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81"./node_modules/onnxruntime-common/dist/esm/backend-impl.js":(c,a,s)=>{s.r(a),s.d(a,{registerBackend:()=>p,resolveBackendAndExecutionProviders:()=>b});const o=new Map,u=[],p=(k,E,B)=>{if(!E||typeof E.init!="function"||typeof E.createInferenceSessionHandler!="function")throw new TypeError("not a valid backend");{const G=o.get(k);if(G===void 0)o.set(k,{backend:E,priority:B});else{if(G.priority>B)return;if(G.priority===B&&G.backend!==E)throw new Error(`cannot register backend "${k}" using priority ${B}`)}if(B>=0){const D=u.indexOf(k);D!==-1&&u.splice(D,1);for(let Q=0;Q<u.length;Q++)if(o.get(u[Q]).priority<=B)return void u.splice(Q,0,k);u.push(k)}}},C=async k=>{const E=o.get(k);if(!E)return"backend not found.";if(E.initialized)return E.backend;if(E.aborted)return E.error;{const B=!!E.initPromise;try{return B||(E.initPromise=E.backend.init(k)),await E.initPromise,E.initialized=!0,E.backend}catch(G){return B||(E.error=`${G}`,E.aborted=!0),E.error}finally{delete E.initPromise}}},b=async k=>{const E=k.executionProviders||[],B=E.map(U=>typeof U=="string"?U:U.name),G=B.length===0?u:B;let D;const Q=[],V=new Set;for(const U of G){const H=await C(U);typeof H=="string"?Q.push({name:U,err:H}):(D||(D=H),D===H&&V.add(U))}if(!D)throw new Error(`no available backend found. ERR: ${Q.map(U=>`[${U.name}] ${U.err}`).join(", ")}`);for(const{name:U,err:H}of Q)B.includes(U)&&console.warn(`removing requested execution provider "${U}" from session options because it is not available: ${H}`);const W=E.filter(U=>V.has(typeof U=="string"?U:U.name));return[D,new Proxy(k,{get:(U,H)=>H==="executionProviders"?W:Reflect.get(U,H)})]}},
vendor: 191 bytes, line 81
81"./node_modules/onnxruntime-common/dist/esm/backend.js":(c,a,s)=>{s.r(a),s.d(a,{registerBackend:()=>o.registerBackend});var o=s("./node_modules/onnxruntime-common/dist/esm/backend-impl.js")},
vendor: 489 bytes, line 81
81"./node_modules/onnxruntime-common/dist/esm/env-impl.js":(c,a,s)=>{s.r(a),s.d(a,{env:()=>p});var o=s("./node_modules/onnxruntime-common/dist/esm/version.js");let u="warning";const p={wasm:{},webgl:{},webgpu:{},versions:{common:o.version},set logLevel(C){if(C!==void 0){if(typeof C!="string"||["verbose","info","warning","error","fatal"].indexOf(C)===-1)throw new Error(`Unsupported logging level: ${C}`);u=C}},get logLevel(){return u}};Object.defineProperty(p,"logLevel",{enumerable:!0})},
vendor: 161 bytes, line 81
81"./node_modules/onnxruntime-common/dist/esm/env.js":(c,a,s)=>{s.r(a),s.d(a,{env:()=>o});const o=s("./node_modules/onnxruntime-common/dist/esm/env-impl.js").env},
vendor: 974 bytes, line 81
81"./node_modules/onnxruntime-common/dist/esm/index.js":(c,a,s)=>{s.r(a),s.d(a,{InferenceSession:()=>p.InferenceSession,TRACE:()=>b.TRACE,TRACE_FUNC_BEGIN:()=>b.TRACE_FUNC_BEGIN,TRACE_FUNC_END:()=>b.TRACE_FUNC_END,Tensor:()=>C.Tensor,TrainingSession:()=>k.TrainingSession,env:()=>u.env,registerBackend:()=>o.registerBackend});var o=s("./node_modules/onnxruntime-common/dist/esm/backend.js"),u=s("./node_modules/onnxruntime-common/dist/esm/env.js"),p=s("./node_modules/onnxruntime-common/dist/esm/inference-session.js"),C=s("./node_modules/onnxruntime-common/dist/esm/tensor.js");s("./node_modules/onnxruntime-common/dist/esm/tensor-conversion.js"),s("./node_modules/onnxruntime-common/dist/esm/tensor-factory.js");var b=s("./node_modules/onnxruntime-common/dist/esm/trace.js");s("./node_modules/onnxruntime-common/dist/esm/onnx-model.js"),s("./node_modules/onnxruntime-common/dist/esm/onnx-value.js");var k=s("./node_modules/onnxruntime-common/dist/esm/training-session.js")},
vendor: 3,665 bytes, line 81
81"./node_modules/onnxruntime-common/dist/esm/inference-session-impl.js":(c,a,s)=>{s.r(a),s.d(a,{InferenceSession:()=>C});var o=s("./node_modules/onnxruntime-common/dist/esm/backend-impl.js"),u=s("./node_modules/onnxruntime-common/dist/esm/tensor.js"),p=s("./node_modules/onnxruntime-common/dist/esm/trace.js");class C{constructor(k){this.handler=k}async run(k,E,B){(0,p.TRACE_FUNC_BEGIN)();const G={};let D={};if(typeof k!="object"||k===null||k instanceof u.Tensor||Array.isArray(k))throw new TypeError("'feeds' must be an object that use input names as keys and OnnxValue as corresponding values.");let Q=!0;if(typeof E=="object"){if(E===null)throw new TypeError("Unexpected argument[1]: cannot be null.");if(E instanceof u.Tensor)throw new TypeError("'fetches' cannot be a Tensor");if(Array.isArray(E)){if(E.length===0)throw new TypeError("'fetches' cannot be an empty array.");Q=!1;for(const U of E){if(typeof U!="string")throw new TypeError("'fetches' must be a string array or an object.");if(this.outputNames.indexOf(U)===-1)throw new RangeError(`'fetches' contains invalid output name: ${U}.`);G[U]=null}if(typeof B=="object"&&B!==null)D=B;else if(B!==void 0)throw new TypeError("'options' must be an object.")}else{let U=!1;const H=Object.getOwnPropertyNames(E);for(const K of this.outputNames)if(H.indexOf(K)!==-1){const J=E[K];(J===null||J instanceof u.Tensor)&&(U=!0,Q=!1,G[K]=J)}if(U){if(typeof B=="object"&&B!==null)D=B;else if(B!==void 0)throw new TypeError("'options' must be an object.")}else D=E}}else if(E!==void 0)throw new TypeError("Unexpected argument[1]: must be 'fetches' or 'options'.");for(const U of this.inputNames)if(k[U]===void 0)throw new Error(`input '${U}' is missing in 'feeds'.`);if(Q)for(const U of this.outputNames)G[U]=null;const V=await this.handler.run(k,G,D),W={};for(const U in V)if(Object.hasOwnProperty.call(V,U)){const H=V[U];H instanceof u.Tensor?W[U]=H:W[U]=new u.Tensor(H.type,H.data,H.dims)}return(0,p.TRACE_FUNC_END)(),W}async release(){return this.handler.dispose()}static async create(k,E,B,G){let D;(0,p.TRACE_FUNC_BEGIN)();let Q={};if(typeof k=="string"){if(D=k,typeof E=="object"&&E!==null)Q=E;else if(E!==void 0)throw new TypeError("'options' must be an object.")}else if(k instanceof Uint8Array){if(D=k,typeof E=="object"&&E!==null)Q=E;else if(E!==void 0)throw new TypeError("'options' must be an object.")}else{if(!(k instanceof ArrayBuffer||typeof SharedArrayBuffer<"u"&&k instanceof SharedArrayBuffer))throw new TypeError("Unexpected argument[0]: must be 'path' or 'buffer'.");{const H=k;let K=0,J=k.byteLength;if(typeof E=="object"&&E!==null)Q=E;else if(typeof E=="number"){if(K=E,!Number.isSafeInteger(K))throw new RangeError("'byteOffset' must be an integer.");if(K<0||K>=H.byteLength)throw new RangeError(`'byteOffset' is out of range [0, ${H.byteLength}).`);if(J=k.byteLength-K,typeof B=="number"){if(J=B,!Number.isSafeInteger(J))throw new RangeError("'byteLength' must be an integer.");if(J<=0||K+J>H.byteLength)throw new RangeError(`'byteLength' is out of range (0, ${H.byteLength-K}].`);if(typeof G=="object"&&G!==null)Q=G;else if(G!==void 0)throw new TypeError("'options' must be an object.")}else if(B!==void 0)throw new TypeError("'byteLength' must be a number.")}else if(E!==void 0)throw new TypeError("'options' must be an object.");D=new Uint8Array(H,K,J)}}const[V,W]=await(0,o.resolveBackendAndExecutionProviders)(Q),U=await V.createInferenceSessionHandler(D,W);return(0,p.TRACE_FUNC_END)(),new C(U)}startProfiling(){this.handler.startProfiling()}endProfiling(){this.handler.endProfiling()}get inputNames(){return this.handler.inputNames}get outputNames(){return this.handler.outputNames}}},
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81"./node_modules/onnxruntime-common/dist/esm/tensor-conversion-impl.js":(c,a,s)=>{s.r(a),s.d(a,{tensorToDataURL:()=>o,tensorToImageData:()=>u});const o=(p,C)=>{const b=typeof document<"u"?document.createElement("canvas"):new OffscreenCanvas(1,1);b.width=p.dims[3],b.height=p.dims[2];const k=b.getContext("2d");if(k!=null){let E,B;(C==null?void 0:C.tensorLayout)!==void 0&&C.tensorLayout==="NHWC"?(E=p.dims[2],B=p.dims[3]):(E=p.dims[3],B=p.dims[2]);const G=(C==null?void 0:C.format)!==void 0?C.format:"RGB",D=C==null?void 0:C.norm;let Q,V;D===void 0||D.mean===void 0?Q=[255,255,255,255]:typeof D.mean=="number"?Q=[D.mean,D.mean,D.mean,D.mean]:(Q=[D.mean[0],D.mean[1],D.mean[2],0],D.mean[3]!==void 0&&(Q[3]=D.mean[3])),D===void 0||D.bias===void 0?V=[0,0,0,0]:typeof D.bias=="number"?V=[D.bias,D.bias,D.bias,D.bias]:(V=[D.bias[0],D.bias[1],D.bias[2],0],D.bias[3]!==void 0&&(V[3]=D.bias[3]));const W=B*E;let U=0,H=W,K=2*W,J=-1;G==="RGBA"?(U=0,H=W,K=2*W,J=3*W):G==="RGB"?(U=0,H=W,K=2*W):G==="RBG"&&(U=0,K=W,H=2*W);for(let g=0;g<B;g++)for(let v=0;v<E;v++){const e=(p.data[U++]-V[0])*Q[0],d=(p.data[H++]-V[1])*Q[1],_=(p.data[K++]-V[2])*Q[2],re=J===-1?255:(p.data[J++]-V[3])*Q[3];k.fillStyle="rgba("+e+","+d+","+_+","+re+")",k.fillRect(v,g,1,1)}if("toDataURL"in b)return b.toDataURL();throw new Error("toDataURL is not supported")}throw new Error("Can not access image data")},u=(p,C)=>{const b=typeof document<"u"?document.createElement("canvas").getContext("2d"):new OffscreenCanvas(1,1).getContext("2d");let k;if(b==null)throw new Error("Can not access image data");{let E,B,G;(C==null?void 0:C.tensorLayout)!==void 0&&C.tensorLayout==="NHWC"?(E=p.dims[2],B=p.dims[1],G=p.dims[3]):(E=p.dims[3],B=p.dims[2],G=p.dims[1]);const D=C!==void 0&&C.format!==void 0?C.format:"RGB",Q=C==null?void 0:C.norm;let V,W;Q===void 0||Q.mean===void 0?V=[255,255,255,255]:typeof Q.mean=="number"?V=[Q.mean,Q.mean,Q.mean,Q.mean]:(V=[Q.mean[0],Q.mean[1],Q.mean[2],255],Q.mean[3]!==void 0&&(V[3]=Q.mean[3])),Q===void 0||Q.bias===void 0?W=[0,0,0,0]:typeof Q.bias=="number"?W=[Q.bias,Q.bias,Q.bias,Q.bias]:(W=[Q.bias[0],Q.bias[1],Q.bias[2],0],Q.bias[3]!==void 0&&(W[3]=Q.bias[3]));const U=B*E;if(C!==void 0&&(C.format!==void 0&&G===4&&C.format!=="RGBA"||G===3&&C.format!=="RGB"&&C.format!=="BGR"))throw new Error("Tensor format doesn't match input tensor dims");const H=4;let K=0,J=1,g=2,v=3,e=0,d=U,_=2*U,re=-1;D==="RGBA"?(e=0,d=U,_=2*U,re=3*U):D==="RGB"?(e=0,d=U,_=2*U):D==="RBG"&&(e=0,_=U,d=2*U),k=b.createImageData(E,B);for(let S=0;S<B*E;K+=H,J+=H,g+=H,v+=H,S++)k.data[K]=(p.data[e++]-W[0])*V[0],k.data[J]=(p.data[d++]-W[1])*V[1],k.data[g]=(p.data[_++]-W[2])*V[2],k.data[v]=re===-1?255:(p.data[re++]-W[3])*V[3]}return k}},
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81"./node_modules/onnxruntime-common/dist/esm/tensor-impl.js":(c,a,s)=>{s.r(a),s.d(a,{Tensor:()=>b});var o=s("./node_modules/onnxruntime-common/dist/esm/tensor-conversion-impl.js"),u=s("./node_modules/onnxruntime-common/dist/esm/tensor-factory-impl.js"),p=s("./node_modules/onnxruntime-common/dist/esm/tensor-impl-type-mapping.js"),C=s("./node_modules/onnxruntime-common/dist/esm/tensor-utils-impl.js");class b{constructor(E,B,G){let D,Q;if((0,p.checkTypedArray)(),typeof E=="object"&&"location"in E)switch(this.dataLocation=E.location,D=E.type,Q=E.dims,E.location){case"cpu-pinned":{const W=p.NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.get(D);if(!W)throw new TypeError(`unsupported type "${D}" to create tensor from pinned buffer`);if(!(E.data instanceof W))throw new TypeError(`buffer should be of type ${W.name}`);this.cpuData=E.data;break}case"texture":if(D!=="float32")throw new TypeError(`unsupported type "${D}" to create tensor from texture`);this.gpuTextureData=E.texture,this.downloader=E.download,this.disposer=E.dispose;break;case"gpu-buffer":if(D!=="float32"&&D!=="float16"&&D!=="int32"&&D!=="int64"&&D!=="uint32"&&D!=="uint8"&&D!=="bool"&&D!=="uint4"&&D!=="int4")throw new TypeError(`unsupported type "${D}" to create tensor from gpu buffer`);this.gpuBufferData=E.gpuBuffer,this.downloader=E.download,this.disposer=E.dispose;break;case"ml-tensor":if(D!=="float32"&&D!=="float16"&&D!=="int32"&&D!=="int64"&&D!=="uint32"&&D!=="uint64"&&D!=="int8"&&D!=="uint8"&&D!=="bool")throw new TypeError(`unsupported type "${D}" to create tensor from MLTensor`);this.mlTensorData=E.mlTensor,this.downloader=E.download,this.disposer=E.dispose;break;default:throw new Error(`Tensor constructor: unsupported location '${this.dataLocation}'`)}else{let W,U;if(typeof E=="string")if(D=E,U=G,E==="string"){if(!Array.isArray(B))throw new TypeError("A string tensor's data must be a string array.");W=B}else{const H=p.NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.get(E);if(H===void 0)throw new TypeError(`Unsupported tensor type: ${E}.`);if(Array.isArray(B)){if(E==="float16"&&H===Uint16Array||E==="uint4"||E==="int4")throw new TypeError(`Creating a ${E} tensor from number array is not supported. 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vendor: 211 bytes, line 81
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se=0;se<I.length-2;se++)Ku.adjustPadAndReturnShape(I[se+(Y?1:2)],x[se],N[se],L[se],j,se,se+I.length-2,Ae)}}static computePoolOutputShape(I,x,N,L,j,Y,Ae){if(x.length<=0)throw new Error("input shape must be of size greater than 0");let se=[x[0],x[1]];return Ku.computeShapeHelper(I,x,se,N,L,j,Y,Ae),se}static computeConvOutputShape(I,x,N,L,j,Y,Ae){if(I.length<=0||x.length<=0)throw new Error("invalid input tensor dims or invalid filter tensor dims");let se=[I[0],x[0]];return Ku.computeShapeHelper(!1,I,se,N,L,j,Y,Ae),se}static computeShapeHelper(I,x,N,L,j,Y,Ae,se){if(I)for(let ne=0;ne<x.length-2;ne++)N.push(1);else for(let ne=0;ne<x.length-2;ne++)N.push(Ku.adjustPadAndReturnShape(x[ne+2],L[ne],j[ne],Y[ne],Ae,ne,ne+x.length-2,se))}static adjustPadAndReturnShape(I,x,N,L,j,Y,Ae,se){let ne=N*(L-1)+1;if(!se||se==="NOTSET")return Math.floor((I+j[Y]+j[Ae]-ne)/x+1);switch(se){case"VALID":return j[Y]=0,j[Ae]=0,Math.floor((I-ne)/x+1);case"SAME_LOWER":case"SAME_UPPER":if(N!==1)throw new Error("Dilation 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indices: ${te.indices}; 99 var current = offset; 100 ${ft} 101 return indices; 102 }`,Mt=[];if(Y>=2)for(let Lt=Y-1;Lt>=0;Lt--)Mt.push(`${PA(et,Lt,Y)} * (indices[${Lt}])`);let Dt=Y<2?"":` 103 fn i2o_${f}(indices: ${te.indices}) -> u32 { 104 return ${Mt.join("+")}; 105 }`,Nt=(...Lt)=>Y===0?"0u":`${te.indices}(${Lt.map(Te).join(",")})`,hA=(Lt,UA)=>Y<2?`${Lt}`:`${PA(Lt,UA,Y)}`,xA={},er=(Lt,UA)=>(()=>{if(te.storage===te.value)return`${f}[${Lt}]=${UA};`;if(te.storage==="vec2<u32>"&&te.value==="i32")return`${f}[${Lt}]=vec2<u32>(u32(${UA}), select(0u, 0xFFFFFFFFu, ${UA} < 0));`;if(te.storage==="vec2<u32>"&&te.value==="u32")return`${f}[${Lt}]=vec2<u32>(u32(${UA}), 0u);`;if(te.storage==="u32"&&te.value==="vec4<bool>")return`${f}[${Lt}]=dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(${UA}));`;throw new Error(`not supported combination of storage type ${te.storage} and value type ${te.value} 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"),UA=Ae.map(HA=>`d${HA}`).join(", ");return` 115 fn set_${f}(${Lt}, value: ${ye}) { 116 set_${f}ByIndices(${Nt(UA)}, value); 117 }`})();return{impl:()=>{let Lt=[],UA=!1;return Oe.offsetToIndices&&(Lt.push(bt),UA=!0),Oe.indicesToOffset&&(Lt.push(Dt),UA=!0),Oe.broadcastedIndicesToOffset&&(Object.values(xA).forEach(HA=>Lt.push(HA)),UA=!0),Oe.set&&(Lt.push(mr),UA=!0),Oe.setByIndices&&(Lt.push(Ir),UA=!0),Oe.get&&(Lt.push(Ar),UA=!0),Oe.getByIndices&&(Lt.push(qr),UA=!0),!j&&UA&&Lt.unshift(`const ${Re} = ${te.indices}(${x.join(",")});`,`const ${et} = ${te.indices}(${gt.computeStrides(x).join(",")});`),Lt.join(` 118`)},type:te,offsetToIndices:Lt=>(Oe.offsetToIndices=!0,Y<2?Lt:`o2i_${f}(${Lt})`),indicesToOffset:Lt=>(Oe.indicesToOffset=!0,Y<2?Lt:`i2o_${f}(${Lt})`),broadcastedIndicesToOffset:(Lt,UA)=>{Oe.broadcastedIndicesToOffset=!0;let HA=`${UA.name}broadcastedIndicesTo${f}Offset`;if(HA in xA)return`${HA}(${Lt})`;let RA=[];for(let is=Y-1;is>=0;is--){let 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Y===0?tr("0u"):Y===1?tr(UA[0]):(Oe.get=!0,Oe.getByIndices=!0,Oe.indicesToOffset=!0,`get_${f}(${UA})`)},getByOffset:tr,getByIndices:Lt=>Y<2?tr(Lt):(Oe.getByIndices=!0,Oe.indicesToOffset=!0,`get_${f}ByIndices(${Lt})`),usage:N,name:f,strides:et,shape:Re,rank:Y}},Yt=(f,I,x,N=1)=>Kr(f,I,x,"input",N),TA=(f,I,x,N=1)=>Kr(f,I,x,"output",N),Js=(f,I,x)=>Kr(f,I,x,"atomicOutput",1),Ss=(f,I,x,N=1)=>Kr(f,I,x,"internal",N),Ds=class{constructor(f,I){this.normalizedDispatchGroup=f,this.limits=I,this.internalVariables=[],this.variables=[],this.uniforms=[],this.variableIndex=0}guardAgainstOutOfBoundsWorkgroupSizes(f){return`if (global_idx >= ${typeof f=="number"?`${f}u`:f}) { return; }`}mainStart(f=Gr){let I=typeof f=="number"?f:f[0],x=typeof f=="number"?1:f[1],N=typeof f=="number"?1:f[2];if(I>this.limits.maxComputeWorkgroupSizeX||x>this.limits.maxComputeWorkgroupSizeY||N>this.limits.maxComputeWorkgroupSizeZ)throw new Error(`workgroup size [${I}, ${x}, ${N}] exceeds the maximum workgroup size [${this.limits.maxComputeWorkgroupSizeX}, ${this.limits.maxComputeWorkgroupSizeY}, ${this.limits.maxComputeWorkgroupSizeZ}].`);if(I*x*N>this.limits.maxComputeInvocationsPerWorkgroup)throw new Error(`workgroup size [${I}, ${x}, ${N}] exceeds the maximum workgroup invocations ${this.limits.maxComputeInvocationsPerWorkgroup}.`);let L=this.normalizedDispatchGroup[1]===1&&this.normalizedDispatchGroup[2]===1;return`@compute @workgroup_size(${I}, ${x}, ${N}) 121 fn main(${L?`@builtin(global_invocation_id) global_id : vec3<u32>, 122 @builtin(workgroup_id) workgroup_id : vec3<u32>, 123 @builtin(local_invocation_index) local_idx : u32, 124 @builtin(local_invocation_id) local_id : vec3<u32>`:`@builtin(global_invocation_id) global_id : vec3<u32>, 125 @builtin(local_invocation_id) local_id : vec3<u32>, 126 @builtin(local_invocation_index) local_idx : u32, 127 @builtin(workgroup_id) workgroup_id : vec3<u32>, 128 @builtin(num_workgroups) num_workgroups : vec3<u32>`}) { 129 ${L?`let global_idx = global_id.x; 130 let workgroup_index = workgroup_id.x;`:`let workgroup_index = workgroup_id.z * num_workgroups[0] * num_workgroups[1] + 131 workgroup_id.y * num_workgroups[0] + workgroup_id.x; 132 let global_idx = workgroup_index * ${I*x*N}u + local_idx;`} 133 `}appendVariableUniforms(f){f.rank!==0&&(f.shape.startsWith("uniforms.")&&this.uniforms.push({name:f.shape.replace("uniforms.",""),type:"u32",length:f.rank}),f.strides.startsWith("uniforms.")&&this.uniforms.push({name:f.strides.replace("uniforms.",""),type:"u32",length:f.rank}))}declareVariable(f,I){if(f.usage==="internal")throw new Error("cannot use internal variable with declareVariable(). use registerInternalVariables() instead.");this.variables.push(f),this.appendVariableUniforms(f);let x=f.usage==="input"?"read":"read_write",N=f.usage==="atomicOutput"?"atomic<i32>":f.type.storage;return`@group(0) @binding(${I}) var<storage, ${x}> ${f.name}: array<${N}>;`}declareVariables(...f){return f.map(I=>this.declareVariable(I,this.variableIndex++)).join(` 134`)}registerInternalVariable(f){if(f.usage!=="internal")throw new Error("cannot use input or output variable with registerInternalVariable(). use declareVariables() instead.");this.internalVariables.push(f),this.appendVariableUniforms(f)}registerInternalVariables(...f){return f.forEach(I=>this.registerInternalVariable(I)),this}registerUniform(f,I,x=1){return this.uniforms.push({name:f,type:I,length:x}),this}registerUniforms(f){return this.uniforms=this.uniforms.concat(f),this}uniformDeclaration(){if(this.uniforms.length===0)return"";let f=[];for(let{name:I,type:x,length:N}of this.uniforms)if(N&&N>4)x==="f16"?f.push(`@align(16) ${I}:array<mat2x4<${x}>, ${Math.ceil(N/8)}>`):f.push(`${I}:array<vec4<${x}>, ${Math.ceil(N/4)}>`);else{let L=N==null||N===1?x:`vec${N}<${x}>`;f.push(`${I}:${L}`)}return` 135 struct Uniforms { ${f.join(", ")} }; 136 @group(0) @binding(${this.variableIndex}) var<uniform> uniforms: Uniforms;`}get additionalImplementations(){return this.uniformDeclaration()+this.variables.map(f=>f.impl()).join(` 137`)+this.internalVariables.map(f=>f.impl()).join(` 138`)}get variablesInfo(){if(this.uniforms.length===0)return;let f=I=>[12,10,1,6][["u32","f16","f32","i32"].indexOf(I)];return this.uniforms.map(I=>[f(I.type),I.length??1])}},ks=(f,I)=>new Ds(f,I)}),h0=me(()=>{hr(),yr(),va(),Qr(),os=(f,I)=>{if(!f||f.length!==1)throw new Error("Transpose requires 1 input.");if(I.length!==0&&I.length!==f[0].dims.length)throw new Error(`perm size ${I.length} does not match input rank ${f[0].dims.length}`)},Tr=(f,I)=>I.length!==0?I:[...new Array(f).keys()].reverse(),As=(f,I)=>gt.sortBasedOnPerm(f,Tr(f.length,I)),yi=(f,I,x,N)=>{let L=`fn perm(i: ${N.type.indices}) -> ${x.type.indices} { 139 var a: ${x.type.indices};`;for(let j=0;j<I;++j)L+=`a[${f[j]}]=i[${j}];`;return L+"return a;}"},Da=(f,I)=>{let x=[],N=[];for(let L=0;L<f.length;++L)f[L]!==1&&x.push(f[L]),f[I[L]]!==1&&N.push(I[L]);return{newShape:x,newPerm:N}},Qn=(f,I)=>{let x=0;for(let N=0;N<f.length;++N)if(I[f[N]]!==1){if(f[N]<x)return!1;x=f[N]}return!0},$s=(f,I)=>{let x,N=f.dataType,L=f.dims.length,j=Tr(L,I),Y=As(f.dims,j),Ae=f.dims,se=Y;if(L<2||Qn(j,f.dims))return x=Te=>{let Oe=Yt("input",N,Ae,4),Ke=TA("output",N,se,4);return` 140 ${Te.registerUniform("output_size","u32").declareVariables(Oe,Ke)} 141 ${Te.mainStart()} 142 ${Te.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} 143 output[global_idx] = input[global_idx]; 144 }`},{name:"TransposeCopy",shaderCache:{inputDependencies:["type"]},getRunData:()=>{let Te=gt.size(Y);return{outputs:[{dims:Y,dataType:f.dataType}],dispatchGroup:{x:Math.ceil(Te/64/4)},programUniforms:[{type:12,data:Math.ceil(Te/4)}]}},getShaderSource:x};let{newShape:ne,newPerm:ye}=Da(f.dims,j),Be=gt.areEqual(ye,[2,3,1]),te=gt.areEqual(ye,[3,1,2]);if(ne.length===2||Be||te){Ae=Be?[ne[0],ne[1]*ne[2]]:te?[ne[0]*ne[1],ne[2]]:ne,se=[Ae[1],Ae[0]];let Te=16;return x=Oe=>{let Ke=Yt("a",N,Ae.length),Re=TA("output",N,se.length);return` 145 ${Oe.registerUniform("output_size","u32").declareVariables(Ke,Re)} 146 var<workgroup> tile : array<array<${Re.type.value}, ${Te+1}>, ${Te}>; 147 ${Oe.mainStart([Te,Te,1])} 148 let stride = (uniforms.output_shape[1] - 1) / ${Te} + 1; 149 let workgroup_id_x = workgroup_index % stride; 150 let workgroup_id_y = workgroup_index / stride; 151 let input_col = workgroup_id_y * ${Te}u + local_id.x; 152 let input_row = workgroup_id_x * ${Te}u + local_id.y; 153 if (input_row < uniforms.a_shape[0] && input_col < uniforms.a_shape[1]) { 154 tile[local_id.y][local_id.x] = ${Ke.getByIndices(`${Ke.type.indices}(input_row, input_col)`)}; 155 } 156 workgroupBarrier(); 157 158 let output_col = workgroup_id_x * ${Te}u + local_id.x; 159 let output_row = workgroup_id_y * ${Te}u + local_id.y; 160 if (output_row < uniforms.output_shape[0] && output_col < uniforms.output_shape[1]) { 161 ${Re.setByIndices(`${Re.type.indices}(output_row, output_col)`,"tile[local_id.x][local_id.y]")} 162 } 163 }`},{name:"TransposeShared",shaderCache:{inputDependencies:["type"]},getRunData:()=>{let Oe=gt.size(Y);return{outputs:[{dims:Y,dataType:f.dataType}],dispatchGroup:{x:Math.ceil(se[1]/Te),y:Math.ceil(se[0]/Te)},programUniforms:[{type:12,data:Oe},..._A(Ae,se)]}},getShaderSource:x}}return x=Te=>{let Oe=Yt("a",N,Ae.length),Ke=TA("output",N,se.length);return` 164 ${Te.registerUniform("output_size","u32").declareVariables(Oe,Ke)} 165 166 ${yi(j,L,Oe,Ke)} 167 168 ${Te.mainStart()} 169 ${Te.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} 170 171 let indices = ${Ke.offsetToIndices("global_idx")}; 172 let aIndices = perm(indices); 173 174 ${Ke.setByOffset("global_idx",Oe.getByIndices("aIndices"))} 175 }`},{name:"Transpose",shaderCache:{hint:`${I}`,inputDependencies:["rank"]},getRunData:()=>{let Te=gt.size(Y);return{outputs:[{dims:Y,dataType:f.dataType}],dispatchGroup:{x:Math.ceil(Te/64)},programUniforms:[{type:12,data:Te},..._A(Ae,se)]}},getShaderSource:x}},Ei=(f,I)=>{os(f.inputs,I.perm),f.compute($s(f.inputs[0],I.perm))},or=f=>vt({perm:f.perm})}),d1=me(()=>{hr(),yr(),Qr(),e2(),h0(),ea={max:"select(bestValue, candidate, candidate > bestValue)",min:"select(bestValue, candidate, candidate < bestValue)",mean:"bestValue + candidate",sum:"bestValue + candidate",prod:"bestValue * candidate",sumSquare:"bestValue + candidate * candidate",logSumExp:"bestValue + exp(candidate)",l1:"bestValue + abs(candidate)",l2:"bestValue + candidate * candidate",logSum:"bestValue + candidate"},ta={max:"select(bestValue, candidate, candidate > bestValue)",min:"select(bestValue, candidate, candidate < bestValue)",mean:"bestValue + candidate",sum:"bestValue + candidate",prod:"bestValue * candidate",sumSquare:"bestValue + candidate",logSumExp:"bestValue + candidate",l1:"bestValue + candidate",l2:"bestValue + candidate",logSum:"bestValue + candidate"},Fo={max:"_A[offset]",min:"_A[offset]",mean:"0",sum:"0",prod:"1",sumSquare:"0",logSumExp:"0",l1:"0",l2:"0",logSum:"0"},io={max:"bestValue",min:"bestValue",sum:"bestValue",prod:"bestValue",sumSquare:"bestValue",logSumExp:"log(bestValue)",l1:"bestValue",l2:"sqrt(bestValue)",logSum:"log(bestValue)"},ui=(f,I)=>{let x=[];for(let N=I-f;N<I;++N)x.push(N);return x},Fi=(f,I)=>{let x=[],N=f.length;for(let j=0;j<N;j++)I.indexOf(j)===-1&&x.push(f[j]);let L=I.map(j=>f[j]);return[x,L]},Wn=(f,I)=>{let x=f.length+I.length,N=[],L=0;for(let j=0;j<x;j++)I.indexOf(j)===-1?N.push(f[L++]):N.push(1);return N},Un=(f,I)=>{for(let x=0;x<f.length;++x)if(f[f.length-x-1]!==I-1-x)return!1;return!0},Lo=(f,I)=>{let x=[];if(!Un(f,I)){for(let N=0;N<I;++N)f.indexOf(N)===-1&&x.push(N);f.forEach(N=>x.push(N))}return x},vn=(f,I,x,N,L,j,Y)=>{let Ae=x[0].dims,se=gt.size(j),ne=gt.size(Y),ye=Yt("_A",x[0].dataType,Ae),Be=TA("output",L,j),te=64;se===1&&(te=256);let Te=` 176 var<workgroup> aBestValues : array<f32, ${te}>; 177 `;return{name:f,shaderCache:{hint:`${I};${te}`,inputDependencies:["type"]},getShaderSource:Oe=>` 178 ${Oe.registerUniform("reduceSize","u32").declareVariables(ye,Be)} 179 ${Te} 180 fn DIV_CEIL(a : u32, b : u32) -> u32 { 181 return ((a - 1u) / b + 1u); 182 } 183 ${Oe.mainStart(te)} 184 185 let outputIndex = global_idx / ${te}; 186 let offset = outputIndex * uniforms.reduceSize; 187 188 var bestValue = f32(${Fo[N]}); 189 let Length = uniforms.reduceSize; 190 for (var k = local_idx; k < Length; k = k + ${te}) { 191 let candidate = f32(${ye.getByOffset("offset + k")}); 192 bestValue = ${ea[N]}; 193 } 194 aBestValues[local_idx] = bestValue; 195 workgroupBarrier(); 196 197 var reduceSize = min(Length, ${te}u); 198 for (var currentSize = reduceSize / 2u; reduceSize > 1u; 199 currentSize = reduceSize / 2u) { 200 let interval = DIV_CEIL(reduceSize, 2u); 201 if (local_idx < currentSize) { 202 let candidate = aBestValues[local_idx + interval]; 203 bestValue = ${ta[N]}; 204 aBestValues[local_idx] = bestValue; 205 } 206 reduceSize = interval; 207 workgroupBarrier(); 208 } 209 210 if (local_idx == 0u) { 211 ${Be.setByOffset("outputIndex",N==="mean"?`${Be.type.storage}(bestValue / f32(uniforms.reduceSize))`:`${Be.type.storage}(${io[N]})`)}; 212 } 213 }`,getRunData:()=>({outputs:[{dims:j,dataType:L}],dispatchGroup:{x:se},programUniforms:[{type:12,data:ne}]})}},Sa=(f,I,x,N)=>{let L=f.inputs.length===1?x:Yi(f.inputs,x),j=L.axes;j.length===0&&!L.noopWithEmptyAxes&&(j=f.inputs[0].dims.map((Te,Oe)=>Oe));let Y=gt.normalizeAxes(j,f.inputs[0].dims.length),Ae=Y,se=f.inputs[0],ne=Lo(Ae,f.inputs[0].dims.length);ne.length>0&&(se=f.compute($s(f.inputs[0],ne),{inputs:[0],outputs:[-1]})[0],Ae=ui(Ae.length,se.dims.length));let[ye,Be]=Fi(se.dims,Ae),te=ye;L.keepDims&&(te=Wn(ye,Y)),f.compute(vn(I,L.cacheKey,[se],N,f.inputs[0].dataType,te,Be),{inputs:[se]})},Ns=(f,I)=>{Sa(f,"ReduceMeanShared",I,"mean")},BA=(f,I)=>{Sa(f,"ReduceL1Shared",I,"l1")},Aa=(f,I)=>{Sa(f,"ReduceL2Shared",I,"l2")},Li=(f,I)=>{Sa(f,"ReduceLogSumExpShared",I,"logSumExp")},Vi=(f,I)=>{Sa(f,"ReduceMaxShared",I,"max")},Go=(f,I)=>{Sa(f,"ReduceMinShared",I,"min")},Kn=(f,I)=>{Sa(f,"ReduceProdShared",I,"prod")},Vo=(f,I)=>{Sa(f,"ReduceSumShared",I,"sum")},Hi=(f,I)=>{Sa(f,"ReduceSumSquareShared",I,"sumSquare")},Bi=(f,I)=>{Sa(f,"ReduceLogSumShared",I,"logSum")}}),e2=me(()=>{hr(),yr(),va(),Qr(),d1(),ca=f=>{if(!f||f.length===0||f.length>2)throw new Error("Reduce op requires 1 or 2 inputs.");if(f.length===2&&f[1].dims.length!==1)throw new Error("Invalid axes input dims.")},Wr=f=>["","",`var value = ${f.getByIndices("input_indices")};`,""],xi=(f,I,x,N,L,j,Y=!1,Ae=!1)=>{let se=[],ne=x[0].dims,ye=ne.length,Be=gt.normalizeAxes(L,ye),te=!Ae&&Be.length===0;ne.forEach((Ke,Re)=>{te||Be.indexOf(Re)>=0?Y&&se.push(1):se.push(Ke)});let Te=se.length,Oe=gt.size(se);return{name:f,shaderCache:I,getShaderSource:Ke=>{let Re=[],et=Yt("_A",x[0].dataType,ye),ft=TA("output",j,Te),bt=N(et,ft,Be),Mt=bt[2];for(let Dt=0,Nt=0;Dt<ye;Dt++)te||Be.indexOf(Dt)>=0?(Y&&Nt++,Mt=`for(var j${Dt}: u32 = 0; j${Dt} < ${ne[Dt]}; j${Dt}++) { 214 ${bt[2].includes("last_index")?`let last_index = j${Dt};`:""} 215 ${et.indicesSet("input_indices",Dt,`j${Dt}`)} 216 ${Mt} 217 }`):(Re.push(`${et.indicesSet("input_indices",Dt,ft.indicesGet("output_indices",Nt))};`),Nt++);return` 218 219 ${Ke.registerUniform("output_size","u32").declareVariables(et,ft)} 220 221 ${Ke.mainStart()} 222 ${Ke.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} 223 var input_indices: ${et.type.indices}; 224 let output_indices = ${ft.offsetToIndices("global_idx")}; 225 226 ${Re.join(` 227`)} 228 ${bt[0]} // init ops for reduce max/min 229 ${bt[1]} 230 ${Mt} 231 ${bt[3]} 232 ${bt.length===4?ft.setByOffset("global_idx","value"):bt.slice(4).join(` 233`)} 234 }`},getRunData:()=>({outputs:[{dims:se,dataType:j}],dispatchGroup:{x:Math.ceil(Oe/64)},programUniforms:[{type:12,data:Oe},..._A(ne,se)]})}},Yi=(f,I)=>{let x=[];return f[1].dims[0]>0&&f[1].getBigInt64Array().forEach(N=>x.push(Number(N))),vt({axes:x,keepDims:I.keepDims,noopWithEmptyAxes:I.noopWithEmptyAxes})},Gs=(f,I,x,N)=>{let L=f.inputs,j=L.length===1?x:Yi(L,x);f.compute(xi(I,{hint:j.cacheKey,inputDependencies:["rank"]},[L[0]],j.noopWithEmptyAxes&&j.axes.length===0?Wr:N,j.axes,L[0].dataType,j.keepDims,j.noopWithEmptyAxes),{inputs:[0]})},fi=(f,I)=>{ca(f.inputs),Gs(f,"ReduceLogSum",I,(x,N)=>[`var value = ${N.type.storage}(0);`,"",`value += ${x.getByIndices("input_indices")};`,"value = log(value);"])},An=(f,I)=>{ca(f.inputs),Gs(f,"ReduceL1",I,(x,N)=>[`var value = ${N.type.storage}(0);`,"",`value += abs(${x.getByIndices("input_indices")});`,""])},Zn=(f,I)=>{ca(f.inputs),Gs(f,"ReduceL2",I,(x,N)=>[`var t = ${N.type.value}(0); var value = ${N.type.value}(0);`,"",`t = ${x.getByIndices("input_indices")}; value += (t * t);`,"value = sqrt(value);"])},Mi=(f,I)=>{ca(f.inputs),Gs(f,"ReduceLogSumExp",I,(x,N)=>[`var value = ${N.type.storage}(0);`,"",`value += exp(${x.getByIndices("input_indices")});`,"value = log(value);"])},Pn=(f,I)=>{ca(f.inputs),Gs(f,"ReduceMax",I,(x,N,L)=>{let j=[];for(let Y=0;Y<x.rank;Y++)(L.indexOf(Y)>=0||L.length===0)&&j.push(x.indicesSet("input_indices",Y,0));return[`${j.join(` 235`)}`,`var value = ${x.getByIndices("input_indices")};`,`value = max(value, ${x.getByIndices("input_indices")});`,""]})},oo=(f,I)=>{ca(f.inputs),Gs(f,"ReduceMean",I,(x,N,L)=>{let j=1;for(let Y=0;Y<x.rank;Y++)(L.indexOf(Y)>=0||L.length===0)&&(j*=f.inputs[0].dims[Y]);return["var sum = f32(0);","",`sum += f32(${x.getByIndices("input_indices")});`,`let value = ${N.type.value}(sum / ${j});`]})},_o=(f,I)=>{ca(f.inputs),Gs(f,"ReduceMin",I,(x,N,L)=>{let j=[];for(let Y=0;Y<x.rank;Y++)(L.indexOf(Y)>=0||L.length===0)&&j.push(`input_indices[${Y}] = 0;`);return[`${j.join(` 236`)}`,`var value = ${x.getByIndices("input_indices")};`,`value = min(value, ${x.getByIndices("input_indices")});`,""]})},Sn=(f,I)=>{ca(f.inputs),Gs(f,"ReduceProd",I,(x,N)=>[`var value = ${N.type.storage}(1);`,"",`value *= ${x.getByIndices("input_indices")};`,""])},uA=(f,I)=>{ca(f.inputs),Gs(f,"ReduceSum",I,(x,N)=>[`var value = ${N.type.storage}(0);`,"",`value += ${x.getByIndices("input_indices")};`,""])},yn=(f,I)=>{ca(f.inputs),Gs(f,"ReduceSumSquare",I,(x,N)=>[`var t = ${N.type.value}(0); var value = ${N.type.value}(0);`,"",`t = ${x.getByIndices("input_indices")}; value += t * t;`,""])},kA=(f,I,x)=>{if(I.length===0)return x;let N=1,L=1;for(let j=0;j<I.length;j++)I.indexOf(j)===-1?N*=f[j]:L*=f[j];return L<32&&N>1024},lo=(f,I)=>{kA(f.inputs[0].dims,I.axes,I.noopWithEmptyAxes)?oo(f,I):Ns(f,I)},co=(f,I)=>{kA(f.inputs[0].dims,I.axes,I.noopWithEmptyAxes)?An(f,I):BA(f,I)},ra=(f,I)=>{kA(f.inputs[0].dims,I.axes,I.noopWithEmptyAxes)?Zn(f,I):Aa(f,I)},js=(f,I)=>{kA(f.inputs[0].dims,I.axes,I.noopWithEmptyAxes)?Mi(f,I):Li(f,I)},Nn=(f,I)=>{kA(f.inputs[0].dims,I.axes,I.noopWithEmptyAxes)?Pn(f,I):Vi(f,I)},di=(f,I)=>{kA(f.inputs[0].dims,I.axes,I.noopWithEmptyAxes)?_o(f,I):Go(f,I)},uo=(f,I)=>{kA(f.inputs[0].dims,I.axes,I.noopWithEmptyAxes)?Sn(f,I):Kn(f,I)},_s=(f,I)=>{kA(f.inputs[0].dims,I.axes,I.noopWithEmptyAxes)?uA(f,I):Vo(f,I)},Xn=(f,I)=>{kA(f.inputs[0].dims,I.axes,I.noopWithEmptyAxes)?yn(f,I):Hi(f,I)},Ho=(f,I)=>{kA(f.inputs[0].dims,I.axes,I.noopWithEmptyAxes)?fi(f,I):Bi(f,I)}}),g1=me(()=>{hr(),va(),e2(),En=f=>{if(!f||f.length===0||f.length>2)throw new Error("ArgMinMaxOp op requires 1 or 2 inputs.");if(f[0].dataType!==1)throw new Error("Invalid input type.")},un=(f,I)=>{En(f.inputs),f.compute(xi("ArgMin",{hint:I.cacheKey,inputDependencies:["rank"]},[f.inputs[0]],(x,N,L)=>{let j=[];for(let Y=0;Y<x.rank;Y++)(L.indexOf(Y)>=0||L.length===0)&&j.push(`input_indices[${Y}] = 0;`);return[`${j.join(` 237`)}`,`var value = ${x.getByIndices("input_indices")}; 238var best_index : i32 = 0;`,`if (${x.getByIndices("input_indices")} ${I.selectLastIndex>0?"<=":"<"} value) { 239 value = ${x.getByIndices("input_indices")}; 240 best_index = i32(last_index); 241 }`,"",N.setByOffset("global_idx","best_index")]},[I.axis],7,I.keepDims),{inputs:[0]})},Ri=(f,I)=>{En(f.inputs),f.compute(xi("argMax",{hint:I.cacheKey,inputDependencies:["rank"]},[f.inputs[0]],(x,N,L)=>{let j=[];for(let Y=0;Y<x.rank;Y++)(L.indexOf(Y)>=0||L.length===0)&&j.push(`input_indices[${Y}] = 0;`);return[`${j.join(` 242`)}`,`var value = ${x.getByIndices("input_indices")}; 243var best_index : i32 = 0;`,`if (${x.getByIndices("input_indices")} ${I.selectLastIndex>0?">=":">"} value) { 244 value = ${x.getByIndices("input_indices")}; 245 best_index = i32(last_index); 246 }`,"",N.setByOffset("global_idx","best_index")]},[I.axis],7,I.keepDims),{inputs:[0]})},Di=f=>vt(f)}),t2=me(()=>{hr(),yr(),$c(),Qr(),gi=(f,I)=>{let x=f[0],N=f[1],L=f[2],j=f[3],Y=f[4],Ae=f[5];if(Y&&Ae)throw new Error("Attention cannot have both past and attention_bias");if(x.dims.length!==3)throw new Error('Input "input" must have 3 dimensions');let se=x.dims[0],ne=x.dims[1],ye=x.dims[2];if(L.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimensions');if(N.dims.length!==2)throw new Error('Input "weights" is expected to have 2 dimensions');if(N.dims[0]!==ye)throw new Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(L.dims[0]!==N.dims[1])throw new Error('Input "bias" dimension 0 should have same length as dimension 1 of input "weights"');let Be=L.dims[0]/3,te=Be,Te=te;if(I.qkvHiddenSizes.length>0){if(I.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let et of I.qkvHiddenSizes)if(et%I.numHeads!=0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");Be=I.qkvHiddenSizes[0],te=I.qkvHiddenSizes[1],Te=I.qkvHiddenSizes[2]}let Oe=ne;if(Be!==te)throw new Error("qkv_hidden_sizes first element should be same as the second");if(L.dims[0]!==Be+te+Te)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let Ke=0;if(Y){if(te!==Te)throw new Error('Input "past" expect k_hidden_size == v_hidden_size');if(Y.dims.length!==5)throw new Error('Input "past" must have 5 dimensions');if(Y.dims[0]!==2)throw new Error('Input "past" first dimension must be 2');if(Y.dims[1]!==se)throw new Error('Input "past" second dimension must be batch_size');if(Y.dims[2]!==I.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(Y.dims[4]!==te/I.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');I.pastPresentShareBuffer||(Ke=Y.dims[3])}let Re=Oe+Ke;if(j)throw new Error("Mask not supported");if(Y)throw new Error("past is not supported");if(Ae){if(Ae.dims.length!==4)throw new Error('Input "attention_bias" must have 4 dimensions');if(Ae.dims[0]!==se||Ae.dims[1]!==I.numHeads||Ae.dims[2]!==ne||Ae.dims[3]!==Re)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:se,sequenceLength:ne,pastSequenceLength:Ke,kvSequenceLength:Oe,totalSequenceLength:Re,maxSequenceLength:-1,inputHiddenSize:ye,hiddenSize:Be,vHiddenSize:Te,headSize:Math.floor(Be/I.numHeads),vHeadSize:Math.floor(Te/I.numHeads),numHeads:I.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:I.maskFilterValue,maskType:0,scale:I.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},wA=(f,I,x)=>I&&f?` 247 let total_sequence_length_input = u32(${I.getByOffset("0")}); 248 let present_sequence_length = max(total_sequence_length_input, uniforms.past_sequence_length); 249 let is_subsequent_prompt: bool = sequence_length > 1 && sequence_length != total_sequence_length_input; 250 let is_first_prompt: bool = is_subsequent_prompt == false && sequence_length == total_sequence_length_input; 251 total_sequence_length = u32(${f==null?void 0:f.getByOffset("batchIdx")}) + 1; 252 var past_sequence_length: u32 = 0; 253 if (is_first_prompt == false) { 254 past_sequence_length = total_sequence_length - sequence_length; 255 } 256 `:` 257 ${x?"let past_sequence_length = uniforms.past_sequence_length":""}; 258 let present_sequence_length = total_sequence_length; 259 `,ua=(f,I,x,N,L,j,Y,Ae)=>{let se=_r(Y?1:j),ne=64,ye=j/se;ye<ne&&(ne=32);let Be=Math.ceil(j/se/ne),te=[{type:12,data:I},{type:12,data:x},{type:12,data:N},{type:12,data:L},{type:12,data:ye},{type:12,data:Be}],Te=ir(f.dataType,se),Oe=IA(1,se),Ke=["type"];return Y&&Ke.push("type"),Ae&&Ke.push("type"),{name:"AttentionProbsSoftmax",shaderCache:{hint:`${ne};${Te};${se}`,inputDependencies:Ke},getShaderSource:Re=>{let et=TA("x",f.dataType,f.dims,se),ft=[et],bt=Y?Yt("seq_lens",Y.dataType,Y.dims):void 0;bt&&ft.push(bt);let Mt=Ae?Yt("total_sequence_length_input",Ae.dataType,Ae.dims):void 0;Mt&&ft.push(Mt);let Dt=IA(f.dataType);return` 260 var<workgroup> thread_max: array<f32, ${ne}>; 261 var<workgroup> thread_sum: array<f32, ${ne}>; 262 ${Re.registerUniforms([{name:"batch_size",type:"u32"},{name:"num_heads",type:"u32"},{name:"past_sequence_length",type:"u32"},{name:"sequence_length",type:"u32"},{name:"total_sequence_length",type:"u32"},{name:"elements_per_thread",type:"u32"}]).declareVariables(...ft)} 263 ${Re.mainStart([ne,1,1])} 264 let batchIdx = workgroup_id.z / uniforms.num_heads; 265 let headIdx = workgroup_id.z % uniforms.num_heads; 266 let sequence_length = uniforms.sequence_length; 267 var total_sequence_length = uniforms.total_sequence_length; 268 ${wA(bt,Mt,!1)} 269 let local_offset = local_idx * uniforms.elements_per_thread; 270 let offset = (global_idx / ${ne}) * uniforms.total_sequence_length + local_offset; 271 let seq_causal_length = ${Y?"u32(past_sequence_length + workgroup_id.y + 1)":"total_sequence_length"}; 272 var thread_max_vector = ${Oe}(-3.402823e+38f); 273 for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) { 274 thread_max_vector = max(${Oe}(x[offset + i]), thread_max_vector); 275 } 276 thread_max[local_idx] = ${(()=>{switch(se){case 1:return"thread_max_vector";case 2:return"max(thread_max_vector.x, thread_max_vector.y)";case 4:return"max(max(thread_max_vector.x, thread_max_vector.y), max(thread_max_vector.z, thread_max_vector.w))";default:throw new Error(`Unsupported components: ${se}`)}})()}; 277 workgroupBarrier(); 278 279 var max_value = f32(-3.402823e+38f); 280 for (var i = 0u; i < ${ne}; i++) { 281 max_value = max(thread_max[i], max_value); 282 } 283 284 var sum_vector = ${Oe}(0); 285 for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) { 286 sum_vector += exp(${Oe}(x[offset + i]) - max_value); 287 } 288 thread_sum[local_idx] = ${(()=>{switch(se){case 1:return"sum_vector";case 2:return"sum_vector.x + sum_vector.y";case 4:return"sum_vector.x + sum_vector.y + sum_vector.z + sum_vector.w";default:throw new Error(`Unsupported components: ${se}`)}})()}; 289 workgroupBarrier(); 290 291 var sum: f32 = 0; 292 for (var i = 0u; i < ${ne}; i++) { 293 sum += thread_sum[i]; 294 } 295 296 if (sum == 0) { 297 for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) { 298 x[offset + i] = ${et.type.value}(${Dt}(1.0) / ${Dt}(seq_causal_length)); 299 } 300 } else { 301 for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) { 302 var f32input = ${Oe}(x[offset + i]); 303 x[offset + i] = ${et.type.value}(exp(f32input - max_value) / sum); 304 } 305 } 306 ${Y?` 307 for (var total_seq_id: u32 = seq_causal_length; total_seq_id + local_offset < uniforms.total_sequence_length; total_seq_id++) { 308 x[offset + total_seq_id] = ${et.type.value}(${Dt}(0)); 309 }`:""}; 310 }`},getRunData:()=>({outputs:[],dispatchGroup:{x:Math.ceil(j/ne),y:L,z:I*x},programUniforms:te})}},To=(f,I,x,N,L,j,Y,Ae,se)=>{let ne=Y+j.kvSequenceLength,ye=[j.batchSize,j.numHeads,j.sequenceLength,ne],Be=f>1&&N,te=j.kvNumHeads?j.kvNumHeads:j.numHeads,Te=Be?[j.batchSize,te,ne,j.headSize]:void 0,Oe=j.nReps?j.nReps:1,Ke=j.scale===0?1/Math.sqrt(j.headSize):j.scale,Re=_r(j.headSize),et=j.headSize/Re,ft=12,bt={x:Math.ceil(ne/ft),y:Math.ceil(j.sequenceLength/ft),z:j.batchSize*j.numHeads},Mt=[{type:12,data:j.sequenceLength},{type:12,data:et},{type:12,data:ne},{type:12,data:j.numHeads},{type:12,data:j.headSize},{type:1,data:Ke},{type:12,data:Y},{type:12,data:j.kvSequenceLength},{type:12,data:Oe}],Dt=Be&&N&>.size(N.dims)>0,Nt=["type","type"];Dt&&Nt.push("type"),L&&Nt.push("type"),Ae&&Nt.push("type"),se&&Nt.push("type");let hA=[{dims:ye,dataType:I.dataType,gpuDataType:0}];return Be&&hA.push({dims:Te,dataType:I.dataType,gpuDataType:0}),{name:"AttentionProbs",shaderCache:{hint:`${Re};${L!==void 0};${N!==void 0};${f}`,inputDependencies:Nt},getRunData:()=>({outputs:hA,dispatchGroup:bt,programUniforms:Mt}),getShaderSource:xA=>{let er=Yt("q",I.dataType,I.dims,Re),tr=[er,Yt("key",x.dataType,x.dims,Re)];if(Dt){let UA=Yt("past_key",N.dataType,N.dims,Re);tr.push(UA)}L&&tr.push(Yt("attention_bias",L.dataType,L.dims));let qr=Ae?Yt("seq_lens",Ae.dataType,Ae.dims):void 0;qr&&tr.push(qr);let Ar=se?Yt("total_sequence_length_input",se.dataType,se.dims):void 0;Ar&&tr.push(Ar);let Ir=TA("output",I.dataType,ye),mr=[Ir];Be&&mr.push(TA("present_key",I.dataType,Te,Re));let Lt=IA(1,Re);return` 311 const TILE_SIZE = 12u; 312 313 var<workgroup> tileQ: array<${er.type.storage}, 144>; 314 var<workgroup> tileK: array<${er.type.storage}, 144>; 315 ${xA.registerUniforms([{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"alpha",type:"f32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"n_reps",type:"u32"}]).declareVariables(...tr,...mr)} 316 ${xA.mainStart([ft,ft,1])} 317 // x holds the N and y holds the M 318 let headIdx = workgroup_id.z % uniforms.num_heads; 319 let kvHeadIdx = ${Oe===1?"headIdx":"headIdx / uniforms.n_reps"}; 320 let kv_num_heads = ${Oe===1?"uniforms.num_heads":"uniforms.num_heads / uniforms.n_reps"}; 321 let batchIdx = workgroup_id.z / uniforms.num_heads; 322 let m = workgroup_id.y * TILE_SIZE; 323 let n = workgroup_id.x * TILE_SIZE; 324 let sequence_length = uniforms.M; 325 var total_sequence_length = uniforms.N; 326 ${wA(qr,Ar,!0)} 327 let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx; 328 let qOffset = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K; 329 ${Dt&&Be?"let pastKeyOffset = absKvHeadIdx * uniforms.past_sequence_length * uniforms.K;":""}; 330 let kOffset = absKvHeadIdx * uniforms.kv_sequence_length * uniforms.K; 331 ${Be?"let presentKeyOffset = absKvHeadIdx * uniforms.N * uniforms.K;":""} 332 var value = ${Lt}(0); 333 for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) { 334 if (global_id.y < uniforms.M && w + local_id.x < uniforms.K) { 335 tileQ[TILE_SIZE * local_id.y + local_id.x] = q[qOffset + local_id.y * uniforms.K + w + local_id.x]; 336 } 337 if (n + local_id.y < uniforms.N && w + local_id.x < uniforms.K) { 338 var idx = TILE_SIZE * local_id.y + local_id.x; 339 ${Dt&&Be?` 340 if (n + local_id.y < past_sequence_length) { 341 tileK[idx] = past_key[pastKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x]; 342 } else if (n + local_id.y - past_sequence_length < uniforms.kv_sequence_length) { 343 tileK[idx] = key[kOffset + (n + local_id.y - past_sequence_length) * uniforms.K + w + local_id.x]; 344 }`:` 345 if (n + local_id.y < uniforms.kv_sequence_length) { 346 tileK[idx] = key[kOffset + (n + local_id.y) * uniforms.K + w + local_id.x]; 347 }`} 348 ${Be?`if (n + local_id.y < present_sequence_length) { 349 present_key[presentKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x] = tileK[idx]; 350 }`:""} 351 } 352 workgroupBarrier(); 353 354 for (var k: u32 = 0u; k < TILE_SIZE && w+k < uniforms.K; k++) { 355 value += ${Lt}(tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * local_id.x + k]); 356 } 357 358 workgroupBarrier(); 359 } 360 361 if (global_id.y < uniforms.M && global_id.x < total_sequence_length) { 362 let headOffset = workgroup_id.z * uniforms.M * uniforms.N; 363 let outputIdx = headOffset + global_id.y * uniforms.N + global_id.x; 364 var sum: f32 = ${(()=>{switch(Re){case 1:return"value";case 2:return"value.x + value.y";case 4:return"value.x + value.y + value.z + value.w";default:throw new Error(`Unsupported components: ${Re}`)}})()}; 365 output[outputIdx] = ${Ir.type.value} (sum * uniforms.alpha) + ${L?"attention_bias[outputIdx]":"0.0"}; 366 } 367 }`}}},qn=(f,I,x,N,L,j,Y=void 0,Ae=void 0)=>{let se=j+L.kvSequenceLength,ne=L.nReps?L.nReps:1,ye=L.vHiddenSize*ne,Be=f>1&&N,te=L.kvNumHeads?L.kvNumHeads:L.numHeads,Te=Be?[L.batchSize,te,se,L.headSize]:void 0,Oe=[L.batchSize,L.sequenceLength,ye],Ke=12,Re={x:Math.ceil(L.vHeadSize/Ke),y:Math.ceil(L.sequenceLength/Ke),z:L.batchSize*L.numHeads},et=[{type:12,data:L.sequenceLength},{type:12,data:se},{type:12,data:L.vHeadSize},{type:12,data:L.numHeads},{type:12,data:L.headSize},{type:12,data:ye},{type:12,data:j},{type:12,data:L.kvSequenceLength},{type:12,data:ne}],ft=Be&&N&>.size(N.dims)>0,bt=["type","type"];ft&&bt.push("type"),Y&&bt.push("type"),Ae&&bt.push("type");let Mt=[{dims:Oe,dataType:I.dataType,gpuDataType:0}];return Be&&Mt.push({dims:Te,dataType:I.dataType,gpuDataType:0}),{name:"AttentionScore",shaderCache:{hint:`${N!==void 0};${f}`,inputDependencies:bt},getRunData:()=>({outputs:Mt,dispatchGroup:Re,programUniforms:et}),getShaderSource:Dt=>{let Nt=Yt("probs",I.dataType,I.dims),hA=[Nt,Yt("v",x.dataType,x.dims)];ft&&hA.push(Yt("past_value",N.dataType,N.dims));let xA=Y?Yt("seq_lens",Y.dataType,Y.dims):void 0;Y&&hA.push(xA);let er=Ae?Yt("total_sequence_length_input",Ae.dataType,Ae.dims):void 0;Ae&&hA.push(er);let tr=[TA("output",I.dataType,Oe)];return Be&&tr.push(TA("present_value",I.dataType,Te)),` 368 const TILE_SIZE = 12u; 369 var<workgroup> tileQ: array<${Nt.type.value}, 144>; 370 var<workgroup> tileV: array<${Nt.type.value}, 144>; 371 ${Dt.registerUniforms([{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"v_hidden_size",type:"u32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"n_reps",type:"u32"}]).declareVariables(...hA,...tr)} 372 ${Dt.mainStart([Ke,Ke,1])} 373 let headIdx = workgroup_id.z % uniforms.num_heads; 374 let batchIdx = workgroup_id.z / uniforms.num_heads; 375 let kvHeadIdx = ${ne===1?"headIdx":"headIdx / uniforms.n_reps"}; 376 let kv_num_heads = ${ne===1?"uniforms.num_heads":"uniforms.num_heads / uniforms.n_reps"}; 377 let m = global_id.y; 378 let n = global_id.x; 379 let sequence_length = uniforms.M; 380 var total_sequence_length = uniforms.K; 381 ${wA(xA,er,!0)} 382 let offsetA = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K; 383 let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx; // kvHeadIdx is relative to the batch 384 ${ft&&Be?"let pastValueOffset = absKvHeadIdx * uniforms.N * uniforms.past_sequence_length + n;":""}; 385 let vOffset = absKvHeadIdx * uniforms.N * uniforms.kv_sequence_length + n; 386 ${Be?"let presentValueOffset = absKvHeadIdx * uniforms.N * uniforms.K + n;":""} 387 var value = ${Nt.type.storage}(0); 388 for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) { 389 if (m < uniforms.M && w + local_id.x < uniforms.K) { 390 tileQ[TILE_SIZE * local_id.y + local_id.x] = probs[offsetA + w + local_id.x]; 391 } 392 if (n < uniforms.N && w + local_id.y < uniforms.K) { 393 var idx = TILE_SIZE * local_id.y + local_id.x; 394 ${ft&&Be?` 395 if (w + local_id.y < past_sequence_length) { 396 tileV[idx] = past_value[pastValueOffset + (w + local_id.y) * uniforms.N]; 397 } else if (w + local_id.y - past_sequence_length < uniforms.kv_sequence_length) { 398 tileV[idx] = v[vOffset + (w + local_id.y - past_sequence_length) * uniforms.N]; 399 } 400 `:` 401 if (w + local_id.y < uniforms.kv_sequence_length) { 402 tileV[idx] = v[vOffset + (w + local_id.y) * uniforms.N]; 403 }`} 404 ${Be?` 405 if (w + local_id.y < present_sequence_length) { 406 present_value[presentValueOffset + (w + local_id.y) * uniforms.N] = tileV[idx]; 407 }`:""} 408 } 409 workgroupBarrier(); 410 for (var k: u32 = 0u; k < TILE_SIZE && w+k < total_sequence_length; k++) { 411 value += tileQ[TILE_SIZE * local_id.y + k] * tileV[TILE_SIZE * k + local_id.x]; 412 } 413 workgroupBarrier(); 414 } 415 416 // we need to transpose output from BNSH_v to BSND_v 417 if (m < uniforms.M && n < uniforms.N) { 418 let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size 419 + headIdx * uniforms.N + n; 420 output[outputIdx] = value; 421 } 422 }`}}},fn=(f,I,x,N,L,j,Y,Ae,se,ne,ye=void 0,Be=void 0)=>{let te=Math.min(f.outputCount,1+(Y?1:0)+(Ae?1:0)),Te=te>1?ne.pastSequenceLength:0,Oe=Te+ne.kvSequenceLength,Ke=se&>.size(se.dims)>0?se:void 0,Re=[I,x];te>1&&Y&>.size(Y.dims)>0&&Re.push(Y),Ke&&Re.push(Ke),ye&&Re.push(ye),Be&&Re.push(Be);let et=f.compute(To(te,I,x,Y,Ke,ne,Te,ye,Be),{inputs:Re,outputs:te>1?[-1,1]:[-1]})[0];f.compute(ua(et,ne.batchSize,ne.numHeads,Te,ne.sequenceLength,Oe,ye,Be),{inputs:ye&&Be?[et,ye,Be]:[et],outputs:[]});let ft=[et,N];te>1&&Ae&>.size(Ae.dims)>0&&ft.push(Ae),ye&&ft.push(ye),Be&&ft.push(Be),f.compute(qn(te,et,N,Ae,ne,Te,ye,Be),{inputs:ft,outputs:te>1?[0,2]:[0]})},fo=(f,I)=>{let x=[I.batchSize,I.numHeads,I.sequenceLength,I.headSize],N=I.sequenceLength,L=I.inputHiddenSize,j=I.headSize,Y=12,Ae={x:Math.ceil(I.headSize/Y),y:Math.ceil(I.sequenceLength/Y),z:I.batchSize*I.numHeads},se=[f.inputs[0],f.inputs[1],f.inputs[2]],ne=[{type:12,data:N},{type:12,data:L},{type:12,data:j},{type:12,data:I.numHeads},{type:12,data:I.headSize},{type:12,data:I.hiddenSize},{type:12,data:I.hiddenSize+I.hiddenSize+I.vHiddenSize}];return f.compute({name:"AttentionPrepare",shaderCache:{inputDependencies:["type","type","type"]},getRunData:()=>({outputs:[{dims:x,dataType:f.inputs[0].dataType,gpuDataType:0},{dims:x,dataType:f.inputs[0].dataType,gpuDataType:0},{dims:x,dataType:f.inputs[0].dataType,gpuDataType:0}],dispatchGroup:Ae,programUniforms:ne}),getShaderSource:ye=>{let Be=TA("output_q",se[0].dataType,x),te=TA("output_k",se[0].dataType,x),Te=TA("output_v",se[0].dataType,x),Oe=Yt("input",se[0].dataType,se[0].dims),Ke=Yt("weight",se[1].dataType,se[1].dims),Re=Yt("bias",se[2].dataType,se[2].dims),et=Oe.type.storage;return` 423 const TILE_SIZE = 12u; 424 var<workgroup> tileInput: array<${et}, 144>; 425 var<workgroup> tileWeightQ: array<${et}, 144>; 426 var<workgroup> tileWeightK: array<${et}, 144>; 427 var<workgroup> tileWeightV: array<${et}, 144>; 428 ${ye.registerUniforms([{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"hidden_size",type:"u32"},{name:"ldb",type:"u32"}]).declareVariables(Oe,Ke,Re,Be,te,Te)} 429 ${ye.mainStart([Y,Y,1])} 430 let batchIndex = workgroup_id.z / uniforms.num_heads; 431 let headNumber = workgroup_id.z % uniforms.num_heads; 432 let m = global_id.y; 433 let n = global_id.x; 434 435 let inputOffset = batchIndex * (uniforms.M * uniforms.K) + m * uniforms.K; 436 let biasOffsetQ = headNumber * uniforms.head_size; 437 let biasOffsetK = uniforms.hidden_size + biasOffsetQ; 438 let biasOffsetV = uniforms.hidden_size + biasOffsetK; 439 440 var valueQ = ${et}(0); 441 var valueK = ${et}(0); 442 var valueV = ${et}(0); 443 for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) { 444 if (m < uniforms.M && w + local_id.x < uniforms.K) { 445 tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x]; 446 } 447 if (n < uniforms.N && w + local_id.y < uniforms.K) { 448 let offset = n + (w + local_id.y) * uniforms.ldb; 449 tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset]; 450 tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset]; 451 tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset]; 452 } 453 workgroupBarrier(); 454 for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) { 455 let inputTileOffset = TILE_SIZE * local_id.y + k; 456 let weightTileOffset = TILE_SIZE * k + local_id.x; 457 valueQ += tileInput[inputTileOffset] * tileWeightQ[weightTileOffset]; 458 valueK += tileInput[inputTileOffset] * tileWeightK[weightTileOffset]; 459 valueV += tileInput[inputTileOffset] * tileWeightV[weightTileOffset]; 460 } 461 462 workgroupBarrier(); 463 } 464 465 let headOffset = (m * uniforms.N + n) % uniforms.head_size; 466 valueQ += bias[headOffset + biasOffsetQ]; 467 valueK += bias[headOffset + biasOffsetK]; 468 valueV += bias[headOffset + biasOffsetV]; 469 470 let offset = workgroup_id.z * uniforms.M * uniforms.N; 471 if (m < uniforms.M && n < uniforms.N) { 472 let outputIdx = offset + m * uniforms.N + n; 473 output_q[outputIdx] = valueQ; 474 output_k[outputIdx] = valueK; 475 output_v[outputIdx] = valueV; 476 } 477 }`}},{inputs:se,outputs:[-1,-1,-1]})},Yo=(f,I)=>{let x=gi(f.inputs,I),[N,L,j]=fo(f,x);return fn(f,N,L,j,f.inputs[4],void 0,void 0,void 0,f.inputs[5],x)}}),h1=me(()=>{lt(),hr(),yr(),va(),Qr(),go=(f,I)=>{if(!f||f.length!==5)throw new Error("BatchNormalization requires 5 inputs");let x=(N,L,j)=>{let Y=L.length;if(Y!==N.length)throw new Error(`${j}: num dimensions != ${Y}`);L.forEach((Ae,se)=>{if(Ae!==N[se])throw new Error(`${j}: dim[${se}] do not match`)})};if(f[0].dims.length>1){let N=I.format==="NHWC"?I.spatial?f[0].dims.slice(-1):f[0].dims.slice(-1).concat(f[0].dims.slice(1,f[0].dims.length-1)):f[0].dims.slice(1,I.spatial?2:void 0);x(f[1].dims,N,"Invalid input scale"),x(f[2].dims,N,"Invalid input B"),x(f[3].dims,N,"Invalid input mean"),x(f[4].dims,N,"Invalid input var")}else x(f[1].dims,[1],"Invalid input scale"),x(f[2].dims,[1],"Invalid input B"),x(f[3].dims,[1],"Invalid input mean"),x(f[4].dims,[1],"Invalid input var")},zn=(f,I)=>{let{epsilon:x,spatial:N,format:L}=I,j=f[0].dims,Y=N?_r(j[j.length-1]):1,Ae=L==="NHWC"&&j.length>1?Y:1,se=gt.size(j)/Y,ne=N,ye=ne?j.length:j,Be=Yt("x",f[0].dataType,f[0].dims,Y),te=Yt("scale",f[1].dataType,f[1].dims,Ae),Te=Yt("bias",f[2].dataType,f[2].dims,Ae),Oe=Yt("inputMean",f[3].dataType,f[3].dims,Ae),Ke=Yt("inputVar",f[4].dataType,f[4].dims,Ae),Re=TA("y",f[0].dataType,ye,Y);return{name:"BatchNormalization",shaderCache:{hint:`${I.epsilon}_${I.format}_${N}_${Y}`,inputDependencies:ne?["rank","type","type","type","type"]:void 0},getShaderSource:et=>` 478 const epsilon = ${x}; 479 ${et.registerUniform("outputSize","u32").declareVariables(Be,te,Te,Oe,Ke,Re)} 480 ${et.mainStart()} 481 ${et.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} 482 var outputIndices = ${Re.offsetToIndices(`global_idx * ${Y}`)}; 483 ${(()=>{let ft="";if(N)ft=`let cOffset = ${j.length===1?"0u":L==="NHWC"?`outputIndices[${j.length-1}] / ${Y}`:"outputIndices[1]"};`;else if(L==="NCHW")ft=` 484 ${Re.indicesSet("outputIndices","0","0")} 485 let cOffset = ${Re.indicesToOffset("outputIndices")};`;else{ft=`var cIndices = ${te.type.indices}(0); 486 cIndices[0] = outputIndices[${j.length-1}];`;for(let bt=1;bt<te.rank;bt++)ft+=`cIndices[${bt}] = outputIndices[${bt}];`;ft+=`let cOffset = ${te.indicesToOffset("cIndices")};`}return ft})()} 487 let scale = ${te.getByOffset("cOffset")}; 488 let bias = ${Te.getByOffset("cOffset")}; 489 let inputMean = ${Oe.getByOffset("cOffset")}; 490 let inputVar = ${Ke.getByOffset("cOffset")}; 491 let x = ${Be.getByOffset("global_idx")}; 492 let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias; 493 ${Re.setByOffset("global_idx","value")} 494 }`,getRunData:()=>({outputs:[{dims:f[0].dims,dataType:f[0].dataType}],dispatchGroup:{x:Math.ceil(se/64)},programUniforms:ne?[{type:12,data:se},..._A(j)]:[{type:12,data:se}]})}},ji=f=>vt(f),ho=(f,I)=>{let{inputs:x,outputCount:N}=f,L=ji({...I,outputCount:N});if(G.webgpu.validateInputContent&&go(x,L),I.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");f.compute(zn(x,L))}}),p1=me(()=>{yr(),Qr(),Ts=f=>{if(f[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![320,640,1280].includes(f[0].dims[2]))throw new Error("number of channels should be 320, 640 or 1280");if(f[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(f[0].dims[2]!==f[1].dims[0])throw new Error("last dimension of input and bias are not the same")},Ro=f=>{let I=f[0].dims,x=f[0].dims[2],N=gt.size(I)/4,L=f[0].dataType,j=Yt("input",L,I,4),Y=Yt("bias",L,[x],4),Ae=Yt("residual",L,I,4),se=TA("output",L,I,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:I,dataType:f[0].dataType}],dispatchGroup:{x:Math.ceil(N/64)}}),getShaderSource:ne=>` 495 const channels = ${x}u / 4; 496 ${ne.declareVariables(j,Y,Ae,se)} 497 498 ${ne.mainStart()} 499 ${ne.guardAgainstOutOfBoundsWorkgroupSizes(N)} 500 let value = ${j.getByOffset("global_idx")} 501 + ${Y.getByOffset("global_idx % channels")} + ${Ae.getByOffset("global_idx")}; 502 ${se.setByOffset("global_idx","value")} 503 }`}},Jn=f=>{Ts(f.inputs),f.compute(Ro(f.inputs))}}),A2=me(()=>{hr(),yr(),va(),Qr(),rr=(f,I,x,N,L,j,Y)=>{let Ae=Math.ceil(I/4),se="";se=typeof L=="string"?`${L}(a)`:L("a");let ne=Yt("inputData",x,[Ae],4),ye=TA("outputData",N,[Ae],4),Be=[{name:"vec_size",type:"u32"}];return Y&&Be.push(...Y),` 504 ${f.registerUniforms(Be).declareVariables(ne,ye)} 505 506 ${j??""} 507 508 ${f.mainStart()} 509 ${f.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} 510 511 let a = ${ne.getByOffset("global_idx")}; 512 ${ye.setByOffset("global_idx",se)} 513 }`},wr=(f,I,x,N,L,j=f.dataType,Y,Ae)=>{let se=[{type:12,data:Math.ceil(gt.size(f.dims)/4)}];return Y&&se.push(...Y),{name:I,shaderCache:{hint:L,inputDependencies:["type"]},getShaderSource:ne=>rr(ne,gt.size(f.dims),f.dataType,j,x,N,Ae),getRunData:ne=>({outputs:[{dims:f.dims,dataType:j}],dispatchGroup:{x:Math.ceil(gt.size(ne[0].dims)/64/4)},programUniforms:se})}},Gi=f=>{f.compute(wr(f.inputs[0],"Abs","abs"))},Na=f=>{f.compute(wr(f.inputs[0],"Acos","acos"))},po=f=>{f.compute(wr(f.inputs[0],"Acosh","acosh"))},Ws=f=>{f.compute(wr(f.inputs[0],"Asin","asin"))},On=f=>{f.compute(wr(f.inputs[0],"Asinh","asinh"))},fa=f=>{f.compute(wr(f.inputs[0],"Atan","atan"))},$n=f=>{f.compute(wr(f.inputs[0],"Atanh","atanh"))},Wi=f=>vt(f),dn=(f,I)=>{let x;switch(I.to){case 10:x="vec4<f16>";break;case 1:x="vec4<f32>";break;case 12:x="vec4<u32>";break;case 6:x="vec4<i32>";break;case 9:x="vec4<bool>";break;default:throw new RangeError(`not supported type (specified in attribute 'to' from 'Cast' operator): ${I.to}`)}f.compute(wr(f.inputs[0],"Cast",x,void 0,I.cacheKey,I.to))},s0=f=>{let I,x,N=f.length>=2&&f[1].data!==0,L=f.length>=3&&f[2].data!==0;switch(f[0].dataType){case 1:I=N?f[1].getFloat32Array()[0]:-34028234663852886e22,x=L?f[2].getFloat32Array()[0]:34028234663852886e22;break;case 10:I=N?f[1].getUint16Array()[0]:64511,x=L?f[2].getUint16Array()[0]:31743;break;default:throw new Error("Unsupport data type")}return vt({min:I,max:x})},Ui=(f,I)=>{let x=I||s0(f.inputs),N=IA(f.inputs[0].dataType);f.compute(wr(f.inputs[0],"Clip",L=>`clamp(${L}, vec4<${N}>(uniforms.min), vec4<${N}>(uniforms.max))`,void 0,x.cacheKey,void 0,[{type:f.inputs[0].dataType,data:x.min},{type:f.inputs[0].dataType,data:x.max}],[{name:"min",type:N},{name:"max",type:N}]),{inputs:[0]})},Zr=f=>{f.compute(wr(f.inputs[0],"Ceil","ceil"))},sa=f=>{f.compute(wr(f.inputs[0],"Cos","cos"))},jo=f=>{f.compute(wr(f.inputs[0],"Cosh","cosh"))},Us=f=>vt(f),za=(f,I)=>{let x=IA(f.inputs[0].dataType);f.compute(wr(f.inputs[0],"Elu",N=>`elu_vf32(${N})`,` 514 const elu_alpha_ = ${x}(${I.alpha}); 515 516 fn elu_f32(a: ${x}) -> ${x} { 517 return select((exp(a) - 1.0) * elu_alpha_, a, a >= 0.0); 518 } 519 520 fn elu_vf32(v: vec4<${x}>) -> vec4<${x}> { 521 return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w)); 522 }`,I.cacheKey))},ei=(f="f32")=>` 523const r0: ${f} = 0.3275911; 524const r1: ${f} = 0.254829592; 525const r2: ${f} = -0.284496736; 526const r3: ${f} = 1.421413741; 527const r4: ${f} = -1.453152027; 528const r5: ${f} = 1.061405429; 529 530fn erf_vf32(v: vec4<${f}>) -> vec4<${f}> { 531 let absv = abs(v); 532 let x = 1.0 / (1.0 + r0 * absv); 533 return sign(v) * (1.0 - ((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv)); 534}`,mo=f=>{let I=IA(f.inputs[0].dataType);f.compute(wr(f.inputs[0],"Erf",x=>`erf_vf32(${x})`,ei(I)))},gn=f=>{f.compute(wr(f.inputs[0],"Exp","exp"))},Wo=f=>{f.compute(wr(f.inputs[0],"Floor","floor"))},Ki=f=>{let I=IA(f.inputs[0].dataType);f.compute(wr(f.inputs[0],"Gelu",x=>`0.5 * ${x} * (1.0 + erf_vf32(${x} * 0.7071067811865475))`,ei(I)))},aa=(f,I)=>{let x=IA(f.inputs[0].dataType);f.compute(wr(f.inputs[0],"LeakyRelu",N=>`select(leaky_relu_alpha_ * ${N}, ${N}, ${N} >= vec4<${x}>(0.0))`,`const leaky_relu_alpha_ = ${x}(${I.alpha});`,I.cacheKey))},da=f=>{f.compute(wr(f.inputs[0],"Not",I=>`!${I}`))},a0=f=>{f.compute(wr(f.inputs[0],"Neg",I=>`-${I}`))},bo=f=>{f.compute(wr(f.inputs[0],"Reciprocal",I=>`1.0/${I}`))},hn=f=>{let I=IA(f.inputs[0].dataType);f.compute(wr(f.inputs[0],"Relu",x=>`select(vec4<${I}>(0.0), ${x}, ${x} > vec4<${I}>(0.0))`))},ti=f=>{f.compute(wr(f.inputs[0],"Sigmoid",I=>`(1.0 / (1.0 + exp(-${I})))`))},Zi=f=>vt(f),rs=(f,I)=>{let x=IA(f.inputs[0].dataType);f.compute(wr(f.inputs[0],"HardSigmoid",N=>`max(vec4<${x}>(0.0), min(vec4<${x}>(1.0), ${I.alpha} * ${N} + vec4<${x}>(${I.beta})))`,void 0,I.cacheKey))},Uo=f=>{f.compute(wr(f.inputs[0],"Sin","sin"))},Xi=f=>{f.compute(wr(f.inputs[0],"Sinh","sinh"))},Ua=f=>{f.compute(wr(f.inputs[0],"Sqrt","sqrt"))},Ka=f=>{f.compute(wr(f.inputs[0],"Tan","tan"))},_i=f=>`sign(${f}) * (1 - exp(-2 * abs(${f}))) / (1 + exp(-2 * abs(${f})))`,pn=f=>{f.compute(wr(f.inputs[0],"Tanh",_i))},Bn=(f="f32")=>` 535const fast_gelu_a: ${f} = 0.5; 536const fast_gelu_b: ${f} = 0.7978845608028654; 537const fast_gelu_c: ${f} = 0.035677408136300125; 538 539fn tanh_v(v: vec4<${f}>) -> vec4<${f}> { 540 return ${_i("v")}; 541} 542`,Co=f=>`(fast_gelu_a + fast_gelu_a * tanh_v(${f} * (fast_gelu_c * ${f} * ${f} + fast_gelu_b))) * ${f}`,rn=f=>{let I=IA(f.inputs[0].dataType);f.compute(wr(f.inputs[0],"FastGelu",Co,Bn(I),void 0,f.inputs[0].dataType))},Ca=(f,I)=>{let x=IA(f.inputs[0].dataType);return f.compute(wr(f.inputs[0],"ThresholdedRelu",N=>`select(vec4<${x}>(0.0), ${N}, ${N} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${x}>(${I.alpha});`,I.cacheKey)),0},qi=f=>{f.compute(wr(f.inputs[0],"Log","log"))},Io=(f,I)=>` 543const alpha = vec4<${f}>(${I}); 544const one = ${f}(1.0); 545const zero = ${f}(0.0); 546 547fn quick_gelu_impl(x: vec4<${f}>) -> vec4<${f}> { 548 let v = x *alpha; 549 var x1 : vec4<${f}>; 550 for (var i = 0; i < 4; i = i + 1) { 551 if (v[i] >= zero) { 552 x1[i] = one / (one + exp(-v[i])); 553 } else { 554 x1[i] = one - one / (one + exp(v[i])); 555 } 556 } 557 return x * x1; 558} 559`,xn=f=>`quick_gelu_impl(${f})`,n0=(f,I)=>{let x=IA(f.inputs[0].dataType);f.compute(wr(f.inputs[0],"QuickGelu",xn,Io(x,I.alpha),I.cacheKey,f.inputs[0].dataType))}}),m1=me(()=>{yr(),Qr(),A2(),Ks=f=>{if(f[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![2560,5120,10240].includes(f[0].dims[2]))throw new Error("hidden state should be 2560, 5120 or 10240");if(f[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(f[0].dims[2]!==f[1].dims[0])throw new Error("last dimension of input and bias are not the same")},Za=f=>{let I=f[0].dims.slice();I[2]=I[2]/2;let x=Yt("input",f[0].dataType,f[0].dims,4),N=Yt("bias",f[0].dataType,[f[0].dims[2]],4),L=TA("output",f[0].dataType,I,4),j=gt.size(I)/4,Y=ir(f[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:I,dataType:f[0].dataType}],dispatchGroup:{x:Math.ceil(j/64)}}),getShaderSource:Ae=>` 560 const M_SQRT2 = sqrt(2.0); 561 const halfChannels = ${f[0].dims[2]/4/2}u; 562 563 ${Ae.declareVariables(x,N,L)} 564 565 ${ei(Y)} 566 567 ${Ae.mainStart()} 568 ${Ae.guardAgainstOutOfBoundsWorkgroupSizes(j)} 569 let biasIdx = global_idx % halfChannels; 570 let batchIndex = global_idx / halfChannels; 571 let inputOffset = biasIdx + batchIndex * halfChannels * 2; 572 let valueLeft = input[inputOffset] + bias[biasIdx]; 573 let valueRight = input[inputOffset + halfChannels] + bias[biasIdx + halfChannels]; 574 let geluRight = valueRight * 0.5 * (erf_vf32(valueRight / M_SQRT2) + 1); 575 576 ${L.setByOffset("global_idx","valueLeft * geluRight")} 577 }`}},Ia=f=>{Ks(f.inputs),f.compute(Za(f.inputs))}}),b1=me(()=>{hr(),yr(),Qr(),wo=(f,I,x,N,L,j,Y,Ae,se,ne,ye,Be)=>{let te,Te;typeof Ae=="string"?te=Te=(ft,bt)=>`${Ae}((${ft}),(${bt}))`:typeof Ae=="function"?te=Te=Ae:(te=Ae.scalar,Te=Ae.vector);let Oe,Ke=TA("outputData",ye,N.length,4),Re=Yt("aData",se,I.length,4),et=Yt("bData",ne,x.length,4);if(L)if(j){let ft=gt.size(I)===1,bt=gt.size(x)===1,Mt=I.length>0&&I[I.length-1]%4==0,Dt=x.length>0&&x[x.length-1]%4==0;Oe=ft||bt?Ke.setByOffset("global_idx",Te(ft?`${Re.type.value}(${Re.getByOffset("0")}.x)`:Re.getByOffset("global_idx"),bt?`${et.type.value}(${et.getByOffset("0")}.x)`:et.getByOffset("global_idx"))):` 578 let outputIndices = ${Ke.offsetToIndices("global_idx * 4u")}; 579 let offsetA = ${Re.broadcastedIndicesToOffset("outputIndices",Ke)}; 580 let offsetB = ${et.broadcastedIndicesToOffset("outputIndices",Ke)}; 581 ${Ke.setByOffset("global_idx",Te(Y||Mt?Re.getByOffset("offsetA / 4u"):`${Re.type.value}(${Re.getByOffset("offsetA / 4u")}[offsetA % 4u])`,Y||Dt?et.getByOffset("offsetB / 4u"):`${et.type.value}(${et.getByOffset("offsetB / 4u")}[offsetB % 4u])`))} 582 `}else Oe=Ke.setByOffset("global_idx",Te(Re.getByOffset("global_idx"),et.getByOffset("global_idx")));else{if(!j)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let ft=(bt,Mt,Dt="")=>{let Nt=`aData[indexA${Mt}][componentA${Mt}]`,hA=`bData[indexB${Mt}][componentB${Mt}]`;return` 583 let outputIndices${Mt} = ${Ke.offsetToIndices(`global_idx * 4u + ${Mt}u`)}; 584 let offsetA${Mt} = ${Re.broadcastedIndicesToOffset(`outputIndices${Mt}`,Ke)}; 585 let offsetB${Mt} = ${et.broadcastedIndicesToOffset(`outputIndices${Mt}`,Ke)}; 586 let indexA${Mt} = offsetA${Mt} / 4u; 587 let indexB${Mt} = offsetB${Mt} / 4u; 588 let componentA${Mt} = offsetA${Mt} % 4u; 589 let componentB${Mt} = offsetB${Mt} % 4u; 590 ${bt}[${Mt}] = ${Dt}(${te(Nt,hA)}); 591 `};Oe=ye===9?` 592 var data = vec4<u32>(0); 593 ${ft("data",0,"u32")} 594 ${ft("data",1,"u32")} 595 ${ft("data",2,"u32")} 596 ${ft("data",3,"u32")} 597 outputData[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:` 598 ${ft("outputData[global_idx]",0)} 599 ${ft("outputData[global_idx]",1)} 600 ${ft("outputData[global_idx]",2)} 601 ${ft("outputData[global_idx]",3)} 602 `}return` 603 ${f.registerUniform("vec_size","u32").declareVariables(Re,et,Ke)} 604 605 ${Be??""} 606 607 ${f.mainStart()} 608 ${f.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} 609 ${Oe} 610 }`},sn=(f,I,x,N,L,j,Y=x.dataType)=>{let Ae=x.dims.map(Ke=>Number(Ke)??1),se=N.dims.map(Ke=>Number(Ke)??1),ne=!gt.areEqual(Ae,se),ye=Ae,Be=gt.size(Ae),te=!1,Te=!1,Oe=[ne];if(ne){let Ke=lA.calcShape(Ae,se,!1);if(!Ke)throw new Error("Can't perform binary op on the given tensors");ye=Ke.slice(),Be=gt.size(ye);let Re=gt.size(Ae)===1,et=gt.size(se)===1,ft=Ae.length>0&&Ae[Ae.length-1]%4==0,bt=se.length>0&&se[se.length-1]%4==0;Oe.push(Re),Oe.push(et),Oe.push(ft),Oe.push(bt);let Mt=1;for(let Dt=1;Dt<ye.length;Dt++){let Nt=Ae[Ae.length-Dt];if(Nt!==se[se.length-Dt])break;Mt*=Nt}Mt%4==0?(Te=!0,te=!0):(Re||et||ft||bt)&&(te=!0)}else te=!0;return Oe.push(te),{name:f,shaderCache:{hint:I+Oe.map(Ke=>Ke.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:Ke=>wo(Ke,Ae,se,ye,te,ne,Te,L,x.dataType,N.dataType,Y,j),getRunData:()=>({outputs:[{dims:ye,dataType:Y}],dispatchGroup:{x:Math.ceil(Be/64/4)},programUniforms:[{type:12,data:Math.ceil(gt.size(ye)/4)},..._A(Ae,se,ye)]})}},mn=(f,I,x,N,L,j)=>{f.compute(sn(I,L??"",f.inputs[0],f.inputs[1],x,N,j))},ss=f=>{mn(f,"Add",(I,x)=>`${I}+${x}`)},bn=f=>{mn(f,"Div",(I,x)=>`${I}/${x}`)},hi=f=>{mn(f,"Equal",{scalar:(I,x)=>`u32(${I}==${x})`,vector:(I,x)=>`vec4<u32>(${I}==${x})`},void 0,void 0,9)},pi=f=>{mn(f,"Mul",(I,x)=>`${I}*${x}`)},Mn=f=>{let I=Yt("input",f.inputs[0].dataType,f.inputs[0].dims).type.value;mn(f,"Pow",{scalar:(x,N)=>`pow_custom(${x},${N})`,vector:(x,N)=>`pow_vector_custom(${x},${N})`},` 611 fn pow_custom(a : ${I}, b : ${I}) -> ${I} { 612 if (b == ${I}(0.0)) { 613 return ${I}(1.0); 614 } else if (a < ${I}(0.0) && f32(b) != floor(f32(b))) { 615 return ${I}(pow(f32(a), f32(b))); // NaN 616 } 617 return select(sign(a), ${I}(1.0), round(f32(abs(b) % ${I}(2.0))) != 1.0) * ${I}(${I==="i32"?"round":""}(pow(f32(abs(a)), f32(b)))); 618 } 619 fn pow_vector_custom(a : vec4<${I}>, b : vec4<${I}>) -> vec4<${I}> { 620 // TODO: implement vectorized pow 621 return vec4<${I}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w)); 622 } 623 `)},Ko=f=>{mn(f,"Sub",(I,x)=>`${I}-${x}`)},ko=f=>{mn(f,"Greater",{scalar:(I,x)=>`u32(${I}>${x})`,vector:(I,x)=>`vec4<u32>(${I}>${x})`},void 0,void 0,9)},Dn=f=>{mn(f,"Less",{scalar:(I,x)=>`u32(${I}<${x})`,vector:(I,x)=>`vec4<u32>(${I}<${x})`},void 0,void 0,9)},mi=f=>{mn(f,"GreaterOrEqual",{scalar:(I,x)=>`u32(${I}>=${x})`,vector:(I,x)=>`vec4<u32>(${I}>=${x})`},void 0,void 0,9)},an=f=>{mn(f,"LessOrEqual",{scalar:(I,x)=>`u32(${I}<=${x})`,vector:(I,x)=>`vec4<u32>(${I}<=${x})`},void 0,void 0,9)}}),C1=me(()=>{hr(),yr(),va(),Qr(),Cn=(f,I)=>{if(!f||f.length<1)throw new Error("too few inputs");let x=f[0],N=x.dataType,L=x.dims.length;f.forEach((j,Y)=>{if(Y!==0){if(j.dataType!==N)throw new Error("input tensors should be one type");if(j.dims.length!==L)throw new Error("input tensors should have the same shape");j.dims.forEach((Ae,se)=>{if(se!==I&&Ae!==x.dims[se])throw new Error("non concat dimensions must match")})}})},vo=(f,I)=>` 624 fn calculateInputIndex(index: u32) -> u32 { 625 let sizeInConcatAxis = array<u32, ${f}u>(${I}); 626 for (var i: u32 = 0u; i < ${f}; i += 1u ) { 627 if (index < sizeInConcatAxis[i]) { 628 return i; 629 } 630 } 631 return ${f}u; 632 }`,Hs=(f,I)=>{let x=f.length,N=[];for(let L=0;L<x;++L){let j=I.setByOffset("global_idx",f[L].getByIndices("indices"));x===1?N.push(j):L===0?N.push(`if (inputIndex == ${L}u) { ${j} }`):L===x-1?N.push(`else { ${j} }`):N.push(`else if (inputIndex == ${L}) { ${j} }`)}return N.join(` 633`)},vr=(f,I,x,N)=>{let L=gt.size(x),j=new Array(f.length),Y=new Array(f.length),Ae=0,se=[],ne=[],ye=[{type:12,data:L}];for(let Oe=0;Oe<f.length;++Oe)Ae+=f[Oe].dims[I],j[Oe]=Ae,ne.push(f[Oe].dims.length),Y[Oe]=Yt(`input${Oe}`,N,ne[Oe]),se.push("rank"),ye.push({type:12,data:j[Oe]});for(let Oe=0;Oe<f.length;++Oe)ye.push(..._A(f[Oe].dims));ye.push(..._A(x));let Be=TA("output",N,x.length),te=Be.indicesGet("indices",I),Te=Array.from(Array(j.length).keys()).map(Oe=>`uniforms.sizeInConcatAxis${Oe}`).join(",");return{name:"Concat",shaderCache:{hint:`${I}`,inputDependencies:se},getRunData:()=>({outputs:[{dims:x,dataType:N}],dispatchGroup:{x:Math.ceil(L/64)},programUniforms:ye}),getShaderSource:Oe=>` 634 635 ${(()=>{Oe.registerUniform("outputSize","u32");for(let Ke=0;Ke<f.length;Ke++)Oe.registerUniform(`sizeInConcatAxis${Ke}`,"u32");return Oe.declareVariables(...Y,Be)})()} 636 637 ${vo(j.length,Te)} 638 639 ${Oe.mainStart()} 640 ${Oe.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} 641 642 var indices = ${Be.offsetToIndices("global_idx")}; 643 644 let inputIndex = calculateInputIndex(${te}); 645 if (inputIndex != 0u) { 646 let sizeInConcatAxis = array<u32, ${j.length}u>(${Te}); 647 ${te} -= sizeInConcatAxis[inputIndex - 1u]; 648 } 649 650 ${Hs(Y,Be)} 651 }`}},Ji=(f,I)=>{let x=f.inputs,N=x[0].dims,L=gt.normalizeAxis(I.axis,N.length);Cn(x,L);let j=N.slice();j[L]=x.reduce((Ae,se)=>Ae+(se.dims.length>L?se.dims[L]:0),0);let Y=x.filter(Ae=>gt.size(Ae.dims)>0);f.compute(vr(Y,L,j,x[0].dataType),{inputs:Y})},Qo=f=>vt({axis:f.axis})}),_0=me(()=>{hr(),yr(),wa=(f,I,x="f32")=>{switch(f.activation){case"Relu":return`value = max(value, ${I}(0.0));`;case"Sigmoid":return`value = (${I}(1.0) / (${I}(1.0) + exp(-value)));`;case"Clip":return`value = clamp(value, ${I}(${x}(uniforms.clip_min)), ${I}(${x}(uniforms.clip_max)));`;case"HardSigmoid":return`value = max(${I}(0.0), min(${I}(1.0), ${x}(uniforms.alpha) * value + ${x}(uniforms.beta)));`;case"LeakyRelu":return`value = select(${x}(uniforms.alpha) * value, value, value >= ${I}(0.0));`;case"Tanh":return`let e2x = exp(-2.0 * abs(value)); 652 value = sign(value) * (1.0 - e2x) / (1.0 + e2x); 653 `;case"":return"";default:throw new Error(`Unsupported activation ${f.activation}`)}},bi=(f,I)=>{f.activation==="Clip"?I.push({type:1,data:f.clipMax},{type:1,data:f.clipMin}):f.activation==="HardSigmoid"?I.push({type:1,data:f.alpha},{type:1,data:f.beta}):f.activation==="LeakyRelu"&&I.push({type:1,data:f.alpha})},Ai=(f,I)=>{f.activation==="Clip"?I.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):f.activation==="HardSigmoid"?I.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):f.activation==="LeakyRelu"&&I.push({name:"alpha",type:"f32"})},In=f=>{let I=(f==null?void 0:f.activation)||"";if(I==="HardSigmoid"){let[x,N]=(f==null?void 0:f.activation_params)||[.2,.5];return{activation:I,alpha:x,beta:N}}if(I==="Clip"){let[x,N]=(f==null?void 0:f.activation_params)||[sr,jr];return{activation:I,clipMax:N,clipMin:x}}if(I==="LeakyRelu"){let[x]=(f==null?void 0:f.activation_params)||[.01];return{activation:I,alpha:x}}return{activation:I}}}),r2=me(()=>{ka=(f,I)=>{switch(f){case 1:return I;case 2:return`vec2<${I}>`;case 3:return`vec3<${I}>`;case 4:return`vec4<${I}>`;default:throw new Error(`${f}-component is not supported.`)}},Po=f=>` 654 ${f?"value = value + getBiasByOutputCoords(coords);":""} 655 `}),I1=me(()=>{wn=f=>` 656fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 { 657 return dot(coords, vec4<i32>( 658 shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1)); 659} 660fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 { 661 return dot(coords, vec4<i32>( 662 i32(${f}.x), i32(${f}.y), i32(${f}.z), 1)); 663} 664`}),s2=me(()=>{hr(),yr(),Qr(),_0(),$i=(f,I,x,N,L)=>{let j=N-x;return` 665 ${Array.from({length:x}).map((Y,Ae)=>` 666 if (${PA(I.shape,Ae,I.rank)} != 1) { 667 ${I.indicesSet(f,Ae,PA(L,Ae+j,N))} 668 } else { 669 ${I.indicesSet(f,Ae,0)} 670 }`).join("")} 671`},Ti=(f,I,x,N,L=!1,j)=>{let Y=f[0].dims,Ae=f[1].dims,se=Y[Y.length-2],ne=Ae[Ae.length-1],ye=Y[Y.length-1],Be=_r(ne),te=_r(ye),Te=_r(se),Oe=gt.size(x)/Be/Te,Ke=f.length>2,Re=N?N.slice(0,-2):x.slice(0,-2),et=[gt.size(Re),se,ne],ft=[{type:12,data:Oe},{type:12,data:se},{type:12,data:ne},{type:12,data:ye}];return bi(I,ft),ft.push(..._A(Re,Y,Ae)),Ke&&ft.push(..._A(f[2].dims)),ft.push(..._A(et)),{name:"MatMulNaive",shaderCache:{hint:`${I.activation};${Be};${te};${Te};${L}`,inputDependencies:Ke?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:j?j(x):x,dataType:f[0].dataType}],dispatchGroup:{x:Math.ceil(Oe/64)},programUniforms:ft}),getShaderSource:bt=>{let Mt=Ss("batch_dims",f[0].dataType,Re.length),Dt=Yt("a",f[0].dataType,Y.length,te),Nt=Yt("b",f[1].dataType,Ae.length,Be),hA=TA("output",f[0].dataType,et.length,Be),xA=ir(hA.type.tensor),er=wa(I,hA.type.value,xA),tr=[Dt,Nt],qr="";if(Ke){let Ir=L?Be:1;tr.push(Yt("bias",f[2].dataType,f[2].dims.length,Ir)),qr=L?`value += bias[col / ${Ir}];`:`value += ${hA.type.value}(bias[row + i]);`}let Ar=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];return Ai(I,Ar),` 672 ${bt.registerUniforms(Ar).registerInternalVariables(Mt).declareVariables(...tr,hA)} 673 ${bt.mainStart()} 674 ${bt.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} 675 let col = (global_idx % (uniforms.N / ${Be})) * ${Be}; 676 var index1 = global_idx / (uniforms.N / ${Be}); 677 let stride1 = uniforms.M / ${Te}; 678 let row = (index1 % stride1) * ${Te}; 679 let batch = index1 / stride1; 680 681 ${x.length===2?"":`let batch_indices = ${Mt.offsetToIndices("batch")};`} 682 683 var a_indices: ${Dt.type.indices}; 684 ${$i("a_indices",Dt,Dt.rank-2,Mt.rank,"batch_indices")} 685 ${Dt.indicesSet("a_indices",Dt.rank-2,0)} 686 ${Dt.indicesSet("a_indices",Dt.rank-1,0)} 687 let a_offset = ${Dt.indicesToOffset("a_indices")}; 688 689 var b_indices: ${Nt.type.indices}; 690 ${$i("b_indices",Nt,Nt.rank-2,Mt.rank,"batch_indices")} 691 ${Nt.indicesSet("b_indices",Nt.rank-2,0)} 692 ${Nt.indicesSet("b_indices",Nt.rank-1,0)} 693 let b_offset = ${Nt.indicesToOffset("b_indices")}; 694 var values: array<${hA.type.value}, ${Te}>; 695 for (var k: u32 = 0u; k < uniforms.K; k = k + ${te}) { 696 ${(()=>{let Ir=`var a_data: ${Dt.type.value};`;for(let mr=0;mr<te;mr++)Ir+=` 697 let b_data${mr} = b[(b_offset + (k + ${mr}) * uniforms.N + col) / ${Be}];`;for(let mr=0;mr<Te;mr++){Ir+=`a_data = a[(a_offset + (row + ${mr}) * uniforms.K + k) / ${te}];`;for(let Lt=0;Lt<te;Lt++)Ir+=` 698 values[${mr}] = fma(${Nt.type.value}(a_data${te===1?"":`[${Lt}]`}), b_data${Lt}, values[${mr}]); 699`}return Ir})()} 700 } 701 for (var i = 0u; i < ${Te}u; i++) { 702 var value = values[i]; 703 ${qr} 704 ${er} 705 let cur_indices = ${hA.type.indices}(batch, row + i, col); 706 let offset = ${hA.indicesToOffset("cur_indices")}; 707 ${hA.setByOffset(`offset / ${Be}`,"value")}; 708 } 709 } 710 `}}}}),a2=me(()=>{hr(),yr(),Qr(),_0(),s2(),r2(),gs=(f,I)=>f?` 711 mm_Asub[inputRow][inputCol] = mm_readA(batch, 712 kStart + inputRow, 713 globalRowStart / innerElementSize + inputCol${I?", batchIndices":""}); 714 `:` 715 mm_Asub[inputRow][inputCol] = mm_readA(batch, 716 globalRow + innerRow, 717 kStart / innerElementSize + inputCol${I?", batchIndices":""}); 718 `,Zo=(f,I)=>f?` 719 let ACached0 = mm_Asub[k * innerElementSize][localRow]; 720 let ACached1 = mm_Asub[k * innerElementSize + 1][localRow]; 721 let ACached2 = mm_Asub[k * innerElementSize + 2][localRow]; 722 ${I===3?"":"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];"} 723 for (var i = 0; i < rowPerThread; i = i + 1) { 724 acc[i] = BCached0 * ACached0[i] + acc[i]; 725 acc[i] = BCached1 * ACached1[i] + acc[i]; 726 acc[i] = BCached2 * ACached2[i] + acc[i]; 727 ${I===3?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"} 728 }`:` 729 for (var i = 0; i < rowPerThread; i = i + 1) { 730 let ACached = mm_Asub[tileRow + i][k]; 731 acc[i] = BCached0 * ACached.x + acc[i]; 732 acc[i] = BCached1 * ACached.y + acc[i]; 733 acc[i] = BCached2 * ACached.z + acc[i]; 734 ${I===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"} 735 }`,eo=(f,I,x="f32",N,L=!1,j=32,Y=!1,Ae=32)=>{let se=I[1]*f[1],ne=I[0]*f[0],ye=L?se:j,Be=L?j:se,te=ye/I[0],Te=j/I[1];if((!L||te!==4||f[1]!==4)&&(L||te!==3&&te!==4)||ye%I[0]!=0||j%I[1]!=0||f[0]!==4)throw new Error(`If transposeA ${L} is true, innerElementSize ${te} and workPerThread[1] ${f[1]} must be 4. 736 Otherwise, innerElementSize ${te} must be 3 or 4. 737 tileAWidth ${ye} must be divisible by workgroupSize[0]${I[0]}. tileInner ${j} must be divisible by workgroupSize[1] ${I[1]}. colPerThread ${f[0]} must be 4.`);return` 738var<workgroup> mm_Asub: array<array<vec${te}<${x}>, ${ye/te}>, ${Be}>; 739var<workgroup> mm_Bsub: array<array<vec4<${x}>, ${ne/f[0]}>, ${j}>; 740 741const rowPerThread = ${f[1]}; 742const colPerThread = ${f[0]}; 743const innerElementSize = ${te}; 744const tileInner = ${j}; 745 746@compute @workgroup_size(${I[0]}, ${I[1]}, ${I[2]}) 747fn main(@builtin(local_invocation_id) localId : vec3<u32>, 748 @builtin(global_invocation_id) globalId : vec3<u32>, 749 @builtin(workgroup_id) workgroupId : vec3<u32>) { 750 let localRow = i32(localId.y); 751 let tileRow = localRow * rowPerThread; 752 let tileCol = i32(localId.x); 753 754 let globalRow =i32(globalId.y) * rowPerThread; 755 let globalCol = i32(globalId.x); 756 let batch = ${Y?"0":"i32(globalId.z)"}; 757 ${N?`let batchIndices = ${N.offsetToIndices("u32(batch)")};`:""} 758 let globalRowStart = i32(workgroupId.y) * ${se}; 759 760 let num_tiles = ${Y?`${Math.ceil(Ae/j)}`:"(uniforms.dim_inner - 1) / tileInner + 1"}; 761 var kStart = ${Y?`i32(globalId.z) * ${Ae}`:"0"}; 762 763 var acc: array<vec4<${x}>, rowPerThread>; 764 765 // Loop over shared dimension. 766 let tileRowB = localRow * ${Te}; 767 for (var t = 0; t < num_tiles; t = t + 1) { 768 // Load one tile of A into local memory. 769 for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { 770 let inputRow = tileRow + innerRow; 771 let inputCol = tileCol; 772 ${gs(L,N)} 773 } 774 775 // Load one tile of B into local memory. 776 for (var innerRow = 0; innerRow < ${Te}; innerRow = innerRow + 1) { 777 let inputRow = tileRowB + innerRow; 778 let inputCol = tileCol; 779 mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${N?", batchIndices":""}); 780 } 781 kStart = kStart + tileInner; 782 workgroupBarrier(); 783 784 // Compute acc values for a single thread. 785 for (var k = 0; k < tileInner / innerElementSize; k = k + 1) { 786 let BCached0 = mm_Bsub[k * innerElementSize][tileCol]; 787 let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol]; 788 let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol]; 789 ${te===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"} 790 791 ${Zo(L,te)} 792 } 793 794 workgroupBarrier(); 795 } 796 797 for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { 798 mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]); 799 } 800}`},Ga=(f,I)=>f?` 801 mm_Asub[inputRow][inputCol] = mm_readA(batch, 802 kStart + inputRow, 803 globalRowStart + inputCol${I?", batchIndices":""}); 804 `:` 805 mm_Asub[inputRow][inputCol] = mm_readA(batch, 806 globalRowStart + inputRow, 807 kStart + inputCol${I?", batchIndices":""}); 808 `,i0=f=>f?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",Ci=(f,I,x="f32",N,L=!1,j=32,Y=!1,Ae=32,se=!1)=>{let ne=f[1]*I[1],ye=f[0]*I[0],Be=L?ne:j,te=L?j:ne;if(te%I[1]!=0||Be%I[0]!=0||j%I[1]!=0)throw new Error(`tileAHight ${te} must be divisible by workgroupSize[1]${I[1]}, tileAWidth ${Be} must be divisible by workgroupSize[0]${I[0]}, tileInner ${j} must be divisible by workgroupSize[1]${I[1]}`);let Te=te/I[1],Oe=Be/I[0],Ke=j/I[1],Re=se?` 809 let localRow = i32(localId.y); 810 let localCol = i32(localId.x); 811 let globalRowStart = i32(workgroupId.y) * ${ne}; 812 let globalColStart = i32(workgroupId.x) * ${ye}; 813 814 // Loop over shared dimension. 815 for (var t = 0; t < num_tiles; t = t + 1) { 816 // Load one tile of A into local memory. 817 for (var inputRow = localRow; inputRow < ${te}; inputRow = inputRow + ${I[1]}) { 818 for (var inputCol = localCol; inputCol < ${Be}; inputCol = inputCol + ${I[0]}) { 819 ${Ga(L,N)} 820 } 821 } 822 // Load one tile of B into local memory. 823 for (var inputRow = localRow; inputRow < ${j}; inputRow = inputRow + ${I[1]}) { 824 for (var inputCol = localCol; inputCol < ${ye}; inputCol = inputCol + ${I[0]}) { 825 mm_Bsub[inputRow][inputCol] = mm_readB(batch, 826 kStart + inputRow, 827 globalColStart + inputCol${N?", batchIndices":""}); 828 } 829 } 830 kStart = kStart + tileInner; 831 workgroupBarrier(); 832 833 // Compute acc values for a single thread. 834 var BCached : array<${x}, colPerThread>; 835 for (var k = 0; k < tileInner; k = k + 1) { 836 for (var inner = 0; inner < colPerThread; inner = inner + 1) { 837 BCached[inner] = mm_Bsub[k][localCol + inner * ${I[0]}]; 838 } 839 for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { 840 let ACached = ${L?`mm_Asub[k][localRow + innerRow * ${I[1]}];`:`mm_Asub[localRow + innerRow * ${I[1]}][k];`} 841 for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { 842 acc[innerRow][innerCol] = acc[innerRow][innerCol] + 843 ACached * BCached[innerCol]; 844 } 845 } 846 } 847 workgroupBarrier(); 848 } 849 for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { 850 let gRow = globalRowStart + localRow + innerRow * ${I[1]}; 851 for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { 852 let gCol = globalColStart + localCol + innerCol * ${I[0]}; 853 mm_write(batch, gRow, gCol, acc[innerRow][innerCol]); 854 } 855 } 856 `:` 857let tileRow = i32(localId.y) * rowPerThread; 858let tileCol = i32(localId.x) * colPerThread; 859 860let globalRow = i32(globalId.y) * rowPerThread; 861let globalCol = i32(globalId.x) * colPerThread; 862let globalRowStart = i32(workgroupId.y) * ${ne}; 863 864let tileRowA = i32(localId.y) * ${Te}; 865let tileColA = i32(localId.x) * ${Oe}; 866let tileRowB = i32(localId.y) * ${Ke}; 867// Loop over shared dimension. 868for (var t = 0; t < num_tiles; t = t + 1) { 869 // Load one tile of A into local memory. 870 for (var innerRow = 0; innerRow < ${Te}; innerRow = innerRow + 1) { 871 for (var innerCol = 0; innerCol < ${Oe}; innerCol = innerCol + 1) { 872 let inputRow = tileRowA + innerRow; 873 let inputCol = tileColA + innerCol; 874 ${Ga(L,N)} 875 } 876 } 877 878 // Load one tile of B into local memory. 879 for (var innerRow = 0; innerRow < ${Ke}; innerRow = innerRow + 1) { 880 for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { 881 let inputRow = tileRowB + innerRow; 882 let inputCol = tileCol + innerCol; 883 mm_Bsub[inputRow][inputCol] = mm_readB(batch, 884 kStart + inputRow, 885 globalCol + innerCol${N?", batchIndices":""}); 886 } 887 } 888 kStart = kStart + tileInner; 889 workgroupBarrier(); 890 891 // Compute acc values for a single thread. 892 var BCached : array<${x}, colPerThread>; 893 for (var k = 0; k < tileInner; k = k + 1) { 894 for (var inner = 0; inner < colPerThread; inner = inner + 1) { 895 BCached[inner] = mm_Bsub[k][tileCol + inner]; 896 } 897 898 for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { 899 ${i0(L)} 900 for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { 901 acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol]; 902 } 903 } 904 } 905 906 workgroupBarrier(); 907} 908 909for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { 910 for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { 911 mm_write(batch, globalRow + innerRow, globalCol + innerCol, 912 acc[innerRow][innerCol]); 913 } 914} 915`;return` 916 var<workgroup> mm_Asub : array<array<${x}, ${Be}>, ${te}>; 917 var<workgroup> mm_Bsub : array<array<${x}, ${ye}>, ${j}>; 918 const rowPerThread = ${f[1]}; 919 const colPerThread = ${f[0]}; 920 const tileInner = ${j}; 921 922@compute @workgroup_size(${I[0]}, ${I[1]}, ${I[2]}) 923fn main(@builtin(local_invocation_id) localId : vec3<u32>, 924 @builtin(global_invocation_id) globalId : vec3<u32>, 925 @builtin(workgroup_id) workgroupId : vec3<u32>) { 926 let batch = ${Y?"0":"i32(globalId.z)"}; 927 ${N?`let batchIndices = ${N.offsetToIndices("u32(batch)")};`:""} 928 let num_tiles = ${Y?`${Math.ceil(Ae/j)}`:"(uniforms.dim_inner - 1) / tileInner + 1"}; 929 var kStart = ${Y?`i32(globalId.z) * ${Ae}`:"0"}; 930 931 var acc : array<array<${x}, colPerThread>, rowPerThread>; 932 ${Re} 933 } 934`},LA=(f,I,x,N,L=!1)=>{let[j,Y,Ae,se]=N,ne=ir(N[0].type.tensor);return` 935 fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${j.type.indices}) -> ${ka(f,ne)} { 936 var value = ${ka(f,ne)}(0.0); 937 let col = colIn * ${f}; 938 if(row < uniforms.dim_a_outer && col < uniforms.dim_inner) 939 { 940 var aIndices: ${Y.type.indices}; 941 ${$i("aIndices",Y,Y.rank-2,j.rank,"batchIndices")} 942 ${Y.indicesSet("aIndices",Y.rank-2,"u32(row)")} 943 ${Y.indicesSet("aIndices",Y.rank-1,"u32(colIn)")} 944 value = ${Y.getByIndices("aIndices")}; 945 } 946 return value; 947 } 948 949 fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${j.type.indices}) -> ${ka(f,ne)} { 950 var value = ${ka(f,ne)}(0.0); 951 let col = colIn * ${f}; 952 if(row < uniforms.dim_inner && col < uniforms.dim_b_outer) 953 { 954 var bIndices: ${Ae.type.indices}; 955 ${$i("bIndices",Ae,Ae.rank-2,j.rank,"batchIndices")} 956 ${Ae.indicesSet("bIndices",Ae.rank-2,"u32(row)")} 957 ${Ae.indicesSet("bIndices",Ae.rank-1,"u32(colIn)")} 958 value = ${Ae.getByIndices("bIndices")}; 959 } 960 return value; 961 } 962 963 fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${ka(f,ne)}) { 964 let col = colIn * ${f}; 965 if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) { 966 var value = valueIn; 967 let coords = vec3<i32>(batch, row, colIn); 968 ${I?`value = value + ${L?"bias[colIn]":`${ka(f,ne)}(bias[row])`};`:""} 969 ${x} 970 ${se.setByIndices("vec3<u32>(coords)","value")} 971 } 972 } 973 `},t=(f,I,x,N,L=!1,j)=>{let Y=f[0].dims,Ae=f[1].dims,se=Y.slice(0,-2),ne=Ae.slice(0,-2),ye=N?N.slice(0,-2):x.slice(0,-2),Be=gt.size(ye),te=Y[Y.length-2],Te=Y[Y.length-1],Oe=Ae[Ae.length-1],Ke=Te%4==0&&Oe%4==0,Re=te<=8?[4,1,1]:[4,4,1],et=[8,8,1],ft=[Math.ceil(Oe/et[0]/Re[0]),Math.ceil(te/et[1]/Re[1]),Math.ceil(Be/et[2]/Re[2])],bt=Ke?4:1,Mt=[...se,te,Te/bt],Dt=Mt.length,Nt=[...ne,Te,Oe/bt],hA=Nt.length,xA=[Be,te,Oe/bt],er=[{type:6,data:te},{type:6,data:Oe},{type:6,data:Te}];bi(I,er),er.push(..._A(ye,Mt,Nt));let tr=["rank","rank"],qr=f.length>2;return qr&&(er.push(..._A(f[2].dims)),tr.push("rank")),er.push(..._A(xA)),{name:"MatMul",shaderCache:{hint:`${Re};${I.activation};${Ke};${L}`,inputDependencies:tr},getRunData:()=>({outputs:[{dims:j?j(x):x,dataType:f[0].dataType}],dispatchGroup:{x:ft[0],y:ft[1],z:ft[2]},programUniforms:er}),getShaderSource:Ar=>{let Ir=ye.length,mr=Ss("batchDims",f[0].dataType,Ir,1),Lt=ir(f[0].dataType),UA=Yt("a",f[0].dataType,Dt,bt),HA=Yt("b",f[1].dataType,hA,bt),RA=TA("result",f[0].dataType,xA.length,bt),is=[UA,HA];if(qr){let fc=L?bt:1;is.push(Yt("bias",f[2].dataType,f[2].dims.length,fc))}let Va=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];Ai(I,Va);let ai=ir(RA.type.tensor),N0=wa(I,RA.type.value,ai),$0=LA(bt,qr,N0,[mr,UA,HA,RA],L);return` 974 ${Ar.registerUniforms(Va).registerInternalVariables(mr).declareVariables(...is,RA)} 975 ${$0} 976 ${Ke?eo(Re,et,Lt,mr):Ci(Re,et,Lt,mr)} 977 `}}}}),w1=me(()=>{hr(),qo(),Qr(),_0(),r2(),I1(),a2(),A=(f,I,x,N,L=!1,j,Y=4,Ae=4,se=4,ne="f32")=>{let ye=xA=>{switch(xA){case 1:return"return w[row * i32(uniforms.w_shape[3]) + colIn];";case 4:return"return w[row * i32(uniforms.w_shape[3]) / 4 + colIn];";default:throw new Error(`innerElementSize ${xA} is not supported.`)}},Be=f?` 978 let coord = vec4<i32>(batch, xRow, xCol, xCh); 979 `:` 980 let coord = vec4<i32>(batch, xCh, xRow, xCol); 981 `,te=f?` 982 let coords = vec4<i32>( 983 batch, 984 row / outWidth, 985 row % outWidth, 986 col); 987 `:` 988 let coords = vec4<i32>( 989 batch, 990 row, 991 col / outWidth, 992 col % outWidth); 993 `,Te=f?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",Oe=f?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",Ke=f?"row":"col",Re=f?"col":"row",et=` 994 let inChannels = i32(uniforms.w_shape[2]); 995 let outWidth = ${f?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; 996 let outRow = ${Ke} / outWidth; 997 let outCol = ${Ke} % outWidth; 998 999 let WRow = ${Re} / (i32(uniforms.w_shape[1]) * inChannels); 1000 let WCol = ${Re} / inChannels % i32(uniforms.w_shape[1]); 1001 let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0]; 1002 let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1]; 1003 let xCh = ${Re} % inChannels; 1004 var resData = ${ka(Y,ne)}(0.0); 1005 // The bounds checking is always needed since we use it to pad zero for 1006 // the 'same' padding type. 1007 if (xRow >= 0 && xRow < ${Te} && xCol >= 0 && xCol < ${Oe}) { 1008 ${Be} 1009 let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape)); 1010 ${(xA=>{switch(xA){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${ne}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${xA} is not supported.`)}})(Y)} 1011 } 1012 return resData;`,ft=f?I&&N?` 1013 let col = colIn * ${Y}; 1014 ${et}`:` 1015 let col = colIn * ${Y}; 1016 if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) { 1017 ${et} 1018 } 1019 return ${ka(Y,ne)}(0.0);`:N&&x?` 1020 let col = colIn * ${Y}; 1021 ${et}`:` 1022 let col = colIn * ${Y}; 1023 if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) { 1024 ${et} 1025 } 1026 return ${ka(Y,ne)}(0.0);`,bt=f?N&&x?ye(Ae):` 1027 let col = colIn * ${Ae}; 1028 if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) { 1029 ${ye(Ae)} 1030 } 1031 return ${ka(Ae,ne)}(0.0);`:` 1032 let col = colIn * ${Ae}; 1033 if (row < uniforms.dim_inner && col < uniforms.dim_a_outer) { 1034 ${ye(Ae)} 1035 } 1036 return ${ka(Ae,ne)}(0.0);`,Mt=ka(se,ne),Dt=ka(f?Y:Ae,ne),Nt=ka(f?Ae:Y,ne),hA=wa(j,Mt,ne);return` 1037 fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${Dt} { 1038 ${f?ft:bt} 1039 } 1040 1041 fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${Nt} { 1042 ${f?bt:ft} 1043 } 1044 1045 fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${Mt}) { 1046 let col = colIn * ${se}; 1047 if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) 1048 { 1049 var value = valueIn; 1050 let outWidth = ${f?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; 1051 ${te} 1052 ${Po(L)} 1053 ${hA} 1054 setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); 1055 } 1056 }`},r=(f,I,x,N,L,j,Y,Ae,se)=>{let ne=I.format==="NHWC",ye=ne?f[0].dims[3]:f[0].dims[1],Be=x[0],te=ne?x[2]:x[3],Te=ne?x[1]:x[2],Oe=ne?x[3]:x[1],Ke=ne&&(ye%4==0||ye%3==0)&&Oe%4==0,Re=ne?Oe:te*Te,et=ne?te*Te:Oe,ft=[8,8,1],bt=N<=8?[4,1,1]:[4,4,1],Mt=[Math.ceil(Re/ft[0]/bt[0]),Math.ceil(et/ft[1]/bt[1]),Math.ceil(Be/ft[2]/bt[2])];fr("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${Mt}`);let Dt=Ke?ne&&ye%4!=0?3:4:1,Nt=ft[1]*bt[1],hA=ft[0]*bt[0],xA=Math.max(ft[0]*Dt,ft[1]),er=N%Nt==0,tr=L%hA==0,qr=j%xA==0,Ar=Ke?[Dt,4,4]:[1,1,1],Ir=[{type:6,data:N},{type:6,data:L},{type:6,data:j},{type:6,data:[I.pads[0],I.pads[1]]},{type:6,data:I.strides},{type:6,data:I.dilations}];bi(I,Ir),Ir.push(..._A(f[0].dims,f[1].dims));let mr=["rank","rank"];return Y&&(Ir.push(..._A(f[2].dims)),mr.push("rank")),Ir.push(..._A(x)),{name:"Conv2DMatMul",shaderCache:{hint:`${I.cacheKey};${Dt};${Ke};${er};${tr};${qr};${Nt};${hA};${xA}`,inputDependencies:mr},getRunData:()=>({outputs:[{dims:se?se(x):x,dataType:f[0].dataType}],dispatchGroup:{x:Mt[0],y:Mt[1],z:Mt[2]},programUniforms:Ir}),getShaderSource:Lt=>{let UA=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"},{name:"pad",type:"i32",length:2},{name:"stride",type:"i32",length:2},{name:"dilation",type:"i32",length:2}];Ai(I,UA);let HA=Ke?4:1,RA=ir(f[0].dataType),is=` 1057 fn setOutputAtIndex(flatIndex : i32, value : ${Ke?`vec4<${RA}>`:RA}) { 1058 result[flatIndex] = ${Ke?`vec4<${RA}>`:RA}(value); 1059 } 1060 fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${Ke?`vec4<${RA}>`:RA}) { 1061 let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3)); 1062 setOutputAtIndex(flatIndex ${Ke?"/ 4":""}, value); 1063 }`,Va=[Yt("x",f[0].dataType,f[0].dims.length,Dt===3?1:Dt),Yt("w",f[1].dataType,f[1].dims.length,HA)],ai=TA("result",f[0].dataType,x.length,HA);if(Y){let N0=Yt("bias",f[2].dataType,f[2].dims.length,HA);Va.push(N0),is+=` 1064 fn getBiasByOutputCoords(coords : vec4<i32>) -> ${Ke?`vec4<${RA}>`:RA} { 1065 return bias[coords.${ne?"w":"y"}${Ke?"/ 4":""}]; 1066 }`}return` 1067 ${wn("uniforms.result_strides")} 1068 //struct Uniforms { xShape : vec4<i32>, wShape : vec4<i32>, outShape : vec4<i32>, 1069 // outShapeStrides: vec3<i32>, filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, 1070 // dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32 }; 1071 ${Lt.registerUniforms(UA).declareVariables(...Va,ai)} 1072 ${is} 1073 ${A(ne,er,tr,qr,Y,I,Ar[0],Ar[1],Ar[2],RA)} 1074 ${Ke?eo(bt,ft,RA,void 0,!ne,xA):Ci(bt,ft,RA,void 0,!ne,xA,!1,void 0,Ae)}`}}}}),k1=me(()=>{hr(),qo(),yr(),Qr(),_0(),r2(),n=f=>{let I=1;for(let x=0;x<f.length;x++)I*=f[x];return I},i=f=>typeof f=="number"?[f,f,f]:f,l=(f,I)=>I<=1?f:f+(f-1)*(I-1),h=(f,I,x,N=1)=>{let L=l(I,N);return Math.floor((f[0]*(x-1)-x+L)/2)},m=(f,I,x,N,L)=>{L==null&&(L=h(f,I[0],N[0]));let j=[0,0,0,x];for(let Y=0;Y<3;Y++)f[Y]+2*L>=I[Y]&&(j[Y]=Math.trunc((f[Y]-I[Y]+2*L)/N[Y]+1));return j},w=(f,I,x,N,L,j,Y,Ae,se,ne)=>{let ye,Be,te,Te;if(f==="VALID"&&(f=0),typeof f=="number"){ye={top:f,bottom:f,left:f,right:f,front:f,back:f};let Oe=m([I,x,N,1],[Ae,se,ne],1,[L,j,Y],f);Be=Oe[0],te=Oe[1],Te=Oe[2]}else if(Array.isArray(f)){if(!f.every((Ke,Re,et)=>Ke===et[0]))throw Error(`Unsupported padding parameter: ${f}`);ye={top:f[0],bottom:f[1],left:f[2],right:f[3],front:f[4],back:f[5]};let Oe=m([I,x,N,1],[Ae,se,ne],1,[L,j,Y],f[0]);Be=Oe[0],te=Oe[1],Te=Oe[2]}else{if(f!=="SAME_UPPER")throw Error(`Unknown padding parameter: ${f}`);{Be=Math.ceil(I/L),te=Math.ceil(x/j),Te=Math.ceil(N/Y);let Oe=(Be-1)*L+Ae-I,Ke=(te-1)*j+se-x,Re=(Te-1)*Y+ne-N,et=Math.floor(Oe/2),ft=Oe-et,bt=Math.floor(Ke/2),Mt=Ke-bt,Dt=Math.floor(Re/2);ye={top:bt,bottom:Mt,left:Dt,right:Re-Dt,front:et,back:ft}}}return{padInfo:ye,outDepth:Be,outHeight:te,outWidth:Te}},M=(f,I,x,N,L,j=!1,Y="channelsLast")=>{let Ae,se,ne,ye,Be;if(Y==="channelsLast")[Ae,se,ne,ye,Be]=f;else{if(Y!=="channelsFirst")throw new Error(`Unknown dataFormat ${Y}`);[Ae,Be,se,ne,ye]=f}let[te,,Te,Oe,Ke]=I,[Re,et,ft]=i(x),[bt,Mt,Dt]=i(N),Nt=l(Te,bt),hA=l(Oe,Mt),xA=l(Ke,Dt),{padInfo:er,outDepth:tr,outHeight:qr,outWidth:Ar}=w(L,se,ne,ye,Re,et,ft,Nt,hA,xA),Ir=j?te*Be:te,mr=[0,0,0,0,0];return Y==="channelsFirst"?mr=[Ae,Ir,tr,qr,Ar]:Y==="channelsLast"&&(mr=[Ae,tr,qr,Ar,Ir]),{batchSize:Ae,dataFormat:Y,inDepth:se,inHeight:ne,inWidth:ye,inChannels:Be,outDepth:tr,outHeight:qr,outWidth:Ar,outChannels:Ir,padInfo:er,strideDepth:Re,strideHeight:et,strideWidth:ft,filterDepth:Te,filterHeight:Oe,filterWidth:Ke,effectiveFilterDepth:Nt,effectiveFilterHeight:hA,effectiveFilterWidth:xA,dilationDepth:bt,dilationHeight:Mt,dilationWidth:Dt,inShape:f,outShape:mr,filterShape:I}},y=(f,I,x,N,L,j)=>{let Y=j==="channelsLast";Y?f[0].dims[3]:f[0].dims[1];let Ae={x:x.map((te,Te)=>Te)},se=[Math.ceil(n(Ae.x.map(te=>x[te]))/64),1,1];fr("verbose",()=>`[conv3d_naive_webgpu] dispatch = ${se}`);let ne=[{type:12,data:gt.size(x)},{type:12,data:N},{type:12,data:L},{type:12,data:I.strides},{type:12,data:I.dilations}];bi(I,ne),ne.push(..._A(f[0].dims,f[1].dims));let ye=["rank","rank"],Be=f.length===3;return Be&&(ne.push(..._A(f[2].dims)),ye.push("rank")),ne.push(..._A(x)),{name:"Conv3DNaive",shaderCache:{hint:`${I.cacheKey};${Y};1;${Be}`,inputDependencies:ye},getRunData:()=>({outputs:[{dims:x,dataType:f[0].dataType}],dispatchGroup:{x:se[0],y:se[1],z:se[2]},programUniforms:ne}),getShaderSource:te=>{let Te=[{name:"output_size",type:"u32"},{name:"filter_dims",type:"u32",length:N.length},{name:"pads",type:"u32",length:L.length},{name:"strides",type:"u32",length:I.strides.length},{name:"dilations",type:"u32",length:I.dilations.length}];Ai(I,Te);let Oe=ir(f[0].dataType),Ke=Yt("x",f[0].dataType,f[0].dims.length,1),Re=Yt("W",f[1].dataType,f[1].dims.length,1),et=[Ke,Re],ft=TA("result",f[0].dataType,x.length,1),bt="";if(Be){let Nt=Yt("bias",f[2].dataType,f[2].dims.length,1);et.push(Nt),bt+=` 1075 fn getBiasByOutputCoords(coords : array<u32, 5>) -> ${Oe} { 1076 return bias[${PA("coords",Y?4:1,5)}]; 1077 }`}let Mt=ka(1,Oe),Dt=wa(I,Mt,Oe);return` 1078 ${bt} 1079 fn getX(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 { 1080 let aIndices = array<u32, 5>(d0, d1, d2, d3, d4); 1081 return ${Ke.getByIndices("aIndices")}; 1082 } 1083 fn getW(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 { 1084 let aIndices = array<u32, 5>(d0, d1, d2, d3, d4); 1085 return ${Re.getByIndices("aIndices")}; 1086 } 1087 ${te.registerUniforms(Te).declareVariables(...et,ft)} 1088 ${te.mainStart()} 1089 ${te.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} 1090 let coords = ${ft.offsetToIndices("global_idx")}; 1091 let batch = ${PA("coords",0,Ke.rank)}; 1092 let d2 = ${PA("coords",Y?Ke.rank-1:1,Ke.rank)}; 1093 let xFRCCorner = vec3<u32>(${PA("coords",Y?1:2,Ke.rank)}, 1094 ${PA("coords",Y?2:3,Ke.rank)}, 1095 ${PA("coords",Y?3:4,Ke.rank)}) * uniforms.strides - uniforms.pads; 1096 let xFCorner = xFRCCorner.x; 1097 let xRCorner = xFRCCorner.y; 1098 let xCCorner = xFRCCorner.z; 1099 let xShapeY = ${PA("uniforms.x_shape",Y?1:2,Ke.rank)}; 1100 let xShapeZ = ${PA("uniforms.x_shape",Y?2:3,Ke.rank)}; 1101 let xShapeW = ${PA("uniforms.x_shape",Y?3:4,Ke.rank)}; 1102 let xShapeU = ${PA("uniforms.x_shape",Y?4:1,Ke.rank)}; 1103 let inputDepthNearestVec4 = (xShapeU / 4) * 4; 1104 let inputDepthVec4Remainder = xShapeU % 4; 1105 1106 var value = 0.0; 1107 for (var wF = 0u; wF < uniforms.filter_dims[0]; wF++) { 1108 let xF = xFCorner + wF * uniforms.dilations[0]; 1109 if (xF < 0 || xF >= xShapeY) { 1110 continue; 1111 } 1112 1113 for (var wR = 0u; wR < uniforms.filter_dims[1]; wR++) { 1114 let xR = xRCorner + wR * uniforms.dilations[1]; 1115 if (xR < 0 || xR >= xShapeZ) { 1116 continue; 1117 } 1118 1119 for (var wC = 0u; wC < uniforms.filter_dims[2]; wC++) { 1120 let xC = xCCorner + wC * uniforms.dilations[2]; 1121 if (xC < 0 || xC >= xShapeW) { 1122 continue; 1123 } 1124 1125 for (var d1 = 0u; d1 < inputDepthNearestVec4; d1 += 4) { 1126 ${Y?`let xValues = vec4<f32>( 1127 getX(batch, xF, xR, xC, d1), 1128 getX(batch, xF, xR, xC, d1 + 1), 1129 getX(batch, xF, xR, xC, d1 + 2), 1130 getX(batch, xF, xR, xC, d1 + 3)); 1131 `:`let xValues = vec4<f32>( 1132 getX(batch, d1, xF, xR, xC), 1133 getX(batch, d1 + 1, xF, xR, xC), 1134 getX(batch, d1 + 2, xF, xR, xC), 1135 getX(batch, d1 + 3, xF, xR, xC)); 1136 `} 1137 let wValues = vec4<f32>( 1138 getW(d2, d1, wF, wR, wC), 1139 getW(d2, d1 + 1, wF, wR, wC), 1140 getW(d2, d1 + 2, wF, wR, wC), 1141 getW(d2, d1 + 3, wF, wR, wC)); 1142 value += dot(xValues, wValues); 1143 } 1144 if (inputDepthVec4Remainder == 1) { 1145 ${Y?`value += getX(batch, xF, xR, xC, inputDepthNearestVec4) 1146 * getW(d2, inputDepthNearestVec4, wF, wR, wC);`:`value += getX(batch, inputDepthNearestVec4, xF, xR, xC) 1147 * getW(d2, inputDepthNearestVec4, wF, wR, wC);`} 1148 } else if (inputDepthVec4Remainder == 2) { 1149 ${Y?`let xValues = vec2<f32>( 1150 getX(batch, xF, xR, xC, inputDepthNearestVec4), 1151 getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1)); 1152 `:`let xValues = vec2<f32>( 1153 getX(batch, inputDepthNearestVec4, xF, xR, xC), 1154 getX(batch, inputDepthNearestVec4 + 1, xF, xR, xC)); 1155 `} 1156 let wValues = vec2<f32>( 1157 getW(d2, inputDepthNearestVec4, wF, wR, wC), 1158 getW(d2, inputDepthNearestVec4 + 1, wF, wR, wC)); 1159 value += dot(xValues, wValues); 1160 } else if (inputDepthVec4Remainder == 3) { 1161 ${Y?`let xValues = vec3<f32>( 1162 getX(batch, xF, xR, xC, inputDepthNearestVec4), 1163 getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1), 1164 getX(batch, xF, xR, xC, inputDepthNearestVec4 + 2)); 1165 `:`let xValues = vec3<f32>( 1166 getX(batch, inputDepthNearestVec4, xF, xR, xC), 1167 getX(batch, inputDepthNearestVec4 + 1, xF, xR, xC), 1168 getX(batch, inputDepthNearestVec4 + 2, xF, xR, xC)); 1169 `} 1170 let wValues = vec3<f32>( 1171 getW(d2, inputDepthNearestVec4, wF, wR, wC), 1172 getW(d2, inputDepthNearestVec4 + 1, wF, wR, wC), 1173 getW(d2, inputDepthNearestVec4 + 2, wF, wR, wC)); 1174 value += dot(xValues, wValues); 1175 } 1176 } 1177 } 1178 } 1179 ${Be?"value = value + getBiasByOutputCoords(coords)":""}; 1180 ${Dt} 1181 result[global_idx] = f32(value); 1182 }`}}}}),rf=me(()=>{hr(),yr(),Qr(),_0(),P=(f,I,x,N)=>{let L=f.length>2,j=L?"value += b[output_channel];":"",Y=f[0].dims,Ae=f[1].dims,se=I.format==="NHWC",ne=se?x[3]:x[1],ye=ne/I.group,Be=se&&ye>=4?_r(ne):1,te=gt.size(x)/Be,Te=[{type:12,data:te},{type:12,data:I.dilations},{type:12,data:[I.strides[0],I.strides[1]]},{type:12,data:[I.pads[0],I.pads[1]]},{type:12,data:ye}];bi(I,Te),Te.push(..._A(Y,[Ae[0],Ae[1],Ae[2],Ae[3]/Be]));let Oe=L?["rank","rank","rank"]:["rank","rank"];return Te.push(..._A([x[0],x[1],x[2],x[3]/Be])),{name:"GroupedConv",shaderCache:{hint:`${I.cacheKey}_${Be}`,inputDependencies:Oe},getRunData:()=>({outputs:[{dims:N?N(x):x,dataType:f[0].dataType}],dispatchGroup:{x:Math.ceil(te/64)},programUniforms:Te}),getShaderSource:Ke=>{let Re=TA("output",f[0].dataType,x.length,Be),et=ir(Re.type.tensor),ft=wa(I,Re.type.value,et),bt=Yt("x",f[0].dataType,Y.length),Mt=Yt("w",f[1].dataType,Ae.length,Be),Dt=[bt,Mt];L&&Dt.push(Yt("b",f[2].dataType,f[2].dims,Be));let Nt=[{name:"output_size",type:"u32"},{name:"dilations",type:"u32",length:I.dilations.length},{name:"strides",type:"u32",length:2},{name:"pads",type:"u32",length:2},{name:"output_channels_per_group",type:"u32"}];Ai(I,Nt);let hA=se?` 1183 for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[0]; wHeight++) { 1184 let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0]; 1185 1186 if (xHeight < 0u || xHeight >= uniforms.x_shape[1]) { 1187 continue; 1188 } 1189 1190 for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[1]; wWidth++) { 1191 let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1]; 1192 if (xWidth < 0u || xWidth >= uniforms.x_shape[2]) { 1193 continue; 1194 } 1195 1196 for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[2]; wInChannel++) { 1197 let input_channel = in_channel_offset + wInChannel; 1198 let xVal = ${bt.get("batch","xHeight","xWidth","input_channel")}; 1199 let wVal = ${Mt.get("wHeight","wWidth","wInChannel","output_channel")}; 1200 value += xVal * wVal; 1201 } 1202 } 1203 } 1204 `:` 1205 for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[1]; wInChannel++) { 1206 let input_channel = in_channel_offset + wInChannel; 1207 for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[2]; wHeight++) { 1208 let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0]; 1209 1210 if (xHeight < 0u || xHeight >= uniforms.x_shape[2]) { 1211 continue; 1212 } 1213 1214 for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[3]; wWidth++) { 1215 let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1]; 1216 if (xWidth < 0u || xWidth >= uniforms.x_shape[3]) { 1217 continue; 1218 } 1219 1220 let xVal = ${bt.get("batch","input_channel","xHeight","xWidth")}; 1221 let wVal = ${Mt.get("output_channel","wInChannel","wHeight","wWidth")}; 1222 value += xVal * wVal; 1223 } 1224 } 1225 } 1226 `;return` 1227 ${Ke.registerUniforms(Nt).declareVariables(...Dt,Re)} 1228 1229 ${Ke.mainStart()} 1230 ${Ke.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} 1231 1232 let outputIndices = ${Re.offsetToIndices("global_idx")}; 1233 let batch: u32 = outputIndices[0]; 1234 let output_channel: u32 = outputIndices[${se?3:1}]; 1235 let xRCCorner: vec2<u32> = vec2<u32>(outputIndices[${se?1:2}], outputIndices[${se?2:3}]) * uniforms.strides - uniforms.pads; 1236 let group_id: u32 = output_channel * ${Be} / uniforms.output_channels_per_group; 1237 var in_channel_offset = group_id * uniforms.w_shape[${se?2:1}]; 1238 1239 var value: ${Re.type.value} = ${Re.type.value}(0); 1240 ${hA} 1241 ${j} 1242 ${ft} 1243 ${Re.setByOffset("global_idx","value")} 1244 }`}}},R=(f,I,x,N)=>{let L=f.length>2,j=_r(x[3]),Y=_r(x[2]),Ae=gt.size(x)/j/Y,se=[f[0].dims[0],f[0].dims[1],f[0].dims[2],f[0].dims[3]/j],ne=[f[1].dims[0],f[1].dims[1],f[1].dims[2],f[1].dims[3]/j],ye=[x[0],x[1],x[2],x[3]/j],Be=[{type:12,data:Ae},{type:6,data:[I.strides[0],I.strides[1]]},{type:6,data:[I.pads[0],I.pads[1]]}];bi(I,Be),Be.push(..._A(se,ne,ye));let te=(Y-1)*I.strides[1]+ne[1];return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${I.cacheKey};${j};${Y};${te};${ne[0]};${ne[1]}`,inputDependencies:L?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:N?N(x):x,dataType:f[0].dataType}],dispatchGroup:{x:Math.ceil(Ae/64)},programUniforms:Be}),getShaderSource:Te=>{let Oe=TA("output",f[0].dataType,ye.length,j),Ke=ir(Oe.type.tensor),Re=wa(I,Oe.type.value,Ke),et=Yt("x",f[0].dataType,se.length,j),ft=Yt("w",f[1].dataType,ne.length,j),bt=[et,ft];L&&bt.push(Yt("b",f[2].dataType,f[2].dims,j));let Mt=L?"value += b[output_channel];":"",Dt=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return Ai(I,Dt),` 1245 ${Te.registerUniforms(Dt).declareVariables(...bt,Oe)} 1246 ${Te.mainStart()} 1247 ${Te.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} 1248 let width0 = uniforms.output_shape[3]; 1249 let output_channel = global_idx % width0; 1250 var index1 = global_idx / width0; 1251 let width1 = uniforms.output_shape[2] / ${Y}u; 1252 let col = (index1 % width1) * ${Y}u; 1253 index1 = index1 / width1; 1254 let row = index1 % uniforms.output_shape[1]; 1255 let batch = index1 / uniforms.output_shape[1]; 1256 1257 let x_corner = vec2<i32>(i32(row), i32(col)) * uniforms.strides - uniforms.pads; 1258 1259 var x_vals: array<${et.type.value}, ${te}>; 1260 var values: array<${Oe.type.value}, ${Y}>; 1261 let input_channel = output_channel; 1262 // Use constant instead of uniform can give better performance for w's height/width. 1263 for (var w_height: u32 = 0u; w_height < ${ne[0]}; w_height++) { 1264 let x_height = x_corner.x + i32(w_height); 1265 if (x_height >= 0 && u32(x_height) < uniforms.x_shape[1]) { 1266 for (var i = 0; i < ${te}; i++) { 1267 let x_width = x_corner.y + i; 1268 if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) { 1269 x_vals[i] = ${et.get("batch","u32(x_height)","u32(x_width)","input_channel")}; 1270 } else { 1271 x_vals[i] = ${et.type.value}(0); 1272 } 1273 } 1274 for (var w_width: u32 = 0u; w_width < ${ne[1]}; w_width++) { 1275 let w_val = ${ft.get("w_height","w_width","0","output_channel")}; 1276 for (var i = 0u; i < ${Y}u; i++) { 1277 values[i] = fma(x_vals[i * u32(uniforms.strides[1]) + w_width], w_val, values[i]); 1278 } 1279 } 1280 } 1281 } 1282 1283 for (var i = 0u; i < ${Y}u; i++) { 1284 var value = values[i]; 1285 ${Mt} 1286 ${Re} 1287 ${Oe.set("batch","row","col + i","output_channel","value")}; 1288 } 1289 }`}}}}),xr=me(()=>{yr(),w1(),k1(),a2(),rf(),_0(),s2(),h0(),ee=(f,I,x,N,L,j)=>{let Y=f[0],Ae=f.slice(j?1:2,j?3:4),se=Ae.length,ne=I[0],ye=I.slice(2).map((te,Te)=>te+(te-1)*(x[Te]-1)),Be=Ae.map((te,Te)=>te+N[Te]+N[Te+se]).map((te,Te)=>Math.floor((te-ye[Te]+L[Te])/L[Te]));return Be.splice(0,0,Y),Be.splice(j?3:1,0,ne),Be},ae=[2,3,1,0],ue=(f,I)=>{if(!f||f.length!==2&&f.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(f[0].dims.length>5)throw new Error("greater than 5D is not supported");if(f[0].dims.length!==f[1].dims.length)throw new Error("filter does not have same dimension as input");if(f[0].dims[I.format==="NHWC"?f[0].dims.length-1:1]!==f[1].dims[1]*I.group)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(f.length===3&&(f[2].dims.length!==1||f[1].dims[0]!==f[2].dims[0]))throw new Error("invalid bias");let x=f[0].dims.length-2;if(I.dilations.length!==x)throw new Error(`dilations should be ${x}D`);if(I.strides.length!==x)throw new Error(`strides should be ${x}D`);if(I.pads.length!==2*x)throw new Error(`pads should be ${2*x}D`);if(I.kernelShape.length!==0&&I.kernelShape.length!==f[1].dims.length-2)throw new Error("invalid kernel shape")},be=(f,I)=>{let x=f.kernelShape.slice();x.length<I[1].dims.length-2&&x.push(...Array(I[1].dims.length-2-x.length).fill(0));for(let j=2;j<I[1].dims.length;++j)x[j-2]===0&&(x[j-2]=I[1].dims[j]);let N=f.pads.slice();pA.adjustPadsBasedOnAutoPad(I[0].dims,f.strides,f.dilations,x,N,f.format==="NHWC",f.autoPad);let L=Object.assign({},f);return Object.assign(L,{kernelShape:x,pads:N}),L},_e=f=>{let I=In(f),x=f.format;return{autoPad:["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][f.auto_pad],format:x,dilations:f.dilations,group:f.group,kernelShape:f.kernel_shape,pads:f.pads,strides:f.strides,wIsConst:f.w_is_const(),...I,cacheKey:`${f.format};${I.activation};`}},Ve=(f,I,x,N)=>{let L=x.format==="NHWC",j=ee(I[0].dims,I[1].dims,x.dilations,x.pads,x.strides,L);if(x.group!==1){let Dt=[I[0]];if(L){let Nt=f.kernelCustomData.wT??f.compute($s(I[1],ae),{inputs:[1],outputs:[x.wIsConst?-2:-1]})[0];x.wIsConst&&!f.kernelCustomData.wT&&(f.kernelCustomData.wT=Nt),Dt.push(Nt)}else Dt.push(I[1]);return I.length===3&&Dt.push(I[2]),void(!f.adapterInfo.isArchitecture("ampere")&&L&&I[1].dims[0]===x.group&&I[1].dims[1]===1&&x.dilations[0]===1&&x.dilations[1]===1?f.compute(R(Dt,x,j,N),{inputs:Dt}):f.compute(P(Dt,x,j,N),{inputs:Dt}))}let Y=I.length===3,Ae=I[0].dims[L?1:2],se=I[0].dims[L?2:3],ne=I[0].dims[L?3:1],ye=I[1].dims[2],Be=I[1].dims[3],te=j[L?1:2],Te=j[L?2:3],Oe=j[L?3:1],Ke=L&&ye===Ae&&Be===se&&x.pads[0]===0&&x.pads[1]===0;if(Ke||ye===1&&Be===1&&x.dilations[0]===1&&x.dilations[1]===1&&x.strides[0]===1&&x.strides[1]===1&&x.pads[0]===0&&x.pads[1]===0){let Dt,Nt,hA,xA=j[0],er=[];if(L){let Ar=f.kernelCustomData.wT??f.compute($s(I[1],ae),{inputs:[1],outputs:[x.wIsConst?-2:-1]})[0];if(x.wIsConst&&!f.kernelCustomData.wT&&(f.kernelCustomData.wT=Ar),Ke){let Ir=Ae*se*ne;Dt=I[0].reshape([1,xA,Ir]),Nt=Ar.reshape([1,Ir,Oe]),hA=[1,xA,Oe]}else Dt=I[0].reshape([xA,Ae*se,ne]),Nt=Ar.reshape([1,ne,Oe]),hA=[xA,te*Te,Oe];er.push(Dt),er.push(Nt)}else Dt=I[0].reshape([xA,ne,Ae*se]),Nt=I[1].reshape([1,Oe,ne]),hA=[xA,Oe,te*Te],er.push(Nt),er.push(Dt);Y&&er.push(I[2]);let tr=hA[2],qr=er[0].dims[er[0].dims.length-1];return void(tr<8&&qr<8?f.compute(Ti(er,x,j,hA,L,N),{inputs:er}):f.compute(t(er,x,j,hA,L,N),{inputs:er}))}let Re=f.kernelCustomData.wT??f.compute($s(I[1],ae),{inputs:[1],outputs:[x.wIsConst?-2:-1]})[0];x.wIsConst&&!f.kernelCustomData.wT&&(f.kernelCustomData.wT=Re);let et=[I[0],Re];Y&&et.push(I[2]);let ft=L?te*Te:Oe,bt=L?Oe:te*Te,Mt=ye*Be*ne;f.compute(r(et,x,j,ft,bt,Mt,Y,!0,N),{inputs:et})},$e=(f,I)=>{let x=I.format==="NHWC",N=[f.inputs[0].reshape(x?[f.inputs[0].dims[0],1,f.inputs[0].dims[1],f.inputs[0].dims[2]]:[f.inputs[0].dims[0],f.inputs[0].dims[1],1,f.inputs[0].dims[2]]),f.inputs[1].reshape([f.inputs[1].dims[0],f.inputs[1].dims[1],1,f.inputs[1].dims[2]])];f.inputs.length===3&&N.push(f.inputs[2]);let L=[0,I.pads[0],0,I.pads[1]],j=[1].concat(I.strides),Y=[1].concat(I.dilations),Ae=[1].concat(I.kernelShape),se=be({...I,pads:L,strides:j,dilations:Y,kernelShape:Ae},N);Ve(f,N,se,ne=>x?[ne[0],ne[2],ne[3]]:[ne[0],ne[1],ne[3]])},it=(f,I,x)=>{let N=x.format==="NHWC"?"channelsLast":"channelsFirst",L=be(x,I),j=x.autoPad==="NOTSET"?x.pads:x.autoPad,Y=M(I[0].dims,I[1].dims,x.strides,x.dilations,j,!1,N);f.compute(y(I,L,Y.outShape,[Y.filterDepth,Y.filterHeight,Y.filterWidth],[Y.padInfo.front,Y.padInfo.top,Y.padInfo.left],N))},ht=(f,I)=>{if(ue(f.inputs,I),f.inputs[0].dims.length===3)$e(f,I);else if(f.inputs[0].dims.length===5)it(f,f.inputs,I);else{let x=be(I,f.inputs);Ve(f,f.inputs,x)}}}),v1=me(()=>{hr(),qo(),yr(),Qr(),yt=(f,I,x)=>{let N=f.length>2,L=I.outputShape,j=I.format==="NHWC",Y=I.group,Ae=f[1].dims,se=Ae[2]/Y,ne=Ae[3],ye=j?_r(se):1,Be=j?_r(ne):1,te=j?ne===1?ye:Be:1,Te=gt.size(L)/Be,Oe=[Math.ceil(Te/64),1,1];fr("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${Oe}`);let Ke=["rank","rank"],Re=[I.strides[0],I.strides[1]],et=[I.kernelShape[j?1:2],I.kernelShape[j?2:3]],ft=[I.dilations[0],I.dilations[1]],bt=[et[0]+(I.dilations[0]<=1?0:(I.kernelShape[j?1:2]-1)*(I.dilations[0]-1)),et[1]+(I.dilations[1]<=1?0:(I.kernelShape[j?2:3]-1)*(I.dilations[1]-1))],Mt=[bt[0]-1-Math.floor((I.pads[0]+I.pads[2])/2),bt[1]-1-Math.floor((I.pads[1]+I.pads[3])/2)],Dt=[{type:12,data:Te},{type:12,data:Re},{type:12,data:et},{type:12,data:ft},{type:12,data:bt},{type:6,data:Mt},{type:12,data:se},{type:12,data:ne},..._A(f[0].dims,f[1].dims)];return N&&(Dt.push(..._A(f[2].dims)),Ke.push("rank")),Dt.push(..._A(L)),{name:"ConvTranspose2D",shaderCache:{hint:`${I.cacheKey};${ye}${te}${Be}${ne===1}`,inputDependencies:Ke},getRunData:()=>({dispatchGroup:{x:Oe[0],y:Oe[1],z:Oe[2]},outputs:[{dims:x?x(L):L,dataType:f[0].dataType}],programUniforms:Dt}),getShaderSource:Nt=>{let hA=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:Re.length},{name:"filter_dims",type:"u32",length:et.length},{name:"dilations",type:"u32",length:et.length},{name:"effective_filter_dims",type:"u32",length:bt.length},{name:"pads",type:"i32",length:Mt.length},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],xA=ir(f[0].dataType),er=j?1:2,tr=j?2:3,qr=j?3:1,Ar=Yt("W",f[1].dataType,f[1].dims.length,te),Ir=Yt("Dy",f[0].dataType,f[0].dims.length,ye),mr=[Ir,Ar];N&&mr.push(Yt("bias",f[2].dataType,[L[qr]].length,Be));let Lt=TA("result",f[0].dataType,L.length,Be),UA=` 1290 let outputIndices = ${Lt.offsetToIndices(`global_idx * ${Be}`)}; 1291 let batch = ${Lt.indicesGet("outputIndices",0)}; 1292 let d1 = ${Lt.indicesGet("outputIndices",qr)}; 1293 let r = ${Lt.indicesGet("outputIndices",er)}; 1294 let c = ${Lt.indicesGet("outputIndices",tr)}; 1295 let dyCorner = vec2<i32>(i32(r), i32(c)) - uniforms.pads; 1296 let dyRCorner = dyCorner.x; 1297 let dyCCorner = dyCorner.y; 1298 let groupId = d1 / uniforms.output_channels_per_group; 1299 let wOutChannel = d1 - groupId * uniforms.output_channels_per_group; 1300 // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1). 1301 // ? = to be determined. : = across all values in that axis. 1302 var dotProd = ${Lt.type.value}(0.0); 1303 var wR: u32 = 0; 1304 if (uniforms.dilations.x == 1) { 1305 // Minimum wR >= 0 that satisfies (dyRCorner + wR) % (uniforms.strides.x) == 0 1306 wR = u32(((dyRCorner + i32(uniforms.strides.x) - 1) / i32(uniforms.strides.x)) * i32(uniforms.strides.x) - dyRCorner); 1307 } 1308 for (; wR < uniforms.effective_filter_dims.x; wR = wR + 1) { 1309 if (wR % uniforms.dilations.x != 0) { 1310 continue; 1311 } 1312 let dyR = (${xA}(dyRCorner) + ${xA}(wR)) / ${xA}(uniforms.strides[0]); 1313 let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x; 1314 if (dyR < 0.0 || dyR >= ${xA}(uniforms.Dy_shape[${er}]) || fract(dyR) > 0.0 || 1315 wRPerm < 0) { 1316 continue; 1317 } 1318 let idyR: u32 = u32(dyR); 1319 var wC: u32 = 0; 1320 if (uniforms.dilations.y == 1) { 1321 // Minimum wC >= 0 that satisfies (dyCCorner + wC) % (uniforms.strides.y) == 0 1322 wC = u32(((dyCCorner + i32(uniforms.strides.y) - 1) / i32(uniforms.strides.y)) * i32(uniforms.strides.y) - dyCCorner); 1323 } 1324 1325 for (; wC < uniforms.effective_filter_dims.y; wC = wC + 1) { 1326 if (wC % uniforms.dilations.y != 0) { 1327 continue; 1328 } 1329 let dyC = (${xA}(dyCCorner) + ${xA}(wC)) / ${xA}(uniforms.strides.y); 1330 let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y; 1331 if (dyC < 0.0 || dyC >= ${xA}(uniforms.Dy_shape[${tr}]) || 1332 fract(dyC) > 0.0 || wCPerm < 0) { 1333 continue; 1334 } 1335 let idyC: u32 = u32(dyC); 1336 var inputChannel = groupId * uniforms.input_channels_per_group; 1337 for (var d2: u32 = 0; d2 < uniforms.input_channels_per_group; d2 = d2 + ${ye}) { 1338 let xValue = ${j?Ir.getByOffset(`${Ir.indicesToOffset(`${Ir.type.indices}(batch, idyR, idyC, inputChannel)`)} / ${ye}`):Ir.get("batch","inputChannel","idyR","idyC")}; 1339 ${(()=>{let HA="";if(ye===1)HA+=` 1340 let w_offset = ${Ar.indicesToOffset(`${Ar.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel, wOutChannel)`)}; 1341 let wValue = ${Ar.getByOffset(`w_offset / ${te}`)}; 1342 dotProd = dotProd + xValue * wValue;`;else if(ne===1)HA+=` 1343 let wValue = ${Ar.getByOffset(`${Ar.indicesToOffset(`${Ar.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel, wOutChannel)`)} / ${te}`)}; 1344 dotProd = dotProd + dot(xValue, wValue);`;else for(let RA=0;RA<ye;RA++)HA+=` 1345 let wValue${RA} = ${Ar.getByOffset(`${Ar.indicesToOffset(`${Ar.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel + ${RA}, wOutChannel)`)} / ${te}`)}; 1346 dotProd = dotProd + xValue[${RA}] * wValue${RA};`;return HA})()} 1347 inputChannel = inputChannel + ${ye}; 1348 } 1349 wC = wC + uniforms.strides.y - 1; 1350 } 1351 wR = wR + uniforms.strides[0] - 1; 1352 } 1353 let value = dotProd${N?` + bias[d1 / ${Be}]`:""}; 1354 ${Lt.setByOffset("global_idx","value")}; 1355 `;return` 1356 ${Nt.registerUniforms(hA).declareVariables(...mr,Lt)} 1357 ${Nt.mainStart()} 1358 ${Nt.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}; 1359 ${UA}}`}}}}),kn=me(()=>{v1(),_0(),h0(),Pt=(f,I,x,N,L,j)=>(f-1)*I+x+(N-1)*L+1-j,Wt=(f,I,x,N,L)=>{let j=Math.floor(f/2);I==="SAME_UPPER"?(x[N]=j,x[L]=f-j):I==="SAME_LOWER"&&(x[N]=f-j,x[L]=j)},cA=(f,I,x,N,L,j,Y,Ae,se,ne)=>{let ye=f.length-2,Be=ne.length===0;se.length<ye&&se.push(...Array(ye-se.length).fill(0));let te=f[0],Te=I[Ae?3:1]*L;for(let 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x=gA(I,f.inputs);qA(f,f.inputs,x)}}}),_n=me(()=>{hr(),yr(),va(),Qr(),Xr=(f,I,x,N)=>{let L=gt.size(I),j=I.length,Y=Yt("input",f,j),Ae=TA("output",f,j),se=x.dataType===6?x.getInt32Array()[0]:Number(x.getBigInt64Array()[0]),ne=gt.normalizeAxis(se,j);return{name:"CumSum",shaderCache:{hint:N.cacheKey,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:I,dataType:f}],dispatchGroup:{x:Math.ceil(L/64)},programUniforms:[{type:12,data:L},{type:12,data:ne},..._A(I,I)]}),getShaderSource:ye=>{let Be=` i32(${Y.indicesGet("inputIndices","uniforms.axis")}) `,te=PA("uniforms.input_shape","uniforms.axis",j),Te=N.reverse?Be+(N.exclusive?" + 1":""):"0",Oe=N.reverse?te:Be+(N.exclusive?"":" + 1");return` 1360 ${ye.registerUniform("outputSize","u32").registerUniform("axis","u32").declareVariables(Y,Ae)} 1361 ${ye.mainStart()} 1362 ${ye.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} 1363 var inputIndices = ${Ae.offsetToIndices("global_idx")}; 1364 var sum = ${Ae.type.value}(0); 1365 let 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1382 let indices = ${Ke.offsetToIndices("global_idx")}; 1383 let aIndices = perm(indices); 1384 1385 ${Ke.setByOffset("global_idx",Oe.getByIndices("aIndices"))} 1386 }`}},Os=(f,I)=>{xs(f.inputs),f.compute(zs(f.inputs[0],I))},ga=f=>vt({blocksize:f.blocksize,mode:f.mode,format:f.format})}),Z0=me(()=>{hr(),yr(),va(),Qr(),pa="^"+(ha="("+(na="[a-zA-Z]|\\.\\.\\.")+")+")+"$",Gn="^"+("("+ha+",)*"+ha)+"$",Fn=class{constructor(f=-1){this.symbolToIndices=new Map,this.inputIndex=f}addSymbol(f,I){let x=this.symbolToIndices.get(f);x===void 0?x=[I]:x.push(I),this.symbolToIndices.set(f,x)}},Ln=class{constructor(f,I){var L;this.equation=I,this.hasEllipsis=!1,this.symbolToInfo=new Map,this.lhs=new Array,this.outputDims=[];let[x,N]=I.includes("->")?I.split("->",2):[I,""];if(!x.match(RegExp(Gn)))throw new Error("Invalid LHS term");if(x.split(",").forEach((j,Y)=>{let Ae=f[Y].dims.slice();if(!j.match(RegExp(pa)))throw new Error("Invalid LHS term");let 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I=f[0].dims,x=Array.from(f[1].getBigInt64Array(),Number),N=o0(I,x),L=f[0].dataType,j=L===9||gt.size(I)===1,Y=L===9||I.length>0&&I[I.length-1]%4==0?4:1,Ae=j||N.length>0&&N[N.length-1]%4==0?4:1,se=Math.ceil(gt.size(N)/Ae),ne=[{type:12,data:se},..._A(I,N)];return{name:"Expand",shaderCache:{hint:`${N.length};${Y}${Ae}`,inputDependencies:["rank"]},getShaderSource:ye=>{let Be,te=Yt("input",L,I.length,Y),Te=TA("output",L,N.length,Ae);if(L===9){let Oe=(Ke,Re,et="")=>` 1398 let outputIndices${Re} = ${Te.offsetToIndices(`outputOffset + ${Re}u`)}; 1399 let offset${Re} = ${te.broadcastedIndicesToOffset(`outputIndices${Re}`,Te)}; 1400 let index${Re} = offset${Re} / 4u; 1401 let component${Re} = offset${Re} % 4u; 1402 ${Ke}[${Re}] = ${et}(${te.getByOffset(`index${Re}`)}[component${Re}]); 1403 `;Be=` 1404 let outputOffset = global_idx * ${Ae}; 1405 var data = vec4<u32>(0); 1406 ${Oe("data",0,"u32")} 1407 ${Oe("data",1,"u32")} 1408 ${Oe("data",2,"u32")} 1409 ${Oe("data",3,"u32")} 1410 ${Te.setByOffset("global_idx","data")} 1411 }`}else Be=` 1412 let outputIndices = ${Te.offsetToIndices(`global_idx * ${Ae}`)}; 1413 let inputOffset = ${te.broadcastedIndicesToOffset("outputIndices",Te)}; 1414 let data = ${Te.type.value}(${te.getByOffset(`inputOffset / ${Y}`)}); 1415 ${Te.setByOffset("global_idx","data")} 1416 }`;return` 1417 ${ye.registerUniform("vec_size","u32").declareVariables(te,Te)} 1418 ${ye.mainStart()} 1419 ${ye.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} 1420 ${Be}`},getRunData:()=>({outputs:[{dims:N,dataType:f[0].dataType}],dispatchGroup:{x:Math.ceil(se/64)},programUniforms:ne})}},ro=f=>{Qi(f.inputs),f.compute(l0(f.inputs),{inputs:[0]})}}),y1=me(()=>{hr(),yr(),Qr(),A2(),fl=f=>{let I=f[0].dataType,x=gt.size(f[0].dims),N=gt.size(f[1].dims),L=N%4==0;return{name:"FastGeluWithBias",shaderCache:{hint:`${L}`,inputDependencies:["type","type"]},getShaderSource:j=>{let Y=Yt("x",I,[1],4),Ae=Yt("bias",I,[1],4),se=TA("y",I,[1],4),ne=Be=>` 1421 let bias${Be}_offset: u32 = (global_idx * 4 + ${Be}) % uniforms.bias_size; 1422 let bias${Be} = ${Ae.getByOffset(`bias${Be}_offset / 4`)}[bias${Be}_offset % 4];`,ye=L?` 1423 let bias = ${Ae.getByOffset("global_idx % (uniforms.bias_size / 4)")};`:`${ne(0)}${ne(1)}${ne(2)}${ne(3)} 1424 let bias = ${Y.type.value}(bias0, bias1, bias2, bias3);`;return`${j.registerUniforms([{name:"output_vec_size",type:"u32"},{name:"bias_size",type:"u32"}]).declareVariables(Y,Ae,se)} 1425 1426 ${Bn(IA(I))} 1427 1428 ${j.mainStart(Gr)} 1429 ${j.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_vec_size")} 1430 1431 let x = ${Y.getByOffset("global_idx")}; 1432 ${ye} 1433 let x_in = x + bias; 1434 ${se.setByOffset("global_idx",Co("x_in"))} 1435 }`},getRunData:j=>({outputs:[{dims:j[0].dims,dataType:j[0].dataType}],programUniforms:[{type:12,data:Math.ceil(x/4)},{type:12,data:N}],dispatchGroup:{x:Math.ceil(x/Gr/4)}})}},dl=f=>{f.inputs.length<2||gt.size(f.inputs[1].dims)===0?rn(f):f.compute(fl(f.inputs))}}),E1=me(()=>{hr(),yr(),va(),Qr(),gl=f=>{if(!f||f.length!==2)throw new Error("Gather requires 2 inputs.")},m0=(f,I)=>{let x=f[0].dims,N=f[1].dims,L=x.length,j=gt.normalizeAxis(I.axis,L),Y=x.slice(0);Y.splice(j,1,...N);let Ae=x[j],se=f[0].dataType===9?4:1,ne=Math.ceil(gt.size(Y)/se),ye=[{type:12,data:ne},{type:6,data:Ae},{type:12,data:j},..._A(f[0].dims,f[1].dims,Y)];return{name:"Gather",shaderCache:{hint:I.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:Y,dataType:f[0].dataType}],dispatchGroup:{x:Math.ceil(ne/64)},programUniforms:ye}),getShaderSource:Be=>{let te,Te=Yt("data",f[0].dataType,f[0].dims.length,se),Oe=Yt("inputIndices",f[1].dataType,f[1].dims.length),Ke=TA("output",f[0].dataType,Y.length,se),Re=et=>{let ft=N.length,bt=`var indicesIndices${et} = ${Oe.type.indices}(0);`;for(let Mt=0;Mt<ft;Mt++)bt+=`${ft>1?`indicesIndices${et}[${Mt}]`:`indicesIndices${et}`} = ${Y.length>1?`outputIndices${et}[uniforms.axis + ${Mt}]`:`outputIndices${et}`};`;bt+=` 1436 var idx${et} = ${Oe.getByIndices(`indicesIndices${et}`)}; 1437 if (idx${et} < 0) { 1438 idx${et} = idx${et} + uniforms.axisDimLimit; 1439 } 1440 var dataIndices${et} : ${Te.type.indices}; 1441 `;for(let Mt=0,Dt=0;Mt<L;Mt++)Mt===j?(bt+=`${L>1?`dataIndices${et}[${Mt}]`:`dataIndices${et}`} = u32(idx${et});`,Dt+=ft):(bt+=`${L>1?`dataIndices${et}[${Mt}]`:`dataIndices${et}`} = ${Y.length>1?`outputIndices${et}[${Dt}]`:`outputIndices${et}`};`,Dt++);return bt};if(f[0].dataType===9){let et=(ft,bt,Mt="")=>` 1442 let outputIndices${bt} = ${Ke.offsetToIndices(`outputOffset + ${bt}u`)}; 1443 ${Re(bt)}; 1444 let offset${bt} = ${Te.indicesToOffset(`dataIndices${bt}`)}; 1445 let index${bt} = offset${bt} / 4u; 1446 let component${bt} = offset${bt} % 4u; 1447 ${ft}[${bt}] = ${Mt}(${Te.getByOffset(`index${bt}`)}[component${bt}]); 1448 `;te=` 1449 let outputOffset = global_idx * ${se}; 1450 var value = vec4<u32>(0); 1451 ${et("value",0,"u32")} 1452 ${et("value",1,"u32")} 1453 ${et("value",2,"u32")} 1454 ${et("value",3,"u32")} 1455 ${Ke.setByOffset("global_idx","value")} 1456 `}else te=` 1457 let outputIndices = ${Ke.offsetToIndices("global_idx")}; 1458 ${Re("")}; 1459 let value = ${Te.getByIndices("dataIndices")}; 1460 ${Ke.setByOffset("global_idx","value")}; 1461 `;return` 1462 ${Be.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(Te,Oe,Ke)} 1463 ${Be.mainStart()} 1464 ${Be.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} 1465 ${te} 1466 }`}}},hl=f=>vt({axis:f.axis}),R0=(f,I)=>{let x=f.inputs;gl(x),f.compute(m0(f.inputs,I))}}),le=me(()=>{hr(),yr(),Qr(),pl=(f,I,x,N,L,j,Y,Ae,se)=>{let ne=[{type:12,data:j},{type:12,data:N},{type:12,data:L},{type:12,data:x},{type:12,data:Y},{type:12,data:Ae},{type:12,data:se}],ye=[j];return ne.push(..._A(I.dims,ye)),f.compute({name:"computeSliceOffsets",shaderCache:{hint:`${L.length}_${x.length}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:ye,dataType:f.inputs[1].dataType}],dispatchGroup:{x:Math.ceil(j/64)},programUniforms:ne}),getShaderSource:Be=>{let te=[Yt("indices_data",I.dataType,I.dims.length),TA("input_slice_offsets_data",12,1,1)],Te=[{name:"output_size",type:"u32"},{name:"batch_dims",type:"u32"},{name:"input_dims",type:"u32",length:L.length},{name:"sizes_from_slice_dims_data",type:"u32",length:x.length},{name:"num_slices_per_batch",type:"u32"},{name:"input_batch_stride",type:"u32"},{name:"num_slice_dims",type:"u32"}];return` 1467 ${Be.registerUniforms(Te).declareVariables(...te)} 1468 ${Be.mainStart()} 1469 ${Be.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} 1470 let batch_idx = global_idx / uniforms.num_slices_per_batch; 1471 let base_offset = batch_idx * uniforms.input_batch_stride; 1472 1473 let slice_indices_base_offset = global_idx * uniforms.num_slice_dims; 1474 var relative_slice_offset = 0; 1475 for (var dim_idx = 0u; dim_idx < uniforms.num_slice_dims; dim_idx ++) { 1476 var index = i32(indices_data[dim_idx + slice_indices_base_offset].x); 1477 let input_dim_idx = uniforms.batch_dims + dim_idx; 1478 if (index < 0) { 1479 ${L.length===1?"index += i32(uniforms.input_dims);":"index += i32(uniforms.input_dims[input_dim_idx]);"} 1480 } 1481 ${x.length===1?"relative_slice_offset += index * i32(uniforms.sizes_from_slice_dims_data);":"relative_slice_offset += index * i32(uniforms.sizes_from_slice_dims_data[dim_idx]);"} 1482 } 1483 1484 input_slice_offsets_data[global_idx] = base_offset + u32(relative_slice_offset); 1485 }`}},{inputs:[I],outputs:[-1]})[0]},b0=(f,I)=>{let x=f.inputs,N=x[0].dims,L=x[0].dataType,j=x[1].dims,Y=j[j.length-1],Ae=gt.sizeToDimension(j,j.length-1),se=gt.sizeFromDimension(N,I.batchDims+Y),ne=gt.sizeToDimension(N,I.batchDims),ye=gt.sizeFromDimension(N,I.batchDims),Be=Ae/ne,te=new Array(Y),Te=se;for(let bt=0;bt<Y;++bt)te[Y-1-bt]=Te,Te*=N[I.batchDims+Y-1-bt];let Oe=pl(f,x[1],te,I.batchDims,N,Ae,Be,ye,Y),Ke=I.batchDims+Y;if(Ke>N.length)throw new Error("last dimension of indices must not be larger than rank of input tensor");let Re=j.slice(0,-1).concat(N.slice(Ke)),et=gt.size(Re),ft=[{type:12,data:et},{type:12,data:se},..._A(x[0].dims,Oe.dims,Re)];f.compute({name:"GatherND",shaderCache:{hint:I.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:Re,dataType:L}],dispatchGroup:{x:Math.ceil(et/64)},programUniforms:ft}),getShaderSource:bt=>{let Mt=Yt("data",x[0].dataType,x[0].dims.length),Dt=Yt("slice_offsets",12,Oe.dims.length),Nt=TA("output",x[0].dataType,Re.length);return` 1486 ${bt.registerUniform("output_size","u32").registerUniform("slice_size","u32").declareVariables(Mt,Dt,Nt)} 1487 ${bt.mainStart()} 1488 ${bt.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} 1489 let slice_offset = slice_offsets[global_idx / uniforms.slice_size]; 1490 output[global_idx] = data[u32(slice_offset) + global_idx % uniforms.slice_size]; 1491 }`}},{inputs:[x[0],Oe]})},j0=f=>({batchDims:f.batch_dims,cacheKey:""})}),xe=me(()=>{hr(),yr(),va(),Qr(),ml=(f,I)=>{if(f.length<3||f.length>4)throw new Error("GatherBlockQuantized requires 3 or 4 inputs.");let x=gt.normalizeAxis(I.quantizeAxis,f[0].dims.length),N=I.blockSize,L=f[0],j=f[2],Y=f.length===4?f[3]:void 0;if(j.dims.length!==L.dims.length||!L.dims.map((Ae,se)=>se===x?Math.ceil(Ae/N)===j.dims[se]:Ae===j.dims[se]).reduce((Ae,se)=>Ae&&se,!0))throw new Error("Scales must have the same rank as the input tensor and the dims should match except on gatherAxis.");if(Y){if(Y.dataType!==L.dataType)throw new Error("Zero point must have the same data type as the input tensor.");if(Y.dims.length!==j.dims.length||!Y.dims.map((Ae,se)=>Ae===j.dims[se]).reduce((Ae,se)=>Ae&&se,!0))throw new Error("Zero point must have the same rank as the input tensor and the dims should match except on quantizeAxis.")}},bl=(f,I)=>{let x=f[0].dims,N=f[1].dims,L=x.length,j=gt.normalizeAxis(I.gatherAxis,L),Y=gt.normalizeAxis(I.quantizeAxis,L),Ae=x.slice(0);Ae.splice(j,1,...N);let se=gt.size(Ae),ne=f[2].dataType,ye=f[0].dataType===22,Be=[{type:12,data:se},{type:12,data:Y},{type:12,data:j},{type:12,data:I.blockSize},..._A(...f.map((te,Te)=>te.dims),Ae)];return{name:"GatherBlockQuantized",shaderCache:{hint:`${I.cacheKey};${f.filter((te,Te)=>Te!==1).map(te=>te.dims.join("_")).join(";")}`,inputDependencies:Array.from({length:f.length},(te,Te)=>"rank")},getRunData:()=>({outputs:[{dims:Ae,dataType:ne}],dispatchGroup:{x:Math.ceil(se/64)},programUniforms:Be}),getShaderSource:te=>{let Te=Yt("data",f[0].dataType,f[0].dims.length),Oe=Yt("inputIndices",f[1].dataType,f[1].dims.length),Ke=Yt("scales",f[2].dataType,f[2].dims.length),Re=f.length>3?Yt("zeroPoint",f[3].dataType,f[3].dims.length):void 0,et=TA("output",ne,Ae.length),ft=[Te,Oe,Ke];return Re&&ft.push(Re),` 1492 ${te.registerUniforms([{name:"output_size",type:"u32"},{name:"quantize_axis",type:"u32"},{name:"gather_axis",type:"u32"},{name:"block_size",type:"u32"}]).declareVariables(...ft,et)} 1493 ${te.mainStart()} 1494 let output_indices = ${et.offsetToIndices("global_idx")}; 1495 var indices_indices = ${Oe.type.indices}(0); 1496 ${N.length>1?` 1497 for (var i: u32 = 0; i < ${N.length}; i++) { 1498 let index = ${et.indicesGet("output_indices","uniforms.gather_axis + i")}; 1499 ${Oe.indicesSet("indices_indices","i","index")}; 1500 }`:`indices_indices = ${et.indicesGet("output_indices","uniforms.gather_axis")};`}; 1501 var data_indices = ${Te.type.indices}(0); 1502 for (var i: u32 = 0; i < uniforms.gather_axis; i++) { 1503 let index = ${et.indicesGet("output_indices","i")}; 1504 ${Te.indicesSet("data_indices","i","index")}; 1505 } 1506 var index_from_indices = ${Oe.getByIndices("indices_indices")}; 1507 if (index_from_indices < 0) { 1508 index_from_indices += ${x[j]}; 1509 } 1510 ${Te.indicesSet("data_indices","uniforms.gather_axis","u32(index_from_indices)")}; 1511 for (var i = uniforms.gather_axis + 1; i < ${Ae.length}; i++) { 1512 let index = ${et.indicesGet("output_indices",`i + ${N.length} - 1`)}; 1513 ${Te.indicesSet("data_indices","i","index")}; 1514 } 1515 let data_offset = ${Te.indicesToOffset("data_indices")}; 1516 let data_index = data_offset % 8; 1517 // Convert 4-bit packed data to 8-bit packed data. 1518 let packed_4bit_quantized_data = ${Te.getByOffset("data_offset / 8")}; 1519 let packed_8bit_quantized_data = (packed_4bit_quantized_data >> (4 * (data_index % 2))) & 0x0f0f0f0f; 1520 let quantized_data_vec = ${ye?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_quantized_data)); 1521 let quantized_data = quantized_data_vec[data_index / 2]; 1522 var scale_indices = data_indices; 1523 let quantize_axis_index = ${Ke.indicesGet("data_indices","uniforms.quantize_axis")} / uniforms.block_size; 1524 ${Ke.indicesSet("scale_indices","uniforms.quantize_axis","quantize_axis_index")}; 1525 var scale = ${Ke.getByIndices("scale_indices")}; 1526 ${Re?` 1527 let zero_point_indices = scale_indices; 1528 let zero_point_offset = ${Re.indicesToOffset("zero_point_indices")}; 1529 let zero_point_index = zero_point_offset % 8; 1530 let packed_4bit_zero_points = ${Re.getByOffset("zero_point_offset / 8")}; 1531 let packed_8bit_zero_points = (packed_4bit_zero_points >> (4 * (zero_point_index % 2))) & 0x0f0f0f0f; 1532 let zero_point_vec = ${ye?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_zero_points)); 1533 let zero_point = zero_point_vec[zero_point_index / 2];`:"var zero_point = 0"}; 1534 let dequantized_data = ${IA(ne)}(quantized_data - zero_point) * scale; 1535 ${et.setByOffset("global_idx","dequantized_data")}; 1536 }`}}},C0=(f,I)=>{let x=f.inputs;ml(x,I),f.compute(bl(f.inputs,I))},Cl=f=>vt({blockSize:f.blockSize,gatherAxis:f.gatherAxis,quantizeAxis:f.quantizeAxis})}),Ye=me(()=>{hr(),yr(),va(),Qr(),W0=f=>{if(!f||f.length!==2)throw new Error("GatherElements requires 2 inputs.");if(f[0].dims.length<1)throw new Error("GatherElements requires that the data input be rank >= 1.");if(f[0].dims.length!==f[1].dims.length)throw new Error(`GatherElements requires that the data input and 1537 indices input tensors be of same rank.`)},Il=(f,I)=>{let x=f[0].dims,N=f[0].dataType,L=x.length,j=f[1].dims,Y=f[1].dataType,Ae=gt.normalizeAxis(I.axis,L),se=x[Ae],ne=j.slice(0),ye=gt.size(ne),Be=Yt("input",N,L),te=Yt("indicesInput",Y,j.length),Te=TA("output",N,ne.length),Oe=[{type:12,data:ye},{type:6,data:se},{type:12,data:Ae}];return Oe.push(..._A(x,j,ne)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:ne,dataType:f[0].dataType}],dispatchGroup:{x:Math.ceil(ye/64)},programUniforms:Oe}),getShaderSource:Ke=>` 1538 ${Ke.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(Be,te,Te)} 1539 ${Ke.mainStart()} 1540 ${Ke.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} 1541 1542 let outputIndices = ${Te.offsetToIndices("global_idx")}; 1543 1544 var idx = ${te.getByOffset("global_idx")}; 1545 if (idx < 0) { 1546 idx = idx + uniforms.axisDimLimit; 1547 } 1548 var inputIndices = ${Be.type.indices}(outputIndices); 1549 ${Be.indicesSet("inputIndices","uniforms.axis","u32(idx)")}; 1550 let value = ${Be.getByIndices("inputIndices")}; 1551 1552 ${Te.setByOffset("global_idx","value")}; 1553 }`}},I0=f=>vt({axis:f.axis}),U0=(f,I)=>{let x=f.inputs;W0(x),f.compute(Il(f.inputs,I))}}),Et=me(()=>{hr(),yr(),Qr(),wl=f=>{if(!f)throw new Error("Input is missing");if(f.length<2||f.length>3)throw new Error("Invaid input number.");if(f.length===3&&f[2].dims.length>2)throw new Error("Invalid input shape of C");if(f[0].dataType!==f[1].dataType||f.length===3&&f[0].dataType!==f[2].dataType)throw new Error("Input types are mismatched")},kl=(f,I)=>{let x=f[0].dims.slice(),N=f[1].dims.slice(),[L,j,Y]=oA.getShapeOfGemmResult(x,I.transA,N,I.transB,f.length===3?f[2].dims:void 0),Ae=[L,j];if(!Ae)throw new Error("Can't use gemm on the given tensors");let se=16,ne=Math.ceil(j/se),ye=Math.ceil(L/se),Be=(gt.size(Ae),[{type:12,data:ne},{type:12,data:L},{type:12,data:j},{type:12,data:Y},{type:1,data:I.alpha},{type:1,data:I.beta}]),te=["type","type"];return f.length===3&&(Be.push(..._A(f[2].dims)),te.push("rank")),Be.push(..._A(Ae)),{name:"GemmShared",shaderCache:{hint:`${I.cacheKey}`,inputDependencies:te},getRunData:()=>({outputs:[{dims:Ae,dataType:f[0].dataType}],dispatchGroup:{x:ne*ye},programUniforms:Be}),getShaderSource:Te=>{let Oe=Yt("a",f[0].dataType,f[0].dims),Ke=Yt("b",f[1].dataType,f[1].dims),Re=null,et=[Oe,Ke];f.length===3&&(Re=Yt("c",f[2].dataType,f[2].dims.length),et.push(Re));let ft=TA("output",f[0].dataType,Ae.length);et.push(ft);let bt="",Mt="";I.transA&&I.transB?(Mt=` 1554 var col = tile_row_start + local_id.x; 1555 var row = k_start + local_id.y; 1556 if (col < uniforms.M && row < uniforms.K) { 1557 tile_a[local_id.y][local_id.x] = a[row * uniforms.M + col]; 1558 } else { 1559 tile_a[local_id.y][local_id.x] = ${Oe.type.value}(0); 1560 } 1561 1562 col = k_start + local_id.x; 1563 row = tile_col_start + local_id.y; 1564 if (col < uniforms.K && row < uniforms.N) { 1565 tile_b[local_id.y][local_id.x] = b[row * uniforms.K + col]; 1566 } else { 1567 tile_b[local_id.y][local_id.x] = ${Ke.type.value}(0); 1568 } 1569 `,bt="value += tile_a[k][local_id.y] * tile_b[local_id.x][k];"):I.transA&&!I.transB?(Mt=` 1570 var col = tile_row_start + local_id.x; 1571 var row = k_start + local_id.y; 1572 if (col < uniforms.M && row < uniforms.K) { 1573 tile_a[local_id.y][local_id.x] = a[row * uniforms.M + col]; 1574 } else { 1575 tile_a[local_id.y][local_id.x] = ${Oe.type.value}(0); 1576 } 1577 1578 col = tile_col_start + local_id.x; 1579 row = k_start + local_id.y; 1580 if (col < uniforms.N && row < uniforms.K) { 1581 tile_b[local_id.y][local_id.x] = b[row * uniforms.N + col]; 1582 } else { 1583 tile_b[local_id.y][local_id.x] = ${Ke.type.value}(0); 1584 } 1585 `,bt="value += tile_a[k][local_id.y] * tile_b[k][local_id.x];"):!I.transA&&I.transB?(Mt=` 1586 var col = k_start + local_id.x; 1587 var row = tile_row_start + local_id.y; 1588 if (col < uniforms.K && row < uniforms.M) { 1589 tile_a[local_id.y][local_id.x] = a[row * uniforms.K + col]; 1590 } else { 1591 tile_a[local_id.y][local_id.x] = ${Oe.type.value}(0); 1592 } 1593 1594 col = k_start + local_id.x; 1595 row = tile_col_start + local_id.y; 1596 if (col < uniforms.K && row < uniforms.N) { 1597 tile_b[local_id.y][local_id.x] = b[row * uniforms.K + col]; 1598 } else { 1599 tile_b[local_id.y][local_id.x] = ${Ke.type.value}(0); 1600 } 1601 `,bt="value += tile_a[local_id.y][k] * tile_b[local_id.x][k];"):!I.transA&&!I.transB&&(Mt=` 1602 var col = k_start + local_id.x; 1603 var row = tile_row_start + local_id.y; 1604 if (col < uniforms.K && row < uniforms.M) { 1605 tile_a[local_id.y][local_id.x] = a[row * uniforms.K + col]; 1606 } else { 1607 tile_a[local_id.y][local_id.x] = ${Oe.type.value}(0); 1608 } 1609 1610 col = tile_col_start + local_id.x; 1611 row = k_start + local_id.y; 1612 if (col < uniforms.N && row < uniforms.K) { 1613 tile_b[local_id.y][local_id.x] = b[row * uniforms.N + col]; 1614 } else { 1615 tile_b[local_id.y][local_id.x] = ${Ke.type.value}(0); 1616 } 1617 `,bt="value += tile_a[local_id.y][k] * tile_b[k][local_id.x];");let Dt=I.alpha===1?"":"value *= uniforms.alpha;";return` 1618 ${Te.registerUniforms([{name:"num_tile_n",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"},{name:"alpha",type:"f32"},{name:"beta",type:"f32"}]).declareVariables(...et)} 1619 var<workgroup> tile_a: array<array<${Oe.type.storage}, 16>, 16>; 1620 var<workgroup> tile_b: array<array<${Ke.type.storage}, 16>, 16>; 1621 ${Te.mainStart([se,se,1])} 1622 let tile_col_start = (workgroup_index % uniforms.num_tile_n) * 16; 1623 let tile_row_start = (workgroup_index / uniforms.num_tile_n) * 16; 1624 let num_tiles = (uniforms.K - 1) / 16 + 1; 1625 var k_start = 0u; 1626 var value = ${ft.type.value}(0); 1627 for (var t: u32 = 0u; t < num_tiles; t++) { 1628 ${Mt} 1629 k_start = k_start + 16; 1630 workgroupBarrier(); 1631 1632 for (var k: u32 = 0u; k < 16; k++) { 1633 ${bt} 1634 } 1635 workgroupBarrier(); 1636 } 1637 1638 ${Dt} 1639 let m = tile_row_start + local_id.y; 1640 let n = tile_col_start + local_id.x; 1641 ${Re!=null?`let cOffset = ${Re.broadcastedIndicesToOffset("vec2(m, n)",ft)}; value += ${ft.type.value}(uniforms.beta) * ${Re.getByOffset("cOffset")};`:""} 1642 if (m < uniforms.M && n < uniforms.N) { 1643 output[m * uniforms.N + n] = value; 1644 } 1645 }`}}},c0=f=>({transA:f.transA,transB:f.transB,alpha:f.alpha,beta:f.beta,cacheKey:`${f.transA};${f.transB};${f.alpha===1}`}),vl=(f,I)=>{wl(f.inputs),f.compute(kl(f.inputs,I))}}),Ht=me(()=>{hr(),yr(),va(),Qr(),[Pi,Si,so,Ni]=[0,1,2,3],yl=f=>{if(f[0].dims.length!==4)throw new Error("only 4-D tensor is supported.");if(f[0].dims.length!==f[1].dims.length)throw new Error("input dimensions must be equal to grid dimensions");if(f[0].dims.length-2!==f[1].dims[f[1].dims.length-1])throw new Error("last dimension of grid must be equal to "+(f[0].dims.length-2));if(f[0].dims[0]!==f[1].dims[0])throw new Error("grid batch size must match input batch size")},El=f=>` 1646 fn gs_bicubic_interpolate(p: mat4x4<${f}>, x: f32, y: f32) -> ${f} { 1647 var v: vec4<f32>; 1648 var coeffs = gs_get_cubic_coeffs(x); 1649 for (var i = 0; i < 4; i++) { 1650 v[i] = coeffs[0] * p[i][0] + coeffs[1] * p[i][1] + coeffs[2] * p[i][2] + coeffs[3] * p[i][3]; 1651 } 1652 coeffs = gs_get_cubic_coeffs(y); 1653 let pixel = ${f}(coeffs[0] * v[0] + coeffs[1] * v[1] + coeffs[2] * v[2] + coeffs[3] * v[3]); 1654 return pixel; 1655 } 1656`,u0=f=>` 1657 fn gs_denormalize(n: f32, length: i32) -> f32 { 1658 ${f.alignCorners===0?` 1659 // alignCorners: false => [-1, 1] to [-0.5, length - 0.5] 1660 return ((n + 1.0) * f32(length) - 1.0) / 2.0; 1661 `:` 1662 // alignCorners: true => [-1, 1] to [0, length - 1] 1663 return (n + 1.0) / 2.0 * (f32(length - 1)); 1664 `} 1665 } 1666`,Bl=f=>` 1667 ${f.paddingMode==="reflection"?` 1668 fn gs_reflect(x: i32, x_min: f32, x_max: f32) -> u32 { 1669 var dx = 0.0; 1670 var fx = f32(x); 1671 let range = x_max - x_min; 1672 if (fx < x_min) { 1673 dx = x_min - fx; 1674 let n = u32(dx / range); 1675 let r = dx - f32(n) * range; 1676 if (n % 2 == 0) { 1677 fx = x_min + r; 1678 } else { 1679 fx = x_max - r; 1680 } 1681 } else if (fx > x_max) { 1682 dx = fx - x_max; 1683 let n = u32(dx / range); 1684 let r = dx - f32(n) * range; 1685 if (n % 2 == 0) { 1686 fx = x_max - r; 1687 } else { 1688 fx = x_min + r; 1689 } 1690 } 1691 return u32(fx); 1692 }`:""} 1693`,xl=(f,I,x)=>` 1694 fn pixel_at_grid(r: i32, c: i32, H: i32, W: i32, batch: u32, channel: u32, border: vec4<f32>) -> ${I} { 1695 var pixel = ${I}(0); 1696 var indices = vec4<u32>(0); 1697 indices[${Pi}] = batch; 1698 indices[${Si}] = channel;`+(()=>{switch(x.paddingMode){case"zeros":return` 1699 if (r >= 0 && r < H && c >=0 && c < W) { 1700 indices[${so}] = u32(r); 1701 indices[${Ni}] = u32(c); 1702 } 1703 `;case"border":return` 1704 indices[${so}] = u32(clamp(r, 0, H - 1)); 1705 indices[${Ni}] = u32(clamp(c, 0, W - 1)); 1706 `;case"reflection":return` 1707 indices[${so}] = gs_reflect(r, border[1], border[3]); 1708 indices[${Ni}] = gs_reflect(c, border[0], border[2]); 1709 `;default:throw new Error(`padding mode ${x.paddingMode} is not supported`)}})()+` 1710 return ${f.getByIndices("indices")}; 1711 } 1712`,Ml=(f,I,x)=>(()=>{switch(x.mode){case"nearest":return` 1713 let result = pixel_at_grid(i32(round(y)), i32(round(x)), H_in, W_in, indices[${Pi}], indices[${Si}], border); 1714 `;case"bilinear":return` 1715 let x1 = i32(floor(x)); 1716 let y1 = i32(floor(y)); 1717 let x2 = x1 + 1; 1718 let y2 = y1 + 1; 1719 1720 let p11 = pixel_at_grid(y1, x1, H_in, W_in, indices[${Pi}], indices[${Si}], border); 1721 let p12 = pixel_at_grid(y1, x2, H_in, W_in, indices[${Pi}], indices[${Si}], border); 1722 let p21 = pixel_at_grid(y2, x1, H_in, W_in, indices[${Pi}], indices[${Si}], border); 1723 let p22 = pixel_at_grid(y2, x2, H_in, W_in, indices[${Pi}], indices[${Si}], border); 1724 1725 let dx2 = ${I}(f32(x2) - x); 1726 let dx1 = ${I}(x - f32(x1)); 1727 let dy2 = ${I}(f32(y2) - y); 1728 let dy1 = ${I}(y - f32(y1)); 1729 let result = dy2 * (dx2 * p11 + dx1 * p12) + dy1 * (dx2 * p21 + dx1 * p22); 1730 `;case"bicubic":return` 1731 let x0 = i32(floor(x)) - 1; 1732 let y0 = i32(floor(y)) - 1; 1733 var p: mat4x4<${I}>; 1734 for (var h = 0; h < 4; h++) { 1735 for (var w = 0; w < 4; w++) { 1736 p[h][w] = pixel_at_grid(h + y0, w + x0, H_in, W_in, indices[${Pi}], indices[${Si}], border); 1737 } 1738 } 1739 1740 let dx = x - f32(x0 + 1); 1741 let dy = y - f32(y0 + 1); 1742 let result = gs_bicubic_interpolate(p, dx, dy); 1743 `;default:throw new Error(`mode ${x.mode} is not supported`)}})()+`${f.setByOffset("global_idx","result")}`,w0=(f,I)=>{let x=Yt("x",f[0].dataType,f[0].dims.length),N=[f[1].dims[0],f[1].dims[1],f[1].dims[2]],L=Yt("grid",f[1].dataType,N.length,2),j=[f[0].dims[0],f[0].dims[1],f[1].dims[1],f[1].dims[2]];I.format==="NHWC"&&(j=[f[0].dims[0],f[1].dims[1],f[1].dims[2],f[0].dims[3]],[Pi,Si,so,Ni]=[0,3,1,2]);let Y=TA("output",f[0].dataType,j.length),Ae=x.type.value,se=[{type:12,data:gt.size(j)},..._A(f[0].dims,N,j)];return{name:"GridSample",shaderCache:{hint:`${I.cacheKey}`,inputDependencies:["type","type"]},getRunData:ne=>{let ye=gt.size(j);return{outputs:[{dims:j,dataType:ne[0].dataType}],dispatchGroup:{x:Math.ceil(ye/64)},programUniforms:se}},getShaderSource:ne=>` 1744 ${ne.registerUniform("output_size","u32").declareVariables(x,L,Y)} 1745 1746 fn gs_get_cubic_coeffs(x: f32) -> vec4<f32> { 1747 let cubic_alpha = -0.75f; 1748 let x_abs = abs(x); 1749 var coeffs: vec4<f32>; 1750 coeffs[0] = (((cubic_alpha * (x_abs + 1) - 5 * cubic_alpha) * (x_abs + 1) + 8 * cubic_alpha) * (x_abs + 1) - 4 * cubic_alpha); 1751 coeffs[1] = (((cubic_alpha + 2) * x_abs - (cubic_alpha + 3)) * x_abs * x_abs + 1); 1752 coeffs[2] = (((cubic_alpha + 2) * (1 - x_abs) - (cubic_alpha + 3)) * (1 - x_abs) * (1 - x_abs) + 1); 1753 coeffs[3] = (((cubic_alpha * (2 - x_abs) - 5 * cubic_alpha) * (2 - x_abs) + 8 * cubic_alpha) * (2 - x_abs) - 4 * cubic_alpha); 1754 return coeffs; 1755 } 1756 1757 ${El(Ae)} 1758 ${u0(I)} 1759 ${Bl(I)} 1760 ${xl(x,Ae,I)} 1761 1762 ${ne.mainStart()} 1763 ${ne.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} 1764 let H_in = i32(uniforms.x_shape[${so}]); 1765 let W_in = i32(uniforms.x_shape[${Ni}]); 1766 1767 ${I.alignCorners===0?` 1768 let x_min = -0.5; 1769 let x_max = f32(W_in) - 0.5; 1770 let y_min = -0.5; 1771 let y_max = f32(H_in) - 0.5; 1772 `:` 1773 let x_min = 0.0; 1774 let x_max = f32(W_in) - 1.0; 1775 let y_min = 0.0; 1776 let y_max = f32(H_in) - 1.0; 1777 `}; 1778 let border = vec4<f32>(x_min, y_min, x_max, y_max); 1779 1780 let indices = ${Y.offsetToIndices("global_idx")}; 1781 var grid_indices = vec3<u32>(indices[${Pi}], indices[${so}], indices[${Ni}]); 1782 let nxy = ${L.getByIndices("grid_indices")}; 1783 var x = gs_denormalize(f32(nxy[0]), W_in); 1784 var y = gs_denormalize(f32(nxy[1]), H_in); 1785 1786 ${Ml(Y,Ae,I)} 1787 }`}},Dl=(f,I)=>{yl(f.inputs),f.compute(w0(f.inputs,I))},Gl=f=>vt({alignCorners:f.align_corners,mode:f.mode,paddingMode:f.padding_mode,format:f.format})}),Vt=me(()=>{hr(),yr(),va(),$c(),t2(),Qr(),h0(),nn=(f,I)=>f.length>I&&f[I].dims.length>0?f[I]:void 0,k0=(f,I)=>{let x=f[0],N=nn(f,1),L=nn(f,2),j=nn(f,3),Y=nn(f,4),Ae=nn(f,5),se=nn(f,6),ne=nn(f,7);if(x.dims.length!==3&&x.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let ye,Be=x.dims[0],te=x.dims[1],Te=x.dims.length===3?x.dims[2]:I.numHeads*x.dims[4],Oe=te,Ke=0,Re=0,et=Math.floor(Te/I.numHeads);if(se&&ne&>.size(se.dims)&>.size(ne.dims)){if(se.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(se.dims[0]!==Be||se.dims[1]!==I.numHeads||se.dims[3]!==et)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(ne.dims[0]!==Be||ne.dims[1]!==I.numHeads||ne.dims[3]!==et)throw new Error('Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)');if(se.dims[2]!==ne.dims[2])throw new Error('Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)');if(ne.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');Ke=se.dims[2],Re=se.dims[2]}else if(se&>.size(se.dims)||ne&>.size(ne.dims))throw new Error('Input "past_key" and "past_value" shall be both present or both absent');if(N&>.size(N.dims)>0){if(x.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(N.dims.length<3||N.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(x.dims[0]!==N.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(N.dims.length===3){if(N.dims[2]!==x.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');ye=2,Oe=N.dims[1]}else if(N.dims.length===5){if(N.dims[2]!==I.numHeads||N.dims[3]!==2||N.dims[4]!==et)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(L)throw new Error('Expect "value" be none when "key" has packed kv format.');ye=5,Oe=N.dims[1]}else{if(N.dims[1]!==I.numHeads||N.dims[3]!==et)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');ye=0,Oe=N.dims[2]}}else{if(x.dims.length!==5)throw new Error('Input "query" is expected to have 5 dimensions when key is empty');if(x.dims[2]!==I.numHeads||x.dims[3]!==3)throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');ye=3}if(j&>.size(j.dims)>0){if(j.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimension');if(N&&N.dims.length===5&&N.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let ft=Ke+Oe,bt=0;if(Y&>.size(Y.dims)>0){bt=8;let Nt=Y.dims;throw Nt.length===1?Nt[0]===Be?bt=1:Nt[0]===3*Be+2&&(bt=3):Nt.length===2&&Nt[0]===Be&&Nt[1]===ft&&(bt=5),bt===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, total_sequence_length)'):new Error("Mask not supported")}let Mt=!1,Dt=Te;if(L&>.size(L.dims)>0){if(L.dims.length!==3&&L.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(x.dims[0]!==L.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(L.dims.length===3){if(Oe!==L.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');Dt=L.dims[2]}else{if(Oe!==L.dims[2])throw new Error('Input "key" and "value" shall have the same dim 2 (kv_sequence_length)');Dt=L.dims[1]*L.dims[3],Mt=!0}}if(Y&>.size(Y.dims)>0)throw new Error("Key padding mask is not supported");if(Ae&>.size(Ae.dims)>0){if(Ae.dims.length!==4)throw new Error('Input "attention_bias" is expected to have 4 dimensions');if(Ae.dims[0]!==Be||Ae.dims[1]!==I.numHeads||Ae.dims[2]!==te||Ae.dims[3]!==ft)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:Be,sequenceLength:te,pastSequenceLength:Ke,kvSequenceLength:Oe,totalSequenceLength:ft,maxSequenceLength:Re,inputHiddenSize:0,hiddenSize:Te,vHiddenSize:Dt,headSize:et,vHeadSize:Math.floor(Dt/I.numHeads),numHeads:I.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:I.maskFilterValue,maskType:bt,scale:I.scale,broadcastResPosBias:!1,passPastInKv:Mt,qkvFormat:ye}},_l=f=>vt({...f}),v0=vt({perm:[0,2,1,3]}),Tl=(f,I,x,N,L,j,Y)=>{let Ae=[N,L,j],se=gt.size(Ae),ne=[{type:12,data:se},{type:12,data:Y},{type:12,data:j}];return f.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:Ae,dataType:I.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(se/64)},programUniforms:ne}),getShaderSource:ye=>{let Be=TA("qkv_with_bias",I.dataType,Ae),te=Yt("qkv",I.dataType,Ae),Te=Yt("bias",x.dataType,Ae);return` 1788 ${ye.registerUniforms([{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}]).declareVariables(te,Te,Be)} 1789 ${ye.mainStart()} 1790 ${ye.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} 1791 let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset; 1792 1793 qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx]; 1794 }`}},{inputs:[I,x],outputs:[-1]})[0]},So=(f,I,x,N,L,j,Y,Ae)=>{let se=j;if(Y&>.size(Y.dims)>0){if(N===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return se=Tl(f,j,Y,I,N,x*L,Ae),se=se.reshape([I,N,x,L]),x===1||N===1?se:f.compute($s(se,v0.perm),{inputs:[se],outputs:[-1]})[0]}return j.dims.length===3&&(se=j.reshape([I,N,x,L])),x===1||N===1?se:f.compute($s(se,v0.perm),{inputs:[se],outputs:[-1]})[0]},Eo=(f,I)=>{let x=k0(f.inputs,I),N=f.inputs[0],L=nn(f.inputs,1),j=nn(f.inputs,2),Y=nn(f.inputs,3),Ae=nn(f.inputs,4),se=nn(f.inputs,5),ne=nn(f.inputs,6),ye=nn(f.inputs,7);if(N.dims.length===5)throw new Error("Packed QKV is not implemented");if((L==null?void 0:L.dims.length)===5)throw new Error("Packed KV is not implemented");let Be=L&&j&&L.dims.length===4&&j.dims.length===4,te=So(f,x.batchSize,x.numHeads,x.sequenceLength,x.headSize,N,Y,0);if(Be)return fn(f,te,L,j,Ae,void 0,ne,ye,se,x);if(!L||!j)throw new Error("key and value must be provided");let Te=So(f,x.batchSize,x.numHeads,x.kvSequenceLength,x.headSize,L,Y,x.hiddenSize),Oe=So(f,x.batchSize,x.numHeads,x.kvSequenceLength,x.vHeadSize,j,Y,2*x.hiddenSize);fn(f,te,Te,Oe,Ae,void 0,ne,ye,se,x)}}),rA=me(()=>{hr(),yr(),va(),Qr(),Ql=f=>{if(!f||f.length<1)throw new Error("too few inputs")},Pl=(f,I)=>{let x=[],N=I.numOutputs;return f[1].dims[0]>0&&(f[1].getBigInt64Array().forEach(L=>x.push(Number(L))),N=x.length),vt({numOutputs:N,axis:I.axis,splitSizes:x})},y0=f=>` 1795fn calculateOutputIndex(index: u32) -> u32 { 1796 for (var i: u32 = 0u; i < ${f}u; i += 1u ) { 1797 if (index < ${PA("uniforms.size_in_split_axis","i",f)}) { 1798 return i; 1799 } 1800 } 1801 return ${f}u; 1802}`,Sl=f=>{let I=f.length,x=[];for(let N=0;N<I;++N){let L=f[N].setByIndices("indices","input[global_idx]");I===1?x.push(L):N===0?x.push(`if (output_number == ${N}u) { ${L} }`):N===I-1?x.push(`else { ${L} }`):x.push(`else if (output_number == ${N}) { ${L} }`)}return` 1803 fn writeBufferData(output_number: u32, indices: ${f[0].type.indices}, global_idx: u32) { 1804 ${x.join(` 1805`)} 1806 }`},E0=(f,I)=>{let x=f[0].dims,N=gt.size(x),L=f[0].dataType,j=gt.normalizeAxis(I.axis,x.length),Y=new Array(I.numOutputs),Ae=Yt("input",L,x.length),se=new Array(I.numOutputs),ne=[],ye=[],Be=0,te=[{type:12,data:N}];for(let Te=0;Te<I.numOutputs;Te++){Be+=I.splitSizes[Te],se[Te]=Be;let Oe=x.slice();Oe[j]=I.splitSizes[Te],ye.push(Oe),Y[Te]=TA(`output${Te}`,L,Oe.length),ne.push({dims:ye[Te],dataType:f[0].dataType})}return te.push({type:12,data:se},..._A(x,...ye)),{name:"Split",shaderCache:{hint:I.cacheKey,inputDependencies:["rank"]},getShaderSource:Te=>` 1807 ${Te.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",se.length).declareVariables(Ae,...Y)} 1808 ${y0(se.length)} 1809 ${Sl(Y)} 1810 1811 ${Te.mainStart()} 1812 ${Te.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.input_size")} 1813 1814 var indices = ${Ae.offsetToIndices("global_idx")}; 1815 var index = ${Ae.indicesGet("indices",j)}; 1816 let output_number = calculateOutputIndex(index); 1817 if (output_number != 0) { 1818 index -= ${PA("uniforms.size_in_split_axis","output_number - 1u",se.length)}; 1819 ${Ae.indicesSet("indices",j,"index")}; 1820 } 1821 writeBufferData(output_number, indices, global_idx); 1822 }`,getRunData:()=>({outputs:ne,dispatchGroup:{x:Math.ceil(N/64)},programUniforms:te})}},Nl=(f,I)=>{Ql(f.inputs);let x=f.inputs.length===1?I:Pl(f.inputs,I);f.compute(E0(f.inputs,x),{inputs:[0]})},zl=f=>{let I=f.axis,x=f.splitSizes,N=f.numOutputs<0?x.length:f.numOutputs;if(N!==x.length)throw new Error("numOutputs and splitSizes lengh must be equal");return vt({axis:I,numOutputs:N,splitSizes:x})}}),DA=me(()=>{va(),t2(),Vt(),rA(),h0(),B0=(f,I)=>{if(I.doRotary)throw new Error("GroupQuerryAttention do_rotary attribute is not supported");if(I.doRotary&&f.length<=7)throw new Error("cos_cache and sin_cache inputs are required if do_rotary is specified");let x=f[0],N=f[1],L=f[2],j=f[3],Y=f[4];if(I.localWindowSize!==-1)throw new Error("Local attention is not supported");if(I.softcap!==0)throw new Error("Softcap is not supported");if(I.rotaryInterleaved!==0)throw new Error("Rotary interleaved is not supported");if(I.smoothSoftmax)throw new Error("Smooth softmax is not supported");if(x.dims.length!==3&&x.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let Ae=x.dims[0],se=x.dims[1],ne=x.dims.length===3?x.dims[2]:I.numHeads*x.dims[4],ye=se,Be=0,te=!N||N.dims.length===0,Te=Math.floor(te?ne/(I.numHeads+2*I.kvNumHeads):ne/I.numHeads);te&&(ne=Te*I.numHeads);let Oe=j&&j.dims.length!==0,Ke=Y&&Y.dims.length!==0;if(Oe&&j.dims.length===4&&j.dims[0]===Ae&&j.dims[1]!==I.kvNumHeads&&j.dims[2]===I.kvNumHeads&&j.dims[3]===Te)throw new Error("BSNH pastKey/pastValue is not supported");if(Oe&&Ke){if(j.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(Y.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');Be=j.dims[2]}else if(Oe||Ke)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let Re=1;if(N&&N.dims.length>0){if(x.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(N.dims.length<3||N.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(x.dims[0]!==N.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(N.dims.length===3){if(x.dims[2]%N.dims[2]!=0)throw new Error('Dimension 2 of "query" should be a multiple of "key"');ye=N.dims[1]}else if(N.dims.length===5){if(N.dims[2]!==I.numHeads||N.dims[3]!==2||N.dims[4]!==Te)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(L)throw new Error('Expect "value" be none when "key" has packed kv format.');ye=N.dims[1]}else{if(N.dims[1]!==I.numHeads||N.dims[3]!==Te)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');ye=N.dims[2]}}else{if(x.dims.length!==3&&x.dims.length!==5)throw new Error('Input "query" is expected to have 3 or 5 dimensions when key is empty');if(x.dims.length===5&&(x.dims[2]!==I.numHeads||x.dims[3]!==3))throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');Re=3}let et=!1,ft=I.kvNumHeads?Te*I.kvNumHeads:ne;if(L&&L.dims.length>0){if(L.dims.length!==3&&L.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(x.dims[0]!==L.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(L.dims.length===3){if(ye!==L.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');ft=L.dims[2]}else{if(ye!==L.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');ft=L.dims[1]*L.dims[3],et=!0}}let bt=f.length>4?f[5]:void 0;if(bt&&bt.dims.length!==1&&bt.dims[0]!==Ae)throw new Error('Input "seqlens" is expected to have 1 dimension and the same dim 0 as batch_size');return{batchSize:Ae,sequenceLength:se,pastSequenceLength:Be,kvSequenceLength:ye,totalSequenceLength:-1,maxSequenceLength:-1,inputHiddenSize:0,hiddenSize:ne,vHiddenSize:ft,headSize:Te,vHeadSize:Math.floor(ft/I.kvNumHeads),numHeads:I.numHeads,kvNumHeads:I.kvNumHeads,nReps:I.numHeads/I.kvNumHeads,pastPresentShareBuffer:!1,maskType:0,scale:I.scale,broadcastResPosBias:!1,passPastInKv:et,qkvFormat:Re}},Ol=vt({perm:[0,2,1,3]}),f0=(f,I,x)=>{let N=I,L=x.kvNumHeads;return I.dims.length===3&&x.kvSequenceLength!==0&&(N=I.reshape([x.batchSize,x.kvSequenceLength,L,x.headSize]),N=f.compute($s(N,Ol.perm),{inputs:[N],outputs:[-1]})[0]),N},Fl=(f,I)=>{var Re;let x=B0(f.inputs,I);if(f.inputs[0].dims.length===5)throw new Error("Packed QKV is not implemented");if(((Re=f.inputs[1])==null?void 0:Re.dims.length)===5)throw new Error("Packed KV is not implemented");let N=f.inputs[0],L=f.inputs[1]&&f.inputs[1].dims.length>0?f.inputs[1]:void 0,j=f.inputs[2]&&f.inputs[2].dims.length>0?f.inputs[2]:void 0,Y=f.inputs[3]&&f.inputs[3].dims.length!==0?f.inputs[3]:void 0,Ae=f.inputs[4]&&f.inputs[4].dims.length!==0?f.inputs[4]:void 0,se=f.inputs.length>4?f.inputs[5]:void 0,ne=f.inputs.length>5?f.inputs[6]:void 0,ye=x.kvNumHeads?x.kvNumHeads:x.numHeads,Be=vt({axis:2,numOutputs:3,splitSizes:[x.numHeads*x.headSize,ye*x.headSize,ye*x.headSize]}),[te,Te,Oe]=L||j?[N,L,j]:f.compute(E0([N],Be),{inputs:[N],outputs:[-1,-1,-1]}),Ke=So(f,x.batchSize,x.numHeads,x.sequenceLength,x.headSize,te,void 0,0);fn(f,Ke,f0(f,Te,x),f0(f,Oe,x),void 0,void 0,Y,Ae,void 0,x,se,ne)}}),GA=me(()=>{hr(),yr(),h0(),Qr(),d0=(f,I,x,N,L,j,Y,Ae)=>{let se=_r(j),ne=se===1?"f32":`vec${se}f`,ye=se===1?"vec2f":`mat2x${se}f`,Be=L*Y,te=64;Be===1&&(te=256);let Te=[L,Y,j/se],Oe=[L,Y,2],Ke=[];return Ke.push(..._A(Te,Oe)),f.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${se};${Ae};${te}`,inputDependencies:["rank","type","type"]},getRunData:()=>({outputs:[{dims:Oe,dataType:1}],dispatchGroup:{x:Be},programUniforms:Ke}),getShaderSource:Re=>{let et=Yt("x",I.dataType,3,se),ft=[et,Yt("scale",x.dataType,x.dims),Yt("bias",N.dataType,N.dims),TA("output",1,3,2)];return` 1823 var<workgroup> workgroup_shared : array<${ye}, ${te}>; 1824 const workgroup_size = ${te}u; 1825 ${Re.declareVariables(...ft)} 1826 ${Re.mainStart(te)} 1827 let batch = workgroup_index / uniforms.x_shape[1]; 1828 let channel = workgroup_index % uniforms.x_shape[1]; 1829 let hight = uniforms.x_shape[2]; 1830 // initialize workgroup memory 1831 var sum = ${ne}(0); 1832 var squared_sum = ${ne}(0); 1833 for (var h = local_idx; h < hight; h += workgroup_size) { 1834 let value = ${ne}(${et.get("batch","channel","h")}); 1835 sum += value; 1836 squared_sum += value * value; 1837 } 1838 workgroup_shared[local_idx] = ${ye}(sum, squared_sum); 1839 workgroupBarrier(); 1840 1841 for (var currSize = workgroup_size >> 1; currSize > 0; currSize = currSize >> 1) { 1842 if (local_idx < currSize) { 1843 workgroup_shared[local_idx] = workgroup_shared[local_idx] + workgroup_shared[local_idx + currSize]; 1844 } 1845 workgroupBarrier(); 1846 } 1847 if (local_idx == 0) { 1848 let sum_final = ${ts("workgroup_shared[0][0]",se)} / f32(hight * ${se}); 1849 let squared_sum_final = ${ts("workgroup_shared[0][1]",se)} / f32(hight * ${se}); 1850 1851 let inv_std_dev = inverseSqrt(squared_sum_final - sum_final * sum_final + f32(${Ae})); 1852 let channel_scale = inv_std_dev * f32(scale[channel]); 1853 let channel_shift = f32(bias[channel]) - sum_final * channel_scale; 1854 output[workgroup_index] = vec2f(channel_scale, channel_shift); 1855 } 1856 }`}},{inputs:[I,x,N],outputs:[-1]})[0]},Ll=(f,I,x)=>{let N=I[0].dims,L=N,j=N[0],Y=N[1],Ae=gt.sizeFromDimension(N,2),se=_r(Ae),ne=gt.size(L)/se,ye=d0(f,I[0],I[1],I[2],j,Ae,Y,x.epsilon),Be=[j,Y,Ae/se],te=[j,Y];f.compute({name:"InstanceNormalization",shaderCache:{hint:`${se}`,inputDependencies:["type","none"]},getRunData:()=>({outputs:[{dims:L,dataType:I[0].dataType}],dispatchGroup:{x:Math.ceil(ne/64)},programUniforms:[{type:12,data:ne},..._A(Be,te,Be)]}),getShaderSource:Te=>{let Oe=Yt("x",I[0].dataType,Be.length,se),Ke=Yt("scale_shift",1,te.length,2),Re=TA("output",I[0].dataType,Be.length,se),et=[Oe,Ke,Re];return` 1857 ${Te.registerUniform("output_size","u32").declareVariables(...et)} 1858 ${Te.mainStart()} 1859 ${Te.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} 1860 let outputIndices = ${Re.offsetToIndices("global_idx")}; 1861 let batch = outputIndices[0]; 1862 let channel = outputIndices[1]; 1863 let scale_shift = ${Ke.getByIndices("vec2<u32>(batch, channel)")}; 1864 let value = ${Oe.getByOffset("global_idx")} * ${Re.type.value}(scale_shift.x) + ${Re.type.value}(scale_shift.y); 1865 ${Re.setByOffset("global_idx","value")}; 1866 }`}},{inputs:[I[0],ye]})},g0=(f,I,x)=>{let N=I[0].dims,L=N,j=N[0],Y=N[N.length-1],Ae=gt.sizeFromDimension(N,1)/Y,se=_r(Y),ne=gt.size(L)/se,ye=[{type:12,data:Ae},{type:12,data:Math.floor(Y/se)}],Be=!1,te=[0,N.length-1];for(let Ke=0;Ke<N.length-2;Ke++)Be=Be||N[Ke+1]!==1,te.push(Ke+1);Be=Be&&N[N.length-1]!==1;let Te=Be?f.compute($s(f.inputs[0],te),{inputs:[f.inputs[0]],outputs:[-1]})[0]:f.inputs[0].reshape(Array.from({length:N.length},(Ke,Re)=>N[te[Re]])),Oe=d0(f,Te,I[1],I[2],j,Ae,Y,x.epsilon);f.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${se}`,inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:L,dataType:I[0].dataType}],dispatchGroup:{x:Math.ceil(ne/64)},programUniforms:ye}),getShaderSource:Ke=>{let Re=ir(I[0].dataType),et=se===1?"vec2f":`mat${se}x2f`,ft=Dt=>{let Nt=Dt===0?"x":"y",hA=se===1?"f32":`vec${se}f`;switch(se){case 1:return`${Re}(${hA}(scale.${Nt}))`;case 2:return`vec2<${Re}>(${hA}(scale[0].${Nt}, scale[1].${Nt}))`;case 4:return`vec4<${Re}>(${hA}(scale[0].${Nt}, scale[1].${Nt}, scale[2].${Nt}, scale[3].${Nt}))`;default:throw new Error(`Not supported compoents ${se}`)}},bt=Yt("input",I[0].dataType,I[0].dims,se),Mt=TA("output",I[0].dataType,L,se);return` 1867 @group(0) @binding(0) var<storage, read> input : array<${bt.type.storage}>; 1868 @group(0) @binding(1) var<storage, read> scale_input : array<${et}>; 1869 @group(0) @binding(2) var<storage, read_write> output : array<${Mt.type.storage}>; 1870 struct Uniforms {H: u32, C : u32}; 1871 @group(0) @binding(3) var<uniform> uniforms: Uniforms; 1872 1873 ${Ke.mainStart()} 1874 let current_image_number = global_idx / (uniforms.C * uniforms.H); 1875 let current_channel_number = global_idx % uniforms.C; 1876 1877 let scale_offset = current_image_number * uniforms.C + current_channel_number; 1878 let scale = scale_input[scale_offset]; 1879 output[global_idx] = fma(input[global_idx], ${ft(0)}, ${ft(1)}); 1880 }`}},{inputs:[I[0],Oe]})},Vl=(f,I)=>{I.format==="NHWC"?g0(f,f.inputs,I):Ll(f,f.inputs,I)}}),yA=me(()=>{hr(),yr(),Qr(),Hl=f=>{if(!f||f.length<2)throw new Error("layerNorm requires at least 2 inputs.")},x0=(f,I,x)=>{let N=I.simplified,L=f[0].dims,j=f[1],Y=!N&&f[2],Ae=L,se=gt.normalizeAxis(I.axis,L.length),ne=gt.sizeToDimension(L,se),ye=gt.sizeFromDimension(L,se),Be=gt.size(j.dims),te=Y?gt.size(Y.dims):0;if(Be!==ye||Y&&te!==ye)throw new Error(`Size of X.shape()[axis:] == ${ye}. 1881 Size of scale and bias (if provided) must match this. 1882 Got scale size of ${Be} and bias size of ${te}`);let Te=[];for(let Mt=0;Mt<L.length;++Mt)Mt<se?Te.push(L[Mt]):Te.push(1);let Oe=_r(ye),Ke=["type","type"],Re=[{type:12,data:ne},{type:1,data:ye},{type:12,data:Math.floor(ye/Oe)},{type:1,data:I.epsilon}];Y&&Ke.push("type");let et=x>1,ft=x>2,bt=[{dims:Ae,dataType:f[0].dataType}];return et&&bt.push({dims:Te,dataType:1}),ft&&bt.push({dims:Te,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${Oe};${x};${N}`,inputDependencies:Ke},getRunData:()=>({outputs:bt,dispatchGroup:{x:Math.ceil(ne/64)},programUniforms:Re}),getShaderSource:Mt=>{let Dt=ir(f[0].dataType),Nt=[Yt("x",f[0].dataType,f[0].dims,Oe),Yt("scale",j.dataType,j.dims,Oe)];return Y&&Nt.push(Yt("bias",Y.dataType,Y.dims,Oe)),Nt.push(TA("output",f[0].dataType,Ae,Oe)),et&&Nt.push(TA("mean_data_output",1,Te)),ft&&Nt.push(TA("inv_std_output",1,Te)),` 1883 ${Mt.registerUniforms([{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}]).declareVariables(...Nt)} 1884 ${Mt.mainStart()} 1885 ${Mt.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")} 1886 let offset = global_idx * uniforms.norm_size_vectorized; 1887 var mean_vector = ${ba("f32",Oe)}; 1888 var mean_square_vector = ${ba("f32",Oe)}; 1889 1890 for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) { 1891 let value = ${Bs(Dt,Oe,"x[h + offset]")}; 1892 mean_vector += value; 1893 mean_square_vector += value * value; 1894 } 1895 let mean = ${ts("mean_vector",Oe)} / uniforms.norm_size; 1896 let inv_std_dev = inverseSqrt(${ts("mean_square_vector",Oe)} / uniforms.norm_size ${N?"":"- mean * mean"} + uniforms.epsilon); 1897 1898 for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) { 1899 let f32input = ${Bs(Dt,Oe,"x[j + offset]")}; 1900 let f32scale = ${Bs(Dt,Oe,"scale[j]")}; 1901 output[j + offset] = ${Nt[0].type.value}((f32input ${N?"":"- mean"}) * inv_std_dev * f32scale 1902 ${Y?`+ ${Bs(Dt,Oe,"bias[j]")}`:""} 1903 ); 1904 } 1905 1906 ${et?"mean_data_output[global_idx] = mean":""}; 1907 ${ft?"inv_std_output[global_idx] = inv_std_dev":""}; 1908 }`}}},Yl=(f,I)=>{Hl(f.inputs),f.compute(x0(f.inputs,I,f.outputCount))}}),OA=me(()=>{yr(),s2(),a2(),M0=f=>{if(!f||f.length!==2)throw new Error("MatMul requires 2 inputs.");if(f[0].dims[f[0].dims.length-1]!==f[1].dims[f[1].dims.length-2])throw new Error("shared dimension does not match.")},Rl=f=>{M0(f.inputs);let I=lA.calcShape(f.inputs[0].dims,f.inputs[1].dims,!0);if(!I)throw new Error("Can't use matmul on the given tensors");let x=I[I.length-1],N=f.inputs[0].dims[f.inputs[0].dims.length-1];if(x<8&&N<8)f.compute(Ti(f.inputs,{activation:""},I));else{let L=I[I.length-2],j=gt.size(f.inputs[0].dims.slice(0,-2)),Y=gt.size(f.inputs[1].dims.slice(0,-2));if(j!==1&&L===1&&Y===1){let Ae=[1,j,x],se=[f.inputs[0].reshape([1,j,N]),f.inputs[1].reshape([1,N,x])];f.compute(t(se,{activation:""},I,Ae),{inputs:se})}else f.compute(t(f.inputs,{activation:""},I))}}}),XA=me(()=>{hr(),yr(),va(),Qr(),jl=(f,I)=>{if(f.length<3||f.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let x=f[0],N=x.dims.length;if(x.dims[N-1]!==I.k)throw new Error("The last dim of input shape does not match the k value");let L=Math.floor((I.k+I.blockSize-1)/I.blockSize),j=I.blockSize/8*I.bits,Y=f[1];if(!gt.areEqual(Y.dims,[I.n,L,j]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let Ae=f[2].dims;if(gt.size(Ae)!==I.n*L)throw new Error("scales input size error.");if(f.length===4){let se=f[3].dims,ne=I.bits>4?I.n*L:I.n*Math.floor((L+1)/2);if(gt.size(se)!==ne)throw new Error("zeroPoints input size error.")}},G2=(f,I)=>{let x=f[0].dims,N=x.length,L=x[N-2],j=I.k,Y=I.n,Ae=x.slice(0,N-2),se=gt.size(Ae),ne=f[1].dims[2]/4,ye=f[0].dataType,Be=_r(I.k),te=_r(ne),Te=_r(Y),Oe=Ae.concat([L,Y]),Ke=L>1&&Y/Te%2==0?2:1,Re=gt.size(Oe)/Te/Ke,et=64,ft=[],bt=[se,L,j/Be],Mt=gt.convertShape(f[1].dims).slice();Mt.splice(-1,1,ne/te),ft.push(..._A(bt)),ft.push(..._A(Mt)),ft.push(..._A(f[2].dims)),f.length===4&&ft.push(..._A(gt.convertShape(f[3].dims)));let Dt=[se,L,Y/Te];return ft.push(..._A(Dt)),{name:"MatMulNBits",shaderCache:{hint:`${I.blockSize};${I.bits};${Be};${te};${Te};${Ke};64`,inputDependencies:Array(f.length).fill("rank")},getRunData:()=>({outputs:[{dims:Oe,dataType:ye}],dispatchGroup:{x:Re},programUniforms:ft}),getShaderSource:Nt=>{let hA=bt.length,xA=Yt("a",f[0].dataType,hA,Be),er=Yt("b",12,Mt.length,te),tr=Yt("scales",f[2].dataType,f[2].dims.length),qr=[xA,er,tr],Ar=f.length===4?Yt("zero_points",12,f[3].dims.length):void 0;Ar&&qr.push(Ar);let Ir=Dt.length,mr=TA("output",f[0].dataType,Ir,Te),Lt=ir(f[0].dataType),UA=(()=>{switch(Be){case 1:return`array<${Lt}, 8>`;case 2:return`mat4x2<${Lt}>`;case 4:return`mat2x4<${Lt}>`;default:throw new Error(`${Be}-component is not supported.`)}})();return` 1909 var<workgroup> workgroup_shared: array<${mr.type.value}, ${Ke*et}>; 1910 ${Nt.declareVariables(...qr,mr)} 1911 ${Nt.mainStart([et,1,1])} 1912 let output_indices = ${mr.offsetToIndices(`(global_idx / 64) * ${Ke}`)}; 1913 let col = output_indices[2]; 1914 let row = output_indices[1]; 1915 let batch = output_indices[0]; 1916 let nBlocksPerCol = uniforms.b_shape[1]; 1917 1918 for (var block = local_id.x; block < nBlocksPerCol; block += 64) { 1919 //process one block 1920 var word_offset: u32 = block * ${I.blockSize/Be}; 1921 ${(()=>{let HA=` 1922 var col_index = col * ${Te}; 1923 ${Ar?` 1924 let zero_point_bytes_per_col = (nBlocksPerCol + 1) / 2; 1925 var zero_point_byte_count: u32; 1926 var zero_point_word_index: u32; 1927 var zero_point_byte_offset: u32; 1928 let zero_point_nibble_offset: u32 = block & 0x1u; 1929 var zero_point_bits_offset: u32; 1930 var zero_point_word: u32;`:` 1931 // The default zero point is 8 for unsigned 4-bit quantization. 1932 let zero_point = ${Lt}(8);`} 1933 `;for(let RA=0;RA<Te*Ke;RA++)HA+=` 1934 let scale${RA} = ${tr.getByOffset("col_index * nBlocksPerCol + block")}; 1935 ${Ar?` 1936 zero_point_byte_count = col_index * zero_point_bytes_per_col + (block >> 0x1u); 1937 zero_point_word_index = zero_point_byte_count >> 0x2u; 1938 zero_point_byte_offset = zero_point_byte_count & 0x3u; 1939 zero_point_bits_offset = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2); 1940 zero_point_word = ${Ar.getByOffset("zero_point_word_index")} >> zero_point_bits_offset; 1941 let zero_point${RA} = ${Lt}((zero_point_word) & 0xFu);`:""} 1942 col_index += 1;`;return HA})()} 1943 for (var word: u32 = 0; word < ${ne}; word += ${te}) { 1944 ${(()=>{let HA=`col_index = col * ${Te};`;for(let RA=0;RA<Te*Ke;RA++)HA+=` 1945 let b${RA}_data = ${er.getByIndices(`${er.type.indices}(col_index, block, word)`)}; 1946 col_index += 1;`;return HA+=` 1947 var b_value: u32; 1948 let b_mask: u32 = 0x0F0F0F0Fu; 1949 var b_value_lower: vec4<u32>; 1950 var b_value_upper: vec4<u32>; 1951 var b_quantized_values: ${UA}; 1952 var b_dequantized_values: ${UA};`,HA})()} 1953 for (var i: u32 = 0; i < ${te}; i++) { 1954 ${(()=>{let HA=` 1955 // reuse a data 1956 var input_offset = ${xA.indicesToOffset(`${xA.type.indices}(batch, row, word_offset)`)}; 1957 var a_data: ${UA}; 1958 for (var j: u32 = 0; j < ${8/Be}; j++) { 1959 a_data[j] = ${xA.getByOffset("input_offset")}; 1960 input_offset++; 1961 } 1962 `;for(let RA=0;RA<Te*Ke;RA++)HA+=` 1963 b_value = ${te===1?`b${RA}_data`:`b${RA}_data[i]`}; 1964 b_value_lower = unpack4xU8(b_value & b_mask); 1965 b_value_upper = unpack4xU8((b_value >> 4) & b_mask); 1966 b_quantized_values = ${UA}(${Array.from({length:4},(is,Va)=>`${Lt}(b_value_lower[${Va}]), ${Lt}(b_value_upper[${Va}])`).join(", ")}); 1967 b_dequantized_values = ${Be===1?`${UA}(${Array.from({length:8},(is,Va)=>`(b_quantized_values[${Va}] - ${Ar?`zero_point${RA}`:"zero_point"}) * scale${RA}`).join(", ")});`:`(b_quantized_values - ${UA}(${Array(8).fill(Ar?`zero_point${RA}`:"zero_point").join(",")})) * scale${RA};`}; 1968 workgroup_shared[local_id.x * ${Ke} + ${Math.floor(RA/Te)}]${Te>1?`[${RA%Te}]`:""} += ${Array.from({length:8/Be},(is,Va)=>Be===1?`a_data[${Va}] * b_dequantized_values[${Va}]`:`dot(a_data[${Va}], b_dequantized_values[${Va}])`).join(" + ")}; 1969 `;return HA})()} 1970 word_offset += ${8/Be}; 1971 } 1972 } 1973 } 1974 workgroupBarrier(); 1975 1976 if (local_id.x < ${Ke}) { 1977 var output_value: ${mr.type.value} = ${mr.type.value}(0); 1978 var workgroup_shared_offset: u32 = local_id.x; 1979 for (var b: u32 = 0u; b < 64u; b++) { 1980 output_value += workgroup_shared[workgroup_shared_offset]; 1981 workgroup_shared_offset += ${Ke}; 1982 } 1983 ${mr.setByIndices(`${mr.type.indices}(batch, row, col + local_id.x)`,"output_value")}; 1984 } 1985 }`}}},e1=(f,I)=>{let x=f[0].dims,N=x.length,L=x[N-2],j=I.k,Y=I.n,Ae=x.slice(0,N-2),se=gt.size(Ae),ne=f[1].dims[2]/4,ye=f[0].dataType,Be=_r(I.k),te=_r(ne),Te=Ae.concat([L,Y]),Oe=Y%8==0?8:Y%4==0?4:1,Ke=128/Oe,Re=Ke*te*8,et=Re/Be,ft=Re/I.blockSize,bt=gt.size(Te)/Oe,Mt=[],Dt=[se,L,j/Be],Nt=gt.convertShape(f[1].dims).slice();Nt.splice(-1,1,ne/te),Mt.push(..._A(Dt)),Mt.push(..._A(Nt)),Mt.push(..._A(f[2].dims)),f.length===4&&Mt.push(..._A(gt.convertShape(f[3].dims)));let hA=[se,L,Y];return Mt.push(..._A(hA)),{name:"BlockwiseMatMulNBits32",shaderCache:{hint:`${I.blockSize};${Be};${te};${Ke};${Oe}`,inputDependencies:Array(f.length).fill("rank")},getRunData:()=>({outputs:[{dims:Te,dataType:ye}],dispatchGroup:{x:bt},programUniforms:Mt}),getShaderSource:xA=>{let er=Dt.length,tr=Yt("a",f[0].dataType,er,Be),qr=Yt("b",12,Nt.length,te),Ar=Yt("scales",f[2].dataType,f[2].dims.length),Ir=[tr,qr,Ar],mr=f.length===4?Yt("zero_points",12,f[3].dims.length):void 0;mr&&Ir.push(mr);let Lt=hA.length,UA=TA("output",f[0].dataType,Lt),HA=ir(f[0].dataType);return` 1986 var<workgroup> sub_a: array<${tr.type.value}, ${et}>; 1987 var<workgroup> inter_results: array<array<${UA.type.value}, ${Ke}>, ${Oe}>; 1988 ${xA.declareVariables(...Ir,UA)} 1989 ${xA.mainStart([Ke,Oe,1])} 1990 let output_indices = ${UA.offsetToIndices(`workgroup_index * ${Oe}`)}; 1991 let col = output_indices[2]; 1992 let row = output_indices[1]; 1993 let batch = output_indices[0]; 1994 let n_blocks_per_col = uniforms.b_shape[1]; 1995 let num_tiles = (n_blocks_per_col - 1) / ${ft} + 1; 1996 1997 // Loop over shared dimension. 1998 for (var tile: u32 = 0; tile < num_tiles; tile += 1) { 1999 let a_col_start = tile * ${et}; 2000 // load one tile A data into shared memory. 2001 for (var a_offset = local_idx; a_offset < ${et}; a_offset += 128) 2002 { 2003 let a_col = a_col_start + a_offset; 2004 if (a_col < uniforms.a_shape[2]) 2005 { 2006 sub_a[a_offset] = ${tr.getByIndices(`${tr.type.indices}(batch, row, a_col)`)}; 2007 } else { 2008 sub_a[a_offset] = ${tr.type.value}(0); 2009 } 2010 } 2011 workgroupBarrier(); 2012 2013 // each thread process one block 2014 let b_row = col + local_id.y; 2015 let block = tile * ${ft} + local_id.x; 2016 ${mr?` 2017 let zero_point_bytes_per_col = (n_blocks_per_col + 1) / 2; 2018 let zero_point_byte_count = b_row * zero_point_bytes_per_col + (block >> 0x1u); 2019 let zero_point_word_index = zero_point_byte_count >> 0x2u; 2020 let zero_point_byte_offset = zero_point_byte_count & 0x3u; 2021 let zero_point_nibble_offset: u32 = block & 0x1u; 2022 let zero_point_bits_offset = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2); 2023 let zero_point_word = ${mr.getByOffset("zero_point_word_index")} >> zero_point_bits_offset; 2024 let zero_point = ${HA}((zero_point_word) & 0xFu);`:` 2025 // The default zero point is 8 for unsigned 4-bit quantization. 2026 let zero_point = ${HA}(8);`} 2027 let scale = ${Ar.getByOffset("b_row * n_blocks_per_col + block")}; 2028 let b_data = ${qr.getByIndices(`${qr.type.indices}(b_row, block, 0)`)}; 2029 var word_offset = local_id.x * ${I.blockSize/Be}; 2030 for (var i: u32 = 0; i < ${te}; i++) { 2031 ${(()=>{switch(Be){case 1:return` 2032 let a_data0 = vec4<${HA}>(sub_a[word_offset], sub_a[word_offset + 1], sub_a[word_offset + 2], sub_a[word_offset + 3]); 2033 let a_data1 = vec4<${HA}>(sub_a[word_offset + 4], sub_a[word_offset + 5], sub_a[word_offset + 6], sub_a[word_offset + 7]);`;case 2:return` 2034 let a_data0 = vec4<${HA}>(sub_a[word_offset], sub_a[word_offset + 1]); 2035 let a_data1 = vec4<${HA}>(sub_a[word_offset + 2], sub_a[word_offset + 3]);`;case 4:return` 2036 let a_data0 = sub_a[word_offset]; 2037 let a_data1 = sub_a[word_offset + 1];`;default:throw new Error(`${Be}-component is not supported.`)}})()} 2038 let b_value = ${te===1?"b_data":"b_data[i]"}; 2039 let b_value_lower = unpack4xU8(b_value & 0x0F0F0F0Fu); 2040 let b_value_upper = unpack4xU8((b_value >> 4) & 0x0F0F0F0Fu); 2041 let b_quantized_values = mat2x4<${HA}>(${Array.from({length:4},(RA,is)=>`${HA}(b_value_lower[${is}]), ${HA}(b_value_upper[${is}])`).join(", ")}); 2042 let b_dequantized_values = (b_quantized_values - mat2x4<${HA}>(${Array(8).fill("zero_point").join(",")})) * scale; 2043 inter_results[local_id.y][local_id.x] += ${Array.from({length:2},(RA,is)=>`dot(a_data${is}, b_dequantized_values[${is}])`).join(" + ")}; 2044 word_offset += ${8/Be}; 2045 } 2046 workgroupBarrier(); 2047 } 2048 2049 if (local_idx < ${Oe}) { 2050 var output_value: ${UA.type.value} = ${UA.type.value}(0); 2051 for (var b = 0u; b < ${Ke}; b++) { 2052 output_value += inter_results[local_idx][b]; 2053 } 2054 if (col + local_idx < uniforms.output_shape[2]) 2055 { 2056 ${UA.setByIndices(`${UA.type.indices}(batch, row, col + local_idx)`,"output_value")} 2057 } 2058 } 2059 }`}}},_2=(f,I)=>{jl(f.inputs,I),I.blockSize===32&&f.adapterInfo.isVendor("intel")&&f.adapterInfo.isArchitecture("gen-12lp")?f.compute(e1(f.inputs,I)):f.compute(G2(f.inputs,I))},T2=f=>vt(f)}),pr=me(()=>{hr(),yr(),Qr(),Q2=f=>{if(!f||f.length<1)throw new Error("Too few inputs");if(f[0].dataType!==1&&f[0].dataType!==10)throw new Error("Input type must be float or float16.");if(f.length>=2){let I=2*f[0].dims.length===f[1].dims[0];if(f.length===4&&(I=2*f[3].dims[0]===f[1].dims[0]),!I)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},K0=(f,I,x)=>{let N="";for(let L=I-1;L>=0;--L)N+=` 2060 k = i32(${f.indicesGet("indices",L)}) - ${PA("uniforms.pads",L,x)}; 2061 if (k < 0) { 2062 break; 2063 } 2064 if (k >= i32(${PA("uniforms.x_shape",L,I)})) { 2065 break; 2066 } 2067 offset += k * i32(${PA("uniforms.x_strides",L,I)}); 2068 `;return` 2069 value = ${f.type.value}(uniforms.constant_value); 2070 for (var i = 0; i < 1; i++) { 2071 var offset = 0; 2072 var k = 0; 2073 ${N} 2074 value = x[offset]; 2075 } 2076 `},P2=(f,I,x)=>{let N="";for(let L=I-1;L>=0;--L)N+=` 2077 k = i32(${f.indicesGet("indices",L)}) - ${PA("uniforms.pads",L,x)}; 2078 if (k < 0) { 2079 k = -k; 2080 } 2081 { 2082 let _2n_1 = 2 * (i32(${PA("uniforms.x_shape",L,I)}) - 1); 2083 k = k % _2n_1; 2084 if(k >= i32(${PA("uniforms.x_shape",L,I)})) { 2085 k = _2n_1 - k; 2086 } 2087 } 2088 offset += k * i32(${PA("uniforms.x_strides",L,I)}); 2089 `;return` 2090 var offset = 0; 2091 var k = 0; 2092 ${N} 2093 value = x[offset]; 2094 `},S2=(f,I,x)=>{let N="";for(let L=I-1;L>=0;--L)N+=` 2095 k = i32(${f.indicesGet("indices",L)}) - ${PA("uniforms.pads",L,x)}; 2096 if (k < 0) { 2097 k = 0; 2098 } 2099 if (k >= i32(${PA("uniforms.x_shape",L,I)})) { 2100 k = i32(${PA("uniforms.x_shape",L,I)}) - 1; 2101 } 2102 offset += k * i32(${PA("uniforms.x_strides",L,I)}); 2103 `;return` 2104 var offset = 0; 2105 var k = 0; 2106 ${N} 2107 value = x[offset]; 2108 `},N2=(f,I,x)=>{let N="";for(let L=I-1;L>=0;--L)N+=` 2109 k = i32(${f.indicesGet("indices",L)}) - ${PA("uniforms.pads",L,x)}; 2110 if (k < 0) { 2111 k += i32(${PA("uniforms.x_shape",L,I)}]); 2112 } 2113 if (k >= i32(${PA("uniforms.x_shape",L,I)})) { 2114 k -= i32(${PA("uniforms.x_shape",L,I)}); 2115 } 2116 offset += k * i32(${PA("uniforms.x_strides",L,I)}); 2117 `;return` 2118 var offset = 0; 2119 var k = 0; 2120 ${N} 2121 value = x[offset]; 2122 `},z2=(f,I,x)=>{switch(x.mode){case 0:return K0(f,I,x.pads.length);case 1:return P2(f,I,x.pads.length);case 2:return S2(f,I,x.pads.length);case 3:return N2(f,I,x.pads.length);default:throw new Error("Invalid mode")}},O2=(f,I)=>{let x=gt.padShape(f[0].dims.slice(),I.pads),N=f[0].dims,L=[{type:12,data:gt.size(x)},{type:6,data:I.pads}],j=f.length>=3&&f[2].data;return I.mode===0&&L.push({type:j?f[2].dataType:1,data:I.value}),L.push(..._A(f[0].dims,x)),{name:"Pad",shaderCache:{hint:`${I.mode}${j}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:x,dataType:f[0].dataType}],dispatchGroup:{x:Math.ceil(gt.size(x)/64)},programUniforms:L}),getShaderSource:Y=>{let Ae=TA("output",f[0].dataType,x.length),se=Yt("x",f[0].dataType,N.length),ne=se.type.value,ye=z2(Ae,N.length,I),Be=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:I.pads.length}];return I.mode===0&&Be.push({name:"constant_value",type:j?ne:"f32"}),` 2123 ${Y.registerUniforms(Be).declareVariables(se,Ae)} 2124 ${Y.mainStart()} 2125 ${Y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} 2126 2127 let indices = ${Ae.offsetToIndices("global_idx")}; 2128 2129 var value = ${ne}(0); 2130 ${ye} 2131 output[global_idx] = value; 2132 }`}}},F2=(f,I)=>{if(f.length>1){let x=f[1].getBigInt64Array(),N=f.length>=3&&f[2].data?f[2].dataType===10?f[2].getUint16Array()[0]:f[2].getFloat32Array()[0]:0,L=f[0].dims.length,j=new Int32Array(2*L).fill(0);if(f.length>=4){let Ae=f[3].getBigInt64Array();for(let se=0;se<Ae.length;se++)j[Number(Ae[se])]=Number(x[se]),j[Number(Ae[se])+L]=Number(x[se+Ae.length])}else x.forEach((Ae,se)=>j[Number(se)]=Number(Ae));let Y=[];return j.forEach(Ae=>Y.push(Ae)),{mode:I.mode,value:N,pads:Y}}return I},L2=(f,I)=>{Q2(f.inputs);let x=F2(f.inputs,I);f.compute(O2(f.inputs,x),{inputs:[0]})}}),cs=me(()=>{lt(),hr(),yr(),Qr(),D0=f=>{if(G.webgpu.validateInputContent&&(!f||f.length!==1))throw new Error("Pool ops requires 1 input.")},V2=(f,I,x)=>{let N=I.format==="NHWC",L=f.dims.slice();N&&L.splice(1,0,L.pop());let j=Object.hasOwnProperty.call(I,"dilations"),Y=I.kernelShape.slice(),Ae=I.strides.slice(),se=j?I.dilations.slice():[],ne=I.pads.slice();pA.adjustPoolAttributes(x,L,Y,Ae,se,ne);let ye=pA.computePoolOutputShape(x,L,Ae,se,Y,ne,I.autoPad),Be=Object.assign({},I);j?Object.assign(Be,{kernelShape:Y,strides:Ae,pads:ne,dilations:se,cacheKey:I.cacheKey}):Object.assign(Be,{kernelShape:Y,strides:Ae,pads:ne,cacheKey:I.cacheKey});let te=ye.slice();return te.push(te.splice(1,1)[0]),[Be,N?te:ye]},vc=(f,I)=>{let x=I.format==="NHWC",N=[{type:12,data:gt.size(f)},{type:12,data:gt.size(I.kernelShape)}],L=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(I.kernelShape.length<=2){let j=I.kernelShape[I.kernelShape.length-1],Y=I.strides[I.strides.length-1],Ae=I.pads[I.pads.length/2-1],se=I.pads[I.pads.length-1],ne=!!(Ae+se);N.push({type:12,data:j},{type:12,data:Y},{type:12,data:Ae},{type:12,data:se}),L.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let ye=!1;if(I.kernelShape.length===2){let Be=I.kernelShape[I.kernelShape.length-2],te=I.strides[I.strides.length-2],Te=I.pads[I.pads.length/2-2],Oe=I.pads[I.pads.length-2];ye=!!(Te+Oe),N.push({type:12,data:Be},{type:12,data:te},{type:12,data:Te},{type:12,data:Oe}),L.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[N,L,!0,ne,ye]}{if(x)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let j=gt.computeStrides(I.kernelShape);N.push({type:12,data:j},{type:12,data:I.pads},{type:12,data:I.strides}),L.push({name:"kernelStrides",type:"u32",length:j.length},{name:"pads",type:"u32",length:I.pads.length},{name:"strides",type:"u32",length:I.strides.length});let Y=I.pads.reduce((Ae,se)=>Ae+se);return[N,L,!!Y,!1,!1]}},yc=(f,I,x,N,L,j,Y,Ae,se,ne,ye,Be)=>{let te=L.format==="NHWC",Te=I.type.value,Oe=TA("output",I.type.tensor,N);if(L.kernelShape.length<=2){let Ke="",Re="",et="",ft=x-(te?2:1);if(Ke=ye?` 2133 for (var i: u32 = 0u; i < uniforms.kw; i++) { 2134 xIndices[${ft}] = indices[${ft}] * uniforms.sw - uniforms.pwStart + i; 2135 if (xIndices[${ft}] < 0 || xIndices[${ft}] 2136 >= uniforms.x_shape[${ft}]) { 2137 pad++; 2138 continue; 2139 } 2140 let x_val = x[${I.indicesToOffset("xIndices")}]; 2141 ${j} 2142 }`:` 2143 for (var i: u32 = 0u; i < uniforms.kw; i++) { 2144 xIndices[${ft}] = indices[${ft}] * uniforms.sw - uniforms.pwStart + i; 2145 let x_val = x[${I.indicesToOffset("xIndices")}]; 2146 ${j} 2147 }`,L.kernelShape.length===2){let bt=x-(te?3:2);Re=Be?` 2148 for (var j: u32 = 0u; j < uniforms.kh; j++) { 2149 xIndices[${bt}] = indices[${bt}] * uniforms.sh - uniforms.phStart + j; 2150 if (xIndices[${bt}] < 0 || xIndices[${bt}] >= uniforms.x_shape[${bt}]) { 2151 pad += i32(uniforms.kw); 2152 continue; 2153 } 2154 `:` 2155 for (var j: u32 = 0u; j < uniforms.kh; j++) { 2156 xIndices[${bt}] = indices[${bt}] * uniforms.sh - uniforms.phStart + j; 2157 `,et=` 2158 } 2159 `}return` 2160 ${f.registerUniforms(se).declareVariables(I,Oe)} 2161 2162 ${f.mainStart()} 2163 ${f.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} 2164 2165 let indices = ${Oe.offsetToIndices("global_idx")}; 2166 var xIndices = ${Oe.offsetToIndices("global_idx")}; 2167 2168 var value = ${Te}(${Ae}); 2169 var pad = 0; 2170 ${Re} 2171 ${Ke} 2172 ${et} 2173 ${Y} 2174 2175 output[global_idx] = value; 2176 }`}{if(te)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let Ke=L.kernelShape.length,Re=L.pads.length,et="";return et=ne?` 2177 if (xIndices[j] >= uniforms.x_shape[j]) { 2178 pad++; 2179 isPad = true; 2180 break; 2181 } 2182 } 2183 if (!isPad) { 2184 let x_val = x[${I.indicesToOffset("xIndices")}]; 2185 ${j} 2186 }`:` 2187 } 2188 let x_val = x[${I.indicesToOffset("xIndices")}]; 2189 ${j} 2190 `,` 2191 ${f.registerUniforms(se).declareVariables(I,Oe)} 2192 2193 ${f.mainStart()} 2194 ${f.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} 2195 let indices = ${Oe.offsetToIndices("global_idx")}; 2196 var xIndices = ${Oe.offsetToIndices("global_idx")}; 2197 2198 var offsets: array<u32, ${Ke}>; 2199 2200 var value = ${Te}(${Ae}); 2201 var pad = 0; 2202 var isPad = false; 2203 2204 for (var i: u32 = 0u; i < uniforms.kernelSize; i++) { 2205 var offset = i; 2206 for (var j = 0u; j < ${Ke-1}u; j++) { 2207 offsets[j] = offset / ${PA("uniforms.kernelStrides","j",Ke)}; 2208 offset -= offsets[j] * ${PA("uniforms.kernelStrides","j",Ke)}; 2209 } 2210 offsets[${Ke-1}] = offset; 2211 2212 isPad = false; 2213 for (var j = ${x-Ke}u; j < ${x}u; j++) { 2214 xIndices[j] = indices[j] * ${PA("uniforms.strides",`j - ${x-Ke}u`,Ke)} 2215 + offsets[j - ${x-Ke}u] - ${PA("uniforms.pads","j - 2u",Re)}; 2216 ${et} 2217 } 2218 ${Y} 2219 2220 output[global_idx] = value; 2221 }`}},Ec=f=>`${f.format};${f.ceilMode};${f.autoPad};${f.kernelShape.length}`,H2=f=>`${Ec(f)};${f.countIncludePad}`,Y2=f=>`${Ec(f)};${f.storageOrder};${f.dilations}`,sc=f=>({format:f.format,autoPad:["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][f.auto_pad],ceilMode:f.ceil_mode,kernelShape:f.kernel_shape,strides:f.strides,pads:f.pads}),Bc=(f,I,x,N)=>{let[L,j]=V2(I,N,x),Y=Yt("x",I.dataType,I.dims.length),Ae=Y.type.value,se="";L.countIncludePad?se+=`value /= ${Ae}(uniforms.kernelSize);`:se+=`value /= ${Ae}(i32(uniforms.kernelSize) - pad);`;let[ne,ye,Be,te,Te]=vc(j,L);return ne.push(..._A(I.dims,j)),{name:f,shaderCache:{hint:`${N.cacheKey};${Be};${te};${Te}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:j,dataType:I.dataType}],dispatchGroup:{x:Math.ceil(gt.size(j)/64)},programUniforms:ne}),getShaderSource:Oe=>yc(Oe,Y,I.dims.length,j.length,L,"value += x_val;",se,0,ye,Be,te,Te)}},R2=f=>{let I=f.count_include_pad!==0,x=sc(f);if(x.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let N={countIncludePad:I,...x,cacheKey:""};return{...N,cacheKey:H2(N)}},xc=(f,I)=>{D0(f.inputs),f.compute(Bc("AveragePool",f.inputs[0],!1,I))},Mc={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},j2=f=>{let I=f.format;return{format:I,...Mc,cacheKey:I}},Dc=(f,I)=>{D0(f.inputs),f.compute(Bc("GlobalAveragePool",f.inputs[0],!0,I))},Gc=(f,I,x,N)=>{let[L,j]=V2(I,N,x),Y=Yt("x",I.dataType,I.dims.length),[Ae,se,ne,ye,Be]=vc(j,L);return Ae.push(..._A(I.dims,j)),{name:f,shaderCache:{hint:`${N.cacheKey};${ne};${ye};${Be}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:j,dataType:I.dataType}],dispatchGroup:{x:Math.ceil(gt.size(j)/64)},programUniforms:Ae}),getShaderSource:te=>yc(te,Y,I.dims.length,j.length,L,` 2222 value = max(x_val, value); 2223 `,"",I.dataType===10?-65504:-1e5,se,ne,ye,Be)}},W2=(f,I)=>{D0(f.inputs),f.compute(Gc("MaxPool",f.inputs[0],!1,I))},ac=f=>{let I=f.storage_order,x=f.dilations,N=sc(f);if(I!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(N.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let L={storageOrder:I,dilations:x,...N,cacheKey:""};return{...L,cacheKey:Y2(L)}},U2=f=>{let I=f.format;return{format:I,...Mc,cacheKey:I}},K2=(f,I)=>{D0(f.inputs),f.compute(Gc("GlobalMaxPool",f.inputs[0],!0,I))}}),br=me(()=>{hr(),yr(),va(),Qr(),Z2=(f,I)=>{if(f.length<2||f.length>3)throw new Error("DequantizeLinear requires 2 or 3 inputs.");if(f.length===3&&f[1].dims===f[2].dims)throw new Error("x-scale and x-zero-point must have the same shape.");if(f.length===3&&f[0].dataType!==f[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(f[0].dataType===6&&f.length>2)throw new Error("In the case of dequantizing int32 there is no zero point.");if(f[1].dims.length!==0&&f[1].dims.length!==1&&f[1].dims.length!==f[0].dims.length)throw new Error("scale input must be a scalar, a 1D tensor, or have the same rank as the input tensor.");if(f.length>2){if(f[0].dataType!==f[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(f[1].dims.length!==f[2].dims.length)throw new Error("scale and zero-point inputs must have the same rank.");if(!f[1].dims.map((x,N)=>x===f[2].dims[N]).reduce((x,N)=>x&&N,!0))throw new Error("scale and zero-point inputs must have the same shape.")}if(I.blockSize>0){if(f[1].dims.length===0||f[1].dims.length===1&&f[1].dims[0]===1)throw new Error("blockSize must be set only for block quantization.");if(!f[1].dims.map((L,j)=>j===I.axis||L===f[0].dims[j]).reduce((L,j)=>L&&j,!0))throw new Error("For block qunatization, scale input shape to match the input shape except for the axis");if(f[1].dims.length!==f[0].dims.length)throw new Error("For block qunatization the scale input rank must be the same as the x rank.");let x=f[0].dims[I.axis],N=f[1].dims[I.axis];if(I.blockSize<Math.ceil(x/N)||I.blockSize>Math.ceil(x/(N-1)-1))throw new Error("blockSize must be with in the range [ceil(dI / Si), ceil(dI / (Si - 1) - 1)].")}},X2=(f,I)=>{let x=gt.normalizeAxis(I.axis,f[0].dims.length),N=f[0].dataType,L=N===3,j=f[0].dims,Y=f[1].dataType,Ae=gt.size(j),se=N===3||N===2,ne=se?[Math.ceil(gt.size(f[0].dims)/4)]:f[0].dims,ye=f[1].dims,Be=f.length>2?f[2]:void 0,te=Be?se?[Math.ceil(gt.size(Be.dims)/4)]:Be.dims:void 0,Te=ye.length===0||ye.length===1&&ye[0]===1,Oe=Te===!1&&ye.length===1,Ke=_r(Ae),Re=Te&&(!se||Ke===4),et=Re?Ke:1,ft=Re&&!se?Ke:1,bt=Yt("input",se?12:N,ne.length,ft),Mt=Yt("scale",Y,ye.length),Dt=Be?Yt("zero_point",se?12:N,te.length):void 0,Nt=TA("output",Y,j.length,et),hA=[bt,Mt];Dt&&hA.push(Dt);let xA=[ne,ye];Be&&xA.push(te);let er=[{type:12,data:Ae/et},{type:12,data:x},{type:12,data:I.blockSize},..._A(...xA,j)];return{name:"DequantizeLinear",shaderCache:{hint:I.cacheKey,inputDependencies:Dt?["rank","rank","rank"]:["rank","rank"]},getShaderSource:tr=>` 2224 ${tr.registerUniforms([{name:"output_size",type:"u32"},{name:"axis",type:"u32"},{name:"block_size",type:"u32"}]).declareVariables(...hA,Nt)} 2225 ${tr.mainStart()} 2226 ${tr.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} 2227 let output_indices = ${Nt.offsetToIndices("global_idx")}; 2228 2229 // Set input x 2230 ${se?` 2231 let input = ${bt.getByOffset("global_idx / 4")}; 2232 let x_vec = ${L?"unpack4xI8(input)":"unpack4xU8(input)"}; 2233 let x_value = ${et===1?"x_vec[global_idx % 4]":"x_vec"};`:`let x_value = ${bt.getByOffset("global_idx")};`}; 2234 2235 // Set scale input 2236 ${Te?`let scale_value= ${Mt.getByOffset("0")}`:Oe?` 2237 let scale_index = ${Nt.indicesGet("output_indices","uniforms.axis")}; 2238 let scale_value= ${Mt.getByOffset("scale_index")};`:` 2239 var scale_indices: ${Mt.type.indices} = output_indices; 2240 let index = ${Mt.indicesGet("scale_indices","uniforms.axis")} / uniforms.block_size; 2241 ${Mt.indicesSet("scale_indices","uniforms.axis","index")}; 2242 let scale_value= ${Mt.getByIndices("scale_indices")};`}; 2243 2244 // Set zero-point input 2245 ${Dt?Te?se?` 2246 let zero_point_input = ${Dt.getByOffset("0")}; 2247 let zero_point_vec = ${L?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"}; 2248 let zero_point_value= zero_point_vec[0]`:`let zero_point_value = ${Dt.getByOffset("0")}`:Oe?se?` 2249 let zero_point_index = ${Nt.indicesGet("output_indices","uniforms.axis")}; 2250 let zero_point_input = ${Dt.getByOffset("zero_point_index / 4")}; 2251 let zero_point_vec = ${L?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"}; 2252 let zero_point_value = zero_point_vec[zero_point_index % 4]`:` 2253 let zero_point_index = ${Nt.indicesGet("output_indices","uniforms.axis")}; 2254 let zero_point_value = ${Dt.getByOffset("zero_point_index")};`:se?` 2255 let zero_point_offset = ${Mt.indicesToOffset("scale_indices")}; 2256 let zero_point_input = ${Dt.getByOffset("zero_point_offset / 4")}; 2257 let zero_point_vec = ${L?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"}; 2258 let zero_point_value = zero_point_vec[zero_point_offset % 4];`:`let zero_point_value = ${Dt.getByIndices("scale_indices")};`:`let zero_point_value = ${se?L?"i32":"u32":bt.type.value}(0);`}; 2259 // Compute and write output 2260 ${Nt.setByOffset("global_idx",`${Nt.type.value}(x_value - zero_point_value) * scale_value`)}; 2261 }`,getRunData:()=>({outputs:[{dims:j,dataType:Y}],dispatchGroup:{x:Math.ceil(Ae/et/64),y:1,z:1},programUniforms:er})}},_c=(f,I)=>{Z2(f.inputs,I),f.compute(X2(f.inputs,I))},nc=f=>vt({axis:f.axis,blockSize:f.blockSize})}),Mr=me(()=>{lt(),hr(),Qr(),t1=(f,I,x)=>{if(f===I||f<I&&x<0||f>I&&x>0)throw new Error("Range these inputs' contents are invalid.")},q2=(f,I,x,N)=>{let L=Math.abs(Math.ceil((I-f)/x)),j=[L],Y=L,Ae=[{type:12,data:Y},{type:N,data:f},{type:N,data:x},..._A(j)];return{name:"Range",shaderCache:{hint:`${N}`},getShaderSource:se=>{let ne=TA("output",N,j.length),ye=ne.type.value,Be=[{name:"outputSize",type:"u32"},{name:"start",type:ye},{name:"delta",type:ye}];return` 2262 ${se.registerUniforms(Be).declareVariables(ne)} 2263 ${se.mainStart()} 2264 ${se.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} 2265 output[global_idx] = uniforms.start + ${ye}(global_idx) * uniforms.delta; 2266 }`},getRunData:()=>({outputs:[{dims:j,dataType:N}],dispatchGroup:{x:Math.ceil(Y/64)},programUniforms:Ae})}},J2=f=>{let I=0,x=0,N=0;f.inputs[0].dataType===6?(I=f.inputs[0].getInt32Array()[0],x=f.inputs[1].getInt32Array()[0],N=f.inputs[2].getInt32Array()[0]):f.inputs[0].dataType===1&&(I=f.inputs[0].getFloat32Array()[0],x=f.inputs[1].getFloat32Array()[0],N=f.inputs[2].getFloat32Array()[0]),G.webgpu.validateInputContent&&t1(I,x,N),f.compute(q2(I,x,N,f.inputs[0].dataType),{inputs:[]})}}),Pr=me(()=>{hr(),yr(),va(),Qr(),Tc=(f,I,x,N)=>{if(f!=="none"&&N!=="i32"&&N!=="u32"&&N!=="f32")throw new Error(`Input ${N} is not supported with reduction ${f}.`);let L=`{ 2267 var oldValue = 0; 2268 loop { 2269 let newValueF32 =`,j=`; 2270 let newValue = bitcast<i32>(newValueF32); 2271 let res = atomicCompareExchangeWeak(&${I}, oldValue, newValue); 2272 if res.exchanged { 2273 break; 2274 } 2275 oldValue = res.old_value; 2276 } 2277 }`;switch(f){case"none":return`${I}=${x};`;case"add":return N==="i32"||N==="u32"?`atomicAdd(&${I}, bitcast<${N}>(${x}));`:` 2278 ${L}bitcast<${N}>(oldValue) + (${x})${j}`;case"max":return N==="i32"||N==="u32"?`atomicMax(&${I}, bitcast<${N}>(${x}));`:` 2279 ${L}max(bitcast<f32>(oldValue), (${x}))${j}`;case"min":return N==="i32"||N==="u32"?`atomicMin(&${I}, bitcast<${N}>(${x}));`:`${L}min(bitcast<${N}>(oldValue), (${x}))${j}`;case"mul":return`${L}(bitcast<${N}>(oldValue) * (${x}))${j}`;default:throw new Error(`Reduction ${f} is not supported.`)}},$2=(f,I)=>{let x=f[0].dims,N=f[1].dims,L=x,j=Math.ceil(gt.size(N)/1),Y=N[N.length-1],Ae=gt.sizeFromDimension(x,Y),se=[{type:12,data:j},{type:12,data:Y},{type:12,data:Ae},..._A(f[1].dims,f[2].dims,L)];return{name:"ScatterND",shaderCache:{hint:`${I.cacheKey}_${I.reduction}`,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:L,dataType:f[0].dataType}],dispatchGroup:{x:Math.ceil(j/64)},programUniforms:se}),getShaderSource:ne=>{let ye=Yt("indices",f[1].dataType,f[1].dims.length),Be=Yt("updates",f[2].dataType,f[2].dims.length,1),te=I.reduction!=="none"&&I.reduction!==""?Js("output",f[0].dataType,L.length):TA("output",f[0].dataType,L.length,1);return` 2280 ${ne.registerUniform("output_size","u32").registerUniform("last_index_dimension","u32").registerUniform("num_updates_elements","u32").declareVariables(ye,Be,te)} 2281 ${ne.mainStart()} 2282 ${ne.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} 2283 var data_offset = 0u; 2284 let indices_start = uniforms.last_index_dimension * global_idx; 2285 let indices_end = indices_start + uniforms.last_index_dimension; 2286 for (var i = indices_start; i < indices_end; i++) { 2287 var index = i32(indices[i].x); 2288 ${f[0].dims.length===1?` 2289 let element_count_dim = uniforms.output_strides; 2290 let dim_value = uniforms.output_shape;`:` 2291 let element_count_dim = uniforms.output_strides[i - indices_start]; 2292 let dim_value = uniforms.output_shape[i - indices_start + uniforms.last_index_dimension];`} 2293 if (index >= 0) { 2294 if (index >= i32(dim_value)) { 2295 index = i32(dim_value - 1); 2296 } 2297 } else { 2298 if (index < -i32(dim_value)) { 2299 index = 0; 2300 } else { 2301 index += i32(dim_value); 2302 } 2303 } 2304 data_offset += u32((u32(index) * element_count_dim)); 2305 } 2306 2307 for (var i = 0u; i < uniforms.num_updates_elements; i++) { 2308 let value = updates[uniforms.num_updates_elements * global_idx + i]; 2309 ${Tc(I.reduction,"output[data_offset + i]","value",te.type.value)} 2310 } 2311 2312 }`}}},eu=f=>vt({reduction:f.reduction}),Qc=(f,I)=>{f.compute($2(f.inputs,I),{inputs:[f.inputs[1],f.inputs[2]],outputs:[]})}}),us=me(()=>{hr(),yr(),va(),Qr(),tu=(f,I)=>{if(f.every(x=>x>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),f.length>0){if(I.mode==="linear"){if(!(f.length===2||f.length===3||f.length===4&&f[0]===1&&f[1]===1||f.length===4&&f[0]===1&&f[3]===1||f.length===5&&f[0]===1&&f[1]===1))throw new Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and 2313 one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(I.mode==="cubic"&&!(f.length===2||f.length===4&&f[0]===1&&f[1]===1||f.length===4&&f[0]===1&&f[3]===1))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")}},Au=(f,I,x)=>{I.every(L=>L>=0&&L<x||(()=>{throw new Error("Resize requires axes input values to be positive and less than rank")}));let N=new Array(x).fill(1);return I.forEach((L,j)=>N[L]=f[j]),N},Pc=(f,I,x,N,L,j)=>{let[Y,Ae,se]=x>10?[1,2,3]:[-1,f.length>1?1:-1,-1],ne=f[0].dims.length;if(Y>0&&f.length>Y&&f[Y].dims.length>0)f[Y].getFloat32Array().forEach(ye=>j.push(ye));else if(I.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(Ae>0&&f.length>Ae&&f[Ae].dims.length===1&&f[Ae].dims[0]>0){if(f[Ae].getFloat32Array().forEach(ye=>N.push(ye)),N.length!==0&&N.length!==ne&&x>=18&&N.length!==I.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");tu(N,I),I.axes.length>0&&Au(N,I.axes,ne).forEach((ye,Be)=>N[Be]=ye)}if(se>0&&f.length>se&&f[se].dims.length===1&&f[se].dims[0]>0&&(f[se].getBigInt64Array().forEach(ye=>L.push(Number(ye))),L.length!==0&&L.length!==ne&&x>=18&&L.length!==I.axes.length))throw new Error("Resize requires sizes input size to be same as input rank or axes size for opset 18 and up");if(I.axes.length>0){if(N.length!==0&&N.length!==I.axes.length)throw new Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(L.length!==0&&L.length!==I.axes.length)throw new Error('Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified')}if(typeof N<"u"&&typeof L<"u"&&N.length>0&&L.length>ne)throw new Error("Resize requires only of scales or sizes to be specified")},ru=(f,I,x,N)=>` 2314 // The whole part and the fractional part are calculated separately due to inaccuracy of floating 2315 // point division. As an example, f32(21) / f32(7) may evaluate to 2.99... instead of 3, causing an 2316 // offset-by-one error later in floor(). 2317 let big = (${f}) * (${I}); 2318 let whole = ${N}(big / (${x})); 2319 let fract = ${N}(big % (${x})) / ${N}(${x}); 2320 return whole + fract; 2321`,A1=(f,I)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32, 2322 lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${I} { `+(()=>{switch(f){case"asymmetric":return` 2323 if (xScale < 1.0 || floor(xScale) != xScale) { 2324 return ${I}(xResized) / ${I}(xScale); 2325 } else { 2326 ${ru("xResized","lengthOriginal","lengthResized",I)} 2327 } 2328 `;case"pytorch_half_pixel":return`if (lengthResized > 1) { 2329 return (${I}(xResized) + 0.5) / ${I}(xScale) - 0.5; 2330 } else { 2331 return 0.0; 2332 }`;case"tf_half_pixel_for_nn":return`return (${I}(xResized) + 0.5) / ${I}(xScale);`;case"align_corners":return`if (lengthResized == 1) { 2333 return 0.0; 2334 } else { 2335 ${ru("xResized","lengthOriginal - 1","lengthResized - 1",I)} 2336 }`;case"tf_crop_and_resize":return`if (lengthResized > 1) { 2337 return ${I}(roiStart) * ${I}(lengthOriginal - 1) + 2338 (${I}(xResized) * ${I}(roiEnd - roiStart) * ${I}(lengthOriginal - 1)) / 2339 ${I}(lengthResized - 1); 2340 } else { 2341 return 0.5 * ${I}(roiStart + roiEnd) * ${I}(lengthOriginal - 1); 2342 }`;case"half_pixel_symmetric":return`const outputWidth = ${I}xScale * ${I}(lengthResized); 2343 const adjustment = ${I}(lengthResized) / outputWidth; 2344 const center = ${I}(lengthOriginal) / 2; 2345 const offset = center * (1 - adjustment); 2346 return offset + ((${I}(xResized) + 0.5) / ${I}(xScale)) - 0.5;`;case"half_pixel":return`return ((${I}(xResized) + 0.5) / ${I}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${f} is not supported`)}})()+"}",Sc=(f,I,x)=>`fn getNearestPixelFromOriginal(xOriginal: ${x}, isDownSample: bool) -> ${x} {`+(()=>{switch(f){case"round_prefer_ceil":return"if (fract(xOriginal) == 0.5) { return ceil(xOriginal); } else { return round(xOriginal); }";case"floor":return"return floor(xOriginal);";case"ceil":return"return ceil(xOriginal);";case"round_prefer_floor":return"if (fract(xOriginal) == 0.5) { return floor(xOriginal); } else { return round(xOriginal); }";default:if(I<11)return"if (isDownSample) { return ceil(xOriginal); } else { return xOriginal; }";throw new Error(`Nearest mode ${f} is not supported`)}})()+"}",Nc=(f,I,x)=>{let N=new Array(x).fill(0).concat(new Array(x).fill(1)),L=f.length===0?N:f.slice();return I.length>0?(I.forEach((j,Y)=>{N[j]=L[Y],N[Y+x]=L[I.length+Y]}),N):L},su=(f,I,x,N)=>{let L=[];if(x.length>0)if(N.length>0){if(f.forEach(j=>L.push(j)),Math.max(...N)>f.length)throw new Error("axes is out of bound");N.forEach((j,Y)=>L[j]=x[Y])}else x.forEach(j=>L.push(j));else{if(I.length===0)throw new Error("Resize requires either scales or sizes.");L=f.map((j,Y)=>Math.round(j*I[Y]))}return L},au=(f,I,x)=>{let N=(()=>{switch(x.keepAspectRatioPolicy){case"not_larger":return x.axes.length>0?Math.min(...x.axes.map(j=>I[j]),Number.MAX_VALUE):Math.min(...I,Number.MAX_VALUE);case"not_smaller":return x.axes.length>0?Math.max(...x.axes.map(j=>I[j]),Number.MIN_VALUE):Math.max(...I,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${x.keepAspectRatioPolicy} is not supported`)}})();I.fill(1,0,I.length);let L=f.slice();return x.axes.length>0?(x.axes.forEach(j=>I[j]=N),x.axes.forEach(j=>L[j]=Math.round(f[j]*I[j]))):(I.fill(N,0,I.length),L.forEach((j,Y)=>L[Y]=Math.round(j*I[Y]))),L},zc=(f,I,x,N,L)=>` 2347 fn calculateOriginalIndicesFromOutputIndices(output_indices: ${f.type.indices}) -> array<${f.type.value}, ${x.length}> { 2348 var original_indices: array<${f.type.value}, ${x.length}>; 2349 for (var i:u32 = 0; i < ${x.length}; i++) { 2350 var output_index = ${f.indicesGet("output_indices","i")}; 2351 var scale = ${PA("uniforms.scales","i",N)}; 2352 var roi_low = ${PA("uniforms.roi","i",L)}; 2353 var roi_hi = ${PA("uniforms.roi",`i + ${I.length}`,L)}; 2354 if (scale == 1.0) { 2355 original_indices[i] = ${f.type.value}(output_index); 2356 } else { 2357 var input_shape_i = ${PA("uniforms.input_shape","i",I.length)}; 2358 var output_shape_i = ${PA("uniforms.output_shape","i",x.length)}; 2359 original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i, 2360 input_shape_i, roi_low, roi_hi); 2361 } 2362 } 2363 return original_indices; 2364 }`,nu=(f,I,x,N,L,j,Y)=>` 2365 fn calculateInputIndicesFromOutputIndices(output_indices: ${I.type.indices}) -> ${f.type.indices} { 2366 var input_indices: ${f.type.indices}; 2367 for (var i:u32 = 0; i < ${N.length}; i++) { 2368 var output_index = ${I.indicesGet("output_indices","i")}; 2369 var input_index: u32; 2370 var scale = ${PA("uniforms.scales","i",L)}; 2371 if (scale == 1.0) { 2372 input_index = output_index; 2373 } else { 2374 var roi_low = ${PA("uniforms.roi","i",j)}; 2375 var roi_hi = ${PA("uniforms.roi",`i + ${x.length}`,j)}; 2376 var input_shape_i = ${PA("uniforms.input_shape","i",x.length)}; 2377 var output_shape_i = ${PA("uniforms.output_shape","i",N.length)}; 2378 var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i, 2379 input_shape_i, roi_low, roi_hi); 2380 if (!${Y} || (original_idx >= 0 && original_idx < ${I.type.value}(input_shape_i))) { 2381 if (original_idx < 0) { 2382 input_index = 0; 2383 } else if (original_idx > ${I.type.value}(input_shape_i - 1)) { 2384 input_index = input_shape_i - 1; 2385 } else { 2386 input_index = u32(getNearestPixelFromOriginal(original_idx, scale < 1)); 2387 } 2388 } else { 2389 input_index = u32(original_idx); 2390 } 2391 } 2392 ${f.indicesSet("input_indices","i","input_index")} 2393 } 2394 return input_indices; 2395 }`,iu=(f,I)=>` 2396 fn checkInputIndices(input_indices: ${f.type.indices}) -> bool { 2397 for (var i:u32 = 0; i < ${I.length}; i++) { 2398 var input_index = ${f.indicesGet("input_indices","i")}; 2399 if (input_index < 0 || input_index >= ${PA("uniforms.input_shape","i",I.length)}) { 2400 return false; 2401 } 2402 } 2403 return true; 2404 }`,ic=(f,I,x,N)=>f.rank>N?` 2405 ${f.indicesSet("input_indices",I,"channel")}; 2406 ${f.indicesSet("input_indices",x,"batch")}; 2407`:"",ou=(f,I,x,N,L)=>{let[j,Y,Ae,se]=x.length===2?[-1,0,1,-1]:[0,2,3,1],ne=f.type.value;return` 2408 fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${ne} { 2409 var input_indices: ${f.type.indices}; 2410 ${f.indicesSet("input_indices",Y,`max(0, min(row, ${x[Y]} - 1))`)}; 2411 ${f.indicesSet("input_indices",Ae,`max(0, min(col, ${x[Ae]} - 1))`)}; 2412 ${ic(f,se,j,2)} 2413 return ${f.getByIndices("input_indices")}; 2414 } 2415 2416 fn bilinearInterpolation(output_indices: ${I.type.indices}) -> ${ne} { 2417 var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices); 2418 var row:${ne} = originalIndices[${Y}]; 2419 var col:${ne} = originalIndices[${Ae}]; 2420 ${N?`if (row < 0 || row > (${x[Y]} - 1) || col < 0 || col > (${x[Ae]} - 1)) { 2421 return ${L}; 2422 }`:""}; 2423 row = max(0, min(row, ${x[Y]} - 1)); 2424 col = max(0, min(col, ${x[Ae]} - 1)); 2425 var row1: u32 = u32(row); 2426 var col1: u32 = u32(col); 2427 var row2: u32 = u32(row + 1); 2428 var col2: u32 = u32(col + 1); 2429 var channel: u32 = ${x.length>2?`u32(originalIndices[${se}])`:"0"}; 2430 var batch: u32 = ${x.length>2?`u32(originalIndices[${j}])`:"0"}; 2431 var x11: ${ne} = getInputValue(batch, channel, row1, col1); 2432 var x12: ${ne} = getInputValue(batch, channel, row1, col2); 2433 var x21: ${ne} = getInputValue(batch, channel, row2, col1); 2434 var x22: ${ne} = getInputValue(batch, channel, row2, col2); 2435 var dx1: ${ne} = abs(row - ${ne}(row1)); 2436 var dx2: ${ne} = abs(${ne}(row2) - row); 2437 var dy1: ${ne} = abs(col - ${ne}(col1)); 2438 var dy2: ${ne} = abs(${ne}(col2) - col); 2439 if (row1 == row2) { 2440 dx1 = 0.5; 2441 dx2 = 0.5; 2442 } 2443 if (col1 == col2) { 2444 dy1 = 0.5; 2445 dy2 = 0.5; 2446 } 2447 return (x11 * dx2 * dy2 + x12 * dx2 * dy1 + x21 * dx1 * dy2 + x22 * dx1 * dy1); 2448 }`},lu=(f,I,x,N,L,j,Y,Ae,se,ne)=>{let ye=x.length===2,[Be,te]=ye?[0,1]:[2,3],Te=f.type.value,Oe=Ke=>{let Re=Ke===Be?"row":"col";return` 2449 fn ${Re}CubicInterpolation(input_indices: ${f.type.indices}, output_indices: ${I.type.indices}) -> ${Te} { 2450 var output_index = ${I.indicesGet("output_indices",Ke)}; 2451 var originalIdx: ${Te} = getOriginalCoordinateFromResizedCoordinate(output_index, ${L[Ke]}, 2452 ${N[Ke]}, ${x[Ke]}, ${j[Ke]}, ${j[Ke]} + ${x.length}); 2453 var fractOriginalIdx: ${Te} = originalIdx - floor(originalIdx); 2454 var coefs = getCubicInterpolationCoefs(fractOriginalIdx); 2455 2456 if (${Ae} && (originalIdx < 0 || originalIdx > (${x[Ke]} - 1))) { 2457 return ${se}; 2458 } 2459 var data: array<${Te}, 4> = array<${Te}, 4>(0.0, 0.0, 0.0, 0.0); 2460 for (var i: i32 = -1; i < 3; i++) { 2461 var ${Re}: ${Te} = originalIdx + ${Te}(i); 2462 if (${Re} < 0 || ${Re} >= ${x[Ke]}) { 2463 ${ne?`coefs[i + 1] = 0.0; 2464 continue;`:Ae?`return ${se};`:`${Re} = max(0, min(${Re}, ${x[Ke]} - 1));`}; 2465 } 2466 var input_indices_copy: ${f.type.indices} = input_indices; 2467 ${f.indicesSet("input_indices_copy",Ke,`u32(${Re})`)}; 2468 data[i + 1] = ${Ke===Be?f.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"}; 2469 } 2470 return cubicInterpolation1D(data, coefs); 2471 }`};return` 2472 ${Oe(Be)}; 2473 ${Oe(te)}; 2474 fn getCubicInterpolationCoefs(s: ${Te}) -> array<${Te}, 4> { 2475 var absS = abs(s); 2476 var coeffs: array<${Te}, 4> = array<${Te}, 4>(0.0, 0.0, 0.0, 0.0); 2477 var oneMinusAbsS: ${Te} = 1.0 - absS; 2478 var twoMinusAbsS: ${Te} = 2.0 - absS; 2479 var onePlusAbsS: ${Te} = 1.0 + absS; 2480 coeffs[0] = ((${Y} * onePlusAbsS - 5 * ${Y}) * onePlusAbsS + 8 * ${Y}) * onePlusAbsS - 4 * ${Y}; 2481 coeffs[1] = ((${Y} + 2) * absS - (${Y} + 3)) * absS * absS + 1; 2482 coeffs[2] = ((${Y} + 2) * oneMinusAbsS - (${Y} + 3)) * oneMinusAbsS * oneMinusAbsS + 1; 2483 coeffs[3] = ((${Y} * twoMinusAbsS - 5 * ${Y}) * twoMinusAbsS + 8 * ${Y}) * twoMinusAbsS - 4 * ${Y}; 2484 return coeffs; 2485 } 2486 2487 fn cubicInterpolation1D(x: array<${Te}, 4>, coefs: array<${Te}, 4>) -> ${Te} { 2488 var coefsSum: ${Te} = coefs[0] + coefs[1] + coefs[2] + coefs[3]; 2489 return (x[0] * coefs[0] + x[1] * coefs[1]+ x[2] * coefs[2]+ x[3] * coefs[3]) / coefsSum; 2490 } 2491 2492 fn bicubicInterpolation(output_indices: ${I.type.indices}) -> ${Te} { 2493 var input_indices: ${f.type.indices} = output_indices; 2494 return colCubicInterpolation(input_indices, output_indices); 2495 } 2496 `},Oc=(f,I,x,N,L)=>{let[j,Y,Ae,se,ne]=x.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],ye=f.type.value;return` 2497 fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${ye} { 2498 var input_indices: ${f.type.indices}; 2499 ${f.indicesSet("input_indices",Y,`max(0, min(depth, ${x[Y]} - 1))`)}; 2500 ${f.indicesSet("input_indices",Ae,`max(0, min(height, ${x[Ae]} - 1))`)}; 2501 ${f.indicesSet("input_indices",se,`max(0, min(width, ${x[se]} - 1))`)}; 2502 ${ic(f,ne,j,3)} 2503 return ${f.getByIndices("input_indices")}; 2504 } 2505 2506 fn trilinearInterpolation(output_indices: ${I.type.indices}) -> ${ye} { 2507 var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices); 2508 var depth:${ye} = originalIndices[${Y}]; 2509 var height:${ye} = originalIndices[${Ae}]; 2510 var width:${ye} = originalIndices[${se}]; 2511 ${N?`if (depth < 0 || depth > (${x[Y]} - 1) || height < 0 || height > (${x[Ae]} - 1) || width < 0 || (width > ${x[se]} - 1)) { 2512 return ${L}; 2513 }`:""}; 2514 2515 depth = max(0, min(depth, ${x[Y]} - 1)); 2516 height = max(0, min(height, ${x[Ae]} - 1)); 2517 width = max(0, min(width, ${x[se]} - 1)); 2518 var depth1: u32 = u32(depth); 2519 var height1: u32 = u32(height); 2520 var width1: u32 = u32(width); 2521 var depth2: u32 = u32(depth + 1); 2522 var height2: u32 = u32(height + 1); 2523 var width2: u32 = u32(width + 1); 2524 var channel: u32 = ${x.length>3?`u32(originalIndices[${ne}])`:"0"}; 2525 var batch: u32 = ${x.length>3?`u32(originalIndices[${j}])`:"0"}; 2526 2527 var x111: ${ye} = getInputValue(batch, channel, depth1, height1, width1); 2528 var x112: ${ye} = getInputValue(batch, channel, depth1, height1, width2); 2529 var x121: ${ye} = getInputValue(batch, channel, depth1, height2, width1); 2530 var x122: ${ye} = getInputValue(batch, channel, depth1, height2, width2); 2531 var x211: ${ye} = getInputValue(batch, channel, depth2, height1, width1); 2532 var x212: ${ye} = getInputValue(batch, channel, depth2, height1, width2); 2533 var x221: ${ye} = getInputValue(batch, channel, depth2, height2, width1); 2534 var x222: ${ye} = getInputValue(batch, channel, depth2, height2, width2); 2535 var dx1: ${ye} = abs(depth - ${ye}(depth1)); 2536 var dx2: ${ye} = abs(${ye}(depth2) - depth); 2537 var dy1: ${ye} = abs(height - ${ye}(height1)); 2538 var dy2: ${ye} = abs(${ye}(height2) - height); 2539 var dz1: ${ye} = abs(width - ${ye}(width1)); 2540 var dz2: ${ye} = abs(${ye}(width2) - width); 2541 if (depth1 == depth2) { 2542 dx1 = 0.5; 2543 dx2 = 0.5; 2544 } 2545 if (height1 == height2) { 2546 dy1 = 0.5; 2547 dy2 = 0.5; 2548 } 2549 if (width1 == width2) { 2550 dz1 = 0.5; 2551 dz2 = 0.5; 2552 } 2553 return (x111 * dx2 * dy2 * dz2 + x112 * dx2 * dy2 * dz1 + x121 * dx2 * dy1 *dz2 + x122 * dx2 * dy1 * dz1 + 2554 x211 * dx1 * dy2 * dz2 + x212 * dx1 * dy2 * dz1 + x221 * dx1 * dy1 *dz2 + x222 * dx1 * dy1 * dz1); 2555 }`},cu=(f,I,x,N,L,j)=>{let Y=f.dims,Ae=Nc(j,I.axes,Y.length),se=su(Y,N,L,I.axes),ne=N.slice();N.length===0&&(ne=Y.map((et,ft)=>et===0?1:se[ft]/et),I.keepAspectRatioPolicy!=="stretch"&&(se=au(Y,ne,I)));let ye=TA("output",f.dataType,se.length),Be=Yt("input",f.dataType,Y.length),te=gt.size(se),Te=Y.length===se.length&&Y.every((et,ft)=>et===se[ft]),Oe=I.coordinateTransformMode==="tf_crop_and_resize",Ke=I.extrapolationValue,Re=Be.type.value;return{name:"Resize",shaderCache:{hint:`${I.cacheKey}|${x}|${ne.length>0?I.mode==="cubic"?ne:ne.length:""}|${L.length>0?L:""}|${Ae.length>0?Ae:""}|${Te}|${I.mode==="nearest"?Y.length:Y}`,inputDependencies:["rank"]},getShaderSource:et=>` 2556 ${Te?"":` 2557 ${A1(I.coordinateTransformMode,Re)}; 2558 ${(()=>{switch(I.mode){case"nearest":return` 2559 ${iu(Be,Y)}; 2560 ${Sc(I.nearestMode,x,Re)}; 2561 ${nu(Be,ye,Y,se,ne.length,Ae.length,Oe)}; 2562 `;case"linear":return` 2563 ${zc(ye,Y,se,ne.length,Ae.length)}; 2564 ${(()=>{if(Y.length===2||Y.length===4)return`${ou(Be,ye,Y,Oe,Ke)}`;if(Y.length===3||Y.length===5)return`${Oc(Be,ye,Y,Oe,Ke)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()}; 2565 `;case"cubic":return` 2566 ${(()=>{if(Y.length===2||Y.length===4)return`${lu(Be,ye,Y,se,ne,Ae,I.cubicCoeffA,Oe,I.extrapolationValue,I.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()}; 2567 `;default:throw Error("Invalid resize mode")}})()}; 2568 `} 2569 ${et.registerUniform("output_size","u32").registerUniform("scales","f32",ne.length).registerUniform("roi","f32",Ae.length).declareVariables(Be,ye)} 2570 ${et.mainStart()} 2571 ${et.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} 2572 ${Te?"output[global_idx] = input[global_idx];":` 2573 let output_indices = ${ye.offsetToIndices("global_idx")}; 2574 var input_indices: ${Be.type.indices}; 2575 ${(()=>{switch(I.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices); 2576 if (checkInputIndices(input_indices)) { 2577 output[global_idx] = ${Be.getByIndices("input_indices")}; 2578 } else { 2579 output[global_idx] = ${I.extrapolationValue}; 2580 }`;case"linear":return`output[global_idx] = ${Y.length===2||Y.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${I.mode}`)}})()}; 2581`} 2582 }`,getRunData:()=>({outputs:[{dims:se,dataType:f.dataType}],dispatchGroup:{x:Math.ceil(te/64)},programUniforms:[{type:12,data:te},{type:1,data:ne},{type:1,data:Ae},..._A(Y,se)]})}},uu=f=>{let I=f.customDataBuffer;return new Uint32Array(I,I.byteOffset,1)[0]},fu=(f,I)=>{let x=[],N=[],L=[],j=uu(f);if(I.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");Pc(f.inputs,I,j,x,N,L),f.compute(cu(f.inputs[0],I,j,x,N,L),{inputs:[0]})},du=f=>{let I=f.antialias,x=f.axes,N=f.coordinateTransformMode,L=f.cubicCoeffA,j=f.excludeOutside!==0,Y=f.extrapolationValue,Ae=f.keepAspectRatioPolicy,se=f.mode,ne=f.nearestMode===""?"simple":f.nearestMode;return vt({antialias:I,axes:x,coordinateTransformMode:N,cubicCoeffA:L,excludeOutside:j,extrapolationValue:Y,keepAspectRatioPolicy:Ae,mode:se,nearestMode:ne})}}),Zs=me(()=>{hr(),yr(),va(),Qr(),gu=(f,I)=>{let[x,N,L,j]=f,{numHeads:Y,rotaryEmbeddingDim:Ae}=I;if(x.dims.length!==3&&x.dims.length!==4)throw new Error(`Input 'x' is expected to have 3 or 4 dimensions, got ${x.dims.length}`);if(!gt.areEqual(N.dims,[])&&!gt.areEqual(N.dims,[1])&&N.dims.length!==2)throw new Error(`Input 'position_ids' is expected to have 0, 1, or 2 dimensions, got ${N.dims.length}`);if(L.dims.length!==2)throw new Error(`Input 'cos_cache' is expected to have 2 dimensions, got ${L.dims.length}`);if(j.dims.length!==2)throw new Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${j.dims.length}`);if(!gt.areEqual(L.dims,j.dims))throw new Error("Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape");if(Ae>0&&Y===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let se=x.dims[0],ne=x.dims[x.dims.length-2],ye=L.dims[0],Be=gt.sizeFromDimension(x.dims,1)/ne,te=Ae===0?2*L.dims[1]:Be/Y;if(Ae>te)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(N.dims.length===2){if(se!==N.dims[0])throw new Error(`Input 'position_ids' dimension 0 should be of size batch_size, got ${N.dims[0]}`);if(ne!==N.dims[1])throw new Error(`Input 'position_ids' dimension 1 should be of size sequence_length, got ${N.dims[1]}`)}if(te/2!==L.dims[1]&&Ae/2!==L.dims[1])throw new Error(`Input 'cos_cache' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got ${L.dims[1]}`);if(ne>ye)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},hu=(f,I)=>{let{interleaved:x,numHeads:N,rotaryEmbeddingDim:L,scale:j}=I,Y=f[0].dims[0],Ae=gt.sizeFromDimension(f[0].dims,1),se=f[0].dims[f[0].dims.length-2],ne=Ae/se,ye=f[2].dims[1],Be=L===0?2*ye:ne/N,te=new Array(Y,se,ne/Be,Be-ye),Te=gt.computeStrides(te),Oe=[{type:1,data:j},{type:12,data:te},{type:12,data:Te},...f[0].dims.length===3?new Array({type:12,data:[Ae,ne,Be,1]}):[],...f[0].dims.length===4?new Array({type:12,data:[Ae,Be,se*Be,1]}):[],..._A(f[0].dims,f[1].dims,f[2].dims,f[3].dims,f[0].dims)];return{name:"RotaryEmbedding",shaderCache:{hint:vt({interleaved:x}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:Ke=>{let Re=Yt("input",f[0].dataType,f[0].dims.length),et=Yt("position_ids",f[1].dataType,f[1].dims.length),ft=Yt("cos_cache",f[2].dataType,f[2].dims.length),bt=Yt("sin_cache",f[3].dataType,f[3].dims.length),Mt=TA("output",f[0].dataType,f[0].dims.length);return Ke.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:te.length},{name:"global_strides",type:"u32",length:Te.length},{name:"input_output_strides",type:"u32",length:Te.length}]),` 2583 ${Ke.declareVariables(Re,et,ft,bt,Mt)} 2584 2585 ${Ke.mainStart(Gr)} 2586 let half_rotary_emb_dim = uniforms.${ft.name}_shape[1]; 2587 let bsnh = global_idx / uniforms.global_strides % uniforms.global_shape; 2588 let size = uniforms.global_shape[0] * uniforms.global_strides[0]; 2589 ${Ke.guardAgainstOutOfBoundsWorkgroupSizes("size")} 2590 2591 if (bsnh[3] < half_rotary_emb_dim) { 2592 let position_ids_idx = 2593 ${et.broadcastedIndicesToOffset("bsnh.xy",TA("",et.type.tensor,2))}; 2594 let position_id = 2595 u32(${et.getByOffset("position_ids_idx")}) + select(0, bsnh[1], position_ids_idx == 0); 2596 let i = dot(bsnh, uniforms.input_output_strides) + select(0, bsnh[3], ${x}); 2597 let j = i + select(half_rotary_emb_dim, 1, ${x}); 2598 let re = ${Re.getByOffset("i")} * ${ft.get("position_id","bsnh[3]")} - 2599 ${Re.getByOffset("j")} * ${bt.get("position_id","bsnh[3]")}; 2600 ${Mt.setByOffset("i","re")} 2601 let im = ${Re.getByOffset("i")} * ${bt.get("position_id","bsnh[3]")} + 2602 ${Re.getByOffset("j")} * ${ft.get("position_id","bsnh[3]")}; 2603 ${Mt.setByOffset("j","im")} 2604 } else { 2605 let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim; 2606 ${Mt.setByOffset("k",Re.getByOffset("k"))} 2607 } 2608 }`},getRunData:()=>({outputs:[{dims:f[0].dims,dataType:f[0].dataType}],dispatchGroup:{x:Math.ceil(gt.size(te)/Gr)},programUniforms:Oe})}},pu=(f,I)=>{gu(f.inputs,I),f.compute(hu(f.inputs,I))}}),ia=me(()=>{hr(),yr(),Qr(),mu=f=>{if(!f||f.length<3)throw new Error("layerNorm requires at least 3 inputs.");let I=f[0],x=f[1],N=f[2];if(I.dataType!==x.dataType||I.dataType!==N.dataType)throw new Error("All inputs must have the same data type");if(I.dims.length!==3&&I.dims.length!==2)throw new Error("Input must be 2D or 3D");if(x.dims.length!==3&&x.dims.length!==2)throw new Error("Skip must be 2D or 3D");let L=I.dims[I.dims.length-1],j=I.dims[I.dims.length-2];if(x.dims[x.dims.length-1]!==L)throw new Error("Skip must have the same hidden size as input");if(x.dims[x.dims.length-2]!==j)throw new Error("Skip must have the same sequence length as input");if(N.dims.length!==1)throw new Error("Gamma must be 1D");if(N.dims[N.dims.length-1]!==L)throw new Error("Gamma must have the same hidden size as input");if(f.length>3){let Y=f[3];if(Y.dims.length!==1)throw new Error("Beta must be 1D");if(Y.dims[Y.dims.length-1]!==L)throw new Error("Beta must have the same hidden size as input")}if(f.length>4){let Y=f[4];if(Y.dims.length!==1)throw new Error("Bias must be 1D");if(Y.dims[Y.dims.length-1]!==L)throw new Error("Bias must have the same hidden size as input")}},bu=(f,I,x,N)=>{let L=I.simplified,j=f[0].dims,Y=gt.size(j),Ae=j,se=Y,ne=j.slice(-1)[0],ye=N?j.slice(0,-1).concat(1):[],Be=!L&&f.length>3,te=f.length>4,Te=N&&x>1,Oe=N&&x>2,Ke=x>3,Re=64,et=_r(ne),ft=[{type:12,data:se},{type:12,data:et},{type:12,data:ne},{type:1,data:I.epsilon}],bt=[{dims:Ae,dataType:f[0].dataType}];return x>1&&bt.push({dims:ye,dataType:1}),x>2&&bt.push({dims:ye,dataType:1}),x>3&&bt.push({dims:j,dataType:f[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${et};${Te};${Oe};${Ke}`,inputDependencies:f.map((Mt,Dt)=>"type")},getShaderSource:Mt=>{let Dt=[Yt("x",f[0].dataType,f[0].dims,et),Yt("skip",f[1].dataType,f[1].dims,et),Yt("gamma",f[2].dataType,f[2].dims,et)];Be&&Dt.push(Yt("beta",f[3].dataType,f[3].dims,et)),te&&Dt.push(Yt("bias",f[4].dataType,f[4].dims,et)),Dt.push(TA("output",f[0].dataType,Ae,et)),Te&&Dt.push(TA("mean_output",1,ye)),Oe&&Dt.push(TA("inv_std_output",1,ye)),Ke&&Dt.push(TA("input_skip_bias_sum",f[0].dataType,Ae,et));let Nt=ir(f[0].dataType),hA=ir(1,et);return` 2609 2610 ${Mt.registerUniforms([{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}]).declareVariables(...Dt)} 2611 var<workgroup> sum_shared : array<${hA}, 64>; 2612 var<workgroup> sum_squared_shared : array<${hA}, 64>; 2613 2614 ${Mt.mainStart([Re,1,1])} 2615 let ix = local_id.x; 2616 let iy = global_id.x / 64; 2617 2618 let hidden_size_vectorized: u32 = uniforms.hidden_size / uniforms.components; 2619 var stride = hidden_size_vectorized / 64; 2620 let offset = ix * stride + iy * hidden_size_vectorized; 2621 let offset1d = stride * ix; 2622 if (ix == 63) { 2623 stride = hidden_size_vectorized - stride * ix; 2624 } 2625 for (var i: u32 = 0; i < stride; i++) { 2626 let skip_value = skip[offset + i]; 2627 let bias_value = ${te?"bias[offset1d + i]":Nt+"(0.0)"}; 2628 let input_value = x[offset + i]; 2629 let value = input_value + skip_value + bias_value; 2630 ${Ke?"input_skip_bias_sum[offset + i] = value;":""} 2631 output[offset + i] = value; 2632 let f32_value = ${Bs(Nt,et,"value")}; 2633 sum_shared[ix] += f32_value; 2634 sum_squared_shared[ix] += f32_value * f32_value; 2635 } 2636 workgroupBarrier(); 2637 2638 var reduce_size : u32 = 64; 2639 for (var curr_size = reduce_size >> 1; curr_size > 0; curr_size = reduce_size >> 1) { 2640 reduce_size = curr_size + (reduce_size & 1); 2641 if (ix < curr_size) { 2642 sum_shared[ix] += sum_shared[ix + reduce_size]; 2643 sum_squared_shared[ix] += sum_squared_shared[ix + reduce_size]; 2644 } 2645 workgroupBarrier(); 2646 } 2647 2648 let sum = sum_shared[0]; 2649 let square_sum = sum_squared_shared[0]; 2650 let mean = ${ts("sum",et)} / f32(uniforms.hidden_size); 2651 let inv_std_dev = inverseSqrt(${ts("square_sum",et)} / f32(uniforms.hidden_size) ${L?"":"- mean * mean"} + uniforms.epsilon); 2652 ${Te?"mean_output[global_idx] = mean;":""} 2653 ${Oe?"inv_std_output[global_idx] = inv_std_dev;":""} 2654 2655 for (var i: u32 = 0; i < stride; i++) { 2656 output[offset + i] = (output[offset + i] ${L?"":`- ${Nt}(mean)`}) * 2657 ${Nt}(inv_std_dev) * gamma[offset1d + i] 2658 ${Be?"+ beta[offset1d + i]":""}; 2659 } 2660 }`},getRunData:()=>({outputs:bt,dispatchGroup:{x:Math.ceil(se/ne)},programUniforms:ft})}},Fc=(f,I)=>{mu(f.inputs);let x=[0];f.outputCount>1&&x.push(-3),f.outputCount>2&&x.push(-3),f.outputCount>3&&x.push(3),f.compute(bu(f.inputs,I,f.outputCount,!1),{outputs:x})}}),Fs=me(()=>{hr(),yr(),va(),Qr(),Cu=(f,I)=>{if(!f||f.length<1)throw new Error("too few inputs");if(I.axes.length!==0){if(I.axes.length!==I.starts.length||I.axes.length!==I.ends.length)throw new Error("axes, starts and ends must have the same length")}else if(I.starts.length!==I.ends.length)throw new Error("starts and ends must have the same length");f.slice(1).forEach((x,N)=>{if(f[N+1].dataType!==6&&f[N+1].dataType!==7)throw new Error(`Input ${N} must be an array of int32 or int64`)})},Wl=(f,I)=>{let x=[];if(f.length>I)if(f[I].dataType===7)f[I].getBigInt64Array().forEach(N=>x.push(Number(N)));else{if(f[I].dataType!==6)throw new Error(`Input ${I} must be an array of int32 or int64`);f[I].getInt32Array().forEach(N=>x.push(Number(N)))}return x},Lc=(f,I)=>{if(f.length>1){let x=Wl(f,1),N=Wl(f,2),L=Wl(f,3);return L.length===0&&(L=[...Array(f[0].dims.length).keys()]),vt({starts:x,ends:N,axes:L})}return I},Vc=(f,I,x,N,L)=>{let j=f;return f<0&&(j+=x[N[I]]),L[I]<0?Math.max(0,Math.min(j,x[N[I]]-1)):Math.max(0,Math.min(j,x[N[I]]))},Iu=(f,I,x)=>`fn calculateInputIndices(output_indices: ${I.type.indices}) -> ${f.type.indices} { 2661 var input_indices: ${f.type.indices}; 2662 var carry = 0u; 2663 for (var i = ${x.length}; i >= 0; i--) { 2664 let input_shape_i = ${PA("uniforms.input_shape","i",x.length)}; 2665 let steps_i = ${PA("uniforms.steps","i",x.length)}; 2666 let signs_i = ${PA("uniforms.signs","i",x.length)}; 2667 let starts_i = ${PA("uniforms.starts","i",x.length)}; 2668 var output_index = ${I.indicesGet("output_indices","i")}; 2669 var input_index = output_index * steps_i + starts_i + carry; 2670 carry = input_index / input_shape_i; 2671 input_index = input_index % input_shape_i; 2672 if (signs_i < 0) { 2673 input_index = input_shape_i - input_index - 1u + starts_i; 2674 } 2675 ${f.indicesSet("input_indices","i","input_index")}; 2676 } 2677 return input_indices; 2678 }`,Is=(f,I)=>{let x=f[0].dims,N=gt.size(x),L=I.axes.length>0?gt.normalizeAxes(I.axes,x.length):[...Array(x.length).keys()],j=Wl(f,4);j.forEach(Re=>Re!==0||(()=>{throw new Error("step cannot be 0")})),j.length===0&&(j=Array(L.length).fill(1));let Y=I.starts.map((Re,et)=>Vc(Re,et,x,L,j)),Ae=I.ends.map((Re,et)=>Vc(Re,et,x,L,j));if(L.length!==Y.length||L.length!==Ae.length)throw new Error("start, ends and axes should have the same number of elements");if(L.length!==x.length)for(let Re=0;Re<x.length;++Re)L.includes(Re)||(Y.splice(Re,0,0),Ae.splice(Re,0,x[Re]),j.splice(Re,0,1));let se=j.map(Re=>Math.sign(Re));j.forEach((Re,et,ft)=>{if(Re<0){let bt=(Ae[et]-Y[et])/Re,Mt=Y[et],Dt=Mt+bt*j[et];Y[et]=Dt,Ae[et]=Mt,ft[et]=-Re}});let ne=x.slice(0);L.forEach((Re,et)=>{ne[Re]=Math.ceil((Ae[Re]-Y[Re])/j[Re])});let ye={dims:ne,dataType:f[0].dataType},Be=TA("output",f[0].dataType,ne.length),te=Yt("input",f[0].dataType,f[0].dims.length),Te=gt.size(ne),Oe=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:Y.length},{name:"signs",type:"i32",length:se.length},{name:"steps",type:"u32",length:j.length}],Ke=[{type:12,data:Te},{type:12,data:Y},{type:6,data:se},{type:12,data:j},..._A(f[0].dims,ne)];return{name:"Slice",shaderCache:{hint:`${se.length}_${Y.length}_${j.length}`,inputDependencies:["rank"]},getShaderSource:Re=>` 2679 ${Re.registerUniforms(Oe).declareVariables(te,Be)} 2680 ${Iu(te,Be,x)} 2681 ${Re.mainStart()} 2682 ${Re.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} 2683 let output_indices = ${Be.offsetToIndices("global_idx")}; 2684 let input_indices = calculateInputIndices(output_indices); 2685 ${Be.setByOffset("global_idx",te.getByIndices("input_indices"))} 2686 }`,getRunData:()=>({outputs:[ye],dispatchGroup:{x:Math.ceil(N/64)},programUniforms:Ke})}},r1=(f,I)=>{Cu(f.inputs,I);let x=Lc(f.inputs,I);f.compute(Is(f.inputs,x),{inputs:[0]})},s1=f=>{let I=f.starts,x=f.ends,N=f.axes;return vt({starts:I,ends:x,axes:N})}}),Xa=me(()=>{hr(),yr(),va(),h0(),Qr(),a1=f=>{if(!f||f.length!==1)throw new Error("Softmax op requires 1 input.")},Hc=(f,I)=>{let x,N=f.inputs[0],L=N.dims,j=gt.size(L),Y=L.length,Ae=gt.normalizeAxis(I.axis,Y),se=Ae<L.length-1,ne=[];se?(ne=Array.from({length:Y},(Dt,Nt)=>Nt),ne[Ae]=Y-1,ne[Y-1]=Ae,x=f.compute($s(N,ne),{inputs:[N],outputs:[-1]})[0]):x=N;let ye=x.dims,Be=ye[Y-1],te=j/Be,Te=_r(Be),Oe=Be/Te,Ke=64;te===1&&(Ke=256);let Re=Yt("x",x.dataType,x.dims,Te),et=TA("result",x.dataType,x.dims,Te),ft=Re.type.value,bt=ir(x.dataType)==="f32"?`var threadMax = ${ft}(-3.402823e+38f);`:`var threadMax = ${ft}(-65504.0h);`,Mt=f.compute({name:"Softmax",shaderCache:{hint:`${Te};${Ke}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:ye,dataType:x.dataType}],dispatchGroup:{x:te},programUniforms:[{type:6,data:Oe}]}),getShaderSource:Dt=>` 2687 var<workgroup> rowMaxShared : ${ft}; 2688 var<workgroup> rowSumShared : ${ft}; 2689 var<workgroup> threadShared : array<${ft}, ${Ke}>; 2690 2691 fn getValue(row: i32, col: i32, row_stride: i32) -> ${ft} { 2692 let index = row * row_stride + col; 2693 return x[index]; 2694 } 2695 2696 fn setValue(row: i32, col: i32, row_stride: i32, value: ${ft}) { 2697 let index = row * row_stride + col; 2698 result[index] = value; 2699 } 2700 ${Dt.registerUniform("packedCols","i32").declareVariables(Re,et)} 2701 ${Dt.mainStart(Ke)} 2702 let gindex = i32(global_idx); 2703 let lindex = i32(local_idx); 2704 const wg = ${Ke}; 2705 let row = gindex / wg; 2706 let cols = uniforms.packedCols; 2707 let row_stride : i32 = uniforms.packedCols; 2708 2709 // find the rows max 2710 ${bt} 2711 for (var col = lindex; col < cols; col += wg) { 2712 let value = getValue(row, col, row_stride); 2713 threadMax = max(threadMax, value); 2714 } 2715 if (lindex < cols) { 2716 threadShared[lindex] = threadMax; 2717 } 2718 workgroupBarrier(); 2719 2720 var reduceSize = min(cols, wg); 2721 for (var currSize = reduceSize >> 1; currSize > 0; currSize = reduceSize >> 1) { 2722 reduceSize = currSize + (reduceSize & 1); 2723 if (lindex < currSize) { 2724 threadShared[lindex] = max(threadShared[lindex], threadShared[lindex + reduceSize]); 2725 } 2726 workgroupBarrier(); 2727 } 2728 if (lindex == 0) { 2729 rowMaxShared = ${ft}(${((Nt,hA)=>hA===4?`max(max(${Nt}.x, ${Nt}.y), max(${Nt}.z, ${Nt}.w))`:hA===2?`max(${Nt}.x, ${Nt}.y)`:hA===3?`max(max(${Nt}.x, ${Nt}.y), ${Nt}.z)`:Nt)("threadShared[0]",Te)}); 2730 } 2731 workgroupBarrier(); 2732 2733 // find the rows sum 2734 var threadSum = ${ft}(0.0); 2735 for (var col = lindex; col < cols; col += wg) { 2736 let subExp = exp(getValue(row, col, row_stride) - rowMaxShared); 2737 threadSum += subExp; 2738 } 2739 threadShared[lindex] = threadSum; 2740 workgroupBarrier(); 2741 2742 for (var currSize = wg >> 1; currSize > 0; currSize = currSize >> 1) { 2743 if (lindex < currSize) { 2744 threadShared[lindex] = threadShared[lindex] + threadShared[lindex + currSize]; 2745 } 2746 workgroupBarrier(); 2747 } 2748 if (lindex == 0) { 2749 rowSumShared = ${ft}(${ts("threadShared[0]",Te)}); 2750 } 2751 workgroupBarrier(); 2752 2753 // calculate final value for each element in the row 2754 for (var col = lindex; col < cols; col += wg) { 2755 let value = exp(getValue(row, col, row_stride) - rowMaxShared) / rowSumShared; 2756 setValue(row, col, row_stride, value); 2757 } 2758 }`},{inputs:[x],outputs:[se?-1:0]})[0];se&&f.compute($s(Mt,ne),{inputs:[Mt]})},wu=(f,I)=>{a1(f.inputs),Hc(f,I)},ku=f=>vt({axis:f.axis})}),Oa=me(()=>{hr(),yr(),Qr(),Yc=f=>Array.from(f.getBigInt64Array(),Number),vu=f=>{if(!f||f.length!==2)throw new Error("Tile requires 2 inputs.");if(f[0].dataType!==1&&f[0].dataType!==10&&f[0].dataType!==6&&f[0].dataType!==12)throw new Error("Tile only support float, float16, int32, and uint32 data types");if(f[1].dataType!==7)throw new Error("Tile `repeats` input should be of int64 data type");if(f[1].dims.length!==1)throw new Error("Tile `repeats` input should be 1-D");if(Yc(f[1]).length!==f[0].dims.length)throw new Error("Tile `repeats` input should have same number of elements as rank of input data tensor")},Rc=(f,I)=>{let x=[];for(let N=0;N<f.length;++N)x.push(f[N]*I[N]);return x},jc=(f,I)=>{let x=f[0].dims,N=I??Yc(f[1]),L=Rc(x,N),j=gt.size(L),Y=f[0].dataType,Ae=Yt("input",Y,x.length),se=TA("output",Y,L.length);return{name:"Tile",shaderCache:{hint:`${N}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:L,dataType:f[0].dataType}],dispatchGroup:{x:Math.ceil(j/64)},programUniforms:[{type:12,data:j},..._A(f[0].dims,L)]}),getShaderSource:ne=>` 2759 const inputShape = ${Ae.indices(...x)}; 2760 ${ne.registerUniform("output_size","u32").declareVariables(Ae,se)} 2761 ${ne.mainStart()} 2762 ${ne.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} 2763 let output_indices = ${se.offsetToIndices("global_idx")}; 2764 var input_indices: ${Ae.type.indices}; 2765 for (var i = 0; i < ${x.length}; i++) { 2766 let input_dim_i = ${Ae.indicesGet("uniforms.input_shape","i")}; 2767 let input_dim_value = ${se.indicesGet("output_indices","i")} % input_dim_i; 2768 2769 ${Ae.indicesSet("input_indices","i","input_dim_value")} 2770 } 2771 ${se.setByOffset("global_idx",Ae.getByIndices("input_indices"))} 2772 }`}},n1=f=>{vu(f.inputs),f.compute(jc(f.inputs),{inputs:[0]})}}),Fa=me(()=>{hr(),yr(),Qr(),yu=(f,I,x,N,L)=>{let j,Y=TA("output_data",L,x.length,4),Ae=Yt("a_data",I[1].dataType,I[1].dims.length,4),se=Yt("b_data",I[2].dataType,I[2].dims.length,4),ne=Yt("c_data",I[0].dataType,I[0].dims.length,4),ye=(Be,te,Te)=>`select(${te}, ${Be}, ${Te})`;if(N){let Be=(te,Te,Oe="")=>{let Ke=`a_data[index_a${Te}][component_a${Te}]`,Re=`b_data[index_b${Te}][component_b${Te}]`,et=`bool(c_data[index_c${Te}] & (0xffu << (component_c${Te} * 8)))`;return` 2773 let output_indices${Te} = ${Y.offsetToIndices(`global_idx * 4u + ${Te}u`)}; 2774 let offset_a${Te} = ${Ae.broadcastedIndicesToOffset(`output_indices${Te}`,Y)}; 2775 let offset_b${Te} = ${se.broadcastedIndicesToOffset(`output_indices${Te}`,Y)}; 2776 let offset_c${Te} = ${ne.broadcastedIndicesToOffset(`output_indices${Te}`,Y)}; 2777 let index_a${Te} = offset_a${Te} / 4u; 2778 let index_b${Te} = offset_b${Te} / 4u; 2779 let index_c${Te} = offset_c${Te} / 4u; 2780 let component_a${Te} = offset_a${Te} % 4u; 2781 let component_b${Te} = offset_b${Te} % 4u; 2782 let component_c${Te} = offset_c${Te} % 4u; 2783 ${te}[${Te}] = ${Oe}(${ye(Ke,Re,et)}); 2784 `};j=L===9?` 2785 var data = vec4<u32>(0); 2786 ${Be("data",0,"u32")} 2787 ${Be("data",1,"u32")} 2788 ${Be("data",2,"u32")} 2789 ${Be("data",3,"u32")} 2790 output_data[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:` 2791 ${Be("output_data[global_idx]",0)} 2792 ${Be("output_data[global_idx]",1)} 2793 ${Be("output_data[global_idx]",2)} 2794 ${Be("output_data[global_idx]",3)} 2795 `}else j=Y.setByOffset("global_idx",ye(Ae.getByOffset("global_idx"),se.getByOffset("global_idx"),ne.getByOffset("global_idx")));return` 2796 ${f.registerUniform("vec_size","u32").declareVariables(ne,Ae,se,Y)} 2797 ${f.mainStart()} 2798 ${f.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} 2799 ${j} 2800 }`},Eu=f=>{let I=f[1].dims,x=f[2].dims,N=f[0].dims,L=f[1].dataType,j=!(gt.areEqual(I,x)&>.areEqual(x,N)),Y=I,Ae=gt.size(I);if(j){let ne=lA.calcShape(lA.calcShape(I,x,!1),N,!1);if(!ne)throw new Error("Can't perform where op on the given tensors");Y=ne,Ae=gt.size(Y)}let se=Math.ceil(Ae/4);return{name:"Where",shaderCache:{inputDependencies:["rank","rank","rank"]},getShaderSource:ne=>yu(ne,f,Y,j,L),getRunData:()=>({outputs:[{dims:Y,dataType:L}],dispatchGroup:{x:Math.ceil(Ae/64/4)},programUniforms:[{type:12,data:se},..._A(N,I,x,Y)]})}},Wc=f=>{f.compute(Eu(f.inputs))}}),_a=me(()=>{g1(),t2(),h1(),p1(),m1(),b1(),C1(),xr(),kn(),_n(),ri(),Z0(),sf(),y1(),E1(),le(),xe(),Ye(),Et(),Ht(),DA(),GA(),yA(),OA(),XA(),Vt(),pr(),cs(),br(),Mr(),Pr(),e2(),us(),Zs(),ia(),Fs(),Xa(),rA(),Oa(),h0(),A2(),Fa(),i1=new 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Releasing tensor."),this.releaseTensor()}this.activeUpload?this.activeUpload.set(f):this.activeUpload=new Uint8Array(f)}async download(f){if(this.activeUpload)return f?void(f instanceof ArrayBuffer?new Uint8Array(f).set(this.activeUpload):new Uint8Array(f.buffer,f.byteOffset,f.byteLength).set(this.activeUpload)):this.activeUpload.buffer;if(!this.wrapper)throw new Error("Tensor has not been created.");return f?this.wrapper.read(f):this.wrapper.read()}},Qu=class{constructor(f){this.backend=f,this.tensorTrackersById=new Map,this.freeTensors=[],this.externalTensors=new Set}reserveTensorId(){let f=Uc();return this.tensorTrackersById.set(f,new Xc(this)),f}releaseTensorId(f){let I=this.tensorTrackersById.get(f);I&&(this.tensorTrackersById.delete(f),I.tensorWrapper&&this.releaseTensor(I.tensorWrapper))}async ensureTensor(f,I,x,N){fr("verbose",()=>`[WebNN] TensorManager.ensureTensor {tensorId: ${f}, dataType: ${I}, shape: ${x}, copyOld: ${N}}`);let L=this.tensorTrackersById.get(f);if(!L)throw new Error("Tensor not found.");return L.ensureTensor(this.backend.currentContext,I,x,N)}upload(f,I){let x=this.tensorTrackersById.get(f);if(!x)throw new Error("Tensor not found.");x.upload(I)}async download(f,I){fr("verbose",()=>`[WebNN] TensorManager.download {tensorId: ${f}, dstBuffer: ${I==null?void 0:I.byteLength}}`);let x=this.tensorTrackersById.get(f);if(!x)throw new Error("Tensor not found.");return x.download(I)}releaseTensorsForSession(f){for(let I of this.freeTensors)I.sessionId===f&&I.destroy();this.freeTensors=this.freeTensors.filter(I=>I.sessionId!==f)}registerTensor(f,I,x,N){let L=Uc(),j=new Zc({sessionId:this.backend.currentSessionId,context:f,tensor:I,dataType:x,shape:N});return this.tensorTrackersById.set(L,new Xc(this,j)),this.externalTensors.add(j),L}async getCachedTensor(f,I,x,N,L){let j=this.backend.currentSessionId,Y=this.backend.currentContext;for(let[se,ne]of this.freeTensors.entries())if(ne.canReuseTensor(Y,f,I)){fr("verbose",()=>`[WebNN] Reusing tensor {dataType: ${f}, shape: ${I}}`);let ye=this.freeTensors.splice(se,1)[0];return ye.sessionId=j,ye}fr("verbose",()=>`[WebNN] MLContext.createTensor {dataType: ${f}, shape: ${I}}`);let Ae=await Y.createTensor({dataType:f,shape:I,dimensions:I,usage:x,writable:N,readable:L});return new Zc({sessionId:j,context:Y,tensor:Ae,dataType:f,shape:I})}releaseTensor(f){this.externalTensors.has(f)&&this.externalTensors.delete(f),this.freeTensors.push(f)}},l1=(...f)=>new Qu(...f)}),T0=me(()=>{hr(),G0(),Fu(),si(),qo(),qc=new Map([[1,"float32"],[10,"float16"],[6,"int32"],[12,"uint32"],[7,"int64"],[13,"uint64"],[22,"int4"],[21,"uint4"],[3,"int8"],[2,"uint8"],[9,"uint8"]]),Pu=(f,I)=>{if(f===I)return!0;if(f===void 0||I===void 0)return!1;let x=Object.keys(f).sort(),N=Object.keys(I).sort();return x.length===N.length&&x.every((L,j)=>L===N[j]&&f[L]===I[L])},Su=class{constructor(f){this.tensorManager=l1(this),this.mlContextBySessionId=new Map,this.sessionIdsByMLContext=new Map,this.mlContextCache=[],ja(f.logLevel,!!f.debug)}get currentSessionId(){if(this.activeSessionId===void 0)throw new Error("No active session");return this.activeSessionId}onRunStart(f){this.activeSessionId=f}async createMLContext(f){if(f instanceof GPUDevice){let x=this.mlContextCache.findIndex(N=>N.gpuDevice===f);if(x!==-1)return this.mlContextCache[x].mlContext;{let N=await navigator.ml.createContext(f);return this.mlContextCache.push({gpuDevice:f,mlContext:N}),N}}if(f===void 0){let x=this.mlContextCache.findIndex(N=>N.options===void 0&&N.gpuDevice===void 0);if(x!==-1)return this.mlContextCache[x].mlContext;{let N=await navigator.ml.createContext();return this.mlContextCache.push({mlContext:N}),N}}let I=this.mlContextCache.findIndex(x=>Pu(x.options,f));if(I!==-1)return this.mlContextCache[I].mlContext;{let x=await navigator.ml.createContext(f);return this.mlContextCache.push({options:f,mlContext:x}),x}}get currentContext(){let f=this.getMLContext(this.currentSessionId);if(!f)throw new Error(`No MLContext found for session ${this.currentSessionId}`);return f}registerMLContext(f,I){this.mlContextBySessionId.set(f,I);let x=this.sessionIdsByMLContext.get(I);x||(x=new Set,this.sessionIdsByMLContext.set(I,x)),x.add(f)}onReleaseSession(f){let I=this.mlContextBySessionId.get(f);if(!I)return;this.tensorManager.releaseTensorsForSession(f),this.mlContextBySessionId.delete(f);let x=this.sessionIdsByMLContext.get(I);if(x.delete(f),x.size===0){this.sessionIdsByMLContext.delete(I);let N=this.mlContextCache.findIndex(L=>L.mlContext===I);N!==-1&&this.mlContextCache.splice(N,1)}}getMLContext(f){return this.mlContextBySessionId.get(f)}reserveTensorId(){return this.tensorManager.reserveTensorId()}releaseTensorId(f){fr("verbose",()=>`[WebNN] releaseTensorId {tensorId: ${f}}`),this.tensorManager.releaseTensorId(f)}async ensureTensor(f,I,x,N){let L=qc.get(I);if(!L)throw new Error(`Unsupported ONNX data type: ${I}`);return this.tensorManager.ensureTensor(f,L,x,N)}uploadTensor(f,I){if(!Er().shouldTransferToMLTensor)throw new Error("Trying to upload to a MLTensor while shouldTransferToMLTensor is false");fr("verbose",()=>`[WebNN] uploadTensor {tensorId: ${f}, data: ${I.byteLength}}`),this.tensorManager.upload(f,I)}async downloadTensor(f,I){return this.tensorManager.download(f,I)}createMLTensorDownloader(f,I){return async()=>{let x=await this.tensorManager.download(f);return la(x,I)}}registerMLTensor(f,I,x){let N=qc.get(I);if(!N)throw new Error(`Unsupported ONNX data type: ${I}`);let L=this.tensorManager.registerTensor(this.currentContext,f,N,x);return fr("verbose",()=>`[WebNN] registerMLTensor {tensor: ${f}, dataType: ${N}, dimensions: ${x}} -> {tensorId: ${L}}`),L}registerMLConstant(f,I,x,N,L,j){if(!j)throw new Error("External mounted files are not available.");let Y=f;f.startsWith("./")&&(Y=f.substring(2));let Ae=j.get(Y);if(!Ae)throw new Error(`File with name ${Y} not found in preloaded 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Gm{constructor(I,x,N,L){this.module=I,this.dataType=x,this.data=N,this.dims=L}getFloat32Array(){if(this.dataType!==1)throw new Error("Invalid data type");let I=gt.size(this.dims);return I===0?new Float32Array:new Float32Array(this.module.HEAP8.buffer,this.data,I)}getBigInt64Array(){if(this.dataType!==7)throw new Error("Invalid data type");let I=gt.size(this.dims);return I===0?new BigInt64Array:new BigInt64Array(this.module.HEAP8.buffer,this.data,I)}getInt32Array(){if(this.dataType!==6)throw new Error("Invalid data type");let I=gt.size(this.dims);return I===0?new Int32Array:new Int32Array(this.module.HEAP8.buffer,this.data,I)}getUint16Array(){if(this.dataType!==10&&this.dataType!==4)throw new Error("Invalid data type");let I=gt.size(this.dims);return I===0?new Uint16Array:new Uint16Array(this.module.HEAP8.buffer,this.data,I)}reshape(I){if(gt.size(I)!==gt.size(this.dims))throw new Error("Invalid new shape");return new Gm(this.module,this.dataType,this.data,I)}},Ul=class{constructor(f,I,x){this.module=f,this.backend=I,this.customDataOffset=0,this.customDataSize=0,this.adapterInfo=I.adapterInfo,this.deviceInfo=I.deviceInfo;let N=f.PTR_SIZE,L=x/f.PTR_SIZE,j=N===4?"i32":"i64";this.opKernelContext=Number(f.getValue(N*L++,j));let Y=Number(f.getValue(N*L++,j));this.outputCount=Number(f.getValue(N*L++,j)),this.customDataOffset=Number(f.getValue(N*L++,"*")),this.customDataSize=Number(f.getValue(N*L++,j));let Ae=[];for(let se=0;se<Y;se++){let ne=Number(f.getValue(N*L++,j)),ye=Number(f.getValue(N*L++,"*")),Be=Number(f.getValue(N*L++,j)),te=[];for(let Te=0;Te<Be;Te++)te.push(Number(f.getValue(N*L++,j)));Ae.push(new Q0(f,ne,ye,te))}this.inputs=Ae}get kernelCustomData(){return this.backend.currentKernelCustomData}get customDataBuffer(){return this.module.HEAPU8.subarray(this.customDataOffset,this.customDataOffset+this.customDataSize)}compute(f,I){var L;let x=((L=I==null?void 0:I.inputs)==null?void 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If you are running with pre-allocated output, please make sure the output type/dims are correct. Error: ${N}`)}finally{this.module.stackRestore(x)}}},Lu=async(f,I,x,N)=>{let L=I.jsepInit;if(!L)throw new Error("Failed to initialize JSEP. The WebAssembly module is not built with JSEP support.");if(f==="webgpu"){let j=new Gu;await j.initialize(x,N),L("webgpu",[j,Y=>j.alloc(Number(Y)),Y=>j.free(Y),(Y,Ae,se,ne=!1)=>{if(ne)fr("verbose",()=>`[WebGPU] jsepCopyGpuToGpu: src=${Number(Y)}, dst=${Number(Ae)}, size=${Number(se)}`),j.memcpy(Number(Y),Number(Ae));else{fr("verbose",()=>`[WebGPU] jsepCopyCpuToGpu: dataOffset=${Number(Y)}, gpuDataId=${Number(Ae)}, size=${Number(se)}`);let ye=I.HEAPU8.subarray(Number(Y>>>0),Number(Y>>>0)+Number(se));j.upload(Number(Ae),ye)}},async(Y,Ae,se)=>{fr("verbose",()=>`[WebGPU] jsepCopyGpuToCpu: gpuDataId=${Y}, dataOffset=${Ae}, size=${se}`),await j.download(Number(Y),()=>I.HEAPU8.subarray(Number(Ae)>>>0,Number(Ae+se)>>>0))},(Y,Ae,se)=>j.createKernel(Y,Number(Ae),se,I.UTF8ToString(I._JsepGetNodeName(Number(Ae)))),Y=>j.releaseKernel(Y),(Y,Ae,se,ne)=>{fr("verbose",()=>`[WebGPU] jsepRun: sessionHandle=${se}, kernel=${Y}, contextDataOffset=${Ae}`);let ye=new Ul(I,j,Number(Ae));return j.computeKernel(Number(Y),ye,ne)},()=>j.captureBegin(),()=>j.captureEnd(),()=>j.replay()])}else{let j=new Su(x);L("webnn",[j,()=>j.reserveTensorId(),Y=>j.releaseTensorId(Y),async(Y,Ae,se,ne)=>j.ensureTensor(Y,Ae,se,ne),(Y,Ae)=>{j.uploadTensor(Y,Ae)},async(Y,Ae)=>j.downloadTensor(Y,Ae)])}}}),kd=me(()=>{zu(),u1(),hr(),G0(),Nu(),Ou(),B1=(f,I)=>{Er()._OrtInit(f,I)!==0&&nr("Can't initialize onnxruntime.")},Ys=async f=>{B1(f.wasm.numThreads,Ma(f.logLevel))},La=async(f,I)=>{{let x=(R3(),Ue(X0)).init;if(I==="webgpu"){if(typeof navigator>"u"||!navigator.gpu)throw new Error("WebGPU is not supported in current environment");let N=f.webgpu.adapter;if(N){if(typeof N.limits!="object"||typeof N.features!="object"||typeof N.requestDevice!="function")throw new Error("Invalid GPU adapter set in `env.webgpu.adapter`. It must be a GPUAdapter object.")}else{let L=f.webgpu.powerPreference;if(L!==void 0&&L!=="low-power"&&L!=="high-performance")throw new Error(`Invalid powerPreference setting: "${L}"`);let j=f.webgpu.forceFallbackAdapter;if(j!==void 0&&typeof j!="boolean")throw new Error(`Invalid forceFallbackAdapter setting: "${j}"`);if(N=await navigator.gpu.requestAdapter({powerPreference:L,forceFallbackAdapter:j}),!N)throw new Error('Failed to get GPU adapter. 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2803"./src/base/feature_extraction_utils.js":(c,a,s)=>{s.r(a),s.d(a,{FeatureExtractor:()=>C,validate_audio_inputs:()=>b});var o=s("./src/utils/constants.js"),u=s("./src/utils/generic.js"),p=s("./src/utils/hub.js");class C extends u.Callable{constructor(E){super(),this.config=E}static async from_pretrained(E,B){return new this(await(0,p.getModelJSON)(E,o.FEATURE_EXTRACTOR_NAME,!0,B))}}function b(k,E){var B;if(!(k instanceof Float32Array||k instanceof Float64Array))throw new Error(`${E} expects input to be a Float32Array or a Float64Array, but got ${((B=k==null?void 0:k.constructor)==null?void 0:B.name)??typeof k} instead. If using the feature extractor directly, remember to use \`read_audio(url, sampling_rate)\` to obtain the raw audio data of the file/url.`)}},
2803"./src/base/image_processors_utils.js":(c,a,s)=>{s.r(a),s.d(a,{ImageProcessor:()=>J,center_to_corners_format:()=>G,post_process_instance_segmentation:()=>K,post_process_object_detection:()=>D,post_process_panoptic_segmentation:()=>H,post_process_semantic_segmentation:()=>Q});var o=s("./src/utils/generic.js"),u=s("./src/utils/tensor.js"),p=s("./src/utils/maths.js");s("./src/utils/image.js");var C=s("./src/utils/core.js"),b=s("./src/utils/hub.js"),k=s("./src/utils/constants.js");function E(g,v,e=0,d=null){const _=g/v;let re=(0,p.bankers_round)(_)*v;return d!==null&&re>d&&(re=Math.floor(_)*v),re<e&&(re=Math.ceil(_)*v),re}function B([g,v],e){return[Math.max(Math.floor(g/e),1)*e,Math.max(Math.floor(v/e),1)*e]}function G([g,v,e,d]){return[g-e/2,v-d/2,g+e/2,v+d/2]}function D(g,v=.5,e=null,d=!1){const _=g.logits,re=g.pred_boxes,[S,F,Ie]=_.dims;if(e!==null&&e.length!==S)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let he=[];for(let ce=0;ce<S;++ce){let ge=e!==null?e[ce]:null,Ce={boxes:[],classes:[],scores:[]},Fe=_[ce],Pe=re[ce];for(let de=0;de<F;++de){let pe,we=Fe[de],Ge=[];if(d){pe=we.sigmoid().data;for(let me=0;me<pe.length;++me)pe[me]>v&&Ge.push(me)}else{let me=(0,p.max)(we.data)[1];if(me===Ie-1||(pe=(0,p.softmax)(we.data),pe[me]<v))continue;Ge.push(me)}for(const me of Ge){let at=Pe[de].data;at=G(at),ge!==null&&(at=at.map((Ue,ot)=>Ue*ge[(ot+1)%2])),Ce.boxes.push(at),Ce.classes.push(me),Ce.scores.push(pe[me])}}he.push(Ce)}return he}function Q(g,v=null){const e=g.logits,d=e.dims[0];if(v!==null&&v.length!==d)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");const _=[];for(let re=0;re<d;++re){const S=v!==null?v[re]:null;let F=e[re];S!==null&&(F=(0,u.interpolate)(F,S,"bilinear",!1));const[Ie,he]=S??F.dims.slice(-2),ce=new u.Tensor("int32",new Int32Array(Ie*he),[Ie,he]),ge=F[0].data,Ce=ce.data;for(let de=1;de<F.dims[0];++de){const pe=F[de].data;for(let we=0;we<pe.length;++we)pe[we]>ge[we]&&(ge[we]=pe[we],Ce[we]=de)}const Fe=new Array(F.dims[0]);for(let de=0;de<Ce.length;++de){const pe=Ce[de];Fe[pe]=pe}const Pe=Fe.filter(de=>de!==void 0);_.push({segmentation:ce,labels:Pe})}return _}function V(g,v,e,d){const _=[],re=[],S=[];for(let F=0;F<g.dims[0];++F){const Ie=g[F],he=v[F],ce=(0,p.max)(Ie.data)[1];if(ce===d)continue;const ge=(0,p.softmax)(Ie.data)[ce];ge>e&&(_.push(he),re.push(ge),S.push(ce))}return[_,re,S]}function W(g,v,e,d=.5,_=.8){const re=[];let S=0,F=0;const Ie=v[e].data;for(let ce=0;ce<g.length;++ce)g[ce]===e&&(re.push(ce),++S),Ie[ce]>=d&&++F;let he=S>0&&F>0;return he&&(he=S/F>_),[he,re]}function U(g,v,e,d,_,re=null,S=null){const[F,Ie]=S??g[0].dims,he=new u.Tensor("int32",new Int32Array(F*Ie),[F,Ie]),ce=[];if(S!==null)for(let de=0;de<g.length;++de)g[de]=(0,u.interpolate)(g[de],S,"bilinear",!1);const ge=new Int32Array(g[0].data.length),Ce=new Float32Array(g[0].data.length);for(let de=0;de<g.length;++de){let pe=v[de];const we=g[de].data;for(let Ge=0;Ge<we.length;++Ge)we[Ge]*=pe,we[Ge]>Ce[Ge]&&(ge[Ge]=de,Ce[Ge]=we[Ge])}let Fe=0;const Pe=he.data;for(let de=0;de<e.length;++de){const pe=e[de],[we,Ge]=W(ge,g,de,d,_);if(we){++Fe;for(const me of Ge)Pe[me]=Fe;ce.push({id:Fe,label_id:pe,score:v[de]})}}return[he,ce]}function H(g,v=.5,e=.5,d=.8,_=null,re=null){_===null&&(console.warn("`label_ids_to_fuse` unset. No instance will be fused."),_=new Set);const S=g.class_queries_logits??g.logits,F=(g.masks_queries_logits??g.pred_masks).sigmoid();let[Ie,he,ce]=S.dims;if(ce-=1,re!==null&&re.length!==Ie)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let ge=[];for(let Ce=0;Ce<Ie;++Ce){let Fe=re!==null?re[Ce]:null,Pe=S[Ce],de=F[Ce],[pe,we,Ge]=V(Pe,de,v,ce);if(Ge.length===0){let[Ue,ot]=Fe??de.dims.slice(-2),pt=new u.Tensor("int32",new Int32Array(Ue*ot).fill(-1),[Ue,ot]);ge.push({segmentation:pt,segments_info:[]});continue}let[me,at]=U(pe,we,Ge,e,d,_,Fe);ge.push({segmentation:me,segments_info:at})}return ge}function K(g,v=.5,e=null){throw new Error("`post_process_instance_segmentation` is not yet implemented.")}class J extends o.Callable{constructor(v){super(),this.image_mean=v.image_mean??v.mean,this.image_std=v.image_std??v.std,this.resample=v.resample??2,this.do_rescale=v.do_rescale??!0,this.rescale_factor=v.rescale_factor??1/255,this.do_normalize=v.do_normalize,this.do_thumbnail=v.do_thumbnail,this.size=v.size??v.image_size,this.do_resize=v.do_resize??this.size!==void 0,this.size_divisibility=v.size_divisibility??v.size_divisor,this.do_center_crop=v.do_center_crop,this.crop_size=v.crop_size,this.do_convert_rgb=v.do_convert_rgb??!0,this.do_crop_margin=v.do_crop_margin,this.pad_size=v.pad_size,this.do_pad=v.do_pad,this.do_pad&&!this.pad_size&&this.size&&this.size.width!==void 0&&this.size.height!==void 0&&(this.pad_size=this.size),this.do_flip_channel_order=v.do_flip_channel_order??!1,this.config=v}async thumbnail(v,e,d=2){const _=v.height,re=v.width,S=e.height,F=e.width;let Ie=Math.min(_,S),he=Math.min(re,F);return Ie===_&&he===re?v:(_>re?he=Math.floor(re*Ie/_):re>_&&(Ie=Math.floor(_*he/re)),await v.resize(he,Ie,{resample:d}))}async crop_margin(v,e=200){const d=v.clone().grayscale(),_=(0,p.min)(d.data)[0],re=(0,p.max)(d.data)[0]-_;if(re===0)return v;const S=e/255;let F=d.width,Ie=d.height,he=0,ce=0;const ge=d.data;for(let Ce=0;Ce<d.height;++Ce){const Fe=Ce*d.width;for(let Pe=0;Pe<d.width;++Pe)(ge[Fe+Pe]-_)/re<S&&(F=Math.min(F,Pe),Ie=Math.min(Ie,Ce),he=Math.max(he,Pe),ce=Math.max(ce,Ce))}return v=await v.crop([F,Ie,he,ce])}pad_image(v,e,d,{mode:_="constant",center:re=!1,constant_values:S=0}={}){const[F,Ie,he]=e;let ce,ge;
2803if(typeof d=="number"?(ce=d,ge=d):d==="square"?ce=ge=Math.max(F,Ie):(ce=d.width,ge=d.height),ce!==Ie||ge!==F){const Ce=new Float32Array(ce*ge*he);if(Array.isArray(S))for(let de=0;de<Ce.length;++de)Ce[de]=S[de%he];else S!==0&&Ce.fill(S);const[Fe,Pe]=re?[Math.floor((ce-Ie)/2),Math.floor((ge-F)/2)]:[0,0];for(let de=0;de<F;++de){const pe=(de+Pe)*ce,we=de*Ie;for(let Ge=0;Ge<Ie;++Ge){const me=(pe+Ge+Fe)*he,at=(we+Ge)*he;for(let Ue=0;Ue<he;++Ue)Ce[me+Ue]=v[at+Ue]}}if(_==="symmetric"){if(re)throw new Error("`center` padding is not supported when `mode` is set to `symmetric`.");const de=F-1,pe=Ie-1;for(let we=0;we<ge;++we){const Ge=we*ce,me=(0,C.calculateReflectOffset)(we,de)*Ie;for(let at=0;at<ce;++at){if(we<F&&at<Ie)continue;const Ue=(Ge+at)*he,ot=(me+(0,C.calculateReflectOffset)(at,pe))*he;for(let pt=0;pt<he;++pt)Ce[Ue+pt]=v[ot+pt]}}}v=Ce,e=[ge,ce,he]}return[v,e]}rescale(v){for(let e=0;e<v.length;++e)v[e]=this.rescale_factor*v[e]}get_resize_output_image_size(v,e){const[d,_]=v.size;let re,S;if(this.do_thumbnail){const{height:F,width:Ie}=e;re=Math.min(F,Ie)}else Number.isInteger(e)?(re=e,S=this.config.max_size??re):e!==void 0&&(re=e.shortest_edge,S=e.longest_edge);if(re!==void 0||S!==void 0){const F=re===void 0?1:Math.max(re/d,re/_),Ie=d*F,he=_*F,ce=S===void 0?1:Math.min(S/Ie,S/he);let ge=Math.floor(Number((Ie*ce).toFixed(2))),Ce=Math.floor(Number((he*ce).toFixed(2)));return this.size_divisibility!==void 0&&([ge,Ce]=B([ge,Ce],this.size_divisibility)),[ge,Ce]}if(e!==void 0&&e.width!==void 0&&e.height!==void 0){let F=e.width,Ie=e.height;if(this.config.keep_aspect_ratio&&this.config.ensure_multiple_of){let he=Ie/_,ce=F/d;Math.abs(1-ce)<Math.abs(1-he)?he=ce:ce=he,Ie=E(he*_,this.config.ensure_multiple_of),F=E(ce*d,this.config.ensure_multiple_of)}return[F,Ie]}if(this.size_divisibility!==void 0)return B([d,_],this.size_divisibility);if(e.min_pixels!==void 0&&e.max_pixels!==void 0){const{min_pixels:F,max_pixels:Ie}=e;return function(he,ce,ge=28,Ce=3136,Fe=1003520){if(he<ge||ce<ge)throw new Error(`height:${he} or width:${ce} must be larger than factor:${ge}`);if(Math.max(he,ce)/Math.min(he,ce)>200)throw new Error("absolute aspect ratio must be smaller than 200, got "+Math.max(he,ce)/Math.min(he,ce));let Pe=Math.round(he/ge)*ge,de=Math.round(ce/ge)*ge;if(Pe*de>Fe){const pe=Math.sqrt(he*ce/Fe);Pe=Math.floor(he/pe/ge)*ge,de=Math.floor(ce/pe/ge)*ge}else if(Pe*de<Ce){const pe=Math.sqrt(Ce/(he*ce));Pe=Math.ceil(he*pe/ge)*ge,de=Math.ceil(ce*pe/ge)*ge}return[Pe,de]}(_,d,this.config.patch_size*this.config.merge_size,F,Ie)}throw new Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(e)}`)}async resize(v){const[e,d]=this.get_resize_output_image_size(v,this.size);return await v.resize(e,d,{resample:this.resample})}async preprocess(v,{do_normalize:e=null,do_pad:d=null,do_convert_rgb:_=null,do_convert_grayscale:re=null,do_flip_channel_order:S=null}={}){this.do_crop_margin&&(v=await this.crop_margin(v));const[F,Ie]=v.size;if(_??this.do_convert_rgb?v=v.rgb():re&&(v=v.grayscale()),this.do_resize&&(v=await this.resize(v)),this.do_thumbnail&&(v=await this.thumbnail(v,this.size,this.resample)),this.do_center_crop){let Ce,Fe;Number.isInteger(this.crop_size)?(Ce=this.crop_size,Fe=this.crop_size):(Ce=this.crop_size.width,Fe=this.crop_size.height),v=await v.center_crop(Ce,Fe)}const he=[v.height,v.width];let ce=Float32Array.from(v.data),ge=[v.height,v.width,v.channels];if(this.do_rescale&&this.rescale(ce),e??this.do_normalize){let Ce=this.image_mean;Array.isArray(this.image_mean)||(Ce=new Array(v.channels).fill(Ce));let Fe=this.image_std;if(Array.isArray(this.image_std)||(Fe=new Array(v.channels).fill(Ce)),Ce.length!==v.channels||Fe.length!==v.channels)throw new Error(`When set to arrays, the length of \`image_mean\` (${Ce.length}) and \`image_std\` (${Fe.length}) must match the number of channels in the image (${v.channels}).`);for(let Pe=0;Pe<ce.length;Pe+=v.channels)for(let de=0;de<v.channels;++de)ce[Pe+de]=(ce[Pe+de]-Ce[de])/Fe[de]}if(d??this.do_pad){if(this.pad_size)[ce,ge]=this.pad_image(ce,[v.height,v.width,v.channels],this.pad_size);else if(this.size_divisibility){const[Ce,Fe]=B([ge[1],ge[0]],this.size_divisibility);[ce,ge]=this.pad_image(ce,ge,{width:Ce,height:Fe})}}if(S??this.do_flip_channel_order){if(ge[2]!==3)throw new Error("Flipping channel order is only supported for RGB images.");for(let Ce=0;Ce<ce.length;Ce+=3){const Fe=ce[Ce];ce[Ce]=ce[Ce+2],ce[Ce+2]=Fe}}return{original_size:[Ie,F],reshaped_input_size:he,pixel_values:new u.Tensor("float32",ce,ge).permute(2,0,1)}}async _call(v,...e){Array.isArray(v)||(v=[v]);const d=await Promise.all(v.map(re=>this.preprocess(re)));return{pixel_values:(0,u.stack)(d.map(re=>re.pixel_values),0),original_sizes:d.map(re=>re.original_size),reshaped_input_sizes:d.map(re=>re.reshaped_input_size)}}static async from_pretrained(v,e){return new this(await(0,b.getModelJSON)(v,k.IMAGE_PROCESSOR_NAME,!0,e))}}},
2803"./src/base/processing_utils.js":(c,a,s)=>{s.r(a),s.d(a,{Processor:()=>C});var o=s("./src/utils/constants.js"),u=s("./src/utils/generic.js"),p=s("./src/utils/hub.js");class C extends u.Callable{constructor(k,E){super(),this.config=k,this.components=E}get image_processor(){return this.components.image_processor}get tokenizer(){return this.components.tokenizer}get feature_extractor(){return this.components.feature_extractor}apply_chat_template(k,E={}){if(!this.tokenizer)throw new Error("Unable to apply chat template without a tokenizer.");return this.tokenizer.apply_chat_template(k,{tokenize:!1,...E})}batch_decode(...k){if(!this.tokenizer)throw new Error("Unable to decode without a tokenizer.");return this.tokenizer.batch_decode(...k)}decode(...k){if(!this.tokenizer)throw new Error("Unable to decode without a tokenizer.");return this.tokenizer.decode(...k)}async _call(k,...E){for(const B of[this.image_processor,this.feature_extractor,this.tokenizer])if(B)return B(k,...E);throw new Error("No image processor, feature extractor, or tokenizer found.")}static async from_pretrained(k,E){const[B,G]=await Promise.all([this.uses_processor_config?(0,p.getModelJSON)(k,o.PROCESSOR_NAME,!0,E):{},Promise.all(this.classes.filter(D=>D in this).map(async D=>{const Q=await this[D].from_pretrained(k,E);return[D.replace(/_class$/,""),Q]})).then(Object.fromEntries)]);return new this(B,G)}}Ct(C,"classes",["image_processor_class","tokenizer_class","feature_extractor_class"]),Ct(C,"uses_processor_config",!1)},
2803"./src/configs.js":(c,a,s)=>{s.r(a),s.d(a,{AutoConfig:()=>k,PretrainedConfig:()=>b,getKeyValueShapes:()=>C});var o=s("./src/utils/core.js"),u=s("./src/utils/hub.js");function p(E){const B={};let G={};switch(E.model_type){case"llava":case"paligemma":case"florence2":case"llava_onevision":case"idefics3":G=p(E.text_config);break;case"moondream1":G=p(E.phi_config);break;case"musicgen":G=p(E.decoder);break;case"multi_modality":G=p(E.language_config);break;case"gpt2":case"gptj":case"jais":case"codegen":case"gpt_bigcode":B.num_heads="n_head",B.num_layers="n_layer",B.hidden_size="n_embd";break;case"gpt_neox":case"stablelm":case"opt":case"falcon":B.num_heads="num_attention_heads",B.num_layers="num_hidden_layers",B.hidden_size="hidden_size";break;case"llama":case"olmo":case"olmo2":case"mobilellm":case"granite":case"cohere":case"mistral":case"starcoder2":case"qwen2":case"qwen2_vl":case"phi":case"phi3":case"phi3_v":B.num_heads="num_key_value_heads",B.num_layers="num_hidden_layers",B.hidden_size="hidden_size",B.num_attention_heads="num_attention_heads";break;case"gemma":case"gemma2":case"glm":case"helium":B.num_heads="num_key_value_heads",B.num_layers="num_hidden_layers",B.dim_kv="head_dim";break;case"openelm":B.num_heads="num_kv_heads",B.num_layers="num_transformer_layers",B.dim_kv="head_dim";break;case"gpt_neo":case"donut-swin":B.num_heads="num_heads",B.num_layers="num_layers",B.hidden_size="hidden_size";break;case"bloom":B.num_heads="n_head",B.num_layers="n_layer",B.hidden_size="hidden_size";break;case"mpt":B.num_heads="n_heads",B.num_layers="n_layers",B.hidden_size="d_model";break;case"exaone":B.num_heads="num_key_value_heads",B.num_layers="num_layers",B.dim_kv="head_dim",B.num_attention_heads="num_attention_heads";break;case"t5":case"mt5":case"longt5":B.num_decoder_layers="num_decoder_layers",B.num_decoder_heads="num_heads",B.decoder_dim_kv="d_kv",B.num_encoder_layers="num_layers",B.num_encoder_heads="num_heads",B.encoder_dim_kv="d_kv";break;case"bart":case"mbart":case"marian":case"whisper":case"m2m_100":case"blenderbot":case"blenderbot-small":case"florence2_language":B.num_decoder_layers="decoder_layers",B.num_decoder_heads="decoder_attention_heads",B.decoder_hidden_size="d_model",B.num_encoder_layers="encoder_layers",B.num_encoder_heads="encoder_attention_heads",B.encoder_hidden_size="d_model";break;case"speecht5":B.num_decoder_layers="decoder_layers",B.num_decoder_heads="decoder_attention_heads",B.decoder_hidden_size="hidden_size",B.num_encoder_layers="encoder_layers",B.num_encoder_heads="encoder_attention_heads",B.encoder_hidden_size="hidden_size";break;case"trocr":B.num_encoder_layers=B.num_decoder_layers="decoder_layers",B.num_encoder_heads=B.num_decoder_heads="decoder_attention_heads",B.encoder_hidden_size=B.decoder_hidden_size="d_model";break;case"musicgen_decoder":B.num_encoder_layers=B.num_decoder_layers="num_hidden_layers",B.num_encoder_heads=B.num_decoder_heads="num_attention_heads",B.encoder_hidden_size=B.decoder_hidden_size="hidden_size";break;case"moonshine":B.num_decoder_layers="decoder_num_hidden_layers",B.num_decoder_heads="decoder_num_key_value_heads",B.num_encoder_layers="encoder_num_hidden_layers",B.num_encoder_heads="encoder_num_key_value_heads",B.encoder_hidden_size=B.decoder_hidden_size="hidden_size";break;case"vision-encoder-decoder":const Q=p(E.decoder),V="num_decoder_layers"in Q,W=(0,o.pick)(E,["model_type","is_encoder_decoder"]);return V?(W.num_decoder_layers=Q.num_decoder_layers,W.num_decoder_heads=Q.num_decoder_heads,W.decoder_hidden_size=Q.decoder_hidden_size,W.num_encoder_layers=Q.num_encoder_layers,W.num_encoder_heads=Q.num_encoder_heads,W.encoder_hidden_size=Q.encoder_hidden_size):(W.num_layers=Q.num_layers,W.num_heads=Q.num_heads,W.hidden_size=Q.hidden_size),W}const D={...G,...(0,o.pick)(E,["model_type","multi_query","is_encoder_decoder"])};for(const Q in B)D[Q]=E[B[Q]];return D}function C(E,{prefix:B="past_key_values",batch_size:G=1}={}){const D={},Q=E.normalized_config;if(Q.is_encoder_decoder&&"num_encoder_heads"in Q&&"num_decoder_heads"in Q){const V=Q.encoder_dim_kv??Q.encoder_hidden_size/Q.num_encoder_heads,W=Q.decoder_dim_kv??Q.decoder_hidden_size/Q.num_decoder_heads,U=[G,Q.num_encoder_heads,0,V],H=[G,Q.num_decoder_heads,0,W];for(let K=0;K<Q.num_decoder_layers;++K)D[`${B}.${K}.encoder.key`]=U,D[`${B}.${K}.encoder.value`]=U,D[`${B}.${K}.decoder.key`]=H,D[`${B}.${K}.decoder.value`]=H}else{const V=Q.num_heads,W=Q.num_layers,U=Q.dim_kv??Q.hidden_size/(Q.num_attention_heads??V);if(Q.model_type==="falcon"){const H=[G*V,0,U];for(let K=0;K<W;++K)D[`${B}.${K}.key`]=H,D[`${B}.${K}.value`]=H}else if(Q.multi_query){const H=[G*V,0,2*U];for(let K=0;K<W;++K)D[`${B}.${K}.key_value`]=H}else if(Q.model_type==="bloom"){const H=[G*V,U,0],K=[G*V,0,U];for(let J=0;J<W;++J)D[`${B}.${J}.key`]=H,D[`${B}.${J}.value`]=K}else if(Q.model_type==="openelm")for(let H=0;H<W;++H){const K=[G,V[H],0,U];D[`${B}.${H}.key`]=K,D[`${B}.${H}.value`]=K}else{const H=[G,V,0,U];for(let K=0;K<W;++K)D[`${B}.${K}.key`]=H,D[`${B}.${K}.value`]=H}}return D}class b{constructor(B){Ct(this,"model_type",null);Ct(this,"is_encoder_decoder",!1);Ct(this,"max_position_embeddings");Ct(this,"transformers.js_config");Object.assign(this,B),this.normalized_config=p(this)}static async from_pretrained(B,{progress_callback:G=null,config:D=null,cache_dir:Q=null,local_files_only:V=!1,revision:W="main"}={}){!D||D instanceof b||(D=new b(D));const U=D??await async function(H,K){return await(0,u.getModelJSON)(H,"config.json",!0,K)}(B,{progress_callback:G,config:D,cache_dir:Q,local_files_only:V,revision:W});return new this(U)}}class k{static async from_pretrained(...B){return b.from_pretrained(...B)}}},
2803"./src/env.js":(c,a,s)=>{var e,d;s.r(a),s.d(a,{apis:()=>W,env:()=>g});var o=s("?569f"),u=s("?3f59"),p=s("?154a");const C=typeof window<"u"&&window.document!==void 0,b=typeof self<"u"&&((e=self.constructor)==null?void 0:e.name)==="DedicatedWorkerGlobalScope",k=typeof self<"u"&&"caches"in self,E=typeof navigator<"u"&&"gpu"in navigator,B=typeof navigator<"u"&&"ml"in navigator,G=typeof process<"u",D=G&&((d=process==null?void 0:process.release)==null?void 0:d.name)==="node",Q=!v(o),V=!v(u),W=Object.freeze({IS_BROWSER_ENV:C,IS_WEBWORKER_ENV:b,IS_WEB_CACHE_AVAILABLE:k,IS_WEBGPU_AVAILABLE:E,IS_WEBNN_AVAILABLE:B,IS_PROCESS_AVAILABLE:G,IS_NODE_ENV:D,IS_FS_AVAILABLE:Q,IS_PATH_AVAILABLE:V}),U=Q&&V;let H="./";if(U){const _=Object(import.meta).url;_?H=u.dirname(u.dirname(p.fileURLToPath(_))):typeof __dirname<"u"&&(H=u.dirname(__dirname))}const K=U?u.join(H,"/.cache/"):null,J="/models/",g={version:"3.3.3",backends:{onnx:{}},allowRemoteModels:!0,remoteHost:"https://huggingface.co/",remotePathTemplate:"{model}/resolve/{revision}/",allowLocalModels:!(C||b),localModelPath:U?u.join(H,J):J,useFS:Q,useBrowserCache:k,useFSCache:Q,cacheDir:K,useCustomCache:!1,customCache:null};function v(_){return Object.keys(_).length===0}},
2803"./src/generation/configuration_utils.js":(c,a,s)=>{s.r(a),s.d(a,{GenerationConfig:()=>u});var o=s("./src/utils/core.js");class u{constructor(C){Ct(this,"max_length",20);Ct(this,"max_new_tokens",null);Ct(this,"min_length",0);Ct(this,"min_new_tokens",null);Ct(this,"early_stopping",!1);Ct(this,"max_time",null);Ct(this,"do_sample",!1);Ct(this,"num_beams",1);Ct(this,"num_beam_groups",1);Ct(this,"penalty_alpha",null);Ct(this,"use_cache",!0);Ct(this,"temperature",1);Ct(this,"top_k",50);Ct(this,"top_p",1);Ct(this,"typical_p",1);Ct(this,"epsilon_cutoff",0);Ct(this,"eta_cutoff",0);Ct(this,"diversity_penalty",0);Ct(this,"repetition_penalty",1);Ct(this,"encoder_repetition_penalty",1);Ct(this,"length_penalty",1);Ct(this,"no_repeat_ngram_size",0);Ct(this,"bad_words_ids",null);Ct(this,"force_words_ids",null);Ct(this,"renormalize_logits",!1);Ct(this,"constraints",null);Ct(this,"forced_bos_token_id",null);Ct(this,"forced_eos_token_id",null);Ct(this,"remove_invalid_values",!1);Ct(this,"exponential_decay_length_penalty",null);Ct(this,"suppress_tokens",null);Ct(this,"streamer",null);Ct(this,"begin_suppress_tokens",null);Ct(this,"forced_decoder_ids",null);Ct(this,"guidance_scale",null);Ct(this,"num_return_sequences",1);Ct(this,"output_attentions",!1);Ct(this,"output_hidden_states",!1);Ct(this,"output_scores",!1);Ct(this,"return_dict_in_generate",!1);Ct(this,"pad_token_id",null);Ct(this,"bos_token_id",null);Ct(this,"eos_token_id",null);Ct(this,"encoder_no_repeat_ngram_size",0);Ct(this,"decoder_start_token_id",null);Ct(this,"generation_kwargs",{});Object.assign(this,(0,o.pick)(C,Object.getOwnPropertyNames(this)))}}},
vendor: 5,404 bytes, line 2803
2803"./src/generation/logits_process.js":(c,a,s)=>{s.r(a),s.d(a,{ClassifierFreeGuidanceLogitsProcessor:()=>H,ForcedBOSTokenLogitsProcessor:()=>k,ForcedEOSTokenLogitsProcessor:()=>E,LogitsProcessor:()=>p,LogitsProcessorList:()=>b,LogitsWarper:()=>C,MinLengthLogitsProcessor:()=>V,MinNewTokensLengthLogitsProcessor:()=>W,NoBadWordsLogitsProcessor:()=>U,NoRepeatNGramLogitsProcessor:()=>D,RepetitionPenaltyLogitsProcessor:()=>Q,SuppressTokensAtBeginLogitsProcessor:()=>B,TemperatureLogitsWarper:()=>K,TopKLogitsWarper:()=>g,TopPLogitsWarper:()=>J,WhisperTimeStampLogitsProcessor:()=>G});var o=s("./src/utils/generic.js");s("./src/utils/tensor.js");var u=s("./src/utils/maths.js");class p extends o.Callable{_call(e,d){throw Error("`_call` should be implemented in a subclass")}}class C extends o.Callable{_call(e,d){throw Error("`_call` should be implemented in a subclass")}}class b extends o.Callable{constructor(){super(),this.processors=[]}push(e){this.processors.push(e)}extend(e){this.processors.push(...e)}_call(e,d){let _=d;for(const re of this.processors)_=re(e,_);return _}[Symbol.iterator](){return this.processors.values()}}class k extends p{constructor(e){super(),this.bos_token_id=e}_call(e,d){for(let _=0;_<e.length;++_)if(e[_].length===1){const re=d[_].data;re.fill(-1/0),re[this.bos_token_id]=0}return d}}class E extends p{constructor(e,d){super(),this.max_length=e,this.eos_token_id=Array.isArray(d)?d:[d]}_call(e,d){for(let _=0;_<e.length;++_)if(e[_].length===this.max_length-1){const re=d[_].data;re.fill(-1/0);for(const S of this.eos_token_id)re[S]=0}return d}}class B extends p{constructor(e,d){super(),this.begin_suppress_tokens=e,this.begin_index=d}_call(e,d){for(let _=0;_<e.length;++_)if(e[_].length===this.begin_index){const re=d[_].data;for(const S of this.begin_suppress_tokens)re[S]=-1/0}return d}}class G extends p{constructor(e,d){super(),this.eos_token_id=Array.isArray(e.eos_token_id)?e.eos_token_id[0]:e.eos_token_id,this.no_timestamps_token_id=e.no_timestamps_token_id,this.timestamp_begin=this.no_timestamps_token_id+1,this.begin_index=d.length,d.at(-1)===this.no_timestamps_token_id&&(this.begin_index-=1),this.max_initial_timestamp_index=e.max_initial_timestamp_index}_call(e,d){for(let _=0;_<e.length;++_){const re=d[_].data;if(re[this.no_timestamps_token_id]=-1/0,e[_].length===this.begin_index-1){re.fill(-1/0),re[this.timestamp_begin]=0;continue}const S=e[_].slice(this.begin_index),F=S.length>=1&&S[S.length-1]>=this.timestamp_begin,Ie=S.length<2||S[S.length-2]>=this.timestamp_begin;if(F&&(Ie?re.subarray(this.timestamp_begin).fill(-1/0):re.subarray(0,this.eos_token_id).fill(-1/0)),e[_].length===this.begin_index&&this.max_initial_timestamp_index!==null){const ge=this.timestamp_begin+this.max_initial_timestamp_index;re.subarray(ge+1).fill(-1/0)}const he=(0,u.log_softmax)(re);Math.log(he.subarray(this.timestamp_begin).map(Math.exp).reduce((ge,Ce)=>ge+Ce))>(0,u.max)(he.subarray(0,this.timestamp_begin))[0]&&re.subarray(0,this.timestamp_begin).fill(-1/0)}return d}}class D extends p{constructor(e){super(),this.no_repeat_ngram_size=e}getNgrams(e){const d=e.length,_=[];for(let S=0;S<d+1-this.no_repeat_ngram_size;++S){const F=[];for(let Ie=0;Ie<this.no_repeat_ngram_size;++Ie)F.push(e[S+Ie]);_.push(F.map(Number))}const re=new Map;for(const S of _){const F=S.slice(0,S.length-1),Ie=JSON.stringify(F),he=re.get(Ie)??[];he.push(S[S.length-1]),re.set(Ie,he)}return re}getGeneratedNgrams(e,d){const _=d.slice(d.length+1-this.no_repeat_ngram_size,d.length);return e.get(JSON.stringify(_.map(Number)))??[]}calcBannedNgramTokens(e){const d=[];if(e.length+1<this.no_repeat_ngram_size)return d;{const _=this.getNgrams(e);return this.getGeneratedNgrams(_,e)}}_call(e,d){for(let _=0;_<e.length;++_){const re=d[_].data,S=this.calcBannedNgramTokens(e[_]);for(const F of S)re[F]=-1/0}return d}}class Q extends p{constructor(e){super(),this.penalty=e}_call(e,d){for(let _=0;_<e.length;++_){const re=d[_].data;for(const S of new Set(e[_])){const F=Number(S);re[F]<0?re[F]*=this.penalty:re[F]/=this.penalty}}return d}}class V extends p{constructor(e,d){super(),this.min_length=e,this.eos_token_id=Array.isArray(d)?d:[d]}_call(e,d){for(let _=0;_<e.length;++_)if(e[_].length<this.min_length){const re=d[_].data;for(const S of this.eos_token_id)re[S]=-1/0}return d}}class W extends p{constructor(e,d,_){super(),this.prompt_length_to_skip=e,this.min_new_tokens=d,this.eos_token_id=Array.isArray(_)?_:[_]}_call(e,d){for(let _=0;_<e.length;++_)if(e[_].length-this.prompt_length_to_skip<this.min_new_tokens){const re=d[_].data;for(const S of this.eos_token_id)re[S]=-1/0}return d}}class U extends p{constructor(e,d){super(),this.bad_words_ids=e,this.eos_token_id=Array.isArray(d)?d:[d]}_call(e,d){for(let _=0;_<e.length;++_){const re=d[_].data,S=e[_];for(const F of this.bad_words_ids){let Ie=!0;for(let he=1;he<=F.length-1&&F.length<S.length;++he)if(F.at(-he-1)!=S.at(-he)){Ie=!1;break}Ie&&(re[F.at(-1)]=-1/0)}}return d}}class H extends p{constructor(e){if(super(),e<=1)throw new Error(`Require guidance scale >1 to use the classifier free guidance processor, got guidance scale ${e}.`);this.guidance_scale=e}_call(e,d){if(d.dims[0]!==2*e.length)throw new Error(`Logits should have twice the batch size of the input ids, the first half of batches corresponding to the conditional inputs, and the second half of batches corresponding to the unconditional inputs. Got batch size ${d.dims[
28030]} for the logits and ${e.length} for the input ids.`);const _=e.length,re=d.slice([0,_],null),S=d.slice([_,d.dims[0]],null);for(let F=0;F<S.data.length;++F)S.data[F]+=(re.data[F]-S.data[F])*this.guidance_scale;return S}}class K extends C{constructor(e){super(),this.temperature=e}_call(e,d){const _=d.data;for(let re=0;re<_.length;++re)_[re]/=this.temperature;return d}}class J extends C{constructor(e,{filter_value:d=-1/0,min_tokens_to_keep:_=1}={}){if(super(),e<0||e>1)throw new Error(`\`top_p\` must be a float > 0 and < 1, but is ${e}`);if(!Number.isInteger(_)||_<1)throw new Error(`\`min_tokens_to_keep\` must be a positive integer, but is ${_}`);this.top_p=e,this.filter_value=d,this.min_tokens_to_keep=_}}class g extends C{constructor(e,{filter_value:d=-1/0,min_tokens_to_keep:_=1}={}){if(super(),!Number.isInteger(e)||e<0)throw new Error(`\`top_k\` must be a positive integer, but is ${e}`);this.top_k=Math.max(e,_),this.filter_value=d}}},
2803"./src/generation/logits_sampler.js":(c,a,s)=>{s.r(a),s.d(a,{LogitsSampler:()=>C});var o=s("./src/utils/generic.js"),u=s("./src/utils/tensor.js"),p=s("./src/utils/maths.js");s("./src/generation/configuration_utils.js");class C extends o.Callable{constructor(G){super(),this.generation_config=G}async _call(G){return this.sample(G)}async sample(G){throw Error("sample should be implemented in subclasses.")}getLogits(G,D){let Q=G.dims.at(-1),V=G.data;if(D===-1)V=V.slice(-Q);else{let W=D*Q;V=V.slice(W,W+Q)}return V}randomSelect(G){let D=0;for(let V=0;V<G.length;++V)D+=G[V];let Q=Math.random()*D;for(let V=0;V<G.length;++V)if(Q-=G[V],Q<=0)return V;return 0}static getSampler(G){if(G.do_sample)return new k(G);if(G.num_beams>1)return new E(G);if(G.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${G.num_return_sequences}.`);return new b(G)}}class b extends C{async sample(G){const D=(0,p.max)(G.data)[1];return[[BigInt(D),0]]}}class k extends C{async sample(G){let D=G.dims.at(-1);this.generation_config.top_k>0&&(D=Math.min(this.generation_config.top_k,D));const[Q,V]=await(0,u.topk)(G,D),W=(0,p.softmax)(Q.data);return Array.from({length:this.generation_config.num_beams},()=>{const U=this.randomSelect(W);return[V.data[U],Math.log(W[U])]})}}class E extends C{async sample(G){let D=G.dims.at(-1);this.generation_config.top_k>0&&(D=Math.min(this.generation_config.top_k,D));const[Q,V]=await(0,u.topk)(G,D),W=(0,p.softmax)(Q.data);return Array.from({length:this.generation_config.num_beams},(U,H)=>[V.data[H],Math.log(W[H])])}}},
2803"./src/generation/stopping_criteria.js":(c,a,s)=>{s.r(a),s.d(a,{EosTokenCriteria:()=>b,InterruptableStoppingCriteria:()=>k,MaxLengthCriteria:()=>C,StoppingCriteria:()=>u,StoppingCriteriaList:()=>p});var o=s("./src/utils/generic.js");class u extends o.Callable{_call(B,G){throw Error("StoppingCriteria needs to be subclassed")}}class p extends o.Callable{constructor(){super(),this.criteria=[]}push(B){this.criteria.push(B)}extend(B){B instanceof p?B=B.criteria:B instanceof u&&(B=[B]),this.criteria.push(...B)}_call(B,G){const D=new Array(B.length).fill(!1);for(const Q of this.criteria){const V=Q(B,G);for(let W=0;W<D.length;++W)D[W]||(D[W]=V[W])}return D}[Symbol.iterator](){return this.criteria.values()}}class C extends u{constructor(B,G=null){super(),this.max_length=B,this.max_position_embeddings=G}_call(B){return B.map(G=>G.length>=this.max_length)}}class b extends u{constructor(B){super(),Array.isArray(B)||(B=[B]),this.eos_token_id=B}_call(B,G){return B.map(D=>{const Q=D.at(-1);return this.eos_token_id.some(V=>Q==V)})}}class k extends u{constructor(){super(),this.interrupted=!1}interrupt(){this.interrupted=!0}reset(){this.interrupted=!1}_call(B,G){return new Array(B.length).fill(this.interrupted)}}},
2803"./src/generation/streamers.js":(c,a,s)=>{s.r(a),s.d(a,{BaseStreamer:()=>C,TextStreamer:()=>k,WhisperTextStreamer:()=>E});var o=s("./src/utils/core.js"),u=s("./src/tokenizers.js"),p=s("./src/env.js");class C{put(G){throw Error("Not implemented")}end(){throw Error("Not implemented")}}const b=p.apis.IS_PROCESS_AVAILABLE?B=>process.stdout.write(B):B=>console.log(B);class k extends C{constructor(G,{skip_prompt:D=!1,callback_function:Q=null,token_callback_function:V=null,skip_special_tokens:W=!0,decode_kwargs:U={},...H}={}){super(),this.tokenizer=G,this.skip_prompt=D,this.callback_function=Q??b,this.token_callback_function=V,this.decode_kwargs={skip_special_tokens:W,...U,...H},this.token_cache=[],this.print_len=0,this.next_tokens_are_prompt=!0}put(G){var W;if(G.length>1)throw Error("TextStreamer only supports batch size of 1");if(this.skip_prompt&&this.next_tokens_are_prompt)return void(this.next_tokens_are_prompt=!1);const D=G[0];(W=this.token_callback_function)==null||W.call(this,D),this.token_cache=(0,o.mergeArrays)(this.token_cache,D);const Q=this.tokenizer.decode(this.token_cache,this.decode_kwargs);let V;Q.endsWith(` 2804`)?(V=Q.slice(this.print_len),this.token_cache=[],this.print_len=0):Q.length>0&&(0,u.is_chinese_char)(Q.charCodeAt(Q.length-1))?(V=Q.slice(this.print_len),this.print_len+=V.length):(V=Q.slice(this.print_len,Q.lastIndexOf(" ")+1),this.print_len+=V.length),this.on_finalized_text(V,!1)}end(){let G;this.token_cache.length>0?(G=this.tokenizer.decode(this.token_cache,this.decode_kwargs).slice(this.print_len),this.token_cache=[],this.print_len=0):G="",this.next_tokens_are_prompt=!0,this.on_finalized_text(G,!0)}on_finalized_text(G,D){var Q,V;G.length>0&&((Q=this.callback_function)==null||Q.call(this,G)),D&&this.callback_function===b&&p.apis.IS_PROCESS_AVAILABLE&&((V=this.callback_function)==null||V.call(this,` 2805`))}}class E extends k{constructor(G,{skip_prompt:D=!1,callback_function:Q=null,token_callback_function:V=null,on_chunk_start:W=null,on_chunk_end:U=null,on_finalize:H=null,time_precision:K=.02,skip_special_tokens:J=!0,decode_kwargs:g={}}={}){super(G,{skip_prompt:D,skip_special_tokens:J,callback_function:Q,token_callback_function:V,decode_kwargs:g}),this.timestamp_begin=G.timestamp_begin,this.on_chunk_start=W,this.on_chunk_end=U,this.on_finalize=H,this.time_precision=K,this.waiting_for_timestamp=!1}put(G){var Q,V;if(G.length>1)throw Error("WhisperTextStreamer only supports batch size of 1");const D=G[0];if(D.length===1){const W=Number(D[0])-this.timestamp_begin;if(W>=0){const U=W*this.time_precision;this.waiting_for_timestamp?(Q=this.on_chunk_end)==null||Q.call(this,U):(V=this.on_chunk_start)==null||V.call(this,U),this.waiting_for_timestamp=!this.waiting_for_timestamp,G=[[]]}}return super.put(G)}end(){var G;super.end(),(G=this.on_finalize)==null||G.call(this)}}},
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2805typeof vs!="string"&&(vs=vs[GA]),br=vs&&vs!==p.DATA_TYPES.auto&&p.DATA_TYPES.hasOwnProperty(vs)?vs:p.DEFAULT_DEVICE_DTYPE_MAPPING[pr]??p.DATA_TYPES.fp32}const Mr=br;if(!p.DEFAULT_DTYPE_SUFFIX_MAPPING.hasOwnProperty(Mr))throw new Error(`Invalid dtype: ${Mr}. Should be one of: ${Object.keys(p.DATA_TYPES).join(", ")}`);if(Mr===p.DATA_TYPES.fp16&&pr==="webgpu"&&!await(0,p.isWebGpuFp16Supported)())throw new Error(`The device (${pr}) does not support fp16.`);const Pr=OA.kv_cache_dtype?typeof OA.kv_cache_dtype=="string"?OA.kv_cache_dtype:OA.kv_cache_dtype[Mr]??"float32":void 0;if(Pr&&!["float32","float16"].includes(Pr))throw new Error(`Invalid kv_cache_dtype: ${Pr}. Should be one of: float32, float16`);const us={dtype:Mr,kv_cache_dtype:Pr},Zs=p.DEFAULT_DTYPE_SUFFIX_MAPPING[Mr],ia=`${yA.subfolder??""}/${GA}${Zs}.onnx`,Fs={...yA.session_options};Fs.executionProviders??(Fs.executionProviders=cs);const Xa=OA.free_dimension_overrides;Xa?Fs.freeDimensionOverrides??(Fs.freeDimensionOverrides=Xa):pr.startsWith("webnn")&&!Fs.freeDimensionOverrides&&console.warn('WebNN does not currently support dynamic shapes and requires `free_dimension_overrides` to be set in config.json as a field within "transformers.js_config". When `free_dimension_overrides` is not set, you may experience significant performance degradation.');const Oa=(0,k.getModelFile)(DA,ia,!0,yA),Fa=yA.use_external_data_format??OA.use_external_data_format;let _a=[];if(Fa&&(Fa===!0||typeof Fa=="object"&&Fa.hasOwnProperty(GA)&&Fa[GA]===!0)){if(H.apis.IS_NODE_ENV)throw new Error("External data format is not yet supported in Node.js");const vs=`${GA}${Zs}.onnx_data`,si=`${yA.subfolder??""}/${vs}`;_a.push(new Promise(async(T0,X0)=>{const Q0=await(0,k.getModelFile)(DA,si,!0,yA);T0({path:vs,data:Q0})}))}else Fs.externalData!==void 0&&(_a=Fs.externalData.map(async vs=>
2805{if(typeof vs.data=="string"){const si=await(0,k.getModelFile)(DA,vs.data,!0,yA);return{...vs,data:si}}return vs}));if(_a.length>0&&(Fs.externalData=await Promise.all(_a)),pr==="webgpu"){const vs=(0,o.getKeyValueShapes)(yA.config,{prefix:"present"});if(Object.keys(vs).length>0&&!(0,u.isONNXProxy)()){const si={};for(const T0 in vs)si[T0]="gpu-buffer";Fs.preferredOutputLocation=si}}return{buffer:await Oa,session_options:Fs,session_config:us}}(le,xe[Et],Ye);return[Et,await(0,u.createInferenceSession)(Ht,Vt,rA)]})))}async function Pe(le,xe,Ye){return Object.fromEntries(await Promise.all(Object.keys(xe).map(async Et=>[Et,await(0,k.getModelJSON)(le,xe[Et],!1,Ye)])))}async function de(le,xe){const Ye=function(Et,Ht){const Vt=Object.create(null),rA=[];for(const yA of Et.inputNames){const OA=Ht[yA];OA instanceof D.Tensor?Vt[yA]=(0,u.isONNXProxy)()?OA.clone():OA:rA.push(yA)}if(rA.length>0)throw new Error(`An error occurred during model execution: "Missing the following inputs: ${rA.join(", ")}.`);const DA=Object.keys(Ht).length,GA=Et.inputNames.length;if(DA>GA){let yA=Object.keys(Ht).filter(OA=>!Et.inputNames.includes(OA));console.warn(`WARNING: Too many inputs were provided (${DA} > ${GA}). The following inputs will be ignored: "${yA.join(", ")}".`)}return Vt}(le,xe);try{const Et=Object.fromEntries(Object.entries(Ye).map(([Vt,rA])=>[Vt,rA.ort_tensor]));let Ht=await le.run(Et);return Ht=pe(Ht),Ht}catch(Et){const Ht=Object.fromEntries(Object.entries(Ye).map(([Vt,{type:rA,dims:DA,data:GA}])=>[Vt,{type:rA,dims:DA,data:GA}]));throw console.error(`An error occurred during model execution: "${Et}".`),console.error("Inputs given to model:",Ht),Et}}function pe(le){for(let xe in le)(0,u.isONNXTensor)(le[xe])?le[xe]=new D.Tensor(le[xe]):typeof le[xe]=="object"&&pe(le[xe]);return le}function we(le){if(le instanceof D.Tensor)return le;if(le.length===0)throw Error("items must be non-empty");if(Array.isArray(le[0])){if(le.some(xe=>xe.length!==le[0].length))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' and/or 'truncation=True' to have batched tensors with the same length.");return new D.Tensor("int64",BigInt64Array.from(le.flat().map(xe=>BigInt(xe))),[le.length,le[0].length])}return new D.Tensor("int64",BigInt64Array.from(le.map(xe=>BigInt(xe))),[1,le.length])}function Ge(le){return new D.Tensor("bool",[le],[1])}async function me(le,xe){let{encoder_outputs:Ye,input_ids:Et,decoder_input_ids:Ht,...Vt}=xe;if(!Ye){const rA=(0,b.pick)(xe,le.sessions.model.inputNames);Ye=(await at(le,rA)).last_hidden_state}return Vt.input_ids=Ht,Vt.encoder_hidden_states=Ye,le.sessions.decoder_model_merged.inputNames.includes("encoder_attention_mask")&&(Vt.encoder_attention_mask=xe.attention_mask),await Ue(le,Vt,!0)}async function at(le,xe){const Ye=le.sessions.model,Et=(0,b.pick)(xe,Ye.inputNames);if(Ye.inputNames.includes("inputs_embeds")&&!Et.inputs_embeds){if(!xe.input_ids)throw new Error("Both `input_ids` and `inputs_embeds` are missing in the model inputs.");Et.inputs_embeds=await le.encode_text({input_ids:xe.input_ids})}if(Ye.inputNames.includes("token_type_ids")&&!Et.token_type_ids){if(!Et.input_ids)throw new Error("Both `input_ids` and `token_type_ids` are missing in the model inputs.");Et.token_type_ids=(0,D.zeros_like)(Et.input_ids)}if(Ye.inputNames.includes("pixel_mask")&&!Et.pixel_mask){if(!Et.pixel_values)throw new Error("Both `pixel_values` and `pixel_mask` are missing in the model inputs.");const Ht=Et.pixel_values.dims;Et.pixel_mask=(0,D.ones)([Ht[0],Ht[2],Ht[3]])}return await de(Ye,Et)}async function Ue(le,xe,Ye=!1){const Et=le.sessions[Ye?"decoder_model_merged":"model"],{past_key_values:Ht,...Vt}=xe;if(Et.inputNames.includes("use_cache_branch")&&(Vt.use_cache_branch=Ge(!!Ht)),Et.inputNames.includes("position_ids")&&Vt.attention_mask&&!Vt.position_ids){const DA=le.config.model_type==="paligemma"?1:0;Vt.position_ids=function(GA,yA=null,OA=0){const{input_ids:XA,inputs_embeds:pr,attention_mask:cs}=GA,{data:br,dims:Mr}=ct(cs,OA);let Pr=new D.Tensor("int64",br,Mr);if(yA){const us=-(XA??pr).dims.at(1);Pr=Pr.slice(null,[us,null])}return Pr}(Vt,Ht,DA)}le.addPastKeyValues(Vt,Ht);const rA=(0,b.pick)(Vt,Et.inputNames);return await de(Et,rA)}function ot({image_token_id:le,inputs_embeds:xe,image_features:Ye,input_ids:Et,attention_mask:Ht}){const Vt=Et.tolist().map(yA=>yA.reduce((OA,XA,pr)=>(XA==le&&OA.push(pr),OA),[])),rA=Vt.reduce((yA,OA)=>yA+OA.length,0),DA=Ye.dims[0];if(rA!==DA)throw new Error(`Image features and image tokens do n
2805ot match: tokens: ${rA}, features ${DA}`);let GA=0;for(let yA=0;yA<Vt.length;++yA){const OA=Vt[yA],XA=xe[yA];for(let pr=0;pr<OA.length;++pr)XA[OA[pr]].data.set(Ye[GA++].data)}return{inputs_embeds:xe,attention_mask:Ht}}async function pt(le,{input_ids:xe=null,attention_mask:Ye=null,pixel_values:Et=null,position_ids:Ht=null,inputs_embeds:Vt=null,past_key_values:rA=null,generation_config:DA=null,logits_processor:GA=null,...yA}){if(!Vt){if(Vt=await le.encode_text({input_ids:xe,...yA}),Et&&xe.dims[1]!==1){const OA=await le.encode_image({pixel_values:Et,...yA});({inputs_embeds:Vt,attention_mask:Ye}=le._merge_input_ids_with_image_features({image_features:OA,inputs_embeds:Vt,input_ids:xe,attention_mask:Ye}))}else if(rA&&Et&&xe.dims[1]===1){const OA=xe.dims[1],XA=Object.values(rA)[0].dims.at(-2);Ye=(0,D.cat)([(0,D.ones)([xe.dims[0],XA]),Ye.slice(null,[Ye.dims[1]-OA,Ye.dims[1]])],1)}}if(!Ht&&le.config.model_type==="qwen2_vl"){const{image_grid_thw:OA,video_grid_thw:XA}=yA;[Ht]=le.get_rope_index(xe,OA,XA,Ye)}return await Ue(le,{inputs_embeds:Vt,past_key_values:rA,attention_mask:Ye,position_ids:Ht,generation_config:DA,logits_processor:GA},!0)}function ct(le,xe=0){const[Ye,Et]=le.dims,Ht=le.data,Vt=new BigInt64Array(Ht.length);for(let rA=0;rA<Ye;++rA){const DA=rA*Et;let GA=BigInt(xe);for(let yA=0;yA<Et;++yA){const OA=DA+yA;Ht[OA]===0n?Vt[OA]=BigInt(1):(Vt[OA]=GA,GA+=Ht[OA])}}return{data:Vt,dims:le.dims}}function je(le,xe,Ye,Et){if(Ye.past_key_values){const Ht=Object.values(Ye.past_key_values)[0].dims.at(-2),{input_ids:Vt,attention_mask:rA}=Ye;if(!(rA&&rA.dims[1]>Vt.dims[1])){if(Ht<Vt.dims[1])Ye.input_ids=Vt.slice(null,[Ht,null]);else if(le.config.image_token_index!=null&&Vt.data.some(DA=>DA==le.config.image_token_index)){const DA=le.config.num_image_tokens;if(!DA)throw new Error("`num_image_tokens` is missing in the model configuration.");const GA=Vt.dims[1]-(Ht-DA);Ye.input_ids=Vt.slice(null,[-GA,null]),Ye.attention_mask=(0,D.ones)([1,Ht+GA])}}}return Ye}function At(le,xe,Ye,Et){return Ye.past_key_values&&(xe=xe.map(Ht=>[Ht.at(-1)])),{...Ye,decoder_input_ids:we(xe)}}function It(le,...xe){return le.config.is_encoder_decoder?At(le,...xe):je(le,...xe)}function wt(le,xe,Ye,Et){const Ht=!!Ye.past_key_values;
2805return Et.guidance_scale!==null&&Et.guidance_scale>1&&(Ht?Ye.input_ids=(0,D.cat)([Ye.input_ids,Ye.input_ids],0):(Ye.input_ids=(0,D.cat)([Ye.input_ids,(0,D.full_like)(Ye.input_ids,BigInt(Et.pad_token_id))],0),Ye.attention_mask=(0,D.cat)([Ye.attention_mask,(0,D.full_like)(Ye.attention_mask,0n)],0))),!Ht&&Ye.pixel_values||(Ye.pixel_values=(0,D.full)([0,0,3,384,384],1)),Ht&&(Ye.images_seq_mask=new D.Tensor("bool",new Array(1).fill(!0).fill(!1,0,1),[1,1]),Ye.images_emb_mask=new D.Tensor("bool",new Array(0).fill(!1),[1,1,0])),Ye}class ze extends C.Callable{constructor(Ye,Et,Ht){super();Ct(this,"main_input_name","input_ids");Ct(this,"forward_params",["input_ids","attention_mask"]);this.config=Ye,this.sessions=Et,this.configs=Ht;const Vt=Ce.get(this.constructor),rA=ce.get(Vt);switch(this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,rA){case _:this.can_generate=!0,this._forward=Ue,this._prepare_inputs_for_generation=je;break;case e:case d:case F:this.can_generate=!0,this._forward=me,this._prepare_inputs_for_generation=At;break;case v:this._forward=me;break;case S:this.can_generate=!0,this._forward=pt,this._prepare_inputs_for_generation=It;break;case he:this.can_generate=!0,this._prepare_inputs_for_generation=It;break;case Ie:this.can_generate=!0,this._prepare_inputs_for_generation=wt;break;default:this._forward=at}this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){var Et;const Ye=[];for(const Ht of Object.values(this.sessions))(Et=Ht==null?void 0:Ht.handler)!=null&&Et.dispose&&Ye.push(Ht.handler.dispose());return await Promise.all(Ye)}static async from_pretrained(Ye,{progress_callback:Et=null,config:Ht=null,cache_dir:Vt=null,local_files_only:rA=!1,revision:DA="main",model_file_name:GA=null,subfolder:yA="onnx",device:OA=null,dtype:XA=null,use_external_data_format:pr=null,session_options:cs={}}={}){let br={progress_callback:Et,config:Ht,cache_dir:Vt,local_files_only:rA,revision:DA,model_file_name:GA,subfolder:yA,device:OA,dtype:XA,use_external_data_format:pr,session_options:cs};const Mr=Ce.get(this),Pr=ce.get(Mr);let us;if(Ht=br.config=await o.AutoConfig.from_pretrained(Ye,br),Pr===_)us=await Promise.all([Fe(Ye,{model:br.model_file_name??"model"},br),Pe(Ye,{generation_config:"generation_config.json"},br)]);else if(Pr===e||Pr===d)us=await Promise.all([Fe(Ye,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},br),Pe(Ye,{generation_config:"generation_config.json"},br)]);else if(Pr===re)us=await Promise.all([Fe(Ye,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},br)]);else if(Pr===v)us=await Promise.all([Fe(Ye,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},br)]);else if(Pr===S){const Zs={embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"};Ht.is_encoder_decoder&&(Zs.model="encoder_model"),us=await Promise.all([Fe(Ye,Zs,br),Pe(Ye,{generation_config:"generation_config.json"},br)])}else if(Pr===F)us=await Promise.all([Fe(Ye,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},br),Pe(Ye,{generation_config:"generation_config.json"},br)]);else if(Pr===Ie)us=await Promise.all([Fe(Ye,{prepare_inputs_embeds:"prepare_inputs_embeds",model:"language_model",lm_head:"lm_head",gen_head:"gen_head",gen_img_embeds:"gen_img_embeds",image_decode:"image_decode"},br),Pe(Ye,{generation_config:"generation_config.json"},br)]);else if(Pr===he)us=await Promise.all([Fe(Ye,{prepare_inputs_embeds:"prepare_inputs_embeds",model:"model",vision_encoder:"vision_encoder"},br),Pe(Ye,{generation_config:"generation_config.json"},br)]);else{if(Pr!==g){const Zs=Mr??(Ht==null?void 0:Ht.model_type);Zs!=="custom"&&console.warn(`Model type for '${Zs}' not found, assuming encoder-only architecture. Please report this at ${E.GITHUB_ISSUE_URL}.`)}us=await Promise.all([Fe(Ye,{model:br.model_file_name??"model"},br)])}return new this(Ht,...us)}async _call(Ye){return await this.forward(Ye)}async forward(Ye){return await this._forward(this,Ye)}get generation_config(){var Ye;return((Ye=this.configs)==null?void 0:Ye.generation_config)??null}_get_logits_warper(Ye){const Et=new B.LogitsProcessorList;return Ye.temperature!==null&&Ye.temperature!==1&&Et.push(new B.TemperatureLogitsWarper(Ye.temperature)),Ye.top_k!==null&&Ye.top_k!==0&&Et.push(new B.TopKLogitsWarper(Ye.top_k)),Ye.top_p!==null&&Ye.top_p<1&&Et.push(new B.TopPLogitsWarper(Ye.top_p)),Et}_get_logits_processor(Ye,Et,Ht=null){const Vt=new B.LogitsProcessorList;if(Ye.repetition_penalty!==null&&Ye.repetition_penalty!==1&&Vt.push(new B.RepetitionPenaltyLogitsProcessor(Ye.repetition_penalty)),Ye.no_repeat_ngram_size!==null&&Ye.no_repeat_ngram_size>0&&Vt.push(new B.NoRepeatNGramLogitsProcessor(Ye.no_repeat_ngram_size)),Ye.bad_words_ids!==null&&Vt.push(new B.NoBadWordsLogitsProcessor(Ye.bad_words_ids,Ye.eos_token_id)),Ye.min_length!==null&&Ye.eos_token_id!==null&&Ye.min_length>0&&Vt.push(new B.MinLengthLogitsProcessor(Ye.min_length,Ye.eos_token_id)),Ye.min_new_tokens!==null&&Ye.eos_token_id!==null&&Ye.min_new_tokens>0&&Vt.push(new B.MinNewTokensLengthLogitsProcessor(Et,Ye.min_new_tokens,Ye.eos_token_id)),Ye.forced_bos_token_id!==null&&Vt.push(new B.ForcedBOSTokenLogitsProcessor(Ye.forced_bos_token_id)),Ye.forced_eos_token_id!==null&&Vt.push(new B.ForcedEOSTokenLogitsProcessor(Ye.max_length,Ye.forced_eos_token_id)),Ye.begin_suppress_tokens!==null){const rA=Et>1||Ye.forced_bos_token_id===null?Et:Et+1;Vt.push(new B.SuppressTokensAtBeginLogitsProcessor(Ye.begin_suppress_tokens,rA))}return Ye.guidance_scale!==null&&Ye.guidance_scale>
28051&&Vt.push(new B.ClassifierFreeGuidanceLogitsProcessor(Ye.guidance_scale)),Ht!==null&&Vt.extend(Ht),Vt}_prepare_generation_config(Ye,Et,Ht=G.GenerationConfig){const Vt={...this.config};for(const DA of["decoder","generator","text_config"])DA in Vt&&Object.assign(Vt,Vt[DA]);const rA=new Ht(Vt);return Object.assign(rA,this.generation_config??{}),Ye&&Object.assign(rA,Ye),Et&&Object.assign(rA,(0,b.pick)(Et,Object.getOwnPropertyNames(rA))),rA}_get_stopping_criteria(Ye,Et=null){const Ht=new W.StoppingCriteriaList;return Ye.max_length!==null&&Ht.push(new W.MaxLengthCriteria(Ye.max_length,this.config.max_position_embeddings??null)),Ye.eos_token_id!==null&&Ht.push(new W.EosTokenCriteria(Ye.eos_token_id)),Et&&Ht.extend(Et),Ht}_validate_model_class(){if(!this.can_generate){const Ye=[jc,Wc,Rc,Hc],Et=Ce.get(this.constructor),Ht=new Set,Vt=this.config.model_type;for(const DA of Ye){const GA=DA.get(Vt);GA&&Ht.add(GA[0])}let rA=`The current model class (${Et}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw Ht.size>0&&(rA+=` Please use the following class instead: ${[...Ht].join(", ")}`),Error(rA)}}prepare_inputs_for_generation(...Ye){return this._prepare_inputs_for_generation(this,...Ye)}_update_model_kwargs_for_generation({generated_input_ids:Ye,outputs:Et,model_inputs:Ht,is_encoder_decoder:Vt}){return Ht.past_key_values=this.getPastKeyValues(Et,Ht.past_key_values),Ht.input_ids=new D.Tensor("int64",Ye.flat(),[Ye.length,1]),Vt||(Ht.attention_mask=(0,D.cat)([Ht.attention_mask,(0,D.ones)([Ht.attention_mask.dims[
28050],1])],1)),Ht.position_ids=null,Ht}_prepare_model_inputs({inputs:Ye,bos_token_id:Et,model_kwargs:Ht}){const Vt=(0,b.pick)(Ht,this.forward_params),rA=this.main_input_name;if(rA in Vt){if(Ye)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else Vt[rA]=Ye;return{inputs_tensor:Vt[rA],model_inputs:Vt,model_input_name:rA}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:Ye,model_inputs:Et,model_input_name:Ht,generation_config:Vt}){if(this.sessions.model.inputNames.includes("inputs_embeds")&&!Et.inputs_embeds&&"_prepare_inputs_embeds"in this){const{input_ids:DA,pixel_values:GA,attention_mask:yA,...OA}=Et,XA=await this._prepare_inputs_embeds(Et);Et={...OA,...(0,b.pick)(XA,["inputs_embeds","attention_mask"])}}let{last_hidden_state:rA}=await at(this,Et);if(Vt.guidance_scale!==null&&Vt.guidance_scale>1)rA=(0,D.cat)([rA,(0,D.full_like)(rA,0)],0),"attention_mask"in Et&&(Et.attention_mask=(0,D.cat)([Et.attention_mask,(0,D.zeros_like)(Et.attention_mask)],0));else if(Et.decoder_input_ids){const DA=we(Et.decoder_input_ids).dims[0];if(DA!==rA.dims[0]){if(rA.dims[0]!==1)throw new Error(`The encoder outputs have a different batch size (${rA.dims[0]}) than the decoder inputs (${DA}).`);rA=(0,D.cat)(Array.from({length:DA},()=>rA),0)}}return Et.encoder_outputs=rA,Et}_prepare_decoder_input_ids_for_generation({batch_size:Ye,model_input_name:Et,model_kwargs:Ht,decoder_start_token_id:Vt,bos_token_id:rA,generation_config:DA}){let{decoder_input_ids:GA,...yA}=Ht;if(!(GA instanceof D.Tensor)){if(GA)Array.isArray(GA[0])||(GA=Array.from({length:Ye},()=>GA));else if(Vt??(Vt=rA),this.config.model_type==="musicgen")GA=Array.from({length:Ye*this.config.decoder.num_codebooks},()=>[Vt]);else if(Array.isArray(Vt)){if(Vt.length!==Ye)throw new Error(`\`decoder_start_token_id\` expcted to have length ${Ye} but got ${Vt.length}`);GA=Vt}else GA=Array.from({length:Ye},()=>[Vt]);GA=we(GA)}return Ht.decoder_attention_mask=(0,D.ones_like)(GA),{input_ids:GA,model_inputs:yA}}async generate({inputs:Ye=null,generation_config:Et=null,logits_processor:Ht=null,stopping_criteria:Vt=null,streamer:rA=null,...DA}){this._validate_model_class(),Et=this._prepare_generation_config(Et,DA);let{inputs_tensor:GA,model_inputs:yA,model_input_name:OA}=this._prepare_model_inputs({inputs:Ye,model_kwargs:DA});const XA=this.config.is_encoder_decoder;let pr;XA&&("encoder_outputs"in yA||(yA=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:GA,model_inputs:yA,model_input_name:OA,generation_config:Et}))),XA?{input_ids:pr,model_inputs:yA}=this._prepare_decoder_input_ids_for_generation({batch_size:yA[OA].dims.at(0),model_input_name:OA,model_kwargs:yA,decoder_start_token_id:Et.decoder_start_token_id,bos_token_id:Et.bos_token_id,generation_config:Et}):pr=yA[OA];let cs=pr.dims.at(-1);Et.max_new_tokens!==null&&(Et.max_length=cs+Et.max_new_tokens);const br=this._get_logits_processor(Et,cs,Ht),Mr=this._get_stopping_criteria(Et,Vt),Pr=yA[OA].dims.at(0),us=U.LogitsSampler.getSampler(Et),Zs=new Array(Pr).fill(0),ia=pr.tolist();let Fs;rA&&rA.put(ia);let Xa={};for(;;){if(yA=this.prepare_inputs_for_generation(ia,yA,Et),Fs=await this.forward(yA),Et.output_attentions&&Et.return_dict_in_generate){const vs=this.getAttentions(Fs);for(const si in vs)si in Xa||(Xa[si]=[]),Xa[si].push(vs[si])}const _a=br(ia,Fs.logits.slice(null,-1,null)),Ii=[];for(let vs=0;vs<_a.dims.at(0);++vs){const si=_a[vs],T0=await us(si);for(const[X0,Q0]of T0){const Ul=BigInt(X0);Zs[vs]+=Q0,ia[vs].push(Ul),Ii.push([Ul]);break}}if(rA&&rA.put(Ii),Mr(ia).every(vs=>vs))break;yA=this._update_model_kwargs_for_generation({generated_input_ids:Ii,outputs:Fs,model_inputs:yA,is_encoder_decoder:XA})}rA&&rA.end();const Oa=this.getPastKeyValues(Fs,yA.past_key_values,!0),Fa=new D.Tensor("int64",ia.flat(),[ia.length,ia[0].length]);if(Et.return_dict_in_generate)return{sequences:Fa,past_key_values:Oa,...Xa};for(const _a of Object.values(Fs))_a.location==="gpu-buffer"&&_a.dispose();return Fa}getPastKeyValues(Ye,Et,Ht=!1){const Vt=Object.create(null);for(const rA in Ye)if(rA.startsWith("present")){const DA=rA.replace("present","past_key_values"),GA=rA.includes("encoder");if(Vt[DA]=GA&&Et?Et[DA]:Ye[rA],Et&&(!GA||Ht)){const yA=Et[DA];
2805yA.location==="gpu-buffer"&&yA.dispose()}}return Vt}getAttentions(Ye){const Et={};for(const Ht of["cross_attentions","encoder_attentions","decoder_attentions"])for(const Vt in Ye)Vt.startsWith(Ht)&&(Ht in Et||(Et[Ht]=[]),Et[Ht].push(Ye[Vt]));return Et}addPastKeyValues(Ye,Et){var Ht,Vt,rA;if(Et)Object.assign(Ye,Et);else{const DA=this.sessions.decoder_model_merged??this.sessions.model,GA=((Ht=DA==null?void 0:DA.config)==null?void 0:Ht.kv_cache_dtype)??"float32",yA=GA==="float16"?new Uint16Array:[],OA=((rA=(Vt=Ye[this.main_input_name]??Ye.attention_mask)==null?void 0:Vt.dims)==null?void 0:rA[0])??1,XA=(0,o.getKeyValueShapes)(this.config,{batch_size:OA});for(const pr in XA)Ye[pr]=new D.Tensor(GA,yA,XA[pr])}}async encode_image({pixel_values:Ye}){const Et=(await de(this.sessions.vision_encoder,{pixel_values:Ye})).image_features;return this.config.num_image_tokens||(console.warn(`The number of image tokens was not set in the model configuration. Setting it to the number of features detected by the vision encoder (${Et.dims[1]}).`),this.config.num_image_tokens=Et.dims[1]),Et}async encode_text({input_ids:Ye}){return(await de(this.sessions.embed_tokens,{input_ids:Ye})).inputs_embeds}}class rt{}class Bt extends rt{constructor({last_hidden_state:xe,hidden_states:Ye=null,attentions:Et=null}){super(),this.last_hidden_state=xe,this.hidden_states=Ye,this.attentions=Et}}class Ot extends ze{}class St extends Ot{}class oe extends Ot{async _call(xe){return new _n(await super._call(xe))}}class zt extends Ot{async _call(xe){return new xr(await super._call(xe))}}class ke extends Ot{async _call(xe){return new kn(await super._call(xe))}}class fe extends Ot{async _call(xe){return new ri(await super._call(xe))}}class Qe extends ze{}class Je extends Qe{}class st extends Qe{async _call(xe){return new _n(await super._call(xe))}}class lt extends Qe{async _call(xe){return new xr(await super._call(xe))}}class xt extends Qe{async _call(xe){return new kn(await super._call(xe))}}class jt extends ze{}class $t extends jt{}class Ft extends ze{}class dA extends Ft{}class eA extends Ft{async _call(xe){return new _n(await super._call(xe))}}class nA extends Ft{async _call(xe){return new xr(await super._call(xe))}}class aA extends Ft{async _call(xe){return new kn(await super._call(xe))}}class bA extends Ft{async _call(xe){return new ri(await super._call(xe))}}class vA extends ze{}class Gt extends vA{}class mA extends vA{async _call(xe){return new _n(await super._call(xe))}}class AA extends vA{async _call(xe){return new xr(await super._call(xe))}}class gr extends vA{async _call(xe){return new kn(await super._call(xe))}}class CA extends vA{async _call(xe){return new ri(await super._call(xe))}}class NA extends ze{}class tA extends NA{}class iA extends NA{async _call(xe){return new _n(await super._call(xe))}}class qt extends NA{async _call(xe){return new xr(await super._call(xe))}}class zA extends NA{async _call(xe){return new kn(await super._call(xe))}}class Fr extends NA{async _call(xe){return new ri(await super._call(xe))}}class Vr extends ze{}class Tt extends Vr{}class MA extends Vr{async _call(xe){return new _n(await super._call(xe))}}class QA extends Vr{async _call(xe){return new xr(await super._call(xe))}}class ZA extends Vr{async _call(xe){return new kn(await super._call(xe))}}class FA extends Vr{async _call(xe){return new ri(await super._call(xe))}}class EA extends ze{}class Rr extends EA{}class Er extends EA{async _call(xe){return new _n(await super._call(xe))}}class Dr extends EA{async _call(xe){return new xr(await super._call(xe))}}class Vs extends EA{async _call(xe){return new kn(await super._call(xe))}}class nr extends EA{async _call(xe){return new ri(await super._call(xe))}}class qs extends ze{}class xa extends qs{}class Ja extends qs{async _call(xe){return new _n(await super._call(xe))}}class ma extends qs{async _call(xe){return new xr(await super._call(xe))}}class Qa extends qs{async _call(xe){return new kn(await super._call(xe))}}class $a extends qs{async _call(xe){return new ri(await super._call(xe))}}class ds extends ze{}class en extends ds{}class oa extends ds{async _call(xe){return new xr(await super._call(xe))}}class Yn extends ds{async _call(xe){return new kn(await super._call(xe))}}class Ma extends ds{async _call(xe){return new ri(await super._call(xe))}}class Br extends ds{async _call(xe){return new _n(await super._call(xe))}}class Hr extends ze{}class cn extends Hr{}class Rn extends Hr{async _call(xe){return new _n(await super._call(xe))}}class Oi extends Hr{async _call(xe){return new xr(await super._call(xe))}}class Ra extends Hr{async _call(xe){return new kn(await super._call(xe))}}class jn extends ze{}class Tn extends jn{}class ja extends jn{async _call(xe){return new _n(await super._call(xe))}}class $A extends jn{async _call(xe){return new xr(await super._call(xe))}}class fr extends jn{async _call(xe){return new ri(await super._call(xe))}}class la extends ze{}class vi extends la{}class Pa extends la{async _call(xe){return new _n(await super._call(xe))}}class ci extends la{async _call(xe){return new xr(await super._call(xe))}}class tn extends la{async _call(xe){return new kn(await super._call(xe))}}class Wa extends la{async _call(xe){return new ri(await super._call(xe))}}class _t extends ze{}class Ee extends _t{}class qe extends _t{async _call(xe){return new _n(await super._call(xe))}}class ut extends _t{async _call(xe){return new xr(await super._call(xe))}}class mt extends _t{async _call(xe){return new ri(await super._call(xe))}}class vt extends ze{}class Rt extends vt{}class lA extends vt{async _call(xe){return new xr(await super._call(xe))}}class gt extends vt{async _call(xe){return new ri(await super._call(xe))}}class pA extends vt{async _call(xe){return new _n(await super._call(xe))}}class oA extends ze{constructor(){super(...arguments);Ct(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"])}}class sr extends oA{}class jr extends oA{}class Gr extends ze{}class Or extends Gr{}class ir extends Gr{}class IA extends ze{}class _A extends IA{}class _r extends IA{}class ba extends ze{}class Bs extends ba{}class ts extends ba{}class PA extends ba{async _call(xe){return new xr(await super._call(xe))}}class Kr extends ze{}class Yt extends Kr{}class TA extends Kr{}class Js extends Kr{async _call(xe){return new xr(await super._call(xe))}}class Ss extends Kr{}class Ds extends ze{}class ks extends Ds{}class os extends Ds{}class Tr extends ze{}class As extends Tr{}class yi extends Tr{}class Da extends ze{}class Qn extends Da{}class $s extends Da{async _call(xe){return new _n(await super._call(xe))}}class Ei extends Da{async _call(xe){return new xr(await super._call(xe))}}class or extends Da{async _call(xe){return new kn(await super._call(xe))}}class ea extends Da{async _call(xe){return new ri(await super._call(xe))}}class ta extends ze{}class Fo extends ta{}class io extends ta{async _call(xe){return new _n(await super._call(xe))}}class ui extends ta{async _call(xe){return new xr(await super._call(xe))}}class Fi extends ta{async _call(xe){return new kn(await super._call(xe))}}
2805class Wn extends ta{async _call(xe){return new ri(await super._call(xe))}}class Un extends ze{}class Lo extends Un{}class vn extends Un{async _call(xe){return new _n(await super._call(xe))}}class Sa extends Un{async _call(xe){return new xr(await super._call(xe))}}class Ns extends Un{async _call(xe){return new kn(await super._call(xe))}}class BA extends Un{async _call(xe){return new ri(await super._call(xe))}}class Aa extends ze{}class Li extends Aa{}class Vi extends Aa{}class Go extends ze{constructor(){super(...arguments);Ct(this,"requires_attention_mask",!1);Ct(this,"main_input_name","input_features");Ct(this,"forward_params",["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"])}}class Kn extends Go{}class Vo extends Go{_prepare_generation_config(xe,Ye){return super._prepare_generation_config(xe,Ye,K.WhisperGenerationConfig)}_retrieve_init_tokens(xe){const Ye=[xe.decoder_start_token_id];let Et=xe.language;const Ht=xe.task;if(xe.is_multilingual){Et||(console.warn("No language specified - defaulting to English (en)."),Et="en");const Vt=`<|${(0,J.whisper_language_to_code)(Et)}|>`;Ye.push(xe.lang_to_id[Vt]),Ye.push(xe.task_to_id[Ht??"transcribe"])}else if(Et||Ht)throw new Error("Cannot specify `task` or `language` for an English-only model. If the model is intended to be multilingual, pass `is_multilingual=true` to generate, or update the generation config.");return!xe.return_timestamps&&xe.no_timestamps_token_id&&Ye.at(-1)!==xe.no_timestamps_token_id?Ye.push(xe.no_timestamps_token_id):xe.return_timestamps&&Ye.at(-1)===xe.no_timestamps_token_id&&(console.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`."),Ye.pop()),Ye.filter(Vt=>Vt!=null)}async generate({inputs:xe=null,generation_config:Ye=null,logits_processor:Et=null,stopping_criteria:Ht=null,...Vt}){Ye=this._prepare_generation_config(Ye,Vt);const rA=Vt.decoder_input_ids??this._retrieve_init_tokens(Ye);if(Ye.return_timestamps&&(Et??(Et=new B.LogitsProcessorList),Et.push(new B.WhisperTimeStampLogitsProcessor(Ye,rA))),Ye.begin_suppress_tokens&&(Et??(Et=new B.LogitsProcessorList),Et.push(new B.SuppressTokensAtBeginLogitsProcessor(Ye.begin_suppress_tokens,rA.length))),Ye.return_token_timestamps){if(!Ye.alignment_heads)throw new Error("Model generation config has no `alignment_heads`, token-level timestamps not available. See https://gist.github.com/hollance/42e32852f24243b748ae6bc1f985b13a on how to add this property to the generation config.");Ye.task==="translate"&&console.warn("Token-level timestamps may not be reliable for task 'translate'."),Ye.output_attentions=!0,Ye.return_dict_in_generate=!0}const DA=await super.generate({inputs:xe,generation_config:Ye,logits_processor:Et,decoder_input_ids:rA,...Vt});return Ye.return_token_timestamps&&(DA.token_timestamps=this._extract_token_timestamps(DA,Ye.alignment_heads,Ye.num_frames)),DA}_extract_token_timestamps(xe,Ye,Et=null,Ht=.02){if(!xe.cross_attentions)throw new Error("Model outputs must contain cross attentions to extract timestamps. This is most likely because the model was not exported with `output_attentions=True`.");Et==null&&console.warn("`num_frames` has not been set, meaning the entire audio will be analyzed. This may lead to inaccurate token-level timestamps for short audios (< 30 seconds).");let Vt=this.config.median_filter_width;Vt===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),Vt=7);const rA=xe.cross_attentions,DA=Array.from({length:this.config.decoder_layers},(Mr,Pr)=>(0,D.cat)(rA.map(us=>us[Pr]),2)),GA=(0,D.stack)(Ye.map(([Mr,Pr])=>{if(Mr>=DA.length)throw new Error(`Layer index ${Mr} is out of bounds for cross attentions (length ${DA.length}).`);return Et?DA[Mr].slice(null,Pr,null,[0,Et]):DA[Mr].slice(null,Pr)})).transpose(1,0,2,3),[yA,OA]=(0,D.std_mean)(GA,-2,0,!0),XA=GA.clone();for(let Mr=0;Mr<XA.dims[0];++Mr){const Pr=XA[Mr];for(let us=0;us<Pr.dims[0];++us){const Zs=Pr[us],ia=yA[Mr][us][0].data,Fs=OA[Mr][us][0].data;for(let Xa=0;Xa<Zs.dims[0];++Xa){let Oa=Zs[Xa].data;for(let Fa=0;Fa<Oa.length;++Fa)Oa[Fa]=(Oa[Fa]-Fs[Fa])/ia[Fa];Oa.set((0,V.medianFilter)(Oa,Vt))}}}const pr=[(0,D.mean)(XA,1)],cs=xe.sequences.dims,br=new D.Tensor("float32",new Float32Array(cs[0]*cs[1]),cs);for(let Mr=0;Mr<cs[0];++Mr){const Pr=pr[Mr].neg().squeeze_(0),[us,Zs]=(0,V.dynamic_time_warping)(Pr.tolist()),ia=Array.from({length:us.length-1},(Oa,Fa)=>us[Fa+1]-us[Fa]),Fs=(0,b.mergeArrays)([1],ia).map(Oa=>!!Oa),Xa=[];for(let Oa=0;Oa<Fs.length;++Oa)Fs[Oa]&&Xa.push(Zs[Oa]*Ht);br[Mr].data.set(Xa,1)}return br}}class Hi extends ze{constructor(){super(...arguments);Ct(this,"requires_attention_mask",!1);
2805Ct(this,"main_input_name","input_values");Ct(this,"forward_params",["input_values","decoder_input_ids","past_key_values"])}}class Bi extends Hi{}class ca extends Hi{}class Wr extends ze{constructor(){super(...arguments);Ct(this,"main_input_name","pixel_values");Ct(this,"forward_params",["pixel_values","decoder_input_ids","encoder_hidden_states","past_key_values"])}}class xi extends ze{constructor(){super(...arguments);Ct(this,"forward_params",["input_ids","attention_mask","pixel_values","position_ids","past_key_values"])}}class Yi extends xi{_merge_input_ids_with_image_features({inputs_embeds:xe,image_features:Ye,input_ids:Et,attention_mask:Ht}){const Vt=this.config.image_token_index,rA=Et.tolist().map(XA=>XA.findIndex(pr=>pr==Vt)),DA=rA.every(XA=>XA===-1),GA=rA.every(XA=>XA!==-1);if(!DA&&!GA)throw new Error("Every input should contain either 0 or 1 image token.");if(DA)return{inputs_embeds:xe,attention_mask:Ht};const yA=[],OA=[];for(let XA=0;XA<rA.length;++XA){const pr=rA[XA],cs=xe[XA],br=Ye[XA],Mr=Ht[XA];yA.push((0,D.cat)([cs.slice([0,pr]),br,cs.slice([pr+1,cs.dims[0]])],0)),OA.push((0,D.cat)([Mr.slice([0,pr]),(0,D.ones)([br.dims[0]]),Mr.slice([pr+1,Mr.dims[0]])],0))}return{inputs_embeds:(0,D.stack)(yA,0),attention_mask:(0,D.stack)(OA,0)}}}class Gs extends Yi{}class fi extends Yi{}class An extends ze{constructor(){super(...arguments);Ct(this,"forward_params",["input_ids","inputs_embeds","attention_mask","pixel_values","encoder_outputs","decoder_input_ids","decoder_inputs_embeds","decoder_attention_mask","past_key_values"]);Ct(this,"main_input_name","inputs_embeds")}}class Zn extends An{_merge_input_ids_with_image_features({inputs_embeds:xe,image_features:Ye,input_ids:Et,attention_mask:Ht}){return{inputs_embeds:(0,D.cat)([Ye,xe],1),attention_mask:(0,D.cat)([(0,D.ones)(Ye.dims.slice(0,2)),Ht],1)}}async _prepare_inputs_embeds({input_ids:xe,pixel_values:Ye,inputs_embeds:Et,attention_mask:Ht}){if(!xe&&!Ye)throw new Error("Either `input_ids` or `pixel_values` should be provided.");let Vt,rA;return xe&&(Vt=await this.encode_text({input_ids:xe})),Ye&&(rA=await this.encode_image({pixel_values:Ye})),Vt&&rA?{inputs_embeds:Et,attention_mask:Ht}=this._merge_input_ids_with_image_features({inputs_embeds:Vt,image_features:rA,input_ids:xe,attention_mask:Ht}):Et=Vt||rA,{inputs_embeds:Et,attention_mask:Ht}}async forward({input_ids:xe,pixel_values:Ye,attention_mask:Et,decoder_input_ids:Ht,decoder_attention_mask:Vt,encoder_outputs:rA,past_key_values:DA,inputs_embeds:GA,decoder_inputs_embeds:yA}){if(GA||({inputs_embeds:GA,attention_mask:Et}=await this._prepare_inputs_embeds({input_ids:xe,pixel_values:Ye,inputs_embeds:GA,attention_mask:Et})),!rA){let{last_hidden_state:XA}=await at(this,{inputs_embeds:GA,attention_mask:Et});rA=XA}if(!yA){if(!Ht)throw new Error("Either `decoder_input_ids` or `decoder_inputs_embeds` should be provided.");yA=await this.encode_text({input_ids:Ht})}return await Ue(this,{inputs_embeds:yA,attention_mask:Vt,encoder_attention_mask:Et,encoder_hidden_states:rA,past_key_values:DA},!0)}}class Mi extends ze{constructor(){super(...arguments);Ct(this,"forward_params",["input_ids","attention_mask","pixel_values","position_ids","past_key_values"])}}class Pn extends Mi{_merge_input_ids_with_image_features(xe){const Ye=xe.image_features.dims.at(-1),Et=xe.image_features.view(-1,Ye);return ot({image_token_id:this.config.image_token_index,...xe,image_features:Et})}}class oo extends ze{constructor(){super(...arguments);Ct(this,"forward_params",["input_ids","attention_mask","pixel_values","pixel_attention_mask","position_ids","past_key_values"])}}class _o extends oo{async encode_image({pixel_values:xe,pixel_attention_mask:Ye}){return(await de(this.sessions.vision_encoder,{pixel_values:xe,pixel_attention_mask:Ye})).image_features}_merge_input_ids_with_image_features(xe){const Ye=xe.image_features.dims.at(-1),Et=xe.image_features.view(-1,Ye);return ot({image_token_id:this.config.image_token_id,...xe,image_features:Et})}}class Sn extends ze{constructor(){super(...arguments);Ct(this,"forward_params",["input_ids","inputs_embeds","attention_mask","position_ids","pixel_values","image_sizes","past_key_values"])}}class uA extends Sn{async forward({input_ids:xe=null,attention_mask:Ye=null,pixel_values:Et=null,image_sizes:Ht=null,position_ids:Vt=null,inputs_embeds:rA=null,past_key_values:DA=null,generation_config:GA=null,logits_processor:yA=null,...OA}){if(!rA){let XA;if(Et&&xe.dims[1]!==1){if(!Ht)throw new Error("`image_sizes` must be provided when `pixel_values` is provided.");({image_features:XA}=await de(this.sessions.vision_encoder,{pixel_values:Et,image_sizes:Ht}))}else{const pr=this.config.normalized_config.hidden_size;XA=new D.Tensor("float32",[],[0,pr])}({inputs_embeds:rA}=await de(this.sessions.prepare_inputs_embeds,{input_ids:xe,image_features:XA}))}return await Ue(this,{inputs_embeds:rA,past_key_values:DA,attention_mask:Ye,position_ids:Vt,generation_config:GA,logits_processor:yA},!1)}}class yn extends ze{}class kA extends yn{}class lo extends yn{static async from_pretrained(xe,Ye={}){return super.from_pretrained(xe,{...Ye,model_file_name:Ye.model_file_name??"text_model"})}}class co extends yn{static async from_pretrained(xe,Ye={}){return super.from_pretrained(xe,{...Ye,model_file_name:Ye.model_file_name??"text_model"})}}class ra extends yn{static async from_pretrained(xe,Ye={}){return super.from_pretrained(xe,{...Ye,model_file_name:Ye.model_file_name??"vision_model"})}}class js extends yn{static async from_pretrained(xe,Ye={}){return super.from_pretrained(xe,{...Ye,model_file_name:Ye.model_file_name??"vision_model"})}}class Nn extends ze{}class di extends Nn{}class uo extends Nn{static async from_pretrained(xe,Ye={}){return super.from_pretrained(xe,{...Ye,model_file_name:Ye.model_file_name??"text_model"})}}class _s extends yn{static async from_pretrained(xe,Ye={}){return super.from_pretrained(xe,{...Ye,model_file_name:Ye.model_file_name??"vision_model"})}}class Xn extends ze{}class Ho extends Xn{}class En extends ze{}
2805class un extends En{async forward(xe){const Ye=!xe.input_ids,Et=!xe.pixel_values;if(Ye&&Et)throw new Error("Either `input_ids` or `pixel_values` should be provided.");if(Ye&&(xe.input_ids=(0,D.ones)([xe.pixel_values.dims[0],1])),Et){const{image_size:yA}=this.config.vision_config;xe.pixel_values=(0,D.full)([0,3,yA,yA],0)}const{text_embeddings:Ht,image_embeddings:Vt,l2norm_text_embeddings:rA,l2norm_image_embeddings:DA}=await super.forward(xe),GA={};return Ye||(GA.text_embeddings=Ht,GA.l2norm_text_embeddings=rA),Et||(GA.image_embeddings=Vt,GA.l2norm_image_embeddings=DA),GA}}class Ri extends En{static async from_pretrained(xe,Ye={}){return super.from_pretrained(xe,{...Ye,model_file_name:Ye.model_file_name??"text_model"})}}class Di extends En{static async from_pretrained(xe,Ye={}){return super.from_pretrained(xe,{...Ye,model_file_name:Ye.model_file_name??"vision_model"})}}class gi extends ze{}class wA extends gi{}class ua extends gi{}class To extends ze{}class qn extends To{}class fn extends To{}class fo extends ze{}class Yo extends fo{}class go extends fo{}class zn extends ze{}class ji extends zn{}class ho extends zn{}class Ts extends ze{}class Ro extends Ts{}class Jn extends Ts{}class rr extends ze{}class wr extends rr{}class Gi extends rr{}class Na extends ze{}class po extends Na{}class Ws extends Na{}class On extends ze{}class fa extends On{}class $n extends On{}class Wi extends ze{}class dn extends Wi{}class s0 extends Wi{}class Ui extends ze{}class Zr extends Ui{}class sa extends Ui{}class jo extends ze{}class Us extends jo{}class za extends jo{}class ei extends ze{}class mo extends ei{}class gn extends ei{}class Wo extends ze{}class Ki extends Wo{}class aa extends Wo{}class da extends ze{}class a0 extends da{}class bo extends da{}class hn extends ze{}class ti extends hn{}class Zi extends hn{}class rs extends ze{}class Uo extends rs{}class Xi extends rs{}class Ua extends ze{}class Ka extends Ua{}class _i extends Ua{}class pn extends ze{}class Bn extends pn{}class Co extends pn{}class rn extends ze{}class Ca extends rn{}class qi extends rn{}class Io extends ze{}class xn extends Io{}class n0 extends Io{}class Ks extends ze{}class Za extends Ks{}class Ia extends Ks{}class wo extends ze{constructor(){super(...arguments);Ct(this,"forward_params",["input_ids","attention_mask","position_ids","past_key_values","pixel_values","image_grid_thw"])}}class sn extends wo{get_rope_index(xe,Ye,Et,Ht){const{vision_config:Vt,image_token_id:rA,video_token_id:DA,vision_start_token_id:GA}=this.config,yA=Vt.spatial_merge_size??2,OA=[];if(Ye||Et){let XA=xe.tolist();Ht||(Ht=(0,D.ones_like)(xe));const pr=Ht.tolist(),cs=Array.from({length:3},Zs=>Array.from({length:xe.dims[0]},ia=>Array.from({length:xe.dims[1]},Fs=>1))),br=Ye?Ye.tolist():[],Mr=Et?Et.tolist():[];let Pr=0,us=0;for(let Zs=0;Zs<XA.length;++Zs){const ia=XA[Zs].filter((Ys,La)=>pr[Zs][La]==1),Fs=ia.reduce((Ys,La,wi)=>(La==GA&&Ys.push(wi),Ys),[]),Xa=Fs.map(Ys=>ia[Ys+1]),Oa=Xa.filter(Ys=>Ys==rA).length,Fa=Xa.filter(Ys=>Ys==DA).length;let _a=[],Ii=0,vs=Oa,si=Fa;for(let Ys=0;Ys<Xa.length;++Ys){const La=ia.findIndex((ao,Hn)=>Hn>Ii&&ao==rA),wi=ia.findIndex((ao,Hn)=>Hn>Ii&&ao==DA),P0=vs>0&&La!==-1?La:ia.length+1,p0=si>0&&wi!==-1?wi:ia.length+1;let Kl,oc,lc,cc;P0<p0?([oc,lc,cc]=br[Pr],++Pr,--vs,Kl=P0):([oc,lc,cc]=Mr[us],++us,--si,Kl=p0);const[Vu,uc,No]=[Number(oc),Math.floor(Number(lc)/yA),Math.floor(Number(cc)/yA)],ki=Kl-Ii,q0=_a.length>0?(0,V.max)(_a.at(-1))[0]+1:0;_a.push(Array.from({length:3*ki},(ao,Hn)=>q0+Hn%ki));const J0=ki+q0,S0=Vu*uc*No,n2=Array.from({length:S0},(ao,Hn)=>J0+Math.floor(Hn/(uc*No))),Hu=Array.from({length:S0},(ao,Hn)=>J0+Math.floor(Hn/No)%uc),i2=Array.from({length:S0},(ao,Hn)=>J0+Hn%No);_a.push([n2,Hu,i2].flat()),Ii=Kl+S0}if(Ii<ia.length){const Ys=_a.length>0?(0,V.max)(_a.at(-1))[0]+1:0,La=ia.length-Ii;_a.push(Array.from({length:3*La},(wi,P0)=>Ys+P0%La))}const T0=_a.reduce((Ys,La)=>Ys+La.length,0),X0=new Array(T0);let Q0=0;for(let Ys=0;Ys<3;++Ys)for(let La=0;La<_a.length;++La){const wi=_a[La],P0=wi.length/3;for(let p0=Ys*P0;p0<(Ys+1)*P0;++p0)X0[Q0++]=wi[p0]}let Ul=0;const Lu=pr[Zs];for(let Ys=0;Ys<Lu.length;++Ys)if(Lu[Ys]==1){for(let La=0;La<3;++La)cs[La][Zs][Ys]=X0[La*T0/3+Ul];++Ul}const B1=(0,V.max)(X0)[0];OA.push(B1+1-XA[Zs].length)}return[new D.Tensor("int64",cs.flat(1/0),[3,xe.dims[0],xe.dims[1]]),new D.Tensor("int64",OA,[OA.length,1])]}if(Ht){const{data:XA,dims:pr}=ct(Ht),cs=BigInt64Array.from({length:3*XA.length},(Mr,Pr)=>XA[Pr%XA.length]),br=Array.from({length:pr[0]},(Mr,Pr)=>(0,V.max)(XA.subarray(pr[1]*Pr,pr[1]*(Pr+1)))[0]+1n+BigInt(pr[1]));return[new D.Tensor("int64",cs,[3,...pr]),new D.Tensor("int64",br,[br.length,1])]}{const[XA,pr]=xe.dims,cs=BigInt64Array.from({length:3*XA*pr}
vendor: 5,822 bytes, line 2805
2805,(br,Mr)=>BigInt(Math.floor(Mr%pr/XA)));return[new D.Tensor("int64",cs,[3,...xe.dims]),(0,D.zeros)([XA,1])]}}async encode_image({pixel_values:xe,image_grid_thw:Ye}){return(await de(this.sessions.vision_encoder,{pixel_values:xe,grid_thw:Ye})).image_features}_merge_input_ids_with_image_features(xe){return ot({image_token_id:this.config.image_token_id,...xe})}prepare_inputs_for_generation(xe,Ye,Et){if(Ye.attention_mask&&!Ye.position_ids)if(Ye.past_key_values){Ye.pixel_values=null;const Ht=BigInt(Object.values(Ye.past_key_values)[0].dims.at(-2)),Vt=Ye.rope_deltas.map(rA=>Ht+rA);Ye.position_ids=(0,D.stack)([Vt,Vt,Vt],0)}else[Ye.position_ids,Ye.rope_deltas]=this.get_rope_index(Ye.input_ids,Ye.image_grid_thw,Ye.video_grid_thw,Ye.attention_mask);return Ye}}class mn extends ze{}class ss extends mn{}class bn extends mn{}class hi extends ze{}class pi extends hi{}class Mn extends hi{}class Ko extends ze{}class ko extends Ko{}class Dn extends Ko{}class mi extends ze{}class an extends mi{}class Cn extends mi{}class vo extends ze{}class Hs extends vo{}class vr extends vo{}class Ji extends ze{}class Qo extends Ji{}class wa extends Ji{async _call(xe){return new xr(await super._call(xe))}}class bi extends ze{}class Ai extends bi{}class In extends bi{async _call(xe){return new xr(await super._call(xe))}}class ka extends ze{}class Po extends ka{}class wn extends ze{}class $i extends wn{}class Ti extends wn{async _call(xe){return new xr(await super._call(xe))}}class gs extends ze{}class Zo extends gs{}class eo extends ze{}class Ga extends eo{}class i0 extends eo{async _call(xe){return new xr(await super._call(xe))}}class Ci extends ze{}class LA extends Ci{}class t extends ze{}class A extends t{}class r extends t{async _call(xe){return new xr(await super._call(xe))}}class n extends ze{}class i extends n{async _call(xe){return new y1(await super._call(xe))}}class l extends ze{}class h extends l{}class m extends l{async _call(xe){return new xr(await super._call(xe))}}class w extends ze{}class M extends w{}class y extends w{async _call(xe){return new xr(await super._call(xe))}}class P extends ze{}class R extends P{}class ee extends P{}class ae extends ze{}class ue extends ae{}class be extends ae{}class _e extends ze{}class Ve extends _e{}class $e extends _e{async _call(xe){return new xr(await super._call(xe))}}class it extends ze{}class ht extends it{}class yt extends it{async _call(xe){return new Wt(await super._call(xe))}}class Pt extends it{async _call(xe){return new cA(await super._call(xe))}}class Wt extends rt{constructor({logits:xe,pred_boxes:Ye}){super(),this.logits=xe,this.pred_boxes=Ye}}class cA extends rt{constructor({logits:xe,pred_boxes:Ye,pred_masks:Et}){super(),this.logits=xe,this.pred_boxes=Ye,this.pred_masks=Et}}class gA extends ze{}class SA extends gA{}class VA extends gA{async _call(xe){return new qA(await super._call(xe))}}class qA extends rt{constructor({logits:xe,pred_boxes:Ye}){super(),this.logits=xe,this.pred_boxes=Ye}}class Cr extends ze{}class Yr extends Cr{}class Xr extends Cr{async _call(xe){return new ls(await super._call(xe))}}class ls extends Wt{}class as extends ze{}class xs extends as{}class Ms extends as{async _call(xe){return new xr(await super._call(xe))}}class zs extends ze{}class Os extends zs{}class ga extends zs{async _call(xe){return new xr(await super._call(xe))}}class na extends ze{}class ha extends na{}class pa extends na{async _call(xe){return new xr(await super._call(xe))}}class Gn extends ze{}class Fn extends Gn{}class Ln extends Gn{async _call(xe){return new xr(await super._call(xe))}}class Vn extends ze{}class yo extends Vn{}class to extends Vn{}class Ao extends ze{}class Qi extends Ao{}class Xo extends Ao{}class o0 extends ze{}class l0 extends o0{}class ro extends ze{}class fl extends ro{}class dl extends ro{}class gl extends ro{}class m0 extends ze{}class hl extends m0{}class R0 extends ze{}class pl extends R0{}class b0 extends R0{}class j0 extends ze{}class ml extends j0{}class bl extends j0{}class C0 extends ze{}class Cl extends C0{}class W0 extends ze{}class Il extends W0{}class I0 extends W0{async _call(xe){return new xr(await super._call(xe))}}class U0 extends ze{}class wl extends U0{}class kl extends U0{async _call(xe){return new xr(await super._call(xe))}}class c0 extends ze{}class vl extends c0{}class Pi extends c0{async _call(xe){return new xr(await super._call(xe))}}class Si extends ze{}class so extends Si{}class Ni extends Si{async _call(xe){return new xr(await super._call(xe))}}class yl extends ze{}class El extends yl{}class u0 extends ze{}class Bl extends u0{}class xl extends u0{async _call(xe){return new Ml(await super._call(xe))}}class Ml extends rt{constructor({logits:xe,pred_boxes:Ye}){super(),this.logits=xe,this.pred_boxes=Ye}}class w0 extends ze{}class Dl extends w0{async get_image_embeddings({pixel_values:xe}){return await at(this,{pixel_values:xe})}async forward(xe){if(xe.image_embeddings&&xe.image_positional_embeddings||(xe={...xe,...await this.get_image_embeddings(xe)}),!xe.input_labels&&xe.input_points){const Et=xe.input_points.dims.slice(0,-1),Ht=Et.reduce((Vt,rA)=>Vt*rA,1);xe.input_labels=new D.Tensor("int64",new BigInt64Array(Ht).fill(1n),Et)}const Ye={image_embeddings:xe.image_embeddings,image_positional_embeddings:xe.image_positional_embeddings};return xe.input_points&&(Ye.input_points=xe.input_points),xe.input_labels&&(Ye.input_labels=xe.input_labels),xe.input_boxes&&(Ye.input_boxes=xe.input_boxes),await de(this.sessions.prompt_encoder_mask_decoder,Ye)}async _call(xe){return new Gl(await super._call(xe))}}class Gl extends rt{constructor({iou_scores:xe,pred_masks:Ye}){super(),this.iou_scores=xe,this.pred_masks=Ye}}class nn extends ze{}class k0 extends nn{}class _l extends nn{}class v0 extends ze{}class Tl extends v0{}class So extends v0{}class Eo extends ze{}
2805class Ql extends Eo{}class Pl extends Eo{async _call(xe){return new Z0(await super._call(xe))}}class y0 extends Eo{async _call(xe){return new xr(await super._call(xe))}}class Sl extends Eo{async _call(xe){return new kn(await super._call(xe))}}class E0 extends ze{}class Nl extends E0{}class zl extends E0{async _call(xe){return new kn(await super._call(xe))}}class B0 extends ze{}class Ol extends B0{}class f0 extends ze{}class Fl extends f0{}class d0 extends f0{async _call(xe){return new Z0(await super._call(xe))}}class Ll extends f0{async _call(xe){return new xr(await super._call(xe))}}class g0 extends ze{}class Vl extends g0{}class Hl extends g0{async _call(xe){return new Z0(await super._call(xe))}}class x0 extends g0{async _call(xe){return new xr(await super._call(xe))}}class Yl extends g0{async _call(xe){return new kn(await super._call(xe))}}class M0 extends ze{}class Rl extends M0{}class jl extends M0{async _call(xe){return new Z0(await super._call(xe))}}class G2 extends M0{async _call(xe){return new xr(await super._call(xe))}}class e1 extends ze{}class _2 extends Eo{}class T2 extends Eo{async _call(xe){return new Z0(await super._call(xe))}}class Q2 extends Eo{async _call(xe){return new xr(await super._call(xe))}}class K0 extends ze{}class P2 extends K0{}class S2 extends K0{async _call(xe){return new Z0(await super._call(xe))}}class N2 extends K0{async _call(xe){return new xr(await super._call(xe))}}class z2 extends K0{async _call(xe){return new v1(await super._call(xe))}}class O2 extends K0{async _call(xe){return new kn(await super._call(xe))}}class F2 extends ze{}class L2 extends F2{}class D0 extends ze{}class V2 extends D0{}class vc extends D0{}class yc extends D0{async generate_speech(xe,Ye,{threshold:Et=.5,minlenratio:Ht=0,maxlenratio:Vt=20,vocoder:rA=null}={}){const DA={input_ids:xe},{encoder_outputs:GA,encoder_attention_mask:yA}=await at(this,DA),OA=GA.dims[1]/this.config.reduction_factor,XA=Math.floor(OA*Vt),pr=Math.floor(OA*Ht),cs=this.config.num_mel_bins;let br=[],Mr=null,Pr=null,us=0;for(;;){++us;const Fs=Ge(!!Pr);let Xa;Xa=Pr?Pr.output_sequence_out:new D.Tensor("float32",new Float32Array(cs),[1,1,cs]);let Oa={use_cache_branch:Fs,output_sequence:Xa,encoder_attention_mask:yA,speaker_embeddings:Ye,encoder_hidden_states:GA};this.addPastKeyValues(Oa,Mr),Pr=await de(this.sessions.decoder_model_merged,Oa),Mr=this.getPastKeyValues(Pr,Mr);const{prob:Fa,spectrum:_a}=Pr;if(br.push(_a),us>=pr&&(Array.from(Fa.data).filter(Ii=>Ii>=Et).length>0||us>=XA))break}const Zs=(0,D.cat)(br),{waveform:ia}=await de(rA.sessions.model,{spectrogram:Zs});return{spectrogram:Zs,waveform:ia}}}class Ec extends ze{constructor(){super(...arguments);Ct(this,"main_input_name","spectrogram")}}class H2 extends ze{}class Y2 extends H2{}class sc extends ze{}class Bc extends sc{}class R2 extends sc{}class xc extends ze{}class Mc extends xc{}class j2 extends xc{}class Dc extends ze{}class Gc extends Dc{}class W2 extends Dc{}class ac extends ze{}class U2 extends ac{}class K2 extends ac{static async from_pretrained(xe,Ye={}){return super.from_pretrained(xe,{...Ye,model_file_name:Ye.model_file_name??"text_model"})}}class Z2 extends ac{static async from_pretrained(xe,Ye={}){return super.from_pretrained(xe,{...Ye,model_file_name:Ye.model_file_name??"audio_model"})}}class X2 extends ze{}class _c extends X2{async _call(xe){return new E1(await super._call(xe))}}class nc extends ze{}class t1 extends nc{}class q2 extends nc{}class J2 extends nc{}class Tc extends ze{}class $2 extends Tc{}class eu extends Tc{}class Qc extends ze{}class tu extends Qc{}class Au extends Qc{async _call(xe){return new xr(await super._call(xe))}}class Pc extends ze{}class ru extends Pc{}class A1 extends Pc{}class Sc extends ze{constructor(){super(...arguments);Ct(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"])}_apply_and_filter_by_delay_pattern_mask(Ye){const[Et,Ht]=Ye.dims,Vt=this.config.decoder.num_codebooks,rA=Ht-Vt;let DA=0;for(let OA=0;OA<Ye.size;++OA){if(Ye.data[OA]===this.config.decoder.pad_token_id)continue;const XA=OA%Ht-Math.floor(OA/Ht)%Vt;XA>0&&XA<=rA&&(Ye.data[DA++]=Ye.data[OA])}const GA=Math.floor(Et/Vt),yA=DA/(GA*Vt);return new D.Tensor(Ye.type,Ye.data.slice(0,DA),[GA,Vt,yA])}prepare_inputs_for_generation(Ye,Et,Ht){let Vt=structuredClone(Ye);for(let rA=0;rA<Vt.length;++rA)for(let DA=0;DA<Vt[rA].length;++DA)rA%this.config.decoder.num_codebooks>=DA&&(Vt[rA][DA]=BigInt(this.config.decoder.pad_token_id));return Ht.guidance_scale!==null&&Ht.guidance_scale>1&&(Vt=Vt.concat(Vt)),super.prepare_inputs_for_generation(Vt,Et,Ht)}async generate(Ye){const Et=await super.generate(Ye),Ht=this._apply_and_filter_by_delay_pattern_mask(Et).unsqueeze_(0),{audio_values:Vt}=await de(this.sessions.encodec_decode,{audio_codes:Ht});return Vt}}class Nc extends ze{}class su extends Nc{}class au extends Nc{async _call(xe){return new xr(await super._call(xe))}}class zc extends ze{}class nu extends zc{}class iu extends zc{async _call(xe){return new xr(await super._call(xe))}}class ic extends ze{}class ou extends ic{}class lu extends ic{async _call(xe){return new xr(await super._call(xe))}}class Oc extends ze{}class cu extends Oc{}class uu extends Oc{async _call(xe){return new xr(await super._call(xe))}}class fu extends ze{}class du extends fu{}class gu extends ze{}class hu extends gu{constructor(...Ye){super(...Ye);Ct(this,"forward_params",["input_ids","pixel_values","images_seq_mask","images_emb_mask","attention_mask","position_ids","past_key_values"]);this._generation_mode="text"}async forward(Ye){const Et=this._generation_mode??"text";let Ht;if(Et!=="text"&&Ye.past_key_values){const yA=this.sessions.gen_img_embeds,OA=(0,b.pick)({image_ids:Ye.input_ids},yA.inputNames);Ht=await de(yA,OA)}else{const yA=this.sessions.prepare_inputs_embeds,OA=(0,b.pick)(Ye,yA.inputNames);Ht=await de(yA,OA)}const Vt={...Ye,...Ht},rA=await Ue(this,Vt),DA=this.sessions[Et==="text"?"lm_head":"gen_head"];if(!DA)throw new Error(`Unable to find "${DA}" generation head`);const GA=await de(DA,(0,b.pick)(rA,DA.inputNames));return{...Ht,...rA,...GA}}async generate(Ye){return this._generation_mode="text",super.generate(Ye)}async generate_images(Ye){this._generation_mode="image";const Et=(Ye.inputs??Ye[this.main_input_name]).dims[1],Ht=(await super.generate(Ye)).slice(null,[Et,null]),Vt=this.sessions.image_decode,{decoded_image:rA}=await de(Vt,{generated_tokens:Ht}),DA=rA.add_(1).mul_(127.5).clamp_(0,255).to("uint8"),GA=[];for(const yA of DA){const OA=Q.RawImage.fromTensor(yA);GA.push(OA)}return GA}}class pu extends rt{constructor({char_logits:xe,bpe_logits:Ye,wp_logits:Et}){super(),this.char_logits=xe,this.bpe_logits=Ye,this.wp_logits=Et}get logits(){return[this.char_logits,this.bpe_logits,this.wp_logits]}}class mu extends ze{}class bu extends mu{async _call(xe){return new pu(await super._call(xe))}}class Fc extends ze{}class Cu extends Fc{}class Wl extends Fc{}class Lc extends ze{}class Vc extends Lc{}class Iu extends Lc{}class Is{static async from_pretrained(xe,{progress_callback:Ye=null,config:Et=null,cache_dir:Ht=null,local_files_only:Vt=!1,revision:rA="main",model_file_name:DA=null,subfolder:GA="onnx",device:yA=null,dtype:OA=null,use_external_data_format:XA=null,session_options:pr={}}={}){const cs={progress_callback:Ye,config:Et,cache_dir:Ht,local_files_only:Vt,revision:rA,model_file_name:DA,subfolder:GA,device:yA,dtype:OA,use_external_data_format:XA,session_options:pr};if(cs.config=await o.AutoConfig.from_pretrained(xe,cs),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(const br of this.MODEL_CLASS_MAPPINGS){const Mr=br.get(cs.config.model_type);if(Mr)return await Mr[1].from_pretrained(xe,cs)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${cs.config.model_type}", attempting to construct from base class.`),await ze.from_pretrained(xe,cs);throw Error(`Unsupported model type: ${cs.config.model_type}`)}}Ct(Is,"MODEL_CLASS_MAPPINGS",null),Ct(Is,"BASE_IF_FAIL",!1);const r1=new Map([["bert",["BertModel",St]],["modernbert",["ModernBertModel",Je]],["nomic_bert",["NomicBertModel",$t]],["roformer",["RoFormerModel",dA]],["electra",["ElectraModel",tA]],["esm",["EsmModel",cn]],["convbert",["ConvBertModel",Gt]],["camembert",["CamembertModel",Tt]],["deberta",["DebertaModel",Rr]],["deberta-v2",["DebertaV2Model",xa]],["mpnet",["MPNetModel",vi]],["albert",["AlbertModel",Rt]],["distilbert",["DistilBertModel",en]],["roberta",["RobertaModel",Qn]],["xlm",["XLMMo
2805del",Fo]],["xlm-roberta",["XLMRobertaModel",Lo]],["clap",["ClapModel",U2]],["clip",["CLIPModel",kA]],["clipseg",["CLIPSegModel",wA]],["chinese_clip",["ChineseCLIPModel",Ho]],["siglip",["SiglipModel",di]],["jina_clip",["JinaCLIPModel",un]],["mobilebert",["MobileBertModel",Tn]],["squeezebert",["SqueezeBertModel",Ee]],["wav2vec2",["Wav2Vec2Model",Ql]],["wav2vec2-bert",["Wav2Vec2BertModel",Rl]],["unispeech",["UniSpeechModel",Fl]],["unispeech-sat",["UniSpeechSatModel",Vl]],["hubert",["HubertModel",_2]],["wavlm",["WavLMModel",P2]],["audio-spectrogram-transformer",["ASTModel",Li]],["vits",["VitsModel",_c]],["pyannote",["PyAnnoteModel",Nl]],["wespeaker-resnet",["WeSpeakerResNetModel",Ol]],["detr",["DetrModel",ht]],["rt_detr",["RTDetrModel",SA]],["table-transformer",["TableTransformerModel",Yr]],["vit",["ViTModel",Qo]],["ijepa",["IJepaModel",Ai]],["pvt",["PvtModel",$i]],["vit_msn",["ViTMSNModel",Ga]],["vit_mae",["ViTMAEModel",Zo]],["groupvit",["GroupViTModel",LA]],["fastvit",["FastViTModel",A]],["mobilevit",["MobileViTModel",h]],["mobilevitv2",["MobileViTV2Model",M]],["owlvit",["OwlViTModel",R]],["owlv2",["Owlv2Model",ue]],["beit",["BeitModel",Ve]],["deit",["DeiTModel",xs]],["hiera",["HieraModel",Os]],["convnext",["ConvNextModel",Il]],["convnextv2",["ConvNextV2Model",wl]],["dinov2",["Dinov2Model",vl]],["dinov2_with_registers",["Dinov2WithRegistersModel",so]],["resnet",["ResNetModel",ha]],["swin",["SwinModel",Fn]],["swin2sr",["Swin2SRModel",yo]],["donut-swin",["DonutSwinModel",Cl]],["yolos",["YolosModel",Bl]],["dpt",["DPTModel",Qi
2805]],["glpn",["GLPNModel",ml]],["hifigan",["SpeechT5HifiGan",Ec]],["efficientnet",["EfficientNetModel",tu]],["decision_transformer",["DecisionTransformerModel",du]],["patchtst",["PatchTSTForPrediction",Cu]],["patchtsmixer",["PatchTSMixerForPrediction",Vc]],["mobilenet_v1",["MobileNetV1Model",su]],["mobilenet_v2",["MobileNetV2Model",nu]],["mobilenet_v3",["MobileNetV3Model",ou]],["mobilenet_v4",["MobileNetV4Model",cu]],["maskformer",["MaskFormerModel",pl]],["mgp-str",["MgpstrForSceneTextRecognition",bu]],["style_text_to_speech_2",["StyleTextToSpeech2Model",L2]]]),s1=new Map([["t5",["T5Model",sr]],["longt5",["LongT5Model",Or]],["mt5",["MT5Model",_A]],["bart",["BartModel",Bs]],["mbart",["MBartModel",Yt]],["marian",["MarianModel",k0]],["whisper",["WhisperModel",Kn]],["m2m_100",["M2M100Model",Tl]],["blenderbot",["BlenderbotModel",ks]],["blenderbot-small",["BlenderbotSmallModel",As]]]),a1=new Map([["bloom",["BloomModel",ko]],["jais",["JAISModel",Yo]],["gpt2",["GPT2Model",qn]],["gptj",["GPTJModel",wr]],["gpt_bigcode",["GPTBigCodeModel",po]],["gpt_neo",["GPTNeoModel",ji]],["gpt_neox",["GPTNeoXModel",Ro]],["codegen",["CodeGenModel",fa]],["llama",["LlamaModel",dn]],["exaone",["ExaoneModel",mo]],["olmo",["OlmoModel",a0]],["olmo2",["Olmo2Model",ti]],["mobilellm",["MobileLLMModel",Ki]],["granite",["GraniteModel",Uo]],["cohere",["CohereModel",Ka]],["gemma",["GemmaModel",Bn]],["gemma2",["Gemma2Model",Ca]],["helium",["HeliumModel",Zr]],["glm",["GlmModel",Us]],["openelm",["OpenELMModel",xn]],["qwen2",["Qwen2Model",Za]],["phi",["PhiModel",ss]],["phi3",["Phi3Model",pi]],["mpt",["MptModel",an]],["opt",["OPTModel",Hs]],["mistral",["MistralModel",Bc]],["starcoder2",["Starcoder2Model",Mc]],["falcon",["FalconModel",Gc]],["stablelm",["StableLmModel",$2]]]),Hc=new Map([["speecht5",["SpeechT5ForSpeechToText",vc]],["whisper",["WhisperForConditionalGeneration",Vo]],["moonshine",["MoonshineForConditionalGeneration",ca]]]),wu=new Map([["speecht5",["SpeechT5ForTextToSpeech",yc]]]),ku=new Map([["vits",["VitsModel",_c]],["musicgen",["MusicgenForConditionalGeneration",Sc]]]),Yc=new Map([["bert",["BertForSequenceClassification",zt]],["modernbert",["ModernBertForSequenceClassification",lt]],["roformer",["RoFormerForSequenceClassification",nA]],["electra",["ElectraForSequenceClassification",qt]],["esm",["EsmForSequenceClassification",Oi]],["convbert",["ConvBertForSequenceClassification",AA]],["camembert",["CamembertForSequenceClassification",QA]],["deberta",["DebertaForSequenceClassification",Dr]],["deberta-v2",["DebertaV2ForSequenceClassification",ma]],["mpnet",["MPNetForSequenceClassification",ci]],["albert",["AlbertForSequenceClassification",lA]],["distilbert",["DistilBertForSequenceClassification",oa]],["roberta",["RobertaForSequenceClassification",Ei]],["xlm",["XLMForSequenceClassification",ui]],["xlm-roberta",["XLMRobertaForSequenceClassification",Sa]],["bart",["BartForSequenceClassification",PA]],["mbart",["MBartForSequenceClassification",Js]],["mobilebert",["MobileBertForSequenceClassification",$A]],["squeezebert",["SqueezeBertForSequenceClassification",ut]]]),vu=new Map([["bert",["BertForTokenClassification",ke]],["modernbert",["ModernBertForTokenClassification",xt]],["roformer",["RoFormerForTokenClassification",aA]],["electra",["ElectraForTokenClassification",zA]],["esm",["EsmForTokenClassification",Ra]],["convbert",["ConvBertForTokenClassification",gr]],["camembert",["CamembertForTokenClassification",ZA]],["deberta",["DebertaForTokenClassification",Vs]],["deberta-v2",["DebertaV2ForTokenClassification",Qa]],["mpnet",["MPNetForTokenClassification",tn]],["distilbert",["DistilBertForTokenClassification",Yn]],["roberta",["RobertaForTokenClassification",or]],["xlm",["XLMForTokenClassification",Fi]],["xlm-roberta",["XLMRobertaForTokenClassification",Ns]]]),Rc=new Map([["t5",["T5ForConditionalGeneration",jr]],["longt5",["LongT5ForConditionalGeneration",ir]],["mt5",["MT5ForConditionalGeneration",_r]],["bart",["BartForConditionalGeneration",ts]],["mbart",["MBartForConditionalGeneration",TA]],["marian",["MarianMTModel",_l]],["m2m_100",["M2M100ForConditionalGeneration",So]],["blenderbot",["BlenderbotForConditionalGeneration",os]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",yi]]]),jc=new Map([["bloom",["BloomForCausalLM",Dn]],["gpt2",["GPT2LMHeadModel",fn]],["jais",["JAISLMHeadModel",go]],["gptj",["GPTJForCausalLM",Gi]],["gpt_bigcode",["GPTBigCodeForCausalLM",Ws]],["gpt_neo",["GPTNeoForCausalLM",ho]],["gpt_neox",["GPTNeoXFor
2805CausalLM",Jn]],["codegen",["CodeGenForCausalLM",$n]],["llama",["LlamaForCausalLM",s0]],["exaone",["ExaoneForCausalLM",gn]],["olmo",["OlmoForCausalLM",bo]],["olmo2",["Olmo2ForCausalLM",Zi]],["mobilellm",["MobileLLMForCausalLM",aa]],["granite",["GraniteForCausalLM",Xi]],["cohere",["CohereForCausalLM",_i]],["gemma",["GemmaForCausalLM",Co]],["gemma2",["Gemma2ForCausalLM",qi]],["helium",["HeliumForCausalLM",sa]],["glm",["GlmForCausalLM",za]],["openelm",["OpenELMForCausalLM",n0]],["qwen2",["Qwen2ForCausalLM",Ia]],["phi",["PhiForCausalLM",bn]],["phi3",["Phi3ForCausalLM",Mn]],["mpt",["MptForCausalLM",Cn]],["opt",["OPTForCausalLM",vr]],["mbart",["MBartForCausalLM",Ss]],["mistral",["MistralForCausalLM",R2]],["starcoder2",["Starcoder2ForCausalLM",j2]],["falcon",["FalconForCausalLM",W2]],["trocr",["TrOCRForCausalLM",Y2]],["stablelm",["StableLmForCausalLM",eu]],["phi3_v",["Phi3VForCausalLM",uA]]]),n1=new Map([["multi_modality",["MultiModalityCausalLM",hu]]]),yu=new Map([["bert",["BertForMaskedLM",oe]],["modernbert",["ModernBertForMaskedLM",st]],["roformer",["RoFormerForMaskedLM",eA]],["electra",["ElectraForMaskedLM",iA]],["esm",["EsmForMaskedLM",Rn]],["convbert",["ConvBertForMaskedLM",mA]],["camembert",["CamembertForMaskedLM",MA]],["deberta",["DebertaForMaskedLM",Er]],["deberta-v2",["DebertaV2ForMaskedLM",Ja]],["mpnet",["MPNetForMaskedLM",Pa]],["albert",["AlbertForMaskedLM",pA]],["distilbert",["DistilBertForMaskedLM",Br]],["roberta",["RobertaForMaskedLM",$s]],["xlm",["XLMWithLMHeadModel",io]],["xlm-roberta",["XLMRobertaForMaskedLM",vn]],["mobilebert",["MobileBertForMaskedLM",ja]],["squeezebert",["SqueezeBertForMaskedLM",qe]]]),Eu=new Map([["bert",["BertForQuestionAnswering",fe]],["roformer",["RoFormerForQuestionAnswering",bA]],["electra",["ElectraForQuestionAnswering",Fr]],["convbert",["ConvBertForQuestionAnswering",CA]],["camembert",["CamembertForQuestionAnswering",FA]],["deberta",["DebertaForQuestionAnswering",nr]],["deberta-v2",["DebertaV2ForQuestionAnswering",$a]],["mpnet",["MPNetForQuestionAnswering",Wa]],["albert",["AlbertForQuestionAnswering",gt]],["distilbert",["DistilBertForQuestionAnswering",Ma]],["roberta",["RobertaForQuestionAnswering",ea]],["xlm",["XLMForQuestionAnswering",Wn]],["xlm-roberta",["XLMRobertaForQuestionAnswering",BA]],["mobilebert",["MobileBertForQuestionAnswering",fr]],["squeezebert",["SqueezeBertForQuestionAnswering",mt]]]),Wc=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",Wr]],["idefics3",["Idefics3ForConditionalGeneration",_o]]]),i1=new Map([["llava",["LlavaForConditionalGeneration",Yi]],["llava_onevision",["LlavaOnevisionForConditionalGeneration",Gs]],["moondream1",["Moondream1ForConditionalGeneration",fi]],["florence2",["Florence2ForConditionalGeneration",Zn]],["qwen2-vl",["Qwen2VLForConditionalGeneration",sn]],["idefics3",["Idefics3ForConditionalGeneration",_o]],["paligemma",["PaliGemmaForConditionalGeneration",Pn]]]),o1=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",Wr]]]),Bu=new Map([["vit",["ViTForImageClassification",wa]],["ijepa",["IJepaForImageClassification",In]],["pvt",["PvtForImageClassification",Ti]],["vit_msn",["ViTMSNForImageClassification",i0]],["fastvit",["FastViTForImageClassification",r]],["mobilevit",["MobileViTForImageClassification",m]],["mobilevitv2",["MobileViTV2ForImageClassification",y]],["beit",["BeitForImageClassification",$e]],["deit",["DeiTForImageClassification",Ms]],["hiera",["HieraForImageClassification",ga]],["convnext",["ConvNextForImageClassification",I0]],["convnextv2",["ConvNextV2ForImageClassification",kl]],["dinov2",["Dinov2ForImageClassification",Pi]],["dinov2_with_registers",["Dinov2WithRegistersForImageClassification",Ni]],["resnet",["ResNetForImageClassification",pa]],["swin",["SwinForImageClassification",Ln]],["segformer",["SegformerForImageClassification",q2]],["efficientnet",["EfficientNetForImageClassification",Au]],["mobilenet_v1",["MobileNetV1ForImageClassification",au]],["mobilenet_v2",["MobileNetV2ForImageClassification",iu]],["mobilenet_v3",["MobileNetV3ForImageClassification",lu]],["mobilenet_v4",["MobileNetV4ForImageClassification",uu]]]),xu=new Map([["detr",["DetrForObjectDetection",yt]],["rt_detr",["RTDetrForObjectDetection",VA]],["table-transformer",["TableTransformerForObjectDetection",Xr]],["yolos",["YolosForObjectDetection",xl]]]),Mu=new Map([["owlvit",["OwlViTForObjectDetection",ee]],["owlv2",["Owlv2ForObjectDetection",be]],["grounding-dino",["GroundingDinoForObjectDetection",El]]]),Du=new Map([["detr",["DetrForSegmentation",Pt]],["clipseg",["CLIPSegForImageSegmentation",ua]]]),Gu=new Map([["segformer",["SegformerForSemanticSegmentation",J2]],["sapiens",["SapiensForSemanticSegmentation",fl]]]),_u=new Map([["detr",["DetrForSegmentation",Pt]],["maskformer",["MaskFormerForInstanceSegmentation",b0]]]),Uc=new Map([["sam",["SamModel",Dl]]]),Tu=new Map([["wav2vec2",["Wav2Vec2ForCTC",Pl]],["wav2vec2-bert",["Wav2Vec2BertForCTC",jl]],["unispeech",["UniSpeechForCTC",d0]],["unispeech-sat",["UniSpeechSatForCTC",Hl]],["wavlm",["WavLMForCTC",S2]],["hubert",["HubertForCTC",T2]]]),Kc=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",y0]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",G2]],["unispeech",["UniSpeechForSequenceClassification",Ll]],["unispeech-sat",["UniSpeechSatForSequenceClassification",x0]],["wavlm",["WavLMForSequenceClassification",N2]],["hubert",["HubertForSequenceClassification",Q2]],["audio-spectrogram-transformer",["ASTForAudioClassification",Vi]]]),Zc=new Map([["wavlm",["WavLMForXVector",z2]]]),Xc=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",Yl]],["wavlm",["WavLMForAudioFrameClassification",O2]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",Sl]],["pyannote",["PyAnnoteForAudioFrameClassification",zl]]]),Qu=new Map([["vitmatte",["VitMatteForImageMatting",i]]]),l1=new Map([["patchtst",["PatchTSTForPrediction",Wl]],["patchtsmixer",["PatchTSMixerForPrediction",Iu]]]),qc=new Map([["swin2sr",["Swin2SRForImageSuperResolution",to]]]),Pu=ne
2805w Map([["dpt",["DPTForDepthEstimation",Xo]],["depth_anything",["DepthAnythingForDepthEstimation",l0]],["glpn",["GLPNForDepthEstimation",bl]],["sapiens",["SapiensForDepthEstimation",dl]],["depth_pro",["DepthProForDepthEstimation",hl]]]),Su=new Map([["sapiens",["SapiensForNormalEstimation",gl]]]),c1=new Map([["vitpose",["VitPoseForPoseEstimation",Po]]]),Jc=new Map([["clip",["CLIPVisionModelWithProjection",js]],["siglip",["SiglipVisionModel",_s]],["jina_clip",["JinaCLIPVisionModel",Di]]]),G0=[[r1,g],[s1,v],[a1,_],[Yc,g],[vu,g],[Rc,e],[Hc,e],[jc,_],[n1,Ie],[yu,g],[Eu,g],[Wc,d],[i1,S],[Bu,g],[Du,g],[_u,g],[Gu,g],[Qu,g],[l1,g],[qc,g],[Pu,g],[Su,g],[c1,g],[xu,g],[Mu,g],[Uc,re],[Tu,g],[Kc,g],[wu,e],[ku,g],[Zc,g],[Xc,g],[Jc,g]];for(const[le,xe]of G0)for(const[Ye,Et]of le.values())ce.set(Ye,xe),Ce.set(Et,Ye),ge.set(Ye,Et);const Nu=[["MusicgenForConditionalGeneration",Sc,F],["Phi3VForCausalLM",uA,he],["CLIPTextModelWithProjection",co,g],["SiglipTextModel",uo,g],["JinaCLIPTextModel",Ri,g],["ClapTextModelWithProjection",K2,g],["ClapAudioModelWithProjection",Z2,g]];for(const[le,xe,Ye]of Nu)ce.set(le,Ye),Ce.set(xe,le),ge.set(le,xe);class zu extends Is{}Ct(zu,"MODEL_CLASS_MAPPINGS",G0.map(xe=>xe[0])),Ct(zu,"BASE_IF_FAIL",!0);class u1 extends Is{}Ct(u1,"MODEL_CLASS_MAPPINGS",[Yc]);class hr extends Is{}Ct(hr,"MODEL_CLASS_MAPPINGS",[vu]);class Ou extends Is{}Ct(Ou,"MODEL_CLASS_MAPPINGS",[Rc]);class qo extends Is{}Ct(qo,"MODEL_CLASS_MAPPINGS",[Hc]);class Fu extends Is{}Ct(Fu,"MODEL_CLASS_MAPPINGS",[wu]);class $c extends Is{}Ct($c,"MODEL_CLASS_MAPPINGS",[ku]);class f1 extends Is{}Ct(f1,"MODEL_CLASS_MAPPINGS",[jc]);class va extends Is{}Ct(va,"MODEL_CLASS_MAPPINGS",[yu]);class yr extends Is{}Ct(yr,"MODEL_CLASS_MAPPINGS",[Eu]);class Qr extends Is{}Ct(Qr,"MODEL_CLASS_MAPPINGS",[Wc]);class h0 extends Is{}Ct(h0,"MODEL_CLASS_MAPPINGS",[Bu]);class d1 extends Is{}Ct(d1,"MODEL_CLASS_MAPPINGS",[Du]);class e2 extends Is{}Ct(e2,"MODEL_CLASS_MAPPINGS",[Gu]);class g1 extends Is{}Ct(g1,"MODEL_CLASS_MAPPINGS",[_u]);class t2 extends Is{}Ct(t2,"MODEL_CLASS_MAPPINGS",[xu]);class h1 extends Is{}Ct(h1,"MODEL_CLASS_MAPPINGS",[Mu]);class p1 extends Is{}Ct(p1,"MODEL_CLASS_MAPPINGS",[Uc]);class A2 extends Is{}Ct(A2,"MODEL_CLASS_MAPPINGS",[Tu]);class m1 extends Is{}Ct(m1,"MODEL_CLASS_MAPPINGS",[Kc]);class b1 extends Is{}Ct(b1,"MODEL_CLASS_MAPPINGS",[Zc]);class C1 extends Is{}Ct(C1,"MODEL_CLASS_MAPPINGS",[Xc]);class _0 extends Is{}Ct(_0,"MODEL_CLASS_MAPPINGS",[o1]);class r2 extends Is{}Ct(r2,"MODEL_CLASS_MAPPINGS",[Qu]);class I1 extends Is{}Ct(I1,"MODEL_CLASS_MAPPINGS",[qc]);class s2 extends Is{}Ct(s2,"MODEL_CLASS_MAPPINGS",[Pu]);class a2 extends Is{}Ct(a2,"MODEL_CLASS_MAPPINGS",[Su]);class w1 extends Is{}Ct(w1,"MODEL_CLASS_MAPPINGS",[c1]);class k1 extends Is{}Ct(k1,"MODEL_CLASS_MAPPINGS",[Jc]);class rf extends rt{constructor({logits:xe,past_key_values:Ye,encoder_outputs:Et,decoder_attentions:Ht=null,cross_attentions:Vt=null}){super(),this.logits=xe,this.past_key_values=Ye,this.encoder_outputs=Et,this.decoder_attentions=Ht,this.cross_attentions=Vt}}class xr extends rt{constructor({logits:xe,...Ye}){super(),this.logits=xe;const Et=Object.values(Ye);Et.length>0&&(this.attentions=Et)}}class v1 extends rt{constructor({logits:xe,embeddings:Ye}){super(),this.logits=xe,this.embeddings=Ye}}class kn extends rt{constructor({logits:xe}){super(),this.logits=xe}}class _n extends rt{constructor({logits:xe}){super(),this.logits=xe}}class ri extends rt{constructor({start_logits:xe,end_logits:Ye}){super(),this.start_logits=xe,this.end_logits=Ye}}class Z0 extends rt{constructor({logits:xe}){super(),this.logits=xe}}class sf extends rt{constructor({logits:xe,past_key_values:Ye}){super(),this.logits=xe,this.past_key_values=Ye}}class y1 extends rt{constructor({alphas:xe}){super(),this.alphas=xe}}class E1 extends rt{constructor({waveform:xe,spectrogram:Ye}){super(),this.waveform=xe,this.spectrogram=Ye}}},
2805"./src/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js":(c,a,s)=>{s.r(a),s.d(a,{ASTFeatureExtractor:()=>p});var o=s("./src/base/feature_extraction_utils.js");s("./src/utils/tensor.js");var u=s("./src/utils/audio.js");class p extends o.FeatureExtractor{constructor(b){super(b);const k=this.config.sampling_rate,E=(0,u.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(k/2),k,null,"kaldi",!0);for(let B=0;B<E.length;++B)E[B].push(0);this.mel_filters=E,this.window=(0,u.window_function)(400,"hann",{periodic:!1}),this.mean=this.config.mean,this.std=this.config.std}async _extract_fbank_features(b,k){return(0,u.spectrogram)(b,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:k,transpose:!0})}async _call(b){(0,o.validate_audio_inputs)(b,"ASTFeatureExtractor");const k=await this._extract_fbank_features(b,this.config.max_length);if(this.config.do_normalize){const E=2*this.std,B=k.data;for(let G=0;G<B.length;++G)B[G]=(B[G]-this.mean)/E}return{input_values:k.unsqueeze_(0)}}}},
2805"./src/models/auto/feature_extraction_auto.js":(c,a,s)=>{s.r(a),s.d(a,{AutoFeatureExtractor:()=>C});var o=s("./src/utils/constants.js"),u=s("./src/utils/hub.js");s("./src/base/feature_extraction_utils.js");var p=s("./src/models/feature_extractors.js");class C{static async from_pretrained(k,E={}){const B=await(0,u.getModelJSON)(k,o.FEATURE_EXTRACTOR_NAME,!0,E),G=B.feature_extractor_type,D=p[G];if(!D)throw new Error(`Unknown feature_extractor_type: '${G}'. Please report this at ${o.GITHUB_ISSUE_URL}.`);return new D(B)}}},
2805"./src/models/auto/image_processing_auto.js":(c,a,s)=>{s.r(a),s.d(a,{AutoImageProcessor:()=>b});var o=s("./src/utils/constants.js"),u=s("./src/utils/hub.js"),p=s("./src/base/image_processors_utils.js"),C=s("./src/models/image_processors.js");class b{static async from_pretrained(E,B={}){const G=await(0,u.getModelJSON)(E,o.IMAGE_PROCESSOR_NAME,!0,B),D=G.image_processor_type??G.feature_extractor_type;let Q=C[D];return Q||(D!==void 0&&console.warn(`Image processor type '${D}' not found, assuming base ImageProcessor. Please report this at ${o.GITHUB_ISSUE_URL}.`),Q=p.ImageProcessor),new Q(G)}}},
vendor: 943 bytes, line 2805
2805"./src/models/auto/processing_auto.js":(c,a,s)=>{s.r(a),s.d(a,{AutoProcessor:()=>E});var o=s("./src/utils/constants.js"),u=s("./src/utils/hub.js"),p=s("./src/base/processing_utils.js"),C=s("./src/models/processors.js"),b=s("./src/models/image_processors.js"),k=s("./src/models/feature_extractors.js");class E{static async from_pretrained(G,D={}){const Q=await(0,u.getModelJSON)(G,o.IMAGE_PROCESSOR_NAME,!0,D),{image_processor_type:V,feature_extractor_type:W,processor_class:U}=Q;if(U&&C[U])return C[U].from_pretrained(G,D);if(!V&&!W)throw new Error("No `image_processor_type` or `feature_extractor_type` found in the config.");const H={};if(V){const K=b[V];if(!K)throw new Error(`Unknown image_processor_type: '${V}'.`);H.image_processor=new K(Q)}if(W){const K=b[W];if(K)H.image_processor=new K(Q);else{const J=k[W];if(!J)throw new Error(`Unknown feature_extractor_type: '${W}'.`);H.feature_extractor=new J(Q)}}return new p.Processor({},H)}}},
2805"./src/models/beit/image_processing_beit.js":(c,a,s)=>{s.r(a),s.d(a,{BeitFeatureExtractor:()=>u});var o=s("./src/base/image_processors_utils.js");class u extends o.ImageProcessor{}},
2805"./src/models/bit/image_processing_bit.js":(c,a,s)=>{s.r(a),s.d(a,{BitImageProcessor:()=>u});var o=s("./src/base/image_processors_utils.js");class u extends o.ImageProcessor{}},
2805"./src/models/chinese_clip/image_processing_chinese_clip.js":(c,a,s)=>{s.r(a),s.d(a,{ChineseCLIPFeatureExtractor:()=>u});var o=s("./src/base/image_processors_utils.js");class u extends o.ImageProcessor{}},
2805"./src/models/clap/feature_extraction_clap.js":(c,a,s)=>{s.r(a),s.d(a,{ClapFeatureExtractor:()=>p});var o=s("./src/base/feature_extraction_utils.js");s("./src/utils/tensor.js");var u=s("./src/utils/audio.js");class p extends o.FeatureExtractor{constructor(b){super(b),this.mel_filters=(0,u.mel_filter_bank)(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,null,"htk"),this.mel_filters_slaney=(0,u.mel_filter_bank)(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,"slaney","slaney"),this.window=(0,u.window_function)(this.config.fft_window_size,"hann")}async _get_input_mel(b,k,E,B){let G;const D=b.length-k;if(D>0){if(E!=="rand_trunc")throw new Error(`Truncation strategy "${E}" not implemented`);{const Q=Math.floor(Math.random()*(D+1));b=b.subarray(Q,Q+k),G=await this._extract_fbank_features(b,this.mel_filters_slaney,this.config.nb_max_samples)}}else{if(D<0){let Q=new Float64Array(k);if(Q.set(b),B==="repeat")for(let V=b.length;V<k;V+=b.length)Q.set(b.subarray(0,Math.min(b.length,k-V)),V);else if(B==="repeatpad")for(let V=b.length;V<-D;V+=b.length)Q.set(b,V);b=Q}if(E==="fusion")throw new Error(`Truncation strategy "${E}" not implemented`);G=await this._extract_fbank_features(b,this.mel_filters_slaney,this.config.nb_max_samples)}return G.unsqueeze_(0)}async _extract_fbank_features(b,k,E=null){return(0,u.spectrogram)(b,this.window,this.config.fft_window_size,this.config.hop_length,{power:2,mel_filters:k,log_mel:"dB",max_num_frames:E,do_pad:!1,transpose:!0})}async _call(b,{max_length:k=null}={}){return(0,o.validate_audio_inputs)(b,"ClapFeatureExtractor"),{input_features:(await this._get_input_mel(b,k??this.config.nb_max_samples,this.config.truncation,this.config.padding)).unsqueeze_(0)}}}},
2805"./src/models/clip/image_processing_clip.js":(c,a,s)=>{s.r(a),s.d(a,{CLIPFeatureExtractor:()=>p,CLIPImageProcessor:()=>u});var o=s("./src/base/image_processors_utils.js");class u extends o.ImageProcessor{}class p extends u{}},
2805"./src/models/convnext/image_processing_convnext.js":(c,a,s)=>{s.r(a),s.d(a,{ConvNextFeatureExtractor:()=>p,ConvNextImageProcessor:()=>u});var o=s("./src/base/image_processors_utils.js");class u extends o.ImageProcessor{constructor(b){super(b),this.crop_pct=this.config.crop_pct??.875}async resize(b){var E;const k=(E=this.size)==null?void 0:E.shortest_edge;if(k===void 0)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(k<384){const B=Math.floor(k/this.crop_pct),[G,D]=this.get_resize_output_image_size(b,{shortest_edge:B});b=await b.resize(G,D,{resample:this.resample}),b=await b.center_crop(k,k)}else b=await b.resize(k,k,{resample:this.resample});return b}}class p extends u{}},
2805"./src/models/deit/image_processing_deit.js":(c,a,s)=>{s.r(a),s.d(a,{DeiTFeatureExtractor:()=>p,DeiTImageProcessor:()=>u});var o=s("./src/base/image_processors_utils.js");class u extends o.ImageProcessor{}class p extends u{}},
vendor: 649 bytes, line 2805
2805"./src/models/detr/image_processing_detr.js":(c,a,s)=>{s.r(a),s.d(a,{DetrFeatureExtractor:()=>C,DetrImageProcessor:()=>p});var o=s("./src/base/image_processors_utils.js"),u=s("./src/utils/tensor.js");class p extends o.ImageProcessor{async _call(k){const E=await super._call(k),B=[E.pixel_values.dims[0],64,64],G=(0,u.full)(B,1n);return{...E,pixel_mask:G}}post_process_object_detection(...k){return(0,o.post_process_object_detection)(...k)}post_process_panoptic_segmentation(...k){return(0,o.post_process_panoptic_segmentation)(...k)}post_process_instance_segmentation(...k){return(0,o.post_process_instance_segmentation)(...k)}}class C extends p{}},
2805"./src/models/donut/image_processing_donut.js":(c,a,s)=>{s.r(a),s.d(a,{DonutFeatureExtractor:()=>p,DonutImageProcessor:()=>u});var o=s("./src/base/image_processors_utils.js");class u extends o.ImageProcessor{pad_image(b,k,E,B={}){const[G,D,Q]=k;let V=this.image_mean;Array.isArray(this.image_mean)||(V=new Array(Q).fill(V));let W=this.image_std;Array.isArray(W)||(W=new Array(Q).fill(V));const U=V.map((H,K)=>-H/W[K]);return super.pad_image(b,k,E,{center:!0,constant_values:U,...B})}}class p extends u{}},
2805"./src/models/dpt/image_processing_dpt.js":(c,a,s)=>{s.r(a),s.d(a,{DPTFeatureExtractor:()=>p,DPTImageProcessor:()=>u});var o=s("./src/base/image_processors_utils.js");class u extends o.ImageProcessor{}class p extends u{}},
2805"./src/models/efficientnet/image_processing_efficientnet.js":(c,a,s)=>{s.r(a),s.d(a,{EfficientNetImageProcessor:()=>u});var o=s("./src/base/image_processors_utils.js");class u extends o.ImageProcessor{constructor(C){super(C),this.include_top=this.config.include_top??!0,this.include_top&&(this.image_std=this.image_std.map(b=>b*b))}}},
2805"./src/models/feature_extractors.js":(c,a,s)=>{s.r(a),s.d(a,{ASTFeatureExtractor:()=>o.ASTFeatureExtractor,ClapFeatureExtractor:()=>u.ClapFeatureExtractor,ImageFeatureExtractor:()=>D.ImageProcessor,MoonshineFeatureExtractor:()=>p.MoonshineFeatureExtractor,PyAnnoteFeatureExtractor:()=>C.PyAnnoteFeatureExtractor,SeamlessM4TFeatureExtractor:()=>b.SeamlessM4TFeatureExtractor,SpeechT5FeatureExtractor:()=>k.SpeechT5FeatureExtractor,Wav2Vec2FeatureExtractor:()=>E.Wav2Vec2FeatureExtractor,WeSpeakerFeatureExtractor:()=>B.WeSpeakerFeatureExtractor,WhisperFeatureExtractor:()=>G.WhisperFeatureExtractor});var o=s("./src/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js"),u=s("./src/models/clap/feature_extraction_clap.js"),p=s("./src/models/moonshine/feature_extraction_moonshine.js"),C=s("./src/models/pyannote/feature_extraction_pyannote.js"),b=s("./src/models/seamless_m4t/feature_extraction_seamless_m4t.js"),k=s("./src/models/speecht5/feature_extraction_speecht5.js"),E=s("./src/models/wav2vec2/feature_extraction_wav2vec2.js"),B=s("./src/models/wespeaker/feature_extraction_wespeaker.js"),G=s("./src/models/whisper/feature_extraction_whisper.js"),D=s("./src/base/image_processors_utils.js")},
2805"./src/models/florence2/processing_florence2.js":(c,a,s)=>{s.r(a),s.d(a,{Florence2Processor:()=>C});var o=s("./src/base/processing_utils.js"),u=s("./src/models/auto/image_processing_auto.js"),p=s("./src/tokenizers.js");class C extends o.Processor{constructor(k,E){super(k,E);const{tasks_answer_post_processing_type:B,task_prompts_without_inputs:G,task_prompts_with_input:D}=this.image_processor.config;this.tasks_answer_post_processing_type=new Map(Object.entries(B??{})),this.task_prompts_without_inputs=new Map(Object.entries(G??{})),this.task_prompts_with_input=new Map(Object.entries(D??{})),this.regexes={quad_boxes:/(.+?)<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>/gm,bboxes:/([^<]+)?<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>/gm},this.size_per_bin=1e3}construct_prompts(k){typeof k=="string"&&(k=[k]);const E=[];for(const B of k)if(this.task_prompts_without_inputs.has(B))E.push(this.task_prompts_without_inputs.get(B));else{for(const[G,D]of this.task_prompts_with_input)if(B.includes(G)){E.push(D.replaceAll("{input}",B).replaceAll(G,""));break}E.length!==k.length&&E.push(B)}return E}post_process_generation(k,E,B){const G=this.tasks_answer_post_processing_type.get(E)??"pure_text";let D;switch(k=k.replaceAll("<s>","").replaceAll("</s>",""),G){case"pure_text":D=k;break;case"description_with_bboxes":case"bboxes":case"phrase_grounding":case"ocr":const Q=G==="ocr"?"quad_boxes":"bboxes",V=k.matchAll(this.regexes[Q]),W=[],U=[];for(const[H,K,...J]of V)W.push(K?K.trim():W.at(-1)??""),U.push(J.map((g,v)=>(Number(g)+.5)/this.size_per_bin*B[v%2]));D={labels:W,[Q]:U};break;default:throw new Error(`Task "${E}" (of type "${G}") not yet implemented.`)}return{[E]:D}}async _call(k,E=null,B={}){if(!k&&!E)throw new Error("Either text or images must be provided");return{...await this.image_processor(k,B),...E?this.tokenizer(E,B):{}}}}Ct(C,"tokenizer_class",p.AutoTokenizer),Ct(C,"image_processor_class",u.AutoImageProcessor)},
2805"./src/models/glpn/image_processing_glpn.js":(c,a,s)=>{s.r(a),s.d(a,{GLPNFeatureExtractor:()=>u});var o=s("./src/base/image_processors_utils.js");class u extends o.ImageProcessor{}},
2805"./src/models/grounding_dino/image_processing_grounding_dino.js":(c,a,s)=>{s.r(a),s.d(a,{GroundingDinoImageProcessor:()=>p});var o=s("./src/base/image_processors_utils.js"),u=s("./src/utils/tensor.js");class p extends o.ImageProcessor{async _call(b){const k=await super._call(b),E=k.pixel_values.dims,B=(0,u.ones)([E[0],E[2],E[3]]);return{...k,pixel_mask:B}}}},
2805"./src/models/grounding_dino/processing_grounding_dino.js":(c,a,s)=>{s.r(a),s.d(a,{GroundingDinoProcessor:()=>k});var o=s("./src/base/processing_utils.js"),u=s("./src/models/auto/image_processing_auto.js"),p=s("./src/tokenizers.js"),C=s("./src/base/image_processors_utils.js");function b(E,B){const G=E.dims.at(-1)-1,D=E.tolist();D.fill(!1,0,1),D.fill(!1,G);const Q=B.tolist();return D.map((V,W)=>V?W:null).filter(V=>V!==null).map(V=>Q[V])}class k extends o.Processor{async _call(B,G,D={}){const Q=B?await this.image_processor(B,D):{};return{...G?this.tokenizer(G,D):{},...Q}}post_process_grounded_object_detection(B,G,{box_threshold:D=.25,text_threshold:Q=.25,target_sizes:V=null}={}){const{logits:W,pred_boxes:U}=B,H=W.dims[0];if(V!==null&&V.length!==H)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");const K=W.dims.at(1),J=W.sigmoid(),g=J.max(-1).tolist(),v=U.tolist().map(d=>d.map(_=>(0,C.center_to_corners_format)(_))),e=[];for(let d=0;d<H;++d){const _=V!==null?V[d]:null;_!==null&&(v[d]=v[d].map(he=>he.map((ce,ge)=>ce*_[(ge+1)%2])));const re=g[d],S=[],F=[],Ie=[];for(let he=0;he<K;++he){const ce=re[he];if(ce<=D)continue;const ge=v[d][he],Ce=J[d][he];S.push(ce),Ie.push(ge);const Fe=b(Ce.gt(Q),G[d]);F.push(Fe)}e.push({scores:S,boxes:Ie,labels:this.batch_decode(F)})}return e}}Ct(k,"tokenizer_class",p.AutoTokenizer),Ct(k,"image_processor_class",u.AutoImageProcessor)},
2805"./src/models/idefics3/image_processing_idefics3.js":(c,a,s)=>{s.r(a),s.d(a,{Idefics3ImageProcessor:()=>p});var o=s("./src/base/image_processors_utils.js"),u=s("./src/utils/tensor.js");class p extends o.ImageProcessor{constructor(b){super(b),this.do_image_splitting=b.do_image_splitting??!0,this.max_image_size=b.max_image_size}get_resize_for_vision_encoder(b,k){let[E,B]=b.dims.slice(-2);const G=B/E;return B>=E?(B=Math.ceil(B/k)*k,E=Math.floor(B/G),E=Math.ceil(E/k)*k):(E=Math.ceil(E/k)*k,B=Math.floor(E*G),B=Math.ceil(B/k)*k),{height:E,width:B}}async _call(b,{do_image_splitting:k=null,return_row_col_info:E=!1}={}){let B;if(Array.isArray(b)){if(b.length===0||!b[0])throw new Error("No images provided.");B=Array.isArray(b[0])?b:[b]}else B=[[b]];let G=[],D=[],Q=[];const V=[],W=[];for(const d of B){let _=await Promise.all(d.map(F=>this.preprocess(F)));V.push(..._.map(F=>F.original_size)),W.push(..._.map(F=>F.reshaped_input_size)),_.forEach(F=>F.pixel_values.unsqueeze_(0));const{longest_edge:re}=this.max_image_size;let S;if(k??this.do_image_splitting){let F=new Array(_.length),Ie=new Array(_.length);S=await Promise.all(_.map(async(he,ce)=>{const ge=this.get_resize_for_vision_encoder(he.pixel_values,re),Ce=await(0,u.interpolate_4d)(he.pixel_values,{size:[ge.height,ge.width]}),{frames:Fe,num_splits_h:Pe,num_splits_w:de}=await this.split_image(Ce,this.max_image_size);return F[ce]=Pe,Ie[ce]=de,(0,u.cat)(Fe,0)})),D.push(F),Q.push(Ie)}else{const F=[re,re];S=await Promise.all(_.map(Ie=>(0,u.interpolate_4d)(Ie.pixel_values,{size:F}))),D.push(new Array(_.length).fill(0)),Q.push(new Array(_.length).fill(0))}G.push((0,u.cat)(S,0))}const U=G.length,[H,K,J,g]=G[0].dims;let v,e;if(U===1)v=G[0].unsqueeze_(0),e=(0,u.full)([U,H,J,g],!0);else{const d=Math.max(...G.map(S=>S.dims.at(0)));e=(0,u.full)([U,d,J,g],!0);const _=e.data,re=d*J*g;for(let S=0;S<U;++S){const F=G[S].dims[0];if(F<d){G[S]=(0,u.cat)([G[S],(0,u.full)([d-F,K,J,g],0)],0);const Ie=S*re+F*J*g,he=(S+1)*re;_.fill(!1,Ie,he)}}v=(0,u.stack)(G,0)}return{pixel_values:v,pixel_attention_mask:e,original_sizes:V,reshaped_input_sizes:W,...E?{rows:D,cols:Q}:{}}}async split_image(b,{longest_edge:k}){const E=k,B=k,G=[],[D,Q]=b.dims.slice(-2);let V=0,W=0;if(D>E||Q>B){V=Math.ceil(D/E),W=Math.ceil(Q/B);const U=Math.ceil(D/V),H=Math.ceil(Q/W);for(let g=0;g<V;++g)for(let v=0;v<W;++v){let e,d,_,re;g===V-1?(d=D-U,re=D):(d=g*U,re=(g+1)*U),v===W-1?(e=Q-H,_=Q):(e=v*H,_=(v+1)*H);const S=[d,e],F=[re,_],Ie=await(0,u.slice)(b,S,F,[2,3]);G.push(Ie)}const K=E,J=B;D===K&&Q===J||(b=await(0,u.interpolate_4d)(b,{size:[K,J]}))}return G.push(b),{frames:G,num_splits_h:V,num_splits_w:W}}}},
2805"./src/models/idefics3/processing_idefics3.js":(c,a,s)=>{s.r(a),s.d(a,{Idefics3Processor:()=>k});var o=s("./src/base/processing_utils.js"),u=s("./src/models/auto/image_processing_auto.js"),p=s("./src/tokenizers.js");s("./src/utils/image.js");var C=s("./src/utils/core.js");function b(E,B,G,D,Q,V){return E===0&&B===0?function(W,U,H,K){return`${U}${K}`+H.repeat(W)+`${U}`}(G,D,Q,V):function(W,U,H,K,J,g){let v="";for(let e=0;e<U;++e){for(let d=0;d<H;++d)v+=K+`<row_${e+1}_col_${d+1}>`+J.repeat(W);v+=` 2806`}return v+=` 2807${K}${g}`+J.repeat(W)+`${K}`,v}(G,E,B,D,Q,V)}class k extends o.Processor{constructor(){super(...arguments);Ct(this,"fake_image_token","<fake_token_around_image>");Ct(this,"image_token","<image>");Ct(this,"global_img_token","<global-img>")}async _call(G,D=null,Q={}){let V;Q.return_row_col_info??(Q.return_row_col_info=!0),D&&(V=await this.image_processor(D,Q)),Array.isArray(G)||(G=[G]);const W=V.rows??[new Array(G.length).fill(0)],U=V.cols??[new Array(G.length).fill(0)],H=this.config.image_seq_len,K=[],J=[];for(let g=0;g<G.length;++g){const v=G[g],e=W[g],d=U[g];K.push((0,C.count)(v,this.image_token));const _=e.map((F,Ie)=>b(F,d[Ie],H,this.fake_image_token,this.image_token,this.global_img_token)),re=v.split(this.image_token);if(re.length===0)throw new Error("The image token should be present in the text.");let S=re[0];for(let F=0;F<_.length;++F)S+=_[F]+re[F+1];J.push(S)}return{...this.tokenizer(J),...V}}}Ct(k,"image_processor_class",u.AutoImageProcessor),Ct(k,"tokenizer_class",p.AutoTokenizer),Ct(k,"uses_processor_config",!0)},
2807"./src/models/image_processors.js":(c,a,s)=>{s.r(a),s.d(a,{BeitFeatureExtractor:()=>o.BeitFeatureExtractor,BitImageProcessor:()=>u.BitImageProcessor,CLIPFeatureExtractor:()=>C.CLIPFeatureExtractor,CLIPImageProcessor:()=>C.CLIPImageProcessor,ChineseCLIPFeatureExtractor:()=>p.ChineseCLIPFeatureExtractor,ConvNextFeatureExtractor:()=>b.ConvNextFeatureExtractor,ConvNextImageProcessor:()=>b.ConvNextImageProcessor,DPTFeatureExtractor:()=>G.DPTFeatureExtractor,DPTImageProcessor:()=>G.DPTImageProcessor,DeiTFeatureExtractor:()=>k.DeiTFeatureExtractor,DeiTImageProcessor:()=>k.DeiTImageProcessor,DetrFeatureExtractor:()=>E.DetrFeatureExtractor,DetrImageProcessor:()=>E.DetrImageProcessor,DonutFeatureExtractor:()=>B.DonutFeatureExtractor,DonutImageProcessor:()=>B.DonutImageProcessor,EfficientNetImageProcessor:()=>D.EfficientNetImageProcessor,GLPNFeatureExtractor:()=>Q.GLPNFeatureExtractor,GroundingDinoImageProcessor:()=>V.GroundingDinoImageProcessor,Idefics3ImageProcessor:()=>W.Idefics3ImageProcessor,JinaCLIPImageProcessor:()=>H.JinaCLIPImageProcessor,LlavaOnevisionImageProcessor:()=>K.LlavaOnevisionImageProcessor,Mask2FormerImageProcessor:()=>J.Mask2FormerImageProcessor,MaskFormerFeatureExtractor:()=>g.MaskFormerFeatureExtractor,MaskFormerImageProcessor:()=>g.MaskFormerImageProcessor,MobileNetV1FeatureExtractor:()=>v.MobileNetV1FeatureExtractor,MobileNetV1ImageProcessor:()=>v.MobileNetV1ImageProcessor,MobileNetV2FeatureExtractor:()=>e.MobileNetV2FeatureExtractor,MobileNetV2ImageProcessor:()=>e.MobileNetV2ImageProcessor,MobileNetV3FeatureExtractor:()=>d.MobileNetV3FeatureExtractor,MobileNetV3ImageProcessor:()=>d.MobileNetV3ImageProcessor,MobileNetV4FeatureExtractor:()=>_.MobileNetV4FeatureExtractor,MobileNetV4ImageProcessor:()=>_.MobileNetV4ImageProcessor,MobileViTFeatureExtractor:()=>re.MobileViTFeatureExtractor,MobileViTImageProcessor:()=>re.MobileViTImageProcessor,NougatImageProcessor:()=>S.NougatImageProcessor,OwlViTFeatureExtractor:()=>Ie.OwlViTFeatureExtractor,OwlViTImageProcessor:()=>Ie.OwlViTImageProcessor,Owlv2ImageProcessor:()=>F.Owlv2ImageProcessor,Phi3VImageProcessor:()=>he.Phi3VImageProcessor,PvtImageProcessor:()=>ce.PvtImageProcessor,Qwen2VLImageProcessor:()=>ge.Qwen2VLImageProcessor,RTDetrImageProcessor:()=>Ce.RTDetrImageProcessor,SamImageProcessor:()=>Fe.SamImageProcessor,SegformerFeatureExtractor:()=>Pe.SegformerFeatureExtractor,SegformerImageProcessor:()=>Pe.SegformerImageProcessor,SiglipImageProcessor:()=>de.SiglipImageProcessor,Swin2SRImageProcessor:()=>pe.Swin2SRImageProcessor,VLMImageProcessor:()=>U.VLMImageProcessor,ViTFeatureExtractor:()=>we.ViTFeatureExtractor,ViTImageProcessor:()=>we.ViTImageProcessor,VitMatteImageProcessor:()=>Ge.VitMatteImageProcessor,VitPoseImageProcessor:()=>me.VitPoseImageProcessor,YolosFeatureExtractor:()=>at.YolosFeatureExtractor,YolosImageProcessor:()=>at.YolosImageProcessor});var o=s("./src/models/beit/image_processing_beit.js"),u=s("./src/models/bit/image_processing_bit.js"),p=s("./src/models/chinese_clip/image_processing_chinese_clip.js"),C=s("./src/models/clip/image_processing_clip.js"),b=s("./src/models/convnext/image_processing_convnext.js"),k=s("./src/models/deit/image_processing_deit.js"),E=s("./src/models/detr/image_processing_detr.js"),B=s("./src/models/donut/image_processing_donut.js"),G=s("./src/models/dpt/image_processing_dpt.js"),D=s("./src/models/efficientnet/image_processing_efficientnet.js"),Q=s("./src/models/glpn/image_processing_glpn.js"),V=s("./src/models/grounding_dino/image_processing_grounding_dino.js"),W=s("./src/models/idefics3/image_processing_idefics3.js"),U=s("./src/models/janus/image_processing_janus.js"),H=s("./src/models/jina_clip/image_processing_jina_clip.js"),K=s("./src/models/llava_onevision/image_processing_llava_onevision.js"),J=s("./src/models/mask2former/image_processing_mask2former.js"),g=s("./src/models/maskformer/image_processing_maskformer.js"),v=s("./src/models/mobilenet_v1/image_processing_mobilenet_v1.js"),e=s("./src/models/mobilenet_v2/image_processing_mobilenet_v2.js"),d=s("./src/models/mobilenet_v3/image_processing_mobilenet_v3.js"),_=s("./src/models/mobilenet_v4/image_processing_mobilenet_v4.js"),re=s("./src/models/mobilevit/image_processing_mobilevit.js"),S=s("./src/models/nougat/image_processing_nougat.js"),F=s("./src/models/owlv2/image_processing_owlv2.js"),Ie=s("./src/models/owlvit/image_processing_owlvit.js"),he=s("./src/models/phi3_v/image_processing_phi3_v.js"),ce=s("./src/models/pvt/image_processing_pvt.js"),ge=s("./src/models/qwen2_vl/image_processing_qwen2_vl.js"),Ce=s("./src/models/rt_detr/image_processing_rt_detr.js"),Fe=s("./src/models/sam/image_processing_sam.js"),Pe=s("./src/models/segformer/image_processing_segformer.js"),de=s("./src/models/siglip/image_processing_siglip.js"),pe=s("./src/models/swin2sr/image_processing_swin2sr.js"),we=s("./src/models/vit/image_processing_vit.js"),Ge=s("./src/models/vitmatte/image_processing_vitmatte.js"),me=s("./src/models/vitpose/image_processing_vitpose.js"),at=s("./src/models/yolos/image_processing_yolos.js")},
2807"./src/models/janus/image_processing_janus.js":(c,a,s)=>{s.r(a),s.d(a,{VLMImageProcessor:()=>u});var o=s("./src/base/image_processors_utils.js");class u extends o.ImageProcessor{constructor(C){super({do_pad:!0,pad_size:{width:C.image_size,height:C.image_size},...C}),this.constant_values=this.config.background_color.map(b=>b*this.rescale_factor)}pad_image(C,b,k,E){return super.pad_image(C,b,k,{constant_values:this.constant_values,center:!0,...E})}}},
2807"./src/models/janus/processing_janus.js":(c,a,s)=>{s.r(a),s.d(a,{VLChatProcessor:()=>E});var o=s("./src/base/processing_utils.js"),u=s("./src/models/auto/image_processing_auto.js"),p=s("./src/tokenizers.js"),C=s("./src/utils/core.js"),b=s("./src/utils/tensor.js"),k=s("./src/utils/image.js");class E extends o.Processor{constructor(G,D){super(G,D),this.image_tag=this.config.image_tag,this.image_start_tag=this.config.image_start_tag,this.image_end_tag=this.config.image_end_tag,this.num_image_tokens=this.config.num_image_tokens}async _call(G,{images:D=null,chat_template:Q="default"}={}){D?Array.isArray(D)||(D=[D]):D=await Promise.all(G.filter(re=>re.images).flatMap(re=>re.images).map(re=>k.RawImage.read(re)));const V=this.tokenizer,W=re=>V.encode(re,{add_special_tokens:!1}),U=V.apply_chat_template(G,{tokenize:!1,add_generation_prompt:!0,chat_template:Q}).split(this.image_tag),H=U.length-1;if(D.length!==H)throw new Error(`Number of images provided (${D.length}) does not match number of "${this.image_tag}" image tags (${H})`);const[K,J,g]=V.model.convert_tokens_to_ids([this.image_tag,this.image_start_tag,this.image_end_tag]);let v=W(U[0]),e=new Array(v.length).fill(!1);for(let re=1;re<U.length;++re){const S=new Array(this.num_image_tokens).fill(K),F=W(U[re]);v=(0,C.mergeArrays)(v,[J],S,[g],F);const Ie=new Array(this.num_image_tokens).fill(!0);e=(0,C.mergeArrays)(e,[!1],Ie,[!1],new Array(F.length).fill(!1))}const d=[1,v.length],_={input_ids:new b.Tensor("int64",v,d),attention_mask:new b.Tensor("int64",new Array(v.length).fill(1),d),images_seq_mask:new b.Tensor("bool",e,d),images_emb_mask:new b.Tensor("bool",new Array(H*this.num_image_tokens).fill(!0),[1,H,this.num_image_tokens])};if(D&&D.length>0){const re=await this.image_processor(D);return re.pixel_values.unsqueeze_(0),{..._,...re}}return _}}Ct(E,"image_processor_class",u.AutoImageProcessor),Ct(E,"tokenizer_class",p.AutoTokenizer),Ct(E,"uses_processor_config",!0)},
2807"./src/models/jina_clip/image_processing_jina_clip.js":(c,a,s)=>{s.r(a),s.d(a,{JinaCLIPImageProcessor:()=>u});var o=s("./src/base/image_processors_utils.js");class u extends o.ImageProcessor{constructor(C){const{resize_mode:b,fill_color:k,interpolation:E,size:B,...G}=C;super({...G,size:b==="squash"?{width:B,height:B}:b==="shortest"?{shortest_edge:B}:{longest_edge:B},resample:E==="bicubic"?3:2,do_center_crop:!0,crop_size:B,do_normalize:!0})}}},
2807"./src/models/jina_clip/processing_jina_clip.js":(c,a,s)=>{s.r(a),s.d(a,{JinaCLIPProcessor:()=>C});var o=s("./src/base/processing_utils.js"),u=s("./src/models/auto/image_processing_auto.js"),p=s("./src/tokenizers.js");class C extends o.Processor{async _call(k=null,E=null,B={}){if(!k&&!E)throw new Error("Either text or images must be provided");return{...k?this.tokenizer(k,B):{},...E?await this.image_processor(E,B):{}}}}Ct(C,"tokenizer_class",p.AutoTokenizer),Ct(C,"image_processor_class",u.AutoImageProcessor)},
2807"./src/models/llava_onevision/image_processing_llava_onevision.js":(c,a,s)=>{s.r(a),s.d(a,{LlavaOnevisionImageProcessor:()=>u});var o=s("./src/base/image_processors_utils.js");class u extends o.ImageProcessor{}},
2807"./src/models/mask2former/image_processing_mask2former.js":(c,a,s)=>{s.r(a),s.d(a,{Mask2FormerImageProcessor:()=>u});var o=s("./src/models/maskformer/image_processing_maskformer.js");class u extends o.MaskFormerImageProcessor{}},
2807"./src/models/maskformer/image_processing_maskformer.js":(c,a,s)=>{s.r(a),s.d(a,{MaskFormerFeatureExtractor:()=>p,MaskFormerImageProcessor:()=>u});var o=s("./src/base/image_processors_utils.js");class u extends o.ImageProcessor{post_process_panoptic_segmentation(...b){return(0,o.post_process_panoptic_segmentation)(...b)}post_process_instance_segmentation(...b){return(0,o.post_process_instance_segmentation)(...b)}}class p extends u{}},
2807"./src/models/mgp_str/processing_mgp_str.js":(c,a,s)=>{s.r(a),s.d(a,{MgpstrProcessor:()=>k});var o=s("./src/base/processing_utils.js"),u=s("./src/models/auto/image_processing_auto.js"),p=s("./src/tokenizers.js"),C=s("./src/utils/maths.js");const b={char:["char_decode",1],bpe:["bpe_decode",2],wp:["wp_decode",102]};class k extends o.Processor{get char_tokenizer(){return this.components.char_tokenizer}get bpe_tokenizer(){return this.components.bpe_tokenizer}get wp_tokenizer(){return this.components.wp_tokenizer}_decode_helper(B,G){if(!b.hasOwnProperty(G))throw new Error(`Format ${G} is not supported.`);const[D,Q]=b[G],V=this[D].bind(this),[W,U]=B.dims,H=[],K=[],J=B.tolist();for(let g=0;g<W;++g){const v=J[g],e=[],d=[];for(let re=1;re<U;++re){const[S,F]=(0,C.max)((0,C.softmax)(v[re]));if(d.push(S),F==Q)break;e.push(F)}const _=d.length>0?d.reduce((re,S)=>re*S,1):0;K.push(e),H.push(_)}return[V(K),H]}char_decode(B){return this.char_tokenizer.batch_decode(B).map(G=>G.replaceAll(" ",""))}bpe_decode(B){return this.bpe_tokenizer.batch_decode(B)}wp_decode(B){return this.wp_tokenizer.batch_decode(B).map(G=>G.replaceAll(" ",""))}batch_decode([B,G,D]){const[Q,V]=this._decode_helper(B,"char"),[W,U]=this._decode_helper(G,"bpe"),[H,K]=this._decode_helper(D,"wp"),J=[],g=[];for(let v=0;v<Q.length;++v){const[e,d]=(0,C.max)([V[v],U[v],K[v]]);J.push([Q[v],W[v],H[v]][d]),g.push(e)}return{generated_text:J,scores:g,char_preds:Q,bpe_preds:W,wp_preds:H}}static async from_pretrained(...B){const G=await super.from_pretrained(...B),D=await p.AutoTokenizer.from_pretrained("Xenova/gpt2"),Q=await p.AutoTokenizer.from_pretrained("Xenova/bert-base-uncased");return G.components={image_processor:G.image_processor,char_tokenizer:G.tokenizer,bpe_tokenizer:D,wp_tokenizer:Q},G}async _call(B,G=null){const D=await this.image_processor(B);return G&&(D.labels=this.tokenizer(G).input_ids),D}}Ct(k,"tokenizer_class",p.AutoTokenizer),Ct(k,"image_processor_class",u.AutoImageProcessor)},
2807"./src/models/mobilenet_v1/image_processing_mobilenet_v1.js":(c,a,s)=>{s.r(a),s.d(a,{MobileNetV1FeatureExtractor:()=>p,MobileNetV1ImageProcessor:()=>u});var o=s("./src/base/image_processors_utils.js");class u extends o.ImageProcessor{}class p extends u{}},
2807"./src/models/mobilenet_v2/image_processing_mobilenet_v2.js":(c,a,s)=>{s.r(a),s.d(a,{MobileNetV2FeatureExtractor:()=>p,MobileNetV2ImageProcessor:()=>u});var o=s("./src/base/image_processors_utils.js");class u extends o.ImageProcessor{}class p extends u{}},
2807"./src/models/mobilenet_v3/image_processing_mobilenet_v3.js":(c,a,s)=>{s.r(a),s.d(a,{MobileNetV3FeatureExtractor:()=>p,MobileNetV3ImageProcessor:()=>u});var o=s("./src/base/image_processors_utils.js");class u extends o.ImageProcessor{}class p extends u{}},
2807"./src/models/mobilenet_v4/image_processing_mobilenet_v4.js":(c,a,s)=>{s.r(a),s.d(a,{MobileNetV4FeatureExtractor:()=>p,MobileNetV4ImageProcessor:()=>u});var o=s("./src/base/image_processors_utils.js");class u extends o.ImageProcessor{}class p extends u{}},
2807"./src/models/mobilevit/image_processing_mobilevit.js":(c,a,s)=>{s.r(a),s.d(a,{MobileViTFeatureExtractor:()=>p,MobileViTImageProcessor:()=>u});var o=s("./src/base/image_processors_utils.js");class u extends o.ImageProcessor{}class p extends u{}},
2807"./src/models/moonshine/feature_extraction_moonshine.js":(c,a,s)=>{s.r(a),s.d(a,{MoonshineFeatureExtractor:()=>p});var o=s("./src/base/feature_extraction_utils.js"),u=s("./src/utils/tensor.js");class p extends o.FeatureExtractor{async _call(b){(0,o.validate_audio_inputs)(b,"MoonshineFeatureExtractor"),b instanceof Float64Array&&(b=new Float32Array(b));const k=[1,b.length];return{input_values:new u.Tensor("float32",b,k)}}}},
2807"./src/models/moonshine/processing_moonshine.js":(c,a,s)=>{s.r(a),s.d(a,{MoonshineProcessor:()=>C});var o=s("./src/models/auto/feature_extraction_auto.js"),u=s("./src/tokenizers.js"),p=s("./src/base/processing_utils.js");class C extends p.Processor{async _call(k){return await this.feature_extractor(k)}}Ct(C,"tokenizer_class",u.AutoTokenizer),Ct(C,"feature_extractor_class",o.AutoFeatureExtractor)},
2807"./src/models/nougat/image_processing_nougat.js":(c,a,s)=>{s.r(a),s.d(a,{NougatImageProcessor:()=>u});var o=s("./src/models/donut/image_processing_donut.js");class u extends o.DonutImageProcessor{}},
2807"./src/models/owlv2/image_processing_owlv2.js":(c,a,s)=>{s.r(a),s.d(a,{Owlv2ImageProcessor:()=>u});var o=s("./src/models/owlvit/image_processing_owlvit.js");class u extends o.OwlViTImageProcessor{}},
2807"./src/models/owlvit/image_processing_owlvit.js":(c,a,s)=>{s.r(a),s.d(a,{OwlViTFeatureExtractor:()=>p,OwlViTImageProcessor:()=>u});var o=s("./src/base/image_processors_utils.js");class u extends o.ImageProcessor{post_process_object_detection(...b){return(0,o.post_process_object_detection)(...b)}}class p extends u{}},
2807"./src/models/owlvit/processing_owlvit.js":(c,a,s)=>{s.r(a),s.d(a,{OwlViTProcessor:()=>C});var o=s("./src/base/processing_utils.js"),u=s("./src/models/auto/image_processing_auto.js"),p=s("./src/tokenizers.js");class C extends o.Processor{}Ct(C,"tokenizer_class",p.AutoTokenizer),Ct(C,"image_processor_class",u.AutoImageProcessor)},
2807"./src/models/paligemma/processing_paligemma.js":(c,a,s)=>{s.r(a),s.d(a,{PaliGemmaProcessor:()=>b});var o=s("./src/base/processing_utils.js"),u=s("./src/models/auto/image_processing_auto.js"),p=s("./src/tokenizers.js");const C="<image>";class b extends o.Processor{async _call(E,B=null,G={}){B||(console.warn("You are using PaliGemma without a text prefix. It will perform as a picture-captioning model."),B=""),Array.isArray(E)||(E=[E]),Array.isArray(B)||(B=[B]);const D=this.tokenizer.bos_token,Q=this.image_processor.config.image_seq_length;let V;B.some(U=>U.includes(C))?V=B.map(U=>{const H=U.replaceAll(C,C.repeat(Q)),K=H.lastIndexOf(C),J=K===-1?0:K+7;return H.slice(0,J)+D+H.slice(J)+` 2808`}):(console.warn("You are passing both `text` and `images` to `PaliGemmaProcessor`. The processor expects special image tokens in the text, as many tokens as there are images per each text. It is recommended to add `<image>` tokens in the very beginning of your text. For this call, we will infer how many images each text has and add special tokens."),V=B.map(U=>function(H,K,J,g,v){return`${g.repeat(J*v)}${K}${H} 2809`}(U,D,Q,C,E.length)));const W=this.tokenizer(V,G);return{...await this.image_processor(E,G),...W}}}Ct(b,"tokenizer_class",p.AutoTokenizer),Ct(b,"image_processor_class",u.AutoImageProcessor),Ct(b,"uses_processor_config",!1)},
2809"./src/models/phi3_v/image_processing_phi3_v.js":(c,a,s)=>{s.r(a),s.d(a,{Phi3VImageProcessor:()=>B});var o=s("./src/base/image_processors_utils.js"),u=s("./src/utils/tensor.js");const p=336,C=[2,3],{ceil:b,floor:k,sqrt:E}=Math;class B extends o.ImageProcessor{constructor(D){super({...D,do_normalize:!0,do_pad:!0,pad_size:"custom",do_convert_rgb:!0,do_resize:!0}),this._num_crops=D.num_crops}calc_num_image_tokens_from_image_size(D,Q){const{num_img_tokens:V}=this.config;return k((k(Q/p)*k(D/p)+1)*V+1+(k(Q/p)+1)*E(V))}get_resize_output_image_size(D,Q){const V=this._num_crops,[W,U]=D.size;let H=W/U,K=1;for(;K*Math.ceil(K/H)<=V;)K+=1;K-=1;const J=Math.floor(336*K);return[J,Math.floor(J/H)]}pad_image(D,Q,V,W={}){const[U,H]=Q,K=p*b(U/p),J=p*b(H/p),g=[1,1,1].map((v,e)=>(v-this.image_mean[e])/this.image_std[e]);return super.pad_image(D,Q,{width:J,height:K},{center:!0,constant_values:g,...W})}async _call(D,{num_crops:Q=null}={}){if(this._num_crops=Q??(Q=this.config.num_crops),Q<4||E(Q)%1!=0)throw new Error("num_crops must be a square number >= 4");Array.isArray(D)||(D=[D]);const V=D.length,W=await Promise.all(D.map(v=>this.preprocess(v))),U=W.map(v=>v.original_size),H=W.map(v=>v.reshaped_input_size),K=[];for(const{pixel_values:v}of W){v.unsqueeze_(0);const[e,d]=v.dims.slice(-2),_=await(0,u.interpolate_4d)(v,{size:[p,p],mode:"bicubic"});if(Q>0){const re=[],S=E(Q),F=k(d/S),Ie=k(e/S);for(let ce=0;ce<S;++ce)for(let ge=0;ge<S;++ge){let Ce,Fe,Pe,de;ce===S-1?(Fe=e-Ie,de=e):(Fe=ce*Ie,de=(ce+1)*Ie),ge===S-1?(Ce=d-F,Pe=d):(Ce=ge*F,Pe=(ge+1)*F);const pe=[Fe,Ce],we=[de,Pe],Ge=await(0,u.slice)(v,pe,we,C);re.push(Ge)}const he=await(0,u.interpolate_4d)((0,u.cat)(re,0),{size:[p,p],mode:"bicubic"});K.push((0,u.cat)([_,he],0))}else K.push(_)}const J=(0,u.stack)(K,0),g=H.map(v=>v.map(e=>p*b(e/p)));return{pixel_values:J,original_sizes:U,reshaped_input_sizes:H,image_sizes:new u.Tensor("int64",g.flat(),[V,2]),num_img_tokens:g.map(([v,e])=>this.calc_num_image_tokens_from_image_size(e,v))}}}},
2809"./src/models/phi3_v/processing_phi3_v.js":(c,a,s)=>{s.r(a),s.d(a,{Phi3VProcessor:()=>k});var o=s("./src/base/processing_utils.js"),u=s("./src/models/auto/image_processing_auto.js"),p=s("./src/tokenizers.js");s("./src/utils/image.js");const C="<|image|>",b=/<\|image_\d+\|>/g;class k extends o.Processor{async _call(B,G=null,{padding:D=!0,truncation:Q=!0,num_crops:V=null}={}){let W,U;if(Array.isArray(B)||(B=[B]),G){U=await this.image_processor(G,{num_crops:V});const{num_img_tokens:H}=U,K=B.map((g,v)=>g.split(b).join(C.repeat(H[v])));W=this.tokenizer(K,{padding:D,truncation:Q});const J=this.tokenizer.model.convert_tokens_to_ids([C])[0];W.input_ids.map_(g=>g==J?-g:g)}else W=this.tokenizer(B);return{...W,...U}}}Ct(k,"image_processor_class",u.AutoImageProcessor),Ct(k,"tokenizer_class",p.AutoTokenizer)},
2809"./src/models/processors.js":(c,a,s)=>{s.r(a),s.d(a,{Florence2Processor:()=>o.Florence2Processor,GroundingDinoProcessor:()=>u.GroundingDinoProcessor,Idefics3Processor:()=>p.Idefics3Processor,JinaCLIPProcessor:()=>b.JinaCLIPProcessor,MgpstrProcessor:()=>k.MgpstrProcessor,MoonshineProcessor:()=>E.MoonshineProcessor,OwlViTProcessor:()=>B.OwlViTProcessor,PaliGemmaProcessor:()=>D.PaliGemmaProcessor,Phi3VProcessor:()=>G.Phi3VProcessor,PyAnnoteProcessor:()=>Q.PyAnnoteProcessor,Qwen2VLProcessor:()=>V.Qwen2VLProcessor,SamProcessor:()=>W.SamProcessor,SpeechT5Processor:()=>U.SpeechT5Processor,VLChatProcessor:()=>C.VLChatProcessor,Wav2Vec2Processor:()=>H.Wav2Vec2Processor,Wav2Vec2ProcessorWithLM:()=>K.Wav2Vec2ProcessorWithLM,WhisperProcessor:()=>J.WhisperProcessor});var o=s("./src/models/florence2/processing_florence2.js"),u=s("./src/models/grounding_dino/processing_grounding_dino.js"),p=s("./src/models/idefics3/processing_idefics3.js"),C=s("./src/models/janus/processing_janus.js"),b=s("./src/models/jina_clip/processing_jina_clip.js"),k=s("./src/models/mgp_str/processing_mgp_str.js"),E=s("./src/models/moonshine/processing_moonshine.js"),B=s("./src/models/owlvit/processing_owlvit.js"),G=s("./src/models/phi3_v/processing_phi3_v.js"),D=s("./src/models/paligemma/processing_paligemma.js"),Q=s("./src/models/pyannote/processing_pyannote.js"),V=s("./src/models/qwen2_vl/processing_qwen2_vl.js"),W=s("./src/models/sam/processing_sam.js"),U=s("./src/models/speecht5/processing_speecht5.js"),H=s("./src/models/wav2vec2/processing_wav2vec2.js"),K=s("./src/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.js"),J=s("./src/models/whisper/processing_whisper.js")},
2809"./src/models/pvt/image_processing_pvt.js":(c,a,s)=>{s.r(a),s.d(a,{PvtImageProcessor:()=>u});var o=s("./src/base/image_processors_utils.js");class u extends o.ImageProcessor{}},
vendor: 947 bytes, line 2809
2809"./src/models/pyannote/feature_extraction_pyannote.js":(c,a,s)=>{s.r(a),s.d(a,{PyAnnoteFeatureExtractor:()=>C});var o=s("./src/base/feature_extraction_utils.js"),u=s("./src/utils/tensor.js"),p=s("./src/utils/maths.js");class C extends o.FeatureExtractor{async _call(k){(0,o.validate_audio_inputs)(k,"PyAnnoteFeatureExtractor"),k instanceof Float64Array&&(k=new Float32Array(k));const E=[1,1,k.length];return{input_values:new u.Tensor("float32",k,E)}}samples_to_frames(k){return(k-this.config.offset)/this.config.step}post_process_speaker_diarization(k,E){const B=E/this.samples_to_frames(E)/this.config.sampling_rate,G=[];for(const D of k.tolist()){const Q=[];let V=-1;for(let W=0;W<D.length;++W){const U=(0,p.softmax)(D[W]),[H,K]=(0,p.max)(U),[J,g]=[W,W+1];K!==V?(V=K,Q.push({id:K,start:J,end:g,score:H})):(Q.at(-1).end=g,Q.at(-1).score+=H)}G.push(Q.map(({id:W,start:U,end:H,score:K})=>({id:W,start:U*B,end:H*B,confidence:K/(H-U)})))}return G}}},
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2809"./src/models/qwen2_vl/image_processing_qwen2_vl.js":(c,a,s)=>{s.r(a),s.d(a,{Qwen2VLImageProcessor:()=>p});var o=s("./src/base/image_processors_utils.js"),u=s("./src/utils/tensor.js");class p extends o.ImageProcessor{async _call(b,...k){const{pixel_values:E,original_sizes:B,reshaped_input_sizes:G}=await super._call(b,...k);let D=E;const{temporal_patch_size:Q,merge_size:V,patch_size:W}=this.config;D.dims[0]===1&&(D=(0,u.cat)(Array.from({length:Q},()=>D),0));const U=D.dims[0]/Q,H=D.dims[1],K=Math.floor(D.dims[2]/W),J=Math.floor(D.dims[3]/W);return{pixel_values:D.view(U,Q,H,Math.floor(K/V),V,W,Math.floor(J/V),V,W).permute(0,3,6,4,7,2,1,5,8).view(U*K*J,H*Q*W*W),image_grid_thw:new u.Tensor("int64",[U,K,J],[1,3]),original_sizes:B,reshaped_input_sizes:G}}}},
2809"./src/models/qwen2_vl/processing_qwen2_vl.js":(c,a,s)=>{s.r(a),s.d(a,{Qwen2VLProcessor:()=>C});var o=s("./src/base/processing_utils.js"),u=s("./src/models/auto/image_processing_auto.js"),p=s("./src/tokenizers.js");s("./src/utils/image.js");class C extends o.Processor{async _call(k,E=null,...B){let G,D;if(Array.isArray(k)||(k=[k]),E&&(G=await this.image_processor(E),D=G.image_grid_thw),D){let Q=this.image_processor.config.merge_size**2,V=0;const W=D.tolist();k=k.map(U=>{for(;U.includes("<|image_pad|>");){const H=Number(W[V++].reduce((K,J)=>K*J,1n));U=U.replace("<|image_pad|>","<|placeholder|>".repeat(Math.floor(H/Q)))}return U.replaceAll("<|placeholder|>","<|image_pad|>")})}return{...this.tokenizer(k),...G}}}Ct(C,"image_processor_class",u.AutoImageProcessor),Ct(C,"tokenizer_class",p.AutoTokenizer)},
2809"./src/models/rt_detr/image_processing_rt_detr.js":(c,a,s)=>{s.r(a),s.d(a,{RTDetrImageProcessor:()=>u});var o=s("./src/base/image_processors_utils.js");class u extends o.ImageProcessor{post_process_object_detection(...C){return(0,o.post_process_object_detection)(...C)}}},
2809"./src/models/sam/image_processing_sam.js":(c,a,s)=>{s.r(a),s.d(a,{SamImageProcessor:()=>C});var o=s("./src/base/image_processors_utils.js"),u=s("./src/utils/core.js"),p=s("./src/utils/tensor.js");class C extends o.ImageProcessor{reshape_input_points(k,E,B,G=!1){k=structuredClone(k);let D=(0,u.calculateDimensions)(k);if(D.length===3)G||(D=[1,...D]),k=[k];else if(D.length!==4)throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.");for(let Q=0;Q<k.length;++Q){let V=E[Q],W=B[Q],U=[W[0]/V[0],W[1]/V[1]];for(let H=0;H<k[Q].length;++H)for(let K=0;K<k[Q][H].length;++K)for(let J=0;J<k[Q][H][K].length;++J)k[Q][H][K][J]*=U[J%2]}return new p.Tensor("float32",Float32Array.from(k.flat(1/0)),D)}add_input_labels(k,E){let B=(0,u.calculateDimensions)(k);if(B.length===2)B=[1,...B],k=[k];else if(B.length!==3)throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.");if(B.some((G,D)=>G!==E.dims[D]))throw Error(`The first ${B.length} dimensions of 'input_points' and 'input_labels' must be the same.`);return new p.Tensor("int64",k.flat(1/0).map(BigInt),B)}async _call(k,{input_points:E=null,input_labels:B=null,input_boxes:G=null}={}){const D=await super._call(k);if(E&&(D.input_points=this.reshape_input_points(E,D.original_sizes,D.reshaped_input_sizes)),B){if(!D.input_points)throw Error("`input_points` must be provided if `input_labels` are provided.");D.input_labels=this.add_input_labels(B,D.input_points)}return G&&(D.input_boxes=this.reshape_input_points(G,D.original_sizes,D.reshaped_input_sizes,!0)),D}async post_process_masks(k,E,B,{mask_threshold:G=0,binarize:D=!0,pad_size:Q=null}={}){const V=[],W=[(Q=Q??this.pad_size).height,Q.width];for(let U=0;U<E.length;++U){const H=E[U],K=B[U];let J=await(0,p.interpolate_4d)(k[U],{mode:"bilinear",size:W});if(J=J.slice(null,null,[0,K[0]],[0,K[1]]),J=await(0,p.interpolate_4d)(J,{mode:"bilinear",size:H}),D){const g=J.data,v=new Uint8Array(g.length);for(let e=0;e<g.length;++e)g[e]>G&&(v[e]=1);J=new p.Tensor("bool",v,J.dims)}V.push(J)}return V}generate_crop_boxes(k,E,{crop_n_layers:B=0,overlap_ratio:G=512/1500,points_per_crop:D=32,crop_n_points_downscale_factor:Q=1}={}){}}},
2809"./src/models/sam/processing_sam.js":(c,a,s)=>{s.r(a),s.d(a,{SamProcessor:()=>p});var o=s("./src/base/processing_utils.js"),u=s("./src/models/auto/image_processing_auto.js");class p extends o.Processor{async _call(...b){return await this.image_processor(...b)}post_process_masks(...b){return this.image_processor.post_process_masks(...b)}reshape_input_points(...b){return this.image_processor.reshape_input_points(...b)}}Ct(p,"image_processor_class",u.AutoImageProcessor)},
2809"./src/models/seamless_m4t/feature_extraction_seamless_m4t.js":(c,a,s)=>{s.r(a),s.d(a,{SeamlessM4TFeatureExtractor:()=>C});var o=s("./src/base/feature_extraction_utils.js"),u=s("./src/utils/tensor.js"),p=s("./src/utils/audio.js");class C extends o.FeatureExtractor{constructor(k){super(k);const E=this.config.sampling_rate,B=(0,p.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(E/2),E,null,"kaldi",!0);for(let G=0;G<B.length;++G)B[G].push(0);this.mel_filters=B,this.window=(0,p.window_function)(400,"povey",{periodic:!1})}async _extract_fbank_features(k,E){return k=k.map(B=>32768*B),(0,p.spectrogram)(k,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:E,transpose:!0})}async _call(k,{padding:E=!0,pad_to_multiple_of:B=2,do_normalize_per_mel_bins:G=!0,return_attention_mask:D=!0}={}){(0,o.validate_audio_inputs)(k,"SeamlessM4TFeatureExtractor");let Q,V=await this._extract_fbank_features(k,this.config.max_length);if(G){const[g,v]=V.dims,e=V.data;for(let d=0;d<v;++d){let _=0;for(let Ie=0;Ie<g;++Ie)_+=e[Ie*v+d];const re=_/g;let S=0;for(let Ie=0;Ie<g;++Ie)S+=(e[Ie*v+d]-re)**2;S/=g-1;const F=Math.sqrt(S+1e-7);for(let Ie=0;Ie<g;++Ie){const he=Ie*v+d;e[he]=(e[he]-re)/F}}}if(E){const[g,v]=V.dims,e=V.data,d=g%B;if(d>0){const _=new Float32Array(v*(g+d));_.set(e),_.fill(this.config.padding_value,e.length);const re=g+d;V=new u.Tensor(V.type,_,[re,v]),D&&(Q=new u.Tensor("int64",new BigInt64Array(re),[1,re]),Q.data.fill(1n,0,g))}}const[W,U]=V.dims,H=this.config.stride;if(W%H!==0)throw new Error(`The number of frames (${W}) must be a multiple of the stride (${H}).`);const K=V.view(1,Math.floor(W/H),U*H),J={input_features:K};if(D){const g=K.dims[1],v=new BigInt64Array(g);if(Q){const e=Q.data;for(let d=1,_=0;d<W;d+=H,++_)v[_]=e[d]}else v.fill(1n);J.attention_mask=new u.Tensor("int64",v,[1,g])}return J}}},
2809"./src/models/segformer/image_processing_segformer.js":(c,a,s)=>{s.r(a),s.d(a,{SegformerFeatureExtractor:()=>p,SegformerImageProcessor:()=>u});var o=s("./src/base/image_processors_utils.js");class u extends o.ImageProcessor{post_process_semantic_segmentation(...b){return(0,o.post_process_semantic_segmentation)(...b)}}class p extends u{}},
2809"./src/models/siglip/image_processing_siglip.js":(c,a,s)=>{s.r(a),s.d(a,{SiglipImageProcessor:()=>u});var o=s("./src/base/image_processors_utils.js");class u extends o.ImageProcessor{}},
2809"./src/models/speecht5/feature_extraction_speecht5.js":(c,a,s)=>{s.r(a),s.d(a,{SpeechT5FeatureExtractor:()=>u});var o=s("./src/base/feature_extraction_utils.js");class u extends o.FeatureExtractor{}},
2809"./src/models/speecht5/processing_speecht5.js":(c,a,s)=>{s.r(a),s.d(a,{SpeechT5Processor:()=>C});var o=s("./src/base/processing_utils.js"),u=s("./src/tokenizers.js"),p=s("./src/models/auto/feature_extraction_auto.js");class C extends o.Processor{async _call(k){return await this.feature_extractor(k)}}Ct(C,"tokenizer_class",u.AutoTokenizer),Ct(C,"feature_extractor_class",p.AutoFeatureExtractor)},
vendor: 346 bytes, line 2809
2809"./src/models/swin2sr/image_processing_swin2sr.js":(c,a,s)=>{s.r(a),s.d(a,{Swin2SRImageProcessor:()=>u});var o=s("./src/base/image_processors_utils.js");class u extends o.ImageProcessor{pad_image(C,b,k,E={}){const[B,G,D]=b;return super.pad_image(C,b,{width:G+(k-G%k)%k,height:B+(k-B%k)%k},{mode:"symmetric",center:!1,constant_values:-1,...E})}}},
2809"./src/models/vit/image_processing_vit.js":(c,a,s)=>{s.r(a),s.d(a,{ViTFeatureExtractor:()=>p,ViTImageProcessor:()=>u});var o=s("./src/base/image_processors_utils.js");class u extends o.ImageProcessor{}class p extends u{}},
2809"./src/models/vitmatte/image_processing_vitmatte.js":(c,a,s)=>{s.r(a),s.d(a,{VitMatteImageProcessor:()=>p});var o=s("./src/base/image_processors_utils.js"),u=s("./src/utils/tensor.js");class p extends o.ImageProcessor{async _call(b,k){Array.isArray(b)||(b=[b]),Array.isArray(k)||(k=[k]);const E=await Promise.all(b.map(D=>this.preprocess(D))),B=await Promise.all(k.map(D=>this.preprocess(D,{do_normalize:!1,do_convert_rgb:!1,do_convert_grayscale:!0})));return{pixel_values:(0,u.stack)(E.map((D,Q)=>(0,u.cat)([D.pixel_values,B[Q].pixel_values],0)),0),original_sizes:E.map(D=>D.original_size),reshaped_input_sizes:E.map(D=>D.reshaped_input_size)}}}},
vendor: 784 bytes, line 2809
2809"./src/models/vitpose/image_processing_vitpose.js":(c,a,s)=>{s.r(a),s.d(a,{VitPoseImageProcessor:()=>u});var o=s("./src/base/image_processors_utils.js");class u extends o.ImageProcessor{post_process_pose_estimation(C,b,{threshold:k=null}={}){const E=C.tolist(),[B,G,D,Q]=C.dims,V=[];for(let W=0;W<B;++W){const U=E[W],H=b[W],K=[];for(let J=0;J<H.length;++J){const g=H[J],v=[],e=[],d=[],_=g.at(-2)/Q,re=g.at(-1)/D;for(let S=0;S<U.length;++S){let[F,Ie]=[0,0],he=0,ce=-1/0;const ge=U[S];for(let Fe=0;Fe<ge.length;++Fe){const Pe=ge[Fe];for(let de=0;de<Pe.length;++de){const pe=Pe[de];he+=pe,ce=Math.max(ce,pe),F+=(de+.5)*pe,Ie+=Fe*pe}}if(k!=null&&ce<k)continue;const Ce=[_*F/he,re*Ie/he];v.push(Ce),d.push(S),e.push(ce)}K.push({bbox:g,scores:e,labels:d,keypoints:v})}V.push(K)}return V}}},
2809"./src/models/wav2vec2/feature_extraction_wav2vec2.js":(c,a,s)=>{s.r(a),s.d(a,{Wav2Vec2FeatureExtractor:()=>p});var o=s("./src/base/feature_extraction_utils.js"),u=s("./src/utils/tensor.js");class p extends o.FeatureExtractor{_zero_mean_unit_var_norm(b){const k=b.reduce((G,D)=>G+D,0),E=k/b.length,B=b.reduce((G,D)=>G+(D-E)**2,0)/b.length;return b.map(G=>(G-E)/Math.sqrt(B+1e-7))}async _call(b){(0,o.validate_audio_inputs)(b,"Wav2Vec2FeatureExtractor"),b instanceof Float64Array&&(b=new Float32Array(b));let k=b;this.config.do_normalize&&(k=this._zero_mean_unit_var_norm(k));const E=[1,k.length];return{input_values:new u.Tensor("float32",k,E),attention_mask:new u.Tensor("int64",new BigInt64Array(k.length).fill(1n),E)}}}},
2809"./src/models/wav2vec2/processing_wav2vec2.js":(c,a,s)=>{s.r(a),s.d(a,{Wav2Vec2Processor:()=>C});var o=s("./src/tokenizers.js"),u=s("./src/models/auto/feature_extraction_auto.js"),p=s("./src/base/processing_utils.js");class C extends p.Processor{async _call(k){return await this.feature_extractor(k)}}Ct(C,"tokenizer_class",o.AutoTokenizer),Ct(C,"feature_extractor_class",u.AutoFeatureExtractor)},
2809"./src/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.js":(c,a,s)=>{s.r(a),s.d(a,{Wav2Vec2ProcessorWithLM:()=>C});var o=s("./src/tokenizers.js"),u=s("./src/models/auto/feature_extraction_auto.js"),p=s("./src/base/processing_utils.js");class C extends p.Processor{async _call(k){return await this.feature_extractor(k)}}Ct(C,"tokenizer_class",o.AutoTokenizer),Ct(C,"feature_extractor_class",u.AutoFeatureExtractor)},
2809"./src/models/wespeaker/feature_extraction_wespeaker.js":(c,a,s)=>{s.r(a),s.d(a,{WeSpeakerFeatureExtractor:()=>p});var o=s("./src/base/feature_extraction_utils.js");s("./src/utils/tensor.js");var u=s("./src/utils/audio.js");class p extends o.FeatureExtractor{constructor(b){super(b);const k=this.config.sampling_rate,E=(0,u.mel_filter_bank)(256,this.config.num_mel_bins,20,Math.floor(k/2),k,null,"kaldi",!0);for(let B=0;B<E.length;++B)E[B].push(0);this.mel_filters=E,this.window=(0,u.window_function)(400,"hamming",{periodic:!1}),this.min_num_frames=this.config.min_num_frames}async _extract_fbank_features(b){return b=b.map(k=>32768*k),(0,u.spectrogram)(b,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,transpose:!0,min_num_frames:this.min_num_frames})}async _call(b){(0,o.validate_audio_inputs)(b,"WeSpeakerFeatureExtractor");const k=(await this._extract_fbank_features(b)).unsqueeze_(0);if(this.config.fbank_centering_span===null){const E=k.mean(1).data,B=k.data,[G,D,Q]=k.dims;for(let V=0;V<G;++V){const W=V*D*Q,U=V*Q;for(let H=0;H<D;++H){const K=W+H*Q;for(let J=0;J<Q;++J)B[K+J]-=E[U+J]}}}return{input_features:k}}}},
2809"./src/models/whisper/common_whisper.js":(c,a,s)=>{s.r(a),s.d(a,{WHISPER_LANGUAGE_MAPPING:()=>u,WHISPER_TO_LANGUAGE_CODE_MAPPING:()=>p,whisper_language_to_code:()=>C});const o=[["en","english"],["zh","chinese"],["de","german"],["es","spanish"],["ru","russian"],["ko","korean"],["fr","french"],["ja","japanese"],["pt","portuguese"],["tr","turkish"],["pl","polish"],["ca","catalan"],["nl","dutch"],["ar","arabic"],["sv","swedish"],["it","italian"],["id","indonesian"],["hi","hindi"],["fi","finnish"],["vi","vietnamese"],["he","hebrew"],["uk","ukrainian"],["el","greek"],["ms","malay"],["cs","czech"],["ro","romanian"],["da","danish"],["hu","hungarian"],["ta","tamil"],["no","norwegian"],["th","thai"],["ur","urdu"],["hr","croatian"],["bg","bulgarian"],["lt","lithuanian"],["la","latin"],["mi","maori"],["ml","malayalam"],["cy","welsh"],["sk","slovak"],["te","telugu"],["fa","persian"],["lv","latvian"],["bn","bengali"],["sr","serbian"],["az","azerbaijani"],["sl","slovenian"],["kn","kannada"],["et","estonian"],["mk","macedonian"],["br","breton"],["eu","basque"],["is","icelandic"],["hy","armenian"],["ne","nepali"],["mn","mongolian"],["bs","bosnian"],["kk","kazakh"],["sq","albanian"],["sw","swahili"],["gl","galician"],["mr","marathi"],["pa","punjabi"],["si","sinhala"],["km","khmer"],["sn","shona"],["yo","yoruba"],["so","somali"],["af","afrikaans"],["oc","occitan"],["ka","georgian"],["be","belarusian"],["tg","tajik"],["sd","sindhi"],["gu","gujarati"],["am","amharic"],["yi","yiddish"],["lo","lao"],["uz","uzbek"],["fo","faroese"],["ht","haitian creole"],["ps","pashto"],["tk","turkmen"],["nn","nynorsk"],["mt","maltese"],["sa","sanskrit"],["lb","luxembourgish"],["my","myanmar"],["bo","tibetan"],["tl","tagalog"],["mg","malagasy"],["as","assamese"],["tt","tatar"],["haw","hawaiian"],["ln","lingala"],["ha","hausa"],["ba","bashkir"],["jw","javanese"],["su","sundanese"]],u=new Map(o),p=new Map([...o.map(([b,k])=>[k,b]),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);function C(b){b=b.toLowerCase();let k=p.get(b);if(k===void 0){if(!u.has(b)){const E=b.length===2?u.keys():u.values();throw new Error(`Language "${b}" is not supported. Must be one of: ${JSON.stringify(E)}`)}k=b}return k}},
2809"./src/models/whisper/feature_extraction_whisper.js":(c,a,s)=>{s.r(a),s.d(a,{WhisperFeatureExtractor:()=>C});var o=s("./src/base/feature_extraction_utils.js");s("./src/utils/tensor.js");var u=s("./src/utils/audio.js"),p=s("./src/utils/maths.js");class C extends o.FeatureExtractor{constructor(k){var E;super(k),(E=this.config).mel_filters??(E.mel_filters=(0,u.mel_filter_bank)(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,8e3,this.config.sampling_rate,"slaney","slaney")),this.window=(0,u.window_function)(this.config.n_fft,"hann")}async _extract_fbank_features(k){const E=await(0,u.spectrogram)(k,this.window,this.config.n_fft,this.config.hop_length,{power:2,mel_filters:this.config.mel_filters,log_mel:"log10",max_num_frames:this.config.nb_max_frames}),B=E.data,G=(0,p.max)(B)[0];for(let D=0;D<B.length;++D)B[D]=(Math.max(B[D],G-8)+4)/4;return E}async _call(k){let E;return(0,o.validate_audio_inputs)(k,"WhisperFeatureExtractor"),k.length>this.config.n_samples?(console.warn("Attempting to extract features for audio longer than 30 seconds. If using a pipeline to extract transcript from a long audio clip, remember to specify `chunk_length_s` and/or `stride_length_s`."),E=k.slice(0,this.config.n_samples)):(E=new Float32Array(this.config.n_samples),E.set(k)),{input_features:(await this._extract_fbank_features(E)).unsqueeze_(0)}}}},
2809"./src/models/whisper/generation_whisper.js":(c,a,s)=>{s.r(a),s.d(a,{WhisperGenerationConfig:()=>u});var o=s("./src/generation/configuration_utils.js");class u extends o.GenerationConfig{constructor(){super(...arguments);Ct(this,"return_timestamps",null);Ct(this,"return_token_timestamps",null);Ct(this,"num_frames",null);Ct(this,"alignment_heads",null);Ct(this,"task",null);Ct(this,"language",null);Ct(this,"no_timestamps_token_id",null);Ct(this,"prompt_ids",null);Ct(this,"is_multilingual",null);Ct(this,"lang_to_id",null);Ct(this,"task_to_id",null);Ct(this,"max_initial_timestamp_index",1)}}},
2809"./src/models/whisper/processing_whisper.js":(c,a,s)=>{s.r(a),s.d(a,{WhisperProcessor:()=>C});var o=s("./src/models/auto/feature_extraction_auto.js"),u=s("./src/tokenizers.js"),p=s("./src/base/processing_utils.js");class C extends p.Processor{async _call(k){return await this.feature_extractor(k)}}Ct(C,"tokenizer_class",u.AutoTokenizer),Ct(C,"feature_extractor_class",o.AutoFeatureExtractor)},
2809"./src/models/yolos/image_processing_yolos.js":(c,a,s)=>{s.r(a),s.d(a,{YolosFeatureExtractor:()=>p,YolosImageProcessor:()=>u});var o=s("./src/base/image_processors_utils.js");class u extends o.ImageProcessor{post_process_object_detection(...b){return(0,o.post_process_object_detection)(...b)}}class p extends u{}},
2809"./src/ops/registry.js":(c,a,s)=>{s.r(a),s.d(a,{TensorOpRegistry:()=>k});var o=s("./src/backends/onnx.js"),u=s("./src/utils/tensor.js"),p=s("./src/env.js");const C=p.apis.IS_BROWSER_ENV||p.apis.IS_WEBWORKER_ENV,b=async(E,B,G)=>{const D=await(0,o.createInferenceSession)(new Uint8Array(E),B);let Q=Promise.resolve();return async V=>{const W=(0,o.isONNXProxy)(),U=Object.fromEntries(Object.entries(V).map(([K,J])=>[K,(W?J.clone():J).ort_tensor])),H=await(Q=C?Q.then(()=>D.run(U)):D.run(U));return Array.isArray(G)?G.map(K=>new u.Tensor(H[K])):new u.Tensor(H[G])}};class k{static get nearest_interpolate_4d(){return this._nearest_interpolate_4d||(this._nearest_interpolate_4d=b([8,10,18,0,58,129,1,10,41,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,18,10,4,109,111,100,101,34,7,110,101,97,114,101,115,116,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,21],this.session_options,"y")),this._nearest_interpolate_4d}static get bilinear_interpolate_4d(){return this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=b([8,9,18,0,58,128,1,10,40,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,17,10,4,109,111,100,101,34,6,108,105,110,101,97,114,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20],this.session_options,"y")),this._bilinear_interpolate_4d}static get bicubic_interpolate_4d(){return this._bicubic_interpolate_4d||(this._bicubic_interpolate_4d=b([8,9,18,0,58,127,10,39,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,16,10,4,109,111,100,101,34,5,99,117,98,105,99,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20],this.session_options,"y")),this._bicubic_interpolate_4d}static get matmul(){return this._matmul||(this._matmul=b([8,9,18,0,58,55,10,17,10,1,97,10,1,98,18,1,99,34,6,77,97,116,77,117,108,18,1,114,90,9,10,1,97,18,4,10,2,8,1,90,9,10,1,98,18,4,10,2,8,1,98,9,10,1,99,18,4,10,2,8,1,66,2,16,20],this.session_options,"c")),this._matmul}static get stft(){return this._stft||(this._stft=b([8,7,18,0,58,148,1,10,38,10,1,115,10,1,106,10,1,119,10,1,108,18,1,111,34,4,83,84,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,115,90,26,10,1,115,18,21,10,19,8,1,18,15,10,3,18,1,98,10,3,18,1,115,10,3,18,1,99,90,11,10,1,106,18,6,10,4,8,7,18,0,90,16,10,1,119,18,11,10,9,8,1,18,5,10,3,18,1,119,90,11,10,1,108,18,6,10,4,8,7,18,0,98,31,10,1,111,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,102,10,3,18,1,100,10,3,18,1,99,66,2,16,17],this.session_options,"o")),this._stft}static get rfft(){return this._rfft||(this._rfft=b([8,9,18,0,58,97,10,33,10,1,120,10,0,10,1,97,18,1,121,34,3,68,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,100,90,21,10,1,120,18,16,10,14,8,1,18,10,10,3,18,1,115,10,3,18,1,99,90,11,10,1,97,18,6,10,4,8,7,18,0,98,21,10,1,121,18,16,10,14,8,1,18,10,10,3,18,1,115,10,3,18,1,99,66,2,16,20],this.session_options,"y")),this._rfft}static get top_k(){return this._top_k||(this._top_k=b([8,10,18,0,58,73,10,18,10,1,120,10,1,107,18,1,118,18,1,105,34,4,84,111,112,75,18,1,116,90,9,10,1,120,18,4,10,2,8,1,90,15,10,1,107,18,10,10,8,8,7,18,4,10,2,8,1,98,9,10,1,118,18,4,10,2,8,1,98,9,10,1,105,18,4,10,2,8,7,66,2,16,21],this.session_options,["v","i"])),this._top_k}static get slice(){return this._slice||(this._slice=b([8,7,18,0,58,96,10,25,10,1,120,10,1,115,10,1,101,10,1,97,10,1,116,18,1,121,34,5,83,108,105,99,101,18,1,114,90,9,10,1,120,18,4,10,2,8,1,90,9,10,1,115,18,4,10,2,8,7,90,9,10,1,101,18,4,10,2,8,7,90,9,10,1,97,18,4,10,2,8,7,90,9,10,1,116,18,4,10,2,8,7,98,9,10,1,121,18,4,10,2,8,1,66,2,16,13],this.session_options,"y")),this._slice}}Ct(k,"session_options",{})},
2809"./src/pipelines.js":(c,a,s)=>{s.r(a),s.d(a,{AudioClassificationPipeline:()=>Ie,AutomaticSpeechRecognitionPipeline:()=>ce,DepthEstimationPipeline:()=>at,DocumentQuestionAnsweringPipeline:()=>we,FeatureExtractionPipeline:()=>S,FillMaskPipeline:()=>J,ImageClassificationPipeline:()=>Ce,ImageFeatureExtractionPipeline:()=>F,ImageSegmentationPipeline:()=>Fe,ImageToImagePipeline:()=>me,ImageToTextPipeline:()=>ge,ObjectDetectionPipeline:()=>de,Pipeline:()=>W,QuestionAnsweringPipeline:()=>K,SummarizationPipeline:()=>v,Text2TextGenerationPipeline:()=>g,TextClassificationPipeline:()=>U,TextGenerationPipeline:()=>_,TextToAudioPipeline:()=>Ge,TokenClassificationPipeline:()=>H,TranslationPipeline:()=>e,ZeroShotAudioClassificationPipeline:()=>he,ZeroShotClassificationPipeline:()=>re,ZeroShotImageClassificationPipeline:()=>Pe,ZeroShotObjectDetectionPipeline:()=>pe,pipeline:()=>pt});var o=s("./src/tokenizers.js"),u=s("./src/models.js"),p=s("./src/models/auto/processing_auto.js");s("./src/base/processing_utils.js");var C=s("./src/utils/generic.js"),b=s("./src/utils/core.js"),k=s("./src/utils/maths.js"),E=s("./src/utils/audio.js"),B=s("./src/utils/tensor.js"),G=s("./src/utils/image.js");async function D(ct){return Array.isArray(ct)||(ct=[ct]),await Promise.all(ct.map(je=>G.RawImage.read(je)))}async function Q(ct,je){return Array.isArray(ct)||(ct=[ct]),await Promise.all(ct.map(At=>typeof At=="string"||At instanceof URL?(0,E.read_audio)(At,je):At instanceof Float64Array?new Float32Array(At):At))}function V(ct,je){je&&(ct=ct.map(rt=>0|rt));const[At,It,wt,ze]=ct;return{xmin:At,ymin:It,xmax:wt,ymax:ze}}class W extends C.Callable{constructor({task:je,model:At,tokenizer:It=null,processor:wt=null}){super(),this.task=je,this.model=At,this.tokenizer=It,this.processor=wt}async dispose(){await this.model.dispose()}}class U extends W{constructor(je){super(je)}async _call(je,{top_k:At=1}={}){const It=this.tokenizer(je,{padding:!0,truncation:!0}),wt=await this.model(It),ze=this.model.config.problem_type==="multi_label_classification"?Ot=>Ot.sigmoid():Ot=>new B.Tensor("float32",(0,k.softmax)(Ot.data),Ot.dims),rt=this.model.config.id2label,Bt=[];for(const Ot of wt.logits){const St=ze(Ot),oe=await(0,B.topk)(St,At),zt=oe[0].tolist(),ke=oe[1].tolist().map((fe,Qe)=>({label:rt?rt[fe]:`LABEL_${fe}`,score:zt[Qe]}));At===1?Bt.push(...ke):Bt.push(ke)}return Array.isArray(je)||At===1?Bt:Bt[0]}}class H extends W{constructor(je){super(je)}async _call(je,{ignore_labels:At=["O"]}={}){const It=Array.isArray(je),wt=this.tokenizer(It?je:[je],{padding:!0,truncation:!0}),ze=(await this.model(wt)).logits,rt=this.model.config.id2label,Bt=[];for(let Ot=0;Ot<ze.dims[0];++Ot){const St=wt.input_ids[Ot],oe=ze[Ot],zt=[];for(let ke=0;ke<oe.dims[0];++ke){const fe=oe[ke],Qe=(0,k.max)(fe.data)[1],Je=rt?rt[Qe]:`LABEL_${Qe}`;if(At.includes(Je))continue;const st=this.tokenizer.decode([St[ke].item()],{skip_special_tokens:!0});if(st==="")continue;const lt=(0,k.softmax)(fe.data);zt.push({entity:Je,score:lt[Qe],index:ke,word:st})}Bt.push(zt)}return It?Bt:Bt[0]}}class K extends W{constructor(je){super(je)}async _call(je,At,{top_k:It=1}={}){const wt=this.tokenizer(je,{text_pair:At,padding:!0,truncation:!0}),{start_logits:ze,end_logits:rt}=await this.model(wt),Bt=wt.input_ids.tolist(),Ot=wt.attention_mask.tolist(),St=this.tokenizer.all_special_ids,oe=[];for(let zt=0;zt<ze.dims[0];++zt){const ke=Bt[zt],fe=ke.findIndex(jt=>jt==this.tokenizer.sep_token_id);Ot[zt].map((jt,$t)=>jt==1&&($t===0||$t>fe&&St.findIndex(Ft=>Ft==ke[$t])===-1));const Qe=ze[zt].tolist(),Je=rt[zt].tolist();for(let jt=1;jt<Qe.length;++jt)(Ot[zt]==0||jt<=fe||St.findIndex($t=>$t==ke[jt])!==-1)&&(Qe[jt]=-1/0,Je[jt]=-1/0);const st=(0,k.softmax)(Qe).map((jt,$t)=>[jt,$t]),lt=(0,k.softmax)(Je).map((jt,$t)=>[jt,$t]);st[0][0]=0,lt[0][0]=0;const xt=(0,b.product)(st,lt).filter(jt=>jt[0][1]<=jt[1][1]).map(jt=>[jt[0][1],jt[1][1],jt[0][0]*jt[1][0]]).sort((jt,$t)=>$t[2]-jt[2]);for(let jt=0;jt<Math.min(xt.length,It);++jt){const[$t,Ft,dA]=xt[jt],eA=ke.slice($t,Ft+1),nA=this.tokenizer.decode(eA,{skip_special_tokens:!0});oe.push({answer:nA,score:dA})}}return It===1?oe[0]:oe}}class J extends W{constructor(je){super(je)}async _call(je,{top_k:At=5}={}){const It=this.tokenizer(je,{padding:!0,truncation:!0}
2809),{logits:wt}=await this.model(It),ze=[],rt=It.input_ids.tolist();for(let Bt=0;Bt<rt.length;++Bt){const Ot=rt[Bt],St=Ot.findIndex(Qe=>Qe==this.tokenizer.mask_token_id);if(St===-1)throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`);const oe=wt[Bt][St],zt=await(0,B.topk)(new B.Tensor("float32",(0,k.softmax)(oe.data),oe.dims),At),ke=zt[0].tolist(),fe=zt[1].tolist();ze.push(fe.map((Qe,Je)=>{const st=Ot.slice();return st[St]=Qe,{score:ke[Je],token:Number(Qe),token_str:this.tokenizer.decode([Qe]),sequence:this.tokenizer.decode(st,{skip_special_tokens:!0})}}))}return Array.isArray(je)?ze:ze[0]}}class g extends W{constructor(At){super(At);Ct(this,"_key","generated_text")}async _call(At,It={}){Array.isArray(At)||(At=[At]),this.model.config.prefix&&(At=At.map(St=>this.model.config.prefix+St));const wt=this.model.config.task_specific_params;wt&&wt[this.task]&&wt[this.task].prefix&&(At=At.map(St=>wt[this.task].prefix+St));const ze=this.tokenizer,rt={padding:!0,truncation:!0};let Bt;Bt=this instanceof e&&"_build_translation_inputs"in ze?ze._build_translation_inputs(At,rt,It):ze(At,rt);const Ot=await this.model.generate({...Bt,...It});return ze.batch_decode(Ot,{skip_special_tokens:!0}).map(St=>({[this._key]:St}))}}class v extends g{constructor(At){super(At);Ct(this,"_key","summary_text")}}class e extends g{constructor(At){super(At);Ct(this,"_key","translation_text")}}function d(ct){return Array.isArray(ct)&&ct.every(je=>"role"in je&&"content"in je)}class _ extends W{constructor(je){super(je)}async _call(je,At={}){let It,wt=!1,ze=!1;if(typeof je=="string")It=je=[je];else if(Array.isArray(je)&&je.every(fe=>typeof fe=="string"))wt=!0,It=je;else{if(d(je))je=[je];else{if(!Array.isArray(je)||!je.every(d))throw new Error("Input must be a string, an array of strings, a Chat, or an array of Chats");wt=!0}ze=!0,It=je.map(fe=>this.tokenizer.apply_chat_template(fe,{tokenize:!1,add_generation_prompt:!0}))}const rt=At.add_special_tokens??!1,Bt=!ze&&(At.return_full_text??!0);this.tokenizer.padding_side="left";const Ot=this.tokenizer(It,{add_special_tokens:rt,padding:!0,truncation:!0}),St=await this.model.generate({...Ot,...At}),oe=this.tokenizer.batch_decode(St,{skip_special_tokens:!0});let zt;!Bt&&Ot.input_ids.dims.at(-1)>0&&(zt=this.tokenizer.batch_decode(Ot.input_ids,{skip_special_tokens:!0}).map(fe=>fe.length));const ke=Array.from({length:je.length},fe=>[]);for(let fe=0;fe<oe.length;++fe){const Qe=Math.floor(fe/St.dims[0]*je.length);zt&&(oe[fe]=oe[fe].slice(zt[Qe])),ke[Qe].push({generated_text:ze?[...je[Qe],{role:"assistant",content:oe[fe]}]:oe[fe]})}return wt||ke.length!==1?ke:ke[0]}}class re extends W{constructor(je){super(je),this.label2id=Object.fromEntries(Object.entries(this.model.config.label2id).map(([At,It])=>[At.toLowerCase(),It])),this.entailment_id=this.label2id.entailment,this.entailment_id===void 0&&(console.warn("Could not find 'entailment' in label2id mapping. Using 2 as entailment_id."),this.entailment_id=2),this.contradiction_id=this.label2id.contradiction??this.label2id.not_entailment,this.contradiction_id===void 0&&(console.warn("Could not find 'contradiction' in label2id mapping. Using 0 as contradiction_id."),this.contradiction_id=0)}async _call(je,At,{hypothesis_template:It="This example is {}.",multi_label:wt=!1}={}){const ze=Array.isArray(je);ze||(je=[je]),Array.isArray(At)||(At=[At]);const rt=At.map(St=>It.replace("{}",St)),Bt=wt||At.length===1,Ot=[];for(const St of je){const oe=[];for(const fe of rt){const Qe=this.tokenizer(St,{text_pair:fe,padding:!0,truncation:!0}),Je=await this.model(Qe);Bt?oe.push([Je.logits.data[this.contradiction_id],Je.logits.data[this.entailment_id]]):oe.push(Je.logits.data[this.entailment_id])}const zt=Bt?oe.map(fe=>(0,k.softmax)(fe)[1]):(0,k.softmax)(oe),ke=zt.map((fe,Qe)=>[fe,Qe]).sort((fe,Qe)=>Qe[0]-fe[0]);Ot.push({sequence:St,labels:ke.map(fe=>At[fe[1]]),scores:ke.map(fe=>fe[0])})}return ze?Ot:Ot[0]}}class S extends W{constructor(je){super(je)}async _call(je,{pooling:At="none",normalize:It=!1,quantize:wt=!1,precision:ze="binary"}={}){const rt=this.tokenizer(je,{padding:!0,truncation:!0}
2809),Bt=await this.model(rt);let Ot=Bt.last_hidden_state??Bt.logits??Bt.token_embeddings;if(At!=="none")if(At==="mean")Ot=(0,B.mean_pooling)(Ot,rt.attention_mask);else{if(At!=="cls")throw Error(`Pooling method '${At}' not supported.`);Ot=Ot.slice(null,0)}return It&&(Ot=Ot.normalize(2,-1)),wt&&(Ot=(0,B.quantize_embeddings)(Ot,ze)),Ot}}class F extends W{constructor(je){super(je)}async _call(je,{pool:At=null}={}){const It=await D(je),{pixel_values:wt}=await this.processor(It),ze=await this.model({pixel_values:wt});let rt;if(At){if(!("pooler_output"in ze))throw Error("No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.");rt=ze.pooler_output}else rt=ze.last_hidden_state??ze.logits??ze.image_embeds;return rt}}class Ie extends W{constructor(je){super(je)}async _call(je,{top_k:At=5}={}){const It=this.processor.feature_extractor.config.sampling_rate,wt=await Q(je,It),ze=this.model.config.id2label,rt=[];for(const Bt of wt){const Ot=await this.processor(Bt),St=(await this.model(Ot)).logits[0],oe=await(0,B.topk)(new B.Tensor("float32",(0,k.softmax)(St.data),St.dims),At),zt=oe[0].tolist(),ke=oe[1].tolist().map((fe,Qe)=>({label:ze?ze[fe]:`LABEL_${fe}`,score:zt[Qe]}));rt.push(ke)}return Array.isArray(je)?rt:rt[0]}}class he extends W{constructor(je){super(je)}async _call(je,At,{hypothesis_template:It="This is a sound of {}."}={}){const wt=!Array.isArray(je);wt&&(je=[je]);const ze=At.map(oe=>It.replace("{}",oe)),rt=this.tokenizer(ze,{padding:!0,truncation:!0}),Bt=this.processor.feature_extractor.config.sampling_rate,Ot=await Q(je,Bt),St=[];for(const oe of Ot){const zt=await this.processor(oe),ke=await this.model({...rt,...zt}),fe=(0,k.softmax)(ke.logits_per_audio.data);St.push([...fe].map((Qe,Je)=>({score:Qe,label:At[Je]})))}return wt?St[0]:St}}class ce extends W{constructor(je){super(je)}async _call(je,At={}){switch(this.model.config.model_type){case"whisper":return this._call_whisper(je,At);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":return this._call_wav2vec2(je,At);case"moonshine":return this._call_moonshine(je,At);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(je,At){At.language&&console.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'),At.task&&console.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');const It=!Array.isArray(je);It&&(je=[je]);const wt=this.processor.feature_extractor.config.sampling_rate,ze=await Q(je,wt),rt=[];for(const Bt of ze){const Ot=await this.processor(Bt),St=(await this.model(Ot)).logits[0],oe=[];for(const ke of St)oe.push((0,k.max)(ke.data)[1]);const zt=this.tokenizer.decode(oe);rt.push({text:zt})}return It?rt[0]:rt}async _call_whisper(je,At){const It=At.return_timestamps??!1,wt=At.chunk_length_s??0,ze=At.force_full_sequences??!1;let rt=At.stride_length_s??null;const Bt={...At};It==="word"&&(Bt.return_token_timestamps=!0,Bt.return_timestamps=!1);const Ot=!Array.isArray(je);Ot&&(je=[je]);const St=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,oe=this.processor.feature_extractor.config.hop_length,zt=this.processor.feature_extractor.config.sampling_rate,ke=await Q(je,zt),fe=[];for(const Qe of ke){let Je=[];if(wt>0){if(rt===null)rt=wt/6;else if(wt<=rt)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");const xt=zt*wt,jt=zt*rt,$t=xt-2*jt;let Ft=0;for(;;){const dA=Ft+xt,eA=Qe.subarray(Ft,dA),nA=await this.processor(eA),aA=Ft===0,bA=dA>=Qe.length;if(Je.push({stride:[eA.length,aA?0:jt,bA?0:jt],input_features:nA.input_features,is_last:bA}),bA)break;Ft+=$t}}else Je=[{stride:[Qe.length,0,0],input_features:(await this.processor(Qe)).input_features,is_last:!0}];for(const xt of Je){Bt.num_frames=Math.floor(xt.stride[0]/oe);const jt=await this.model.generate({inputs:xt.input_features,...Bt});It==="word"?(xt.tokens=jt.sequences.tolist()[0],xt.token_timestamps=jt.token_timestamps.tolist()[0].map($t=>(0,k.round)($t,2))):xt.tokens=jt[0].tolist(),xt.stride=xt.stride.map($t=>$t/zt)}const[st,lt]=this.tokenizer._decode_asr(Je,{time_precision:St,return_timestamps:It,force_full_sequences:ze});fe.push({text:st,...lt})}return Ot?fe[0]:fe}async _call_moonshine(je,At){const It=!Array.isArray(je);It&&(je=[je]);const wt=this.processor.feature_extractor.config.sampling_rate,ze=await Q(je,wt),rt=[];for(const Bt of ze){const Ot=await this.processor(Bt),St=6*Math.floor(Bt.length/wt),oe=await this.model.generate({max_new_tokens:St,...At,...Ot}),zt=this.processor.batch_decode(oe,{skip_special_tokens:!0})[0];rt.push({text:zt})}return It?rt[0]:rt}}class ge extends W{constructor(je){super(je)}async _call(je,At={}){const It=Array.isArray(je),wt=await D(je),{pixel_values:ze}=await this.processor(wt),rt=[];for(const Bt of ze){Bt.dims=[1,...Bt.dims];const Ot=await this.model.generate({inputs:Bt,...At}),St=this.tokenizer.batch_decode(Ot,{skip_special_tokens:!0}).map(oe=>({generated_text:oe.trim()}));rt.push(St)}return It?rt:rt[0]}}class Ce extends W{constructor(je){super(je)}async _call(je,{top_k:At=5}={}){const It=await D(je),{pixel_values:wt}=await this.processor(It),ze=await this.model({pixel_values:wt}),rt=this.model.config.id2label,Bt=[];for(const Ot of ze.logits){const St=await(0,B.topk)(new B.Tensor("float32",(0,k.softmax)(Ot.data),Ot.dims),At),oe=St[0].tolist(),zt=St[1].tolist().map((ke,fe)=>({label:rt?rt[ke]:`LABEL_${ke}`,score:oe[fe]}));Bt.push(zt)}return Array.isArray(je)?Bt:Bt[0]}}class Fe extends W{constructor(je){super(je),this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(je,{threshold:At=.5,mask_threshold:It=.5,overlap_mask_area_threshold:wt=.8,label_ids_to_fuse:ze=null,target_sizes:rt=null,subtask:Bt=null}={}){if(Array.isArray(je)&&je.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");const Ot=await D(je),St=Ot.map(st=>[st.height,st.width]),{pixel_values:oe,pixel_mask:zt}=await this.processor(Ot),ke=await this.model({pixel_values:oe,pixel_mask:zt});let fe=null;if(Bt!==null)fe=this.subtasks_mapping[Bt];else for(let[st,lt]of Object.entries(this.subtasks_mapping))if(lt in this.processor.image_processor){fe=this.processor.image_processor[lt].bind(this.processor.image_processor),Bt=st;break}const Qe=this.model.config.id2label,Je=[];
2809if(Bt==="panoptic"||Bt==="instance"){const st=fe(ke,At,It,wt,ze,rt??St)[0],lt=st.segmentation;for(const xt of st.segments_info){const jt=new Uint8ClampedArray(lt.data.length);for(let Ft=0;Ft<lt.data.length;++Ft)lt.data[Ft]===xt.id&&(jt[Ft]=255);const $t=new G.RawImage(jt,lt.dims[1],lt.dims[0],1);Je.push({score:xt.score,label:Qe[xt.label_id],mask:$t})}}else{if(Bt!=="semantic")throw Error(`Subtask ${Bt} not supported.`);{const{segmentation:st,labels:lt}=fe(ke,rt??St)[0];for(const xt of lt){const jt=new Uint8ClampedArray(st.data.length);for(let Ft=0;Ft<st.data.length;++Ft)st.data[Ft]===xt&&(jt[Ft]=255);const $t=new G.RawImage(jt,st.dims[1],st.dims[0],1);Je.push({score:null,label:Qe[xt],mask:$t})}}}return Je}}class Pe extends W{constructor(je){super(je)}async _call(je,At,{hypothesis_template:It="This is a photo of {}"}={}){const wt=Array.isArray(je),ze=await D(je),rt=At.map(ke=>It.replace("{}",ke)),Bt=this.tokenizer(rt,{padding:this.model.config.model_type!=="siglip"||"max_length",truncation:!0}),{pixel_values:Ot}=await this.processor(ze),St=await this.model({...Bt,pixel_values:Ot}),oe=this.model.config.model_type==="siglip"?ke=>ke.sigmoid().data:ke=>(0,k.softmax)(ke.data),zt=[];for(const ke of St.logits_per_image){const fe=[...oe(ke)].map((Qe,Je)=>({score:Qe,label:At[Je]}));fe.sort((Qe,Je)=>Je.score-Qe.score),zt.push(fe)}return wt?zt:zt[0]}}class de extends W{constructor(je){super(je)}async _call(je,{threshold:At=.9,percentage:It=!1}={}){const wt=Array.isArray(je);if(wt&&je.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");const ze=await D(je),rt=It?null:ze.map(fe=>[fe.height,fe.width]),{pixel_values:Bt,pixel_mask:Ot}=await this.processor(ze),St=await this.model({pixel_values:Bt,pixel_mask:Ot}),oe=this.processor.image_processor.post_process_object_detection(St,At,rt),zt=this.model.config.id2label,ke=oe.map(fe=>fe.boxes.map((Qe,Je)=>({score:fe.scores[Je],label:zt[fe.classes[Je]],box:V(Qe,!It)})));return wt?ke:ke[0]}}class pe extends W{constructor(je){super(je)}async _call(je,At,{threshold:It=.1,top_k:wt=null,percentage:ze=!1}={}){const rt=Array.isArray(je),Bt=await D(je),Ot=this.tokenizer(At,{padding:!0,truncation:!0}),St=await this.processor(Bt),oe=[];for(let zt=0;zt<Bt.length;++zt){const ke=Bt[zt],fe=ze?null:[[ke.height,ke.width]],Qe=St.pixel_values[zt].unsqueeze_(0),Je=await this.model({...Ot,pixel_values:Qe});let st;if("post_process_grounded_object_detection"in this.processor){const lt=this.processor.post_process_grounded_object_detection(Je,Ot.input_ids,{box_threshold:It,text_threshold:It,target_sizes:fe})[0];st=lt.boxes.map((xt,jt)=>({score:lt.scores[jt],label:lt.labels[jt],box:V(xt,!ze)}))}else{const lt=this.processor.image_processor.post_process_object_detection(Je,It,fe,!0)[0];st=lt.boxes.map((xt,jt)=>({score:lt.scores[jt],label:At[lt.classes[jt]],box:V(xt,!ze)}))}st.sort((lt,xt)=>xt.score-lt.score),wt!==null&&(st=st.slice(0,wt)),oe.push(st)}return rt?oe:oe[0]}}class we extends W{constructor(je){super(je)}async _call(je,At,It={}){const wt=(await D(je))[0],{pixel_values:ze}=await this.processor(wt),rt=`<s_docvqa><s_question>${At}</s_question><s_answer>`,Bt=this.tokenizer(rt,{add_special_tokens:!1,padding:!0,truncation:!0}).input_ids,Ot=await this.model.generate({inputs:ze,max_length:this.model.config.decoder.max_position_embeddings,decoder_input_ids:Bt,...It}),St=this.tokenizer.batch_decode(Ot)[0].match(/<s_answer>(.*?)<\/s_answer>/);let oe=null;return St&&St.length>=2&&(oe=St[1].trim()),[{answer:oe}]}}class Ge extends W{constructor(At){super(At);Ct(this,"DEFAULT_VOCODER_ID","Xenova/speecht5_hifigan");this.vocoder=At.vocoder??null}async _call(At,{speaker_embeddings:It=null}={}){return this.processor?this._call_text_to_spectrogram(At,{speaker_embeddings:It}):this._call_text_to_waveform(At)}async _call_text_to_waveform(At){const It=this.tokenizer(At,{padding:!0,truncation:!0}),{waveform:wt}=await this.model(It),ze=this.model.config.sampling_rate;return new E.RawAudio(wt.data,ze)}async _call_text_to_spectrogram(At,{speaker_embeddings:It}){if(this.vocoder||(console.log("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await u.AutoModel.from_pretrained(this.DEFAULT_VOCODER_ID,{dtype:"fp32"})),(typeof It=="string"||It instanceof URL)&&(It=new Float32Array(await(await fetch(It)).arrayBuffer())),It instanceof Float32Array)It=new B.Tensor("float32",It,[1,It.length]);else if(!(It instanceof B.Tensor))throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");const{input_ids:wt}=this.tokenizer(At,{padding:!0,truncation:!0}
vendor: 4,479 bytes, line 2809
2809),{waveform:ze}=await this.model.generate_speech(wt,It,{vocoder:this.vocoder}),rt=this.processor.feature_extractor.config.sampling_rate;return new E.RawAudio(ze.data,rt)}}class me extends W{constructor(je){super(je)}async _call(je){const At=await D(je),It=await this.processor(At),wt=await this.model(It),ze=[];for(const rt of wt.reconstruction){const Bt=rt.squeeze().clamp_(0,1).mul_(255).round_().to("uint8");ze.push(G.RawImage.fromTensor(Bt))}return ze.length>1?ze:ze[0]}}class at extends W{constructor(je){super(je)}async _call(je){const At=await D(je),It=await this.processor(At),{predicted_depth:wt}=await this.model(It),ze=[];for(let rt=0;rt<At.length;++rt){const Bt=wt[rt],[Ot,St]=Bt.dims.slice(-2),[oe,zt]=At[rt].size,ke=(await(0,B.interpolate_4d)(Bt.view(1,1,Ot,St),{size:[zt,oe],mode:"bilinear"})).view(zt,oe),fe=ke.min().item(),Qe=ke.max().item(),Je=ke.sub(fe).div_(Qe-fe).mul_(255).to("uint8").unsqueeze(0),st=G.RawImage.fromTensor(Je);ze.push({predicted_depth:ke,depth:st})}return ze.length>1?ze:ze[0]}}const Ue=Object.freeze({"text-classification":{tokenizer:o.AutoTokenizer,pipeline:U,model:u.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:o.AutoTokenizer,pipeline:H,model:u.AutoModelForTokenClassification,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:o.AutoTokenizer,pipeline:K,model:u.AutoModelForQuestionAnswering,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{tokenizer:o.AutoTokenizer,pipeline:J,model:u.AutoModelForMaskedLM,default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:o.AutoTokenizer,pipeline:v,model:u.AutoModelForSeq2SeqLM,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:o.AutoTokenizer,pipeline:e,model:u.AutoModelForSeq2SeqLM,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:o.AutoTokenizer,pipeline:g,model:u.AutoModelForSeq2SeqLM,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:o.AutoTokenizer,pipeline:_,model:u.AutoModelForCausalLM,default:{model:"Xenova/gpt2"},type:"text"},"zero-shot-classification":{tokenizer:o.AutoTokenizer,pipeline:re,model:u.AutoModelForSequenceClassification,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"audio-classification":{pipeline:Ie,model:u.AutoModelForAudioClassification,processor:p.AutoProcessor,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"zero-shot-audio-classification":{tokenizer:o.AutoTokenizer,pipeline:he,model:u.AutoModel,processor:p.AutoProcessor,default:{model:"Xenova/clap-htsat-unfused"},type:"multimodal"},"automatic-speech-recognition":{tokenizer:o.AutoTokenizer,pipeline:ce,model:[u.AutoModelForSpeechSeq2Seq,u.AutoModelForCTC],processor:p.AutoProcessor,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"text-to-audio":{tokenizer:o.AutoTokenizer,pipeline:Ge,model:[u.AutoModelForTextToWaveform,u.AutoModelForTextToSpectrogram],processor:[p.AutoProcessor,null],default:{model:"Xenova/speecht5_tts"},type:"text"},"image-to-text":{tokenizer:o.AutoTokenizer,pipeline:ge,model:u.AutoModelForVision2Seq,processor:p.AutoProcessor,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:Ce,model:u.AutoModelForImageClassification,processor:p.AutoProcessor,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:Fe,model:[u.AutoModelForImageSegmentation,u.AutoModelForSemanticSegmentation,u.AutoModelForUniversalSegmentation],processor:p.AutoProcessor,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"zero-shot-image-classification":{tokenizer:o.AutoTokenizer,pipeline:Pe,model:u.AutoModel,processor:p.AutoProcessor,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:de,model:u.AutoModelForObjectDetection,processor:p.AutoProcessor,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"zero-shot-object-detection":{tokenizer:o.AutoTokenizer,pipeline:pe,model:u.AutoModelForZeroShotObjectDetection,processor:p.AutoProcessor,default:{model:"Xenova/owlvit-base-patch32"},type:"multimodal"},"document-question-answering":{tokenizer:o.AutoTokenizer,pipeline:we,model:u.AutoModelForDocumentQuestionAnswering,processor:p.AutoProcessor,default:{model:"Xenova/d
2809onut-base-finetuned-docvqa"},type:"multimodal"},"image-to-image":{pipeline:me,model:u.AutoModelForImageToImage,processor:p.AutoProcessor,default:{model:"Xenova/swin2SR-classical-sr-x2-64"},type:"image"},"depth-estimation":{pipeline:at,model:u.AutoModelForDepthEstimation,processor:p.AutoProcessor,default:{model:"Xenova/dpt-large"},type:"image"},"feature-extraction":{tokenizer:o.AutoTokenizer,pipeline:S,model:u.AutoModel,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"},"image-feature-extraction":{processor:p.AutoProcessor,pipeline:F,model:[u.AutoModelForImageFeatureExtraction,u.AutoModel],default:{model:"Xenova/vit-base-patch16-224-in21k"},type:"image"}}),ot=Object.freeze({"sentiment-analysis":"text-classification",ner:"token-classification",asr:"automatic-speech-recognition","text-to-speech":"text-to-audio",embeddings:"feature-extraction"});async function pt(ct,je=null,{progress_callback:At=null,config:It=null,cache_dir:wt=null,local_files_only:ze=!1,revision:rt="main",device:Bt=null,dtype:Ot=null,model_file_name:St=null,session_options:oe={}}={}){ct=ot[ct]??ct;const zt=Ue[ct.split("_",1)[0]];if(!zt)throw Error(`Unsupported pipeline: ${ct}. Must be one of [${Object.keys(Ue)}]`);je||(je=zt.default.model,console.log(`No model specified. Using default model: "${je}".`));const ke={progress_callback:At,config:It,cache_dir:wt,local_files_only:ze,revision:rt,device:Bt,dtype:Ot,model_file_name:St,session_options:oe},fe=new Map([["tokenizer",zt.tokenizer],["model",zt.model],["processor",zt.processor]]),Qe=await async function(Je,st,lt){const xt=Object.create(null),jt=[];for(const[$t,Ft]of Je.entries()){if(!Ft)continue;let dA;dA=Array.isArray(Ft)?new Promise(async(eA,nA)=>{var bA,vA;let aA;for(const Gt of Ft){if(Gt===null)return void eA(null);try{return void eA(await Gt.from_pretrained(st,lt))}catch(mA){if((bA=mA.message)!=null&&bA.includes("Unsupported model type"))aA=mA;else{if(!((vA=mA.message)!=null&&vA.includes("Could not locate file")))return void nA(mA);aA=mA}}}nA(aA)}):Ft.from_pretrained(st,lt),xt[$t]=dA,jt.push(dA)}await Promise.all(jt);for(const[$t,Ft]of Object.entries(xt))xt[$t]=await Ft;return xt}(fe,je,ke);return Qe.task=ct,(0,b.dispatchCallback)(At,{status:"ready",task:ct,model:je}),new zt.pipeline(Qe)}},
2809"./src/tokenizers.js":(c,a,s)=>{s.r(a),s.d(a,{AlbertTokenizer:()=>CA,AutoTokenizer:()=>Wa,BartTokenizer:()=>Rr,BertTokenizer:()=>gr,BlenderbotSmallTokenizer:()=>fr,BlenderbotTokenizer:()=>$A,BloomTokenizer:()=>nr,CLIPTokenizer:()=>Ra,CamembertTokenizer:()=>MA,CodeGenTokenizer:()=>Oi,CodeLlamaTokenizer:()=>Ja,CohereTokenizer:()=>ci,ConvBertTokenizer:()=>Fr,DebertaTokenizer:()=>iA,DebertaV2Tokenizer:()=>qt,DistilBertTokenizer:()=>Tt,ElectraTokenizer:()=>ZA,EsmTokenizer:()=>en,FalconTokenizer:()=>$a,GPT2Tokenizer:()=>EA,GPTNeoXTokenizer:()=>ds,GemmaTokenizer:()=>Yn,Grok1Tokenizer:()=>Ma,HerbertTokenizer:()=>zA,LlamaTokenizer:()=>xa,M2M100Tokenizer:()=>cn,MBart50Tokenizer:()=>Dr,MBartTokenizer:()=>Er,MPNetTokenizer:()=>Qa,MarianTokenizer:()=>Tn,MgpstrTokenizer:()=>tn,MobileBertTokenizer:()=>NA,NllbTokenizer:()=>Hr,NougatTokenizer:()=>vi,PreTrainedTokenizer:()=>AA,Qwen2Tokenizer:()=>oa,RoFormerTokenizer:()=>Vr,RobertaTokenizer:()=>Vs,SiglipTokenizer:()=>jn,SpeechT5Tokenizer:()=>la,SqueezeBertTokenizer:()=>tA,T5Tokenizer:()=>FA,TokenizerModel:()=>d,VitsTokenizer:()=>Pa,Wav2Vec2CTCTokenizer:()=>ja,WhisperTokenizer:()=>Rn,XLMRobertaTokenizer:()=>ma,XLMTokenizer:()=>QA,is_chinese_char:()=>H});var o=s("./src/utils/generic.js"),u=s("./src/utils/core.js"),p=s("./src/utils/hub.js"),C=s("./src/utils/maths.js"),b=s("./src/utils/tensor.js"),k=s("./src/utils/data-structures.js"),E=s("./node_modules/@huggingface/jinja/dist/index.js"),B=s("./src/models/whisper/common_whisper.js");async function G(_t,Ee){const qe=await Promise.all([(0,p.getModelJSON)(_t,"tokenizer.json",!0,Ee),(0,p.getModelJSON)(_t,"tokenizer_config.json",!0,Ee)]);return Ee.legacy!==null&&(qe[1].legacy=Ee.legacy),qe}function D(_t,Ee=!0){if(_t.Regex!==void 0){let qe=_t.Regex.replace(/\\([#&~])/g,"$1");for(const[ut,mt]of v)qe=qe.replaceAll(ut,mt);return new RegExp(qe,"gu")}if(_t.String!==void 0){const qe=(0,u.escapeRegExp)(_t.String);return new RegExp(Ee?qe:`(${qe})`,"gu")}return console.warn("Unknown pattern type:",_t),null}function Q(_t){return new Map(Object.entries(_t))}function V(_t){const Ee=_t.dims;switch(Ee.length){case 1:return _t.tolist();case 2:if(Ee[0]!==1)throw new Error("Unable to decode tensor with `batch size !== 1`. Use `tokenizer.batch_decode(...)` for batched inputs.");return _t.tolist()[0];default:throw new Error(`Expected tensor to have 1-2 dimensions, got ${Ee.length}.`)}}function W(_t){return _t.replace(/ \./g,".").replace(/ \?/g,"?").replace(/ \!/g,"!").replace(/ ,/g,",").replace(/ \' /g,"'").replace(/ n\'t/g,"n't").replace(/ \'m/g,"'m").replace(/ \'s/g,"'s").replace(/ \'ve/g,"'ve").replace(/ \'re/g,"'re")}function U(_t){return _t.replace(new RegExp("\\p{M}","gu"),"")}function H(_t){return _t>=19968&&_t<=40959||_t>=13312&&_t<=19903||_t>=131072&&_t<=173791||_t>=173824&&_t<=177983||_t>=177984&&_t<=178207||_t>=178208&&_t<=183983||_t>=63744&&_t<=64255||_t>=194560&&_t<=195103}const K="\\p{P}\\u0021-\\u002F\\u003A-\\u0040\\u005B-\\u0060\\u007B-\\u007E",J=new RegExp(`^[${K}]+$`,"gu"),g=".,!?â¦ãï¼ã।ÛØ",v=new Map([["(?i:'s|'t|'re|'ve|'m|'ll|'d)","(?:'([sS]|[tT]|[rR][eE]|[vV][eE]|[mM]|[lL][lL]|[dD]))"],[` ?[^(\\s|[${g}])]+`,` ?[^\\s${g}]+`]]);class e{constructor(Ee){this.content=Ee.content,this.id=Ee.id,this.single_word=Ee.single_word??!1,this.lstrip=Ee.lstrip??!1,this.rstrip=Ee.rstrip??!1,this.special=Ee.special??!1,this.normalized=Ee.normalized??null}}class d extends o.Callable{constructor(Ee){super(),this.config=Ee,this.vocab=[],this.tokens_to_ids=new Map,this.unk_token_id=void 0,this.unk_token=void 0,this.end_of_word_suffix=void 0,this.fuse_unk=this.config.fuse_unk??!1}static fromConfig(Ee,...qe){switch(Ee.type){case"WordPiece":return new _(Ee);case"Unigram":return new re(Ee,...qe);case"BPE":return new Ie(Ee);default:if(Ee.vocab)return Array.isArray(Ee.vocab)?new re(Ee,...qe):typeof Ee.vocab=="object"&&Ee.continuing_subword_prefix&&Ee.unk_token?new _(Ee):new he(Ee,...qe);throw new Error(`Unknown TokenizerModel type: ${Ee.type}`)}}_call(Ee){return Ee=this.encode(Ee),this.fuse_unk&&(Ee=function(qe,ut,mt){const vt=[];let Rt=0;for(;Rt<qe.length;)if(vt.push(qe[Rt]),(ut.get(qe[Rt])??mt)===mt)for(;++Rt<qe.length&&(ut.get(qe[Rt])??mt)===mt;)ut.get(vt.at(-1))!==mt&&(vt[vt.length-1]+=qe[Rt]);else++Rt;return vt}(Ee,this.tokens_to_ids,this.unk_token_id)),Ee}encode(Ee){throw Error("encode should be implemented in subclass.")}convert_tokens_to_ids(Ee){return Ee.map(qe=>this.tokens_to_ids.get(qe)??this.unk_token_id)}convert_ids_to_tokens(Ee){return Ee.map(qe=>this.vocab[qe]??this.unk_token)}}class _ extends d{constructor(Ee){super(Ee),this.tokens_to_ids=Q(Ee.vocab),this.unk_token_id=this.tokens_to_ids.get(Ee.unk_token),this.unk_token=Ee.unk_token,this.max_input_chars_per_word=Ee.max_input_chars_per_word??100,this.vocab=new Array(this.tokens_to_ids.size);for(const[qe,ut]of this.tokens_to_ids)this.vocab[ut]=qe}
2809encode(Ee){const qe=[];for(const ut of Ee){const mt=[...ut];if(mt.length>this.max_input_chars_per_word){qe.push(this.unk_token);continue}let vt=!1,Rt=0;const lA=[];for(;Rt<mt.length;){let gt=mt.length,pA=null;for(;Rt<gt;){let oA=mt.slice(Rt,gt).join("");if(Rt>0&&(oA=this.config.continuing_subword_prefix+oA),this.tokens_to_ids.has(oA)){pA=oA;break}--gt}if(pA===null){vt=!0;break}lA.push(pA),Rt=gt}vt?qe.push(this.unk_token):qe.push(...lA)}return qe}}class re extends d{constructor(Ee,qe){super(Ee);const ut=Ee.vocab.length;this.vocab=new Array(ut),this.scores=new Array(ut);for(let mt=0;mt<ut;++mt)[this.vocab[mt],this.scores[mt]]=Ee.vocab[mt];this.unk_token_id=Ee.unk_id,this.unk_token=this.vocab[Ee.unk_id],this.tokens_to_ids=new Map(this.vocab.map((mt,vt)=>[mt,vt])),this.bos_token=" ",this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=qe.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.unk_token=this.vocab[this.unk_token_id],this.minScore=(0,C.min)(this.scores)[0],this.unk_score=this.minScore-10,this.scores[this.unk_token_id]=this.unk_score,this.trie=new k.CharTrie,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(Ee){const qe=Ee.chars;let ut=0;for(;ut<qe.length;){let mt=!1;const vt=qe.slice(ut).join(""),Rt=this.trie.commonPrefixSearch(vt);for(const lA of Rt){const gt=this.tokens_to_ids.get(lA),pA=this.scores[gt],oA=(0,u.len)(lA);Ee.insert(ut,oA,pA,gt),mt||oA!==1||(mt=!0)}mt||Ee.insert(ut,1,this.unk_score,this.unk_token_id),ut+=1}}tokenize(Ee){const qe=new k.TokenLattice(Ee,this.bos_token_id,this.eos_token_id);return this.populateNodes(qe),qe.tokens()}encode(Ee){const qe=[];for(const ut of Ee){const mt=this.tokenize(ut);qe.push(...mt)}return qe}}const S=(()=>{const _t=[...Array.from({length:94},(mt,vt)=>vt+33),...Array.from({length:12},(mt,vt)=>vt+161),...Array.from({length:82},(mt,vt)=>vt+174)],Ee=_t.slice();let qe=0;for(let mt=0;mt<256;++mt)_t.includes(mt)||(_t.push(mt),Ee.push(256+qe),qe+=1);const ut=Ee.map(mt=>String.fromCharCode(mt));return Object.fromEntries(_t.map((mt,vt)=>[mt,ut[vt]]))})(),F=(0,u.reverseDictionary)(S);class Ie extends d{constructor(Ee){super(Ee),this.tokens_to_ids=Q(Ee.vocab),this.unk_token_id=this.tokens_to_ids.get(Ee.unk_token),this.unk_token=Ee.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[ut,mt]of this.tokens_to_ids)this.vocab[mt]=ut;const qe=Array.isArray(Ee.merges[0]);this.merges=qe?Ee.merges:Ee.merges.map(ut=>ut.split(" ",2)),this.bpe_ranks=new Map(this.merges.map((ut,mt)=>[JSON.stringify(ut),mt])),this.end_of_word_suffix=Ee.end_of_word_suffix,this.continuing_subword_suffix=Ee.continuing_subword_suffix??null,this.byte_fallback=this.config.byte_fallback??!1,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.ignore_merges=this.config.ignore_merges??!1,this.cache=new Map}bpe(Ee){if(Ee.length===0)return[];const qe=this.cache.get(Ee);if(qe!==void 0)return qe;const ut=Array.from(Ee);this.end_of_word_suffix&&(ut[ut.length-1]+=this.end_of_word_suffix);let mt=[];if(ut.length>1){const vt=new k.PriorityQueue((gt,pA)=>gt.score<pA.score);let Rt={token:ut[0],bias:0,prev:null,next:null},lA=Rt;for(let gt=1;gt<ut.length;++gt){const pA={bias:gt/ut.length,token:ut[gt],prev:lA,next:null};lA.next=pA,this._add_node(vt,lA),lA=pA}for(;!vt.isEmpty();){const gt=vt.pop();if(gt.deleted||!gt.next||gt.next.deleted)continue;if(gt.deleted=!0,gt.next.deleted=!0,gt.prev){const oA={...gt.prev};gt.prev.deleted=!0,gt.prev=oA,oA.prev?oA.prev.next=oA:Rt=oA}const pA={token:gt.token+gt.next.token,bias:gt.bias,prev:gt.prev,next:gt.next.next};pA.prev?(pA.prev.next=pA,this._add_node(vt,pA.prev)):Rt=pA,pA.next&&(pA.next.prev=pA,this._add_node(vt,pA))}for(let gt=Rt;gt!==null;gt=gt.next)mt.push(gt.token)}else mt=ut;if(this.continuing_subword_suffix)for(let vt=0;vt<mt.length-1;++vt)mt[vt]+=this.continuing_subword_suffix;return this.cache.set(Ee,mt),mt}_add_node(Ee,qe){const ut=this.bpe_ranks.get(JSON.stringify([qe.token,qe.next.token]));ut!==void 0&&(qe.score=ut+qe.bias,Ee.push(qe))}encode(Ee){const qe=[];for(const ut of Ee){if(this.ignore_merges&&this.tokens_to_ids.has(ut)){qe.push(ut);continue}const mt=this.bpe(ut);for(const vt of mt)if(this.tokens_to_ids.has(vt))qe.push(vt);else if(this.byte_fallback){const Rt=Array.from(this.text_encoder.encode(vt)).map(lA=>`<0x${lA.toString(16).toUpperCase().padStart(2,"0")}>`);Rt.every(lA=>this.tokens_to_ids.has(lA))?qe.push(...Rt):qe.push(this.unk_token)}else qe.push(this.unk_token)}return qe}}class he extends d{constructor(Ee,qe){super(Ee),this.tokens_to_ids=Q(qe.target_lang?Ee.vocab[qe.target_lang]:Ee.vocab),this.bos_token=qe.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=qe.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=qe.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=qe.unk_token,this.unk_token_id=this.tokens_to_ids.get(this.unk_token),this.vocab=new Array(this.tokens_to_ids.size);for(const[ut,mt]of this.tokens_to_ids)this.vocab[mt]=ut}encode(Ee){return Ee}}
2809class ce extends o.Callable{constructor(Ee){super(),this.config=Ee}static fromConfig(Ee){if(Ee===null)return null;switch(Ee.type){case"BertNormalizer":return new at(Ee);case"Precompiled":return new dA(Ee);case"Sequence":return new me(Ee);case"Replace":return new ge(Ee);case"NFC":return new Ce(Ee);case"NFKC":return new Fe(Ee);case"NFKD":return new Pe(Ee);case"Strip":return new de(Ee);case"StripAccents":return new pe(Ee);case"Lowercase":return new we(Ee);case"Prepend":return new Ge(Ee);default:throw new Error(`Unknown Normalizer type: ${Ee.type}`)}}normalize(Ee){throw Error("normalize should be implemented in subclass.")}_call(Ee){return this.normalize(Ee)}}class ge extends ce{normalize(Ee){const qe=D(this.config.pattern);return qe===null?Ee:Ee.replaceAll(qe,this.config.content)}}class Ce extends ce{normalize(Ee){return Ee=Ee.normalize("NFC")}}class Fe extends ce{normalize(Ee){return Ee=Ee.normalize("NFKC")}}class Pe extends ce{normalize(Ee){return Ee=Ee.normalize("NFKD")}}class de extends ce{normalize(Ee){return this.config.strip_left&&this.config.strip_right?Ee=Ee.trim():(this.config.strip_left&&(Ee=Ee.trimStart()),this.config.strip_right&&(Ee=Ee.trimEnd())),Ee}}class pe extends ce{normalize(Ee){return Ee=U(Ee)}}class we extends ce{normalize(Ee){return Ee=Ee.toLowerCase()}}class Ge extends ce{normalize(Ee){return Ee=this.config.prepend+Ee}}class me extends ce{constructor(Ee){super(Ee),this.normalizers=Ee.normalizers.map(qe=>ce.fromConfig(qe))}normalize(Ee){return this.normalizers.reduce((qe,ut)=>ut.normalize(qe),Ee)}}class at extends ce{_tokenize_chinese_chars(Ee){const qe=[];for(let ut=0;ut<Ee.length;++ut){const mt=Ee[ut];H(mt.charCodeAt(0))?(qe.push(" "),qe.push(mt),qe.push(" ")):qe.push(mt)}return qe.join("")}stripAccents(Ee){return Ee.normalize("NFD").replace(new RegExp("\\p{Mn}","gu"),"")}_is_control(Ee){switch(Ee){case" ":case` 2810`:case"\r":return!1;default:return new RegExp("^\\p{Cc}|\\p{Cf}|\\p{Co}|\\p{Cs}$","u").test(Ee)}}_clean_text(Ee){const qe=[];for(const ut of Ee){const mt=ut.charCodeAt(0);mt===0||mt===65533||this._is_control(ut)||(/^\s$/.test(ut)?qe.push(" "):qe.push(ut))}return qe.join("")}normalize(Ee){return this.config.clean_text&&(Ee=this._clean_text(Ee)),this.config.handle_chinese_chars&&(Ee=this._tokenize_chinese_chars(Ee)),this.config.lowercase?(Ee=Ee.toLowerCase(),this.config.strip_accents!==!1&&(Ee=this.stripAccents(Ee))):this.config.strip_accents&&(Ee=this.stripAccents(Ee)),Ee}}class Ue extends o.Callable{static fromConfig(Ee){if(Ee===null)return null;switch(Ee.type){case"BertPreTokenizer":return new ot(Ee);case"Sequence":return new eA(Ee);case"Whitespace":return new nA(Ee);case"WhitespaceSplit":return new aA(Ee);case"Metaspace":return new $t(Ee);case"ByteLevel":return new pt(Ee);case"Split":return new ct(Ee);case"Punctuation":return new je(Ee);case"Digits":return new At(Ee);case"Replace":return new bA(Ee);default:throw new Error(`Unknown PreTokenizer type: ${Ee.type}`)}}pre_tokenize_text(Ee,qe){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize(Ee,qe){return(Array.isArray(Ee)?Ee.map(ut=>this.pre_tokenize_text(ut,qe)):this.pre_tokenize_text(Ee,qe)).flat()}_call(Ee,qe){return this.pre_tokenize(Ee,qe)}}class ot extends Ue{constructor(Ee){super(),this.pattern=new RegExp(`[^\\s${K}]+|[${K}]`,"gu")}pre_tokenize_text(Ee,qe){return Ee.trim().match(this.pattern)||[]}}class pt extends Ue{constructor(Ee){super(),this.config=Ee,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=new RegExp("'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)|\\s+","gu"),this.byte_encoder=S,this.text_encoder=new TextEncoder}pre_tokenize_text(Ee,qe){return this.add_prefix_space&&!Ee.startsWith(" ")&&(Ee=" "+Ee),(this.use_regex?Ee.match(this.pattern)||[]:[Ee]).map(ut=>Array.from(this.text_encoder.encode(ut),mt=>this.byte_encoder[mt]).join(""))}}class ct extends Ue{constructor(Ee){super(),this.config=Ee,this.pattern=D(this.config.pattern,this.config.invert)}pre_tokenize_text(Ee,qe){var ut;return this.pattern===null?[]:this.config.invert?Ee.match(this.pattern)||[]:((ut=this.config.behavior)==null?void 0:ut.toLowerCase())==="removed"?Ee.split(this.pattern).filter(mt=>mt):function(mt,vt){const Rt=[];let lA=0;for(const gt of mt.matchAll(vt)){const pA=gt[0];lA<gt.index&&Rt.push(mt.slice(lA,gt.index)),pA.length>0&&Rt.push(pA),lA=gt.index+pA.length}return lA<mt.length&&Rt.push(mt.slice(lA)),Rt}(Ee,this.pattern)}}class je extends Ue{constructor(Ee){super(),this.config=Ee,this.pattern=new RegExp(`[^${K}]+|[${K}]+`,"gu")}pre_tokenize_text(Ee,qe){return Ee.match(this.pattern)||[]}}class At extends Ue{constructor(Ee){super(),this.config=Ee;const qe="[^\\d]+|\\d"+(this.config.individual_digits?"":"+");this.pattern=new RegExp(qe,"gu")}pre_tokenize_text(Ee,qe){return Ee.match(this.pattern)||[]}}class It extends o.Callable{constructor(Ee){super(),this.config=Ee}static fromConfig(Ee){if(Ee===null)return null;switch(Ee.type){case"TemplateProcessing":return new rt(Ee);case"ByteLevel":return new Bt(Ee);case"RobertaProcessing":return new ze(Ee);case"BertProcessing":return new wt(Ee);case"Sequence":return new Ot(Ee);default:throw new Error(`Unknown PostProcessor type: ${Ee.type}`)}}post_process(Ee,...qe){throw Error("post_process should be implemented in subclass.")}_call(Ee,...qe){return this.post_process(Ee,...qe)}}class wt extends It{constructor(Ee){super(Ee),this.cls=Ee.cls[0],this.sep=Ee.sep[0]}post_process(Ee,qe=null,{add_special_tokens:ut=!0}={}){ut&&(Ee=(0,u.mergeArrays)([this.cls],Ee,[this.sep]));let mt=new Array(Ee.length).fill(0);if(qe!==null){const vt=ut&&this instanceof ze?[this.sep]:[],Rt=ut?[this.sep]:[];Ee=(0,u.mergeArrays)(Ee,vt,qe,Rt),mt=(0,u.mergeArrays)(mt,new Array(qe.length+vt.length+Rt.length).fill(1))}return{tokens:Ee,token_type_ids:mt}}}class ze extends wt{}class rt extends It{constructor(Ee){super(Ee),this.single=Ee.single,this.pair=Ee.pair}post_process(Ee,qe=null,{add_special_tokens:ut=!0}={}){const mt=qe===null?this.single:this.pair;let vt=[],Rt=[];for(const lA of mt)"SpecialToken"in lA?ut&&(vt.push(lA.SpecialToken.id),Rt.push(lA.SpecialToken.type_id)):"Sequence"in lA&&(lA.Sequence.id==="A"?(vt=(0,u.mergeArrays)(vt,Ee),Rt=(0,u.mergeArrays)(Rt,new Array(Ee.length).fill(lA.Sequence.type_id
2810))):lA.Sequence.id==="B"&&(vt=(0,u.mergeArrays)(vt,qe),Rt=(0,u.mergeArrays)(Rt,new Array(qe.length).fill(lA.Sequence.type_id))));return{tokens:vt,token_type_ids:Rt}}}class Bt extends It{post_process(Ee,qe=null){return qe&&(Ee=(0,u.mergeArrays)(Ee,qe)),{tokens:Ee}}}class Ot extends It{constructor(Ee){super(Ee),this.processors=Ee.processors.map(qe=>It.fromConfig(qe))}post_process(Ee,qe=null,ut={}){let mt;for(const vt of this.processors)if(vt instanceof Bt)Ee=vt.post_process(Ee).tokens,qe&&(qe=vt.post_process(qe).tokens);else{const Rt=vt.post_process(Ee,qe,ut);Ee=Rt.tokens,mt=Rt.token_type_ids}return{tokens:Ee,token_type_ids:mt}}}class St extends o.Callable{constructor(Ee){super(),this.config=Ee,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=Ee.trim_offsets}static fromConfig(Ee){if(Ee===null)return null;switch(Ee.type){case"WordPiece":return new Qe(Ee);case"Metaspace":return new Ft(Ee);case"ByteLevel":return new Je(Ee);case"Replace":return new oe(Ee);case"ByteFallback":return new zt(Ee);case"Fuse":return new ke(Ee);case"Strip":return new fe(Ee);case"Sequence":return new lt(Ee);case"CTC":return new st(Ee);case"BPEDecoder":return new xt(Ee);default:throw new Error(`Unknown Decoder type: ${Ee.type}`)}}_call(Ee){return this.decode(Ee)}decode(Ee){return this.decode_chain(Ee).join("")}decode_chain(Ee){throw Error("`decode_chain` should be implemented in subclass.")}}class oe extends St{decode_chain(Ee){const qe=D(this.config.pattern);return qe===null?Ee:Ee.map(ut=>ut.replaceAll(qe,this.config.content))}}class zt extends St{constructor(Ee){super(Ee),this.text_decoder=new TextDecoder}decode_chain(Ee){const qe=[];let ut=[];for(const mt of Ee){let vt=null;if(mt.length===6&&mt.startsWith("<0x")&&mt.endsWith(">")){const Rt=parseInt(mt.slice(3,5),16);isNaN(Rt)||(vt=Rt)}if(vt!==null)ut.push(vt);else{if(ut.length>0){const Rt=this.text_decoder.decode(Uint8Array.from(ut));qe.push(Rt),ut=[]}qe.push(mt)}}if(ut.length>0){const mt=this.text_decoder.decode(Uint8Array.from(ut));qe.push(mt),ut=[]}return qe}}class ke extends St{decode_chain(Ee){return[Ee.join("")]}}class fe extends St{constructor(Ee){super(Ee),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(Ee){return Ee.map(qe=>{let ut=0;for(let vt=0;vt<this.start&&qe[vt]===this.content;++vt)ut=vt+1;let mt=qe.length;for(let vt=0;vt<this.stop;++vt){const Rt=qe.length-vt-1;if(qe[Rt]!==this.content)break;mt=Rt}return qe.slice(ut,mt)})}}class Qe extends St{constructor(Ee){super(Ee),this.cleanup=Ee.cleanup}decode_chain(Ee){return Ee.map((qe,ut)=>(ut!==0&&(qe=qe.startsWith(this.config.prefix)?qe.replace(this.config.prefix,""):" "+qe),this.cleanup&&(qe=W(qe)),qe))}}class Je extends St{constructor(Ee){super(Ee),this.byte_decoder=F,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(Ee){const qe=Ee.join(""),ut=new Uint8Array([...qe].map(mt=>this.byte_decoder[mt]));return this.text_decoder.decode(ut)}decode_chain(Ee){const qe=[];let ut=[];for(const mt of Ee)this.added_tokens.find(vt=>vt.content===mt)!==void 0?(ut.length>0&&(qe.push(this.convert_tokens_to_string(ut)),ut=[]),qe.push(mt)):ut.push(mt);return ut.length>0&&qe.push(this.convert_tokens_to_string(ut)),qe}}class st extends St{constructor(Ee){super(Ee),this.pad_token=this.config.pad_token,this.word_delimiter_token=this.config.word_delimiter_token,this.cleanup=this.config.cleanup}convert_tokens_to_string(Ee){if(Ee.length===0)return"";const qe=[Ee[0]];for(let mt=1;mt<Ee.length;++mt)Ee[mt]!==qe.at(-1)&&qe.push(Ee[mt]);let ut=qe.filter(mt=>mt!==this.pad_token).join("");return this.cleanup&&(ut=W(ut).replaceAll(this.word_delimiter_token," ").trim()),ut}decode_chain(Ee){return[this.convert_tokens_to_string(Ee)]}}class lt extends St{constructor(Ee){super(Ee),this.decoders=Ee.decoders.map(qe=>St.fromConfig(qe))}decode_chain(Ee){return this.decoders.reduce((qe,ut)=>ut.decode_chain(qe),Ee)}}class xt extends St{constructor(Ee){super(Ee),this.suffix=this.config.suffix}decode_chain(Ee){return Ee.map((qe,ut)=>qe.replaceAll(this.suffix,ut===Ee.length-1?"":" "))}}class jt extends St{decode_chain(Ee){let qe="";for(let ut=1;ut<Ee.length;ut+=2)qe+=Ee[ut];return[qe]}}class $t extends Ue{constructor(Ee){super(),this.addPrefixSpace=Ee.add_prefix_space,this.replacement=Ee.replacement,this.strRep=Ee.str_rep||this.replacement,this.prepend_scheme=Ee.prepend_scheme??"always"}pre_tokenize_text(Ee,{section_index:qe}={}){let ut=Ee.replaceAll(" ",this.strRep);return this.addPrefixSpace&&!ut.startsWith(this.replacement)&&(this.prepend_scheme==="always"||this.prepend_scheme==="first"&&qe===0)&&(ut=this.strRep+ut),[ut]}}class Ft extends St{constructor(Ee){super(Ee),this.addPrefixSpace=Ee.add_prefix_space,this.replacement=Ee.replacement}decode_chain(Ee){const qe=[];for(let ut=0;ut<Ee.length;++ut){let mt=Ee[ut].replaceAll(this.replacement," ");this.addPrefixSpace&&ut==0&&mt.startsWith(" ")&&(mt=mt.substring(1)),qe.push(mt)}return qe}}class dA extends ce{constructor(Ee){super(Ee),this.charsmap=Ee.precompiled_charsmap}normalize(Ee){return(Ee=(Ee=Ee.replace(/[\u0001-\u0008\u000B\u000E-\u001F\u007F\u008F\u009F]/gm,"")).replace(/[\u0009\u000A\u000C\u000D\u00A0\u1680\u2000-\u200F\u2028\u2029\u202F\u205F\u2581\u3000\uFEFF\uFFFD]/gm," ")).includes("ï½")?Ee=Ee.split("ï½").map(ut=>
2810ut.normalize("NFKC")).join("ï½"):Ee=Ee.normalize("NFKC"),Ee}}class eA extends Ue{constructor(Ee){super(),this.tokenizers=Ee.pretokenizers.map(qe=>Ue.fromConfig(qe))}pre_tokenize_text(Ee,qe){return this.tokenizers.reduce((ut,mt)=>mt.pre_tokenize(ut,qe),[Ee])}}class nA extends Ue{constructor(Ee){super()}pre_tokenize_text(Ee,qe){return Ee.match(/\w+|[^\w\s]+/g)||[]}}class aA extends Ue{constructor(Ee){super()}pre_tokenize_text(Ee,qe){return function(ut){return ut.match(/\S+/g)||[]}(Ee)}}class bA extends Ue{constructor(Ee){super(),this.config=Ee,this.pattern=D(this.config.pattern),this.content=this.config.content}pre_tokenize_text(Ee,qe){return this.pattern===null?[Ee]:[Ee.replaceAll(this.pattern,this.config.content)]}}const vA=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function Gt(_t,Ee,qe,ut){for(const mt of Object.keys(_t)){const vt=Ee-_t[mt].length,Rt=qe(mt),lA=new Array(vt).fill(Rt);_t[mt]=ut==="right"?(0,u.mergeArrays)(_t[mt],lA):(0,u.mergeArrays)(lA,_t[mt])}}function mA(_t,Ee){for(const qe of Object.keys(_t))_t[qe].length=Ee}class AA extends o.Callable{constructor(qe,ut){super();Ct(this,"return_token_type_ids",!1);Ct(this,"padding_side","right");this._tokenizer_config=ut,this.normalizer=ce.fromConfig(qe.normalizer),this.pre_tokenizer=Ue.fromConfig(qe.pre_tokenizer),this.model=d.fromConfig(qe.model,ut),this.post_processor=It.fromConfig(qe.post_processor),this.decoder=St.fromConfig(qe.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const mt of qe.added_tokens){const vt=new e(mt);this.added_tokens.push(vt),this.model.tokens_to_ids.set(vt.content,vt.id),this.model.vocab[vt.id]=vt.content,vt.special&&(this.special_tokens.push(vt.content),this.all_special_ids.push(vt.id))}if(this.additional_special_tokens=ut.additional_special_tokens??[],this.special_tokens.push(...this.additional_special_tokens),this.special_tokens=[...new Set(this.special_tokens)],this.decoder&&(this.decoder.added_tokens=this.added_tokens,this.decoder.end_of_word_suffix=this.model.end_of_word_suffix),this.added_tokens_regex=this.added_tokens.length>0?new RegExp(this.added_tokens.slice().sort((mt,vt)=>vt.content.length-mt.content.length).map(mt=>`${mt.lstrip?"\\s*":""}(${(0,u.escapeRegExp)(mt.content)})${mt.rstrip?"\\s*":""}`).join("|")):null,this.mask_token=this.getToken("mask_token"),this.mask_token_id=this.model.tokens_to_ids.get(this.mask_token),this.pad_token=this.getToken("pad_token","eos_token"),this.pad_token_id=this.model.tokens_to_ids.get(this.pad_token),this.sep_token=this.getToken("sep_token"),this.sep_token_id=this.model.tokens_to_ids.get(this.sep_token),this.unk_token=this.getToken("unk_token"),this.unk_token_id=this.model.tokens_to_ids.get(this.unk_token),this.bos_token=this.getToken("bos_token"),this.bos_token_id=this.model.tokens_to_ids.get(this.bos_token),this.eos_token=this.getToken("eos_token"),this.eos_token_id=this.model.tokens_to_ids.get(this.eos_token),this.model_max_length=ut.model_max_length,this.remove_space=ut.remove_space,this.clean_up_tokenization_spaces=ut.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=ut.do_lowercase_and_remove_accent??!1,ut.padding_side&&(this.padding_side=ut.padding_side),this.legacy=!1,this.chat_template=ut.chat_template??null,Array.isArray(this.chat_template)){const mt=Object.create(null);for(const{name:vt,template:Rt}of this.chat_template){if(typeof vt!="string"||typeof Rt!="string")throw new Error('Chat template must be a list of objects with "name" and "template" properties');mt[vt]=Rt}this.chat_template=mt}this._compiled_template_cache=new Map}getToken(...qe){for(const ut of qe){const mt=this._tokenizer_config[ut];if(mt){if(typeof mt=="object"){if(mt.__type==="AddedToken")return mt.content;throw Error(`Unknown token: ${mt}`)}return mt}}return null}static async from_pretrained(qe,{progress_callback:ut=null,config:mt=null,cache_dir:vt=null,local_files_only:Rt=!1,revision:lA="main",legacy:gt=null}={}){return new this(...await G(qe,{progress_callback:ut,config:mt,cache_dir:vt,local_files_only:Rt,revision:lA,legacy:gt}))}_call(qe,{text_pair:ut=null,add_special_tokens:mt=!0,padding:vt=!1,truncation:Rt=null,max_length:lA=null,return_tensor:gt=!0,return_token_type_ids:pA=null}={}
2810){const oA=Array.isArray(qe);let sr;if(oA){if(qe.length===0)throw Error("text array must be non-empty");if(ut!==null){if(!Array.isArray(ut))throw Error("text_pair must also be an array");if(qe.length!==ut.length)throw Error("text and text_pair must have the same length");sr=qe.map((Gr,Or)=>this._encode_plus(Gr,{text_pair:ut[Or],add_special_tokens:mt,return_token_type_ids:pA}))}else sr=qe.map(Gr=>this._encode_plus(Gr,{add_special_tokens:mt,return_token_type_ids:pA}))}else{if(qe==null)throw Error("text may not be null or undefined");if(Array.isArray(ut))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");sr=[this._encode_plus(qe,{text_pair:ut,add_special_tokens:mt,return_token_type_ids:pA})]}if(lA===null?lA=vt==="max_length"?this.model_max_length:(0,C.max)(sr.map(Gr=>Gr.input_ids.length))[0]:Rt||console.warn("Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=true` to explicitly truncate examples to max length."),lA=Math.min(lA,this.model_max_length??1/0),vt||Rt)for(let Gr=0;Gr<sr.length;++Gr)sr[Gr].input_ids.length!==lA&&(sr[Gr].input_ids.length>lA?Rt&&mA(sr[Gr],lA):vt&&Gt(sr[Gr],lA,Or=>Or==="input_ids"?this.pad_token_id:0,this.padding_side));const jr={};if(gt){if((!vt||!Rt)&&sr.some(Or=>{var ir;for(const IA of Object.keys(Or))if(Or[IA].length!==((ir=sr[0][IA])==null?void 0:ir.length))return!0;return!1}))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=true' and 'truncation=true' to have batched tensors with the same length.");const Gr=[sr.length,sr[0].input_ids.length];for(const Or of Object.keys(sr[0]))jr[Or]=new b.Tensor("int64",BigInt64Array.from(sr.flatMap(ir=>ir[Or]).map(BigInt)),Gr)}else{for(const Gr of Object.keys(sr[0]))jr[Gr]=sr.map(Or=>Or[Gr]);if(!oA)for(const Gr of Object.keys(jr))jr[Gr]=jr[Gr][0]}return jr}_encode_text(qe){return qe===null?null:(this.added_tokens_regex?qe.split(this.added_tokens_regex).filter(vt=>vt):[qe]).map((vt,Rt)=>{if(this.added_tokens.find(gt=>gt.content===vt)!==void 0)return vt;{if(this.remove_space===!0&&(vt=vt.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(vt=function(pA){return U(pA.toLowerCase())}(vt)),this.normalizer!==null&&(vt=this.normalizer(vt)),vt.length===0)return[];const gt=this.pre_tokenizer!==null?this.pre_tokenizer(vt,{section_index:Rt}):[vt];return this.model(gt)}}).flat()}_encode_plus(qe,{text_pair:ut=null,add_special_tokens:mt=!0,return_token_type_ids:vt=null}={}){const{tokens:Rt,token_type_ids:lA}=this._tokenize_helper(qe,{pair:ut,add_special_tokens:mt}),gt=this.model.convert_tokens_to_ids(Rt),pA={input_ids:gt,attention_mask:new Array(gt.length).fill(1)};return(vt??this.return_token_type_ids)&&lA&&(pA.token_type_ids=lA),pA}_tokenize_helper(qe,{pair:ut=null,add_special_tokens:mt=!1}={}){const vt=this._encode_text(qe),Rt=this._encode_text(ut);return this.post_processor?this.post_processor(vt,Rt,{add_special_tokens:mt}):{tokens:(0,u.mergeArrays)(vt??[],Rt??[])}}tokenize(qe,{pair:ut=null,add_special_tokens:mt=!1}={}){return this._tokenize_helper(qe,{pair:ut,add_special_tokens:mt}).tokens}encode(qe,{text_pair:ut=null,add_special_tokens:mt=!0,return_token_type_ids:vt=null}={}){return this._encode_plus(qe,{text_pair:ut,add_special_tokens:mt,return_token_type_ids:vt}).input_ids}batch_decode(qe,ut={}){return qe instanceof b.Tensor&&(qe=qe.tolist()),qe.map(mt=>this.decode(mt,ut))}decode(qe,ut={}){if(qe instanceof b.Tensor&&(qe=V(qe)),!Array.isArray(qe)||qe.length===0||!(0,u.isIntegralNumber)(qe[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(qe,ut)}decode_single(qe,{skip_special_tokens:ut=!1,clean_up_tokenization_spaces:mt=null}){let vt=this.model.convert_ids_to_tokens(qe);ut&&(vt=vt.filter(lA=>!this.special_tokens.includes(lA)));let Rt=this.decoder?this.decoder(vt):vt.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(Rt=Rt.replaceAll(this.decoder.end_of_word_suffix," "),ut&&(Rt=Rt.trim())),(mt??this.clean_up_tokenization_spaces)&&
2810(Rt=W(Rt)),Rt}get_chat_template({chat_template:qe=null,tools:ut=null}={}){if(this.chat_template&&typeof this.chat_template=="object"){const mt=this.chat_template;if(qe!==null&&Object.hasOwn(mt,qe))qe=mt[qe];else if(qe===null)if(ut!==null&&"tool_use"in mt)qe=mt.tool_use;else{if(!("default"in mt))throw Error(`This model has multiple chat templates with no default specified! Please either pass a chat template or the name of the template you wish to use to the 'chat_template' argument. Available template names are ${Object.keys(mt).sort()}.`);qe=mt.default}}else if(qe===null){if(!this.chat_template)throw Error("Cannot use apply_chat_template() because tokenizer.chat_template is not set and no template argument was passed! For information about writing templates and setting the tokenizer.chat_template attribute, please see the documentation at https://huggingface.co/docs/transformers/main/en/chat_templating");qe=this.chat_template}return qe}apply_chat_template(qe,{tools:ut=null,documents:mt=null,chat_template:vt=null,add_generation_prompt:Rt=!1,tokenize:lA=!0,padding:gt=!1,truncation:pA=!1,max_length:oA=null,return_tensor:sr=!0,return_dict:jr=!1,tokenizer_kwargs:Gr={},...Or}={}){if(typeof(vt=this.get_chat_template({chat_template:vt,tools:ut}))!="string")throw Error("chat_template must be a string, but got "+typeof vt);let ir=this._compiled_template_cache.get(vt);ir===void 0&&(ir=new E.Template(vt),this._compiled_template_cache.set(vt,ir));const IA=Object.create(null);for(const _r of vA){const ba=this.getToken(_r);ba&&(IA[_r]=ba)}const _A=ir.render({messages:qe,add_generation_prompt:Rt,tools:ut,documents:mt,...IA,...Or});if(lA){const _r=this._call(_A,{add_special_tokens:!1,padding:gt,truncation:pA,max_length:oA,return_tensor:sr,...Gr});return jr?_r:_r.input_ids}return _A}}class gr extends AA{constructor(){super(...arguments);Ct(this,"return_token_type_ids",!0)}}class CA extends AA{constructor(){super(...arguments);Ct(this,"return_token_type_ids",!0)}}class NA extends AA{constructor(){super(...arguments);Ct(this,"return_token_type_ids",!0)}}class tA extends AA{constructor(){super(...arguments);Ct(this,"return_token_type_ids",!0)}}class iA extends AA{constructor(){super(...arguments);Ct(this,"return_token_type_ids",!0)}}class qt extends AA{constructor(){super(...arguments);Ct(this,"return_token_type_ids",!0)}}class zA extends AA{constructor(){super(...arguments);Ct(this,"return_token_type_ids",!0)}}class Fr extends AA{constructor(){super(...arguments);Ct(this,"return_token_type_ids",!0)}}class Vr extends AA{constructor(){super(...arguments);Ct(this,"return_token_type_ids",!0)}}class Tt extends AA{}class MA extends AA{}class QA extends AA{constructor(qe,ut){super(qe,ut);Ct(this,"return_token_type_ids",!0);console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class ZA extends AA{constructor(){super(...arguments);Ct(this,"return_token_type_ids",!0)}}class FA extends AA{}class EA extends AA{}class Rr extends AA{}class Er extends AA{constructor(Ee,qe){super(Ee,qe),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter(ut=>this.languageRegex.test(ut)),this.lang_to_token=ut=>ut}_build_translation_inputs(Ee,qe,ut){return Br(this,Ee,qe,ut)}}class Dr extends Er{}class Vs extends AA{}class nr extends AA{}const qs="â";class xa extends AA{constructor(qe,ut){super(qe,ut);Ct(this,"padding_side","left");this.legacy=ut.legacy??!0,this.legacy||(this.normalizer=null,this.pre_tokenizer=new $t({replacement:qs,add_prefix_space:!0,prepend_scheme:"first"}))}_encode_text(qe){if(qe===null)return null;if(this.legacy||qe.length===0)return super._encode_text(qe);let ut=super._encode_text(qs+qe.replaceAll(qs," "));return ut.length>1&&ut[0]===qs&&this.special_tokens.includes(ut[1])&&(ut=ut.slice(1)),ut}}class Ja extends AA{}class ma extends AA{}class Qa extends AA{}class $a extends AA{}class ds extends AA{}class en extends AA{}class oa extends AA{}class Yn extends AA{}class Ma extends AA{}function Br(_t,Ee,qe,ut){if(!("language_codes"in _t)||!Array.isArray(_t.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");
2810if(!("languageRegex"in _t&&_t.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in _t)||typeof _t.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const mt=ut.src_lang,vt=ut.tgt_lang;if(!_t.language_codes.includes(vt))throw new Error(`Target language code "${vt}" is not valid. Must be one of: {${_t.language_codes.join(", ")}}`);if(mt!==void 0){if(!_t.language_codes.includes(mt))throw new Error(`Source language code "${mt}" is not valid. Must be one of: {${_t.language_codes.join(", ")}}`);for(const Rt of _t.post_processor.config.single)if("SpecialToken"in Rt&&_t.languageRegex.test(Rt.SpecialToken.id)){Rt.SpecialToken.id=_t.lang_to_token(mt);break}}return ut.forced_bos_token_id=_t.model.convert_tokens_to_ids([_t.lang_to_token(vt)])[0],_t._call(Ee,qe)}class Hr extends AA{constructor(Ee,qe){super(Ee,qe),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(ut=>this.languageRegex.test(ut)),this.lang_to_token=ut=>ut}_build_translation_inputs(Ee,qe,ut){return Br(this,Ee,qe,ut)}}class cn extends AA{constructor(Ee,qe){super(Ee,qe),this.languageRegex=/^__[a-z]{2,3}__$/,this.language_codes=this.special_tokens.filter(ut=>this.languageRegex.test(ut)).map(ut=>ut.slice(2,-2)),this.lang_to_token=ut=>`__${ut}__`}_build_translation_inputs(Ee,qe,ut){return Br(this,Ee,qe,ut)}}class Rn extends AA{get timestamp_begin(){return this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1}_decode_asr(Ee,{return_timestamps:qe=!1,return_language:ut=!1,time_precision:mt=null,force_full_sequences:vt=!0}={}){if(mt===null)throw Error("Must specify time_precision");let Rt=null;const lA=qe==="word";function gt(){return{language:Rt,timestamp:[null,null],text:""}}const pA=[];let oA=gt(),sr=0;const jr=this.timestamp_begin,Gr=jr+1500;let Or=[],ir=[],IA=!1,_A=null;const _r=new Set(this.all_special_ids);for(const ts of Ee){const PA=ts.tokens,Kr=lA?ts.token_timestamps:null;let Yt=null,TA=jr;if("stride"in ts){const[Ds,ks,os]=ts.stride;if(sr-=ks,_A=Ds-os,ks&&(TA=ks/mt+jr),os)for(let Tr=PA.length-1;Tr>=0;--Tr){const As=Number(PA[Tr]);if(As>=jr){if(Yt!==null&&(As-jr)*mt<_A)break;Yt=As}}}let Js=[],Ss=[];for(let Ds=0;Ds<PA.length;++Ds){const ks=Number(PA[Ds]);if(_r.has(ks)){const os=this.decode([ks]),Tr=B.WHISPER_LANGUAGE_MAPPING.get(os.slice(2,-2));if(Tr!==void 0){if(Rt!==null&&Tr!==Rt&&!qe){Or.push(Js);const As=this.findLongestCommonSequence(Or)[0],yi=this.decode(As);oA.text=yi,pA.push(oA),Or=[],Js=[],oA=gt()}Rt=oA.language=Tr}}else if(ks>=jr&&ks<=Gr){const os=(ks-jr)*mt+sr,Tr=(0,C.round)(os,2);if(Yt!==null&&ks>=Yt)IA=!0;else if(IA||Or.length>0&&ks<TA)IA=!1;else if(oA.timestamp[0]===null)oA.timestamp[0]=Tr;else if(Tr!==oA.timestamp[0]){oA.timestamp[1]=Tr,Or.push(Js),lA&&ir.push(Ss);const[As,yi]=this.findLongestCommonSequence(Or,ir),Da=this.decode(As);oA.text=Da,lA&&(oA.words=this.collateWordTimestamps(As,yi,Rt)),pA.push(oA),Or=[],Js=[],ir=[],Ss=[],oA=gt()}}else if(Js.push(ks),lA){let os,Tr=(0,C.round)(Kr[Ds]+sr,2);if(Ds+1<Kr.length){os=(0,C.round)(Kr[Ds+1]+sr,2);const As=this.decode([ks]);J.test(As)&&(os=(0,C.round)(Math.min(Tr+mt,os),2))}else os=null;Ss.push([Tr,os])}}if("stride"in ts){const[Ds,ks,os]=ts.stride;sr+=Ds-os}Js.length>0?(Or.push(Js),lA&&ir.push(Ss)):Or.every(Ds=>Ds.length===0)&&(oA=gt(),Or=[],Js=[],ir=[],Ss=[])}if(Or.length>0){if(vt&&qe)throw new Error("Whisper did not predict an ending timestamp, which can happen if audio is cut off in the middle of a word. Also make sure WhisperTimeStampLogitsProcessor was used during generation.");const[ts,PA]=this.findLongestCommonSequence(Or,ir),Kr=this.decode(ts);oA.text=Kr,lA&&(oA.words=this.collateWordTimestamps(ts,PA,Rt)),pA.push(oA)}let ba=Object.create(null);const Bs=pA.map(ts=>ts.text).join("");if(qe||ut){for(let ts=0;ts<pA.length;++ts){const PA=pA[ts];qe||delete PA.timestamp,ut||delete PA.language}if(lA){const ts=[];for(const PA of pA)for(const Kr of PA.words)ts.push(Kr);ba={chunks:ts}}else ba={chunks:pA}}return[Bs,ba]}findLongestCommonSequence(Ee,qe=null){let ut=Ee[0],mt=ut.length,vt=[];const Rt=Array.isArray(qe)&&qe.length>0;
2810let lA=Rt?[]:null,gt=Rt?qe[0]:null;for(let pA=1;pA<Ee.length;++pA){const oA=Ee[pA];let sr=0,jr=[mt,mt,0,0];const Gr=oA.length;for(let Bs=1;Bs<mt+Gr;++Bs){const ts=Math.max(0,mt-Bs),PA=Math.min(mt,mt+Gr-Bs),Kr=ut.slice(ts,PA),Yt=Math.max(0,Bs-mt),TA=Math.min(Gr,Bs),Js=oA.slice(Yt,TA);if(Kr.length!==Js.length)throw new Error("There is a bug within whisper `decode_asr` function, please report it. Dropping to prevent bad inference.");let Ss;Ss=Rt?Kr.filter((ks,os)=>ks===Js[os]&>[ts+os]<=qe[pA][Yt+os]).length:Kr.filter((ks,os)=>ks===Js[os]).length;const Ds=Ss/Bs+Bs/1e4;Ss>1&&Ds>sr&&(sr=Ds,jr=[ts,PA,Yt,TA])}const[Or,ir,IA,_A]=jr,_r=Math.floor((ir+Or)/2),ba=Math.floor((_A+IA)/2);vt.push(...ut.slice(0,_r)),ut=oA.slice(ba),mt=ut.length,Rt&&(lA.push(...gt.slice(0,_r)),gt=qe[pA].slice(ba))}return vt.push(...ut),Rt?(lA.push(...gt),[vt,lA]):[vt,[]]}collateWordTimestamps(Ee,qe,ut){const[mt,vt,Rt]=this.combineTokensIntoWords(Ee,ut),lA=[];for(let gt=0;gt<mt.length;++gt){const pA=Rt[gt];lA.push({text:mt[gt],timestamp:[qe[pA.at(0)][0],qe[pA.at(-1)][1]]})}return lA}combineTokensIntoWords(Ee,qe,ut=`"'â¡¿([{-`,mt=`"'.ã,ï¼!ï¼?ï¼:ï¼â)]}ã`){let vt,Rt,lA;return["chinese","japanese","thai","lao","myanmar"].includes(qe=qe??"english")?[vt,Rt,lA]=this.splitTokensOnUnicode(Ee):[vt,Rt,lA]=this.splitTokensOnSpaces(Ee),this.mergePunctuations(vt,Rt,lA,ut,mt)}decode(Ee,qe){let ut;return qe!=null&&qe.decode_with_timestamps?(Ee instanceof b.Tensor&&(Ee=V(Ee)),ut=this.decodeWithTimestamps(Ee,qe)):ut=super.decode(Ee,qe),ut}decodeWithTimestamps(Ee,qe){const ut=(qe==null?void 0:qe.time_precision)??.02,mt=Array.from(this.all_special_ids).at(-1)+1;let vt=[[]];for(let Rt of Ee)if(Rt=Number(Rt),Rt>=mt){const lA=((Rt-mt)*ut).toFixed(2);vt.push(`<|${lA}|>`),vt.push([])}else vt[vt.length-1].push(Rt);return vt=vt.map(Rt=>typeof Rt=="string"?Rt:super.decode(Rt,qe)),vt.join("")}splitTokensOnUnicode(Ee){const qe=this.decode(Ee,{decode_with_timestamps:!0}),ut=[],mt=[],vt=[];let Rt=[],lA=[],gt=0;for(let pA=0;pA<Ee.length;++pA){const oA=Ee[pA];Rt.push(oA),lA.push(pA);const sr=this.decode(Rt,{decode_with_timestamps:!0});sr.includes("�")&&qe[gt+sr.indexOf("�")]!=="�"||(ut.push(sr),mt.push(Rt),vt.push(lA),Rt=[],lA=[],gt+=sr.length)}return[ut,mt,vt]}splitTokensOnSpaces(Ee){const[qe,ut,mt]=this.splitTokensOnUnicode(Ee),vt=[],Rt=[],lA=[],gt=new RegExp(`^[${K}]$`,"gu");for(let pA=0;pA<qe.length;++pA){const oA=qe[pA],sr=ut[pA],jr=mt[pA],Gr=sr[0]>=this.model.tokens_to_ids.get("<|endoftext|>"),Or=oA.startsWith(" "),ir=oA.trim(),IA=gt.test(ir);if(Gr||Or||IA||vt.length===0)vt.push(oA),Rt.push(sr),lA.push(jr);else{const _A=vt.length-1;vt[_A]+=oA,Rt[_A].push(...sr),lA[_A].push(...jr)}}return[vt,Rt,lA]}mergePunctuations(Ee,qe,ut,mt,vt){const Rt=structuredClone(Ee),lA=structuredClone(qe),gt=structuredClone(ut);let pA=Rt.length-2,oA=Rt.length-1;for(;pA>=0;)Rt[pA].startsWith(" ")&&mt.includes(Rt[pA].trim())?(Rt[oA]=Rt[pA]+Rt[oA],lA[oA]=(0,u.mergeArrays)(lA[pA],lA[oA]),gt[oA]=(0,u.mergeArrays)(gt[pA],gt[oA]),Rt[pA]="",lA[pA]=[],gt[pA]=[]):oA=pA,--pA;for(pA=0,oA=1;oA<Rt.length;)!Rt[pA].endsWith(" ")&&vt.includes(Rt[oA])?(Rt[pA]+=Rt[oA],lA[pA]=(0,u.mergeArrays)(lA[pA],lA[oA]),gt[pA]=(0,u.mergeArrays)(gt[pA],gt[oA]),Rt[oA]="",lA[oA]=[],gt[oA]=[]):pA=oA,++oA;return[Rt.filter(sr=>sr),lA.filter(sr=>sr.length>0),gt.filter(sr=>sr.length>0)]}}class Oi extends AA{}class Ra extends AA{}class jn extends AA{}class Tn extends AA{constructor(Ee,qe){super(Ee,qe),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter(ut=>this.languageRegex.test(ut)),console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}_encode_text(Ee){if(Ee===null)return null;const[qe,...ut]=Ee.trim().split(this.languageRegex);if(ut.length===0)return super._encode_text(qe);if(ut.length===2){const[mt,vt]=ut;return this.supported_language_codes.includes(mt)||console.warn(`Unsupported language code "${mt}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`),(0,u.mergeArrays)([mt],super._encode_text(vt))}}}class ja extends AA{}class $A extends AA{}class fr extends AA{}class la extends AA{}class vi extends AA{}class Pa extends AA{constructor(Ee,qe){super(Ee,qe),this.decoder=new jt({})}}class ci extends AA{}class tn extends AA{}class Wa{static async from_pretrained(Ee,{progress_callback:qe=null,config:ut=null,cache_dir:mt=null,local_files_only:vt=!1,revision:Rt="main",legacy:lA=null}={}){var jr;const[gt,pA]=await G(Ee,{progress_callback:qe,config:ut,cache_dir:mt,local_files_only:vt,revision:Rt,legacy:lA}),oA=((jr=pA.tokenizer_class)==null?void 0:jr.replace(/Fast$/,""))??"PreTrainedTokenizer";let sr=this.TOKENIZER_CLASS_MAPPING[oA];return sr||(console.warn(`Unknown tokenizer class "${oA}", attempting to construct from base class.`),sr=AA),new sr(gt,pA)}}Ct(Wa,"TOKENIZER_CLASS_MAPPING",{T5Tokenizer:FA,DistilBertTokenizer:Tt,CamembertTokenizer:MA,DebertaTokenizer:iA,DebertaV2Tokenizer:qt,BertTokenizer:gr,HerbertTokenizer:zA,ConvBertTokenizer:Fr,RoFormerTokenizer:Vr,XLMTokenizer:QA,ElectraTokenizer:ZA,MobileBertTokenizer:NA,SqueezeBertTokenizer:tA,AlbertTokenizer:CA,GPT2Tokenizer:EA,BartTokenizer:Rr,MBartTokenizer:Er,MBart50Tokenizer:Dr,RobertaTokenizer:Vs,WhisperTokenizer:Rn,CodeGenTokenizer:Oi,CLIPTokenizer:Ra,SiglipTokenizer:jn,MarianTokenizer:Tn,BloomTokenizer:nr,NllbTokenizer:Hr,M2M100Tokenizer:cn,LlamaTokenizer:xa,CodeLlamaTokenizer:Ja,XLMR
2810obertaTokenizer:ma,MPNetTokenizer:Qa,FalconTokenizer:$a,GPTNeoXTokenizer:ds,EsmTokenizer:en,Wav2Vec2CTCTokenizer:ja,BlenderbotTokenizer:$A,BlenderbotSmallTokenizer:fr,SpeechT5Tokenizer:la,NougatTokenizer:vi,VitsTokenizer:Pa,Qwen2Tokenizer:oa,GemmaTokenizer:Yn,Grok1Tokenizer:Ma,CohereTokenizer:ci,MgpstrTokenizer:tn,PreTrainedTokenizer:AA})},
2810"./src/utils/audio.js":(c,a,s)=>{s.r(a),s.d(a,{RawAudio:()=>e,hamming:()=>D,hanning:()=>G,mel_filter_bank:()=>H,read_audio:()=>E,spectrogram:()=>J,window_function:()=>g});var o=s("./src/utils/hub.js"),u=s("./src/utils/maths.js"),p=s("./src/utils/core.js"),C=s("./src/env.js"),b=s("?7a2c"),k=s("./src/utils/tensor.js");async function E(d,_){if(typeof AudioContext>"u")throw Error("Unable to load audio from path/URL since `AudioContext` is not available in your environment. Instead, audio data should be passed directly to the pipeline/processor. 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2810"./src/utils/constants.js":(c,a,s)=>{s.r(a),s.d(a,{CHAT_TEMPLATE_NAME:()=>k,CONFIG_NAME:()=>u,FEATURE_EXTRACTOR_NAME:()=>p,GENERATION_CONFIG_NAME:()=>E,GITHUB_ISSUE_URL:()=>o,IMAGE_PROCESSOR_NAME:()=>C,PROCESSOR_NAME:()=>b});const o="https://github.com/huggingface/transformers.js/issues/new/choose",u="config.json",p="preprocessor_config.json",C=p,b="processor_config.json",k="chat_template.json",E="generation_config.json"},
2810"./src/utils/core.js":(c,a,s)=>{function o(K,J){K&&K(J)}function u(K){return Object.fromEntries(Object.entries(K).map(([J,g])=>[g,J]))}function p(K){return K.replace(/[.*+?^${}()|[\]\\]/g,"\\$&")}function C(K){var J,g,v;return((v=(g=(J=K==null?void 0:K.prototype)==null?void 0:J.__proto__)==null?void 0:g.constructor)==null?void 0:v.name)==="TypedArray"}function b(K){return Number.isInteger(K)||typeof K=="bigint"}function k(K){return K==null||K===-1}function E(K){const J=[];let g=K;for(;Array.isArray(g);)J.push(g.length),g=g[0];return J}function B(K,J,g=void 0){const v=K[J];if(v!==void 0)return delete K[J],v;if(g===void 0)throw Error(`Key ${J} does not exist in object.`);return g}function G(...K){return Array.prototype.concat.apply([],K)}function D(...K){return K.reduce((J,g)=>J.flatMap(v=>g.map(e=>[v,e])))}function Q(K,J){return Math.abs((K+J)%(2*J)-J)}function V(K,J){const g=URL.createObjectURL(J),v=document.createElement("a");v.href=g,v.download=K,v.click(),v.remove(),URL.revokeObjectURL(g)}function W(K,J){return Object.assign({},...J.map(g=>{if(K[g]!==void 0)return{[g]:K[g]}}))}function U(K){let J=0;for(const g of K)++J;return J}function H(K,J){let g=0;for(const v of K)v===J&&++g;return g}s.r(a),s.d(a,{calculateDimensions:()=>E,calculateReflectOffset:()=>Q,count:()=>H,dispatchCallback:()=>o,escapeRegExp:()=>p,isIntegralNumber:()=>b,isNullishDimension:()=>k,isTypedArray:()=>C,len:()=>U,mergeArrays:()=>G,pick:()=>W,pop:()=>B,product:()=>D,reverseDictionary:()=>u,saveBlob:()=>V})},
2810"./src/utils/data-structures.js":(c,a,s)=>{s.r(a),s.d(a,{CharTrie:()=>u,PriorityQueue:()=>o,TokenLattice:()=>C});class o{constructor(E=(G,D)=>G>D,B=1/0){this._heap=[],this._comparator=E,this._maxSize=B}get size(){return this._heap.length}isEmpty(){return this.size===0}peek(){return this._heap[0]}push(...E){return this.extend(E)}extend(E){for(const B of E)if(this.size<this._maxSize)this._heap.push(B),this._siftUp();else{const G=this._smallest();this._comparator(B,this._heap[G])&&(this._heap[G]=B,this._siftUpFrom(G))}return this.size}pop(){const E=this.peek(),B=this.size-1;return B>0&&this._swap(0,B),this._heap.pop(),this._siftDown(),E}replace(E){const B=this.peek();return this._heap[0]=E,this._siftDown(),B}_parent(E){return(E+1>>>1)-1}_left(E){return 1+(E<<1)}_right(E){return E+1<<1}_greater(E,B){return this._comparator(this._heap[E],this._heap[B])}_swap(E,B){const G=this._heap[E];this._heap[E]=this._heap[B],this._heap[B]=G}_siftUp(){this._siftUpFrom(this.size-1)}_siftUpFrom(E){for(;E>0&&this._greater(E,this._parent(E));)this._swap(E,this._parent(E)),E=this._parent(E)}_siftDown(){let E=0;for(;this._left(E)<this.size&&this._greater(this._left(E),E)||this._right(E)<this.size&&this._greater(this._right(E),E);){const B=this._right(E)<this.size&&this._greater(this._right(E),this._left(E))?this._right(E):this._left(E);this._swap(E,B),E=B}}_smallest(){return 2**Math.floor(Math.log2(this.size))-1}}class u{constructor(){this.root=p.default()}extend(E){for(const B of E)this.push(B)}push(E){let B=this.root;for(const G of E){let D=B.children.get(G);D===void 0&&(D=p.default(),B.children.set(G,D)),B=D}B.isLeaf=!0}*commonPrefixSearch(E){let B=this.root;if(B===void 0)return;let G="";for(const D of E){if(G+=D,B=B.children.get(D),B===void 0)return;B.isLeaf&&(yield G)}}}class p{constructor(E,B){this.isLeaf=E,this.children=B}static default(){return new p(!1,new Map)}}class C{constructor(E,B,G){this.chars=Array.from(E),this.len=this.chars.length,this.bosTokenId=B,this.eosTokenId=G,this.nodes=[],this.beginNodes=Array.from({length:this.len+1},()=>[]),this.endNodes=Array.from({length:this.len+1},()=>[]);const D=new b(this.bosTokenId,0,0,0,0),Q=new b(this.eosTokenId,1,this.len,0,0);this.nodes.push(D.clone()),this.nodes.push(Q.clone()),this.beginNodes[this.len].push(Q),this.endNodes[0].push(D)}insert(E,B,G,D){const Q=this.nodes.length,V=new b(D,Q,E,B,G);this.beginNodes[E].push(V),this.endNodes[E+B].push(V),this.nodes.push(V)}viterbi(){const E=this.len;let B=0;for(;B<=E;){if(this.beginNodes[B].length==0)return[];for(let V of this.beginNodes[B]){V.prev=null;let W=0,U=null;for(let H of this.endNodes[B]){const K=H.backtraceScore+V.score;(U===null||K>W)&&(U=H.clone(),W=K)}if(U===null)return[];V.prev=U,V.backtraceScore=W}++B}const G=[],D=this.beginNodes[E][0].prev;if(D===null)return[];let Q=D.clone();for(;Q.prev!==null;)G.push(Q.clone()),Q=Q.clone().prev.clone();return G.reverse(),G}piece(E){return this.chars.slice(E.pos,E.pos+E.length).join("")}tokens(){return this.viterbi().map(E=>this.piece(E))}tokenIds(){return this.viterbi().map(E=>E.tokenId)}}class b{constructor(E,B,G,D,Q){this.tokenId=E,this.nodeId=B,this.pos=G,this.length=D,this.score=Q,this.prev=null,this.backtraceScore=0}clone(){const E=new b(this.tokenId,this.nodeId,this.pos,this.length,this.score);return E.prev=this.prev,E.backtraceScore=this.backtraceScore,E}}},
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2810"./src/utils/dtypes.js":(c,a,s)=>{s.r(a),s.d(a,{DATA_TYPES:()=>C,DEFAULT_DEVICE_DTYPE_MAPPING:()=>b,DEFAULT_DTYPE_SUFFIX_MAPPING:()=>k,isWebGpuFp16Supported:()=>p});var o=s("./src/env.js"),u=s("./src/utils/devices.js");const p=function(){let E;return async function(){if(E===void 0)if(o.apis.IS_WEBGPU_AVAILABLE)try{E=(await navigator.gpu.requestAdapter()).features.has("shader-f16")}catch{E=!1}else E=!1;return E}}(),C=Object.freeze({auto:"auto",fp32:"fp32",fp16:"fp16",q8:"q8",int8:"int8",uint8:"uint8",q4:"q4",bnb4:"bnb4",q4f16:"q4f16"}),b=Object.freeze({[u.DEVICE_TYPES.wasm]:C.q8}),k=Object.freeze({[C.fp32]:"",[C.fp16]:"_fp16",[C.int8]:"_int8",[C.uint8]:"_uint8",[C.q8]:"_quantized",[C.q4]:"_q4",[C.q4f16]:"_q4f16",[C.bnb4]:"_bnb4"})},
2810"./src/utils/generic.js":(c,a,s)=>{s.r(a),s.d(a,{Callable:()=>o});const o=class{constructor(){let u=function(...p){return u._call(...p)};return Object.setPrototypeOf(u,new.target.prototype)}_call(...u){throw Error("Must implement _call method in subclass")}}},
2810"./src/utils/hub.js":(c,a,s)=>{s.r(a),s.d(a,{getFile:()=>B,getModelFile:()=>Q,getModelJSON:()=>V});var o=s("?7a2c"),u=s("?a42a"),p=s("./src/env.js"),C=s("./src/utils/core.js");const b={txt:"text/plain",html:"text/html",css:"text/css",js:"text/javascript",json:"application/json",png:"image/png",jpg:"image/jpeg",jpeg:"image/jpeg",gif:"image/gif"};class k{constructor(H){if(this.filePath=H,this.headers=new Headers,this.exists=o.existsSync(H),this.exists){this.status=200,this.statusText="OK";let K=o.statSync(H);this.headers.set("content-length",K.size.toString()),this.updateContentType();let J=this;this.body=new ReadableStream({start(g){J.arrayBuffer().then(v=>{g.enqueue(new Uint8Array(v)),g.close()})}})}else this.status=404,this.statusText="Not Found",this.body=null}updateContentType(){const H=this.filePath.toString().split(".").pop().toLowerCase();this.headers.set("content-type",b[H]??"application/octet-stream")}clone(){let H=new k(this.filePath);return H.exists=this.exists,H.status=this.status,H.statusText=this.statusText,H.headers=new Headers(this.headers),H}async arrayBuffer(){return(await o.promises.readFile(this.filePath)).buffer}async blob(){const H=await o.promises.readFile(this.filePath);return new Blob([H],{type:this.headers.get("content-type")})}async text(){return await o.promises.readFile(this.filePath,"utf8")}async json(){return JSON.parse(await this.text())}}function E(U,H=null,K=null){let J;try{J=new URL(U)}catch{return!1}return!(H&&!H.includes(J.protocol))&&!(K&&!K.includes(J.hostname))}async function B(U){var H;if(p.env.useFS&&!E(U,["http:","https:","blob:"]))return new k(U);if(typeof process<"u"&&((H=process==null?void 0:process.release)==null?void 0:H.name)==="node"){const K=!!(rl!=null&&rl.TESTING_REMOTELY),J=p.env.version,g=new Headers;if(g.set("User-Agent",`transformers.js/${J}; is_ci/${K};`),E(U,["http:","https:"],["huggingface.co","hf.co"])){const v=(rl==null?void 0:rl.HF_TOKEN)??(rl==null?void 0:rl.HF_ACCESS_TOKEN);v&&g.set("Authorization",`Bearer ${v}`)}return fetch(U,{headers:g})}return fetch(U)}const G={400:"Bad request error occurred while trying to load file",401:"Unauthorized access to file",403:"Forbidden access to file",404:"Could not locate file",408:"Request timeout error occurred while trying to load file",500:"Internal server error error occurred while trying to load file",502:"Bad gateway error occurred while trying to load file",503:"Service unavailable error occurred while trying to load file",504:"Gateway timeout error occurred while trying to load file"};class D{constructor(H){this.path=H}async match(H){let K=u.join(this.path,H),J=new k(K);return J.exists?J:void 0}async put(H,K){const J=Buffer.from(await K.arrayBuffer());let g=u.join(this.path,H);try{await o.promises.mkdir(u.dirname(g),{recursive:!0}),await o.promises.writeFile(g,J)}catch(v){console.warn("An error occurred while writing the file to cache:",v)}}}async function Q(U,H,K=!0,J={}){if(!p.env.allowLocalModels){if(J.local_files_only)throw Error("Invalid configuration detected: local models are disabled (`env.allowLocalModels=false`) but you have requested to only use local models (`local_files_only=true`).");if(!p.env.allowRemoteModels)throw Error("Invalid configuration detected: both local and remote models are disabled. Fix by setting `env.allowLocalModels` or `env.allowRemoteModels` to `true`.")}let g;if((0,C.dispatchCallback)(J.progress_callback,{status:"initiate",name:U,file:H}),!g&&p.env.useBrowserCache){if(typeof caches>"u")throw Error("Browser cache is not available in this environment.");try{g=await caches.open("transformers-cache")}catch(Ce){console.warn("An error occurred while opening the browser cache:",Ce)}}if(!g&&p.env.useFSCache&&(g=new D(J.cache_dir??p.env.cacheDir)),!g&&p.env.useCustomCache){if(!p.env.customCache)throw Error("`env.useCustomCache=true`, but `env.customCache` is not defined.");if(!p.env.customCache.match||!p.env.customCache.put)throw new Error("`env.customCache` must be an object which implements the `match` and `put` functions of the Web Cache API. For more information, see https://developer.mozilla.org/en-US/docs/Web/API/Cache");g=p.env.customCache}const v=J.revision??"main";let e,d,_=W(U,H),re=W(p.env.localModelPath,_),S=W(p.env.remoteHost,p.env.remotePathTemplate.replaceAll("{model}",U).replaceAll("{revision}",encodeURIComponent(v)),H),F=v==="main"?_:W(U,v,H),Ie=g instanceof D?F:S,he=!1;g&&(d=await async function(Ce,...Fe){for(let Pe of Fe)try{let de=await Ce.match(Pe);
2810if(de)return de}catch{continue}}(g,re,Ie));const ce=d!==void 0;if(d===void 0){if(p.env.allowLocalModels)if(E(_,["http:","https:"])){if(J.local_files_only)throw new Error(`\`local_files_only=true\`, but attempted to load a remote file from: ${_}.`);if(!p.env.allowRemoteModels)throw new Error(`\`env.allowRemoteModels=false\`, but attempted to load a remote file from: ${_}.`)}else try{d=await B(re),e=re}catch(Ce){console.warn(`Unable to load from local path "${re}": "${Ce}"`)}if(d===void 0||d.status===404){if(J.local_files_only||!p.env.allowRemoteModels){if(K)throw Error(`\`local_files_only=true\` or \`env.allowRemoteModels=false\` and file was not found locally at "${re}".`);return null}if(d=await B(S),d.status!==200)return function(Ce,Fe,Pe){if(!Pe)return null;const de=G[Ce]??`Error (${Ce}) occurred while trying to load file`;throw Error(`${de}: "${Fe}".`)}(d.status,S,K);e=Ie}he=g&&typeof Response<"u"&&d instanceof Response&&d.status===200}let ge;return(0,C.dispatchCallback)(J.progress_callback,{status:"download",name:U,file:H}),J.progress_callback?ce&&typeof navigator<"u"&&/firefox/i.test(navigator.userAgent)?(ge=new Uint8Array(await d.arrayBuffer()),(0,C.dispatchCallback)(J.progress_callback,{status:"progress",name:U,file:H,progress:100,loaded:ge.length,total:ge.length})):ge=await async function(Ce,Fe){const Pe=Ce.headers.get("Content-Length");Pe===null&&console.warn("Unable to determine content-length from response headers. Will expand buffer when needed.");let de=parseInt(Pe??"0"),pe=new Uint8Array(de),we=0;const Ge=Ce.body.getReader();async function me(){const{done:at,value:Ue}=await Ge.read();if(at)return;let ot=we+Ue.length;if(ot>de){de=ot;let pt=new Uint8Array(de);pt.set(pe),pe=pt}return pe.set(Ue,we),we=ot,Fe({progress:we/de*100,loaded:we,total:de}),me()}return await me(),pe}(d,Ce=>{(0,C.dispatchCallback)(J.progress_callback,{status:"progress",name:U,file:H,...Ce})}):ge=new Uint8Array(await d.arrayBuffer()),he&&e&&await g.match(e)===void 0&&await g.put(e,new Response(ge,{headers:d.headers})).catch(Ce=>{console.warn(`Unable to add response to browser cache: ${Ce}.`)}),(0,C.dispatchCallback)(J.progress_callback,{status:"done",name:U,file:H}),ge}async function V(U,H,K=!0,J={}){let g=await Q(U,H,K,J);if(g===null)return{};let v=new TextDecoder("utf-8").decode(g);return JSON.parse(v)}function W(...U){return(U=U.map((H,K)=>(K&&(H=H.replace(new RegExp("^/"),"")),K!==U.length-1&&(H=H.replace(new RegExp("/$"),"")),H))).join("/")}},
2810"./src/utils/image.js":(c,a,s)=>{s.r(a),s.d(a,{RawImage:()=>V,load_image:()=>W});var o=s("./src/utils/core.js"),u=s("./src/utils/hub.js"),p=s("./src/env.js"),C=s("./src/utils/tensor.js"),b=s("?2b25");let k,E,B;const G=p.apis.IS_BROWSER_ENV||p.apis.IS_WEBWORKER_ENV;if(G)k=(U,H)=>{if(!self.OffscreenCanvas)throw new Error("OffscreenCanvas not supported by this browser.");return new self.OffscreenCanvas(U,H)},B=self.createImageBitmap,E=self.ImageData;else{if(!b)throw new Error("Unable to load image processing library.");B=async U=>{const H=(await U.metadata()).channels,{data:K,info:J}=await U.rotate().raw().toBuffer({resolveWithObject:!0}),g=new V(new Uint8ClampedArray(K),J.width,J.height,J.channels);return H!==void 0&&H!==J.channels&&g.convert(H),g}}const D={0:"nearest",1:"lanczos",2:"bilinear",3:"bicubic",4:"box",5:"hamming"},Q=new Map([["png","image/png"],["jpg","image/jpeg"],["jpeg","image/jpeg"],["gif","image/gif"]]);class V{constructor(H,K,J,g){this.data=H,this.width=K,this.height=J,this.channels=g}get size(){return[this.width,this.height]}static async read(H){if(H instanceof V)return H;if(typeof H=="string"||H instanceof URL)return await this.fromURL(H);throw new Error("Unsupported input type: "+typeof H)}static fromCanvas(H){if(!G)throw new Error("fromCanvas() is only supported in browser environments.");const K=H.getContext("2d").getImageData(0,0,H.width,H.height).data;return new V(K,H.width,H.height,4)}static async fromURL(H){const K=await(0,u.getFile)(H);if(K.status!==200)throw new Error(`Unable to read image from "${H}" (${K.status} ${K.statusText})`);const J=await K.blob();return this.fromBlob(J)}static async fromBlob(H){if(G){const K=await B(H),J=k(K.width,K.height).getContext("2d");return J.drawImage(K,0,0),new this(J.getImageData(0,0,K.width,K.height).data,K.width,K.height,4)}{const K=b(await H.arrayBuffer());return await B(K)}}static fromTensor(H,K="CHW"){if(H.dims.length!==3)throw new Error(`Tensor should have 3 dimensions, but has ${H.dims.length} dimensions.`);if(K==="CHW")H=H.transpose(1,2,0);else if(K!=="HWC")throw new Error(`Unsupported channel format: ${K}`);if(!(H.data instanceof Uint8ClampedArray||H.data instanceof Uint8Array))throw new Error(`Unsupported tensor type: ${H.type}`);switch(H.dims[2]){case 1:case 2:case 3:case 4:return new V(H.data,H.dims[1],H.dims[0],H.dims[2]);default:throw new Error(`Unsupported number of channels: ${H.dims[2]}`)}}grayscale(){if(this.channels===1)return this;const H=new Uint8ClampedArray(this.width*this.height*1);switch(this.channels){case 3:case 4:for(let K=0,J=0;K<this.data.length;K+=this.channels){const g=this.data[K],v=this.data[K+1],e=this.data[K+2];H[J++]=Math.round(.2989*g+.587*v+.114*e)}break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(H,this.width,this.height,1)}rgb(){if(this.channels===3)return this;const H=new Uint8ClampedArray(this.width*this.height*3);switch(this.channels){case 1:for(let K=0,J=0;K<this.data.length;++K)H[J++]=this.data[K],H[J++]=this.data[K],H[J++]=this.data[K];break;case 4:for(let K=0,J=0;K<this.data.length;K+=4)H[J++]=this.data[K],H[J++]=this.data[K+1],H[J++]=this.data[K+2];break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(H,this.width,this.height,3)}rgba(){if(this.channels===4)return this;const H=new Uint8ClampedArray(this.width*this.height*4);switch(this.channels){case 1:for(let K=0,J=0;K<this.data.length;++K)H[J++]=this.data[K],H[J++]=this.data[K],H[J++]=this.data[K],H[J++]=255;break;case 3:for(let K=0,J=0;K<this.data.length;K+=3)H[J++]=this.data[K],H[J++]=this.data[K+1],H[J++]=this.data[K+2],H[J++]=255;break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(H,this.width,this.height,4)}putAlpha(H){if(H.width!==this.width||H.height!==this.height)throw new Error(`Expected mask size to be ${this.width}x${this.height}, but got ${H.width}x${H.height}`);if(H.channels!==1)throw new Error(`Expected mask to have 1 channel, but got ${H.channels}`);const K=this.data,J=H.data,g=this.width*this.height;if(this.channels===3){const v=new Uint8ClampedArray(4*g);for(let e=0,d=0,_=0;e<g;++e)v[_++]=K[d++],v[_++]=K[d++],v[_++]=K[d++],v[_++]=J[e];return this._update(v,this.width,this.height,4)}if(this.channels===4){for(let v=0;v<g;++v)K[4*v+3]=J[v];return this}throw new Error(`Expected image to have 3 or 4 channels, but got ${this.channels}`)}async resize(H,K,{resample:J=2}={}){if(this.width===H&&this.height===K)return this;let g=D[J]??J;const v=(0,o.isNullishDimension)(H),e=(0,o.isNullishDimension)(K);if(v&&e)return this;if(v?H=K/this.height*this.width:e&&(K=H/this.width*this.height),G){const d=this.channels,_=this.toCanvas(),re=k(H,K).getContext("2d");return re.drawImage(_,0,0,H,K),new V(re.getImageData(0,0,H,K).data,H,K,4).convert(d)}{let d=this.toSharp();switch(g){case"box":case"hamming":g!=="box"&&g!=="hamming"||(console.warn(`Resampling method ${g} is not yet supported. Using bilinear instead.`),g="bilinear");case"nearest":case"bilinear":case"bicubic":d=d.affine([H/this.width,0,0,K/this.height],{interpolator:g});break;case"lanczos":d=d.resize({width:H,height:K,fit:"fill",kernel:"lanczos3"});break;default:throw new Error(`Resampling method ${g} is not supported.`)}return await B(d)}}async pad([H,K,J,g]){if(H=Math.max(H,0),K=Math.max(K,0),J=Math.max(J,0),g=Math.max(g,0),H===0&&K===0&&J===0&&g===0)return this;if(G){const v=this.channels,e=this.toCanvas(),d=this.width+H+K,_=this.height+J+g,re=k(d,_).getContext("2d");return re.drawImage(e,0,0,this.width,this.height,H,J,this.width,this.height),new V(re.getImageData(0,0,d,_).data,d,_,4).
2810convert(v)}{const v=this.toSharp().extend({left:H,right:K,top:J,bottom:g});return await B(v)}}async crop([H,K,J,g]){if(H=Math.max(H,0),K=Math.max(K,0),J=Math.min(J,this.width-1),g=Math.min(g,this.height-1),H===0&&K===0&&J===this.width-1&&g===this.height-1)return this;const v=J-H+1,e=g-K+1;if(G){const d=this.channels,_=this.toCanvas(),re=k(v,e).getContext("2d");return re.drawImage(_,H,K,v,e,0,0,v,e),new V(re.getImageData(0,0,v,e).data,v,e,4).convert(d)}{const d=this.toSharp().extract({left:H,top:K,width:v,height:e});return await B(d)}}async center_crop(H,K){if(this.width===H&&this.height===K)return this;const J=(this.width-H)/2,g=(this.height-K)/2;if(G){const v=this.channels,e=this.toCanvas(),d=k(H,K).getContext("2d");let _=0,re=0,S=0,F=0;return J>=0?_=J:S=-J,g>=0?re=g:F=-g,d.drawImage(e,_,re,H,K,S,F,H,K),new V(d.getImageData(0,0,H,K).data,H,K,4).convert(v)}{let v=this.toSharp();if(J>=0&&g>=0)v=v.extract({left:Math.floor(J),top:Math.floor(g),width:H,height:K});else if(J<=0&&g<=0){const e=Math.floor(-g),d=Math.floor(-J);v=v.extend({top:e,left:d,right:H-this.width-d,bottom:K-this.height-e})}else{let e=[0,0],d=0;g<0?(e[0]=Math.floor(-g),e[1]=K-this.height-e[0]):d=Math.floor(g);let _=[0,0],re=0;J<0?(_[0]=Math.floor(-J),_[1]=H-this.width-_[0]):re=Math.floor(J),v=v.extend({top:e[0],bottom:e[1],left:_[0],right:_[1]}).extract({left:re,top:d,width:H,height:K})}return await B(v)}}async toBlob(H="image/png",K=1){if(!G)throw new Error("toBlob() is only supported in browser environments.");return await this.toCanvas().convertToBlob({type:H,quality:K})}toTensor(H="CHW"){let K=new C.Tensor("uint8",new Uint8Array(this.data),[this.height,this.width,this.channels]);if(H!=="HWC"){if(H!=="CHW")throw new Error(`Unsupported channel format: ${H}`);K=K.permute(2,0,1)}return K}toCanvas(){if(!G)throw new Error("toCanvas() is only supported in browser environments.");const H=this.clone().rgba(),K=k(H.width,H.height),J=new E(H.data,H.width,H.height);return K.getContext("2d").putImageData(J,0,0),K}split(){const{data:H,width:K,height:J,channels:g}=this,v=H.constructor,e=H.length/g,d=Array.from({length:g},()=>new v(e));for(let _=0;_<e;++_){const re=g*_;for(let S=0;S<g;++S)d[S][_]=H[re+S]}return d.map(_=>new V(_,K,J,1))}_update(H,K,J,g=null){return this.data=H,this.width=K,this.height=J,g!==null&&(this.channels=g),this}clone(){return new V(this.data.slice(),this.width,this.height,this.channels)}convert(H){if(this.channels===H)return this;switch(H){case 1:this.grayscale();break;case 3:this.rgb();break;case 4:this.rgba();break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this}async save(H){if(!G){if(p.apis.IS_FS_AVAILABLE)return await this.toSharp().toFile(H);throw new Error("Unable to save the image because filesystem is disabled in this environment.")}{if(p.apis.IS_WEBWORKER_ENV)throw new Error("Unable to save an image from a Web Worker.");const K=H.split(".").pop().toLowerCase(),J=Q.get(K)??"image/png",g=await this.toBlob(J);(0,o.saveBlob)(H,g)}}toSharp(){if(G)throw new Error("toSharp() is only supported in server-side environments.");return b(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}})}}const W=V.read.bind(V)},
vendor: 8,914 bytes, line 2810
2810"./src/utils/maths.js":(c,a,s)=>{function o(g,[v,e,d],[_,re],S="bilinear",F=!1){const Ie=re/d,he=_/e,ce=new g.constructor(_*re*v),ge=e*d,Ce=_*re;for(let Fe=0;Fe<_;++Fe)for(let Pe=0;Pe<re;++Pe){const de=Fe*re+Pe,pe=(Pe+.5)/Ie-.5,we=(Fe+.5)/he-.5;let Ge=Math.floor(pe),me=Math.floor(we);const at=Math.min(Ge+1,d-1),Ue=Math.min(me+1,e-1);Ge=Math.max(Ge,0),me=Math.max(me,0);const ot=pe-Ge,pt=we-me,ct=(1-ot)*(1-pt),je=ot*(1-pt),At=(1-ot)*pt,It=ot*pt,wt=me*d,ze=Ue*d,rt=wt+Ge,Bt=wt+at,Ot=ze+Ge,St=ze+at;for(let oe=0;oe<v;++oe){const zt=oe*ge;ce[oe*Ce+de]=ct*g[zt+rt]+je*g[zt+Bt]+At*g[zt+Ot]+It*g[zt+St]}}return ce}function u(g,v,e){const d=new Array(e.length),_=new Array(e.length);for(let F=e.length-1,Ie=1;F>=0;--F)_[F]=Ie,d[F]=v[e[F]],Ie*=d[F];const re=e.map((F,Ie)=>_[e.indexOf(Ie)]),S=new g.constructor(g.length);for(let F=0;F<g.length;++F){let Ie=0;for(let he=v.length-1,ce=F;he>=0;--he)Ie+=ce%v[he]*re[he],ce=Math.floor(ce/v[he]);S[Ie]=g[F]}return[S,d]}function p(g){const v=G(g)[0],e=g.map(re=>Math.exp(re-v)),d=e.reduce((re,S)=>re+S,0);return e.map(re=>re/d)}function C(g){const v=G(g)[0];let e=0;for(let re=0;re<g.length;++re)e+=Math.exp(g[re]-v);const d=Math.log(e);return g.map(re=>re-v-d)}function b(g,v){let e=0;for(let d=0;d<g.length;++d)e+=g[d]*v[d];return e}function k(g,v){return b(g,v)/(E(g)*E(v))}function E(g){return Math.sqrt(g.reduce((v,e)=>v+e*e,0))}function B(g){if(g.length===0)throw Error("Array must not be empty");let v=g[0],e=0;for(let d=1;d<g.length;++d)g[d]<v&&(v=g[d],e=d);return[v,e]}function G(g){if(g.length===0)throw Error("Array must not be empty");let v=g[0],e=0;for(let d=1;d<g.length;++d)g[d]>v&&(v=g[d],e=d);return[v,e]}function D(g){return g>0&&!(g&g-1)}s.r(a),s.d(a,{FFT:()=>W,bankers_round:()=>K,cos_sim:()=>k,dot:()=>b,dynamic_time_warping:()=>J,interpolate_data:()=>o,log_softmax:()=>C,magnitude:()=>E,max:()=>G,medianFilter:()=>U,min:()=>B,permute_data:()=>u,round:()=>H,softmax:()=>p});class Q{constructor(v){if(this.size=0|v,this.size<=1||!D(this.size))throw new Error("FFT size must be a power of two larger than 1");this._csize=v<<1,this.table=new Float64Array(2*this.size);for(let d=0;d<this.table.length;d+=2){const _=Math.PI*d/this.size;this.table[d]=Math.cos(_),this.table[d+1]=-Math.sin(_)}let e=0;for(let d=1;this.size>d;d<<=1)++e;this._width=e%2==0?e-1:e,this._bitrev=new Int32Array(1<<this._width);for(let d=0;d<this._bitrev.length;++d){this._bitrev[d]=0;for(let _=0;_<this._width;_+=2){const re=this._width-_-2;this._bitrev[d]|=(d>>>_&3)<<re}}}createComplexArray(){return new Float64Array(this._csize)}fromComplexArray(v,e){const d=e||new Array(v.length>>>1);for(let _=0;_<v.length;_+=2)d[_>>>1]=v[_];return d}toComplexArray(v,e){const d=e||this.createComplexArray();for(let _=0;_<d.length;_+=2)d[_]=v[_>>>1],d[_+1]=0;return d}transform(v,e){if(v===e)throw new Error("Input and output buffers must be different");this._transform4(v,e,1)}realTransform(v,e){if(v===e)throw new Error("Input and output buffers must be different");this._realTransform4(v,e,1)}inverseTransform(v,e){if(v===e)throw new Error("Input and output buffers must be different");this._transform4(v,e,-1);for(let d=0;d<v.length;++d)v[d]/=this.size}_transform4(v,e,d){const _=this._csize;let re,S,F=1<<this._width,Ie=_/F<<1;const he=this._bitrev;if(Ie===4)for(re=0,S=0;re<_;re+=Ie,++S){const ge=he[S];this._singleTransform2(e,v,re,ge,F)}else for(re=0,S=0;re<_;re+=Ie,++S){const ge=he[S];this._singleTransform4(e,v,re,ge,F,d)}const ce=this.table;for(F>>=2;F>=2;F>>=2){Ie=_/F<<1;const ge=Ie>>>2;for(re=0;re<_;re+=Ie){const Ce=re+ge-1;for(let Fe=re,Pe=0;Fe<Ce;Fe+=2,Pe+=F){const de=Fe,pe=de+ge,we=pe+ge,Ge=we+ge,me=v[de],at=v[de+1],Ue=v[pe],ot=v[pe+1],pt=v[we],ct=v[we+1],je=v[Ge],At=v[Ge+1],It=ce[Pe],wt=d*ce[Pe+1],ze=Ue*It-ot*wt,rt=Ue*wt+ot*It,Bt=ce[2*Pe],Ot=d*ce[2*Pe+1],St=pt*Bt-ct*Ot,oe=pt*Ot+ct*Bt,zt=ce[3*Pe],ke=d*ce[3*Pe+1],fe=je*zt-At*ke,Qe=je*ke+At*zt,Je=me+St,st=at+oe,lt=me-St,xt=at-oe,jt=ze+fe,$t=rt+Qe,Ft=d*(ze-fe),dA=d*(rt-Qe);v[de]=Je+jt,v[de+1]=st+$t,v[pe]=lt+dA,v[pe+1]=xt-Ft,v[we]=Je-jt,v[we+1]=st-$t,v[Ge]=lt-dA,v[Ge+1]=xt+Ft}}}}_singleTransform2(v,e,d,_,re){const S=v[_],F=v[_+1],Ie=v[_+re],he=v[_+re+1];e[d]=S+Ie,e[d+1]=F+he,e[d+2]=S-Ie,e[d+3]=F-he}_singleTransform4(v,e,d,_,re,S){const F=2*re,Ie=3*re,he=v[_],ce=v[_+1],ge=v[_+re],Ce=v[_+re+1],Fe=v[_+F],Pe=v[_+F+1],de=v[_+Ie],pe=v[_+Ie+1],we=he+Fe,Ge=ce+Pe,me=he-Fe,at=ce-Pe,Ue=ge+de,ot=Ce+pe,pt=S*(ge-de),ct=S*(Ce-pe);e[d]=we+Ue,e[d+1]=Ge+ot,e[d+2]=me+ct,e[d+3]=at-pt,e[d+4]=we-Ue,e[d+5]=Ge-ot,e[d+6]=me-ct,e[d+7]=at+pt}_realTransform4(v,e,d){const _=this._csize;let re,S,F=1<<this._width,Ie=_/F<<1;const he=this._bitrev;if(Ie===4)for(re=0,S=0;re<_;re+=Ie,++S){const Ce=he[S];this._singleRealTransform2(e,v,re,Ce>>>1,F>>>1)}else for(re=0,S=0;re<_;re+=Ie,++S){const Ce=he[S];this._singleRealTransform4(e,v,re,Ce>>>1,F>>>1,d)}const ce=this.table;for(F>>=2;F>=2;F>>=2){Ie=_/F<<1;const Ce=Ie>>>1,Fe=Ce>>>1,Pe=Fe>>>1;for(re=0;re<_;re+=Ie)for(let de=0,pe=0;de<=Pe;de+=2,pe+=F){const we=re+de,Ge=we+Fe,me=Ge+Fe,at=me+Fe,Ue=v[we],ot=v[we+1],pt=v[Ge],ct=v[Ge+1],je=v[me],At=v[me+1],It=v[at],wt=v[at+1],ze=Ue,rt=ot,Bt=ce[pe],Ot=d*ce[pe+1],St=pt*Bt-ct*Ot,oe=pt*Ot+ct*Bt,zt=ce[2*pe],ke=d*ce[2*pe+1],fe=je*zt-At*ke,Qe=je*ke+At*zt,Je=ce[3*pe],st=d*ce[3*pe+1],lt=It*Je-wt*st,xt=It*st+wt*Je,jt=ze+fe,$t=rt+Qe,Ft=ze-fe,dA=rt-Qe,eA=St+lt,nA=oe+xt,aA=d*(St-lt),bA=d*(oe-xt);if(v[we]=jt+eA,v[we+1]=$t+nA,v[Ge]=Ft+bA,v[Ge+1]=dA-aA,de===0){v[me]=jt-eA,v[me+1]=$t-nA;continue}if(de===Pe)continue;const vA=re+Fe-de,Gt=re+Ce-de;v[vA]=Ft-d*bA,v[vA+1]=-dA-d*aA,v[Gt]=jt-d*eA,v[Gt+1]=d*nA-$t}}const ge=_>>>1;for(let Ce=2;Ce<ge;Ce+=2)v[_-Ce]=v[Ce],v[_-Ce+1]=-v[Ce+1]}_singleRealTransform2(v,e,d,_,re){const S=v[_],F=v[_+re];e[d]=S+F,e[d+1]=0,e[d+2]=S-F,e[d+3]=0}_singleRealTransform4(v,e,d,_,re,S){const F=2*re,Ie=3*re,he=v[_],ce=v[_+re],ge=v[_+F],Ce=v[_+Ie],Fe=he+ge,Pe=he-ge,de=ce+Ce,pe=S*(ce-Ce);e[d]=Fe+de,e[d+1]=0,e[d+2]=Pe,e[d+3]=-pe,e[d+4]=Fe-de,e[d+5]=0,e[d+6]=Pe,e[d+7]=pe}}class V{constructor(v){const e=2*(v-1),d=2*(2*v-1),_=2**Math.ceil(Math.log2(d));this.bufferSize=_,this._a=e;const re=new Float64Array(d),S=new Float64Array(_);this._chirpBuffer=new Float64Array(_),this._buffer1=new Float64Array(_),this._buffer2=new Float64Array(_),this._outBuffer1=new Float64Array(_),this._outBuffer2=new Float64Array(_);const F=-2*Math.PI/v,Ie=Math.cos(F),he=Math.sin(F);for(let ce=0;ce<d>>1;++ce){const ge=(ce+1-v)**2/2,Ce=Math.sqrt(Ie**2+he**2)**ge,Fe=ge*Math.atan2(he,Ie),Pe=2*ce;re[Pe]=Ce*Math.cos(Fe),re[Pe+1]=Ce*Math.sin(Fe),S[Pe]=re[Pe],S[Pe+1]=-re[Pe+1]}this._slicedChirpBuffer=re.subarray(e,d),this._f=new Q(_>>1),this._f.transform(this._chirpBuffer,S)}_transform(v,e,d){const _=this._buffer1,re=this._buffer2,S=this._outBuffer1,F=this._outBuffer2,Ie=this._chirpBuffer,he=this._slicedChirpBuffer,ce=this._a;if(d)for(let ge=0;ge<he.length;ge+=2){const Ce=ge+1,Fe=e[ge>>1];_[ge]=Fe*he[ge],_[Ce]=Fe*he[Ce]}else for(let ge=0;ge<he.length;ge+=2){const Ce=ge+1;_[ge]=e[ge]*he[ge]-e[Ce]*he[Ce],_[Ce]=e[ge]*he[Ce]+e[Ce]*he[ge]}this._f.transform(S,_);for(let ge=0;ge<Ie.length;ge+=2){const Ce=ge+1;re[ge]=S[ge]*Ie[ge]-S[Ce]*Ie[Ce],re[Ce]=S[ge]*Ie[Ce]+S[Ce]*Ie[ge]}this._f.inverseTransform(F,re);for(let ge=0;ge<F.length;ge+=2){const Ce=F[ge+ce],Fe=F[ge+ce+1],Pe=he[ge],de=he[ge+1];v[ge]=Ce*Pe-Fe*de,v[ge+1]=Ce*de+Fe*Pe}}transform(v,e){this._transform(v,e,!1)}realTransform(v,e){this._transform(v,e,!0)}}class W{constructor(v){this.fft_length=v,this.isPowerOfTwo=D(v),this.isPowerOfTwo?(this.fft=new Q(v),this.outputBufferSize=2*v):(this.fft=new V(v),this.outputBufferSize=this.fft.bufferSize)}realTransform(v,e){this.fft.realTransform(v,e)}transform(v,e){this.fft.transform(v,e)}}function U(g,v){if(v%2==0||v<=0)throw new Error("Window size must be a positive odd number");const e=new g.constructor(g.length),d=new g.constructor(v),_=Math.floor(v/2);for(let re=0;re<g.length;++re){let S=0;for(let F=-_;F<=_;++F){let Ie=re+F;Ie<0?Ie=Math.abs(Ie):Ie>=g.length&&(Ie=2*(g.length-1)-Ie),d[S++]=g[Ie]}d.sort(),e[re]=d[_]}return e}function H(g,v){const e=Math.pow(10,v);return Math.round(g*e)/e}function K(g){const v=Math.round(g);return Math.abs(g)%1==.5?v%2==0?v:v-1:v}function J(g){const v=g.length,e=g[0].length,d=[v+1,e+1],_=Array.from({length:d[0]},()=>Array(d[1]).fill(1/0));_[0][0]=0;const re=Array.from({length:d[0]},()=>Array(d[1]).fill(-1));for(let ce=1;ce<d[1];++ce)for(let ge=1;ge<d[0];++ge){const Ce=_[ge-1][ce-1],Fe=_[ge-1][ce],Pe=_[ge][ce-1];let de,pe;Ce<Fe&&Ce<Pe?(de=Ce,pe=0):Fe<Ce&&Fe<Pe?(de=Fe,pe=1):(de=Pe,pe=2),_[ge][ce]=g[ge-1][ce-1]+de,re[ge][ce]=pe}for(let ce=0;ce<d[1];++ce)re[0][ce]=2;for(let ce=0;ce<d[0];++ce)re[ce][0]=1;let S=v,F=e,Ie=[],he=[];for(;S>0||F>0;)switch(Ie.push(S-1),he.push(F-1),re[S][F]){case 0:--S,--F;break;case 1:--S;break;case 2:--F;break;default:throw new Error(`Internal error in dynamic time warping. Unexpected trace[${S}, ${F}]. Please file a bug report.`)}return Ie.reverse(),he.reverse(),[Ie,he]}},
2810"./src/utils/tensor.js":(c,a,s)=>{s.r(a),s.d(a,{Tensor:()=>b,cat:()=>v,full:()=>F,full_like:()=>Ie,interpolate:()=>E,interpolate_4d:()=>B,layer_norm:()=>H,matmul:()=>G,mean:()=>re,mean_pooling:()=>U,ones:()=>he,ones_like:()=>ce,permute:()=>k,quantize_embeddings:()=>Pe,rand:()=>Fe,rfft:()=>D,slice:()=>W,stack:()=>e,std_mean:()=>_,topk:()=>Q,zeros:()=>ge,zeros_like:()=>Ce});var o=s("./src/utils/maths.js"),u=s("./src/backends/onnx.js"),p=s("./src/ops/registry.js");const C=Object.freeze({float32:Float32Array,float16:Uint16Array,float64:Float64Array,string:Array,int8:Int8Array,uint8:Uint8Array,int16:Int16Array,uint16:Uint16Array,int32:Int32Array,uint32:Uint32Array,int64:BigInt64Array,uint64:BigUint64Array,bool:Uint8Array,uint4:Uint8Array,int4:Int8Array});class b{constructor(...pe){Ct(this,"ort_tensor");return(0,u.isONNXTensor)(pe[0])?this.ort_tensor=pe[0]:this.ort_tensor=new u.Tensor(pe[0],pe[1],pe[2]),new Proxy(this,{get:(we,Ge)=>{if(typeof Ge=="string"){let me=Number(Ge);if(Number.isInteger(me))return we._getitem(me)}return we[Ge]}
2810,set:(we,Ge,me)=>we[Ge]=me})}get dims(){return this.ort_tensor.dims}set dims(pe){this.ort_tensor.dims=pe}get type(){return this.ort_tensor.type}get data(){return this.ort_tensor.data}get size(){return this.ort_tensor.size}get location(){return this.ort_tensor.location}dispose(){this.ort_tensor.dispose()}*[Symbol.iterator](){const[pe,...we]=this.dims;if(we.length>0){const Ge=we.reduce((me,at)=>me*at);for(let me=0;me<pe;++me)yield this._subarray(me,Ge,we)}else yield*this.data}_getitem(pe){const[we,...Ge]=this.dims;if(pe=g(pe,we),Ge.length>0){const me=Ge.reduce((at,Ue)=>at*Ue);return this._subarray(pe,me,Ge)}return new b(this.type,[this.data[pe]],Ge)}indexOf(pe){const we=this.data;for(let Ge=0;Ge<we.length;++Ge)if(we[Ge]==pe)return Ge;return-1}_subarray(pe,we,Ge){const me=pe*we,at=(pe+1)*we,Ue="subarray"in this.data?this.data.subarray(me,at):this.data.slice(me,at);return new b(this.type,Ue,Ge)}item(){const pe=this.data;if(pe.length!==1)throw new Error(`a Tensor with ${pe.length} elements cannot be converted to Scalar`);return pe[0]}tolist(){return function(pe,we){const Ge=pe.length,me=we.reduce((Ue,ot)=>Ue*ot);if(Ge!==me)throw Error(`cannot reshape array of size ${Ge} into shape (${we})`);let at=pe;for(let Ue=we.length-1;Ue>=0;Ue--)at=at.reduce((ot,pt)=>{let ct=ot[ot.length-1];return ct.length<we[Ue]?ct.push(pt):ot.push([pt]),ot},[[]]);return at[0]}(this.data,this.dims)}sigmoid(){return this.clone().sigmoid_()}sigmoid_(){const pe=this.data;for(let we=0;we<pe.length;++we)pe[we]=1/(1+Math.exp(-pe[we]));return this}map(pe){return this.clone().map_(pe)}map_(pe){const we=this.data;for(let Ge=0;Ge<we.length;++Ge)we[Ge]=pe(we[Ge],Ge,we);return this}mul(pe){return this.clone().mul_(pe)}mul_(pe){const we=this.data;for(let Ge=0;Ge<we.length;++Ge)we[Ge]*=pe;return this}div(pe){return this.clone().div_(pe)}div_(pe){const we=this.data;for(let Ge=0;Ge<we.length;++Ge)we[Ge]/=pe;return this}add(pe){return this.clone().add_(pe)}add_(pe){const we=this.data;for(let Ge=0;Ge<we.length;++Ge)we[Ge]+=pe;return this}sub(pe){return this.clone().sub_(pe)}sub_(pe){const we=this.data;for(let Ge=0;Ge<we.length;++Ge)we[Ge]-=pe;return this}clone(){return new b(this.type,this.data.slice(),this.dims.slice())}slice(...pe){const we=[],Ge=[];for(let ct=0;ct<this.dims.length;++ct){let je=pe[ct];if(je==null)Ge.push([0,this.dims[ct]]),we.push(this.dims[ct]);else if(typeof je=="number")je=g(je,this.dims[ct],ct),Ge.push([je,je+1]);else{if(!Array.isArray(je)||je.length!==2)throw new Error(`Invalid slice: ${je}`);{let[At,It]=je;if(At=At===null?0:g(At,this.dims[ct],ct,!1),It=It===null?this.dims[ct]:g(It,this.dims[ct],ct,!1),At>It)throw new Error(`Invalid slice: ${je}`);const wt=[Math.max(At,0),Math.min(It,this.dims[ct])];Ge.push(wt),we.push(wt[1]-wt[0])}}}const me=Ge.map(([ct,je])=>je-ct),at=me.reduce((ct,je)=>ct*je),Ue=this.data,ot=new Ue.constructor(at),pt=this.stride();for(let ct=0;ct<at;++ct){let je=0;for(let At=me.length-1,It=ct;At>=0;--At){const wt=me[At];je+=(It%wt+Ge[At][0])*pt[At],It=Math.floor(It/wt)}ot[ct]=Ue[je]}return new b(this.type,ot,we)}permute(...pe){return k(this,pe)}transpose(...pe){return this.permute(...pe)}sum(pe=null,we=!1){return this.norm(1,pe,we)}norm(pe="fro",we=null,Ge=!1){if(pe==="fro")pe=2;else if(typeof pe=="string")throw Error(`Unsupported norm: ${pe}`);const me=this.data,at=(ct,je)=>ct+je**pe;if(we===null){const ct=me.reduce(at,0)**(1/pe);return new b(this.type,[ct],[])}const[Ue,ot,pt]=d(at,this,we,Ge);if(pe!==1)for(let ct=0;ct<ot.length;++ct)ot[ct]=ot[ct]**(1/pe);return new b(Ue,ot,pt)}normalize_(pe=2,we=1){we=g(we,this.dims.length);const Ge=this.norm(pe,we,!0),me=this.data,at=Ge.data;for(let Ue=0;Ue<me.length;++Ue){let ot=0;for(let pt=this.dims.length-1,ct=Ue,je=1;pt>=0;--pt){const At=this.dims[pt];pt!==we&&(ot+=ct%At*je,je*=this.dims[pt]),ct=Math.floor(ct/At)}me[Ue]/=at[ot]}return this}normalize(pe=2,we=1){return this.clone().normalize_(pe,we)}stride(){return function(pe){const we=new Array(pe.length);for(let Ge=pe.length-1,me=1;Ge>=0;--Ge)we[Ge]=me,me*=pe[Ge];return we}(this.dims)}squeeze(pe=null){return new b(this.type,this.data,K(this.dims,pe))}squeeze_(pe=null){return this.dims=K(this.dims,pe),this}unsqueeze(pe=null){return new b(this.type,this.data,J(this.dims,pe))}unsqueeze_(pe=null){return this.dims=J(this.dims,pe),this}flatten_(pe=0,we=-1){we=(we+this.dims.length)%this.dims.length;let Ge=this.dims.slice(0,pe),me=this.dims.slice(pe,we+1),at=this.dims.slice(we+1);return this.dims=[...Ge,me.reduce((Ue,ot)=>Ue*ot,1),...at],this}flatten(pe=0,we=-1){return this.clone().flatten_(pe,we)}view(...pe){let we=-1;for(let me=0;me<pe.length;++me)if(pe[me]===-1){if(we!==-1)throw new Error("Only one dimension can be inferred");
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vendor: 3,913 bytes, line 2810
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vendor: 4,447 bytes, line 2810
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28102]),r|!t|i||!(e[32524]<0)){if(!Ki(e[h+2156>>2])){if((i=(0|(t=e[49828]))>0)&(0|(r=t))<(0|(t=e[33284])))break a;if(!((0|(r=e[49845]))<=0|(0|t)<(0|r))){e[49845]=0,g[134760]=1,e[33285]=e[h+2152>>2],n=16384;break e}}yt=e[h+2156>>2],e[h+2156>>2]=e[h+2152>>2];n:{c:{o:{f:{l:{if((0|(r=e[32524]))>=0){if(d[r+134736|0])break l;e[32524]=-1}if(r=e[33285])break o;if(t=e[33283],e[t>>2]!=e[t+4>>2])break f;r=32;break n}e[33691]|r||(e[h+2156>>2]=g[134736],r=1),e[32524]=r+1,r=g[r+134736|0];break n}if(!(r=e[33285]))break c}e[33285]=0;break n}e[33284]=e[33284]+1,t=e[33283],r=0|LA[e[t+8>>2]](t)}e[h+2152>>2]=r,e[33691]=0;n:if(!(M|!e[47203])){c:{if((0|(t=e[h+2156>>2]))!=60){if((0|r)!=35&r-97>>>0>25|(0|t)!=38)break n;for(i=e[33285],l=0;;){o:{if(e[h+2156>>2]=r,!i){if(t=e[33283],e[t>>2]==e[t+4>>2])break o;r=e[h+2156>>2]}if(!(!((r=!!(0|Ki(r)))|(0|(t=e[h+2156>>2]))==35)|l>>>0>19)){g[(h+112|0)+l|0]=t,l=l+1|0,(r=e[33285])?(e[33285]=0,i=0):(e[33284]=e[33284]+1,t=e[33283],r=0|LA[e[t+8>>2]](t),i=e[33285]);continue}}break}g[(h+112|0)+l|0]=0;o:{f:{if(!(r=e[33285])){if(r=0,t=e[33283],e[t>>2]==e[t+4>>2])break o;if(!(r=e[33285]))break f}e[33285]=0;break o}e[33284]=e[33284]+1,t=e[33283],r=0|LA[e[t+8>>2]](t)}e[h+2152>>2]=r,e[h+100>>2]=e[h+2156>>2],e[h+104>>2]=r,e[h+96>>2]=h+112,uA(134736,84252,h+96|0);o:{if(e[h+2156>>2]==59){l=h+2156|0,ae=h+2152|0,oe=i=oe-32|0;f:if(d[0|(r=h+112|0)]!=35)t=-1,(0|(r=ti(130752,r)))!=-1&&(e[l>>2]=r,e[ae>>2]||(e[ae>>2]=32),t=r);else{if(d[0|(t=r+1|0)]==120){e[i>>2]=l,t=nA(r+2|0,90005,i);break f}e[i+16>>2]=l,t=nA(t,90070,i+16|0)}if(oe=i+32|0,(0|t)>0)break o}e[32524]=0,e[h+2156>>2]=38,e[h+2152>>2]=32;break n}if((0|(t=e[h+2156>>2]))>32)break n;if(!(r=e[33692]-20|0)|(0|r)==16)break c;break n}if((0|r)!=47&&!aa(r)&&(0|(t=e[h+2152>>2]))!=63&&(0|t)!=33)break n;if((0|(t=e[h+2148>>2]))>780){e[33691]=e[h+2156>>2],g[0|(t=t+189424|0)]=32,g[t+1|0]=0,e[33285]=e[h+2152>>2],n=16384;break e}for(ae=e[33285],l=0,r=e[h+2152>>2];e[h+2156>>2]=r,i=0,ae||(t=e[33283],i=e[t>>2]==e[t+4>>2],r=e[h+2156>>2]),!((0|r)==62|i|l>>>0>499);)e[(h+144|0)+(l<<2)>>2]=r,l=l+1|0,(r=e[33285])?(ae=0,e[33285]=0):(e[33284]=e[33284]+1,t=e[33283],r=0|LA[e[t+8>>2]](t),ae=e[33285]);e[(i=h+144|0)+(l<<2)>>2]=0,e[h+2152>>2]=32,Pt=h+2148|0,ht=e[32525],r=0,oe=it=oe-560|0;o:if(jo(i,84333,3)&&jo(i,84477,4)){for(t=(i+(xn(i)<<2)|0)-4|0,(0|(gA=e[t>>2]))==47&&(e[t>>2]=32);;){if(t=e[i+(r<<2)>>2]){if(Ts(t))t=r;else if(g[(it+512|0)+r|0]=wr(t<<24>>24),t=39,(0|(r=r+1|0))!=39)continue}else t=r;break}if(g[(it+512|0)+t|0]=0,d[it+512|0]!=47){if((0|(ae=ti(130480,it+512|0)))!=16&&(r=e[Pt>>2],e[Pt>>2]=r+1,g[r+189424|0]=32),(0|gA)==47&&(r=0,!(502241>>>ae&1)))break o}else(0|(r=ti(130480,it+512|1)))!=16&&(l=e[Pt>>2],e[Pt>>2]=l+1,g[l+189424|0]=32),ae=r+32|0;l=i+(t<<2)|0,t=e[33708],M=F(t,76)+133076|0,r=262174;f:{l:{i:{h:switch(ae-1|0){case 33:b:if(!((0|t)<=1)){for(;;){if(e[F(r=t-1|0,76)+133152>>2]==2)break b;if(e[33708]=r,i=t>>>0>2,t=r,!i)break}t=1}r=Rr(l,34,t);break o;case 32:b:if(!((0|t)<=1)){for(;;){if(e[F(r=t-1|0,76)+133152>>2]==1)break b;if(e[33708]=r,i=t>>>0>2,t=r,!i)break}t=1}r=Rr(l,33,t)+524328|0;break o;case 9:(0|(r=e[33709]))<=18&&(e[33709]=r+1),e[(t=134912+(M=r<<6)|0)>>2]=10,e[t+4>>2]=-1,e[t+8>>2]=-1,e[t+52>>2]=-1,e[t+56>>2]=-1,e[t+44>>2]=-1,e[t+48>>2]=-1,e[t+36>>2]=-1,e[t+40>>2]=-1,e[t+28>>2]=-1,e[t+32>>2]=-1,e[t+20>>2]=-1,e[t+24>>2]=-1,e[t+12>>2]=-1,e[t+16>>2]=-1,e[t+60>>2]=-1,t=Tr(l,88301),i=Tr(l,88390),fo(t,88479)?fo(t,88528)||(e[28+(134912+(r<<6)|0)>>2]=Gs(i,130224)):e[24+(M+134912|0)>>2]=Gs(i,130192),Ma(Pt,e[33709]);break i;case 2:for((0|(t=e[33709]))<=18&&(e[33709]=t+1),e[(t=134912+(yo=t<<6)|0)>>2]=3,e[t+4>>2]=-1,e[t+8>>2]=-1,e[t+52>>2]=-1,e[t+56>>2]=-1,e[t+44>>2]=-1,e[t+48>>2]=-1,e[t+36>>2]=-1,e[t+40>>2]=-1,e[t+28>>2]=-1,e[t+32>>2]=-1,e[t+20>>2]=-1,e[t+24>>2]=-1,e[t+12>>2]=-1,e[t+16>>2]=-1,e[t+60>>2]=-1,Xr=1;;){if(i=Tr(l,e[130448+(ls=Xr<<2)>>2])){gA=0,t=e[ls+131072>>2];b:if(ae=e[t>>2])for(;;){for(r=0;M=g[r+ae|0],(ht=e[(r<<2)+i>>2])&&(r=r+1|0,(0|M)==(0|ht)););switch(ht-34|0){case 0:case 5:if(!M)break b}if(!(ae=e[t+((gA=gA+1|0)<<3)>>2]))break}Xo=ls+(yo+134912|0)|0;b:if((0|(t=e[4+(t+(gA<<3)|0)>>2]))>=0)t=(0|F(t,e[4+(ls+134912|0)>>2]))/100|0;else{for(;i=(t=i)+4|0,Ts(e[t>>2]););for(to=e[t>>2]==43,i=((Ao=e[(t=t+(to<<2)|0)>>2]==45)<<2)+t|0,gA=it+96|0,oe=ae=(oe=M=oe-16|0)-224|0,IA(ae+16|0,0,144),t=ae+160|4,e[ae+24>>2]=t,e[ae+60>>2]=t,e[ae+92>>2]=-1,e[ae+64>>2]=60,e[ae+20>>2]=t,e[ae+48>>2]=19,r=i;r=(t=r)+4|0,ht=(ht=e[t>>2])?rs(124960,ht):0;);e[ae+100>>
28102]=t,ua(r=ae+16|0,0,0),jt(ae,r,1,1),r=e[ae+8>>2],o0=e[ae+12>>2],ht=e[ae>>2],l0=e[ae+4>>2],gA&&(ro=gA,gA=e[ae+136>>2]+(e[ae+20>>2]-e[ae+60>>2]|0)|0,e[ro>>2]=gA?t+(gA<<2)|0:i),e[(t=M)+8>>2]=r,e[t+12>>2]=o0,e[t>>2]=ht,e[t+4>>2]=l0,oe=ae+224|0,$e=Pa(e[t>>2],e[t+4>>2],e[t+8>>2],e[t+12>>2]),oe=t+16|0;m:{r=100;C:if((0|(t=i))!=(0|(i=e[it+96>>2]))){r=Ao?-1:to;p:{if((0|(t=e[i>>2]))!=115){if((0|t)!=37)break p;if(he($e=r?+(0|r)*$e+100:$e)<2147483648){r=~~$e;break C}r=-2147483648;break C}if(e[i+4>>2]==116){V(+(Cr=$e*+(0|r)/12)),t=0|G(1),G(0);x:{if((r=(t=t>>>20&2047)-969|0)>>>0>=63){if($e=Cr+1,(0|r)<0)break x;if(V(+Cr),r=0|G(1),i=0|G(0),!(t>>>0<1033)){if($e=0,!i&(0|r)==-1048576||($e=Cr+1,t>>>0>=2047))break x;if((0|r)>0|(0|r)>=0){S[8+(t=oe-16|0)>>3]=3105036184601418e216,$e=3105036184601418e216*S[t+8>>3];break x}if(!(r>>>0<3230714880)){S[8+(t=oe-16|0)>>3]=12882297539194267e-247,$e=12882297539194267e-247*S[t+8>>3];break x}}M=t,t=!(i<<1)&(0|(t=r<<1|i>>>31))==-2129002496|t>>>0<2165964800?M:0}pa=(Cr=($e=Cr-((ga=($e=S[14416])+Cr)-$e))*$e)*Cr*($e*S[14421]+S[14420]),Cr*=$e*S[14419]+S[14418],$e*=S[14417],V(+ga),G(1),M=0|G(0),$e=pa+(Cr+($e+S[115376+(i=M<<4&2032)>>3])),ht=e[(i=i+115384|0)>>2],ae=e[i+4>>2],i=(r=ht)+(ht=0)|0,r=(M<<13)+ae|0,r=i>>>0<ht>>>0?r+1|0:r,t?(D(0,0|i),D(1,0|r),$e=(Cr=+Q())*$e+Cr):-2147483648&M?(D(0,0|i),D(1,r+1071644672|0),($e=(ga=(Cr=+Q())*$e)+Cr)<1&&(e[8+(t=oe-16|0)>>2]=0,e[t+12>>2]=1048576,S[t+8>>3]=22250738585072014e-324*S[t+8>>3],$e=($e=(pa=$e+1)+(ga+(Cr-$e)+($e+(1-pa)))+-1)==0?0:$e),$e*=22250738585072014e-324):(D(0,0|i),D(1,r+-1048576|0),$e=(Cr=+Q())*$e+Cr,$e+=$e)}if(he($e*=100)<2147483648){r=~~$e;break C}r=-2147483648;break C}}if((0|Xr)!=1)break m;if(r)r=100+(t=he($e=$e*+(0|r)*100)<2147483648?~~$e:-2147483648)|0;else{if(he($e*=100)<2147483648){r=~~$e;break C}r=-2147483648}}t=(0|F(r,e[ls+134848>>2]))/100|0;break b}t=he($e)<2147483648?~~$e:-2147483648,r&&(t=e[ls+134848>>2]+F(t,r)|0)}e[Xo+4>>2]=t}if((0|(Xr=Xr+1|0))==5)break}Ma(Pt,e[33709]);break i;case 11:(0|(r=e[33709]))<=18&&(e[33709]=r+1),e[(t=134912+(r<<6)|0)>>2]=12,e[t+4>>2]=-1,e[t+8>>2]=-1,e[t+52>>2]=-1,e[t+56>>2]=-1,e[t+44>>2]=-1,e[t+48>>2]=-1,e[t+36>>2]=-1,e[t+40>>2]=-1,e[t+28>>2]=-1,e[t+32>>2]=-1,e[t+20>>2]=-1,e[t+24>>2]=-1,e[t+12>>2]=-1,e[t+16>>2]=-1,e[t+60>>2]=-1,t=(t=Tr(l,88658))?Gs(t,130400):3,i=134912+(r<<6)|0,e[e[47192]+148>>2]!=1?(e[52+(134912+(r<<6)|0)>>2]=t,t=d[t+102776|0]):(e[20+(134912+(r<<6)|0)>>2]=d[t+102764|0],t=d[t+102770|0]),e[i+12>>2]=t,Ma(Pt,e[33709]);break i;case 34:case 41:case 43:if(!((0|(t=e[33709]))<=0)){if(l=ae-32|0,ae=0,i=0,r=0,t>>>0>=4)for(M=-4&t,gA=0;Xr=2|r,ls=1|r,i=(0|l)==e[134912+((ht=3|r)<<6)>>2]?ht:(0|l)==e[134912+(Xr<<6)>>2]?Xr:(0|l)==e[134912+(ls<<6)>>2]?ls:(0|l)==e[134912+(r<<6)>>2]?r:i,r=r+4|0,(0|M)!=(0|(gA=gA+4|0)););if(M=3&t)for(;i=(0|l)==e[134912+(r<<6)>>2]?r:i,r=r+1|0,(0|M)!=(0|(ae=ae+1|0)););(0|i)<=0||(e[33709]=i,t=i)}Ma(Pt,t);break i;case 7:if(t=Tr(l,88741),r=Tr(l,88860),(0|Gs(t,130176))!=1)break i;t=e[Pt>>2],e[Pt>>2]=t+1,g[t+189424|0]=91,t=e[Pt>>2],e[Pt>>2]=t+1,g[t+189424|0]=91,t=ca((t=e[Pt>>2])+189424|0,r,800-t|0)+e[Pt>>2]|0,e[Pt>>2]=t+1,g[t+189424|0]=93,t=e[Pt>>2],e[Pt>>2]=t+1,g[t+189424|0]=93;break i;case 35:e[33692]==36&&(g[e[Pt>>2]+189424|0]=0,(i=ti(131104,r=(t=e[33707])+189424|0))&&(e[Pt>>2]=_s(i,r)+t)),t=e[Pt>>2],e[Pt>>2]=t+1,g[t+189424|0]=1,t=e[Pt>>2],e[Pt>>2]=t+1,g[t+189424|0]=89,e[33692]=0;break i;case 8:if(!(t=Tr(l,89299)))break i;g[134824]=1,r=e[Pt>>2],e[Pt>>2]=ca(r+189424|0,t,800-r|0)+e[Pt>>2];break i;case 13:g[134824]=1;break i;case 40:case 45:g[134824]=0;break i;case 4:if(!(t=Tr(l,89360)))break i;if(ca(it+352|0,t,160),d[it+352|0]&&!Zr(199328,it+352|0)){g[134760]=1,g[199328]=0,r=16384;break o}if((0|(t=Wi(it+352|0)))<0)break i;e[it+20>>2]=t,e[it+16>>2]=1,uA(t=it+352|0,89460,it+16|0),BA(e[Pt>>2]+189424|0,t),e[Pt>>2]=e[Pt>>2]+wA(t);break i;case 10:(0|(r=e[33709]))<=18&&(e[33709]=r+1),e[(t=134912+(r<<6)|0)>>2]=11,e[t+4>>2]=-1,e[t+8>>2]=-1,e[t+52>>2]=-1,e[t+56>>2]=-1,e[t+44>>2]=-1,e[t+48>>2]=-1,e[t+36>>2]=-1,e[t+40>>2]=-1,e[t+28>>2]=-1,e[t+32>>2]=-1,e[t+20>>2]=-1,e[t+24>>2]=-1,e[t+12>>2]=-1,e[t+16>>2]=-1,e[t+60>>2]=-1;b:if(t=Tr(l,89514)){if(ca(it+352|0,t,160),e[34441]){if((0|(t=Wi(it+352|0)))<0||0|LA[e[34441]](1,t+e[33282]|0,ht))break b;e[it+68>>2]=t,e[it+64>>2]=1,uA(it+352|0,89658,it- -64|0)}else{if(!ht|d[it+352|0]==47?t=Vo(it+352|0):(e[it+48>>2]=ht,e[it+52>>
28102]=it+352,uA(t=it+96|0,89564,it+48|0),t=Vo(t)),(0|t)<0)break b;e[it+36>>2]=t,e[it+32>>2]=1,uA(it+352|0,89623,it+32|0)}t=it+352|0,BA(e[Pt>>2]+189424|0,t),e[Pt>>2]=e[Pt>>2]+wA(t),e[4+(134912+(r<<6)|0)>>2]=1}if(Ma(Pt,e[33709]),(0|gA)==47){Fo(11,Pt),r=16384;break o}g[134772]=1,r=16384;break o;case 42:Fo(43,Pt),g[134772]=0,r=16384;break o;case 12:b:{if(t=Tr(l,89714)){if(r=16384,(M=(0|(t=Gs(t,130336)))<0?2:t)>>>0<=2&&(t=e[Pt>>2],e[it+84>>2]=M,e[it+80>>2]=1,uA(t+189424|0,89770,it+80|0),e[Pt>>2]=e[Pt>>2]+3,r=0),t=e[102784+(M<<2)>>2],!(i=Tr(l,89907)))break b;break l}if(r=16384,i=Tr(l,89907))break l;t=21;break f}if(M>>>0<3)break i;break f;case 0:(t=Tr(l,89965))&&(ca(r=it+352|0,t,160),Wi(r)),r=Rr(l,1,e[33708])?147456:0;break o;case 1:r=Rr(l,2,t)?147456:0;break o;case 5:r=0,e[M>>2]==6&&(r=Rr(l,38,t),t=e[33708]),r=524358+(Rr(l,6,t)|r)|0;break o;case 6:r=0,(0|(i=e[M>>2]))==6&&(r=Rr(l,38,t),i=e[M>>2]),(0|i)==7&&(r=Rr(l,39,e[33708])|r),r=524358+(r|Rr(l,7,e[33708]))|0;break o;case 37:if(r=524328,e[M>>2]!=6)break o;r=Rr(l,38,t)+524328|0;break o;case 38:if(r=524358,(-2&e[M>>2])!=6)break o;r=Rr(l,39,t)+524358|0;break o;case 14:case 46:break o;case 3:break h;default:break i}t=Tr(l,88893),r=Tr(l,88992),i=Tr(l,89153),l=Gs(t,130272),r=Gs(r,130320),t=ho(i,0),e[it>>2]=1,i=(0|t)<2?193:t- -64|0,r=(0|(t=(0|r)==1?19:l))==64?i:t,e[it+4>>2]=r,uA(t=it+352|0,89230,it),BA(e[Pt>>2]+189424|0,t),t=e[Pt>>2]+wA(t)|0,e[Pt>>2]=t,e[33707]=t,e[33692]=r}r=0;break o}t=ho(i,1),i=e[33722],gn(1,e[33713]),(0|(t=(0|(i=(0|F(t,i))/100<<8))/(0|F(e[36429],10))|0))<=199&&(t=(0|i)/(0|F(e[36428],10))|0),r=r||16384}i=t>>>5|0,l=t,r=((t=(0|t)>4095)?i>>>0>=4095?4095:i:l)+(t?8388608|r:r)|0}if(oe=it+560|0,r){if(t=e[h+2148>>2]+189424|0,g[0|t]=32,g[t+1|0]=0,!(131072&r)){n=r;break e}BA(189360,134784),n=r;break e}e[h+2156>>2]=32;o:{f:{if(!(r=e[33285])){if(M=0,t=e[33283],e[t>>2]==e[t+4>>2])continue;if(!(r=e[33285]))break f}e[33285]=0;break o}e[33284]=e[33284]+1,t=e[33283],r=0|LA[e[t+8>>2]](t)}e[h+2152>>2]=r,M=0;continue}e[h+2156>>2]=t+57344}if(d[134824])continue;if(i=e[h+2156>>2],!((0|(t=e[h+2152>>2]))!=10|e[47268]!=-1)){(0|(n=Tn(i)))==16384?(t=e[h+2148>>2],v[cA+(t<<1)>>1]=e[33284]-e[47353],e[Os>>2]=t,n=524328,t=_s(e[h+2156>>2],t+189424|0)+e[h+2148>>2]|0):t=e[h+2148>>2],g[0|(t=t+189424|0)]=32,g[t+1|0]=0;break e}n:if((0|i)==1){if((0|t)!=66){if((0|t)!=86)break n;for(t=e[h+2148>>2],e[h+2148>>2]=t+1,g[t+189424|0]=0;;){c:{o:{f:{if(!(r=e[33285])){if(t=e[33283],e[t>>2]==e[t+4>>2])break c;if(!(r=e[33285]))break f}e[33285]=0;break o}e[33284]=e[33284]+1,t=e[33283],r=0|LA[e[t+8>>2]](t)}if(e[h+2156>>2]=r,!(Ts(r)||(0|(t=e[h+2148>>2]))>=799)){e[h+2148>>2]=t+1,g[t+189424|0]=e[h+2156>>2];continue}}break}g[e[h+2148>>2]+189424|0]=0,n=147456;break e}r=e[h+2148>>2],g[0|(t=r+189424|0)]=32,g[t+1|0]=32,g[t+2|0]=32,g[t+3|0]=0,e[h+2148>>2]=r+3;c:{o:{f:{l:{i:{h:{if(!(r=e[33285])){if(t=e[33283],e[t>>2]==e[t+4>>2])break l;if(!(r=e[33285]))break h}e[33285]=0;break i}e[33284]=e[33284]+1,t=e[33283],r=0|LA[e[t+8>>2]](t)}if(e[h+2152>>2]=r,i=0,(0|r)!=48)break f;break o}r=e[h+2152>>2]}if(e[47208]=0,e[47201]=1,(0|r)==49)break c;for(i=e[33285],l=0;;){f:{if(!i){if(t=e[33283],e[t>>2]==e[t+4>>2])break f;r=e[h+2152>>2]}if(!(Ts(r)|l>>>0>58)){e[188832+(l<<2)>>2]=e[h+2152>>2],(r=e[33285])?(e[33285]=0,i=0):(e[33284]=e[33284]+1,t=e[33283],r=0|LA[e[t+8>>2]](t),i=e[33285]),l=l+1|0,e[h+2152>>2]=r,t=e[h+2148>>2],e[h+2148>>2]=t+1,g[t+189424|0]=32;continue}}break}i=2,e[188832+(l<<2)>>2]=0}e[47201]=i}c:{if(!(r=e[33285])){if(t=e[33283],e[t>>2]==e[t+4>>2])continue;if(!(r=e[33285]))break c}e[33285]=0,e[h+2152>>2]=r;continue}e[33284]=e[33284]+1,t=e[33283],e[h+2152>>2]=LA[e[t+8>>2]](t);continue}xs=xs+1|0,r=0,t=e[ue+340>>2];n:if(l=_[t>>1]){for(;;){if((65535&l)!=(0|i)){if(l=_[t+((r=r+2|0)<<1)>>1])continue;break n}break}switch(0|(t=_[t+(r<<1|2)>>1])){case 1:continue;case 0:break n}e[h+2156>>2]=t,i=t}Ki(i)?(_e=1,r=e[h+2156>>2]):VA?(e[33285]=e[h+2152>>2],r=1328,e[h+2156>>2]=1328,e[h+2152>>2]=32,VA=0):(0|(r=e[h+2156>>2]))!=3851?(VA=0,(0|r)!=3405|e[h+2152>>2]!=8205||(r=3406,e[h+2156>>2]=3406)):(r=32,e[h+2156>>2]=32,VA=0);n:if($n(r)){if(e[ue+8216>>2]=e[ue+8216>>2]+1,e[33692]|e[47200]!=2||$n(yt)||(e[h+2544>>2]=0,e[h+2548>>2]=0,e[h+2304>>2]=84731,!Hr(ue,h+2304|0,h+2160|0,h+2544|0,0,0))||(bA(ue,t=h+2160|0,h+2544|0,-1,0),TA(r=t,t=h+2336|0),e[h+80>>2]=t,uA(r=h+2240|0,85451,h+80|0),t=e[h+2148>>2],(0|(r=wA(r)+t|0))>=800))break n;BA(t+189424|0,h+2240|0),e[h+2148>>2]=r}else aa(e[h+2156>>2])&&(e[ue+8220>>2]=e[ue+8220>>2]+1);if(r=e[h+2152>>2],t=e[h+2156>>2],e[47204])if((0|M)>0)M=M-1|0;else{if(!((0|t)!=91|(0|r)!=91)){i=0,M=-1;break t}M=(0|t)==93&&(0|r)==93?2:M}if((0|t)==10){for(i=e[33285],l=0;;){n:{if(!i){if(t=e[33283],e[t>>2]==e[t+4>>2])break n;r=e[h+2152>>2]}if(Ts(r)){l=(e[h+2152>>2]==10)+l|0,(r=e[33285])?(e[33285]=0,i=0):(e[33284]=e[33284]+1,t=e[33283],r=0|LA[e[t+8>>2]](t),i=e[33285]),e[h+2152>>2]=r;continue}}break}if((0|l)>
28100){n&&IA(t=Ms+189424|0,32,or(h+2336|0,t)),t=e[h+2148>>2]+189424|0,g[0|t]=32,g[t+1|0]=0,e[33285]=e[h+2152>>2],n=e[47203]?524358:F((0|l)>=3?3:l,30)+524328|0;break e}if(t=e[47268]<(0|xs),xs=0,!t){t=e[h+2148>>2]+189424|0,g[0|t]=32,g[t+1|0]=0,e[33285]=e[h+2152>>2],n=262174;break e}}if(i=0,e[33692]|M)break t;if(t=0,!n)break r;if(Ts(e[h+2156>>2])){t=n;break r}if(Wr(e[h+2156>>2])&&ei(e[h+2156>>2]))break r;e[33691]=e[h+2156>>2],g[0|(t=Ms+189424|0)]=32,g[t+1|0]=0,e[33285]=e[h+2152>>2];break e}VA&&(e[h+2148>>2]=_s(1328,e[h+2148>>2]+189424|0)+e[h+2148>>2]),n&&IA(t=Ms+189424|0,32,or(h+2336|0,t)),t=e[h+2148>>2]+189424|0,g[0|t]=32,g[t+1|0]=0}n=589864;break e}if(!((0|(r=e[h+2156>>2]))!=46|e[h+2152>>2]!=46)){r:{a:{n:{if(!(r=e[33285])){if(r=e[33283],e[r>>2]==e[r+4>>2])break r;if(!(r=e[33285]))break n}e[33285]=0;break a}e[33284]=e[33284]+1,r=e[33283],r=0|LA[e[r+8>>2]](r)}if(m=r,(0|r)==46)for(e[h+2152>>2]=32,e[h+2156>>2]=8230,m=e[33285];;){a:{n:{if(!m){if(r=e[33283],e[r>>2]==e[r+4>>2]){m=46;break r}if(!(m=e[33285]))break n}e[33285]=0,r=0;break a}e[33284]=e[33284]+1,r=e[33283],m=0|LA[e[r+8>>2]](r),r=e[33285]}if((0|m)!=46)break r;e[h+2152>>2]=32,e[h+2156>>2]=8230,m=r}}(0|(r=e[h+2156>>2]))!=8230?e[33285]=m:(e[h+2152>>2]=m,r=8230)}if(gA=0,(0|(ae=Tn(r)))!=16384){r:if(536621&ae)for(r=e[33285];;){if(!r&&(r=e[33283],e[r>>2]==e[r+4>>2])||!(536621&Tn(e[h+2152>>2])))break r;(m=e[33285])?(e[33285]=0,r=0):(e[33284]=e[33284]+1,r=e[33283],m=0|LA[e[r+8>>2]](r),r=e[33285]),e[h+2152>>2]=m}if(1048576&ae){e[w+780>>2]=ae>>>12&15,VA=1,M=0,n=t;continue}(Ts(e[h+2152>>2])|32768&ae||pi(e[h+2152>>2])||(0|(r=e[h+2152>>2]))==63||(l=0,e[33285]||(r=e[33283],l=e[r>>2]==e[r+4>>2],r=e[h+2152>>2]),l||(0|r)==1))&&(gA=1)}if((0|(r=e[h+2156>>2]))==57404&&(e[h+2156>>2]=60,r=60),e[47201]){n=0;r:if(!(1<<(l=ta(r))&1879048255&&l>>>0<=30)){if(!((l=ta(r))>>>0>27)){if(116672&(M=1<<l))break r;if(134227968&M){n=!(1024&Je(r,l));break r}}n=1}if(!(d[134772]|!n)&&(e[47201]==1||rs(188832,e[h+2156>>2]))){e[ue+288>>2]=0,i=e[h+2156>>2],g[h+2336|0]=0,M=e[h+2152>>2],r=0;r:{a:if(!((0|(n=e[34064]))<=0))for(;;){if((0|i)==e[136272+(r<<4)>>2]){if(e[136276+(r<<4)>>2])break r;if(Qa(0,r))break a;break r}if((0|n)==(0|(r=r+1|0)))break}r=-1}r:if((0|r)>=0)e[h>>2]=r,uA(h+2336|0,86007,h),e[33285]=M;else if((l=!gA)|(0|i)!=46|(0|M)==46||(e[h+2600>>2]=0,e[h+2604>>2]=0,e[h+2540>>2]=86036,!Hr(ue,h+2540|0,h+2544|0,h+2600|0,0,0))?r=Yn(h+2240|0,ue,i,0):(bA(ue,r=h+2544|0,h+2600|0,-1,0),TA(n=r,r=h+2160|0),e[h+64>>2]=r,uA(r=h+2304|0,85451,h- -64|0)),n=r,l|!e[h+2148>>2]|2&d[ue+76|0]){for(r=e[33285],l=1;;){a:{n:{if(!r){if(r=e[33283],e[r>>2]==e[r+4>>2]|(0|i)==60|(0|i)!=(0|M))break a;if(l=l+1|0,M=e[33285])break n;e[33284]=e[33284]+1,r=e[33283],M=0|LA[e[r+8>>2]](r),r=e[33285];continue}if((0|i)==60|(0|i)!=(0|M))break a;M=r,l=l+1|0}r=0,e[33285]=0;continue}break}if(e[h+2152>>2]=M,gA&&(e[33285]=M),(0|l)==1){e[h+16>>2]=n,uA(h+2336|0,86219,h+16|0);break r}if((0|l)<=3){if(g[h+2336|0]=0,(0|(r=e[50786]))<=299&&(e[h+2336>>2]=d[86728]|d[86729]<<8|d[86730]<<16|d[86731]<<24,v[h+2340>>1]=d[86732]|d[86733]<<8),(0|l)>0){for(;e[h+32>>
28102]=n,uA(r=h+2160|0,86219,h+32|0),M=l>>>0>1,gs(h+2336|0,r),l=l-1|0,M;);r=e[50786]}if((0|r)>299)break r;e[h+2160>>2]=d[86857]|d[86858]<<8|d[86859]<<16|d[86860]<<24,r=d[86860]|d[86861]<<8|d[86862]<<16|d[86863]<<24,g[h+2163|0]=r,g[h+2164|0]=r>>>8,g[h+2165|0]=r>>>16,g[h+2166|0]=r>>>24,gs(h+2336|0,h+2160|0);break r}e[h+56>>2]=n,e[h+52>>2]=l,e[h+48>>2]=n,uA(h+2336|0,86932,h+48|0)}else e[33691]=i,e[33285]=M,v[h+2336>>1]=32;if(l=wA(n=h+2336|0),BA((r=e[h+2148>>2])+189424|0,n),e[h+2148>>2]=r+l,gA){if((0|i)==45){n=16384;break e}if(n=Tn(i),!(2&d[ue+76|0]|(0|r)<=0)){n=(-32769&n)==266270?262148:(28672&n)==4096?266244:262148;break e}if(!(524288&n)){n=(28672&n)==4096?266244:262148;break e}if((0|n)>=0)break e}i=e[h+2156>>2]}}if(i|!(2097152&ae)||(r=BA(e[h+2148>>2]+189424|0,Yn(h+2336|0,ue,e[h+2156>>2],1)),d[0|r]?(e[h+2148>>2]=e[h+2148>>2]+wA(r),ae&=-28673,i=e[h+2156>>2]):i=0),M=0,gA){r:if(Ts(m=e[h+2152>>2]))for(l=e[33285],r=0;;){if(!l&&(n=e[33283],e[n>>2]==e[n+4>>2])||!Ts(m))break r;r=((0|m)==10)+r|0,(m=e[33285])?(e[33285]=0,l=0):(e[33284]=e[33284]+1,n=e[33283],m=0|LA[e[n+8>>2]](n),l=e[33285])}else r=0;n=(0|(M=e[h+2156>>2]))==46&&(0|r)<2?4194304|ae:ae;r:{if(!r){l=1,(0|M)!=44|(0|yt)!=46|e[ue+212>>2]!=26741|ht-48>>>0>=10||m-48>>>0>=10&&!ei(m)||(e[h+2156>>2]=1367,l=0),(0|(ae=e[h+2156>>2]))!=46|(0|m)!=39||(M=l,ae=e[33283],(0|(it=e[ae>>2]))==e[ae+4>>2]?l=0:(l=0|LA[e[ae+8>>2]](ae),e[ae>>2]=it),ae=e[h+2156>>2],l=(0|l)!=115&M);a:if((0|ae)!=46)l&=_e;else{n:if(1&g[ue+106|0]){if(!(yt-48>>>0<10)&&((M=yt-73|0)>>>0>15|!(1<<M&40969)||!(1<<(M=ht-73|0)&40969&&M>>>0<=15||Ts(ht))))break n;l=yt-48>>>0>=10?0:!ei(m)&(0|m)!=45&l}if(ei(m)&&(l=d[ue+208|0]!=0&l),_e){ae=e[h+2156>>2];break a}ae=32,e[h+2156>>2]=32,l=0}if(!(!l|(0|ae)!=46|!e[47203]|(0|m)!=60)){Ms=e[h+2148>>2],t=n;break r}if(!l)break r}if(t=e[h+2148>>2]+189424|0,g[0|t]=32,g[t+1|0]=0,e[33285]=m,yt-48>>>0<10&&(n=Wr(m)?n:-4194305&n),(0|r)<2)break e;n=(0|n)==536621?536656:(0|n)==532520?532555:524358;break e}(e[33285]||(M=0,r=e[33283],e[r>>2]!=e[r+4>>2]))&&(M=0,Ts(e[h+2152>>2])&&(e[33285]=m))}n=t}if(e[33712]!=1){if((0|i)!=(0|(r=e[h+2156>>2])))l=e[h+2148>>2],(0|r)==57404&&(r=60,e[h+2156>>2]=60);else{t:{if(pi(i))r=57384;else{if(r=45,e[h+2156>>2]==45)break t;r=32}e[h+2156>>2]=r}l=e[h+2148>>2]}e[h+2148>>2]=_s(r,l+189424|0)+e[h+2148>>2],Ts(e[h+2156>>2])||pi(e[h+2156>>2])||(t=e[h+2148>>2],v[cA+(t<<1)>>1]=e[33284]-e[47353],(0|t)<=(l+1|0)||IA(Qi+(l<<1)|0,255,t+~l<<1)),r=e[h+2148>>2],e[Os>>2]=r;t:{r:{if((0|r)>725){if(!Wr(e[h+2156>>2]))break r;r=e[h+2148>>2]}if((0|r)<796)continue;break t}if(r=e[h+2148>>2],!(e[h+2156>>2]-48>>>0>=10)&&(0|r)<796)continue}break}}g[0|(t=r+189424|0)]=32,g[t+1|0]=0,e[33285]=e[h+2152>>2],n=16384}oe=h+2608|0,VA=n,ha&&(t=e[w+780>>2],e[ha>>2]=t||VA>>>12&7),t=(w+5184|0)+(e[w+6800>>2]<<1)|0,v[t+6>>1]=0,v[t+2>>1]=0,v[t+4>>1]=32767,ae=F(4095&VA,8388608&VA?320:10),n=189424;e:{t:{r:if(t=d[189424]){for(;;){if(!!(255&(t=t<<24>>24))&t>>>0<33){if(t=d[0|(n=n+1|0)])continue;break r}break}if(d[0|n])break t}ae=(0|(r=ae-(t=e[47566])|0))>0?r:0,e[47566]=ae+t,VA=d[190268]?524288|VA:VA,e[ue+8240>>2]=VA;break e}e[47566]=ae,t=d[190268],e[ue+8240>>2]=VA,t&&(qA=1,e[47568]=e[47568]+1,(0|(t=e[47569]))<=0||(t=t-1|0,e[47569]=t,t||(g[190280]=0)))}e[49572]=1,e[47572]=655360,e[47573]=0,e[ue+8184>>2]=0,e[ue+8188>>2]=0,t=0,e[ue+288>>2]=0,e[(r=ue- -8192|0)>>2]=0,e[r+4>>2]=0,e[ue+8200>>2]=0,e[ue+8224>>2]=0,e[ue+8228>>2]=0,e[(r=ue+8232|0)>>2]=0,e[r+4>>2]=0,g[w+786|0]=32,v[w+784>>1]=8192,e[w+6812>>2]=32,v[w+1588>>1]=3,e[w+1584>>2]=0,n=0;e:if(!((0|(r=e[w+6800>>2]))<=0)){for(;;){if(v[(w+5184|0)+(n<<1)>>1]>0)break e;if((0|r)==(0|(n=n+1|0)))break}n=r}if(r=_[(w+5184|0)+(n<<1)>>1],v[w+1592>>1]=r,r)for(;t=!!(65535&~r)+t|0,r=_[(w+5184|0)+((n=n+1|0)<<1)>>1];);for(g[w+1594|0]=t,l=3,cA=1,n=0;;){ht=e[w+6808>>2],$s(w+6808|0,(Pt=(w+784|0)+l|0)-1|0),!d[ue+170|0]|e[w+6808>>2]-48>>>0>=10||Wr(ht)&&(e[w+6808>>2]=97),Ve?e[w+6812>>2]=Ve:P&&$s(w+6812|0,P+189423|0),r=P;e:{t:if(n||(r=or(w+6816|0,P+189424|0)+P|0,n=e[w+6816>>2])){if(_e=or(w+6804|0,t=r+189424|0),(0|n)==1){if(i=r-1|0,Ve=32,yt=0,e[w+6812>>2]!=32){r=i,t=32;break t}P=0,n=r;r:{a:switch(d[0|t]-43|0){case 0:n=r+1|0,P=64;break r;case 2:break a;default:break r}n=r+1|0,P=96}if(g[0|(t=n+189424|0)]-48>>>0>=10)m=n+1|0,_e=-1;else{for(_e=Pn(t);n=(t=n)+1|0,g[t+189424|0]-48>>>0<10;);m=n,n=t}if((0|(Ve=e[47350]))>247)t=0;else if(t=0,!((0|(n=g[n+189424|0]))<0)&&(n=Wn(84868,255&n,14))){r=1+(t=n-84868|0)|0,(0|_e)==-1&&(_e=e[105536+(r<<2)>>2],P=0);r:{a:switch(t-8|0){case 0:e[49574]=0,e[49573]=_e;break r;case 4:break a;default:break r}g[199304]=(0|_e)>=3?1:0}t=1,e[47350]=Ve+1,e[198304+(Ve<<2)>>2]=(r+P|0)+(_e<<8),r=m}Ve=e[w+6812>>2],IA(i+189424|0,32,r-i|0),na=t+na|0,n=0;break e}Ve=0,(0|n)==32|e[49573]!=36?(yt=0,t=n):(e[w+6812>>2]!=32|e[w+6804>>2]!=32||(e[49573]=20),yt=0,t=Za(n,ue))}else e[w+6804>>
28102]=32,yt=1,Ve=0,_e=0,t=32;t:if(as){if(as=1,cA=8,n=0,(0|t)!=93|e[w+6804>>2]!=93)break t;r=r+1|0,t=32,as=0}else if((240&(n=e[49573]))!=64)if(as=0,16&n)n=0;else{r:{a:{n:{c:{if(!((0|(m=(0|t)==8242||(0|t)==8217||(0|t)==146||(0|t)==180?39:t))!=8216&(0|m)!=63))if(Wr(e[w+6808>>2])){if(m=t,Wr(e[w+6804>>2])){m=39;break c}}else m=t;o:{if((0|m)!=1367){if((0|m)==1328){ee|=1024,m=32;break c}if((t=m-44032|0)>>>0>11183)break c;if(i=((n=((M=65535&t)>>>0)/28|0)>>>0)%21|0,t=t-F(n,28)&65535,m-50500>>>0>587)break o;n=t?t+4519|0:0,i=i+4449|0;break a}ee|=131072,Ms=e[w+6804>>2],t=e[w+6812>>2],m=32;break n}n=50500+(t+F(i,28)|0)|0,i=(M>>>0)/588|4352;break a}if(Ms=e[w+6804>>2],t=e[w+6812>>2],!((n=m-12592|0)>>>0>51)){i=4352|d[n+103296|0],n=0;break a}}h=r+189424|0;n:if(!((0|(n=e[ue+212>>2]))!=28268&(0|n)!=24934|(0|m)!=39)&&!aa(t)&&(or(w+6820|0,h+1|0),qi(e[w+6820>>2]))){i=601,n=0;c:switch(Ms-110|0){case 6:break a;case 0:break c;default:break n}if(e[ue+212>>2]!=24934)break a;g[0|h]=32;break a}if(e[w+6824>>2]=32,(0|(t=e[49897]))>0)e[49897]=t-1,n=0;else{if(!m){n=0,i=0;break r}n:{c:{o:{f:if((gA=e[ue+180>>2])&&(M=m,(it=$n(m))&&(M=Za(m,ue)),!ko(gA)))for(;;){e[w+16>>2]=0,e[w+624>>2]=M,t=or(w+16|0,gA)+gA|0;l:if(e[w+624>>2]==e[w+16>>2]){if(d[0|t]){for(i=1,Xr=0,n=h;ls=or(w+16|0,t),xs=or(w+624|0,n),Os=Za(e[w+624>>2],ue),e[w+624>>2]=Os,n=n+xs|0,Xr=(xs=(0|Os)==e[w+16>>2])+Xr|0,i&=xs,d[0|(t=t+ls|0)];);if(!i)break l;e[49897]=Xr}if(!(t=t+1|0))break f;if(8&d[188788]&&(e[w>>2]=gA,e[w+4>>2]=t,ss(e[47195],85187,w)),t=or(w+6828|0,t)+t|0,d[0|t])break o;n=0;break c}for(;n=t,t=t+1|0,d[0|n];);for(;d[0|(n=(t=n)+1|0)];);if(ko(gA=t+2|0))break}n=0,i=m;break n}or(w+6824|0,t),it&&$n(Ms)&&(e[w+6824>>2]=Gi(e[w+6824>>2])),n=e[w+6824>>2]}i=e[w+6828>>2],ee|=2097152,it&&(i=Gi(i))}if((0|i)!=8)break a}i=r;break e}n?e[w+6804>>2]=n:n=0}Wr(i)||qi(i)||rs(e[ue+336>>2],i)||!Wr(e[w+6808>>2])|!(!d[ue+170|0]|i-48>>>0>=10)&e[w+6804>>2]-48>>>0>=10||(i=32,Wt=1);r:{a:{n:{c:{o:{if(e[w+6808>>2]-48>>>0<10){if(i-48>>>0<10){t=R;break o}if(1<<(t=i-32|0)&20481&&t>>>0<=14)break c;Wt=1}else if(t=0,e[w+6812>>2]!=44||(t=R,(0|i)!=44))break o;i=32;break c}if((0|i)==91&&((0|(m=e[w+6804>>2]))==2||(i=91,(0|m)==91&&e[47204])))break n;R=t}if(Wr(i)){c:{o:{f:{if(Wr(e[w+6808>>2])){if(!d[ue+171|0]||(t=e[w+6808>>2],!((0|i)>12352)&&(0|t)<12353))break f}else t=e[w+6808>>2];if(zs=rs(e[ue+336>>2],t)?zs:0,(0|(t=e[w+6808>>2]))!=32&&!rs(e[ue+336>>2],t)){t=32,be=pi(e[w+6808>>2])?be:256|be;break o}ee=$n(i)?2|ee:ee,e[w+6808>>2]!=32|g[Pt-2|0]-48>>>0>=10|e[w+6812>>2]-48>>>0<10||(g[(w+784|0)+l|0]=32,t=1588+(F(SA,12)+w|0)|0,v[t>>1]=_[t>>1]+1,l=l+1|0)}if(t=32,(0|i)==32)break c;if(zs=zs+1|0,(0|(m=e[ue+600>>2]))<=0){t=i;break c}if(!((0|i)<=591&(0|(M=e[w+6808>>2]))>=(0|m))){if((0|i)<(0|m)){t=i;break c}if((0|zs)<2){t=i;break c}if(!((0|M)<=591)){t=i;break c}}if(!Wr(M)){t=i;break c}ee|=16384,be|=128}Wt=1}if(Gn=Gn+1|0,$n(t)){if(m=Za(t,ue),e[ue- -64>>2]){t=Fn?m:712,n=Fn?n:m,Fn=1;break t}if(ei(e[w+6812>>2])){if(e[w+6808>>2]==32){t=m;break t}if(t=32,e[ue+212>>2]!=26465)break r;for(i=85240,M=(w+784|0)+l|0,_e=0;;){if(h=wA(i),d[0|(ht=M-h|0)]==32&&!ra(ht+1|0,i,h=h-1|0)){if((0|(i=g[i+h|0]))==(0|m)){t=m;break t}if((0|i)==65&&Ho(ue,m)){t=m;break t}}if(i=e[131184+((_e=_e+1|0)<<2)>>2],(0|_e)==11)break}break r}if(t=32,(0|m)==32)break t;if(!$n(e[w+6812>>2])){t=m;break t}if(!ei(e[w+6804>>2])){t=m;break t}if(or(w+16|0,189424+(r+_e|0)|0),!(e[ue+212>>2]!=28268|(0|zs)!=2|(0|m)!=106|e[w+6812>>2]!=73)){t=m;break t}if(e[w+6808>>2]==32){t=m;break t}if(!Wr(e[w+16>>2])){t=m;break t}be|=256,Ve=32,Wt=1;break t}if(!cA){cA=0;break t}if((0|zs)<3){cA=0;break t}if((0|t)!=115){cA=0;break t}if(e[ue+212>>2]!=25966){cA=0;break t}if(e[w+6804>>2]!=32){cA=0;break t}if(cA|=4,t=32,d[783+(i=l+w|0)|0]!=39)break t;g[i+783|0]=32;break t}t=32;c:{o:{f:{l:{switch(i-39|0){default:if((0|i)==95)break t;case 1:case 2:case 3:case 4:case 5:if(i-48>>>0>=10||d[ue+170|0]&&Wr(e[w+6808>>2])&&!((m=e[w+6804>>2])-48>>>0<10|m-2406>>>0<10))break c;if((0|(M=e[w+6808>>2]))==32)break o;if(m=e[w+6808>>2],M-48>>>0<10)break f;if((0|(M=m))==(0|(m=e[ue+128>>2])))break l;Wt=1;break t;case 6:if(!qi(e[w+6812>>2])&&Wr(e[w+6804>>2])){if(e[w+6808>>2]!=32){Wt=1;break t}if(ee|=128,(0|SA)<=0)break t;i=1572+(F(SA,12)+w|0)|0,e[i>>2]=16384|e[i>>2];break t}if(i=e[w+6804>>2],!(e[w+6812>>
28102]!=32|(0|i)!=32)){Yr=4;break t}if((0|i)==45){r=r+1|0,Yr=4;break t}if(t=45,e[w+6808>>2]!=32||!Wr(ht)||Wr(e[w+6812>>2]))break t;g[(w+784|0)+l|0]=32,i=1588+(F(SA,12)+w|0)|0,v[i>>1]=_[i>>1]+1,l=l+1|0;break t;case 7:if(e[w+6808>>2]==46){Wt=1;break t}if(t=46,(0|SA)<=0||(i=1572+(F(SA,12)+w|0)|0,1&g[i+1|0])||!Wr(e[w+6812>>2]))break t;e[i>>2]=65536|e[i>>2],t=(t=qi(e[w+6804>>2]))||e[w+6804>>2]==45?32:46;break t;case 0:}i:{if((0|(m=e[w+6812>>2]))!=46||(i=115,e[w+6804>>2]!=115)){if(!Ki(m))break i;i=e[w+6804>>2]}if(Wr(i))break a}if(1&(i=e[ue+88>>2])){if(Wr(e[w+6804>>2]))break a;i=e[ue+88>>2]}if(2&i&&Wr(e[w+6812>>2]))break a;if(!(!rs(e[ue+332>>2],e[w+6812>>2])|(0|ht)!=32)){r=(e[w+6804>>2]==32)+r|0;break a}if(m=(0|(i=e[w+6808>>2]))!=115|Ln,Ln=0,!(1&m))break t;Ln=!!(0|qi(i)),Yr=4;break t}if((0|m)==44&R){Wt=1;break t}R=1;break c}if((0|m)!=32)break c}Wr(ht)&&(Wr(e[w+6812>>2])||(g[(w+784|0)+l|0]=32,t=1588+(F(SA,12)+w|0)|0,v[t>>1]=_[t>>1]+1,l=l+1|0))}t=i;break t}as=1,i=r+1|0,R=t;break e}t=39,Ln=0;break t}Wt=1,Ve=32}else{if(t-48>>>0<10){n=0,i=(0|(m=e[49574]+1|0))>(15&e[49573]),e[49574]=i?0:m,t=i?32:t,Wt|=i,as=0;break t}n=0,e[49574]=0,t=(i=e[w+6808>>2]-48>>>0<10)?32:t,Wt|=i,as=0}if(qi(t)){if(e[w+6808>>2]==32){ee|=262144,i=r;break e}if(i=e[w+6816>>2]-9>>>0<2,M=1&Wt){_e=0;t:if(!((0|y)>(0|(t=r-1|0))))for(;;){if(!(m=v[(w+5184|0)+(t<<1)>>1]))break t;if(_e=((0|m)>0)+_e|0,!((0|y)<=(0|(t=t-1|0))))break}g[1594+(F(SA,12)+w|0)|0]=_e}if(be=i?262144|be:be,g[(w+784|0)+l|0]=32,t=l+1|0,!((0|SA)>298||(i=(w+1584|0)+F(SA,12)|0,(0|(m=_[i+4>>1]))>=(0|t)))){if((0|na)<=0?y=e[i>>2]:(y=198300+(e[47350]<<2)|0,e[y>>2]=128|e[y>>2],na=0,y=64|e[i>>2]),h=e[47352],g[i+6|0]=h,e[i>>2]=y|(Gn?cA:-2&cA)|(d[199304]?2048:0)|ee,(0|h)>0){for(;ee=(y=w+784|0)+t|0,y=y+(t=t-1|0)|0,g[0|ee]=d[0|y],(0|t)>(0|m););g[0|y]=32,v[i+4>>1]=m+1,t=l+2|0}m=(w+1584|0)+F(SA=SA+1|0,12)|0,e[m>>2]=0,v[m+4>>1]=t,l=r;t:if(!((0|(i=e[w+6800>>2]))<=(0|r))){for(;;){if(v[(w+5184|0)+(l<<1)>>1]>0)break t;if((0|i)==(0|(l=l+1|0)))break}l=i}if(_e=_[(w+5184|0)+(l<<1)>>1],v[m+8>>1]=_e,Gn=0,i=0,_e)for(;i=!!(65535&~_e)+i|0,_e=_[(w+5184|0)+((l=l+1|0)<<1)>>1];);g[m+10|0]=i,e[47352]=0,cA=1,ee=be,be=0,Fn=0}Wt=0,n=M?0:n,i=M?P:r}else(0|l)>795?(i=r,r=y,t=l):(t=_s(t,(w+784|0)+l|0)+l|0,i=r,r=y);e[47352]<(0|Yr)&&(e[47352]=Yr),Yr=0,y=r,l=t}if(yt||(P=i,!((0|l)<799)))break}(0|na)<=0|SA||(t=198300+(e[47350]<<2)|0,e[t>>2]=128|e[t>>2],e[w+1584>>2]=64|e[w+1584>>2],SA=1),t=(w+784|0)+l|0,e[ue+8204>>2]=t-1,r=0,g[0|t]=0,g[w+1590|0]=0,g[1590+(F(SA,12)+w|0)|0]=8;e:if((0|SA)<=0)e[w+1584>>2]=512|e[w+1584>>2],l=e[49572];else{t=SA-1|0;t:if((0|SA)!=1)for(n=t;;){if(!pi(g[_[1588+(F(n,12)+w|0)>>
vendor: 9,444 bytes, line 2810
28101]+(w+784|0)|0])){r=n;break t}if(m=(0|n)>1,n=n-1|0,!m)break}if(r=(w+1584|0)+F(r,12)|0,e[r>>2]=16|e[r>>2],4194304&VA&&(t=(w+1584|0)+F(t,12)|0,256&(r=e[t>>2])||(e[t>>2]=65536|r)),e[w+1584>>2]=512|e[w+1584>>2],!((0|SA)<=0|(0|(l=e[49572]))>990))for(m=3|(t=w+624|0),M=2|t,Ve=w+754|0,yt=!(4194304&VA),P=0,y=0;;){e[47354]=e[47354]+1;t:{if((0|(t=e[49827]))<=0||(t=t-1|0,e[49827]=t,t)){if(d[190280])break t}else g[190280]=0;t=_[1588+(F(P,12)+w|0)>>1]+(w+784|0)|0;r:if(!(g[0|t]-48>>>0>=10)&&(r=w+624|0,n=t,e[ue+112>>2]!=1227133512)){for(;;){a:{if(g[0|n]-48>>>0<10)g[0|r]=d[0|n],r=r+1|0,n=n+1|0;else{if(e[ue+124>>2]!=g[0|n]|d[n+1|0]!=32||(l=n+2|0,d[n+3|0]==32|g[0|l]-48>>>0>=10|d[n+4|0]==32))break a;P=P+1|0,n=l}if(r>>>0<Ve>>>0)continue;break r}break}IA(t+(r=r-(R=w+624|0)|0)|0,32,(n=(l=n-t|0)-r|0)>>>0<=l>>>0?n:0),$A(t,R,r)}for(r=0;n=r,r=r+1|0,g[t+n|0]-48>>>0<10;);r:if(n-5>>>0<=27){for(g[w+626|0]=32,v[w+624>>1]=8224,d[0|t]!=48&e[ue+132>>2]>=(0|n)||(r=(w+1584|0)+F(P,12)|0,e[r>>2]=524288|e[r>>2]),be=(w+1584|0)+F(P,12)|0,_e=0,l=m;r=t,!((t=g[0|t])-48>>>0>=10&(0|t)!=e[ue+128>>2])&&(g[0|l]=t,t=l+1|0,R=n,(0|(n=n-1|0))<=0?l=t:e[ue+112>>2]>>>n&1?(h=e[be+4>>2],ee=(w+16|0)+F(_e,12)|0,e[ee>>2]=e[be>>2],e[ee+4>>2]=h,e[ee+8>>2]=e[be+8>>2],_e=_e+1|0,(0|(ee=e[ue+124>>2]))!=32&&(g[l+1|0]=ee,t=l+2|0),g[0|t]=32,l=t+1|0,8&d[be+2|0]||((Yr=e[ue+112>>2])>>>R-2&1&&(g[t+1|0]=48,g[t+2|0]=48,Yr=e[ue+112>>2],l=t+3|0),Yr>>>R-3&1&&(g[0|l]=48,l=l+1|0))):l=t,t=r+1|0,l>>>0<Ve>>>0););if(n=e[be+4>>2],t=(w+16|0)+F(_e,12)|0,e[t>>2]=e[be>>2],e[t+4>>2]=n,n=e[be+20>>2],e[t+16>>2]=e[be+16>>2],e[t+20>>2]=n,n=e[be+12>>2],e[t+8>>2]=e[be+8>>2],e[t+12>>2]=n,n=1,(0|_e)>0)for(;t=(w+16|0)+F(n,12)|0,e[t>>2]=-262209&e[t>>2],(0|_e)>=(0|(n=n+1|0)););if(t=d[r+4|0]|d[r+5|0]<<8|d[r+6|0]<<16|d[r+7|0]<<24,n=d[0|r]|d[r+1|0]<<8|d[r+2|0]<<16|d[r+3|0]<<24,g[0|l]=n,g[l+1|0]=n>>>8,g[l+2|0]=n>>>16,g[l+3|0]=n>>>24,g[l+4|0]=t,g[l+5|0]=t>>>8,g[l+6|0]=t>>>16,g[l+7|0]=t>>>24,t=d[r+12|0]|d[r+13|0]<<8|d[r+14|0]<<16|d[r+15|0]<<24,r=d[r+8|0]|d[r+9|0]<<8|d[r+10|0]<<16|d[r+11|0]<<24,g[l+8|0]=r,g[l+9|0]=r>>>8,g[l+10|0]=r>>>16,g[l+11|0]=r>>>24,g[l+12|0]=t,g[l+13|0]=t>>>8,g[l+14|0]=t>>>16,g[l+15|0]=t>>>24,g[l+16|0]=0,l>>>0<=m>>>0)break r;for(t=d[be+6|0],_e=0,n=m;;){for(y=AA(ue,n,(w+16|0)+F(_e,12)|0,255&t);t=d[0|n],n=n+1|0,(0|t)!=32;);if(t=0,g[be+6|0]=0,_e=_e+1|0,!(n>>>0<l>>>0))break}}else{if(e[47352]=0,y=AA(ue,t,r=(w+1584|0)+F(P,12)|0,d[r+6|0]),(0|(n=e[47352]))>d[r+18|0]&&(g[r+18|0]=n,e[47352]=0),!(!(4096&y)|d[0|t]==32))for(;IA(w+624|0,0,150),e[w+624>>2]=538976288,e[w+628>>2]=538976288,g[w+632|0]=32,AA(ue,$A(M,t,n=or(w+16|0,t)),r,0),d[0|(t=t+n|0)]!=32;);50331648&y&&(ae=(t=yt|(~e[33264]+SA|0)!=(0|P))?ae:10,t|!ha||(e[ha>>2]=4,ae=10))}if(128&y&&!((0|(r=e[33264]))<=0)){if(t=0,n=r,l=3&r)for(;R=(w+1584|0)+F(n+P|0,12)|0,e[R>>2]=1048576|e[R>>2],n=n-1|0,(0|l)!=(0|(t=t+1|0)););if(r>>>0>=4)for(;t=(w+1584|0)+F(n+P|0,12)|0,e[t>>2]=1048576|e[t>>2],e[(r=t-12|0)>>2]=1048576|e[r>>2],e[(r=t-24|0)>>2]=1048576|e[r>>2],e[(t=t-36|0)>>2]=1048576|e[t>>2],n=n-4|0;);e[33264]=n}}if(l=e[49572],(0|SA)<=(0|(P=P+1|0)))break e;if(!((0|l)<991))break}}if((0|(n=e[47351]))<(0|(m=e[47350]))){for(Ve=e[47202],yt=e[49846],_e=e[47352];;){t=(r=e[198304+(n<<2)>>2])>>8;e:{t:switch((31&r)-9|0){case 0:Ve=t;break e;case 4:yt=t;break e;case 3:break t;default:break e}_e=r>>>0>=256?t+_e|0:0}if(!(!(128&r)&(0|m)>(0|(n=n+1|0))))break}e[47352]=_e,e[47351]=n,e[49846]=yt,e[47202]=Ve}e[49572]=l+2,e[(t=190288+(l<<3)|0)>>2]=589824,v[t+4>>1]=i,e[t+8>>2]=589824,v[t+12>>1]=i,r=SA&&e[47199]?ae:10,e[33285]?t=0:(t=e[33283],t=e[t>>2]==e[t+4>>2]),ae=t?r:ae,P=qA,m=0,SA=0,oe=M=oe-32192|0,e[M+24>>2]=0,e[M+28>>2]=0,e[M+16>>2]=0,e[M+20>>2]=0,e[M+8>>2]=0,e[M+12>>2]=0,e[M>>2]=0,e[M+4>>2]=0,i=e[49572],h=_[190284+(i<<3)>>1];e:{if((0|(n=i-3|0))<0)t=n;else{for(;;){if(m=(0|(r=127&d[3+(t=190288+(n<<3)|0)|0]))<(0|m)?m:r,_[t+4>>1])t=n;else if(t=-1,r=(0|n)>0,n=n-1|0,r)continue;break}if(m>>>0>3)break e}for(;;){if((0|(t=t-1|0))<0)break e;if(64&d[0|(r=190288+(t<<3)|0)]){g[r+3|0]=4;break e}if(!(d[r+3|0]<4))break}}if(t=e[ue+292>>2],n=0,(0|i)<=0)m=0;else for(R=-1,m=0;;){r=t,e[ue+292>>2]!=(0|t)&&(v[(t=190288+(n<<3)|0)>>1]=32|_[t>>1]),(0|m)>0&&(be=e[4+(y=190288+(n<<3)|0)>>2],e[(l=190288+(t=n-m<<3)|0)>>2]=e[y>>2],e[l+4>>2]=be,(0|R)!=-1&&(v[4+(t+190288|0)>>1]=R),R=-1);e:{if(d[2+(190288+(l=n<<3)|0)|0]==21){if(t=d[7+(y=l+190288|0)|0],2&d[0|y])break e;t:if((0|t)!=(0|r)){if(y=d[10+(l+190288|0)|0]-9|0){if((0|y)==12)break t;break e}if(d[18+(l+190288|0)|0]!=21)break e}(0|R)==-1&&(R=(t=_[4+(l+190288|0)>>1])||-1),m=m+1|0}t=r}if((0|i)==(0|(n=n+1|0)))break}if(e[49572]=i-m,Ia(t),(r=e[ue+36>>2])&&!((0|(t=(m=e[49572])-1|0))<0))for(Ve=256&r,ee=4&r,yt=8&r,be=15&r,Wt=16&r,qA=2&r,r=r>>>8&1,n=0;;){if(l=m,i=n,m=t,(0|(t=d[2+(y=190288+(cA=t<<3)|0)|0]))==21){e:{t:{if((0|(n=l-2|0))>=0)for(;;){if(d[2+(190288+(t=n<<3)|0)|0]==21)break t;if(t=(0|n)>0,n=n-1|0,!t)break}t=e[ue+292>>2];break e}t=d[7+(t+190288|0)|0]}Ia(t),t=d[y+2|0]}if(n=i,(t=e[144464+((255&t)<<2)>>2])&&(n=r,!(32&d[0|y]))){n=d[t+11|0],R=0,qA&&((0|(l=d[0|t]))!=118&(0|l)!=82||(i=Wt?0:i,R=1));e:{t:{r:{a:switch((l=253&n)-4|0){case 1:break r;case 0:break a;default:break t}if(be&&(n=1,!i)||(0|(n=i))!=2||(n=2,!(t=d[t+13|0])))break e;g[y+2|0]=t;break e}if(be&&(n=2,!i)||(0|(n=i))!=1||(n=1,!(t=d[t+13|0])))break e;g[y+2|0]=t;break e}n=0,yt&&(n=l?i:0)}n=t=R?0:n,_[4+(cA+190288|0)>>1]&&(n=t=ee?0:t,Ve&&(n=t||1))}if(!((0|(t=m-1|0))>=0))break}if(Ia(e[ue+292>>2]),e[49572]<=0)Wt=-2,n=0;else{for(n=-1,i=0,y=0,yt=0;;){t=y<<3,(0|n)!=-1&&(v[4+(t+190288|0)>>1]=n),d[2+(R=t+190288|0)|0]==21&&Ia(d[7+(t+190288|0)|0]),be=e[49572];e:{if(!(32&d[0|(l=t+190288|0)])){i=(0|(r=be-1|0))>(0|y)?e[144464+(d[10+(t+190288|0)|0]<<2)>>2]:i,!(_[l+12>>1]|(0|r)==(0|y))&&(_e=0,d[i+11|0]|!i)||(_e=1),m=d[R+2|0];t:if(!((0|(Wt=e[49848]))<=0))for(r=t+190288|0,n=0;;){if(Ve=F(n,3),d[Ve+199408|0]==(255&m)&&!((ee=d[2+(Ve+199408|0)|0])&(1^_e)|(4&d[r+3|0]?2&ee:0)|(_[r+4>>1]?0:4&ee))){if(m=d[1+(Ve+199408|0)|0],g[R+2|0]=m,!(2&d[e[144464+(m<<2)>>2]+4|0])|d[r+3|0]<2)break t;g[r+3|0]=0;break t}if((0|Wt)==(0|(n=n+1|0)))break}if(!(255&m)){n=_[4+(t+190288|0)>>1];break e}}n=e[l+4>>2],t=(M+32|0)+(yt<<5)|0,r=e[l>>2],e[t>>2]=r,e[t+4>>2]=n,r=e[144464+(r>>>14&1020)>>2],e[t+8>>2]=r,g[t+17|0]=d[r+11|0],yt=yt+1|0,n=-1}if(!((0|yt)<1e3&(0|be)>(0|(y=y+1|0))))break}if(m=0,n=0,!((0|(Wt=yt-2|0))<=0))for(;;){e:if(_[4+((M+32|0)+(m<<5)|0)>>1]){for(r=(0|m)>(0|Wt)?m:Wt,n=0,t=m;;){if((0|t)!=(0|r)){if(n=(0|n)>(0|(l=d[3+((i=M+32|0)+(t<<5)|0)|0]))?n:l,!_[4+(i+((t=t+1|0)<<5)|0)>>1])continue}else t=r;break}if((0|t)<=(0|m))break e;if(r=~m+t|0,i=0,l=t-m&7)for(;g[6+((M+32|0)+(m<<5)|0)|0]=n,m=m+1|0,(0|l)!=(0|(i=i+1|0)););if(r>>>0<7)break e;for(;g[6+(r=(M+32|0)+(m<<5)|0)|0]=n,g[r+38|0]=n,g[r+70|0]=n,g[r+102|0]=n,g[r+134|0]=n,g[r+166|0]=n,g[r+198|0]=n,g[r+230|0]=n,(0|(m=m+8|0))!=(0|t););}else t=m+1|0;if(m=t,!((0|Wt)>(0|t)))break}}for(e[M+40>>2]=e[36125],Ia(e[ue+292>>2]),ht=(0|n)<4,y=1,Ve=1,be=0,r=0,n=0,qA=0;;){e:{t:{r:{a:{if(r){if(i=(t=M+32|0)+((R=n-1|0)<<5)|0,be=d[2+(t+(n<<5)|0)|0],(0|R)>0){if(t=y-(n=(0|y)>0)|0,R>>>0>=(m=n?y:2)>>>0)for(;l=(n=(M+32|0)+(m<<5)|0)-32|0,y=e[n+12>>2],e[l+8>>2]=e[n+8>>2],e[l+12>>2]=y,y=e[n+4>>2],e[l>>2]=e[n>>2],e[l+4>>2]=y,y=e[n+28>>2],e[l+24>>2]=e[n+24>>2],e[l+28>>2]=y,y=e[n+20>>2],e[l+16>>2]=e[n+16>>2],e[l+20>>2]=y,(0|R)>=(0|(m=m+1|0)););y=t}_e=e[144464+(be<<2)>>2],e[i>>2]=0,e[i+4>>2]=0,e[i+24>>2]=0,e[i+28>>2]=0,e[i+16>>2]=0,e[i+20>>2]=0,e[i+8>>2]=0,e[i+12>>2]=0,g[i+2|0]=r,t=e[144464+(r<<2)>>2],e[i+8>>2]=t,be=i}else{if((0|n)>=(0|Wt)|(0|qA)>=997)break a;l=d[2+(i=(m=n<<5)+(M+32|0)|0)|0],t=e[144464+(l<<2)>>2],e[i+8>>2]=t,R=_[i+4>>1],(0|l)==21&&Ia(d[7+(m+(M+32|0)|0)|0]),y=R?n:y,_e=e[144464+(d[i+34|0]<<2)>>2],e[i+40>>2]=_e,R=n}if(!t){r=0,n=R+1|0;continue}if(Gt(ue,256,i,M+32040|0,M),(0|(n=e[M+32052>>2]))>0&&(m=(M+32|0)+(R<<5)|0,_e=e[144464+(n<<2)>>2],e[m+40>>2]=_e,g[m+34|0]=n,g[m+49|0]=d[_e+11|0]),n=0,r)r=t;else if((0|(m=e[M+32056>>2]))<=0)r=t;else{r=e[144464+(m<<2)>>2],e[i+8>>2]=r,n=d[i+2|0],g[i+2|0]=m,m=_[i>>1];n:if(d[r+11|0]!=2)v[i>>1]=65531&m;else{if(v[i>>1]=4|m,d[t+11|0]==2)break n;g[i+3|0]=0}Gt(ue,256,i,M+32040|0,M)}if((0|(l=e[M+32048>>2]))<=0)m=r;else{if(m=e[144464+(l<<2)>>2],g[i+2|0]=l,e[i+8>>2]=m,t=d[m+11|0],yt=1,(0|l)==1){cA=(0|t)==2;break t}l=_[i>>1];n:if((0|t)!=2)v[i>>1]=65531&l;else{if(v[i>>1]=4|l,d[r+11|0]==2)break n;g[i+3|0]=0}Gt(ue,256,i,M+32040|0,M)}if(cA=0,(0|(t=d[m+11|0]))!=2){yt=0;break t}if(cA=1,yt=0,t=2,d[i+3|0]>1){SA=0;break t}l=i+3|0,SA=SA+1|0,r=i;n:{if(8&(ee=e[ue+12>>2])){for(;;){c:switch(ee=r,r=r+32|0,d[ee+49|0]){case 0:break t;case 2:break c;default:continue}break}if(d[0|(r=ee+35|0)]>1)break t;if(d[i+6|0]<=3&&(g[0|l]=0),d[ee+38|0]<4)break n;break t}if(1&SA|(0|SA)<2)break t;if(2&ee)break r;if(ht)r=l;else if(r=l,_[i+36>>1])break r}g[0|r]=0;break t}e[36423]=qA+2,v[(t=145840+(qA<<5)|0)>>1]=0,g[t+2|0]=9,g[t+20|0]=2,e[t+12>>2]=ae,v[t+4>>1]=h,g[t+17|0]=0,g[t+18|0]=0,e[t+8>>2]=e[36125],v[t+32>>1]=0,g[t+34|0]=9,g[t+52|0]=0,e[t+44>>2]=0,v[t+36>>1]=0,g[t+49|0]=0,g[t+50|0]=0,e[t+40>>2]=e[36126],Ia(e[ue+292>>2]),oe=M+32192|0;break e}SA=1}if(!(8&(r=_[i+32>>1]))|(0|R)<=0||(l=d[_e+11|0])>>>0>15|!(1<<l&457)||(n=d[_e+10|0],v[i+32>>1]=8^r),it=_[i+36>>1]){t:if(r=e[ue+4>>2]){switch(0|t){default:n=512&r?11:n;break;case 0:break t;case 2:}if(d[_e+11|0]==2){(l=12&r)&&(n=(0|l)!=12?23:11);r:if(cA){switch(3&r){case 2:n=10;break r;case 0:break r}n=23}d[i+35|0]<4||(n=256&r?10:n)}}if(!((0|i)==(0|be)|(0|qA)<=0)){t:{r:{a:switch(0|(r=7&e[ue>>2])){case 0:break t;case 1:break a;default:break r}if(n-12>>
2810>0>4294967293)break t}n=d[r+101916|0]}n=e[47205]>0?24:n}}if(e[i+72>>2]=e[144464+(d[i+66|0]<<2)>>2],r=e[M+32060>>2],r=n||r||n,!yt){g[17+(l=145840+(ee=qA<<5)|0)|0]=t,e[l+8>>2]=m,g[l+16|0]=0,v[l>>1]=_[i>>1],g[l+3|0]=15&d[i+3|0],g[l+6|0]=d[i+6|0],n=d[i+7|0],v[l+4>>1]=0,g[l+7|0]=n,yt=d[m+10|0],g[l+2|0]=yt;t:if(n=_[i+4>>1]){if(v[l+4>>1]=n,P=1&P?5:1,g[20+(i=ee+145840|0)|0]=P,n=Ve,Ve=0,!n){P=0;break t}g[i+20|0]=8|P,P=0}else g[20+(ee+145840|0)|0]=0;e[12+(n=ee+145840|0)>>2]=e[M+32084>>2]<<1,!it|(0|yt)!=24||(0|(i=e[47205]))<=0||(e[l+8>>2]=e[36126],e[n+12>>2]=F(i,14)),(1<<t&428?t>>>0<=8:0)|2&d[m+7|0]&&(e[n+12>>2]=128,g[l+16|0]=0),g[21+(t=ee+145840|0)|0]=255,g[t+22|0]=255,v[t+18>>1]=5120,qA=qA+1|0}n=R+1|0;continue}break}v[88922]=1,e[44462]=0,na&&(v[145776+(e[36423]<<5)>>1]=2,t=198304+(e[47350]<<2)|0,e[t>>2]=128,e[(t=t-4|0)>>2]=128|e[t>>2]),g[190268]=VA>>>19&1,Vn&&(e[Vn>>2]=VA<<14>>31&189360)}oe=w+6832|0,Wt=e[47192],cA=e[A+12>>2],r=0,l=0,y=0,ee=0,be=0,Yr=0,ae=0,oe=h=oe-6e3|0;e:if(!((0|(i=(qA=e[36423])-1|0))<=0)){for(;g[2+(h+F(r,6)|0)|0]=0,4&d[145840+(t=r<<5)|0]?(n=h+F(ee,6)|0,g[n+1|0]=0,t=t+145840|0,g[n+3|0]=d[t+49|0],t=d[t+3|0],g[0|n]=t,ee=ee+1|0,Yr=(t>>>0>3)+Yr|0):d[e[8+(t+145840|0)>>2]+10|0]!=27|(0|ee)<=0||(t=(h+F(ee,6)|0)-4|0,g[0|t]=4|d[0|t]),(0|i)!=(0|(r=r+1|0)););if(g[h+F(ee,6)|0]=0,ee)if(e[Wt+148>>2]==1){if(!((0|qA)<=0)){for(t=-2&qA,n=1&qA,r=145840;y=d[r+17|0]==2&&d[r+3|0]>3?l:y,y=d[r+49|0]==2&&d[r+35|0]>3?1|l:y,r=r- -64|0,l=l+2|0,(0|t)!=(0|(be=be+2|0)););!n|d[r+17|0]!=2||(y=d[r+3|0]>3?l:y)}if(g[3+(m=145840+(t=y<<5)|0)|0]=7,e[Wt+212>>2]==30313&&(d[7+(t=t+145840|0)|0]||(g[t+7|0]=Us(55),qA=e[36423])),!((0|qA)<=0)){for(be=0,r=145840,t=145840,l=R=e[36125],i=0,Ve=1;;){if(d[r+17|0]?P=e[36125]:(P=e[36125],R=(n=d[e[r+8>>2]+14|0]>50)?P:R,Ve|=n),n=d[r+20|0]?P:l,4&d[0|r]){P=d[r+7|0],l=e[144464+(P<<2)>>2];t:{if((0|(ee=e[Wt+212>>2]))==6840683){if(e[n>>2]!=49||(ee=e[l>>2]-49|0)>>>0>5|!(1<<ee&41))break t;g[t+7|0]=Us(50),ee=e[Wt+212>>2]}if(!((0|ee)!=6516078&(0|ee)!=31336)){ee=0,P||(i=Us(1&(ee=i|Ve)?13621:12593),g[r+7|0]=i,l=e[144464+(i<<2)>>2]),(0|y)!=(0|be)|(1024|e[l>>2])!=13621||(g[m+3|0]=6),e[R>>2]==3420466&&(g[t+7|0]=Us(e[l>>2]==3420466?13619:12594));r:{if(e[n>>2]==12597){if((0|(P=e[l>>2]))!=12597)break r;g[t+7|0]=Us(13109)}P=e[l>>2]}i=ee,(0|P)==12593&&((0|(P=e[R>>2]))==13621&&(g[r+7|0]=Us(12850),P=e[R>>2]),(0|P)==13619&&(g[r+7|0]=Us(13107),P=e[R>>2]),(0|P)==3420466&&(g[r+7|0]=Us(13364)),g[r+3|0]=0)}}Ve=0,R=l,t=r}else l=n;if(r=r+32|0,!((0|(be=be+1|0))<(0|(n=e[36423]))))break}if(l=0,r=145840,!((0|n)<=0))for(;4&d[0|r]&&((t=d[r+7|0])||(g[r+7|0]=17,t=17),t=e[144464+(t<<2)>>2],g[r+21|0]=d[t+12|0],g[r+22|0]=d[t+13|0]),r=r+32|0,(0|n)!=(0|(l=l+1|0)););}}else{if(t=e[Wt+152>>2],t=Wt+F(ht=(0|t)>7?1:t,6)|0,ue=d[0|(ht?t+637:Wt+157)],VA=d[0|(ht?636+(t+cA|0):156+(Wt+cA|0))],g[133068]=(0|cA)==4,!((0|ee)<=0)){for(yt=ee-1|0,_e=cA-1>>>0>1,n=0,i=0;;){w=h+F(i,6)|0,ae=((t=d[0|w])<<24>>24>3)+ae|0;t:if((0|t)==6){t=i-3|0,r=i;r:{for(;;){if((0|r)<=(0|n)|(0|t)>=(0|r))break r;a:switch(m=h+F(r=r-1|0,6)|0,d[0|m]-4|0){case 2:break r;case 0:break a;default:continue}break}g[0|m]=3}r=i;r:{for(;;){if((0|ee)<=(0|(r=r+1|0)))break r;a:switch(d[h+F(r,6)|0]-4|0){case 0:break r;case 2:break a;default:continue}break}g[w+2|0]=2,g[0|w]=5,t=n;break t}if(d[0|w]==6){g[w+2|0]=2,M=0;r:if((0|ee)<=(0|(t=i+1|0)))m=i,as=0;else if(as=1,(0|(l=g[h+F(t,6)|0]))>4)m=i;else{for(y=(Yr-ae|0)>1,m=i;;){if(r=t,(255&l)==4&&(t=y+1|0,y=1,!((0|t)<=1))){t=r;break r}if(as=(0|ee)>(0|(t=r+1|0)),(0|t)==(0|ee))break;if(m=r,(0|(l=g[h+F(t,6)|0]))>4)break r}m=yt,t=ee}l=-1,P=0,y=0,Ve=-1,be=0,R=-1;r:{if((0|(r=n))<(0|t)){for(;l=(it=(0|(R=g[h+F(r,6)|0]))>3)&&(0|l)<0?r-n|0:l,M=(be=(0|y)>(0|R))?M:(0|y)<(0|R)?r:P,Ve=it?r:Ve,P=be?P:r,it=(0|r)!=(0|m),y=be?y:R,r=r+1|0,it;);if(be=P,R=Ve,(0|l)>=0)break r}l=t,P=be,Ve=R}e[33269]=m-P,e[33268]=l,e[33270]=P,e[33271]=M;r:if(d[133068])e[33270]=t,e[33271]=t;else if((0|Ve)>=0){if((0|t)!=(0|ee))break r;g[h+F(Ve,6)|0]=7}else g[h+F(P,6)|0]=7;gr(h,ht,n,t,VA),!as&!!(0|cA)||(VA=_e?d[Wt+156|0]:d[Wt+157|0])}else t=n}else t=n;if((0|t)>=(0|i))n=t;else if(4&d[w+2|0]){for(n=i+1|0,l=-1,Ve=0,P=0,y=0,r=t,M=-1;l=(be=(0|(m=g[h+F(r,6)|0]))>3)&&(0|l)<0?r-t|0:l,Ve=(R=(0|m)<(0|y))?Ve:(0|m)>(0|y)?r:P,M=be?r:M,P=R?P:r,be=(0|r)!=(0|i),y=R?y:m,r=r+1|0,be;);e[33269]=i-P,e[33270]=P,e[33271]=Ve,e[33268]=(0|l)<0?n:l,d[133068]?(e[33270]=n,e[33271]=n):(0|M)>=0?g[h+F(M,6)|0]=7:g[h+F(P,6)|0]=7,gr(h,ht,t,n,ue)}else n=t;if((0|ee)==(0|(i=i+1|0)))break}if(!((0|n)>=(0|ee))){for(l=-1,Ve=0,P=0,y=0,r=n,M=-1;l=(m=(0|(t=g[h+F(r,6)|0]))>
28103)&&(0|l)<0?r-n|0:l,Ve=(i=(0|t)<(0|y))?Ve:(0|t)>(0|y)?r:P,M=m?r:M,P=i?P:r,y=i?y:t,(0|ee)!=(0|(r=r+1|0)););e[33270]=P,e[33271]=Ve,e[33269]=~P+ee,e[33268]=(0|l)<0?ee:l,d[133068]?(e[33270]=ee,e[33271]=ee):(0|M)>=0?g[h+F(M,6)|0]=7:g[h+F(P,6)|0]=7,gr(h,ht,n,ee,VA)}}if((0|qA)<=0)break e;for(r=0,be=0;;){if(R=n=145840+(m=r<<5)|0,t=h+F(be,6)|0,l=d[0|t],g[n+3|0]=l,4&d[0|n]){i=m+145840|0,n=d[t+4|0],g[i+21|0]=n,y=d[t+5|0],g[i+16|0]=0,g[i+22|0]=y;t:{if(1&(P=d[t+2|0]))t=2;else{if(l>>>0<6)break t;t=d[t+1|0]}g[i+16|0]=t}n>>>0<=(255&y)>>>0?(t=y,y=n):(g[i+21|0]=y,g[i+22|0]=n,t=n),(n=d[7+(m+145840|0)|0])&&(t=(255&t)+(255&y)>>>1|0,n=e[144464+(n<<2)>>2],g[i+22|0]=t+d[n+13|0],g[i+21|0]=t+d[n+12|0]),2&P&&(g[R+3|0]=8|l),be=be+1|0}if((0|qA)==(0|(r=r+1|0)))break}}}if(oe=h+6e3|0,be=e[47192],r=0,_e=0,M=0,ae=0,oe=h=oe-160|0,e[36423]>=2)for(cA=e[30450],l=1;;){if(l=(t=l)+1|0,ee=d[3+(i=145840+(y=t<<5)|0)|0],2&(P=_[i>>1])){for(;(31&(m=e[198304+(ae<<2)>>2]))==2&&(cn(127&m,m>>>8|0),e[36432]=110,e[36433]=100,e[36434]=450,e[36430]=5,P=e[50786],R=e[32972],(0|(n=e[R+84>>2]))>0&&(P=(0|F(n,P))/100|0),Ve=d[((0|(n=(0|P)>=359?359:P))<=80?80:n)+101856|0],n=(0|(n=(0|P)>=450?450:P))>399?6:(0|n)>379?7:Ve,e[32526]=(0|F(n,e[R+72>>2]))/256,e[32527]=(0|F(n,e[R+76>>2]))/256,e[32528]=(0|F(n,e[R+80>>2]))/256,n>>>0>7||(R=n-1|0,e[32528]=R,e[32526]=n,e[32527]=R)),ae=ae+1|0,!(128&m););P=_[i>>1]}R=l<<5,Ve=t-1|0,n=7ⅇe:{t:{r:{a:{n:{c:{o:{f:{l:{i:switch(VA=d[17+(y+145840|0)|0],0|(m=4&P?2:VA)){case 2:break n;case 3:case 8:break c;case 5:break o;case 6:case 7:break f;case 4:break l;case 0:break i;default:break e}r=0;break e}if((0|(t=d[17+(145840+(Ve<<5)|0)|0]))!=6?(n=(0|t)==4?60:e[34063]>0||n>>>0<4?48:60,g[18+(y+145840|0)|0]=n):(n=25,g[18+(y+145840|0)|0]=25),!(16&d[0|be])|!d[20+(y+145840|0)|0]||(g[18+(y+145840|0)|0]=60,n=60),64&d[e[8+(y+145840|0)>>2]+6|0]&&(n=n+30|0,g[18+(y+145840|0)|0]=n),r=0,!(8&P))break e;g[18+(y+145840|0)|0]=d[be+164|0]+n;break e}!(i=d[20+(t=y+145840|0)|0])|1&g[e[t+8>>2]+7|0]&d[17+(145840+(Ve<<5)|0)|0]==2||(g[18+(y+145840|0)|0]=15),n=d[17+(R+145840|0)|0],8&d[e[8+(y+145840|0)>>2]+4|0]|n|d[17+(145840+(Ve<<5)|0)|0]!=8||(g[18+(y+145840|0)|0]=25),64&d[e[8+(145840+(t=Ve<<5)|0)>>2]+5|0]&&(g[18+(y+145840|0)|0]=30),!i|!(16&e[be>>2])||(g[18+(y+145840|0)|0]=30);f:if(d[20+(R+145840|0)|0]|!(32&d[e[8+(y+145840|0)>>2]+4|0])|(0|n)!=4)e[12+(y+145840|0)>>2]=256;else{if(i=y+145840|0,d[17+(t+145840|0)|0]==2){e[i+12>>2]=200;break f}e[i+12>>2]=150}if((0|m)!=7||(_e|=(0|n)==2,(254&d[17+(t+145840|0)|0])!=2))break e;e[12+(y+145840|0)>>2]=e[12+(t+145840|0)>>2]+255>>>1;break e}(254&(n=d[17+(145840+(t=Ve<<5)|0)|0]))==6|(0|n)==3|32&e[e[8+(t+145840|0)>>2]+4>>2]&&(g[18+(y+145840|0)|0]=30);o:if((254&(i=d[17+(R+145840|0)|0]))==2){_e=d[20+(R+145840|0)|0]&&(0|i)!=2?_e:1,g[18+(i=y+145840|0)|0]=40,ee=0;f:{switch(0|n){case 0:if((t=e[12+(t+145840|0)>>2])>>>0>39)break f;ee=40-t|0;break f;case 2:break f}if(d[20+(y+145840|0)|0])break o;ee=20;l:switch(n-3|0){case 1:if(ee=0,!(8&d[e[8+(t+145840|0)>>2]+4|0]))break f;break o;case 0:break f;case 5:break l;default:break o}ee=12}g[i+18|0]=ee}if(!(16&d[0|be])|!d[20+(y+145840|0)|0]||d[18+(t=y+145840|0)|0]>19)break e;g[t+18|0]=20;break e}i=d[be+296|0],m=n=y+145840|0,e[n+12>>2]=256,g[n+19|0]=i;c:if(d[n+20|0]){P=25;o:switch(d[17+(145840+(Ve<<5)|0)|0]-2|0){case 0:if(P=12,1&g[e[8+(y+145840|0)>>2]+7|0])break c;break;case 1:break o;default:break c}g[18+(y+145840|0)|0]=P}if((0|(R=d[17+(R+145840|0)|0]))==2){M=1;break e}if(g[22+(i=y+145840|0)|0]=r,(254&d[17+(145840+(n=Ve<<5)|0)|0])==2)break a;if(n=r,(0|(m=e[36423]))<=(0|t))break t;for(;;){if(d[17+(145840+(n=t<<5)|0)|0]==2){n=d[22+(n+145840|0)|0],g[i+22|0]=n;break t}if((0|m)==(0|(t=t+1|0)))break}break r}if(Wt=y+145840|0,yt=n^n>>>0<2,n=(qA=8&ee)?25:d[296+(yt+be|0)|0]-M|0,g[Wt+19|0]=n,(e[36423]-3|0)>(0|t)||(0|(m=255&n))<=(0|(n=e[be+52>>2]))||(g[Wt+19|0]=n),n=0,ee=0,!(P=d[i+52|0]))for(;m=e[i+40>>2],ee=d[i+49|0]==2?(~e[m+4>>2]>>>20&1)+ee|0:ee,n=d[m+10|0]==27?2:n,m=i,i=i+32|0,!(P=d[m+84|0]););m=Wt+96|0,t=145840+(ht=t+2<<5)|0,e[34063]=ee,w=d[e[i+40>>2]+10|0],R=R+145840|0,d[17+(ht+145840|0)|0]|d[e[R+8>>2]+10|0]!=23?(i=m,m=t,t=R):i=y+145968|0,R=d[e[m+8>>2]+15|0];n:if(ee)R=d[e[be+96>>2]+(d[e[t+8>>2]+15|0]+F(R,10)|0)|0],d[t+17|0]!=8|(254&d[m+17|0])!=4||(R=8&d[e[i+8>>2]+4|0]?R-15|0:R);else{if(ue=e[be+100>>2],ht=d[e[t+8>>2]+15|0],i=d[t+20|0],R=d[ue+(ht+F(i|d[m+20|0]?(0|R)==1:R,10)|0)|0],!i|!(32&d[0|be]))break n;R=d[1+(ue+F(ht,10)|0)|0]+R>>>1|0}i=P>>>1|0,ht=!ee,P=(0|(R=(0|F(e[130104+(ee?(0|ee)==1?4:8:0)>>2],R))/128|0))<=8?8:R;n:if((0|yt)!=7)qA&&(P=e[be+200>>2]+P|0);else{if(P=(R=e[be+200>>2])+P|0,!qA)break n;P=((0|R)/2|0)+P|0}R=i&ht|(0|w)==27,(i=_[304+(be+(yt<<1)|0)>>1])||(i=_[be+316>>
28101]),P=F(i<<16>>16,P),(ee=d[7+(i=y+145840|0)|0])&&(yt=d[e[144464+(ee<<2)>>2]+14|0])&&(P=(0|F(P,yt))/100|0),(R|(0|n)==2)==1&&(2097152&(n=e[be+12>>2])||(P=(0|F(262144&n?282:256+((280-(d[e[8+(y+145840|0)>>2]+14|0]<<1)|0)/3|0)&65535,P))/256|0)),yt=y+145840|0,n=F(e[32526],e[be+196>>2]),qA=(0|VA)!=2?256:(0|((0|n)>(0|P)?P:n))/128|0,e[yt+12>>2]=qA,(n=d[yt+16|0])>>>0>=19&&(bn(84371,28,cA),g[yt+16|0]=0,ee=d[i+7|0],n=0),P=n+1|0,(n=255&ee)?(zn(n,h+8|0),n=wo(e[h+132>>2])):n=e[129280+((255&P)<<2)>>2],R=y+145840|0,1&(M|_e)&&(i=145840+(y=Ve<<5)|0,M=d[0|n],n=d[R+21|0],n=((0|F(M,d[R+22|0]-n|0))/256|0)+n|0,g[i+22|0]=n,r=(n-(r=(0|n)==255?255:r)|0)>16?n-16|0:r,g[i+21|0]=r,i=0,(0|r)<(0|n)&&(g[yt+16|0]=P,i=2),e[12+(r=y+145840|0)>>2]=qA,g[r+16|0]=i,n=d[Wt+19|0],g[r+19|0]=d[r+17|0]!=3&&n>>>0>18?18:n),r=(0|VA)!=2,i=-2&(n=_[t>>1]),v[t>>1]=i;n:{c:{o:switch(d[t+17|0]-3|0){case 5:if(d[m+17|0]==2)break n;i=1|n;break c;case 0:break o;default:break n}if(v[t>>1]=1|n,d[m+17|0]!=2&&e[e[t+8>>2]>>2]!=12146)break n}v[t>>1]=i}r?(0|(t=r<<4))<=((n=d[R+22|0])-(i=d[R+21|0])|0)||(i=(0|(t=n-t|0))>0?t:0,g[R+21|0]=i):(n=d[R+22|0],i=d[R+21|0]),t=255&i,r=((0|F(d[e[129280+(d[yt+16|0]<<2)>>2]+127|0],n-t|0))/256|0)+t|0,M=0,_e=0;break e}i=e[12+(n+145840|0)>>2],e[m+12>>2]=i,(0|VA)==3&&(i=e[32526],e[m+12>>2]=i),n=r;a:switch(R-5|0){case 0:e[m+12>>2]=(F(i,160)>>>0)/100;break r;case 2:break a;default:break t}e[m+12>>2]=(F(i,120)>>>0)/100}n=r}_e=0,g[16+(t=y+145840|0)|0]=0,i=t,n=(t=255&n)-16|0,g[i+21|0]=t>>>0>=n>>>0?n:0}if(!(e[36423]>(0|l)))break}if(oe=h+160|0,15&(i=e[47197])|e[36456]){t=0,m=0,oe=n=oe-80|0;e:if((r=e[33222])||(e[33223]=500,r=aA(500),e[33222]=r,r)){if(!((e[36423]-2|0)<2)){for(t=i>>8,be=128&i?0:t,ee=t&i<<24>>31,R=2&i,i=n+32|1,y=1;;){if(tn(n,e[8+(l=145840+(Ve=y<<5)|0)>>2],l,R,n+72|0),t=n+32|0,(13&(r=d[l+20|0]))==1&&(g[n+32|0]=32,t=i),!be|(0|be)!=32&!!(0|r)|y>>>0<2||(or(n+76|0,n),e[n+76>>2]-880>>>0>4294967103||(t=_s(be,t)+t|0)),4&d[0|l]&&((r=d[3+(Ve+145840|0)|0])>>>0<2||(r=r>>>0>=5?5:r,r=R?r>>>0>3?712:716:g[r+94144|0],e[n+76>>2]=r,t=_s(r,t)+t|0)),P=0,e[n+72>>2]=0,d[0|(r=n)])for(;r=or(n+76|0,r)+r|0,e[n+72>>2]>>>P-1&1|!ee|(0|P)<=0||(M=e[n+76>>2])-880>>>0>4294967103||_i(M)&&(t=_s(ee,t)+t|0),P=P+1|0,t=_s(e[n+76>>2],t)+t|0,d[0|r];);if(d[e[l+8>>2]+10|0]!=21&&(8&(r=_[l>>1])&&(t=tn(t,e[36128],l,R,0),r=_[l>>1]),!(4&r)|d[17+(Ve+145840|0)|0]==2||(t=tn(t,e[36136],l,R,0)),(r=d[7+(Ve+145840|0)|0])&&(t=tn(t,e[144464+(r<<2)>>2],l,R,0))),(t=(l=t-(n+32|0)|0)+m|0)>>>0<re[33223])r=e[33222];else{if(r=t+500|0,e[33223]=r,!(r=ds(e[33222],r))){e[33223]=0,r=86135;break e}e[33222]=r}if(g[(P=l)+(l=n+32|0)|0]=0,BA(r+m|0,l),m=t,!((0|(y=y+1|0))<(e[36423]-2|0)))break}if(!r){r=86135;break e}}g[t+r|0]=0}else e[33223]=0,r=86135;oe=n+80|0,15&d[188788]&&(e[A>>2]=r,ss(e[47195],84367,A)),(t=e[36456])&&LA[0|t](r)}d[190280]?(e[36423]=0,t=1):(xt(0),(t=e[A+8>>2])?(oe=r=oe+-64|0,vr(r,t,60),vn(r,1),n=0,(t=eA(r,0))&&(n=t,d[202976]&&(n=eA(202976,2))),oe=r- -64|0,e[44468]=n):n=e[44468],t=1,n&&(r=e[32972],(n=aA(1344))&&(r=$A(n,r,1344),n=216192+(e[50758]<<4)|0,e[n>>2]=11,e[n+8>>2]=r,r=e[50758]+1|0,e[50758]=(0|r)<=169?r:0),e[44468]=0))}else t=0,g[190280]=0;else t=0,e[36423]=0,e[50758]=0,e[50757]=0;return oe=A+16|0,t}function Je(t,A){var r=0,n=0,i=0,l=0;r=1073741825;e:{t:{r:{a:{n:{c:{o:{f:{l:{i:{h:{b:{m:{C:{p:{x:{w:{B:{F:{Y:{R:{M:{y:{G:{S:{X:{L:{K:{N:{ae:{V:{Be:{W:{d:{g:{$:{k:{ee:{I:{s:{Ye:{q:{A:{ge:{H:{he:{pe:{Re:{le:{z:{xe:{E:{Me:{J:{me:{De:{je:{Ge:{_e:{Te:{Qe:{Pe:{Se:{be:{We:{te:{Ne:{Ue:{ze:{Oe:{D:{Ce:{Ke:{pt:{mt:{bt:{Ct:{It:{wt:{kt:{vt:{yt:{Et:{Bt:{xt:{Mt:{Dt:{Gt:{_t:{Tt:{Qt:{Pt:{St:{Nt:{zt:{Ot:{Ft:{Lt:{Vt:{Ht:{Yt:{Rt:{jt:switch(0|A){case 0:A=t-9>>>0<5?1073741825:0,A=(t=(0|t)==133)?1073741825:A;break S;case 1:u:{_:{T:{Q:{P:{O:{Z:{j:{Ae:{ne:{re:{ie:{ce:{ue:{if((0|(A=-256&t))<=2047){if(!A)break ue;if((0|A)==1536)break ce;if((0|A)!=1792||(r=0,n=1,(0|t)!=1807))break u;break A}if((0|A)<=69631){if((0|A)==2048)break ie;if((0|A)!=8192)break u;switch(r=131076,t-8204|0){case 1:break re;case 0:break A;default:break ne}}if((0|A)==69632)break Ae;if((0|A)!=917504)break u;switch(r=8388608,t-917505|0){case 62:break T;case 58:break Q;case 57:break P;case 45:break O;case 43:break Z;case 32:break j;case 0:break A;default:break _}}if(r=16,(0|t)!=173)break u;break A}if(r=0,n=1,t-1536>>>0<6)break A;n=(0|t)==1757,A=(t=(0|t)==1564)?2:0;break G}if(r=0,n=1,(0|t)!=2274)break u;break A}return fe=64,4}if(r=1073741826,(-2&t)==8206)break A;if(t-8234>>>0<5)return fe=0,2;if(r=128,t-8289>>>0<4||(r=2,t-8294>>>0<4))break A;if(r=8388608,t-8298>>>0>=6)break u;break A}A=!(t-69821&-17),t=0;break y}fe=536870976;break M}fe=268435520;break M}fe=-2147483584;break M}fe=134217792;break M}fe=67108928;break M}fe=1073741888;break M}if(r=131072,n=64,t-917536>>>0<96)break A}break s;case 2:u:{_:{T:{Q:{P:{O:{Z:{if((0|(A=-256&t))<=130303){if((0|A)<=127743){if((0|A)<=64767){if((0|A)<=11007){if((0|A)==8192)break Z;if((0|A)!=9216)break u;if(t-9255>>>0>=25)break O;break I}if((0|A)==11008)break P;if((0|A)!=11776||(r=-2147483648,t-11845>>>0>=59))break u;break A}if((0|A)<=126975){if((0|A)==64768)break Q;
2810if((0|A)!=65280||(r=4194304,t-65520>>>0>=9))break u;break A}if((0|A)==126976|(0|A)==127232|(0|A)==127488)break q;break u}if((0|A)<=129023){if((0|A)<=128255){if((0|A)==127744|(0|A)==128e3)break q;break u}if((0|A)==128256|(0|A)==128512|(0|A)==128768)break q;break u}if((0|A)<=129535){if((0|A)==129024|(0|A)==129280)break q;break u}if((0|A)==129536|(0|A)==129792|(0|A)==130048)break q;break u}if((0|A)<=919039){if((0|A)<=917759){if((0|A)<=130815){if((0|A)==130304)break q;if((0|A)!=130560)break u;break q}if((0|A)==130816)break q;if((0|A)!=917504)break u;if((-128&t)!=917632)break T;break ee}if((0|A)<=918271){if((0|A)==917760)break _;if(r=4194304,(0|A)!=918016)break u;break A}if((0|A)==918272|(0|A)==918528)break ee;if(r=4194304,(0|A)!=918784)break u;break A}if((0|A)<=920319){if((0|A)<=919551){if((0|A)==919040)break ee;if(r=4194304,(0|A)!=919296)break u;break A}if((0|A)==919552|(0|A)==919808)break ee;if(r=4194304,(0|A)!=920064)break u;break A}if((0|A)<=920831){if((0|A)==920320)break ee;if(r=4194304,(0|A)!=920576)break u;break A}if((0|A)==920832|(0|A)==921088)break ee;if(r=4194304,(0|A)!=921344)break u;break A}if(r=4194304,(0|t)!=8293)break u;break A}if(r=-2147483648,t-9291>>>0>=21)break u;break A}if((-16&t)==11248|t-11219>>>0<25|(0|t)==11209|t-11194>>>0<3||(0|(A=-2&t))==11124)break I;if(r=-2147483648,(0|A)!=11158)break u;break A}if(r=65536,t-64976>>>0>=32)break u;break A}if((0|t)==917504)break ee;if(r=4194304,t-917506>>>0>=30)break u;break A}if(r=4194304,t>>>0>917999)break A}A=(t=!(65534&~t))>>>16|0,t<<=16;break y;case 6:u:{_:{T:{Q:{P:{O:{Z:{j:{Ae:{ne:{re:{ie:{if((0|(A=-256&t))<=7679){if((0|A)<=767){if(!A)break ie;if((0|A)==256)break re;if((0|A)!=512)break s;if((0|t)!=585)break ne;break R}if((0|A)==768)break Ae;if((0|A)==1024)break j;if((0|A)!=7424||(r=16777216,(0|t)!=7574))break s;break A}if((0|A)<=119807){if((0|A)==7680)break Z;if((0|A)==8448)break O;if((0|A)!=65280||(r=256,t-65345>>>0>=6))break s;break A}if((0|A)<=120319){if((0|A)==119808)break P;if((0|A)!=120064)break s;if(t>>>0>=120070)break Q;break k}if((0|A)==120320)break T;if((0|A)!=120576)break s;if(t>>>0>=120597)break _;break k}if(r=768,t-97>>>0<6)break A;if(r=16777216,t-105>>>0>=2)break s;break A}r=(A=(0|t)==329)>>>9|0,A=(t=(0|t)==303)?16777216:A<<23;break Y}if((0|t)==616)break R;if(r=16777216,(0|t)!=669)break s;break A}switch(r=128,t-976|0){case 35:break u;case 0:case 1:case 2:case 5:case 32:case 33:break A}if((-2&t)!=1012)break s;break A}n=(t=!(t-1110&-3))>>>8|0,t<<=24;break F}n=(A=(0|t)==7883)>>>8|0,A=(t=(0|t)==7725)?16777216:A<<24;break G}if(t-8458>>>0<10)break k;if((A=t-8495|0)>>>0<11)break Rt;break ge}if((0|(A=-2&t))==119842)break $;if(t-119808>>>0<85)break k;if(t-119894>>>0<2|t-119946>>>0<2)break $;if((0|t)==119995|t-119896>>>0<69|t-119982>>>0<12)break k;if((0|A)==119998)break $;if(t-119997>>>0<7)break k;if(r=16777344,(0|A)==120050)break A;if(r=128,t>>>0<=120004)break s;break A}if((-2&t)==120102)break $;if(t-120094>>>0<28)break k;Q:{if((0|t)<=120257){if(t-120154>>>0<2)break $;if(r=16777344,t-120206>>>0>=2)break Q;break A}if(t-120258>>>0<2)break $;if(r=16777344,t-120310>>>0<2)break A}if(r=128,t>>>0<=120145)break s;break A}if(t-120362>>>0<2|t-120414>>>0<2)break $;if(r=16777344,t-120466>>>0<2)break A;if(t-120540>>>0<31|t>>>0>120571|t>>>0<120486)break k;if(r=128,t-120514>>>0>=25)break s;break A}if(t-120772>>>0<8|t-120746>>>0<25|t-120714>>>0<31|t-120688>>>0<25||(0|t)!=120597&t>>>0<120629|t-120656>>>0<31)break k;if(r=128,t-120630>>>0<25)break A;break s}break R;case 7:u:{_:{T:{Q:{P:{O:{Z:{j:{Ae:{ne:{re:{ie:{ce:{ue:{v:{oe:{se:{U:{fe:{de:{Ie:{we:{if((0|(A=-256&t))<=11263){if((0|A)<=3583){if((0|A)<=1535){if((0|A)==512)break we;if((0|A)==768)break Ie;if((0|A)!=1280||(r=4096,(0|t)!=1369))break s;break A}if((0|A)==1536)break de;if((0|A)==1792)break fe;if((0|A)!=2304||(r=4096,(0|t)!=2417))break s;break A}if((0|A)<=7167){if((0|A)==3584)break U;if((0|A)==6144)break se;if((0|A)!=6656||(r=8192,(0|t)!=6823))break s;break A}if((0|A)==7168)break oe;if((0|A)==7424)break v;if((0|A)!=8192)break s;if(r=16793600,!(A=t-8305|0))break A;if((0|A)==14)break ue;break ce}if((0|A)<=43263){if((0|A)<=40959){if((0|A)==11264)break ie;if((0|A)==11776)break re;if((0|A)!=12288)break s;switch(r=8192,t-12293|0){case 1:case 2:case 3:case 4:case 5:case
28106:case 7:case 8:case 9:case 10:case 11:case 12:case 13:case 14:case 15:case 16:case 17:case 18:case 19:case 20:case 21:case 22:case 23:case 24:case 25:case 26:case 27:case 28:case 29:case 30:case 31:case 32:case 33:case 34:case 35:case 36:case 37:case 38:case 39:case 40:case 41:case 42:case 43:break Ae;case 0:case 44:case 45:case 46:case 47:case 48:break A;default:break ne}}if((0|A)==40960)break j;if((0|A)==42496)break Z;if((0|A)!=42752)break s;if(t-42775>>>0>=9)break O;break g}if((0|A)<=65279){if((0|A)==43264)break P;if((0|A)==43520)break Q;if((0|A)!=43776||(r=20480,(-4&t)!=43868))break s;break A}if((0|A)==65280)break T;if((0|A)==92928)break _;if((0|A)!=93952)break s;if(r=4096,t-94099>>>0<13)break A;if(r=8192,(-2&t)!=94176)break s;break A}if(r=16797696,(0|t)==690)break A;if(t-688>>>0<9)return fe=0,20480;if(t-697>>>0<7)break g;if((0|(A=-2&t))==704)return fe=0,20480;if(r=4096,t-710>>>0<10||(r=12288,(0|A)==720)||(r=20480,t-736>>>0<5))break A;r=(t=(-3&t)==748)>>>20|0,t<<=12;break B}A=(0|t)==890?20480:0,A=(t=(0|t)==884)?4096:A;break S}if(r=8192,(0|t)==1600)break A;if(r=4096,t-1765>>>0>=2)break s;break A}if(r=4096,(-2&t)==2036)break A;if(r=8192,(0|t)!=2042)break s;break A}A=(t=!(t-3654&-129))>>>19|0,t<<=13;break y}if(r=8192,(0|t)!=6211)break s;break A}if(r=12288,(0|t)==7291)break A;if(r=4096,t-7288>>>0>=6)break s;break A}if(r=16797696,(0|t)==7522||(r=20480,t-7468>>>0<63))break A;switch(r=16384,t-7588|0){default:if((0|t)==7544)break A;case 1:case 2:case 3:if(t-7579>>>0>=37)break s;break A;case 0:case 4:}return fe=0,16793600}return fe=0,16384}if(r=16384,t-8336>>>0>=13)break s;break A}A=(r=(0|t)==11389)>>>18|0,r=(t=(0|t)==11388)?16793600:r<<14;break w}if(r=-2147479552,(0|t)!=11823)break s;break A}if(t-12445>>>0<2)break A;if((0|t)==12540)break u}if(t-12541>>>0>=2)break s;break A}if(r=8192,(0|t)!=40981)break s;break A}if((0|t)==42508)break x;if((0|t)==42623)break g;if(r=20480,(-2&t)!=42652)break s;break A}if((0|t)==42864)return fe=0,16384;if((0|t)==42888)break g;if(r=20480,(-2&t)!=43e3)break s;break A}r=(A=(0|t)==43494)>>>19|0,A=(t=(0|t)==43471)?8192:A<<13;break Y}if((0|t)==43632||(0|t)==43741)break x;if(r=8192,t-43763>>>0>=2)break s;break A}if(r=12288,(0|t)==65392)break A;if(r=135168,(-2&t)!=65438)break s;break A}if(r=8192,(-2&t)==92994)break A;break s}return fe=0,12288;case 8:r=128;u:{_:{T:{Q:{P:{O:{Z:{j:{Ae:{if((0|(A=-256&t))<=12543){if((0|A)<=5887){if((0|A)<=3583){if(!A)break Ae;if((0|A)!=1536||(A=8388608,(0|t)!=1651))break E;break le}if((0|A)==3584)break j;if((0|A)!=4352||(r=4194304,t-4447>>>0>=2))break E;break z}if((0|A)<=8447){if((0|A)==5888)break Z;if((0|A)!=6400||(A=t-6581|0)>>>0>=6)break E;r=e[(A=81432+(A<<3)|0)>>2],n=e[A+4>>2];break z}if((0|A)==8448)break O;if((0|A)!=12288||(A=2048,(0|t)!=12294))break E;break le}if((0|A)<=68863){if((0|A)<=63999){if((0|A)==12544)break P;if((0|A)!=43520)break E;if((A=t-43701|0)>>>0<8)break _;break xe}if((0|A)==64e3)break u;if((0|A)!=65280||(A=4194304,(0|t)!=65440))break E;break le}if((0|A)<=100095){if((0|A)==68864)break Q;if((0|A)!=70400||(r=8192,(0|t)!=70493))break E;break pe}if((0|A)==100096)break T;if((0|A)!=126464)break E;break z}n=(A=!(t-170&-17))>>>18|0,r=A<<14;break z}if(r=33554432,t-3648>>>0<5)break z;if(A=0,l=33554432,(0|t)==3759)break le;if(t-3776>>>0>=5)break E;break z}if(r=8388608,t-6051>>>0>=2)break E;break z}if(t-8501>>>0>=4)break E;break z}if(A=4194304,(0|t)!=12644)break E;break le}if(r=4096,(-2&t)!=68898)break E;break z}if(r=2048,t-100333>>>0<5)break z;break E}if(r=33554432,!(211>>>A&1))break xe;break z}if((A=t-64014|0)>>>0>=28)break E;r=e[(A=81480+(A<<3)|0)>>2],n=e[A+4>>2];break z;case 10:u:{_:{T:{Q:{P:{O:{if((0|(A=-256&t))<=119807){if((0|A)<=8447){if(!A)break O;if((0|A)!=768)break s;switch(r=128,t-976|0){case 0:case 1:case 2:case 36:case 37:break A;default:break s}}if((0|A)==8448)break P;if((0|A)!=65280||(r=256,t-65313>>>0>=6))break s;break A}if((0|A)<=120319){if((0|A)==119808)break Q;
2810if((0|A)!=120064)break s;if(t>>>0>=120070)break T;break k}if((0|A)==120320)break _;if((0|A)!=120576)break s;if(t-120772>>>0>=8)break u;break k}if(r=768,t-65>>>0>=6)break s;break A}switch(r=128,t-8450|0){case 0:case 5:break A}if(t-8458>>>0<10)break k;if((A=t-8469|0)>>>0<20)break Yt;if((-4&t)==8508)break A;break Me}if(t-119982>>>0<12|t>>>0>120004|t-119977>>>0<4|t-119973>>>0<2||(0|t)==119970|(-2&t)==119966|t-119808>>>0<85)break k;if(r=128,t-119894>>>0>=71)break s;break A}if(t-120138>>>0<7|t>>>0>120145|(0|t)==120134|t-120128>>>0<5||t-120123>>>0<4|t-120094>>>0<28|(0|t)!=120070&t>>>0<120075|t-120086>>>0<7)break k;if(r=128,t-120077>>>0>=8)break s;break A}if(t-120540>>>0<31|t>>>0<120486)break k;if(r=128,t-120488>>>0>=25)break s;break A}if(t-120714>>>0<31|t-120598>>>0<31)break k;if(r=128,t-120656>>>0<31)break A;break s;case 11:u:{_:{T:{Q:{P:{O:{Z:{j:{Ae:{ne:{re:{ie:{ce:{ue:{v:{oe:{se:{if((0|(A=-256&t))<=43263){if((0|A)<=3839){if((0|A)<=3071){if((0|A)==2304)break se;if((0|A)!=2816)break d;if((0|t)>3005)break v;if((0|t)!=2878)break oe;return fe=0,132096}if((0|A)==3072)break ue;if((0|A)!=3328)break d;switch(r=132096,t-3535|0){case 0:case 16:break A;case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 10:case 11:case 12:case 13:case 14:case 15:break d;default:break ce}}if((0|A)<=6911){if((0|A)==3840)break ie;if((0|A)!=4096)break d;if(r=1024,t-4139>>>0<2)break A;switch(t-4145|0){case 0:case 7:case 10:case 11:case 37:case 38:case 49:case 54:case 55:case 82:case 83:break A;case 86:case 87:case 88:case 89:case 90:case 91:case 94:case 105:case 106:break u;default:break re}}if((0|A)==6912)break ne;if((0|A)==7168)break Ae;if((0|A)!=12288||(r=135168,(-2&t)!=12334))break d;break A}if((0|A)<=70399){if((0|A)<=43775){if((0|A)==43264)break j;if((0|A)!=43520)break d;return fe=0,t-43643&-3?1024:4096}if((0|A)==43776)break Z;if((0|A)==69888)break O;if((0|A)!=70144||(r=4096,(0|t)!=70197))break d;break A}if((0|A)<=70911){if((0|A)==70400)break P;if((0|A)!=70656)break d;A=(0|t)==70845?132096:1024,A=(t=(0|t)==70832)?132096:A;break S}if((0|A)==70912)break Q;if((0|A)==71168)break T;if((0|A)!=119040)break d;switch(r=131072,t-119141|0){case 8:break g;case 1:break s;case 0:break A;default:break _}}A=(0|t)==2519?132096:1024,A=(t=(0|t)==2494)?132096:A;break S}if(r=132096,(0|t)!=2903)break d;break A}if((0|t)==3006)return fe=0,132096;if(r=132096,(0|t)!=3031)break d;break A}if((t=t-3266|0)>>>0>20||(r=132096,!(1<<t&1572865)))break d;break A}if((0|t)==3390)break A;if((0|t)!=3415)break d;break A}if(r=4096,(-2&t)!=3902)break d;break A}r=(t=(0|t)==4252)>>>22|0,t<<=10;break B}switch(r=1024,t-6965|0){default:if((0|t)==6916)break A;break;case 0:case 6:break A;case 1:case 2:case 3:case 4:case 5:}if(t-6973>>>0<5)break A;switch(t-6979|0){case 1:break g;case 0:break A}if((0|t)==7042|(0|t)==7073|(-2&t)==7078)break A;if((0|t)==7082)break g;if((0|t)==7143|t-7146>>>0<3)break A;r=(t=(0|t)==7150)>>>22|0,t<<=10;break B}A=(0|t)==7415?4096:1024,A=(t=(0|t)==7393)?4096:A;break S}A=(0|t)==43456?4096:1024,A=(t=(0|t)==43347)?4096:A;break S}if(r=4096,(0|t)!=44012)break d;break A}if(r=4096,(0|t)!=70080)break d;break A}r=132096;P:switch(t-70462|0){case 0:case 25:break A;case 15:break P;default:break d}break g}if(r=132096,(0|t)!=71087)break d;break A}if(r=4096,(0|t)!=71350)break d;break A}if(r=135168,t-119150>>>0<5)break A;break d}break g;case 12:A=((0|t)==8419)<<6,t=0;break y;case 13:u:{_:{T:{Q:{P:{O:{Z:{j:{Ae:{ne:{re:{ie:{ce:{ue:{v:{oe:{se:{U:{fe:{de:{Ie:{we:{Fe:{ke:{Le:{Ze:{Xe:{qe:{Je:{$e:{et:{tt:{At:{rt:{st:{ve:{at:{nt:{it:{ot:{lt:{Ve:{ye:{He:{ct:{ut:{ft:{Ee:{dt:{gt:{ht:{if((0|(A=-256&t))<=43775){if((0|A)<=5887){if((0|A)<=2559){if((0|A)<=1535){if((0|A)==768)break ht;if((0|A)==1024)break gt;if((0|A)!=1280)break s;if(t-1425>>>0>=17)break dt;break g}if((0|A)<=2047){if((0|A)==1536)break Ee;if((0|A)!=1792)break s;if(r=1024,(0|t)==1809)break A;if((-16&t)!=1840)break ft;return fe=0,5120}if((0|A)==2048)break ut;if((0|A)!=2304)break s;if(t>>>0>=2307)break ct;break d}if((0|A)<=3583){if((0|A)<=3071){if((0|A)==2560)break He;if((0|A)!=2816)break s;switch(r=1024,t-2876|0){case 0:break g;case 3:break A;case 1:case 2:break Ve;default:break ye}}
2810if((0|A)==3072)break lt;if((0|A)!=3328)break s;if((0|(A=-2&t))!=3328)break ot;break d}if((0|A)<=4095){if((0|A)==3584)break it;if((0|A)!=3840)break s;if((0|(A=-2&t))!=3864)break nt;break g}if((0|A)==4096)break at;if((0|A)!=4864||(r=1024,(0|t)!=4959))break s;break A}if((0|A)<=8191){if((0|A)<=6655){if((0|A)==5888)break ve;if((0|A)==6144)break st;if((0|A)!=6400)break s;if((A=t-6432|0)>>>0<=18&&(r=1024,1<<A&262535))break A;if(r=4096,t-6457>>>0>=3)break s;break A}if((0|A)<=7167){if((0|A)==6656)break rt;if((0|A)!=6912)break s;if(r=1024,(-4&t)==6912)break A;if((0|t)!=6964)break At;break g}if((0|A)==7168)break tt;if((0|A)!=7424)break s;if(r=4096,t-7620>>>0<12||(r=1024,t-7655>>>0<14))break A;if((t=t-7669|0)>>>0>=11)break s;A=e[(t=82104+(t<<3)|0)>>2];break p}if((0|A)<=42495){if((0|A)<=11519){if((0|A)==8192)break et;if((0|A)!=11264||(r=4096,t-11503>>>0>=3))break s;break A}if((0|A)==11520)break $e;if((0|A)!=12288)break s;if(t-12330>>>0>=4)break Je;break g}if((0|A)<=43263){if((0|A)==42496)break qe;if((0|A)!=43008)break s;if(t-43045>>>0>=2)break Xe;break d}if((0|A)==43264)break Ze;if((0|A)!=43520)break s;switch(r=1024,t-43561|0){case 83:case 150:case 152:break g;case 0:case 1:case 2:case 3:case 4:case 5:case 8:case 9:case 12:case 13:case 26:case 35:case 135:case 137:case 138:case 139:case 142:case 143:case 149:break A;default:break Le}}if((0|A)<=71423){if((0|A)<=69375){if((0|A)<=66047){if((0|A)==43776)break ke;if((0|A)==64256)break Fe;if((0|A)!=65024)break s;if(r=536870912,t-65024>>>0<15||(n=64,(0|t)==65039))break A;if(r=4096,n=0,(-16&t)!=65056)break s;break A}if((0|A)<=68095){if((0|A)==66048)break we;if((0|A)!=66304||(r=1024,t-66422>>>0>=5))break s;break A}if((0|A)==68096)break Ie;if((0|A)!=68864||(r=5120,(-4&t)!=68900))break s;break A}if((0|A)<=70399){if((0|A)<=69887){if((0|A)==69376)break de;if((0|A)!=69632)break s;if(t-69688>>>0>=14)break fe;break d}if((0|A)==69888)break U;if((0|A)!=70144)break s;if(r=1024,t-70191>>>0<3)break A;switch(t-70196|0){case 2:break g;case 0:case 3:case 10:break A;case 1:case 4:case 5:case 6:case 7:case 8:case 9:break oe;default:break se}}if((0|A)<=70911){if((0|A)==70400)break v;if((0|A)!=70656)break s;if((-8&t)!=70712)break ue;break d}if((0|A)==70912)break ce;if((0|A)!=71168)break s;if(r=1024,t-71219>>>0<8)break A;switch(t-71229|0){case 2:break g;case 0:case 3:break A;case 1:break re;default:break ie}}if((0|A)<=92927){if((0|A)<=72703){if((0|A)==71424)break zt;if((0|A)==71680)break ne;if((0|A)!=72192)break s;if(t-72193>>>0>=10)break Ae;break d}if((0|A)<=73215){if((0|A)==72704)break j;if((0|A)!=72960)break s;if((A=t-73009|0)>>>0<19)break St;break J}if((0|A)==73216)break Z;if((0|A)!=92672||(r=4096,t-92912>>>0>=5))break s;break A}if((0|A)<=122879){if((0|A)<=113663){if((0|A)==92928)break O;if((0|A)!=93952||(r=4096,t-94095>>>0>=4))break s;break A}if((0|A)==113664)break P;if((0|A)!=119040)break s;switch(r=4096,t-119143|0){case 0:case 1:case 2:case 20:case 21:case 22:case 23:case 24:case 25:case 26:case 27:case 30:case 31:case 32:case 33:case 34:case 35:case 36:case 67:case 68:case 69:case 70:break A;default:break s}}if((0|A)<=125183){if((0|A)==122880)break Q;if((0|A)!=124928||(r=4096,t-125136>>>0>=7))break s;break A}if((0|A)==125184)break T;if((0|A)!=917760||(r=536870912,t-917760>>>0>=240))break s;break A}if(t-768>>>0<69)break g;if(r=21504,(0|t)==837)break A;if(t-838>>>0<9)break g;if(r=4194304,(0|t)==847)break A;if((-8&t)==848)break g;if(r=4096,t-861>>>0>=6)break s;break A}if(r=4096,t-1155>>>0>=5)break s;break A}if(r=4096,t-1443>>>0<13||(r=5120,t-1456>>>0<14))break A;if((t=t-1471|0)>>>0>=9)break s;A=e[(t=81944+(t<<3)|0)>>2];break p}if(t-1552>>>0<11)break d;if(r=5120,t-1611>>>0<8)break A;if(t-1619>>>0<4)break d;switch(t-1623|0){case 1:break g;case 0:break A;case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 25:case 127:case 128:case 129:case 130:case 131:case 132:case 133:break d}if(r=4096,t-1759>>>0<2)break A;if((A=t-1761|0)>>>0<8)break Ht;break me}if(t-1856>>>0<11)break g;if(r=5120,t-1958>>>0<11)break A;if(r=4096,t-2027>>>0>=9)break s;break A}switch(r=1024,(-2&t)-2070|0){case 2:break g;case 0:break A}if(t-2260>>>
28100<12|t-2089>>>0<4|t-2075>>>0<9|t-2085>>>0<3)break d;if(t-2275>>>0<7)return fe=0,5120;if(r=4096,t-2282>>>0<6||(r=5120,t-2288>>>0<15))break A;if(r=1024,(0|t)!=2303)break s;break A}switch(r=1024,t-2362|0){case 2:break g;case 0:break A}if(t-2369>>>0<8)break d;switch(r=4096,t-2381|0){case 0:case 4:case 5:case 6:case 7:case 111:break A;case 8:case 9:case 10:case 21:case 22:case 52:break d}if(t-2497>>>0<4)break d;if((0|t)==2509)break A;if(r=1024,(-2&t)!=2530)break s;break A}if(t-2561>>>0<2)break d;switch(r=4096,t-2620|0){case 0:case 17:case 128:case 145:break A;case 5:case 6:case 11:case 12:case 15:case 16:case 21:case 52:case 53:case 57:case 69:case 70:case 133:case 134:case 135:case 136:case 137:case 139:case 140:case 166:case 167:case 190:case 191:case 192:break d}if(t-2813>>>0>=3)break s;break A}if((0|t)==2817)break d}if(t-2881>>>0<4)break d;switch(r=4096,t-2893|0){case 0:break A;case 9:break d}if((-2&t)==2914)break d;switch(r=1024,t-3008|0){default:if((0|t)!=2946)break s;break A;case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 10:case 11:case 12:break s;case 0:break A;case 13:}break g}switch(r=1024,t-3072|0){case 77:case 188:case 205:break g;case 0:case 62:case 63:case 64:case 70:case 71:case 72:case 74:case 75:case 76:case 85:case 86:case 98:case 99:case 129:case 191:case 198:case 204:break A}if((-2&t)!=3298)break s;break A}if(r=4096,t-3387>>>0<2)break A;if(t-3393>>>0<4)break d;if((0|t)==3405)break A;if((0|A)==3426)break d;switch(t-3530|0){case 0:break A;case 8:case 9:case 10:case 12:break d;default:break s}}if((A=t-3633|0)>>>0<10)break Vt;break De}if((r=t-3893|0)>>>0>4|!(1<<r&21))break je;break g}switch(r=1024,t-4141|0){case 10:case 12:case 13:break g;case 0:case 1:case 2:case 3:case 5:case 6:case 7:case 8:case 9:case 16:case 17:case 43:case 44:case 49:case 50:case 51:case 68:case 69:case 70:case 71:case 85:case 88:case 89:break A}A=(r=(0|t)==4253)>>>22|0,r=(t=(0|t)==4237)?4096:r<<10;break w}r=1024;ve:{switch(t-5906|0){case 0:case 1:case 32:case 33:break A;case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 10:case 11:case 12:case 13:case 14:case 15:case 16:case 17:case 18:case 19:case 20:case 21:case 22:case 23:case 24:case 25:case 26:case 27:case 28:case 29:case 30:case 31:break ve}switch(t-5970|0){case 0:case 1:case 32:case 33:break A;default:break ve}}if(r=4194304,(-2&t)==6068)break A;if((A=t-6071|0)>>>0<16)break Lt;break Ge}if(r=536870912,t-6155>>>0<3||(r=67109888,t-6277>>>0<2))break A;if(r=1024,(0|t)!=6313)break s;break A}if(t-6679>>>0<2)break d;switch(r=1024,t-6683|0){case 0:case 59:case 61:case 62:case 63:case 64:case 65:case 66:case
281067:case 71:case 74:case 75:case 76:case 77:case 78:case 79:case 80:case 81:case 88:case 89:break A}if(r=4096,t-6832>>>0<14)break A;if((t=t-6773|0)>>>0>=11)break s;A=e[(t=82016+(t<<3)|0)>>2];break p}if(t-6966>>>0<5)break A;switch(t-6972|0){case 0:case 6:break A}if(t-7019>>>0<9)break g;switch(t-7040|0){case 43:break g;case 0:case 1:case 34:case 35:case 36:case 37:case 40:case 41:case 44:case 45:case 104:case 105:case 109:case 111:case 112:case 113:break A;default:break s}}if(r=1024,t-7212>>>0<8)break A;switch(r=12288,t-7222|0){case 1:break g;case 0:break A}if(t-7380>>>0<13)break g;switch(r=4096,t-7376|0){case 0:case 1:case 2:case 18:case 19:case 20:case 21:case 22:case 23:case 24:case 29:case 36:break A}if((-2&t)!=7416)break s;break A}if(r=128,t-8400>>>0<13)break A;if((t=t-8417|0)>>>0>=15)break s;A=e[(t=82192+(t<<3)|0)>>2];break p}if(r=1024,(-32&t)!=11744)break s;break A}if(r=4096,t-12441>>>0>=2)break s;break A}if((0|t)==42607)break g;if(t-42612>>>0<8)break d;if((0|(t&=-2))==42620)break g;if((0|t)==42654)break u;if(r=4096,(0|t)!=42736)break s;break A}switch(r=4096,t-43204|0){case 0:break A;case 1:break d}if(t-43232>>>0>=18)break s;break A}if(t-43302>>>0<5)break d;if(t-43307>>>0<3)break g;if(t-43335>>>0<11|t-43392>>>0<3)break d;if(r=4096,(0|t)==43443||(r=1024,t-43446>>>0<4))break A;n=(A=(0|t)==43493)>>>20|0,A=(t=(0|t)==43452)?1024:A<<12;break G}if((-2&t)==43756)break A;if(r=4096,(0|t)!=43766)break s;break A}r=1024;ke:switch(t-44005|0){case 0:case 3:break A;case 8:break ke;default:break s}break g}if(r=5120,(0|t)!=64286)break s;break A}if(r=4096,(0|t)!=66272)break s;break A}if((-4&t)==68108)break d;if((A=t-68097|0)>>>0<6)break Ft;break _e}if(r=4096,t-69446>>>0>=11)break s;break A}if(t-69811>>>0<4)break d;if(r=1024,(0|t)==69633)break A;if(r=4096,t-69817>>>0>=2)break s;break A}switch(r=1024,t-69888|0){case 51:case 52:case 115:break g;case 0:case 1:case 2:case 39:case 40:case 41:case 42:case 43:case 45:case 46:case 47:case 48:case 49:case 50:break A}if((-2&t)==70016|t-70070>>>0<9)break A;if(r=4096,t-70090>>>0>=3)break s;break A}if((0|t)==70367)break A}if(t-70371>>>0<6)break A;if(r=4096,t-70377>>>0>=2)break s;break A}if((-2&t)==70400)break d;r=4096;v:switch(t-70460|0){case 0:case 42:case 43:case 44:case 45:case 46:case 47:case 48:case 52:case 53:case 54:case 55:case 56:break A;case 4:break v;default:break s}break d}if((0|t)==70722)break g;if(t-70723>>>0<2)break d;if((A=t-70835|0)>>>0<=13)break _;break Te}if((A=t-71090|0)>>>0<12)break Ot;break Qe}switch(t-71339|0){case 0:case 2:break A;default:break re}}if(t-71344>>>0<6)break A;if(r=4096,(0|t)!=71351)break s;break A}if(r=1024,t-71727>>>0<10)break A;if(r=4096,t-71737>>>0>=2)break s;break A}if((0|t)==72244)break g;if(t-72245>>>0<10)break d;if(r=4096,(0|t)==72263)break A;if(t-72273>>>0<11)break d;if(r=1024,t-72330>>>0<13)break A;r=(A=(0|t)==72345)>>>20|0,A=(t=(0|t)==72344)?8192:A<<12;break Y}if((A=t-72752|0)>>>0<16)break Nt;break Pe}if(r=1024,t-73459>>>0>=2)break s;break A}if(r=1024,t-92976>>>0>=7)break s;break A}if(r=1024,(0|t)!=113822)break s;break A}if(r=1024,t-122888>>>0<17)break A;if((t=t-122880|0)>>>0>=43)break s;A=e[(t=82816+(t<<3)|0)>>2];break p}if(r=12288,t-125252>>>0<3||(r=1024,(0|t)==125255)||(r=4096,t-125256>>>0<3))break A;break s}if(!(1<<A&12479))break Te;break d}break d;case 14:u:{if((0|(A=-256&t))!=120576){if((0|A)!=65280){if(A||(r=768,n=66,t-48>>>0>=10))break u;break A}if(r=256,t-65296>>>0>=10)break u;break A}if(r=128,t-120782>>>0<50)break A}break s;case 15:u:{if((0|(A=-256&t))!=12288){if((0|A)!=8448)break u;A=(t=(0|(r=-16&t))==8560)>>>18|0,n=t<<14,r=(t=(0|r)==8544)?32768:n;break w}if(t-12321>>>0<9||t-12344>>>0<3)return fe=0,2048;if(r=2048,(0|t)==12295)break A}break s;case 16:u:{_:{T:{if((0|(A=-256&t))<=9215){if((0|A)==4864)break T;if((0|A)!=6400||(r=134217728,(0|t)!=6618))break u;break A}if((0|A)==9216)break _;if((0|A)!=127232)break u;if(r=0,n=-2147483648,(0|t)==127232)break A;if(n=268435456,t-127233>>>0>=10)break u;break A}if(r=134217728,t-4969>>>0>=9)break u;break A}if(r=0,n=-2147483648,t-9352>>>0<20)break A}break s;case 17:n=(t=(0|t)==8256)>>>25|0,t<<=7;break F;
2810case 18:u:{_:{T:{Q:{if((0|(A=-256&t))<=11775){if((0|A)<=6143){if(r=-2147483624,!A)break A;if((0|A)!=1280||(r=24,(0|t)!=1418))break u;break A}if((0|A)==6144)break Q;if((0|A)!=8192)break u;if(r=-2147483624,(-2&t)==8208)break A;t=t-8211>>>0<2,A=-2147483640;break C}if((0|A)<=65023){if((0|A)==11776)break T;if((0|A)!=12288)break u;A=(0|t)==12336,r=(t=(0|t)==12316)||A?-2147483640:8,fe=t?0:A?130:0;break m}if((0|A)==65024)break _;if((0|A)!=65280||(r=24,(0|t)!=65293))break u;break A}if(r=24,(0|t)!=6150)break u;break A}if(r=-2147483624,(0|t)==11799)break A;t=(-2&t)==11834,A=-2147483640;break C}if(r=8,n=8388608,t-65073>>>0<2||(r=152,n=0,(0|t)==65123))break A}return fe=0,8;case 19:u:{_:{T:{Q:{P:{O:{if((0|(A=-256&t))<=11775){if((0|A)<=8959){if(A)break O;break I}if((0|A)==8960)break P;if((0|A)==9984)break Q;if((0|A)!=10496)break s;break W}if((0|A)<=64767){if(r=-2147483648,(0|A)==11776)break A;if((0|A)!=12288)break s;switch(r=-2147483616,t-12301|0){case 0:case 2:break A;default:break T}}if((0|A)==64768)break _;if((0|A)==65024)break u;if((0|A)!=65280)break s;if(r=32,(0|t)==65379)break A;break s}if((0|A)!=8192)break s;switch(t-8318|0){default:if(r=-2147483648,(0|t)!=8262)break s;break A;case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 10:case 11:case 12:case 13:case 14:case 15:break s;case 0:case 16:}break k}switch(r=-2147483520,t-8969|0){case 1:break s;case 0:case 2:break A}if((0|t)!=9002)break s;return fe=0,-2139095040}if(r=-2147483520,(0|t)==10182)break A;break Be}return fe=0,(-2&t)==12318?-2147483616:-2147483648}if(r=-2147483648,(0|t)!=64830)break s;break A}n=(t=!(t-65090&-3))>>>27|0,t<<=5;break F;case 20:case 21:if(!(t&=-256))break b;if(r=-2147483616,(0|t)==8192)break A;break Ye;case 22:u:{_:{T:{Q:{P:{O:{Z:{j:{Ae:{ne:{re:{ie:{ce:{ue:{v:{oe:{se:{U:{fe:{de:{Ie:{we:{Fe:{ke:{Le:{Ze:{Xe:{qe:{Je:{$e:{et:{tt:{At:{rt:{st:{ve:{at:{nt:{it:{ot:{lt:{Ve:{ye:{He:{ct:{ut:{ft:{Ee:{if((0|(A=-256&t))<=43519){if((0|A)<=5887){if((0|A)<=2303){if((0|A)<=1535){if(!A)break Ee;if((0|A)==768)break ft;if((0|A)!=1280)break s;if(r=0,n=538968064,t-1371>>>0<2)break A;switch(n=268435456,t-1373|0){case 0:break A;case 1:break ct;default:break ut}}if((0|A)==1536)break ye;if((0|A)==1792)break Ve;if((0|A)!=2048)break s;if((A=t-2103|0)>>>0<8)break Qt;break Se}if((0|A)<=3839){if((0|A)==2304)break lt;if((0|A)==3328)break ot;if((0|A)!=3584||(r=64,(-2&t)!=3674))break s;break A}if((0|A)<=4863){if((0|A)==3840)break it;if((0|A)!=4096)break s;if(r=268435520,(-2&t)==4170)break A;if(r=0,n=16777216,(0|t)!=4347)break s;break A}if((0|A)==4864)break nt;if((0|A)!=5632)break s;switch(r=64,t-5741|0){case 1:break n;case 0:break A;default:break at}}if((0|A)<=11263){if((0|A)<=6655){if((0|A)==5888)break ve;if((0|A)==6144)break _t;if((0|A)!=6400)break s;A=(0|t)==6469,r=(t=(0|t)==6468)||A?268435520:0,fe=t?536870912:A?1073741824:0;break m}if((0|A)<=7167){if((0|A)==6656)break st;if((0|A)!=6912)break s;if((0|(A=-2&t))!=7002)break rt;break V}if((0|A)==7168)break At;if((0|A)!=8192)break s;switch(r=-2147483520,t-8214|0){case 1:break I;case 0:break A;case 16:break tt;default:break et}}if((0|A)<=41983){if((0|A)==11264)break $e;if((0|A)==11776)break Je;if((0|A)!=12288)break s;switch(r=-2147483584,n=272629760,t-12289|0){case 2:break I;case 0:break A;case 1:break Xe;default:break qe}}if((0|A)<=43007){if((0|A)==41984)break Ze;if((0|A)!=42496)break s;switch(r=64,n=268435456,t-42739|0){case 4:break c;case 0:break n;case 3:break r;case 2:break A;case 1:break ke;default:break Le}}if((0|A)==43008)break Fe;if((0|A)!=43264)break s;switch(r=4096,t-43310|0){case 0:break A;case 1:break V;default:break we}}if((0|A)<=70655){if((0|A)<=67839){if((0|A)<=65279){if((0|A)==43520)break Ie;if((0|A)==43776)break de;if((0|A)!=65024)break s;if(r=0,n=268435456,(0|(l=-2&t))==65040)break A;if((A=t-65042|0)>>>0<8)break Dt;break be}if((0|A)==65280)break fe;if((0|A)==66304)break U;if((0|A)!=67584||(r=64,(0|t)!=67671))break s;break A}if((0|A)<=69375){if((0|A)==67840)break se;if((0|A)==68096)break oe;if((0|A)!=68352)break s;switch(r=64,t-68410|0){case 0:case 1:case 2:case 3:case 4:case 5:case 95:case 96:case 97:case 98:break A;default:break s}}if((0|A)<=69887){if((0|A)==69376)break v;if((0|A)!=69632)break s;if(t-69703>>>0>=2)break ue;break V}if((0|A)==69888)break ce;if((0|A)!=70144)break s;if((A=t-70200|0)>>>0<=4)break ie;if((0|t)!=70313)break s;break V}
2810if((0|A)<=74751){if((0|A)<=71423){if((0|A)==70656)break re;if((0|A)==70912)break ne;if((0|A)!=71168||(r=268435520,t-71233>>>0>=2))break s;break A}if((0|A)<=72703){if((0|A)==71424)break Ae;if((0|A)!=72192)break s;if(t-72258>>>0>=2)break j;break V}if((0|A)==72704)break Z;if((0|A)!=73216||(r=268435520,t-73463>>>0>=2))break s;break A}if((0|A)<=93695){if((0|A)==74752)break O;if((0|A)==92672)break Q;if((0|A)!=92928)break s;if(r=268435520,t-92983>>>0<2)break A;A=(0|t)==92996?268435520:0,A=(t=(0|t)==92985)?64:A;break S}if((0|A)<=121343){if((0|A)==93696)break P;if((0|A)!=113664||(r=268435520,n=-2147483648,(0|t)!=113823))break s;break A}if((0|A)==121344)break Mt;if((0|A)!=125184)break s;r=((0|t)==125279)<<30,A=0,fe=(t=(0|t)==125278)?536870912:r;break i}r=-1879048128,n=536870912;Ee:{dt:{gt:{ht:{switch(t-33|0){default:switch(t-183|0){case 1:case 2:case 3:case 4:case 5:case 6:case 7:break Ee;case 8:break gt;case 0:break ht;default:break dt}case 2:case 9:fe=66;break f;case 11:return fe=268435456,-2147483584;case 13:return fe=-2147483648,-1879048128;case 25:return fe=134217728,-2147483584;case 26:return fe=67108864,-2147483584;case 1:case 6:break b;case 0:break A;case 3:case 4:case 5:case 7:case 8:case 10:case 12:case 14:case 15:case 16:case 17:case 18:case 19:case 20:case 21:case 22:case 23:case 24:case 27:case 28:case 29:break Ee;case 30:}return fe=1073741824,-1879048128}return fe=0,134230016}fe=1078984704;break f}if((0|t)==161)break We}break I}A=(0|t)==903,r=(t=(0|t)==894)?64:A?134217792:0,fe=t?1073741824:A?67108864:0;break m}if((0|t)==1417)break He;if((0|t)!=1475)break s;break ae}fe=1075838976;break o}fe=-2143289344;break a}r=64,n=268435456;ye:switch(t-1548|0){case 15:break r;case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 10:case 11:case 12:case 13:case 14:case 16:case 17:break s;case 0:break A;case 19:break ye;case 18:break V;default:break T}break c}if((A=t-1792|0)>>>0<6)break Pt;if(r=64,n=134217728,(-2&t)==1798)break A;n=67108864;Ve:{ye:{He:switch(t-1800|0){default:switch(t-2040|0){case 1:break Ve;case 0:break ye;default:break s}case 3:break s;case 0:break A;case 1:break He;case 2:case 4:break ae}fe=1073741824;break t}fe=268435456;break t}fe=536870912;break a}A=(0|t)==2405,r=(t=(0|t)==2404)||A?268435520:0,fe=t?-2143289344:A?16777216:0;break m}if(r=0,n=-2143289344,(0|t)!=3572)break s;break A}switch(r=64,t-3848|0){case 5:fe=-2143289344;break t;case 6:fe=16777216;break t;case 0:break A}if(t-3854>>>0<5)break A;if(r=0,n=268435456,(0|t)!=3860)break s;break A}if((A=t-4961|0)>>>0<4)break Tt;if(r=64,n=134217728,t-4965>>>0<2)break A;A=(0|t)==4968,r=(t=(0|t)==4967)||A?268435520:0,fe=t?1073741824:A?16777216:0;break m}if(t-5867>>>0>=3)break s;break A}if(r=268435520,t-5941>>>0<2)break A;if((t=t-6100|0)>>>0>=7)break s;A=e[(t=83240+(t<<3)|0)>>2];break p}if(r=268435520,(-4&t)!=6824)break s;break A}if(r=64,(0|t)==7005)break A;if(r=268435520,(0|A)!=7006)break s;break A}if(t-7227>>>0<2)break V;if(r=64,t-7229>>>0<3||(r=268435520,(-2&t)==7294))break A;if(r=4096,(0|t)!=7379)break s;break A}fe=33554432;break f}if((-8&t)==8224)break I;if(t-8242>>>0<3)break A;if(t-8240>>>0<9)break I;if((A=t-8251|0)>>>0<21)break Gt;break te}if((0|t)==11513){fe=-2147483648;break o}if(r=0,n=1073741824,(-2&t)==11514)break A;if(n=-2147483648,(0|t)!=11518)break s;break A}switch(r=-1879048128,t-11822|0){case 4:case 6:fe=268435456;break f;case 5:fe=-2147483648;break f;case 7:fe=67108864;break f;case 14:return fe=-2147483648,-1879048128;case 19:return fe=268435456,-2147483584;case 30:case 32:return fe=0,-2147483584;case 0:break A}break I}if((0|t)==12349)break N;if((0|t)!=12539)break s;return fe=0,16}return fe=-2143289344,-1879048128}A=(0|t)==42239,r=(t=(0|t)==42238)?64:A?268435520:0,fe=t?268435456:A?-2147483648:0;break m}Le:switch(t-42509|0){case 1:break n;case 0:break A;case 2:break Le;default:break s}break c}fe=134217728;break t}if(t-43126>>>0<2)break V;if(r=268435520,t-43214>>>0>=2)break s;break A}if((0|t)==43463)break ae;if(r=268435520,(-2&t)!=43464)break s;break A}if(t-43613>>>0<3)break V;if(r=64,(0|t)==43743)break A;if(r=268435520,(-2&t)!=43760)break s;break A}if(r=268435520,(0|t)!=44011)break s;break A}r=268435520,n=541065216;fe:switch(t-65281|0){case 1:case 6:return fe=0,32;case 11:fe=272629760;break t;case 13:fe=-2143289344;break a;case 100:return fe=0,16;case 25:fe=138412032;break t;case 26:fe=71303168;break t;case 30:fe=1077936128;break a;case 96:break n;case 59:break k;case 0:break A;case 99:break fe;default:break s}fe=268435456;break t}r=(A=(0|t)==66512)>>>
281026|0,A=(t=(0|t)==66463)?64:A<<6;break Y}if(r=64,(0|t)!=67871)break s;break A}if(r=268435520,(-2&t)==68182)break A;if(r=64,t-68336>>>0>=6)break s;break A}if(r=268435520,t-69461>>>0>=5)break s;break A}if(r=64,t-69705>>>0<5)break A;if(r=268435520,t-69822>>>0>=4)break s;break A}if(t-69953>>>0<2)break V;if((A=t-70085|0)>>>0<=26)break _;break Ne}if((0|A)!=2)break V;break ae}if(r=268435520,t-70731>>>0<2)break A;n=(A=(0|t)==70747)>>>26|0,A=(t=(0|t)==70733)?64:A<<6,fe=t?268435456:n;break i}switch(r=268435520,(-2&t)-71106|0){case 0:break A;case 2:break u}if(r=8192,t-71110>>>0<3)break A;if(r=268435520,t-71113>>>0>=15)break s;break A}if(r=268435520,t-71484>>>0>=3)break s;break A}if(r=268435520,t-72347>>>0<2)break A;if(r=64,t-72353>>>0>=2)break s;break A}if(r=268435520,t-72769>>>0<2)break A;r=(A=(0|t)==72817)>>>26|0,A=(t=(0|t)==72771)?64:A<<6;break Y}if(r=64,n=134217728,t-74865>>>0<2)break A;if(n=0,t-74864>>>0>=5)break s;break A}A=(0|t)==93848?268435520:0,A=(t=(0|t)==93847)?64:A;break S}if(r=268435520,(-2&t)==92782)break A;if(n=-2147483648,(0|t)!=92917)break s;break A}if((0|t)==1748)break n;break s}if(!(1<<A&100663555))break Ne;break V}break ae;case 23:u:{_:{T:{Q:{P:{O:{if((0|(A=-256&t))<=11775){if((0|A)<=8959){if(A)break O;break I}if((0|A)==8960)break P;if((0|A)==9984)break Q;if((0|A)!=10496)break s;break W}if((0|A)<=64767){if((0|A)==11776)break T;if((0|A)!=12288)break s;if((t=t-12300|0)>>>0<=17&&(r=-2147483616,1<<t&131077))break A;break I}if((0|A)==64768)break _;if((0|A)==65024)break u;if((0|A)!=65280)break s;if(r=32,(0|t)==65378)break A;break s}if((0|A)!=8192)break s;switch(r=-2147483616,t-8218|0){case 1:case 2:case 3:break s;case 0:case 4:break A}switch(t-8317|0){default:if((0|t)!=8261)break s;break I;case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 10:case 11:case 12:case 13:case 14:case 15:break s;case 0:case 16:}break k}switch(r=-2147483520,t-8968|0){case 1:break s;case 0:case 2:break A}if((0|t)!=9001)break s;return fe=0,-2139095040}if(r=-2147483520,(0|t)==10181)break A;break Be}return fe=0,(0|t)==11842?-2147483616:-2147483648}if(r=-2147483648,(0|t)!=64831)break s;break A}r=(t=!(t-65089&-3))>>>27|0,t<<=5;break B;case 24:r=(t=t>>>0<256)>>>1|0,t<<=31;break B;case 25:u:{_:{T:{Q:{P:{O:{Z:{j:{if((0|(A=-256&t))<=12287){if((0|A)<=767){if(!A)break j;if((0|A)!=512)break u;if(t-751>>>0>=17)break Z;break g}if((0|A)==768)break O;if((0|A)!=7936)break u;switch(r=4096,t-8125|0){case 0:case 2:case 3:case 4:case 16:case 17:case 18:case 32:case 33:case 34:case 48:case 49:case 50:case 64:case 65:break A;default:break u}}if((0|A)<=43775){if((0|A)==12288)break P;if((0|A)!=42752||(r=4096,(-2&t)!=42784))break u;break A}if((0|A)==43776)break Q;if((0|A)==65280)break T;if(r=0,n=78,(0|A)!=127744)break u;break A}switch(t-168|0){default:r=-2147479424;j:switch(t-94|0){case 0:break A;case 2:break j;default:break u}return fe=0,-2147479552;case 0:case 7:break g;case 1:case 2:case 3:case 4:case 5:case 6:case 8:case 9:case 10:case 11:case 13:case 14:case 15:break u;case 12:case 16:}break g}if((0|t)==749|t-741>>>0<7)break g;if(t-706>>>0>=4)break _;break g}if((t=t-885|0)>>>0>16||(r=4096,!(1<<t&98305)))break u;break A}if(r=67112960,t-12443>>>0>=2)break u;break A}if(r=4096,(0|t)!=43867)break u;break A}switch(r=4224,t-65342|0){default:if((0|t)!=65507)break u;break;case 0:break A;case 1:break u;case 2:}break g}if(r=4096,t-722>>>0<14)break A}break s;case 26:u:{_:{T:{Q:{P:{O:{if((0|(A=-256&t))<=9471){if((0|A)<=8447){if(A)break u;break I}if((0|A)==8448)break O;if((0|A)==8704)break P;if(r=-2147483648,(0|A)!=8960)break s;break A}if((0|A)<=10495){if((0|A)==9472)break Q;if((0|A)==9728)break T;if((0|A)==9984)break I;break s}if((0|A)==10496)break _;if((0|A)==10752)break I;if(r=-2147483648,(0|A)!=11008)break s;break A}if((0|t)==8472)return fe=0,67108864;if((0|t)==8596)break K;if(r=-2147483648,t>>>0<=8591)break s;break A}if(r=-2147483640,(0|t)==8722)break A;A=-2147483648,fe=(t=t-8942>>>0<4)?33554432:0;break i}if(r=-2147483648,n=130,t-9723>>>0<2)break A;A=-2147483648,fe=(t=t-9725>>>0<2)?134:0;break i}if(r=-2147483648,n=128,(0|t)==9839)break A;break I}
2810if(r=-2147483648,n=130,(-2&t)==10548)break A;A=-2147483648,fe=(t=(0|t)==10626)?134217728:0;break i}if((0|A)==8192)break Ue;break s;case 27:u:{_:{T:{Q:{P:{O:{Z:{j:{Ae:{ne:{re:{ie:{ce:{ue:{v:{oe:{se:{U:{fe:{de:{Ie:{we:{Fe:{ke:{if((0|(A=-256&t))<=12287){if((0|A)<=9727){if((0|A)<=8959){if(!A)break ke;if((0|A)!=8448)break s;switch(r=0,n=130,t-8482|0){case 7:break k;case 0:break A;case 1:case 2:case 3:case 4:case 5:case 6:case 8:case 9:case 10:case 11:break de;case 12:break we;default:break Fe}}if((0|A)==8960)break fe;if((0|A)==9216)break U;if((0|A)!=9472)break s;switch(r=-2147483520,(-2&t)-9632|0){case 0:break A;case 10:break K;default:break se}}if((0|A)<=11007){if((0|A)==9728)break oe;if((0|A)==9984)break v;if(r=-2147483648,(0|A)!=10240)break s;break A}if((0|A)==11008)break ue;if((0|A)==11776)break ce;if((0|A)!=12032)break s;if(r=1048576,t>>>0<12246)break A;switch(r=262144,(-2&t)-12272|0){case 0:break A;case 2:break u;default:break ie}}if((0|A)<=127999){if((0|A)<=127231){if((0|A)==12288)break re;if((0|A)==12800)break ne;if((0|A)!=126976)break s;A=(0|t)==127183,r=0,fe=(t=(0|t)==126980)||A?134:128;break m}if((0|A)==127232)break Ae;if((0|A)==127488)break j;if((0|A)!=127744)break s;if(t>>>0>=127777)break Z;break L}if((0|A)<=128767){if((0|A)==128e3)break O;if((0|A)==128256)break P;if((0|A)!=128512)break s;if((A=t-128581|0)>>>0<11)break bt;break ze}if((0|A)==128768)break Q;if((0|A)==129280)break T;if((0|A)!=129536)break s;fe=128;break o}A=(0|t)==174,r=-2147483648,fe=(t=(0|t)==169)||A?130:0;break m}switch(t-8616|0){case 0:break I;case 1:case 2:break Ie;default:break de}}return fe=0,67108864}return fe=130,-2147483520}if(r=-2147483520,t-8597>>>0<5)break A;if(t-8604>>>0<18)break W;if((A=t-8624|0)>>>0<8)break xt;break Oe}if(t>>>0<8968)break I;if((0|(A=-2&t))==8986)break l;if(t-8972>>>0<20|t-8994>>>0<6)break I;if((0|t)==9e3)break K;if(t-9003>>>0<81)break I;if(r=-2147483648,n=128,(0|t)==9096)break A;if(t-9085>>>0<30)break I;if(r=-2147483520,n=0,(0|A)==9140)break A;switch(t-9143|0){case 0:case 25:break A;case 24:break K}if(t-9140>>>0<40)break I;if((0|t)==9186)break A;if((A=t-9193|0)>>>0<4)break l;switch(r=-2147483648,n=134,t-9200|0){case 0:case 3:break A}if(A>>>0<11)break K;if(n=130,t-9208>>>0<3)break A;if(n=0,t>>>0<=9186)break s;break A}if(r=-2147483648,t-9216>>>0<75||(r=33792,n=130,(0|t)==9410)||(n=0,t-9398>>>0<26))break A;if(r=17408,t-9424>>>0>=26)break s;break A}if(t-9646>>>0<8)break W;if(n=130,(0|t)==9654)break A;if((-4&t)==9660)break W;switch(t-9664|0){case 0:break A;case 6:case 7:case 10:case 11:case 15:case 16:case 17:case 18:case 19:case 34:case 36:break W}return fe=0,t-9703>>>0<6?-2147483520:-2147483648}oe:switch((-16&t)-9728>>>4|0){case 0:if(t>>>0<9733)break K;r=-2147483520,n=128;se:switch(t-9733|0){case 0:break A;case 9:break se;case 1:break W;default:break D}break K;case 2:if((A=t-9760|0)>>>0<11)break Et;if(r=-2147483648,n=130,t>>>0<=9773)break D;break A;case 3:if(r=-2147483648,n=130,t-9784>>>0>=3)break D;break A;case 4:switch(r=-2147483520,n=130,t-9792|0){case 0:case 2:break A}if(r=-2147483648,n=134,t>>>0<=9799)break D;break A;case 5:if(r=-2147483648,n=134,t>>>0<9812)break A;if(n=130,(0|t)!=9823)break D;break A;case 6:if((0|t)==9734)break W;if((0|t)==9824)return fe=130,-2147483520;if(r=-2147483520,n=128,t-9825>>>0<2)break A;if((A=t-9827|0)>>>0<6)break yt;break Ce;case 8:if(r=-2147483648,t>>>0<=9861)break D;break A;case 10:switch(r=-2147483648,n=130,t-9888|0){case 1:break l;case 0:break A}if(n=134,(-2&t)!=9898)break D;break A;case 11:if(r=-2147483648,n=130,(-2&t)==9904)break A;if(n=134,t-9917>>>0>=2)break D;break A;case 12:if((-2&t)==9924)break l;r=-2147483648,n=130;se:switch(t-9928|0){case 0:case 7:break A;case 6:break se;default:break D}break l;case 14:A=(0|t)==9962,r=-2147483648,fe=(t=(0|t)==9961)?130:A?134:128;break m;case 13:break wt;case 9:break kt;case 15:break oe;case 7:break vt;case 1:break Bt;default:break D}if((0|t)==9972|t>>>0<9970)break K;if(r=-2147483648,n=134,t>>>0<9974||(0|t)!=9974&&(n=130,t>>>0<9977))break A;if((t=t-9977|0)>>>0<5)break It;break D}v:{oe:{se:{U:{fe:switch((-16&t)-9984>>>4|0){case 0:if(r=0,n=130,(0|t)==9986||(n=128,t>>>0<9989)||(n=134,(0|t)==9989))break v;switch(n=150,(-2&t)-9994|0){case 0:break v;case 2:break se}if(n=130,t-9992>>>0<6)break v;A=(0|t)==9999,r=0,n=(t=(0|t)==9998)?128:A?130:0;break v;case 1:if(r=0,n=128,t>>>0<10002)break v;if((t=t-10002|0)>>>0>11||(n=130,!(1<<t&2069)))break U;break v;case 2:A=(0|t)==10024,r=0,n=(t=(0|t)==10017)?130:A?134:0;break v;case 4:r=0,n=130;de:switch(t-10052|0){case 0:case 3:break v;case 8:case 10:break de;default:break U}n=134;break v;case 5:if(r=0,n=1073741958,t-10067>>>0<2)break v;n=(t=t-10069&-3)?0:536871046;break v;case 6:if((A=t-10082|0)>>>0<3)break oe;if(r=0,n=128,t>>>0<=10084)break U;break v;case 9:if(r=0,n=134,t-10133>>>0>=3)break U;break v;case 10:if(r=0,n=130,(0|t)!=10145)break U;break v;case 11:A=(0|t)==10175,r=0,n=(t=(0|t)==10160)||A?134:0;break v;case 3:break fe;default:break U}if(r=0,n=130,t-10035>>>0<2)break v}r=0,n=0;break v}n=146;break v}r=e[(t=83992+(A<<3)|0)>>2],n=e[t+4>>2]}return fe=n,-2147483648|r}
2810if(r=-2147483648,n=130,t-11013>>>0<3||(n=134,t-11035>>>0<2))break A;A=(0|t)==11093,r=-2147483648,fe=(t=(0|t)==11088)||A?134:0;break m}if(t-11904>>>0>=26)break _;return fe=0,1048576}if(t-12276>>>0>=8)break s;break A}if((t=t-12306|0)>>>0>14||(r=-2147483648,!(1<<t&16387)))break s;break A}A=0,fe=(t=t-12951&-3)?0:130;break i}if(r=0,n=128,(0|t)==127279)break A;if(t-127280>>>0<26||t-127312>>>0<26)return fe=0,33792;if((A=t-127344|0)>>>0<=15&&(r=33792,n=130,1<<A&49155)||(r=33792,n=0,A>>>0<26))break A;if((0|t)==127374)break L;if(r=0,n=134,t-127377>>>0<10)break A;if(n=102,t>>>0<=127461)break s;break A}r=0,n=134;j:{switch(t-127489|0){case 0:case 25:break A;case 1:break N;case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 10:case 11:case 12:case 13:case 14:case 15:case 16:case 17:case 18:case 19:case 20:case 21:case 22:case 23:case 24:break j}switch(t-127535|0){case 0:break A;case 8:break N;default:break j}}if((-2&t)==127568|t-127538>>>0<9)break A;if(n=128,t>>>0<=127583)break s;break A}if((0|t)==127777)break N;if(t-127789>>>0<9|t-127799>>>0<70)break L;if((0|t)==127877)break X;if(t-127870>>>0<22)break L;if(t-127780>>>0<112)break N;switch(r=0,n=130,t-127894|0){case 44:case 45:case 46:case 49:break X;case 0:case 1:case 3:case 4:case 5:break A}if(t-127904>>>0<42)break L;if(n=150,(0|t)==127946||(n=146,t-127947>>>0<2))break A;if(t-127951>>>0<5)break L;if(n=134,t-127968>>>0<17||(n=130,t-127902>>>0<83))break A;if((A=t-127987|0)>>>0<5)break Ct;break Ke}switch(r=0,n=130,t-128063|0){case 0:case 2:break A}if((0|t)==128124|(-5&t)-128129>>>0<3|(0|t)==128110|t-128112>>>0<9||(-2&t)==128066|t-128102>>>0<4|t-128070>>>0<11)break X;switch(t-128253|0){case 1:fe=128;break o;case 0:break N}if(n=150,(0|t)==128170)break A;break L}if(t>>>0<128318)break L;if(r=0,t>>>0<128326)break A;if(t-128329>>>0<2)break N;if(t-128331>>>0<4|t-128336>>>0<24)break L;if(t-128367>>>0<2)break N;if((-2&t)==128372){fe=146;break o}if(t-128371>>>0<7)break N;if(n=150,!(A=t-128378|0))break A;if((0|A)==13|t-128394>>>0<4)break N;if(n=146,(0|t)==128400||(n=150,t-128405>>>0<2))break A;switch(n=134,t-128420|0){case 0:break A;case 1:case 4:case 13:case 14:case 24:case 30:case 31:case 32:case 45:case 46:case 47:case 56:case 57:case 58:case 61:case 63:case 68:case 75:case 79:case 86:break N}t=t>>>0>128506,A=0;break h}if(r=0,n=128,t-128981>>>0>=4)break s;break A}if(t>>>0<129292)break s;if(t-129328>>>0<10)break X;switch(r=0,n=150,t-129304|0){case 35:break s;case 0:case 1:case 2:case 3:case 4:case 6:case 7:case 14:break A}if(t-129341>>>0<2)break X;if(n=0,(0|t)==129350||(n=198,(-4&t)==129456))break A;if((A=t-129461|0)>>>0<5)break mt;break pt}if(r=1048576,t-11931>>>0<89)break A;break s}return fe=0,524288;case 29:return fe=16777216,1073741825;case 28:break A;case 30:break jt;default:break s}return fe=0,(0|t)==32?1073741825:1}if(!(1079>>>A&1))break ge;A=e[(t=81344+(A<<3)|0)>>2];break p}if(557553>>>A&1)break k;if((-4&t)!=8508)break Me;break A}if(!(207>>>A&1))break me;break d}if(r=1024,!(1017>>>A&1))break De;break A}if(r=1024,!(32895>>>A&1))break Ge;break A}if(r=1024,!(55>>>A&1))break _e;break A}if(r=1024,!(3087>>>A&1))break Qe;break A}if((t=t-71453|0)>>>0>=15)break s;A=e[(t=82312+(t<<3)|0)>>2];break p}if(!(49023>>>A&1))break Pe;A=e[(t=82432+(A<<3)|0)>>2];break p}if(!(514623>>>A&1))break J;A=e[(t=82664+(A<<3)|0)>>2];break p}A=e[(t=83160+(A<<3)|0)>>2];break p}if(r=268435520,!(197>>>A&1))break Se;break A}A=e[(t=83208+(A<<3)|0)>>2];break p}if((t=t-6145|0)>>>0>=10)break s;A=e[(t=83296+(t<<3)|0)>>2];break p}if(!(1077711>>>A&1))break te;A=e[(t=83376+(A<<3)|0)>>2];break p}if(!(159>>>A&1))break be;A=e[(t=83544+(A<<3)|0)>>2];break p}if((t=t-121479|0)>>>0>=4)break s;A=e[(t=83608+(t<<3)|0)>>2];break p}if(!(195>>>A&1))break Oe;break W}if((t=t-9745|0)>>>0>=13)break D;A=e[(t=83640+(t<<3)|0)>>2];break p}if(1101>>>A&1)break K;if(r=-2147483648,n=130,t>>>0<=9773)break D;break A}if(!(45>>>A&1))break Ce;A=e[(t=83744+(A<<3)|0)>>2];break p}if((t=t-9851|0)>>>0>=5)break D;A=e[(t=83792+(t<<3)|0)>>2];break p}if((t=t-9874|0)>>>0>=11)break D;A=e[(t=83832+(t<<3)|0)>>2];break p}if((t=t-9937|0)>>>0>=4)break D;A=e[(t=83920+(t<<3)|0)>>2];break p}A=e[(t=83952+(t<<3)|0)>>2];break p}if(!(23>>>A&1))break Ke;A=e[(t=84016+(A<<3)|0)>>2];break p}
2810if(r=0,n=150,!(1991>>>A&1))break ze;break A}if(n=150,27>>>A&1)break A}A=0,fe=(t=t-129489>>>0<13)?150:134;break i}t=t-127992>>>0<3,A=0;break h}if(t-9837>>>0<2)break A}fe=128;break f}if(t-8623>>>0<13)break I;if(t-8636>>>0<18)break W;if((A=t-8656|0)>>>0<22&&(n=0,3157995>>>A&1)||(r=-2147483648,n=0,t-8661>>>0<31))break A;break s}if(t>>>0<128592)break L;if(r=0,n=0,t>>>0<128640)break A;if(!((A=t-128675|0)>>>0>29|!(1<<A&537788417)))break X;if(t>>>0<128710)break L;if(n=150,(0|t)==128716)break A;if(t-128715>>>0<5)break N;if(t-128720>>>0<3)break L;if(!((A=t-128736|0)>>>0>=10|!(575>>>A&1)))break N;if(n=134,t-128747>>>0<2)break A;switch(n=130,t-128752|0){case 0:case 3:break A}t=t-128756>>>0<6,A=0;break h}switch(r=-2147483648,t-8260|0){case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 10:case 11:case 12:case 13:break s;case 0:case 14:break A}if((0|(t=t-8315|0))!=16&&t)break s;return fe=0,8}if(r=268435520,n=1073741824,(0|t)!=69955)break s;break A}if(t-8266>>>0<8)break I;if(r=-2147483640,(0|t)==8275)break A;if(r=-2147483648,t-8277>>>0>=10)break s;break A}fe=542113792;break f}if(r=-2147483648,n=0,t-65093>>>0<2||(r=64,n=268435456,(0|l)==65104))break A;r=268435520,n=-2147483648;be:switch(t-65106|0){case 3:fe=134217728;break t;case 5:fe=536870912;break a;case 4:break c;case 2:break r;case 0:break A;case 15:case 22:break be;default:break s}break k}if((0|t)==2142)break ae;if(r=64,t-2096>>>0<15)break A;break s}if(r=1024,t-72850>>>0<22)break A;if((t=t-72874|0)>>>0>=13)break s;A=e[(t=82560+(t<<3)|0)>>2];break p}if(r=4096,t-71103>>>0<2)break A;if(r=1024,(-2&t)!=71132)break s;break A}if((0|t)==70726)break g;if(r=4096,(-2&t)!=70850)break s;break A}if(r=4096,t-68325>>>0>=2)break s;break A}if((0|t)==6109)break g;if(r=4096,t-6089>>>0>=11)break s;break A}switch(t-3959|0){case 0:case 2:return fe=0,8389632}if((0|A)==3968|t-3953>>>0<14)break d;if(!((A=t-3970|0)>>>0>=6|!(55>>>A&1)))break g;if(t-3981>>>0<11)break d;if(r=1024,t-3993>>>0<36)break A;if(r=4096,(0|t)!=4038)break s;break A}if(t-3655>>>0<6)break g;if(!((A=t-3761|0)>>>0>11|!(1<<A&3577)))break d;switch(r=1024,t-3661|0){case 1:break g;case 0:break A}if(r=4096,t-3784>>>0<5)break A;if(r=1024,(0|t)!=3789)break s;break A}if(t-1770>>>0<3)break A;if(r=1024,(0|t)!=1773)break s;break A}if((-2&t)==73028)break g;switch(r=1024,t-73104|0){default:if((0|t)!=73031)break s;break A;case 2:case 3:case 4:case 6:break s;case 0:case 1:case 5:break A;case 7:}break g}if((t=t-8492|0)>>>0>=30)break s;A=e[(t=81704+(t<<3)|0)>>2];break p}r=0;break z}n=(A=(-3&t)==43712)>>>20|0,r=A<<12}if((0|(i=-65536&t))==131072)break Re;if((0|i)==65536)break pe;if(A=r,l=n,i)break he}if(t-13312>>>0<6582||t-19968>>>0<20976)break e;if(i=2048,t-63744>>>0<366)return fe=l,2048|A;if(r=A,n=l,t-64112>>>0>=106)break he;break H}if(i=2099200,t-183984>>>0<7473|t-178208>>>0<5762|t-177984>>>0<222|t-131072>>>0<42711||t-173824>>>0<4149||(i=2048,t-194560>>>0<542))break H;break he}if(i=2048,t-110960>>>0<396|t-94208>>>0<6125|t-100352>>>0<755)break H}i=0}return fe=n,r|i}if((-4&t)==8508)break k;if(r=128,!(t-8517>>>0<3)&&(r=16777344,(-2&t)!=8520))break s}fe=n;break m}A=(t=t>>>0>131069)?65536:0,fe=t?0:128;break i}if((0|t)==11776)break I}fe=0;break o}fe=0;break f}return fe=0,4194304}return fe=0,128}return fe=0,16777344}return fe=0,4096}return fe=0,1024}return fe=0,-2147483520}return fe=0,t-10214>>>0<10?-2147483520:-2147483648}fe=0;break a}fe=0;break t}fe=130;break o}fe=130;break f}fe=134;break o}return fe=150,0}fe=0;break i}fe=t?0:n;break i}return fe=A,t}return 131072}return fe=0,16777216}fe=t?0:r;break i}return fe=n,t}return fe=r,t}fe=t?0:A;break m}return fe=0,8192}fe=e[t+4>>2];break i}fe=t?8388608:0;break i}return r}return fe=0,-2147483616}fe=t?134:128}return A}fe=134}return-2147483648}return 0}fe=1073741824;break a}fe=-2147483648}return 268435520}fe=67108864}return 64}return fe=l,2099200|A}function st(t){var A,r=0,n=0,i=0,l=0,h=0;(A=aA(8244))&&(e[A+328>>2]=2,g[132848]=0,e[A+684>>2]=0,e[A+688>>2]=0,e[A+320>>2]=0,e[A+324>>2]=0,g[A+268|0]=0,g[A+228|0]=0,e[A+8216>>2]=0,e[A+8220>>2]=0,e[A+224>>2]=104944,e[A+216>>2]=383,e[A+220>>2]=96,IA(A+344|0,0,292),e[A+8196>>
vendor: 2,553 bytes, line 2810
28102]=0,e[(r=A+8188|0)>>2]=0,e[r+4>>2]=0,e[A+8180>>2]=0,e[A+8184>>2]=0,g[A+460|0]=22,g[A+461|0]=129,g[A+466|0]=38,g[A+462|0]=38,g[A+463|0]=36,g[A+464|0]=22,g[A+465|0]=224,g[A+456|0]=22,g[A+457|0]=22,g[A+458|0]=44,g[A+459|0]=22,g[A+454|0]=46,g[A+455|0]=129,g[A+446|0]=22,g[A+447|0]=38,g[A+448|0]=28,g[A+449|0]=193,g[A+450|0]=38,g[A+451|0]=22,g[A+452|0]=46,g[A+453|0]=46,g[A+441|0]=129,g[A+442|0]=38,g[A+443|0]=22,g[A+444|0]=38,g[A+445|0]=193,e[A+332>>2]=104912,e[A+336>>2]=104916,e[A+340>>2]=105232,g[A+296|0]=18,g[A+297|0]=18,v[A+304>>1]=182,v[A+306>>1]=140,g[A+298|0]=20,v[A+308>>1]=220,v[A+310>>1]=220,v[A+312>>1]=220,g[A+299|0]=20,g[A+300|0]=20,v[A+314>>1]=240,g[A+301|0]=22,v[A+316>>1]=260,v[A+318>>1]=280,g[A+302|0]=22,g[A+303|0]=20,r=IA(A,0,212),e[r+200>>2]=20,e[r+192>>2]=25966,e[r+196>>2]=500,e[r+80>>2]=95,e[r+16>>2]=1,e[r+20>>2]=3,e[r+8>>2]=2,e[r+52>>2]=19,g[r+168|0]=3,e[r+92>>2]=2,e[r+72>>2]=4,e[r+40>>2]=115,e[r+44>>2]=95,e[r+140>>2]=105244,bo(r,201),e[r+120>>2]=2,e[r+124>>2]=44,e[r+164>>2]=100,e[r+128>>2]=46,e[r+132>>2]=14,e[r+112>>2]=1227133512,e[r+116>>2]=49,e[r+104>>2]=1,n=e[26313],e[r+636>>2]=e[26312],e[r+640>>2]=n,n=e[26315],e[r+644>>2]=e[26314],e[r+648>>2]=n,n=e[26317],e[r+652>>2]=e[26316],e[r+656>>2]=n,n=e[26319],e[r+660>>2]=e[26318],e[r+664>>2]=n,n=e[26321],e[r+668>>2]=e[26320],e[r+672>>2]=n,n=e[26323],e[r+676>>2]=e[26322],e[r+680>>2]=n,n=d[104928]|d[104929]<<8,g[r+160|0]=n,g[r+161|0]=n>>>8,n=d[104924]|d[104925]<<8|d[104926]<<16|d[104927]<<24,g[r+156|0]=n,g[r+157|0]=n>>>8,g[r+158|0]=n>>>16,g[r+159|0]=n>>>24),i=BA(A+228|0,t),r=0;e:{t:if(n=d[0|t]){for(;r=(n<<24>>24)+(r<<8)|0,n=d[0|(t=t+1|0)];);r:{a:{n:{c:{o:{f:{l:{i:{h:{b:{m:{C:{p:{x:{w:{B:{F:{Y:{R:{M:{y:{G:{S:{X:{L:{K:{N:{ae:{V:{Be:{W:{d:{g:{$:{k:{ee:{I:{s:{Ye:{q:{A:{ge:{H:{he:{pe:{Re:{le:{z:{xe:{E:{Me:{J:{me:{De:{je:{Ge:{_e:{Te:{Qe:{Pe:{Se:{be:{We:{te:{Ne:{Ue:{ze:{Oe:{D:{Ce:{if((0|r)<=28008){if((0|r)<=26464){if((0|r)<=25696){switch(r-24934|0){case 20:break i;case 1:case 2:case 3:case 4:case 5:case 6:case 9:case 10:case 11:case 14:case 15:case 16:case 17:case 18:case 19:break t;case 8:break J;case 12:break Se;case 7:break be;case 0:break We;case 13:break Ce}switch(r-25189|0){case 1:case 3:case 4:case 5:case 6:case 7:case 8:case 10:case 11:case 12:case 13:break t;case 14:break ge;case 2:break Qe;case 0:break Pe;case 9:break Ce}switch(r-25441|0){case 18:break B;case 0:break J;case 24:break _e;default:break t}}switch(r-25964|0){case 1:case 4:case 5:case 6:break t;case 8:break xe;case 9:break Me;case 7:break J;case 3:break me;case 2:break De;case 0:break Oe}
2810switch(r-26209|0){case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 9:case 10:case 11:case 12:case 13:case 14:case 15:case 16:break t;case 17:break le;case 8:break z;case 0:break E}switch(r-25697|0){case 4:break je;case 0:break Ge;default:break t}}if((0|r)<=27488){switch(r-26729|0){case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 10:case 13:case 14:case 15:break t;case 16:break Ye;case 12:break q;case 11:break A;case 9:break ge;case 0:break H}switch(r-26977|0){case 1:case 2:case 4:case 5:case 6:case 7:case 8:case 9:case 10:case 11:case 12:case 13:case 15:case 16:case 17:break t;case 19:break k;case 18:break ee;case 3:break I;case 14:break s;case 0:break J}switch(r-26465|0){case 20:break H;case 13:break he;case 0:case 3:break pe;default:break t}}switch(r-27489|0){case 13:break h;case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 12:case 15:case 16:case 17:case 18:case 19:case 21:case 22:case 23:break t;case 24:break ae;case 20:break V;case 14:break Be;case 11:break W;case 10:break d;case 0:break g}switch(r-27745|0){case 19:break K;case 0:break N;case 21:break Ue;default:break t}}if((0|r)<=29792){if((0|r)<=28768){switch(r-28009|0){case 3:break h;case 11:break X;case 2:break L;case 1:case 4:case 5:case 6:case 7:case 8:case 12:case 13:case 14:case 15:break t;case 10:break I;case 9:break H;case 0:case 16:break te}switch(r-28258|0){case 0:break G;case 10:break S;case 1:case 2:case 4:case 5:case 6:case 7:case 8:case 9:break t;case 3:break H}switch(r-28525|0){case 0:break y;case 5:break H;default:break t}}if((0|r)<=29539){switch(r-28769|0){case 19:break R;case 11:break M;case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 9:case 10:case 12:case 13:case 14:case 15:case 16:case 17:case 18:case 20:case 21:case 22:case 23:break t;case 0:break H;case 24:break te}switch(r-29295|0){case 6:break F;case 0:break Y;case 1:case 2:case 3:case 4:case 5:break t}if((0|r)==29045)break te;break t}switch(r-29540|0){case 19:break b;case 18:break m;case 13:break C;case 8:break x;case 5:break w;case 7:break B;case 14:break ge;case 0:break o;default:break t}}if((0|r)>6514801)break D;if((0|r)<=30058)switch(r-29793|0){case 19:break l;case 17:break i;case 0:case 4:break h;case 13:break b;case 7:break te;default:break t}if((0|r)<=30312)switch(r-30059|0){case 0:break f;case 15:break te;case 7:break o;default:break t}if((0|r)==30313)break c;if((0|r)==31336)break n;if((0|r)!=6451321)break t}if(e[A+296>>2]=303174162,e[A+300>>2]=370545684,e[A+600>>2]=2432,e[A+8>>2]=0,e[A+12>>2]=65540,e[A+100>>2]=e[A+96>>2],t=e[25889],e[A+304>>2]=e[25888],e[A+308>>2]=t,t=e[25891],e[A+312>>2]=e[25890],e[A+316>>2]=t,xa(A),g[A+345|0]=2|d[A+345|0],g[A+406|0]=16|d[A+406|0],g[A+407|0]=16|d[A+407|0],g[A+408|0]=16|d[A+408|0],g[A+409|0]=16|d[A+409|0],g[A+410|0]=16|d[A+410|0],g[A+411|0]=16|d[A+411|0],g[A+412|0]=16|d[A+412|0],g[A+413|0]=16|d[A+413|0],g[A+414|0]=16|d[A+414|0],g[A+415|0]=16|d[A+415|0],g[A+416|0]=16|d[A+416|0],g[A+417|0]=16|d[A+417|0],g[A+418|0]=16|d[A+418|0],g[A+419|0]=16|d[A+419|0],g[A+420|0]=16|d[A+420|0],g[A+456|0]=4|d[A+456|0],g[A+457|0]=4|d[A+457|0],e[A+112>>2]=613567144,e[A+104>>2]=16,(0|r)!=6451321)break e;e[A+104>>2]=1,e[A+108>>2]=512,r=6451321;break e}if((0|r)>7364975)break Ne;if((0|r)>6840682)break ze;if((0|r)==6514802)break Te;if((0|r)==6516078)break n;if((0|r)!=6779491)break t}if(e[A+600>>2]=896,e[A+328>>2]=8,e[A+296>>2]=336858127,e[A+300>>2]=353768980,e[A+332>>2]=103632,t=e[25905],e[A+304>>2]=e[25904],e[A+308>>2]=t,t=e[25907],e[A+312>>2]=e[25906],e[A+316>>2]=t,IA(A+344|0,0,256),g[A+388|0]=129,g[A+389|0]=129,g[A+390|0]=129,g[A+391|0]=129,g[A+420|0]=129,g[A+421|0]=129,g[A+422|0]=129,g[A+423|0]=129,g[A+360|0]=129,g[A+392|0]=129,g[A+393|0]=129,g[A+417|0]=129,g[A+418|0]=129,g[A+419|0]=129,g[A+420|0]=129,g[A+408|0]=6,g[A+409|0]=4,g[A+410|0]=6,g[A+411|0]=6,g[A+412|0]=6,g[A+413|0]=193,g[A+414|0]=6,g[A+415|0]=6,g[A+406|0]=6,g[A+407|0]=129,g[A+398|0]=4,g[A+399|0]=193,g[A+400|0]=6,g[A+401|0]=193,g[A+402|0]=6,g[A+403|0]=4,g[A+404|0]=4,g[A+405|0]=4,g[A+394|0]=4,g[A+395|0]=4,g[A+396|0]=4,g[A+397|0]=193,e[A+44>>2]=130,e[A+8>>2]=2,e[A+12>>2]=6,e[A+16>>2]=0,e[A+20>>2]=2,e[A+104>>2]=264,e[A+108>>2]=6146,g[A+391|0]=193,g[A+389|0]=193,g[A+390|0]=193,g[A+421|0]=193,e[A+100>>2]=e[A+96>>2],g[A+416|0]=4|d[A+416|0],(0|r)!=6779491)break e;e[A+40>>2]=1,r=6779491;break e}if((0|r)==6840683)break Re;if((0|r)==6972015)break $;if((0|r)!=7107687)break t}e[A+296>>2]=134875662,e[A+300>>
vendor: 4,489 bytes, line 2810
28102]=252968960,e[A+328>>2]=5,g[A+169|0]=1,e[A+132>>2]=33,e[A+104>>2]=99336,e[A+8>>2]=0,e[A+12>>2]=262182,t=e[26069],e[A+304>>2]=e[26068],e[A+308>>2]=t,t=e[26071],e[A+312>>2]=e[26070],e[A+316>>2]=t;break e}if((0|r)<=7564649){if((0|r)==7364976)break J;if((0|r)==7435619)break te;if((0|r)!=7563374)break t;e[A+148>>2]=1,e[A+112>>2]=24,e[A+104>>2]=1,e[A+100>>2]=e[A+96>>2],r=7563374;break e}if((0|r)==7564650)break p;if((0|r)==7959909)break n;if((0|r)!=1885958500)break t}e[A+104>>2]=0;break e}e[A+4>>2]=48,e[A+8>>2]=0,e[A+144>>2]=1,e[A+104>>2]=16779472,e[A+32>>2]=1,e[A+24>>2]=1,t=e[25881],e[A+304>>2]=e[25880],e[A+308>>2]=t,t=e[25883],e[A+312>>2]=e[25882],e[A+316>>2]=t,g[A+465|0]=64&d[A+465|0]|129,r=24934;break e}e[A+600>>2]=4608,e[A+296>>2]=303173650,e[A+300>>2]=303174162,e[A+8>>2]=0,e[A+12>>2]=36,e[A+104>>2]=1024,e[A+100>>2]=e[A+96>>2],e[A+40>>2]=1,t=e[25865],e[A+304>>2]=e[25864],e[A+308>>2]=t,t=e[25867],e[A+312>>2]=e[25866],e[A+316>>2]=t,r=24941;break e}for(e[A+600>>2]=1536,e[A+224>>2]=0,e[A+216>>2]=1631,e[A+220>>2]=1536,e[A+104>>2]=2884720,e[A+328>>2]=7,e[A+40>>2]=1,oe=r=oe-16|0,e[r+12>>2]=-1,t=89684;i=or(r+12|0,t),(0|(n=e[r+12>>2]))>=33&&(g[0|(l=(A+n|0)-1192|0)]=1|d[0|l]),t=t+i|0,n;);for(e[r+12>>2]=-1,t=89743;i=or(r+12|0,t),(0|(n=e[r+12>>2]))>=33&&(g[0|(l=(A+n|0)-1192|0)]=2|d[0|l]),t=t+i|0,n;);for(e[r+12>>2]=-1,t=89795;i=or(r+12|0,t),(0|(n=e[r+12>>2]))>=33&&(g[0|(l=(A+n|0)-1192|0)]=4|d[0|l]),t=t+i|0,n;);for(e[r+12>>2]=-1,t=89941;i=or(r+12|0,t),(0|(n=e[r+12>>2]))>=33&&(g[0|(l=(A+n|0)-1192|0)]=16|d[0|l]),t=t+i|0,n;);for(e[r+12>>2]=-1,t=90045;i=or(r+12|0,t),(0|(n=e[r+12>>2]))>=33&&(g[0|(l=(A+n|0)-1192|0)]=32|d[0|l]),t=t+i|0,n;);for(e[r+12>>2]=-1,t=90045;i=or(r+12|0,t),(0|(n=e[r+12>>2]))>=33&&(g[0|(l=(A+n|0)-1192|0)]=8|d[0|l]),t=t+i|0,n;);for(e[r+12>>2]=-1,t=90045;i=or(r+12|0,t),(0|(n=e[r+12>>2]))>=33&&(g[0|(l=(A+n|0)-1192|0)]=64|d[0|l]),t=t+i|0,n;);oe=r+16|0,r=24946;break e}e[A+600>>2]=1056,e[A+12>>2]=34,e[A+216>>2]=1118,e[A+220>>2]=1072,IA(A+344|0,0,256),g[A+406|0]=4,g[A+366|0]=4,g[A+367|0]=4,g[A+369|0]=4,g[A+370|0]=4,g[A+371|0]=4,g[A+372|0]=4,g[A+361|0]=4,g[A+362|0]=4,g[A+363|0]=4,g[A+364|0]=4,g[A+373|0]=4,g[A+380|0]=4,g[A+381|0]=4,g[A+382|0]=4,g[A+383|0]=4,g[A+375|0]=4,g[A+376|0]=4,g[A+377|0]=4,g[A+378|0]=4,g[A+384|0]=4,g[A+360|0]=129,e[A+328>>2]=6,e[A+296>>2]=134744588,e[A+300>>2]=286261248,e[A+40>>2]=1,e[A+8>>2]=0,e[A+104>>2]=1032,e[A+108>>2]=66,t=e[25885],e[A+304>>2]=e[25884],e[A+308>>2]=t,t=e[25887],e[A+312>>2]=e[25886],e[A+316>>2]=t,r=25189;break e}Br(A),e[A+328>>2]=6,e[A+56>>2]=2,e[A+36>>2]=263,e[A+40>>2]=1074,e[A+124>>2]=32,e[A+104>>2]=184554728,e[A+8>>2]=2,g[A+386|0]=64&d[A+386|0]|129,r=25191;break e}e[A+12>>2]=262182,e[A+40>>2]=1,r=6514802;break e}e[A+328>>2]=14,e[A+296>>2]=303173393,e[A+300>>2]=336986112,e[A+104>>2]=1024,e[A+16>>2]=0,e[A+20>>2]=2,e[A+8>>2]=2,e[A+12>>2]=22,e[A+44>>2]=120,t=e[25893],e[A+304>>2]=e[25892],e[A+308>>2]=t,t=e[25895],e[A+312>>2]=e[25894],e[A+316>>2]=t,g[A+463|0]=64&d[A+463|0]|129,g[A+465|0]=64&d[A+465|0]|129,r=25465;break e}e[A+8>>2]=0,e[A+104>>2]=184618072,e[A+32>>2]=1,t=e[26101],e[A+304>>2]=e[26100],e[A+308>>2]=t,t=e[26103],e[A+312>>2]=e[26102],e[A+316>>2]=t,g[A+465|0]=64&d[A+465|0]|129,r=25697;break e}e[A+296>>2]=336860180,e[A+300>>2]=336991764,e[A+8>>2]=0,e[A+104>>2]=16846872,e[A>>2]=8,e[A+4>>2]=48,e[A+80>>2]=87,e[A+32>>2]=1,e[A+36>>2]=256,e[A+40>>2]=2,t=e[25897],e[A+304>>2]=e[25896],e[A+308>>2]=t,t=e[25899],e[A+312>>2]=e[25898],e[A+316>>2]=t,g[A+465|0]=64&d[A+465|0]|129,r=25701;break e}e[A+132>>2]=33,e[A+104>>2]=16779328,e[A+8>>2]=0,e[A+12>>2]=8,e[A+204>>2]=101,e[A+60>>2]=2,e[A+40>>2]=2,t=e[25901],e[A+304>>2]=e[25900],e[A+308>>2]=t,t=e[25903],e[A+312>>2]=e[25902],e[A+316>>2]=t,g[A+441|0]=64|d[A+441|0],g[A+445|0]=64|d[A+445|0],g[A+449|0]=64|d[A+449|0],g[A+455|0]=64|d[A+455|0],g[A+461|0]=64|d[A+461|0],g[A+465|0]=64|d[A+465|0],r=25966;break e}e[A+328>>2]=4,e[A+296>>2]=336858640,e[A+300>>2]=353768980,e[A+104>>2]=16782344,e[A+20>>2]=2,e[A+12>>2]=22,e[A+4>>2]=2,e[A+8>>2]=2,e[A+332>>2]=103640,t=e[25997],e[A+304>>2]=e[25996],e[A+308>>2]=t,t=e[25999],e[A+312>>2]=e[25998],e[A+316>>2]=t,r=25967;break e}e[A+296>>2]=269422096,e[A+300>>2]=370545684,e[A+104>>2]=86017320,e[A+108>>2]=6144,e[A+16>>2]=0,e[A+20>>2]=2,e[A+8>>2]=2,e[A+12>>2]=534,e[A+100>>2]=e[A+96>>2],e[A+44>>2]=120,t=e[25913],e[A+304>>2]=e[25912],e[A+308>>2]=t,t=e[25915],e[A+312>>2]=e[25914],e[A+316>>2]=t;J:{me:{if((0|r)<=26976){if((0|r)==24942)break me;if((0|r)!=25441)break J;e[A+12>>2]=566,e[A+336>>2]=103664,r=25441;break e}
2810if((0|r)!=26977){if((0|r)!=7364976)break J;e[A+8>>2]=3,e[A+12>>2]=310,r=7364976;break e}e[A+104>>2]=85984264,r=26977;break e}e[A+104>>2]=153093416,e[A+108>>2]=2048,e[A+140>>2]=103676,r=24942;break e}e[A+40>>2]=2;break e}e[A+296>>2]=303173648,e[A+300>>2]=303174162,e[A+104>>2]=3147080,e[A+12>>2]=65792,e[A+84>>2]=1,t=e[25921],e[A+304>>2]=e[25920],e[A+308>>2]=t,t=e[25923],e[A+312>>2]=e[25922],e[A+316>>2]=t,r=25973;break e}e[A+600>>2]=1536,e[A+216>>2]=1740,e[A+220>>2]=1568,e[A+104>>2]=96,e[A+224>>2]=103696,e[A+340>>2]=103872,e[A+40>>2]=1,r=26209;break e}e[A+328>>2]=5}e[A+104>>2]=86024,e[A+164>>2]=130,g[A+465|0]=64&d[A+465|0]|129;break e}e[A+296>>2]=303173650,e[A+300>>2]=303174162,e[A+8>>2]=3,e[A+12>>2]=36,e[A+144>>2]=2,e[A+104>>2]=118658312,e[A+28>>2]=1,e[A+100>>2]=e[A+96>>2],t=e[25865],e[A+304>>2]=e[25864],e[A+308>>2]=t,t=e[25867],e[A+312>>2]=e[25866],e[A+316>>2]=t,g[A+465|0]=64&d[A+465|0]|129,r=26226;break e}v[A+170>>1]=257,e[A+148>>2]=1,e[A+12>>2]=2,r=6840683;break e}e[A+144>>2]=2,e[A+104>>2]=2098176,e[A+8>>2]=0,e[A+12>>2]=32,e[A+40>>2]=3,e[A+28>>2]=1;break e}e[A+8>>2]=3,e[A+100>>2]=e[A+96>>2],r=26478;break e}e[A+328>>2]=18,e[A+296>>2]=320081425,e[A+300>>2]=353768980,e[A+600>>2]=2304,e[A+112>>2]=84648,e[A+104>>2]=16,e[A+8>>2]=6,e[A+12>>2]=65540,e[A+100>>2]=e[A+96>>2],t=e[25973],e[A+304>>2]=e[25972],e[A+308>>2]=t,t=e[25975],e[A+312>>2]=e[25974],e[A+316>>2]=t;H:{he:{pe:{if((0|r)<=28529){if((0|r)==26485)break pe;if((0|r)!=28261)break H;t=e[25861],e[A+304>>2]=e[25860],e[A+308>>2]=t,t=e[25863],e[A+312>>2]=e[25862],e[A+316>>2]=t,e[A+296>>2]=320017171,e[A+300>>2]=320017171,e[A+132>>2]=22,e[A+112>>2]=-1431655768,e[A+108>>2]=32768|e[A+108>>2],xa(A);break e}if((0|r)==28530)break he;if((0|r)!=28769)break H;e[A+600>>2]=2560,xa(A);break e}t=e[25861],e[A+304>>2]=e[25860],e[A+308>>2]=t,t=e[25863],e[A+312>>2]=e[25862],e[A+316>>2]=t,e[A+600>>2]=2688,e[A+296>>2]=320017171,e[A+300>>2]=320017171,e[A+8>>2]=2,xa(A);break e}e[A+600>>2]=2816}xa(A);break e}g[0|i]=104,g[i+1|0]=98,g[i+2|0]=115,g[i+3|0]=0,(0|r)!=29554?(t=e[26093],e[A+304>>2]=e[26092],e[A+308>>2]=t,t=e[26095],e[A+312>>2]=e[26094],e[A+316>>2]=t):(t=e[25977],e[A+304>>2]=e[25976],e[A+308>>2]=t,t=e[25979],e[A+312>>2]=e[25978],e[A+316>>2]=t),e[A+328>>2]=3,e[A+296>>2]=336859409,e[A+300>>2]=353768980,v[A+168>>1]=261,e[A+8>>2]=0,e[A+12>>2]=16,e[A+144>>2]=1,e[A+184>>2]=1056,e[A+104>>2]=33572172,e[A+108>>2]=330,e[A+36>>2]=3,g[A+465|0]=64&d[A+465|0]|129,g[A+458|0]=64&d[A+458|0]|129;break e}e[A+104>>2]=17990912,e[A+8>>2]=3,e[A+12>>2]=36,r=26740;break e}e[A+328>>2]=3,e[A+296>>2]=320016657,e[A+300>>2]=353768980,e[A+124>>2]=32,e[A+128>>2]=44,e[A+104>>2]=186758144,e[A+12>>2]=1081398,e[A+16>>2]=2,e[A+4>>2]=32,e[A+8>>2]=0,e[A+116>>2]=899,e[A+120>>2]=1,g[A+169|0]=1,e[A+76>>2]=2,t=e[25981],e[A+304>>2]=e[25980],e[A+308>>2]=t,t=e[25983],e[A+312>>2]=e[25982],e[A+316>>2]=t,g[A+465|0]=64&d[A+465|0]|129,bo(A,3),r=26741;break e}for(e[A+600>>2]=1328,e[A+8>>2]=3,t=e[25985],e[A+304>>2]=e[25984],e[A+308>>2]=t,t=e[25987],e[A+312>>2]=e[25986],e[A+316>>2]=t,i=IA(A+344|0,0,256),g[A+429|0]=129,g[A+416|0]=129,g[A+403|0]=129,g[A+399|0]=129,g[A+400|0]=129,g[A+397|0]=129,g[A+393|0]=129,n=103952,l=50,h=50;g[0|(t=i+h|0)]=2|d[0|t],t=i+d[n+1|0]|0,g[0|t]=2|d[0|t],t=i+d[n+2|0]|0,g[0|t]=2|d[0|t],h=d[0|(n=n+3|0)],t=103952,(0|n)!=103982;);for(n=A+344|0;g[0|(i=n+l|0)]=4|d[0|i],i=n+d[t+1|0]|0,g[0|i]=4|d[0|i],i=n+d[t+2|0]|0,g[0|i]=4|d[0|i],l=d[0|(t=t+3|0)],(0|t)!=103982;);g[A+168|0]=6,e[A+104>>2]=5128,g[A+413|0]=4|d[A+413|0];break e}e[A+328>>2]=4,e[A+296>>2]=336858640,e[A+300>>2]=353768980,e[A+104>>2]=16782440,e[A+20>>2]=2,e[A+12>>2]=22,e[A+4>>2]=2,e[A+8>>2]=2,e[A+332>>2]=104e3,t=e[25997],e[A+304>>2]=e[25996],e[A+308>>2]=t,t=e[25999],e[A+312>>2]=e[25998],e[A+316>>2]=t,r=26991;break e}e[A+296>>2]=303174160,e[A+300>>2]=353768980,e[A+104>>2]=16781320,e[A+144>>2]=2,e[A+8>>2]=2,e[A+12>>2]=22,t=e[26005],e[A+304>>2]=e[26004],e[A+308>>2]=t,t=e[26007],e[A+312>>2]=e[26006],e[A+316>>2]=t;break e}for(e[A+8>>2]=0,e[A+12>>2]=16,e[A+56>>2]=2,e[A+28>>2]=17,t=e[26009],e[A+304>>2]=e[26008],e[A+308>>2]=t,t=e[26011],e[A+312>>2]=e[26010],e[A+316>>2]=t,t=0,n=A+344|0;g[0|(i=t+n|0)]=231&d[0|i],g[0|(i=n+(1|t)|0)]=231&d[0|i],g[0|(i=n+(2|t)|0)]=231&d[0|i],g[0|(i=n+(3|t)|0)]=231&d[0|i],(0|(t=t+4|0))!=256;);e[A+104>>2]=2280,e[A+108>>2]=2,e[A+608>>2]=104048,g[A+451|0]=16|d[A+451|0],g[A+456|0]=16|d[A+456|0],g[A+459|0]=16|d[A+459|0],g[A+460|0]=16|d[A+460|0],g[A+450|0]=8|d[A+450|0],g[A+462|0]=8|d[A+462|0],g[A+458|0]=8|d[A+458|
vendor: 6,406 bytes, line 2810
28100],g[A+465|0]=64&d[A+465|0]|129;break e}e[A+296>>2]=269618961,e[A+300>>2]=370546196,e[A+12>>2]=131110,e[A+144>>2]=2,e[A+104>>2]=184559112,e[A+108>>2]=8192,e[A+16>>2]=0,e[A+20>>2]=2,e[A+4>>2]=1,e[A+8>>2]=2,e[A+100>>2]=e[A+96>>2],e[A+140>>2]=103676,e[A+68>>2]=2,e[A+56>>2]=1,e[A+44>>2]=130,e[A+28>>2]=2,t=e[26025],e[A+304>>2]=e[26024],e[A+308>>2]=t,t=e[26027],e[A+312>>2]=e[26026],e[A+316>>2]=t,g[A+465|0]=64&d[A+465|0]|129,r=26996;break e}e[A+4>>2]=524,e[A+8>>2]=2,e[A+196>>2]=368,e[A+104>>2]=0,e[A+336>>2]=104128,e[A- -64>>2]=1,t=e[26029],e[A+304>>2]=e[26028],e[A+308>>2]=t,t=e[26031],e[A+312>>2]=e[26030],e[A+316>>2]=t,g[A+465|0]=64&d[A+465|0]|129,r=6972015;break e}for(e[A+296>>2]=303174162,e[A+300>>2]=370545684,t=e[25873],e[A+304>>2]=e[25872],e[A+308>>2]=t,t=e[25875],e[A+312>>2]=e[25874],e[A+316>>2]=t,i=IA(A+344|0,0,256),g[A+431|0]=1,g[A+429|0]=1,g[A+411|0]=1,g[A+405|0]=1,g[A+400|0]=1,g[A+396|0]=1,g[A+392|0]=1,t=104160,n=49;g[0|(n=n+i|0)]=4|d[0|n],n=i+d[t+1|0]|0,g[0|n]=4|d[0|n],n=i+d[t+2|0]|0,g[0|n]=4|d[0|n],n=d[0|(t=t+3|0)],(0|t)!=104193;);e[A+600>>2]=4256,e[A+8>>2]=0,e[A+12>>2]=16,g[A+168|0]=7,e[A+132>>2]=32,g[A+392|0]=128|d[A+392|0],g[A+396|0]=128|d[A+396|0],g[A+400|0]=128|d[A+400|0],g[A+405|0]=128|d[A+405|0],g[A+411|0]=128|d[A+411|0],g[A+429|0]=128|d[A+429|0],g[A+431|0]=128|d[A+431|0],e[A+188>>2]=1056,e[A+192>>2]=29301,e[A+104>>2]=19964960;break e}for(e[A+600>>2]=1056,i=IA(A+344|0,0,256),g[A+393|0]=1,g[A+365|0]=1,g[A+360|0]=1,g[A+545|0]=1,g[A+529|0]=1,g[A+391|0]=1,g[A+389|0]=1,g[A+390|0]=1,g[A+387|0]=1,g[A+379|0]=1,g[A+374|0]=1,g[A+368|0]=1,g[A+489|0]=1,g[A+487|0]=1,g[A+398|0]=1,t=104224,n=17;g[0|(n=n+i|0)]=4|d[0|n],n=i+d[t+1|0]|0,g[0|n]=4|d[0|n],n=i+d[t+2|0]|0,g[0|n]=4|d[0|n],n=d[0|(t=t+3|0)],(0|t)!=104251;);g[A+360|0]=128|d[A+360|0],g[A+365|0]=128|d[A+365|0],g[A+393|0]=128|d[A+393|0],g[A+368|0]=128|d[A+368|0],g[A+374|0]=128|d[A+374|0],g[A+379|0]=128|d[A+379|0],g[A+387|0]=128|d[A+387|0],g[A+389|0]=128|d[A+389|0],g[A+390|0]=128|d[A+390|0],g[A+391|0]=128|d[A+391|0],g[A+529|0]=128|d[A+529|0],g[A+545|0]=128|d[A+545|0],g[A+489|0]=128|d[A+489|0],g[A+487|0]=128|d[A+487|0],g[A+398|0]=128|d[A+398|0],t=e[26055],e[A+312>>2]=e[26054],e[A+316>>2]=t,t=e[26053],e[A+304>>2]=e[26052],e[A+308>>2]=t,e[A+296>>2]=353636370,e[A+300>>2]=336925972,e[A+200>>2]=0,e[A+8>>2]=7,e[A+12>>2]=2097184,g[A+168|0]=2,e[A+104>>2]=50176,e[A+84>>2]=1,bo(A,3);break e}e[A+296>>2]=320017171,e[A+300>>2]=320017171,e[A+104>>2]=184618072,e[A+8>>2]=12,e[A+12>>2]=32,t=e[25861],e[A+304>>2]=e[25860],e[A+308>>2]=t,t=e[25863],e[A+312>>2]=e[25862],e[A+316>>2]=t,r=27500;break e}e[A+184>>2]=42752,e[A+600>>2]=4352,IA(A+344|0,0,256),g[A+456|0]=1,g[A+457|0]=1,g[A+458|0]=1,g[A+459|0]=1,g[A+449|0]=1,g[A+450|0]=1,g[A+451|0]=1,g[A+452|0]=1,g[A+453|0]=1,g[A+454|0]=1,g[A+455|0]=1,g[A+456|0]=1,g[A+441|0]=1,g[A+442|0]=1,g[A+443|0]=1,g[A+444|0]=1,g[A+445|0]=1,g[A+446|0]=1,g[A+447|0]=1,g[A+448|0]=1,g[A+460|0]=65,g[A+461|0]=65,g[A+532|0]=32,g[A+527|0]=32,g[A+519|0]=32,g[A+515|0]=32,g[A+349|0]=32,g[A+350|0]=32,g[A+346|0]=32,e[A+132>>2]=20,e[A+112>>2]=286331152,e[A+104>>2]=1024,e[A+108>>2]=16384,e[A+40>>2]=1,e[A+8>>2]=8,g[A+458|0]=65,g[A+453|0]=65,g[A+447|0]=65,g[A+448|0]=65,g[A+443|0]=65,g[A+444|0]=65,r=27503;break e}e[A+328>>2]=10,e[A+296>>2]=336859666,e[A+300>>2]=353768980,g[A+168|0]=2,e[A+104>>2]=263264,e[A+8>>2]=7,t=e[26065],e[A+304>>2]=e[26064],e[A+308>>2]=t,t=e[26067],e[A+312>>2]=e[26066],e[A+316>>2]=t,r=27509;break e}e[A+104>>2]=1,r=27513;break e}e[A+116>>2]=5e3,e[A+104>>2]=16777216,e[A+24>>2]=1,e[A+16>>2]=0,e[A+20>>2]=2,e[A+8>>2]=2,e[A+12>>2]=32,e[A+328>>2]=5,r=27745;break e}e[A+116>>2]=5e3,e[A+104>>2]=99336,e[A+108>>2]=256,e[A+24>>2]=1,e[A+16>>2]=0,e[A+20>>2]=2,e[A+8>>2]=2,e[A+12>>2]=32,e[A+328>>2]=5,r=27764;break e}e[A+328>>2]=6,e[A+296>>2]=336859409,e[A+300>>2]=353768980,e[A+600>>2]=1056,e[A+104>>2]=2114600,e[A+108>>2]=138,e[A+8>>2]=4,e[A+632>>2]=104288,e[A+604>>2]=104288,t=e[26093],e[A+304>>2]=e[26092],e[A+308>>2]=t,t=e[26095],e[A+312>>2]=e[26094],e[A+316>>2]=t,r=28011;break e}e[A+328>>2]=4,e[A+104>>2]=1,e[A+8>>2]=2,e[A+36>>2]=256,r=28020;break e}e[A+4>>2]=48,e[A+8>>2]=0,e[A+12>>2]=128,e[A+104>>2]=2169880,e[A+32>>2]=1,e[A+36>>2]=256,e[A+24>>2]=1,e[A+136>>2]=85767,t=e[26097],e[A+304>>2]=e[26096],e[A+308>>2]=t,t=e[26099],e[A+312>>2]=e[26098],e[A+316>>2]=t,g[A+465|0]=64&d[A+465|0]|129,r=28268;break e}e[A+8>>2]=0,e[A+104>>2]=71752,t=e[26101],e[A+304>>2]=e[26100],e[A+308>>2]=t,t=e[26103],e[A+312>>2]=e[26102],e[A+316>>2]=t,g[A+465|0]=64&d[A+465|0]|129,r=28258;break e}e[A+296>>2]=336858898,e[A+300>>2]=370546196,e[A+104>>2]=1088,e[A+108>>2]=512,e[A+8>>2]=2,e[A+12>>2]=524310,t=e[26105],e[A+304>>2]=e[26104],e[A+308>>2]=t,t=e[26107],e[A+312>>2]=e[26106],e[A+316>>2]=t,r=28525;break e}e[A+328>>2]=3,e[A+296>>2]=320015633,e[A+300>>2]=353768980,g[A+168|0]=7,e[A+8>>2]=2,e[A+12>>2]=6,e[A+104>>2]=20488,e[A+108>>2]=192,e[A+36>>2]=9,e[A+60>>2]=260,t=e[26109],e[A+304>>2]=e[26108],e[A+308>>2]=t,t=e[26111],e[A+312>>2]=e[26110],e[A+316>>2]=t,g[A+465|0]=64&d[A+465|0]|129,r=28780;break e}for(e[A+296>>2]=353569552,e[A+300>>2]=353768980,e[A+116>>2]=5e3,e[A+104>>2]=33570920,e[A+108>>2]=14336,e[A+8>>2]=3,e[A+12>>2]=139286,e[A+100>>2]=e[A+96>>2],t=e[26113],e[A+304>>2]=e[26112],e[A+308>>2]=t,t=e[26115],e[A+312>>2]=e[26114],e[A+316>>2]=t,g[A+465|0]=64&d[A+465|0]|129,t=0,n=A+344|0;g[0|(i=t+n|0)]=253&d[0|i],g[0|(i=n+(1|t)|0)]=253&d[0|i],g[0|(i=n+(2|t)|0)]=253&d[0|i],g[0|(i=n+(3|t)|0)]=253&d[0|i],(0|(t=t+4|0))!=256;);g[A+442|0]=2|d[A+442|0],g[A+443|0]=2|d[A+443|0],g[A+444|0]=2|d[A+444|0],g[A+446|0]=2|d[A+446|0],g[A+447|0]=2|d[A+447|0],g[A+450|0]=2|d[A+450|0],g[A+451|0]=2|d[A+451|0],g[A+453|0]=2|d[A+453|0],g[A+454|0]=2|d[A+454|0],g[A+456|0]=2|d[A+456|0],g[A+457|0]=2|d[A+457|0],g[A+459|0]=2|d[A+459|0],g[A+460|0]=2|d[A+460|0],g[A+462|0]=2|d[A+462|0],g[A+464|0]=2|d[A+464|0],g[A+466|0]=2|d[A+466|0],e[A+144>>2]=2,e[A+68>>2]=2;break e}e[A+296>>2]=303172879,e[A+300>>2]=353768980,e[A+328>>2]=3,e[A+8>>2]=3,e[A+12>>2]=262,e[A+104>>2]=16805928,e[A+108>>2]=30,t=e[26117],e[A+304>>2]=e[26116],e[A+308>>2]=t,t=e[26119],e[A+312>>2]=e[26118],e[A+316>>2]=t,r=29295;break e}Go(A),r=29301;break e}for(e[A+328>>2]=3,e[A+296>>2]=336859153,e[A+300>>2]=353768980,v[A+168>>1]=261,e[A+8>>2]=0,e[A+12>>2]=22,e[A+124>>2]=0,e[A+128>>2]=44,e[A+104>>2]=16794624,e[A+108>>2]=128,e[A+36>>2]=3,e[A+60>>2]=4,t=e[25869],e[A+304>>2]=e[25868],e[A+308>>2]=t,t=e[25871],e[A+312>>2]=e[25870],e[A+316>>2]=t,(0|r)==25459&&(e[A+108>>
28102]=136),g[A+465|0]=64&d[A+465|0]|129,g[A+458|0]=64&d[A+458|0]|129,t=0,n=A+344|0;g[0|(i=t+n|0)]=223&d[0|i],g[0|(i=n+(1|t)|0)]=223&d[0|i],g[0|(i=n+(2|t)|0)]=223&d[0|i],g[0|(i=n+(3|t)|0)]=223&d[0|i],(0|(t=t+4|0))!=256;);g[A+442|0]=32|d[A+442|0],g[A+444|0]=32|d[A+444|0],g[A+447|0]=32|d[A+447|0],g[A+450|0]=32|d[A+450|0],g[A+452|0]=32|d[A+452|0],g[A+453|0]=32|d[A+453|0],g[A+454|0]=32|d[A+454|0],g[A+458|0]=32|d[A+458|0],g[A+462|0]=32|d[A+462|0],g[A+463|0]=32|d[A+463|0],g[A+466|0]=32|d[A+466|0],g[A+441|0]=32|d[A+441|0],g[A+445|0]=32|d[A+445|0],g[A+449|0]=32|d[A+449|0],g[A+455|0]=32|d[A+455|0],g[A+461|0]=32|d[A+461|0],g[A+465|0]=32|d[A+465|0];break e}for(e[A+296>>2]=303174162,e[A+300>>2]=370545684,e[A+600>>2]=3456,g[A+169|0]=1,e[A+8>>2]=0,e[A+12>>2]=22,e[A+100>>2]=e[A+96>>2],t=e[25873],e[A+304>>2]=e[25872],e[A+308>>2]=t,t=e[25875],e[A+312>>2]=e[25874],e[A+316>>2]=t,IA(A+344|0,0,256),g[A+365|0]=1,g[A+366|0]=1,g[A+357|0]=1,g[A+358|0]=1,g[A+359|0]=1,g[A+360|0]=1,g[A+361|0]=1,g[A+362|0]=1,g[A+363|0]=1,g[A+364|0]=1,g[A+349|0]=1,g[A+350|0]=1,g[A+351|0]=1,g[A+352|0]=1,g[A+353|0]=1,g[A+354|0]=1,g[A+355|0]=1,g[A+356|0]=1,t=74,n=74;g[344+(i=A+n|0)|0]=1|d[i+344|0],g[i+345|0]=1|d[i+345|0],g[i+346|0]=1|d[i+346|0],(0|(n=n+3|0))!=116;);for(;g[344+(n=t+A|0)|0]=2|d[n+344|0],g[n+345|0]=2|d[n+345|0],g[n+346|0]=2|d[n+346|0],(0|(t=t+3|0))!=116;);for(n=26;g[344+(t=A+n|0)|0]=4|d[t+344|0],g[t+345|0]=4|d[t+345|0],g[t+346|0]=4|d[t+346|0],g[t+347|0]=4|d[t+347|0],g[t+348|0]=4|d[t+348|0],(0|(n=n+5|0))!=71;);e[A+112>>2]=84648,e[A+104>>2]=270589952,e[A+108>>2]=65536,e[A+40>>2]=1,e[A+204>>2]=e[A+600>>2]+74;break e}e[A+8>>2]=2,e[A+12>>2]=32,e[A+328>>2]=3,e[A+124>>2]=32,e[A+104>>2]=16864280,e[A+108>>2]=256,e[A+68>>2]=2,e[A+36>>2]=259,e[A+40>>2]=118,e[A+28>>2]=1,g[A+458|0]=128|d[A+458|0],r=29548;break e}e[A+296>>2]=370544658,e[A+300>>2]=370546196,e[A+164>>2]=130,e[A+8>>2]=0,e[A+12>>2]=86,e[A+104>>2]=87064,g[A+169|0]=1,e[A+152>>2]=3,t=e[26121],e[A+304>>2]=e[26120],e[A+308>>2]=t,t=e[26123],e[A+312>>2]=e[26122],e[A+316>>2]=t,g[A+465|0]=64&d[A+465|0]|129,r=7564650;break e}e[A+296>>2]=269487120,e[A+300>>2]=320148500,e[A+8>>2]=3,e[A+12>>2]=278,e[A+144>>2]=2,e[A+104>>2]=32872,t=e[26125],e[A+304>>2]=e[26124],e[A+308>>2]=t,t=e[26127],e[A+312>>2]=e[26126],e[A+316>>2]=t,g[A+465|0]=64&d[A+465|0]|129,r=29553;break e}e[A+296>>2]=336859152,e[A+300>>2]=353768980,e[A+8>>2]=0,e[A+144>>2]=1,e[A+104>>2]=6408,t=e[26129],e[A+304>>2]=e[26128],e[A+308>>2]=t,t=e[26131],e[A+312>>2]=e[26130],e[A+316>>2]=t,g[A+465|0]=64&d[A+465|0]|129,r=29558;break e}e[A+296>>2]=320015376,e[A+300>>2]=353768980,g[A+168|0]=4,e[A+12>>2]=22,e[A+4>>2]=1,e[A+8>>2]=2,e[A+104>>2]=1248,e[A+100>>2]=e[A+96>>2],t=e[26133],e[A+304>>2]=e[26132],e[A+308>>2]=t,t=e[26135],e[A+312>>2]=e[26134],e[A+316>>2]=t;break e}e[A+296>>2]=303174162,e[A+300>>2]=370545684,g[A+169|0]=1,e[A+8>>2]=0,e[A+12>>2]=22,e[A+112>>2]=5288,e[A+100>>2]=e[A+96>>2],t=e[25877],e[A+304>>2]=e[25876],e[A+308>>2]=t,t=e[25879],e[A+312>>2]=e[25878],e[A+316>>2]=t;h:switch(r-29793|0){default:if((0|r)!=27502){if((0|r)!=28012)break r;t=e[26137],e[A+304>>2]=e[26136],e[A+308>>2]=t,t=e[26139],e[A+312>>2]=e[26138],e[A+316>>2]=t,e[A+600>>2]=3328,e[A+296>>2]=320017171,e[A+300>>2]=320017171,e[A+104>>2]=2098176,e[A+108>>2]=131072,e[A+8>>2]=13;break r}e[A+104>>2]=1,e[A+600>>2]=3200;break r;case 4:break h;case 1:case 2:case 3:break r;case 0:break a}e[A+104>>2]=1,e[A+108>>2]=524288,e[A+600>>2]=3072;break r}e[A+328>>2]=10,e[A+296>>2]=353636370,e[A+300>>2]=336925972,g[A+173|0]=1,e[A+8>>2]=7,e[A+12>>2]=32,g[A+168|0]=2,e[A+84>>2]=1,t=e[26141],e[A+304>>2]=e[26140],e[A+308>>2]=t,e[A+104>>2]=(0|r)==24954?2118920:2114824,t=e[26143],e[A+312>>2]=e[26142],e[A+316>>2]=t;break e}Br(A),e[A+296>>2]=303173650,e[A+300>>2]=303174162,e[A+104>>2]=2131208,e[A+8>>2]=3,e[A+12>>2]=32,t=e[25865],e[A+304>>2]=e[25864],e[A+308>>2]=t,t=e[25867],e[A+312>>2]=e[25866],e[A+316>>2]=t,r=29812;break e}Go(A),r=30059;break e}e[A+112>>2]=21160,e[A+104>>2]=16,e[A+600>>2]=1536,e[A+40>>2]=1;break e}e[A+296>>2]=269488144,e[A+300>>2]=370546198,e[A+8>>2]=0,e[A>>2]=33,e[A+148>>2]=1,e[A+104>>2]=12615688,e[A+16>>2]=2,e[A+100>>2]=e[A+96>>2],e[A+632>>2]=104592,e[A+604>>2]=104592,t=e[26145],e[A+304>>2]=e[26144],e[A+308>>2]=t,t=e[26147],e[A+312>>2]=e[26146],e[A+316>>2]=t,r=30313;break e}if(e[A+296>>2]=370544662,e[A+300>>2]=370546198,e[A+8>>2]=3,e[A+12>>2]=2,e[A+148>>2]=1,e[A+184>>2]=12544,v[A+170>>1]=257,e[A+176>>2]=1,g[A+172|0]=1,e[A>>2]=33,e[A+4>>2]=0,e[A+100>>2]=e[A+96>>2],t=e[26225],e[A+304>>2]=e[26224],e[A+308>>2]=t,t=e[26227],e[A+312>>2]=e[26226],e[A+316>>2]=t,(0|r)!=7959909)break e;e[A+112>>
vendor: 42,088 bytes, line 2810
28102]=24,e[A+104>>2]=1,e[A+108>>2]=1048576,r=7959909;break e}t=e[25873],e[A+304>>2]=e[25872],e[A+308>>2]=t,t=e[25875],e[A+312>>2]=e[25874],e[A+316>>2]=t,e[A+600>>2]=2944,e[A+104>>2]=2097152,e[A+108>>2]=262144,e[A+48>>2]=1}xa(A),g[A+422|0]=2|d[A+422|0];break e}e[A+40>>2]=1}return e[A+212>>2]=r,8&(t=e[A+104>>2])&&(e[A+124>>2]=46,e[A+128>>2]=44),4&t&&(e[A+124>>2]=0),A}function lt(t){var A=0,r=0,n=0,i=0,l=0,h=0,m=0,w=0,M=0,y=0,P=0,R=0,ee=0,ae=0,ue=0,be=0,_e=0,Ve=0,$e=0,it=0,ht=0,yt=0,Pt=0,Wt=0,cA=0;e:{r=e[32538],e[47354]=0,e[47568]=0,e[49828]=0,e[47569]=0,g[199328]=0,e[49827]=0,e[49845]=0,g[190280]=0,g[190268]=1,e[47202]=0,e[49573]=0,e[49846]=0,g[199304]=0,g[199388]=0,e[33691]=0,e[33285]=0,e[33708]=1,e[33709]=1,e[33288]=0,A=e[33730],e[33712]=e[33729],e[33713]=A,A=e[33732],e[33714]=e[33731],e[33715]=A,A=e[33734],e[33716]=e[33733],e[33717]=A,A=e[33736],e[33718]=e[33735],e[33719]=A,A=e[33738],e[33720]=e[33737],e[33721]=A,A=e[33740],e[33722]=e[33739],e[33723]=A,A=e[33742],e[33724]=e[33741],e[33725]=A,e[33726]=e[33743],g[134784]=0,g[134824]=0,g[134772]=0,g[134760]=0,e[33284]=-1,e[33692]=0,e[32525]=0,e[47201]=e[33717],e[47200]=e[33718],mn(),e[34438]=0,e[34437]=0,A=e[33730],e[34048]=e[33729],e[34049]=A,A=e[33732],e[34050]=e[33731],e[34051]=A,A=e[33734],e[34052]=e[33733],e[34053]=A,A=e[33736],e[34054]=e[33735],e[34055]=A,A=e[33738],e[34056]=e[33737],e[34057]=A,A=e[33740],e[34058]=e[33739],e[34059]=A,A=e[33742],e[34060]=e[33741],e[34061]=A,e[34062]=e[33743];t:{r:{if(1&r){if(e[e[32539]>>2]=0,e[47569]|e[49845]|e[49827])break r;break t}if(e[e[32539]>>2]=0,!(e[49845]|e[49827]||e[47569]))break t}g[190280]=1}e[49828]=0,A=268436735;t:if(!(!e[34391]|!e[34388])&&(e[47204]=0,e[47203]=0,e[47199]=0,e[34439]=0,(e[47192]||!(A=io(86228)))&&((A=e[33283])||((A=aA(16))&&(e[A>>2]=0,e[A+4>>2]=0,e[A+8>>2]=0,e[A+12>>2]=0),e[33283]=A),l=268439807,(r=e[e[47192]+328>>2])>>>0>20|!e[129104+(r<<3)>>2]?A=l:(t?(l=4,i=wA(t)+1|0):(l=2,i=0),e[A+8>>2]=l,e[A>>2]=t,e[A+12>>2]=e[129108+(r<<3)>>2],e[A+4>>2]=t?t+i|0:0,A=0),!A))){Qe(0);r:{for(;;){e[34436]=0,t=e[34391],e[51290]=t,e[54046]=t+e[34390],e[50767]<=102399&&(e[50767]=102400);a:if(!((h=e[51290])>>>0>=re[54046]))for(;;){if((((0|(t=(i=e[50757])-(m=e[50758])|0))<=0?t+170|0:t)-171|0)>=-1){if((0|(t=e[54731]))<=0)break a;l=0,e[50763]=0,e[50762]=0,e[50765]=2147483647,t=d[218920]?e[54732]:t;n:{for(;;){if(r=t-1|0,e[54732]=r,(0|t)<=0)break n;if(e[51290]=h+1,A=(t=e[51293])+1|0,e[51293]=(0|A)<=5499?A:0,A=(t=F(e[50755],v[205184+(t<<1)>>1]))>>8,g[0|h]=A,i=e[51290],e[51290]=i+1,g[0|i]=t>>>16,(i=e[50756])?(t=r,(r=e[i+4>>2])&&(LA[0|r](A<<16>>16),t=e[54732])):t=r,i=(r=e[51292])+1|0,e[51292]=i,v[205184+(r<<1)>>1]=A,(0|i)>=5500&&(e[51292]=0),h=e[51290],!(re[54046]>=h+2>>>0))break}l=1}g[218920]=l;break a}t=e[4+(r=216192+(i<<4)|0)>>2];n:{c:{o:{f:{l:{i:{h:{b:{m:{C:{p:{x:{w:{B:{F:{Y:switch((255&(A=e[r>>2]))-1|0){case 9:break o;case 7:break f;case 10:break l;case 11:break i;case 13:break h;case 0:break b;case 1:break m;case 2:break C;case 3:break p;case 6:break x;case 5:break w;case 4:break B;case 15:break F;case 8:break Y;default:break c}if(!e[50759])break c;A=e[r+12>>2],r=e[r+8>>2],e[50768]=0,h=r||99232,e[50766]=h,e[50769]=t?2097152/(0|t)|0:0,t=e[50971],i=(0|F(t,e[50788]))/50|0,r=F(i-t|0,-18),t=((0|(t=e[50785]))>=101?101:t)-e[50790]|0,A=(l=r+((0|F(e[50970],d[((0|t)>0?t:0)+105680|0]))/128|0)|0)+((0|F(i,(0|(t=A>>16))<(0|(r=65535&A))?t:r))/2|0)|0,e[50770]=A,t=(l+((0|F(i,(0|t)>(0|r)?t:r))/2|0)|0)-A|0,e[50771]=t,r=d[0|h],e[33072]=e[50976],e[50767]=A+(F(t,r)>>8);break c}LA[e[e[50756]>>2]](t,e[r+8>>2]),EA(t);break c}if(d[218920]||(e[54731]=e[54731]-t),e[50781]=100,e[50773]=0,Ee(),e[50763]=0,e[50762]=0,e[50765]=2147483647,!t)break c;for(t=d[218920]?e[54732]:t;;){if(r=t-1|0,e[54732]=r,(0|t)<=0)break c;if(t=e[51290],e[51290]=t+1,i=(A=e[51293])+1|0,e[51293]=(0|i)<=5499?i:0,i=t,A=(t=F(e[50755],v[205184+(A<<1)>>1]))>>8,g[0|i]=A,i=e[51290],e[51290]=i+1,g[0|i]=t>>>16,(i=e[50756])?(t=r,(r=e[i+4>>2])&&(LA[0|r](A<<16>>16),t=e[54732])):t=r,i=(r=e[51292])+1|0,e[51292]=i,v[205184+(r<<1)>>1]=A,(0|i)>=5500&&(e[51292]=0),i=1,!(re[54046]>=e[51290]+2>>>0))break}break n}for(e[50773]=0,e[54731]=e[54729],Ee(),A=e[r+12>>2],i=e[r+8>>2],d[218920]?t=e[54733]:e[54734]=0,m=A>>8,l=255&A,e[50762]=0,e[50763]=0;;){if(r=t-1|0,e[54733]=r,(0|t)<=0)break c;if(A=(t=e[54734])+1|0,l?h=F(l,g[t+i|0]):(h=d[t+i|0]|g[A+i|0]<<8,A=t+2|0),e[54734]=A,w=(t=e[51293])+1|0,e[51293]=w,A=(0|(t=(0|(t=((0|F(m,F(e[33037],F(e[33038],h))>>10))/32|0)+(F(e[50755],v[205184+(t<<1)>>1])>>8)|0))<=-32768?-32768:t))>=32767?32767:t,(0|w)>=5500&&(e[51293]=0),g[e[51290]]=A,g[e[51290]+1|0]=A>>>8,(h=e[50756])?(t=r,(r=e[h+12>>2])&&(LA[0|r](A<<16>>16),t=e[54733])):t=r,r=e[51290],e[51290]=r+2,w=(h=e[51292])+1|0,e[51292]=w,v[205184+(h<<1)>>1]=(0|F(A,3))/4,(0|w)>=5500&&(e[51292]=0),!(re[54046]>=r+4>>>0))break}i=1;break n}A=e[r+12>>2],i=t>>>16|0,e[50777]=i,t&=65535,e[50773]=t,l=255&A,e[50774]=l,e[50775]=A>>8,l||(e[50777]=i<<1,e[50773]=t<<1),e[50778]=0,e[50776]=0,e[50772]=e[r+8>>2];break c}e[50773]=0}if(e[54731]=e[54729],A=e[50759],d[218920]){if(!A)break c}else{if(!A)break c;for(l=e[r+12>>2],h=e[r+8>>2],r=t>>16,e[55912]=255&r,g[218960]=1,e[55908]=0,67108864&t&&(e[55908]=3,e[55909]=e[110496+(r>>>6&12)>>2]),134217728&t&&(e[55908]=4,e[55909]=e[110512+(r>>>6&12)>>2]),t&=65504;;){if((0|m)!=(0|(i=(0|(r=i+1|0))<=169?r:0)))if((0|(r=e[216192+(i<<4)>>2]))!=3){if(r-5>>>0>1)continue}else g[218960]=0;break}for(e[55913]=e[50762],t=(t=t+32&131008)||64,e[50763]=t+e[50763],e[55684]=F(v[101997],7800)+(_[102024]<<8)<<8,e[55704]=F(v[101998],9e3)+(_[102025]<<8)<<8,ee=e[50980],n=+(0|t),P=+(t>>>2|0),t=0;(0|t)!=7&&(i=v[218+(w=(r=t<<1)+A|0)>>1]<<8,m=F(t,80)+222176|0,w=v[w+164>>1],R=i+F(w,v[2+(r+h|0)>>1])<<8,e[m>>2]=R,y=+(0|R),S[m+16>>3]=y,S[m+48>>3]=16*(+(i+F(w,v[2+(r+l|0)>>1])<<8)-y)/P),i=(r=F(t,80))+222176|0,w=v[182+(R=A+(t<<1)|0)>>1],ue=F(w,d[18+(m=t+h|0)|0])<<6,e[i+4>>2]=ue,y=+(0|ue),S[i+24>>3]=y,ae=i,i=t+l|0,S[ae+56>>3]=64*(+(F(w,d[i+18|0])<<6)-y)/n,(0|t)>(0|ee)|t>>>0>5||(w=r+222176|0,ue=v[R+200>>1],R=F(ue,d[m+26|0])<<10,e[w+8>>2]=R,y=+(0|R),S[w+32>>3]=y,ae=w- -64|0,w=ue<<10,S[ae>>3]=64*(+(0|F(w,d[i+26|0]))-y)/n,t>>>0<=2?(r=r+222176|0,m=F(w,d[m+32|0]),e[r+12>>2]=m,y=+(0|m),S[r+40>>3]=y,S[r+72>>3]=64*(+(0|F(w,d[i+32|0]))-y)/n):e[r+222188>>2]=R),(0|(t=t+1|0))!=8;);}for(;;){if(t=e[50762],!d[218960]&(0|t)==e[50763])break c;C:{p:{if(63&t){if(7&t)break C;x:if(!((0|(r=e[54736]))<=0||(0|(A=e[54735]))<=0))for(i=e[50826],t=1;;){if(e[(h=(l=t<<2)+i|0)>>2]=e[h>>2]+e[l+203312>>2],t>>>0>28|(0|t)>=(0|r))break x;if(l=(0|t)<(0|A),t=t+1|0,!l)break}if((0|(t=e[33073]))>255)break C;e[33073]=t+1;break C}if(t)if(e[50759]){if(r=e[50768]+e[50769]|0,e[50768]=r,r=(A=e[50766])?F(d[A+((0|(r>>=8))>=127?127:r)|0],e[50771])>>8:0,e[55911]=e[55911]+e[55915],A=(0|(A=e[55914]))<=23551?A:0,e[55914]=A+e[50761],A=(r+e[50770]|0)+F(e[33072],d[110528+(A>>6)|0]-128|0)|0,e[50767]=A,!(i=e[51291])&(0|(r=i?i<<12:A))>102399||(A=(0|r)<=102400?102400:r,e[50767]=A),(0|t)!=e[55913]){if(t=0,!((0|(m=e[50980]))<0)){for(;h=F(t,80),n=S[48+(r=h+222176|0)>>3]+S[r+16>>3],S[r+16>>3]=n,P=S[r+56>>3]+S[r+24>>3],S[r+24>>3]=P,y=S[r- -64>>3]+S[r+32>>3],S[r+32>>3]=y,i=he(n)<2147483648?~~n:-2147483648,e[r>>2]=i,i=he(y)<2147483648?~~y:-2147483648,e[r+8>>2]=i,l=he(P)<2147483648?~~P:-2147483648,e[r+4>>2]=(0|l)>0?l:0,(0|t)>2||(n=S[72+(r=h+222176|0)>>3]+S[r+40>>3],S[r+40>>3]=n,i=he(n)<2147483648?~~n:-2147483648),e[h+222188>>2]=i,(0|m)>=(0|(t=t+1|0)););if((0|t)>=8)break p}for(;(0|t)!=7&&(r=F(t,80)+222176|0,n=S[r+48>>3]+S[r+16>>3],S[r+16>>3]=n,i=he(n)<2147483648?~~n:-2147483648,e[r>>2]=i),r=F(t,80)+222176|0,n=S[r+56>>3]+S[r+24>>3],S[r+24>>3]=n,i=he(n)<2147483648?~~n:-2147483648,e[r+4>>2]=(0|i)>0?i:0,(0|(t=t+1|0))!=8;);}}else A=e[50767];else e[50826]=218976,e[54742]=0,e[54736]=oa(e[50767]<<4,218976,0),A=e[50767],e[54737]=890/(A>>12),e[54739]=(0|F(e[50781],F(e[50779],A>>8)))/8e4}if(e[55906]=A>>11,e[54735]=e[54736],e[55904]=F(e[50760],A>>7),e[55905]=e[50754]/(A>>12),r=1^(t=e[54742]),e[54742]=r,e[50826]=F(t,1600)+218976,e[54736]=oa(A<<4,F(r,1600)+218976|0,1),!(!(A=e[50759])|!e[51022]))for(P=S[25430],y=S[25429],t=1;e[272+(r=A+(t<<2)|0)>>2]&&(i=e[r+308>>2],r=F(t,40)+203456|0,M=Aa(P*+v[2+(F(t,80)+222176|0)>>1]),M*=n=fr(y*+(0|i)),M+=M,S[r+8>>3]=M,n*=-n,S[r+16>>3]=n,S[r>>3]=1-M-n),(0|(t=t+1|0))!=9;);}if(l=e[50762]+1|0,e[50762]=l,h=(t=e[50765])+e[55904]|0,e[50765]=h,(0|h)<0&(0|t)>0){if(w=e[55905],A=e[50800]+((0|w)/-2|0)|0,e[55907]=A,(0|(R=e[50763]))<(0|l))break c;if(ee=e[54738]+1|0,e[54738]=ee,m=e[50767],!((0|(t=(r=e[50980])+1|0))>8)&&(i=m<<3,1&r&&(e[203264+(t<<2)>>2]=(1+(e[F(t,80)+222176>>2]/(0|i)|0)|0)/2,t=r+2|0),(0|r)!=7))for(;r=203264+(t<<2)|0,ue=F(t,80)+222176|0,e[r>>2]=(1+(e[ue>>2]/(0|i)|0)|0)/2,e[r+4>>2]=(1+(e[ue+80>>2]/(0|i)|0)|0)/2,(0|(t=t+2|0))!=9;);t=(0|F(e[50781],F(e[50779],m>>8)))/8e4|0,e[54739]=t;C:if(!((0|(r=e[55908]))<=0)){switch(r-3|0){case 0:if((R-l|0)>=w<<1)break C;e[55908]=2,t=(0|F(e[55909],t))/256|0,e[54739]=t;break C;case 1:e[55908]=2,t=(0|F(e[55909],t))/256|0,e[54739]=t;break C}e[55908]=r-1}(r=e[55910])&&(i=t,t=e[55911]>>8,t=(0|F(i,d[r+((0|t)>=127?127:t)|0]))/128|0,e[54739]=t),(0|(r=e[e[32972]+92>>2]))>7||(r=15&(i=d[e[55912]+(106336+(r<<3)|0)|0]),(i=i>>>4|0)&&((0|i)!=15?(0|ee)%(0|i)|0||(e[54739]=(0|F(t,r))/16):(e[55912]=0,e[54739]=(0|F(t,r))/16)))}else A=e[55907];if(l=A+1|0,e[55907]=l,r=h>>>16|0,i=0,!((0|l)<0|(0|l)>=e[50799])){if(!((0|(t=(A=e[50980])+1|0))>8)){if(m=1&(h=8-A|0),(0|A)!=7)for(w=-2&h,A=0;i=F(e[203216+(R=4+(h=t<<2)|0)>>2],v[106400+(F(r,e[R+203264>>2])>>>4&4094)>>1])+(F(e[h+203216>>2],v[106400+(F(r,e[h+203264>>2])>>>4&4094)>>1])+i|0)|0,t=t+2|0,(0|w)!=(0|(A=A+2|0)););m&&(i=F(e[203216+(t<<=2)>>2],v[106400+(F(r,e[t+203264>>2])>>>4&4094)>>1])+i|0)}i=F(d[l+132160|0],(0|i)/e[55906]|0)}if(t=1,(0|(l=e[54737]))<=0)A=r;else for(h=e[50826],A=r;i=F(e[h+(t<<2)>>2],v[106400+((65504&A)>>>4|0)>>1])+i|0,A=A+r|0,(0|l)>=(0|(t=t+1|0)););if((0|(l=e[54735]))>=(0|t))for(h=e[50826];i=i-F(e[h+(t<<2)>>2],v[106400+((65504&A)>>>4|0)>>1])|0,A=A+r|0,(0|l)>=(0|(t=t+1|0)););if(l=(0|(t=e[54728]))==64?i:F(t,i>>6),e[51022]){if(e[50759])for(r=rr(e[33209],0,1103515245,0),t=fe,t=Cn(r=r+12345|0,t=r>>>0<12345?t+1|0:t),e[33209]=t,P=+((16383&t)- -8192|0),A=e[50759],h=0,t=1;(i=e[272+(A+(t<<2)|0)>>2])&&(m=e[F(t,80)+222180>>2],r=F(t,40)+203456|0,n=S[r+32>>3],y=S[r+24>>3],S[r+32>>3]=y,n=n*S[r+16>>3]+(S[r>>3]*P+y*S[r+8>>3]),S[r+24>>3]=n,r=he(n)<2147483648?~~n:-2147483648,h=F(r,F(i,m>>14))+h|0),(0|(t=t+1|0))!=9;);else h=0;l=l+h|0}A=0,(0|(t=e[50776]))>=e[50773]||(r=e[50778],A=e[50772],(h=e[50774])?(i=t+1|0,e[50776]=i,t=F(h,g[A+(t+r|0)|0])):(h=d[0|(A=A+(t+r|0)|0)],A=g[A+1|0],i=t+2|0,e[50776]=i,t=h|A<<8),A=(0|F(F(t,e[50780])>>10,e[50775]))/32|0,(0|(t=e[50777]))>(r+i|0)||(e[50778]=r+((0|F(t,3))/-4|0))),r=(t=e[51293])+1|0,e[51293]=r,t=((F(e[54739],l>>8)>>13)+A|0)+(F(e[50755],v[205184+(t<<1)>>1])>>8)|0,(0|r)>=5500&&(e[51293]=0),r=e[33073];C:{p:{if((0|(A=F(r,t)))>=8388608){if((0|r)>=(0|(h=8388608/(0|t)|0)))break p;break C}if((0|A)>-8388353||(0|r)<(0|(h=-8388608/(0|t)|0)))break C}r=h-1|0,e[33073]=r,A=F(t,r)}if(t=e[51290],e[51290]=t+1,r=t,t=A>>8,g[0|r]=t,r=e[51290],e[51290]=r+1,g[0|r]=A>>>16,(r=e[50756])&&(r=e[r+8>>2])&&LA[0|r](t<<16>>16),A=(r=e[51292])+1|0,e[51292]=A,v[205184+(r<<1)>>1]=t,(0|A)>=5500&&(e[51292]=0),!(re[54046]>=e[51290]+2>>>0))break}i=1;break n}e[50773]=0}e[54731]=e[54729],i=1,m=65535&t,A=d[218920],h=e[r+8>>2],l=e[r+12>>2],n=0,y=0,oe=r=oe+-64|0,w=e[50759];b:if((0|(t=e[w+132>>2]))!=6){if(!A){for(t-1>>>0<=4&&(e[55921]=t,e[55964]=e[110896+(t<<2)>>2]),t=e[w+88>>2],e[54741]=1,e[55922]=(0|t)/32,A=e[50758],t=e[50757];;){m:if((0|A)!=(0|(t=(0|(t=t+1|0))<=169?t:0)))if((0|(R=e[216192+(t<<4)>>2]))!=1){if(R-5>>>0>1)continue}else{if(e[54741]=0,t=e[8+(216192+(t<<4)|0)>>2],!(_[l+4>>1]!=_[t+4>>1]|_[t+6>>1]!=_[l+6>>1]|_[t+8>>1]!=_[l+8>>1]|_[t+10>>1]!=_[l+10>>1])&&_[t+12>>1]==_[l+12>>1])break m;e[54741]=2}break}for((_[h+4>>1]!=_[113564]|_[h+6>>1]!=_[113565]|_[h+8>>1]!=_[113566]|_[h+10>>1]!=_[113567]||_[h+12>>1]!=_[113568])&&(da(),e[55974]=0,e[55975]=0,e[55972]=0,e[55973]=0,e[55988]=0,e[55989]=0,e[55990]=0,e[55991]=0,e[56004]=0,e[56005]=0,e[56006]=0,e[56007]=0,e[56020]=0,e[56021]=0,e[56022]=0,e[56023]=0,e[56036]=0,e[56037]=0,e[56038]=0,e[56039]=0,e[56052]=0,e[56053]=0,e[56054]=0,e[56055]=0,e[56068]=0,e[56069]=0,e[56070]=0,e[56071]=0,e[56086]=0,e[56087]=0,e[56084]=0,e[56085]=0,e[56102]=0,e[56103]=0,e[56100]=0,e[56101]=0,e[56118]=0,e[56119]=0,e[56116]=0,e[56117]=0,e[56134]=0,e[56135]=0,e[56132]=0,e[56133]=0,e[56150]=0,e[56151]=0,e[56148]=0,e[56149]=0,e[56166]=0,e[56167]=0,e[56164]=0,e[56165]=0,e[56182]=0,e[56183]=0,e[56180]=0,e[56181]=0,e[56198]=0,e[56199]=0,e[56196]=0,e[56197]=0,e[56214]=0,e[56215]=0,e[56212]=0,e[56213]=0,e[56230]=0,e[56231]=0,e[56228]=0,e[56229]=0),t=_[l+4>>1]|_[l+6>>1]<<16,A=_[l>>1]|_[l+2>>1]<<16,v[113562]=A,v[113563]=A>>>16,v[113564]=t,v[113565]=t>>>16,t=_[l+60>>1]|_[l+62>>1]<<16,A=_[l+56>>1]|_[l+58>>1]<<16,v[113590]=A,v[113591]=A>>>16,v[113592]=t,v[113593]=t>>>16,t=_[l+52>>1]|_[l+54>>1]<<16,A=_[l+48>>1]|_[l+50>>1]<<16,v[113586]=A,v[113587]=A>>>16,v[113588]=t,v[113589]=t>>>16,t=_[l+44>>1]|_[l+46>>1]<<16,A=_[l+40>>1]|_[l+42>>1]<<16,v[113582]=A,v[113583]=A>>>16,v[113584]=t,v[113585]=t>>>16,t=_[l+36>>1]|_[l+38>>1]<<16,A=_[l+32>>1]|_[l+34>>1]<<16,v[113578]=A,v[113579]=A>>>16,v[113580]=t,v[113581]=t>>>16,t=_[l+28>>1]|_[l+30>>1]<<16,A=_[l+24>>1]|_[l+26>>1]<<16,v[113574]=A,v[113575]=A>>>16,v[113576]=t,v[113577]=t>>>16,t=_[l+20>>1]|_[l+22>>1]<<16,A=_[l+16>>1]|_[l+18>>1]<<16,v[113570]=A,v[113571]=A>>>16,v[113572]=t,v[113573]=t>>>16,t=_[l+12>>1]|_[l+14>>1]<<16,A=_[l+8>>1]|_[l+10>>1]<<16,v[113566]=A,v[113567]=A>>>16,v[113568]=t,v[113569]=t>>>16,P=+(0|m),(ae=1&v[h>>1])?(t=d[h+39|0],e[56680]=t,S[28364]=t>>>0,S[28354]=+(d[l+39|0]-t<<6)/P,t=d[h+40|0],S[28366]=t>>>0,S[28356]=+(d[l+40|0]-t<<6)/P,t=d[h+41|0],e[56682]=t,S[28368]=t>>>0,S[28358]=+(d[l+41|0]-t<<6)/P,A=d[h+42|0],e[56684]=A,S[28370]=A>>>0,t=d[h+43|0],n=+(d[l+43|0]-t<<6)/P,y=+(d[l+42|0]-A<<6)/P,M=+(t>>>0)):(e[56728]=0,e[56729]=0,t=0,e[56680]=0,e[56708]=0,e[56709]=0,e[56732]=0,e[56733]=0,e[56712]=0,e[56713]=0,e[56682]=0,e[56736]=0,e[56737]=0,e[56716]=0,e[56717]=0,e[56684]=0,e[56740]=0,e[56741]=0,M=0),e[56688]=t,S[28360]=y,S[28372]=M,S[28362]=n,e[56692]=0,e[56748]=0,e[56749]=0,e[56694]=0,e[56752]=0,e[56753]=0,e[56696]=0,e[56756]=0,e[56757]=0,e[56700]=0,e[56760]=0,e[56761]=0,e[56704]=0,e[56764]=0,e[56765]=0,e[50764]=m,t=1;ee=v[164+(R=(m=t<<1)+w|0)>>1],A=(it=F(t,80))+222896|0,y=+v[R+218>>1],n=.00390625*+(0|F(ee,v[2+(h+m|0)>>1]))+y,S[A+16>>3]=n,ue=he(n)<2147483648?~~n:-2147483648,e[A>>2]=ue,S[A+48>>3]=64*(.00390625*+(0|F(ee,v[2+(l+m|0)>>1]))+y-n)/P,t>>>0<=3&&(A=it+222896|0,n=.00390625*+v[R+200>>1]*+(d[35+(t+h|0)|0]<<1),S[A+24>>3]=n,m=he(n)<2147483648?~~n:-2147483648,e[A+4>>2]=m,S[A+56>>3]=64*(+(d[35+(t+l|0)|0]<<1)-n)/P),(0|(t=t+1|0))!=6;);if(n=+((t=d[h+40|0])<<1),S[27864]=n,A=e[56618],t||(n=+(0|A),S[27864]=n),t=he(n)<2147483648?~~n:-2147483648,e[55724]=t,m=d[l+40|0],e[55730]=0,e[55731]=1079394304,e[55738]=0,e[55739]=0,e[55725]=89,t=1,S[27868]=64*(+(0|(m?m<<1:A))-n)/P,ae)for(;A=F(t,80)+222896|0,w=d[56+(m=t+h|0)|0]<<2,e[A+12>>2]=w,n=+(0|w),S[A+40>>3]=n,w=t+l|0,S[A+72>>3]=64*(+(d[w+56|0]<<2)-n)/P,m=d[m+49|0],e[A+8>>2]=m,n=+(m>>>0),S[A+32>>3]=n,S[A- -64>>3]=64*(+d[w+49|0]-n)/P,(0|(t=t+1|0))!=7;);e[56606]=0}for(;;){if((0|(ue=e[50764]))>(0|(l=e[56606]))){for(t=e[50767],e[56609]=e[55724],e[56619]=e[55725],e[56610]=e[55744],e[56611]=e[55764],e[56612]=e[55784],e[56613]=e[55804],ae=F(t,10),e[56607]=(0|ae)/4096,e[56620]=e[55745],e[56621]=e[55765],e[56622]=e[55785],e[56614]=e[55824],e[56630]=e[55746],e[56631]=e[55766],e[56632]=e[55786],e[56633]=e[55806],e[56634]=e[55826],e[56635]=e[55846],it=e[56680],e[56608]=it,h=e[56694],e[56656]=h,m=e[56696],e[56653]=m,w=e[56700],e[56655]=w,R=e[56684],e[56649]=R,e[56651]=e[56704],e[56654]=e[56688],e[56652]=e[56682],e[56650]=e[56692],t=0;A=F(t,80)+222896|0,n=S[A+48>>3]+S[A+16>>3],S[A+16>>3]=n,P=S[A+56>>3]+S[A+24>>3],S[A+24>>3]=P,y=S[A+72>>3]+S[A+40>>3],S[A+40>>3]=y,M=S[A- -64>>3]+S[A+32>>3],S[A+32>>3]=M,ee=he(n)<2147483648?~~n:-2147483648,e[A>>2]=ee,ee=he(P)<2147483648?~~P:-2147483648,e[A+4>>2]=ee,ee=he(y)<2147483648?~~y:-2147483648,e[A+12>>2]=ee,ee=he(M)<2147483648?~~M:-2147483648,e[A+8>>2]=ee,(0|(t=t+1|0))!=9;);for(n=S[28354]+S[28364],S[28364]=n,S[28366]=S[28356]+S[28366],P=S[28358]+S[28368],S[28368]=P,y=S[28360]+S[28370],S[28370]=y,M=S[28362]+S[28372],S[28372]=M,t=he(n)<2147483648?~~n:-2147483648,e[56680]=t,t=he(P)<2147483648?~~P:-2147483648,e[56682]=t,t=he(y)<2147483648?~~y:-2147483648,e[56684]=t,t=he(M)<2147483648?~~M:-2147483648,e[56688]=t,n=S[28374]+0,S[28374]=n,t=he(n)<2147483648?~~n:-2147483648,e[56692]=t,n=S[28376]+0,S[28376]=n,t=he(n)<2147483648?~~n:-2147483648,e[56694]=t,n=S[28378]+0,S[28378]=n,t=he(n)<2147483648?~~n:-2147483648,e[56696]=t,n=S[28380]+0,S[28380]=n,t=he(n)<2147483648?~~n:-2147483648,e[56700]=t,n=S[28382]+0,S[28382]=n,t=he(n)<2147483648?~~n:-2147483648,e[56704]=t,e[56659]=e[55724],e[56669]=e[55725],e[56660]=e[55744],e[56670]=e[55745],e[56661]=e[55764],e[56671]=e[55765],e[56662]=e[55784],e[56672]=e[55785],e[56663]=e[55804],e[56664]=e[55824],e[56665]=e[55844],t=e[50768]+e[50769]|0,e[50768]=t,t>>=8,e[50767]=e[50770]+(F(e[50771],d[e[50766]+((0|t)>=127?127:t)|0])>>8),t=ue-l|0,e[55923]=(0|t)>=64?64:t,e[55961]=(0|ae)/40960,t=it-7|0,e[56658]=(0|t)>0?t:0,S[27974]=R>>>0<=87?.001*+v[111136+(R<<1)>>1]*.05:0,S[27975]=m>>>0<=87?.001*+v[111136+(m<<1)>>1]*.25:0,S[27973]=h>>>0<=87?.001*+v[111136+(h<<1)>>1]:0,S[27971]=w>>>0<=87?.001*+v[111136+(w<<1)>>1]*.05:0,n=(t=e[56629])>>>0<=87?.001*+v[111136+(t<<1)>>1]*.6:0,S[r>>3]=n,n=(t=e[56630])>>>0<=87?.001*+v[111136+(t<<1)>>1]*.4:0,S[r+8>>3]=n,n=(t=e[56631])>>>0<=87?.001*+v[111136+(t<<1)>>1]*.15:0,S[r+16>>3]=n,n=(t=e[56632])>>>0<=87?.001*+v[111136+(t<<1)>>1]*.06:0,S[r+24>>3]=n,n=(t=e[56633])>>>0<=87?.001*+v[111136+(t<<1)>>1]*.04:0,S[r+32>>3]=n,n=(t=e[56634])>>>0<=87?.001*+v[111136+(t<<1)>>1]*.022:0,S[r+40>>3]=n,n=(t=e[56635])>>>0<=87?.001*+v[111136+(t<<1)>>1]*.03:0,S[r+48>>3]=n,n=(t=(0|(t=e[56657]-3|0))<=0?57:t)>>>0<=87?.001*+v[111136+(t<<1)>>1]:0,S[27977]=n/+e[55964],P=S[27968],y=S[27967],t=1;A=223664+(l=t<<6)|0,M=(n=fr(y*+e[48+(m=226428+(h=t<<2)|0)>>2]))*-n,S[A+216>>3]=M,n*=Aa(P*+e[m+8>>2]),n+=n,S[A+208>>3]=n,Ve=1-n-M,S[A+200>>3]=Ve,t>>>0<=5&&(m=e[208+(h=h+226428|0)>>2],A=l+223664|0,_e=(be=fr(y*+e[h+248>>2]))*Aa(P*+(0|m)),_e+=_e,S[A+1488>>3]=_e,be*=-be,S[A+1496>>3]=be,$e=1-_e-be,S[A+1480>>3]=$e,S[A+256>>3]=.015625*(be-M),S[A+248>>3]=.015625*(_e-n),S[A+240>>3]=.015625*($e-Ve)),(0|(t=t+1|0))!=10;);for(be=(n=fr(y*+e[56619]))*-n,S[27985]=be,_e=(n*=Aa(P*+(0-e[56609]|0)))+n,S[27984]=_e,n=1-_e-be,S[27983]=n,n!=0&&(n=1/n,S[27983]=n,be*=M=-n,S[27985]=be,_e*=M,S[27984]=_e),Ve=(M=fr(y*+e[56669]))*-M,S[28145]=Ve,$e=(M*=Aa(P*+(0-e[56659]|0)))+M,S[28144]=$e,M=1-$e-Ve,S[28143]=M,M!=0&&(M=1/M,S[28143]=M,Ve*=Pt=-M,S[28145]=Ve,$e*=Pt,S[28144]=$e),S[27990]=.015625*(Ve-be),S[27989]=.015625*($e-_e),S[27988]=.015625*(M-n),t=0;h=e[8+(l=226428+(t<<2)|0)>>2],A=223664+(t<<6)|0,M=(n=fr(y*+e[l+128>>2]))*Aa(P*+(0|h)),M+=M,S[A+848>>3]=M,n*=-n,S[A+856>>3]=n,S[A+840>>3]=S[r+(t<<3)>>3]*(1-M-n),(0|(t=t+1|0))!=7;);if(y=(n=fr(y*+(e[55918]/2|0)))*-n,S[28137]=y,n*=Aa(0*P),n+=n,S[28136]=n,S[28135]=1-n-y,t=1,(0|tA())!=1)continue;break b}break}t=1,e[54741]>0&&(e[54741]=0,e[55963]=64,e[56606]=l+-64,e[55923]=64,(0|tA())==1)||(t=0)}else{if(oe=R=oe-752|0,!A){IA(t=R+376|0,0,376),jn(w,h,t),jn(w,l,t=IA(R,0,376)),A=e[50768]+F(e[50769],m>>>6|0)|0,e[50768]=A,A>>=8,A=e[50770]+(F(e[50771],d[e[50766]+((0|A)>=127?127:A)|0])>>8)|0,e[50767]=A,S[t+368>>3]=(0|A)/4096|0,e[50773]&&(S[t+736>>3]=S[t+736>>3]/5,S[t+360>>3]=S[t+360>>3]/5),A=e[e[56797]+4>>2],LA[e[e[A>>2]>>2]](A,t+376|0,110,110,-1,0),l=m-110|0,w=e[50758],A=e[50757];m:{for(;;){if((0|w)!=(0|(A=(A+1|0)%170|0))&&!((ee=e[216192+(A<<4)>>2])-5>>>0<2)){if(h=1,(0|ee)!=1)continue;break m}break}l=m-220|0,h=0}(0|l)>0&&(A=e[e[56797]+4>>2],LA[e[e[A>>2]>>2]](A,t,l,l||1,-1,0)),h||(e[t+352>>2]=0,e[t+356>>2]=0,S[t>>3]=S[t+368>>3],A=e[e[56797]+4>>2],LA[e[e[A>>2]>>2]](A,t,55,55,-1,0),e[t+360>>2]=0,e[t+364>>2]=0,A=e[e[56797]+4>>2],LA[e[e[A>>2]>>2]](A,t,55,55,-1,0))}t=e[e[56797]+8>>2],A=e[51290],m=0|LA[e[e[t>>2]>>2]](t,e[54046]-A>>>1|0,A),w=e[51290];m:if(m&&!((0|(A=e[50776]))>=(0|(it=e[50773]))))for(ht=e[50777],Wt=(0|F(ht,3))/-4|0,n=.0009765625*+e[50780],ee=e[50772],l=e[50778],cA=e[50775],ue=e[50774],t=0;;){if(ae=d[ee+(h=A+l|0)|0],ue?ae=F(ue,ae<<24>>24):(A=A+1|0,e[50776]=A,ae|=g[ee+(h=A+l|0)|0]<<8),ae=he(P=n*+(0|ae))<2147483648?~~P:-2147483648,v[(yt=w+(t<<1)|0)>>1]=_[yt>>1]+((0|F(ae,cA))/40|0),(0|h)>=(0|ht)&&(l=l+Wt|0,e[50778]=l),A=A+1|0,e[50776]=A,(0|A)>=(0|it))break m;if(!(m>>>0>(t=t+1|0)>>>0))break}t=w+(m<<1)|0,e[51290]=t,oe=R+752|0,t=re[54046]<=t>>>0}if(oe=r- -64|0,t)break n;break c}e[50781]=t||100;break c}cn(t,e[r+8>>2]);break c}t=e[r+8>>2],e[50759]=$A(203816,t,1344),e[50801]=e[t+108>>2]?105792:106064,t=(0|F(e[t+120>>2],26))/100|0,e[33038]=t,(0|(i=e[50754]))<=11e3&&(g[203300]=1,e[33038]=t<<1),e[54728]=e[50982],t=e[50979],A=e[50978],IA(205184,0,11e3),e[51293]=0,t=(A=(h=(0|(l=e[50789]))>0)?130:(0|A)>=5499?5499:A)?h?l:(0|t)>=100?100:t:0,e[50755]=t,A=(0|F(A,i))/1e3|0,e[51292]=A,e[54729]=(0|t)>20?A<<1:t?A:0,e[33037]=(0|F(500-t|0,(0|F(d[e[50797]+105596|0],(0|F(e[50787],55))/100|0))/16|0))/500,t=256,(0|(A=(0|(A=e[50785]))>=101?101:A))>=51&&(t=256+(((F(A,25)-1250&65535)>>>0)/50|0)|0),v[101990]=(0|F(v[102026],t))/256,v[101991]=(0|F(v[102027],t))/256,v[101992]=(0|F(v[102028],t))/256,v[101993]=(0|F(v[102029],t))/256,v[101994]=(0|F(v[102030],t))/256,v[101995]=(0|F(v[102031],t))/256,t=e[50790],v[101999]=(0|F(v[102035],F(t,-6)+256|0))/256,v[102e3]=(0|F(v[102036],F(t,-3)+256|0))/256,yi(8,0,e[50986],0,e[51290]),EA(e[r+8>>2]);break c}if(!e[50759])break c;A=e[r+12>>2],r=e[r+8>>2],e[55911]=0,e[55915]=t?2097152/(0|t)|0:0,e[55910]=r,t=(0|F(A,e[33037]))/16|0,e[50779]=t,e[50780]=(0|F(F(t,e[50985]),15))/100;break c}yi(A>>8,t,e[r+8>>2],e[r+12>>2],h)}i=0,t=e[50757]+1|0,e[50757]=(0|t)<=169?t:0}if(g[218920]=i,!((h=e[51290])>>>0<re[54046]))break}l=e[34391],r=(e[51290]-l|0)/2|0,e[34439]=r+e[34439],A=e[34436],t=e[34388]+F(A,36)|0,e[t+4>>2]=0,e[t>>2]=0,e[t+24>>2]=e[34438];a:if(2&(t=e[32538])){if(h=e[34388],(-2&t)==2&&(!(r=A?h:0)|e[r>>2]!=8||(0|(r=e[r+28>>2]))!=e[34389]&&(e[34389]=r)),i=1,(0|A)<2)break a;for(;;){r=A?h+F(i,36)|0:0;n:{c:switch(0|t){case 2:case 3:if(!r|e[r>>2]!=8||(0|(r=e[r+28>>2]))==e[34389])break n;e[34389]=r;break n;case 0:break c;default:break n}(t=e[34440])?(LA[0|t](l,0,r),A=e[34436],t=e[32538]):t=0}if(!((0|(i=i+1|0))<(0|A)))break}}else if((t=e[34440])&&0|LA[0|t](l,r,e[34388]))break r;if(!xt(1)&&!(170-((0|(t=e[50757]-e[50758]|0))<=0?t+170|0:t)|0||(t=e[34388],e[t>>2]=0,e[t+4>>2]=e[34437],e[t+24>>2]=e[34438],Qe(1))))break}if(A=0,2&d[130152]||!(t=e[34440])||!(0|LA[0|t](0,0,e[34388])))break t}Qe(2),A=268439295}if((0|A)<=268437502){if(!A|(0|A)==268436479|(0|A)!=268437247)break e;return}}}function xt(t){var A,r=0,n=0,i=0,l=0,h=0,m=0,w=0,M=0,y=0,P=0,R=0,ee=0,ae=0,ue=0,be=0,_e=0,Ve=0,$e=0,it=0,ht=0,yt=0;oe=A=oe-720|0,r=e[47198],t||(e[36443]=0,e[36442]=1,e[36444]=0,e[36440]=0,e[36441]=0,t=e[50758],e[36454]=t,e[36427]=-1,e[36424]=-1,e[36446]=0,e[36447]=0,e[36439]=-1,e[36426]=0,e[36455]=t,e[36448]=0,e[36449]=0,e[36450]=0,e[36451]=0,e[36452]=0,e[36453]=0,CA(),e[36427]=-1,t=216192+(e[50758]<<4)|0,e[t>>2]=5,e[t+4>>2]=0,t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0,e[36426]=0,e[36438]&&(e[36438]=0,t=216192+(e[50758]<<4)|0,e[t>>2]=14,e[t+4>>2]=0,t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0));e:{t:if(!((0|(t=e[36442]))>997|e[36423]<=(0|t)))for(ae=r>>>1&1,ue=A+48|4,be=A+348|0,_e=A+652|0,Ve=e[32322],P=e[32320],$e=A+60|0,it=A+648|0,ht=A+56|0,yt=A+620|0,R=A- -64|0;;){if(n=145840+(t<<5)|0,!(t=e[50756])|!e[t>>2]||(e[A+12>>2]=0,tn(t=A+16|0,e[n+8>>2],n,0,A+12|0),r=sn(t),i=d[n+17|0],t=216192+(e[50758]<<4)|0,e[t>>2]=16,e[t+8>>2]=i,e[t+4>>2]=r,t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0),t=1,(0|(i=(0|(r=e[50757]-e[50758]|0))<=0?r+170|0:r))<=(0|((r=d[n+17|0])?(0|r)==2?25:15:10)))break e;if(M=e[36442],2&d[0|n])for(y=2047&_[n+4>>1];;){if(i=e[36443],m=127&(r=e[198304+(i<<2)>>2])){t=r>>>8|0,e[36443]=i+1;r:{a:{switch((31&r)-2|0){case 0:cn(96&r|8,t),Ra(2);break r;case 5:if((0|t)>=e[34064]||!e[4+(m=136272+(i=t<<4)|0)>>2])break r;Kr(10,0),t=216192+(e[50758]<<4)|0,e[t>>2]=6,m=e[m+4>>2],e[t+8>>2]=e[8+(i+136272|0)>>2]+44,e[t+12>>2]=5376,e[t+4>>2]=m;break a;case 8:if((0|((0|(i=e[50757]-e[50758]|0))<=0?i+170:i))<6)break r;m=e[47353],i=216192+(e[50758]<<4)|0,e[i>>2]=778,e[i+8>>2]=t,e[i+4>>2]=m+y&16777215;break a;case 9:if((0|((0|(i=e[50757]-e[50758]|0))<=0?i+170:i))<6)break r;m=e[33284],i=216192+(e[50758]<<4)|0,e[i>>2]=1034,e[i+8>>2]=t,e[i+4>>2]=m+1&16777215;break a}Kr(10,0),i=216192+(e[50758]<<4)|0,e[i>>2]=12,e[i+8>>2]=t,e[i+4>>2]=m}t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0}if(!(128&r))continue}break}(t=d[n+20|0])&&(1&g[e[47192]+48|0]&d[n+17|0]==2|1&g[e[n+8>>2]+7|0]||(e[36426]=0),r=e[47353]+(2047&_[n+4>>1])|0,e[36445]=r,4&t&&((0|((0|(t=e[50757]-e[50758]|0))<=0?t+170:t))<6||(i=e[47568],t=216192+(e[50758]<<4)|0,e[t>>2]=522,e[t+8>>2]=i,e[t+4>>2]=16777215&r,t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0)),1&g[n+20|0]&&(r=_[n+4>>1],i=e[36444],e[36444]=i+1,(0|((0|(t=e[50757]-e[50758]|0))<=0?t+170:t))<6||(y=e[36445],m=e[47355],t=216192+(e[50758]<<4)|0,e[t>>2]=266,e[t+8>>2]=i+m,e[t+4>>2]=16777215&y|(63488&r)<<13,t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0))),(0|(t=e[36441]))>0&&(r=216192+(e[36425]<<4)|0,e[r+4>>2]||(e[r+4>>2]=t),e[36441]=0),t=M+1<<5,r=M-1<<5,!(i=d[n+18|0])|2&d[e[n+8>>2]+7|0]||Kr(i,1),m=t+145840|0,l=r+145840|0,y=1;r:{a:{if(e[47198]&&(w=e[n+8>>2],d[w+10|0]!=15)){n:if(d[n+17|0]==2)switch(d[l+17|0]-3|0){case 0:case 5:break a;default:break n}y=0,tn(A+704|0,w,n,ae,0),(0|((0|(t=e[50757]-e[50758]|0))<=0?t+170:t))<6||(r=e[36445],t=216192+(e[50758]<<4)|0,e[t>>2]=1802,e[t+4>>2]=16777215&r,r=e[A+708>>2],e[t+8>>2]=e[A+704>>2],e[t+12>>2]=r,t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0)}n:switch(d[n+17|0]){case 0:Kr(e[n+12>>2],0),g[n+23|0]=d[e[n+8>>2]+14|0];break r;case 4:r=e[n+8>>2],(0|(t=d[m+17|0]))==2|!d[m+20|0]&(0|t)==3||(v[n>>1]=8192|_[n>>1]),2&d[r+7|0]&&(e[A+88>>2]=0,e[A+92>>2]=0,e[A+80>>2]=0,e[A+84>>2]=0,e[A+72>>2]=0,e[A+76>>2]=0,e[R>>2]=0,e[R+4>>2]=0,e[A+56>>2]=0,e[A+60>>2]=0,e[A+48>>2]=0,e[A+52>>2]=0,Gt(0,1,n,A+552|0,145784),e[A+56>>2]=e[A+620>>2],e[R>>2]=e[A+640>>2],e[36424]<0&&(i=d[m+19|0],t=e[50758],e[36425]=t,e[36441]=0,e[12+(t=216192+(t<<4)|0)>>2]=i,e[t+8>>2]=0,e[t>>2]=8,e[t+4>>2]=0,t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0,y=d[m+22|0],i=d[m+21|0],m=e[129280+(d[n+16|0]<<2)>>2],(0|(t=e[36424]))<0|(0|(w=e[36440]))<=0||e[4+(t=216192+(t<<4)|0)>>2]||(e[t+4>>2]=w),t=e[50758],e[36424]=t,e[36440]=0,e[(t=216192+(t<<4)|0)>>2]=9,e[t+4>>2]=0,y|=i<<16,i=(0|i)==255,e[t+12>>2]=i?3604556:y,e[t+8>>2]=i?P:m,t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0),$t(r,0,A+48|0,n,0)),Gt(0,0,n,A+552|0,145784),e[A+552>>2]=4|e[A+552>>2],(0|(t=e[36440]))<=0||(0|(r=e[36424]))<0||(e[4+(r=216192+(r<<4)|0)>>2]||(e[r+4>>2]=t),e[36440]=0),e[36426]=0,e[36439]=-1,e[36455]=e[50758],CA(),e[36427]=-1,e[36422]=0,(t=e[A+624>>2])&&(i=t,t=e[A+644>>2],ja(i,2,e[A+596>>2]<<1,e[A+552>>2],0,t?(t<<5)/100|0:32)),e[36426]=0;break r;case 6:Gt(0,0,n,A+552|0,145784),8&d[0|n]&&(t=e[n+12>>2],(0|(r=e[36440]))<=0||(0|(i=e[36424]))<0||(e[4+(i=216192+(i<<4)|0)>>2]||(e[i+4>>2]=r),e[36440]=0),e[36426]=0,e[36439]=-1,e[36455]=e[50758],CA(),e[36427]=-1,e[36422]=0,(r=e[A+624>>2])&&(i=t,t=e[A+644>>2],ja(r,2,e[A+596>>2]<<1,e[A+552>>2],i,t?(t<<5)/100|0:32))),t=e[n+12>>2],(0|(r=e[36440]))<=0||(0|(i=e[36424]))<0||(e[4+(i=216192+(i<<4)|0)>>2]||(e[i+4>>2]=r),e[36440]=0),e[36426]=0,e[36439]=-1,e[36455]=e[50758],CA(),e[36427]=-1,e[36422]=0,(r=e[A+624>>2])&&(i=t,t=e[A+644>>2],ja(r,2,e[A+596>>2]<<1,e[A+552>>2],i,t?(t<<5)/100|0:32)),e[36426]=0;break r;case 5:i=e[n+8>>2],e[ue+40>>2]=0,e[(t=ue)+32>>2]=0,e[t+36>>2]=0,e[t+24>>2]=0,e[t+28>>2]=0,e[t+16>>2]=0,e[t+20>>2]=0,e[t+8>>2]=0,e[t+12>>2]=0,e[t>>2]=0,e[t+4>>2]=0,e[A+48>>2]=4;c:{o:{f:{l:switch(d[m+17|0]-2|0){case 0:r=d[n+19|0],t=e[50758],e[36425]=t,e[36441]=0,e[12+(t=216192+(t<<4)|0)>>2]=r,e[t+8>>2]=0,e[t>>2]=8,e[t+4>>2]=0,t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0,r=d[n+22|0],w=d[n+21|0],y=e[129280+(d[n+16|0]<<2)>>2],(0|(t=e[36424]))<0|(0|(h=e[36440]))<=0||e[4+(t=216192+(t<<4)|0)>>2]||(e[t+4>>2]=h),t=(h=!(255&~w))?P:y,y=1;break o;case 1:break l;default:break f}if(!d[m+20|0]){r=d[m+19|0],t=e[50758],e[36425]=t,e[36441]=0,e[12+(t=216192+(t<<4)|0)>>2]=r,e[t+8>>2]=0,e[t>>2]=8,e[t+4>>2]=0,t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0,r=d[m+22|0],w=d[m+21|0],y=e[129280+(d[m+16|0]<<2)>>2],(0|(t=e[36424]))<0|(0|(h=e[36440]))<=0||e[4+(t=216192+(t<<4)|0)>>2]||(e[t+4>>2]=h),t=(h=!(255&~w))?P:y,y=1;break o}}if(y=0,e[36424]>=0)break c;r=d[m+19|0],t=e[50758],e[36425]=t,e[36441]=0,e[12+(t=216192+(t<<4)|0)>>2]=r,e[t+8>>2]=0,e[t>>2]=8,e[t+4>>2]=0,t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0,r=d[n+22|0],w=d[n+21|0],y=e[129280+(d[n+16|0]<<2)>>2],(0|(t=e[36424]))<0|(0|(h=e[36440]))<=0||e[4+(t=216192+(t<<4)|0)>>2]||(e[t+4>>2]=h),t=(h=!(255&~w))?P:y,y=0}ee=e[50758],e[36424]=ee,e[36440]=0,e[(ee=216192+(ee<<4)|0)>>2]=9,e[ee+4>>2]=0,e[ee+12>>2]=h?3604556:255&r|(255&w)<<16,e[ee+8>>2]=t,t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0}c:if(!(2&d[i+7|0])&d[l+17|0]!=2)8&d[0|n]&&Kr(50,0);else{if(Gt(0,1,n,A+552|0,145784),e[A+56>>2]=e[A+620>>2],e[A+64>>2]=e[A+640>>2],$t(i,0,A+48|0,n,0),!(8&d[0|n]))break c;Kr(25,1),$t(i,0,A+48|0,n,0)}c:if(y){if(e[36455]!=e[36454])break c;e[36455]=e[50758]}else v[n>>1]=8192|_[n>>1];if(Gt(0,0,n,A+552|0,145784),e[A+56>>2]=e[A+620>>2],e[A+64>>2]=e[A+640>>2],e[A+76>>2]=e[A+636>>2],e[A+80>>2]=e[A+656>>2],$t(i,0,A+48|0,n,0),d[n+20|0]|d[84+(145840+(M<<5)|0)|0]||((0|(t=d[m+17|0]))==7&&(Kr(20,0),t=d[m+17|0]),(255&t)!=6))break r;Kr(12,0);break r;case 7:c:{o:{f:{l:{i:switch((t=d[m+17|0])-2|0){case 1:break l;case 0:break i;default:break f}r=d[n+19|0],t=e[50758],e[36425]=t,e[36441]=0,e[12+(t=216192+(t<<4)|0)>>2]=r,e[t+8>>2]=0,e[t>>2]=8,e[t+4>>2]=0,t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0,y=d[n+22|0],t=d[n+21|0],i=e[129280+(d[n+16|0]<<2)>>2],(0|(r=e[36424]))<0|(0|(w=e[36440]))<=0||e[4+(r=216192+(r<<4)|0)>>2]||(e[r+4>>2]=w),r=(w=!(255&~t))?P:i;break o}r=d[m+19|0],t=e[50758],e[36425]=t,e[36441]=0,e[12+(t=216192+(t<<4)|0)>>2]=r,e[t+8>>2]=0,e[t>>2]=8,e[t+4>>2]=0,t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0,y=d[m+22|0],t=d[m+21|0],i=e[129280+(d[m+16|0]<<2)>>2],(0|(r=e[36424]))<0|(0|(w=e[36440]))<=0||e[4+(r=216192+(r<<4)|0)>>2]||(e[r+4>>2]=w),r=(w=!(255&~t))?P:i;break o}if(e[36424]>=0)break c;r=d[n+19|0],t=e[50758],e[36425]=t,e[36441]=0,e[12+(t=216192+(t<<4)|0)>>2]=r,e[t+8>>2]=0,e[t>>2]=8,e[t+4>>2]=0,t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0,y=d[n+22|0],t=d[n+21|0],i=e[129280+(d[n+16|0]<<2)>>2],(0|(r=e[36424]))<0|(0|(w=e[36440]))<=0||e[4+(r=216192+(r<<4)|0)>>2]||(e[r+4>>2]=w),r=(w=!(255&~t))?P:i}i=e[50758],e[36424]=i,e[36440]=0,e[(i=216192+(i<<4)|0)>>2]=9,e[i+4>>2]=0,e[i+12>>2]=w?3604556:(255&t)<<16|y,e[i+8>>2]=r,t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0,t=d[m+17|0]}c:{o:{f:switch((255&t)-2|0){case 1:if(d[m+20|0])break o;break;case 0:break f;default:break o}if(e[36455]!=e[36454])break c;e[36455]=e[50758];break c}v[n>>1]=8192|_[n>>1]}Gt(0,0,n,A+552|0,145784),e[A+56>>2]=0,e[A+60>>2]=0,e[R>>2]=0,e[R+4>>2]=0,e[A+80>>2]=0,e[A+84>>2]=0,e[A+72>>2]=0,e[A+76>>2]=0,e[A+88>>2]=0,e[A+56>>2]=e[A+620>>2],e[R>>2]=e[A+640>>2],e[A+80>>2]=e[A+656>>2],e[A+48>>2]=0,e[A+52>>2]=0,e[A+76>>2]=e[A+636>>2],e[A+92>>2]=e[A+596>>2]<<1,8&d[0|n]&&$t(e[n+8>>2],0,A+48|0,n,0),$t(e[n+8>>2],0,A+48|0,n,0);break r;case 8:if(e[A+88>>2]=0,e[A+92>>2]=0,e[A+80>>2]=0,e[A+84>>2]=0,e[A+72>>2]=0,e[A+76>>2]=0,e[R>>2]=0,e[R+4>>2]=0,e[A+56>>2]=0,e[A+60>>2]=0,e[A+48>>2]=0,e[A+52>>2]=0,1&g[0|n]||(r=d[n+19|0],t=e[50758],e[36425]=t,e[36441]=0,e[12+(t=216192+(t<<4)|0)>>2]=r,e[t+8>>2]=0,e[t>>2]=8,e[t+4>>2]=0,t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0,i=d[n+22|0],r=d[n+21|0],y=e[129280+(d[n+16|0]<<2)>>2],(0|(t=e[36424]))<0|(0|(w=e[36440]))<=0||e[4+(t=216192+(t<<4)|0)>>2]||(e[t+4>>2]=w),t=e[50758],e[36424]=t,e[36440]=0,e[(t=216192+(t<<4)|0)>>2]=9,e[t+4>>2]=0,i|=r<<16,r=(0|r)==255,e[t+12>>2]=r?3604556:i,e[t+8>>2]=r?P:y,t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0),d[l+17|0]==8&&(e[36426]=0),Gt(0,0,n,A+552|0,145784),e[A+56>>2]=e[A+620>>2],e[A+64>>2]=e[A+640>>2],e[A+92>>2]=e[A+596>>2]<<1,d[m+17|0]==2){e[36455]==e[36454]&&(e[36455]=e[50758]),$t(e[n+8>>2],0,A+48|0,n,0);break r}if(!(!(1&g[0|n])|d[l+17|0]!=2)){$t(e[n+8>>2],0,A+48|0,n,0);break r}e[36426]=0,$t(e[n+8>>2],0,A+48|0,n,0),e[36426]=0;break r;case 3:e[A+88>>2]=0,e[A+92>>2]=0,e[A+80>>2]=0,e[A+84>>2]=0,e[A+72>>2]=0,e[A+76>>2]=0,e[R>>2]=0,e[R+4>>2]=0,e[A+56>>2]=0,e[A+60>>2]=0,e[A+48>>2]=0,e[A+52>>2]=0,i=e[e[n+8>>2]+4>>2],1&g[0|n]||(r=d[n+19|0],t=e[50758],e[36425]=t,e[36441]=0,e[12+(t=216192+(t<<4)|0)>>2]=r,e[t+8>>2]=0,e[t>>2]=8,e[t+4>>2]=0,t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0,y=d[n+22|0],r=d[n+21|0],w=e[129280+(d[n+16|0]<<2)>>2],(0|(t=e[36424]))<0|(0|(h=e[36440]))<=0||e[4+(t=216192+(t<<4)|0)>>2]||(e[t+4>>2]=h),t=e[50758],e[36424]=t,e[36440]=0,e[(t=216192+(t<<4)|0)>>2]=9,e[t+4>>2]=0,y|=r<<16,r=(0|r)==255,e[t+12>>2]=r?3604556:y,e[t+8>>2]=r?P:w,t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0),d[l+17|0]==8&&(e[36426]=0),d[m+17|0]!=2|e[36455]!=e[36454]||(e[36455]=e[50758]),Gt(0,0,n,A+552|0,145784),(0|(t=e[A+584>>2]-d[n+18|0]|0))>0&&Kr(t,1),e[A+56>>2]=e[A+620>>2],e[A+64>>2]=e[A+640>>2],e[A+76>>2]=e[A+636>>2],e[A+80>>2]=e[A+656>>2],e[A+92>>2]=e[A+596>>2]<<1,$t(e[n+8>>2],0,A+48|0,n,i<<24>>31&5);break r;case 2:break n;default:break r}w=e[n+8>>2]}M=d[n+3|0],e[A+88>>2]=0,e[A+92>>2]=0,e[A+80>>2]=0,e[A+84>>2]=0,e[A+72>>2]=0,e[A+76>>2]=0,e[R>>2]=0,e[R+4>>2]=0,e[A+56>>2]=0,e[A+60>>2]=0,e[A+48>>2]=0,e[A+52>>2]=0,Gt(0,0,n,A+552|0,145784),t=e[A+628>>2],e[A+56>>2]=t,e[A+92>>2]=e[A+596>>2]<<1;a:{if(!t||(h=0,i=it,r=$e,2&d[A+552|0])){if(d[l+17|0]?(h=0,Gt(0,0,l,A+400|0,0),t=e[A+476>>2],e[A+56>>2]=t,!t|!(2&d[A+400|0])||(e[A+72>>2]=e[A+496>>2],h=1),r=e[A+512>>2],e[A+84>>2]=e[A+508>>2],e[A+88>>2]=r):h=0,t)break a;e[A+48>>2]=1,e[A+52>>2]=1,i=yt,r=ht}e[r>>2]=e[i>>2]}e[A+64>>2]=e[A+640>>2],r=d[n+16|0],t=0,(i=d[n+7|0])?(zn(i,A+96|0),r=wo(e[A+220>>2]),(0|(i=e[A+224>>2]))<=0||(t=wo(i))):r=e[129280+(r<<2)>>2],e[36455]==e[36454]&&(e[36455]=e[50758]),i=(i=15&M)>>>0<2?1:i>>>0>6?3:2;a:{switch(d[l+17|0]-3|0){case 2:case 4:h=d[n+19|0],l=e[50758],e[36425]=l,e[36441]=0,e[12+(l=216192+(l<<4)|0)>>2]=h,e[l+8>>2]=t,e[l>>2]=8,e[l+4>>2]=0,t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0,h=d[n+22|0],l=d[n+21|0],(0|(t=e[36440]))<=0||(0|(M=e[36424]))<0||e[4+(M=216192+(M<<4)|0)>>2]||(e[M+4>>2]=t),t=e[50758],e[36424]=t,e[36440]=0,e[(t=216192+(t<<4)|0)>>2]=9,e[t+4>>2]=0,h|=l<<16,l=(0|l)==255,e[t+12>>2]=l?3604556:h,e[t+8>>2]=l?P:r,t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0,$t(w,1,A+48|0,n,i);break a;case 0:case 5:h=d[n+19|0],l=e[50758],e[36425]=l,e[36441]=0,e[12+(l=216192+(l<<4)|0)>>2]=h,e[l+8>>2]=t,e[l>>2]=8,e[l+4>>2]=0,t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0,$t(w,1,A+48|0,n,i),h=d[n+22|0],l=d[n+21|0],(0|(t=e[36440]))<=0||(0|(M=e[36424]))<0||e[4+(M=216192+(M<<4)|0)>>2]||(e[M+4>>2]=t),t=e[50758],e[36424]=t,e[36440]=0,e[(t=216192+(t<<4)|0)>>2]=9,e[t+4>>2]=0,h|=l<<16,l=(0|l)==255,e[t+12>>2]=l?3604556:h,e[t+8>>2]=l?P:r,t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0;break a}h?(h=d[n+22|0],(0|(l=e[36440]))<=0||(0|(M=e[36424]))<0||e[4+(M=216192+(M<<4)|0)>>2]||(e[M+4>>2]=l),l=e[50758],e[36424]=l,e[36440]=0,e[(l=216192+(l<<4)|0)>>2]=9,e[l+4>>2]=0,e[l+12>>2]=(h|h<<16)-983040,e[l+8>>2]=Ve,l=e[50758]+1|0,e[50758]=(0|l)<=169?l:0,h=d[n+19|0],l=e[50758],e[36425]=l,e[36441]=0,e[(l=216192+(l<<4)|0)>>2]=8,e[l+4>>2]=0,e[l+12>>2]=h-1,e[l+8>>2]=t,t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0,$t(w,1,A+48|0,n,i),l=d[n+21|0],h=d[n+22|0],(0|(t=e[36440]))<=0||(0|(M=e[36424]))<0||e[4+(M=216192+(M<<4)|0)>>2]||(e[M+4>>2]=t),t=e[50758],e[36424]=t,e[36440]=0,e[(t=216192+(t<<4)|0)>>2]=9,e[t+4>>2]=0,h|=l<<16,l=(0|l)==255,e[t+12>>2]=l?3604556:h,e[t+8>>2]=l?P:r,t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0):(1&g[0|n]||(h=d[n+19|0],l=e[50758],e[36425]=l,e[36441]=0,e[12+(l=216192+(l<<4)|0)>>2]=h,e[l+8>>2]=t,e[l>>2]=8,e[l+4>>2]=0,t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0,h=d[n+22|0],l=d[n+21|0],(0|(t=e[36440]))<=0||(0|(M=e[36424]))<0||e[4+(M=216192+(M<<4)|0)>>2]||(e[M+4>>2]=t),t=e[50758],e[36424]=t,e[36440]=0,e[(t=216192+(t<<4)|0)>>2]=9,e[t+4>>2]=0,h|=l<<16,l=(0|l)==255,e[t+12>>2]=l?3604556:h,e[t+8>>2]=l?P:r,t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0),$t(w,1,A+48|0,n,i))}!e[47198]|1^y||(tn(A+704|0,e[n+8>>2],n,ae,0),(0|((0|(t=e[50757]-e[50758]|0))<=0?t+170:t))<6||(r=e[36445],t=216192+(e[50758]<<4)|0,e[t>>2]=1802,e[t+4>>2]=16777215&r,r=e[A+708>>2],e[t+8>>2]=e[A+704>>2],e[t+12>>2]=r,t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0)),e[A+56>>2]=e[A+620>>2],e[A+84>>2]=0,e[A+88>>2]=0,e[A+64>>2]=e[A+640>>2],r=e[A+632>>2],e[A+68>>2]=r,t=_e;a:{if(!r&&(!d[m+17|0]||(e[A+72>>2]=0,Gt(0,0,m,A+248|0,0),e[A+52>>2]=1,t=e[A+368>>2],e[A+84>>2]=e[A+364>>2],e[A+88>>2]=t,r=e[A+328>>2],e[A+68>>2]=r,t=be,!r)))break a;e[A+72>>2]=e[t>>2]}$t(w,2,A+48|0,n,i)}if(t=e[36442]+1|0,e[36442]=t,(0|t)>997)break t;if(!(e[36423]>(0|t)))break}(0|(t=e[36440]))<=0||(0|(r=e[36424]))<0||(e[4+(r=216192+(r<<4)|0)>>2]||(e[r+4>>2]=t),e[36440]=0),e[36426]=0,e[36439]=-1,e[36455]=e[50758],CA(),e[36427]=-1,t=0,e[36423]<=0||(r=e[47568],i=e[33284],(0|((0|(t=e[50757]-e[50758]|0))<=0?t+170:t))>=6&&(t=216192+(e[50758]<<4)|0,e[t>>2]=1290,e[t+8>>2]=r,e[t+4>>2]=16777215&i,t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0),e[36423]=0,t=0)}return oe=A+720|0,t}function jt(t,A,r,n){var i,l=0,h=0,m=0,w=0,M=0,y=0,P=0,R=0,ee=0,ae=0,ue=0,be=0,_e=0,Ve=0,$e=0,it=0,ht=0,yt=0,Pt=0,Wt=0,cA=0,gA=0,SA=0,VA=0,qA=0;oe=i=oe-48|0;e:{if(r>>>0<=2){for(cA=e[124732+(r<<=2)>>2],gA=e[r+124720>>2];(0|(r=e[A+4>>2]))==e[A+104>>2]?r=oA(A):(e[A+4>>2]=r+1,r=d[0|r]),(0|r)==32|r-9>>>0<5;);ee=1;t:{r:switch(r-43|0){case 0:case 2:break r;default:break t}ee=(0|r)==45?-1:1,(0|(r=e[A+4>>2]))==e[A+104>>2]?r=oA(A):(e[A+4>>2]=r+1,r=d[0|r])}t:{r:{for(;;){if(g[m+84056|0]==(32|r)){if(m>>>0>6||((0|(r=e[A+4>>2]))==e[A+104>>2]?r=oA(A):(e[A+4>>2]=r+1,r=d[0|r])),(0|(m=m+1|0))!=8)continue;break r}break}if((0|m)!=3){if((0|m)==8)break r;if(!n|m>>>0<4)break t;if((0|m)==8)break r}if((0|(r=e[A+116>>2]))>0|(0|r)>=0&&(e[A+4>>2]=e[A+4>>2]-1),!(!n|m>>>0<4))for(r=(0|r)<0;r||(e[A+4>>2]=e[A+4>>2]-1),(m=m-1|0)>>>0>3;);}oe=P=oe-16|0,U(Ie(Ie(0|ee)*Ie(1/0))),(A=2147483647&(w=G(2)))-8388608>>>0<=2130706431?(r=A,r<<=25,n=1065353216+(A=A>>>7|0)|0):(r=w<<25,n=w>>>7|2147418112,A>>>0>=2139095040||(r=0,n=0,A&&(ea(P,r=A,0,0,0,(A=ce(A))+81|0),y=e[P>>2],M=e[P+4>>2],r=e[P+8>>2],n=65536^e[P+12>>2]|16265-A<<16))),e[i>>2]=y,e[i+4>>2]=M,e[i+8>>2]=r,e[i+12>>2]=-2147483648&w|n,oe=P+16|0,y=e[i+8>>2],M=e[i+12>>2],w=e[i>>2],R=e[i+4>>2];break e}t:{r:{a:if(!m){for(m=0;;){if(g[m+84473|0]!=(32|r))break a;if(m>>>0>1||((0|(r=e[A+4>>2]))==e[A+104>>2]?r=oA(A):(e[A+4>>2]=r+1,r=d[0|r])),(0|(m=m+1|0))==3)break}break r}switch(0|m){case 0:if((0|r)==48){if((0|(m=e[A+4>>2]))==e[A+104>>2]?m=oA(A):(e[A+4>>2]=m+1,m=d[0|m]),(-33&m)==88){oe=h=oe-432|0,(0|(r=e[A+4>>2]))==e[A+104>>2]?m=oA(A):(e[A+4>>2]=r+1,m=d[0|r]);a:{n:{for(;;){if((0|m)!=48){if((0|m)!=46)break a;if((0|(r=e[A+4>>2]))!=e[A+104>>2]){e[A+4>>2]=r+1,m=d[0|r];break n}break}(0|(r=e[A+4>>2]))!=e[A+104>>2]?(Wt=1,e[A+4>>2]=r+1,m=d[0|r]):(Wt=1,m=oA(A))}m=oA(A)}if(be=1,(0|m)==48){for(;_e=(r=_e)-1|0,Ve=Ve-!r|0,(0|(r=e[A+4>>2]))==e[A+104>>2]?m=oA(A):(e[A+4>>2]=r+1,m=d[0|r]),(0|m)==48;);Wt=1}}for(R=1073676288;;){a:{r=32|m;n:{if(!((SA=m-48|0)>>>0<10)){if((0|m)!=46&r-97>>>0>=6)break a;if((0|m)==46){if(be)break a;be=1,_e=y,Ve=M;break n}}r=(0|m)>57?r-87|0:SA,(0|M)<=0&y>>>0<=7|(0|M)<0?l=r+(l<<4)|0:!M&y>>>0<=28?(js(h+48|0,r),qt(h+32|0,yt,Pt,w,R,0,0,0,1073414144),yt=e[h+32>>2],Pt=e[h+36>>2],w=e[h+40>>2],R=e[h+44>>2],qt(h+16|0,e[h+48>>2],e[h+52>>2],e[h+56>>2],e[h+60>>2],yt,Pt,w,R),FA(h,e[h+16>>2],e[h+20>>2],e[h+24>>2],e[h+28>>2],P,ue,it,ht),it=e[h+8>>2],ht=e[h+12>>2],P=e[h>>2],ue=e[h+4>>2]):ae|!r||(qt(h+80|0,yt,Pt,w,R,0,0,0,1073610752),FA(h- -64|0,e[h+80>>2],e[h+84>>2],e[h+88>>2],e[h+92>>2],P,ue,it,ht),it=e[h+72>>2],ht=e[h+76>>2],ae=1,P=e[h+64>>2],ue=e[h+68>>2]),M=(y=y+1|0)?M:M+1|0,Wt=1}(0|(r=e[A+4>>2]))!=e[A+104>>2]?(e[A+4>>2]=r+1,m=d[0|r]):m=oA(A);continue}break}a:if(Wt){if((0|M)<=0&y>>>0<=7|(0|M)<0)for(w=y,R=M;l<<=4,(0|(w=w+1|0))!=8|(R=w?R:R+1|0););n:{c:{o:{if((-33&m)==80){if(w=Oi(A,n),R=r=fe,w|(0|r)!=-2147483648)break n;if(n){if((0|(r=e[A+116>>2]))>0|(0|r)>=0)break o;break c}P=0,ue=0,ua(A,0,0),r=0,A=0;break a}if(w=0,R=0,e[A+116>>2]<0)break n}e[A+4>>2]=e[A+4>>2]-1}w=0,R=0}if(l)if(r=w+((A=be?_e:y)<<2)|0,A=(M=(be?Ve:M)<<2|A>>>30)+R|0,(y=r-32|0)>>>0>0-cA>>>0&(0|(A=M=(r>>>0<w>>>0?A+1|0:A)-(r>>>0<32)|0))>=0|(0|A)>0)e[56798]=68,js(h+160|0,ee),qt(h+144|0,e[h+160>>2],e[h+164>>2],e[h+168>>2],e[h+172>>2],-1,-1,-1,2147418111),qt(h+128|0,e[h+144>>2],e[h+148>>2],e[h+152>>2],e[h+156>>2],-1,-1,-1,2147418111),P=e[h+128>>2],ue=e[h+132>>2],r=e[h+140>>2],A=e[h+136>>2];else if((0|M)>=(0|(r=(A=cA-226|0)>>31))&A>>>0<=y>>>0|(0|r)<(0|M)){if((0|l)>=0)for(;FA(h+416|0,P,ue,it,ht,0,0,0,-1073807360),FA(h+400|0,P,ue,it,ht,(A=r=(0|(A=Lo(P,ue,it,ht,1073610752)))>=0)?e[h+416>>2]:P,A?e[h+420>>2]:ue,A?e[h+424>>2]:it,A?e[h+428>>2]:ht),y=(A=y)-1|0,M=M-!A|0,it=e[h+408>>2],ht=e[h+412>>2],P=e[h+400>>2],ue=e[h+404>>2],(0|(l=r|l<<1))>=0;);A=M-((cA>>31)+(y>>>0<cA>>>0)|0)|0,(0|(r=(r=32+(y-cA|0)|0)>>>0<gA>>>0&(0|(A=r>>>0<32?A+1|0:A))<=0|(0|A)<0?(0|r)>0?r:0:gA))>=113?(js(h+384|0,ee),_e=e[h+392>>2],Ve=e[h+396>>2],yt=e[h+384>>2],Pt=e[h+388>>2],w=0,A=0):(Ss(h+352|0,fi(1,144-r|0)),js(h+336|0,ee),yt=e[h+336>>2],Pt=e[h+340>>2],_e=e[h+344>>2],Ve=e[h+348>>2],pn(h+368|0,e[h+352>>2],e[h+356>>2],e[h+360>>2],e[h+364>>2],yt,Pt,_e,Ve),$e=e[h+376>>2],VA=e[h+380>>2],w=e[h+372>>2],A=e[h+368>>2]),un(h+320|0,(n=!(1&l)&!!(0|Yt(P,ue,it,ht,0,0,0,0))&(0|r)<32)+l|0),qt(h+304|0,yt,Pt,_e,Ve,e[h+320>>2],e[h+324>>2],e[h+328>>2],e[h+332>>2]),r=A,FA(h+272|0,e[h+304>>2],e[h+308>>2],e[h+312>>2],e[h+316>>2],A,w,$e,VA),qt(h+288|0,yt,Pt,_e,Ve,(A=n)?0:P,A?0:ue,A?0:it,A?0:ht),FA(h+256|0,e[h+288>>2],e[h+292>>2],e[h+296>>2],e[h+300>>2],e[h+272>>2],e[h+276>>2],e[h+280>>2],e[h+284>>2]),Ui(h+240|0,e[h+256>>2],e[h+260>>2],e[h+264>>2],e[h+268>>2],r,w,$e,VA),Yt(A=e[h+240>>2],n=e[h+244>>2],r=e[h+248>>2],w=e[h+252>>2],0,0,0,0)||(e[56798]=68),Gr(h+224|0,A,n,r,w,y),P=e[h+224>>2],ue=e[h+228>>2],r=e[h+236>>2],A=e[h+232>>2]}else e[56798]=68,js(h+208|0,ee),qt(h+192|0,e[h+208>>2],e[h+212>>2],e[h+216>>2],e[h+220>>2],0,0,0,65536),qt(h+176|0,e[h+192>>2],e[h+196>>2],e[h+200>
2810>2],e[h+204>>2],0,0,0,65536),P=e[h+176>>2],ue=e[h+180>>2],r=e[h+188>>2],A=e[h+184>>2];else Ss(h+112|0,0*+(0|ee)),P=e[h+112>>2],ue=e[h+116>>2],r=e[h+124>>2],A=e[h+120>>2]}else{n:{c:{if((0|(r=e[A+116>>2]))>0|(0|r)>=0){if(r=e[A+4>>2],e[A+4>>2]=r-1,!n)break c;if(e[A+4>>2]=r-2,!be)break n;e[A+4>>2]=r-3;break n}if(n)break n}ua(A,0,0)}Ss(h+96|0,0*+(0|ee)),P=e[h+96>>2],ue=e[h+100>>2],r=e[h+108>>2],A=e[h+104>>2]}e[i+16>>2]=P,e[i+20>>2]=ue,e[i+24>>2]=A,e[i+28>>2]=r,oe=h+432|0,y=e[i+24>>2],M=e[i+28>>2],w=e[i+16>>2],R=e[i+20>>2];break e}e[A+116>>2]<0||(e[A+4>>2]=e[A+4>>2]-1)}m=A,$e=ee,h=n,A=0,ee=0,oe=l=oe-8976|0,VA=(SA=0-cA|0)-gA|0;a:{n:{for(;;){if((0|r)!=48){if((0|r)!=46)break a;if((0|(r=e[m+4>>2]))!=e[m+104>>2]){e[m+4>>2]=r+1,r=d[0|r];break n}break}(0|(A=e[m+4>>2]))!=e[m+104>>2]?(e[m+4>>2]=A+1,r=d[0|A]):r=oA(m),A=1}r=oA(m)}if(ae=1,(0|r)==48){for(;y=(A=y)-1|0,M=M-!A|0,(0|(A=e[m+4>>2]))==e[m+104>>2]?r=oA(m):(e[m+4>>2]=A+1,r=d[0|A]),(0|r)==48;);A=1}}e[l+784>>2]=0;a:{n:{c:{o:{f:{if((n=(0|r)==46)|(P=r-48|0)>>>0<=9)for(;;){l:{if(1&n){if(!ae){y=w,M=R,ae=1;break l}n=!A;break f}R=(w=w+1|0)?R:R+1|0,(0|ee)<=2044?(Wt=(0|r)==48?Wt:w,A=(l+784|0)+(ee<<2)|0,be&&(P=(F(e[A>>2],10)+r|0)-48|0),e[A>>2]=P,A=1,be=(r=(0|(n=be+1|0))==9)?0:n,ee=r+ee|0):(0|r)!=48&&(e[l+8960>>2]=1|e[l+8960>>2],Wt=18396)}if((0|(r=e[m+4>>2]))==e[m+104>>2]?r=oA(m):(e[m+4>>2]=r+1,r=d[0|r]),!((n=(0|r)==46)|(P=r-48|0)>>>0<10))break}if(y=ae?y:w,M=ae?M:R,!(!A|(-33&r)!=69)){if(P=Oi(m,h),ue=A=fe,!(P|(0|A)!=-2147483648)){if(!h)break c;P=0,ue=0,e[m+116>>2]<0||(e[m+4>>2]=e[m+4>>2]-1)}M=M+ue|0,M=(y=y+P|0)>>>0<P>>>0?M+1|0:M;break n}if(n=!A,(0|r)<0)break o}e[m+116>>2]<0||(e[m+4>>2]=e[m+4>>2]-1)}if(!n)break n;e[56798]=28}w=0,R=0,ua(m,0,0),r=0,A=0;break a}if(A=e[l+784>>2])if(w>>>0>9&(0|R)>=0|(0|R)>0|(0|w)!=(0|y)|(0|M)!=(0|R)|(A>>>gA|0?(0|gA)<=30:0))if(y>>>0>SA>>>1>>>0&(0|M)>=0|(0|M)>0)e[56798]=68,js(l+96|0,$e),qt(l+80|0,e[l+96>>2],e[l+100>>2],e[l+104>>2],e[l+108>>2],-1,-1,-1,2147418111),qt(l- -64|0,e[l+80>>2],e[l+84>>2],e[l+88>>2],e[l+92>>2],-1,-1,-1,2147418111),w=e[l+64>>2],R=e[l+68>>2],r=e[l+76>>2],A=e[l+72>>2];else if((r=y>>>0<(A=cA-226|0)>>>0)&(0|M)<=(0|(A>>=31))|(0|A)>(0|M))e[56798]=68,js(l+144|0,$e),qt(l+128|0,e[l+144>>2],e[l+148>>2],e[l+152>>2],e[l+156>>2],0,0,0,65536),qt(l+112|0,e[l+128>>2],e[l+132>>2],e[l+136>>2],e[l+140>>2],0,0,0,65536),w=e[l+112>>2],R=e[l+116>>2],r=e[l+124>>2],A=e[l+120>>2];else{if(be){if((0|be)<=8){for(m=e[(A=(l+784|0)+(ee<<2)|0)>>2];m=F(m,10),(0|(be=be+1|0))!=9;);e[A>>2]=m}ee=ee+1|0}if(ae=y,!((0|Wt)>(0|y)|(0|Wt)>=9|(0|y)>17)){if((0|ae)==9){js(l+192|0,$e),un(l+176|0,e[l+784>>2]),qt(l+160|0,e[l+192>>2],e[l+196>>2],e[l+200>>2],e[l+204>>2],e[l+176>>2],e[l+180>>2],e[l+184>>2],e[l+188>>2]),w=e[l+160>>2],R=e[l+164>>2],r=e[l+172>>2],A=e[l+168>>2];break a}if((0|ae)<=8){js(l+272|0,$e),un(l+256|0,e[l+784>>2]),qt(l+240|0,e[l+272>>2],e[l+276>>2],e[l+280>>2],e[l+284>>2],e[l+256>>2],e[l+260>>2],e[l+264>>2],e[l+268>>2]),js(l+224|0,e[124720+(0-ae<<2)>>2]),vA(l+208|0,e[l+240>>2],e[l+244>>2],e[l+248>>2],e[l+252>>2],e[l+224>>2],e[l+228>>2],e[l+232>>2],e[l+236>>2]),w=e[l+208>>2],R=e[l+212>>2],r=e[l+220>>2],A=e[l+216>>2];break a}if(A=27+(F(ae,-3)+gA|0)|0,!((r=e[l+784>>2])>>>A|0&&(0|A)<=30)){js(l+352|0,$e),un(l+336|0,r),qt(l+320|0,e[l+352>>2],e[l+356>>2],e[l+360>>2],e[l+364>>2],e[l+336>>2],e[l+340>>2],e[l+344>>2],e[l+348>>2]),js(l+304|0,e[124648+(ae<<2)>>2]),qt(l+288|0,e[l+320>>2],e[l+324>>2],e[l+328>>2],e[l+332>>2],e[l+304>>2],e[l+308>>2],e[l+312>>2],e[l+316>>2]),w=e[l+288>>2],R=e[l+292>>2],r=e[l+300>>2],A=e[l+296>>2];break a}}for(;!e[(l+784|0)+((ee=(r=ee)-1|0)<<2)>>2];);if(be=0,A=(0|ae)%9|0){if(n=0,A=(0|ae)<0?A+9|0:A,r){for(M=1e9/(0|(y=e[124720+(0-A<<2)>>2]))|0,P=0,m=0;w=(w=P)+(ee=((R=e[(P=(l+784|0)+(m<<2)|0)>>2])>>>0)/(y>>>0)|0)|0,e[P>>2]=w,n=(w=!w&(0|n)==(0|m))?n+1&2047:n,ae=w?ae-9|0:ae,P=F(M,R-F(y,ee)|0),(0|(m=m+1|0))!=(0|r););P&&(e[(l+784|0)+(r<<2)>>2]=P,r=r+1|0)}else r=0;ae=9+(ae-A|0)|0}else n=0;for(;;){m=(l+784|0)+(n<<2)|0;n:{for(;;){if(((0|ae)!=36|re[m>>2]>=10384593)&(0|ae)>=36)break n;for(ee=r+2047|0,P=0,A=r;r=A,y=P,P=(A=e[(ee=(l+784|0)+((w=2047&ee)<<2)|0)>>2])<<29,A=R=A>>>3|0,!(M=(y=y+P|0)>>>0<P>>>0?A+1|0:A)&y>>>0<1000000001?P=0:y=(A=y)-rr(P=$i(A,M,1e9),fe,1e9,0)|0,e[ee>>2]=y,A=(0|w)!=(r-1&2047)||(0|n)==(0|w)||y?r:w,ee=w-1|0,(0|n)!=(0|w););
vendor: 17,076 bytes, line 2810
2810if(be=be-29|0,P)break}(0|(n=n-1&2047))==(0|A)&&(m=r=(y=l+784|0)+((A+2046&2047)<<2)|0,M=e[r>>2],r=A-1&2047,e[m>>2]=M|e[y+(r<<2)>>2]),ae=ae+9|0,e[(l+784|0)+(n<<2)>>2]=P;continue}break}n:{c:for(;;){for(y=r+1&2047,P=(l+784|0)+((r-1&2047)<<2)|0;;){w=(0|ae)>45?9:1;o:{for(;;){A=n,m=0;f:{for(;;){if((0|(n=A+m&2047))!=(0|r)&&!((n=e[(l+784|0)+(n<<2)>>2])>>>0<(M=e[124672+(m<<2)>>2])>>>0)){if(n>>>0>M>>>0)break f;if((0|(m=m+1|0))!=4)continue}break}if((0|ae)==36){for(y=0,M=0,m=0,w=0,R=0;(0|(n=A+m&2047))==(0|r)&&(e[780+(l+((r=r+1&2047)<<2)|0)>>2]=0),un(l+768|0,e[(l+784|0)+(n<<2)>>2]),qt(l+752|0,y,M,w,R,0,0,1342177280,1075633366),FA(l+736|0,e[l+752>>2],e[l+756>>2],e[l+760>>2],e[l+764>>2],e[l+768>>2],e[l+772>>2],e[l+776>>2],e[l+780>>2]),w=e[l+744>>2],R=e[l+748>>2],y=e[l+736>>2],M=e[l+740>>2],(0|(m=m+1|0))!=4;);if(js(l+720|0,$e),qt(l+704|0,y,M,w,R,e[l+720>>2],e[l+724>>2],e[l+728>>2],e[l+732>>2]),w=e[l+712>>2],R=e[l+716>>2],y=0,M=0,P=e[l+704>>2],ue=e[l+708>>2],(0|(n=(ee=(0|(m=(h=be+113|0)-cA|0))<(0|gA))?(0|m)>0?m:0:gA))<=112)break o;break n}}if(be=w+be|0,n=r,(0|A)!=(0|r))break}for(R=1e9>>>w|0,ee=~(-1<<w),m=0,n=A;M=(M=m)+((h=e[(m=(l+784|0)+(A<<2)|0)>>2])>>>w|0)|0,e[m>>2]=M,n=(M=!M&(0|A)==(0|n))?n+1&2047:n,ae=M?ae-9|0:ae,m=F(R,h&ee),(0|r)!=(0|(A=A+1&2047)););if(!m)continue;if((0|n)!=(0|y)){e[(l+784|0)+(r<<2)>>2]=m,r=y;continue c}e[P>>2]=1|e[P>>2];continue}break}break}Ss(l+656|0,fi(1,225-n|0)),pn(l+688|0,e[l+656>>2],e[l+660>>2],e[l+664>>2],e[l+668>>2],P,ue,w,R),yt=e[l+696>>2],Pt=e[l+700>>2],it=e[l+688>>2],ht=e[l+692>>2],Ss(l+640|0,fi(1,113-n|0)),Ja(l+672|0,P,ue,w,R,e[l+640>>2],e[l+644>>2],e[l+648>>2],e[l+652>>2]),Ui(l+624|0,P,ue,w,R,y=e[l+672>>2],M=e[l+676>>2],_e=e[l+680>>2],Ve=e[l+684>>2]),FA(l+608|0,it,ht,yt,Pt,e[l+624>>2],e[l+628>>2],e[l+632>>2],e[l+636>>2]),w=e[l+616>>2],R=e[l+620>>2],P=e[l+608>>2],ue=e[l+612>>2]}if((0|(ae=A+4&2047))!=(0|r)){n:if((ae=e[(l+784|0)+(ae<<2)>>2])>>>0<=499999999){if(!ae&(A+5&2047)==(0|r))break n;Ss(l+496|0,.25*+(0|$e)),FA(l+480|0,y,M,_e,Ve,e[l+496>>2],e[l+500>>2],e[l+504>>2],e[l+508>>2]),_e=e[l+488>>2],Ve=e[l+492>>2],y=e[l+480>>2],M=e[l+484>>2]}else(0|ae)==5e8?(qA=+(0|$e),(A+5&2047)!=(0|r)?(Ss(l+560|0,.75*qA),FA(l+544|0,y,M,_e,Ve,e[l+560>>2],e[l+564>>2],e[l+568>>2],e[l+572>>2]),_e=e[l+552>>2],Ve=e[l+556>>2],y=e[l+544>>2],M=e[l+548>>2]):(Ss(l+528|0,.5*qA),FA(l+512|0,y,M,_e,Ve,e[l+528>>2],e[l+532>>2],e[l+536>>2],e[l+540>>2]),_e=e[l+520>>2],Ve=e[l+524>>2],y=e[l+512>>2],M=e[l+516>>2])):(Ss(l+592|0,.75*+(0|$e)),FA(l+576|0,y,M,_e,Ve,e[l+592>>2],e[l+596>>2],e[l+600>>2],e[l+604>>2]),_e=e[l+584>>2],Ve=e[l+588>>2],y=e[l+576>>2],M=e[l+580>>2]);(0|n)>111||(Ja(l+464|0,y,M,_e,Ve,0,0,0,1073676288),Yt(e[l+464>>2],e[l+468>>2],e[l+472>>2],e[l+476>>2],0,0,0,0)||(FA(l+448|0,y,M,_e,Ve,0,0,0,1073676288),_e=e[l+456>>2],Ve=e[l+460>>2],y=e[l+448>>2],M=e[l+452>>2]))}FA(l+432|0,P,ue,w,R,y,M,_e,Ve),Ui(l+416|0,e[l+432>>2],e[l+436>>2],e[l+440>>2],e[l+444>>2],it,ht,yt,Pt),w=e[l+424>>2],R=e[l+428>>2],P=e[l+416>>2],ue=e[l+420>>2],(VA-2|0)>=(2147483647&h)||(e[l+408>>2]=w,e[l+412>>2]=2147483647&R,e[l+400>>2]=P,e[l+404>>2]=ue,qt(l+384|0,P,ue,w,R,0,0,0,1073610752),w=(A=(0|(A=Lo(e[l+400>>2],e[l+404>>2],e[l+408>>2],e[l+412>>2],1081081856)))>=0)?e[l+392>>2]:w,R=A?e[l+396>>2]:R,P=A?e[l+384>>2]:P,ue=A?e[l+388>>2]:ue,be=A+be|0,!(!!(0|Yt(y,M,_e,Ve,0,0,0,0))&(A?ee&(0|n)!=(0|m):ee))&(be+110|0)<=(0|VA)||(e[56798]=68)),Gr(l+368|0,P,ue,w,R,be),w=e[l+368>>2],R=e[l+372>>2],r=e[l+380>>2],A=e[l+376>>2]}else js(l+48|0,$e),un(l+32|0,A),qt(l+16|0,e[l+48>>2],e[l+52>>2],e[l+56>>2],e[l+60>>2],e[l+32>>2],e[l+36>>2],e[l+40>>2],e[l+44>>2]),w=e[l+16>>2],R=e[l+20>>2],r=e[l+28>>2],A=e[l+24>>2];else Ss(l,0*+(0|$e)),w=e[l>>2],R=e[l+4>>2],r=e[l+12>>2],A=e[l+8>>2]}e[i+40>>2]=A,e[i+44>>2]=r,e[i+32>>2]=w,e[i+36>>2]=R,oe=l+8976|0,y=e[i+40>>2],M=e[i+44>>2],w=e[i+32>>2],R=e[i+36>>2];break e;case 3:break r}(0|(r=e[A+116>>2]))>0|(0|r)>=0&&(e[A+4>>2]=e[A+4>>2]-1);break t}if((0|(r=e[A+4>>2]))==e[A+104>>2]?r=oA(A):(e[A+4>>2]=r+1,r=d[0|r]),(0|r)!=40){if(M=2147450880,e[A+116>>2]<0)break e;e[A+4>>2]=e[A+4>>2]-1;break e}for(m=1;(0|(r=e[A+4>>2]))==e[A+104>>2]?r=oA(A):(e[A+4>>2]=r+1,r=d[0|r]),r-48>>>0<10|r-65>>>0<26|(0|r)==95||!(r-97>>>0>=26);)m=m+1|0;if(M=2147450880,(0|r)==41)break e;(0|(r=e[A+116>>2]))>0|(0|r)>=0&&(e[A+4>>2]=e[A+4>>2]-1);r:{if(n){if(m)break r;break e}break t}for(;m=m-1|0,(0|r)>0|(0|r)>=0&&(e[A+4>>2]=e[A+4>>2]-1),m;);break e}e[56798]=28,ua(A,0,0)}M=0}e[t>>2]=w,e[t+4>>2]=R,e[t+8>>2]=y,e[t+12>>2]=M,oe=i+48|0}function $t(t,A,r,n,i){var l,h=0,m=0,w=0,M=0,y=0,P=0,R=0,ee=0,ae=0,ue=0,be=0,_e=0,Ve=0,$e=0,it=0,ht=0,yt=0,Pt=0,Wt=0,cA=0,gA=0;if(oe=l=oe-112|0,e[r+8>>2]){cA=e[50754]/70|0,ue=(P=e[n+12>>2])||256;e:if((0|A)!=2){if((0|A)==1){t:if(d[t+11|0]!=3)switch(d[n-15|0]-3|0){case 0:case 5:break t;default:break e}ue=(0|(P=e[e[47192]+44>>2]))<(0|ue)?ue:P}}else{if((0|(P=e[e[47192]+80>>2]))<=0|!(8&d[0|n]|P>>>0<=d[t+14|0]|32&d[t+6|0]))break e;cA<<=1}if(e[36436]=0,Ve=t,gA=A,y=n,oe=ae=oe-16|0,t=e[34460]+e[r+8>>2]|0,A=(A=d[t+2|0])>>>0>=24?24:A,e[ae+12>>2]=A,m=e[r+12>>2]+e[r+24>>2]|0,e[36422]=m,A){for(P=t+4|0,h=1&v[t+4>>1];t=145488+(M<<3)|0,n=P+(h?M<<6:F(M,44))|0,e[t+4>>2]=n,ee=_[n>>1],v[t+2>>1]=ee,v[t>>1]=d[n+16|0],w=2&ee?M:w,(0|(M=M+1|0))!=(0|A););P=145488,(0|w)<=0||((0|gA)!=1?(A=A-w|0,e[ae+12>>2]=A,P=145488+(w<<3)|0):(A=w+1|0,e[ae+12>>2]=A,P=145488))}else A=0,P=145488;if(!(!e[r+4>>2]|e[r+20>>2]|d[Ve+11|0]!=2)){if(h=e[r+36>>2],n=e[r+40>>2],t=0,(0|(A=e[ae+12>>2]))>=2){w=h>>>12|0,ee=n>>>26&7,$e=n>>>18&248,be=F(_e=63&n,50),ht=63&(yt=h>>>6|0),M=h<<1&126,Pt=F(n>>>16&31,50)-750|0,Wt=F(n>>>11&31,50)-750|0,it=F(n>>>6&31,50)-750|0;e:{t:if((0|gA)!=1){if(!(w|_e))break e;if(8&w?(A=e[4+(P+((m=A-1|0)<<3)|0)>>2],v[A>>1]<0?t=A:(t=(0|(t=e[44469]+1|0))<=169?t:0,e[44469]=t,m=_[A+20>>1]|_[A+22>>1]<<16,t=177888+(t<<6)|0,h=_[A+16>>1]|_[A+18>>1]<<16,v[t+16>>1]=h,v[t+18>>1]=h>>>16,v[t+20>>1]=m,v[t+22>>1]=m>>>16,m=_[A+4>>1]|_[A+6>>1]<<16,h=_[A>>1]|_[A+2>>1]<<16,v[t>>1]=h,v[t+2>>1]=h>>>16,v[t+4>>1]=m,v[t+6>>1]=m>>>16,m=_[A+12>>1]|_[A+14>>1]<<16,h=_[A+8>>1]|_[A+10>>1]<<16,v[t+8>>1]=h,v[t+10>>1]=h>>>16,v[t+12>>1]=m,v[t+14>>1]=m>>>16,m=_[A+28>>1]|_[A+30>>1]<<16,h=_[A+24>>1]|_[A+26>>1]<<16,v[t+24>>1]=h,v[t+26>>1]=h>>>16,v[t+28>>1]=m,v[t+30>>1]=m>>>16,m=_[A+36>>1]|_[A+38>>1]<<16,h=_[A+32>>1]|_[A+34>>1]<<16,v[t+32>>1]=h,v[t+34>>1]=h>>>16,v[t+36>>1]=m,v[t+38>>1]=m>>>16,m=_[A+44>>1]|_[A+46>>1]<<16,h=_[A+40>>1]|_[A+42>>1]<<16,v[t+40>>1]=h,v[t+42>>1]=h>>>16,v[t+44>>1]=m,v[t+46>>1]=m>>>16,m=_[A+52>>1]|_[A+54>>1]<<16,h=_[A+48>>1]|_[A+50>>1]<<16,v[t+48>>1]=h,v[t+50>>1]=h>>>16,v[t+52>>1]=m,v[t+54>>1]=m>>>16,m=_[A+60>>1]|_[A+62>>1]<<16,A=_[A+56>>1]|_[A+58>>1]<<16,v[t+56>>1]=A,v[t+58>>1]=A>>>16,v[t+60>>1]=m,v[t+62>>1]=m>>>16,g[t+16|0]=0,v[t>>1]=32768|_[t>>1],m=e[ae+12>>2]-1|0),e[4+(P+(m<<3)|0)>>2]=t,A=1792,(0|(m=v[t+4>>1]))<300||(A=1536,m>>>0<400||(A=m>>>0<500?1280:1024)),e[36436]=A,h=35):(e[ae+12>>2]=A+1,v[(t=(m=P+(A<<3)|0)-8|0)>>1]=M,A=e[t+4>>2],t=(0|(t=e[44469]+1|0))<=169?t:0,e[44469]=t,(t=177888+(yt=t<<6)|0)&&(h=_[A+4>>1]|_[A+6>>1]<<16,R=_[A>>1]|_[A+2>>1]<<16,v[t>>1]=R,v[t+2>>1]=R>>>16,v[t+4>>1]=h,v[t+6>>1]=h>>>16,h=_[A+60>>1]|_[A+62>>1]<<16,R=_[A+56>>1]|_[A+58>>1]<<16,v[t+56>>1]=R,v[t+58>>1]=R>>>16,v[t+60>>1]=h,v[t+62>>1]=h>>>16,h=_[A+52>>1]|_[A+54>>1]<<16,R=_[A+48>>1]|_[A+50>>1]<<16,v[t+48>>1]=R,v[t+50>>1]=R>>>16,v[t+52>>1]=h,v[t+54>>1]=h>>>16,h=_[A+44>>1]|_[A+46>>1]<<16,R=_[A+40>>1]|_[A+42>>1]<<16,v[t+40>>1]=R,v[t+42>>1]=R>>>16,v[t+44>>1]=h,v[t+46>>1]=h>>>16,h=_[A+36>>1]|_[A+38>>1]<<16,R=_[A+32>>1]|_[A+34>>1]<<16,v[t+32>>1]=R,v[t+34>>1]=R>>>16,v[t+36>>1]=h,v[t+38>>1]=h>>>16,h=_[A+28>>1]|_[A+30>>1]<<16,R=_[A+24>>1]|_[A+26>>1]<<16,v[t+24>>1]=R,v[t+26>>1]=R>>>16,v[t+28>>1]=h,v[t+30>>1]=h>>>16,h=_[A+20>>1]|_[A+22>>1]<<16,R=_[A+16>>1]|_[A+18>>1]<<16,v[t+16>>1]=R,v[t+18>>1]=R>>>16,v[t+20>>1]=h,v[t+22>>1]=h>>>16,h=_[A+12>>1]|_[A+14>>1]<<16,A=_[A+8>>1]|_[A+10>>1]<<16,v[t+8>>1]=A,v[t+10>>1]=A>>>16,v[t+12>>1]=h,v[t+14>>1]=h>>>16,g[yt+177904|0]=0,v[t>>1]=32768|_[t>>1]),v[m>>1]=0,e[m+4>>2]=t,M>>>0>=37&&(e[36422]=(M+e[36422]|0)-36),h=ht<<1,_e&>(t,be,it,Wt,ee,Pt,$e,w)),e[e[32972]+132>>2]||(A=d[t+17|0])&&(A=v[102896+(((0|(A=(h<<6>>>0)/(A>>>0)|0))>=199?199:A)<<1)>>1],g[t+18|0]=(0|F(A,d[t+18|0]))/512,g[t+19|0]=(0|F(A,d[t+19|0]))/512,g[t+20|0]=(0|F(A,d[t+20|0]))/512,g[t+21|0]=(0|F(A,d[t+21|0]))/512,g[t+22|0]=(0|F(A,d[t+22|0]))/512,g[t+23|0]=(0|F(A,d[t+23|0]))/512,g[t+24|0]=(0|F(A,d[t+24|0]))/512,g[t+25|0]=(0|F(A,d[t+25|0]))/512),n-536870912>>>0<=1073741823){if(ee=e[44469],(0|(m=e[ae+12>>2]))>0)for(t=F(n>>>29|0,10)+102854|0,$e=v[t+4>>1],_e=v[t+2>>1],be=v[t>>1],Pt=v[t+6>>1],Wt=v[t+8>>1],h=0;A=e[4+(it=P+(h<<3)|0)>>2],v[A>>1]<0?t=A:(t=177888+(ht=(ee=(0|(t=ee+1|0))<=169?t:0)<<6)|0)?(n=_[A+4>>1]|_[A+6>>1]<<16,m=_[A>>1]|_[A+2>>1]<<16,v[t>>1]=m,v[t+2>>1]=m>>>16,v[t+4>>1]=n,v[t+6>>1]=n>>>16,n=_[A+60>>1]|_[A+62>>1]<<16,m=_[A+56>>1]|_[A+58>>1]<<16,v[t+56>>1]=m,v[t+58>>1]=m>>>16,v[t+60>>1]=n,v[t+62>>1]=n>>>16,n=_[A+52>>1]|_[A+54>>1]<<16,m=_[A+48>>1]|_[A+50>>1]<<16,v[t+48>>1]=m,v[t+50>>1]=m>>>16,v[t+52>>1]=n,v[t+54>>1]=n>>>16,n=_[A+44>>1]|_[A+46>>1]<<16,m=_[A+40>>1]|_[A+42>>1]<<16,v[t+40>>1]=m,v[t+42>>1]=m>>>16,v[t+44>>1]=n,v[t+46>>1]=n>>>16,n=_[A+36>>1]|_[A+38>>1]<<16,m=_[A+32>>1]|_[A+34>>1]<<16,v[t+32>>1]=m,v[t+34>>1]=m>>>16,v[t+36>>1]=n,v[t+38>>1]=n>>>16,n=_[A+28>>1]|_[A+30>>1]<<16,m=_[A+24>>1]|_[A+26>>1]<<16,v[t+24>>1]=m,v[t+26>>1]=m>>>16,v[t+28>>1]=n,v[t+30>>1]=n>>>16,n=_[A+20>>1]|_[A+22>>1]<<16,m=_[A+16>>1]|_[A+18>>1]<<16,v[t+16>>1]=m,v[t+18>>1]=m>>>16,v[t+20>>1]=n,v[t+22>>1]=n>>>16,n=_[A+12>>1]|_[A+14>>1]<<16,A=_[A+8>>1]|_[A+10>>1]<<16,v[t+8>>1]=A,v[t+10>>1]=A>>>16,v[t+12>>1]=n,v[t+14>>1]=n>>>16,g[ht+177904|0]=0,v[t>>1]=32768|_[t>>1],m=e[ae+12>>2]):t=0,e[it+4>>2]=t,v[t+8>>1]=(0|F($e,v[t+8>>1]))/256,v[t+6>>1]=(0|F(_e,v[t+6>>1]))/256,v[t+4>>1]=(0|F(be,v[t+4>>1]))/256,v[t+12>>1]=(0|F(Wt,v[t+12>>1]))/256,v[t+10>>1]=(0|F(Pt,v[t+10>>1]))/256,(0|m)>(0|(h=h+1|0)););e[44469]=ee}if(!t)break e}else{A=e[P+4>>2],(0|(m=v[A>>1]))<0?t=A:(n=(0|(n=e[44469]+1|0))<=169?n:0,e[44469]=n,(n=177888+(m=n<<6)|0)&&(t=_[A+4>>1]|_[A+6>>1]<<16,R=_[A>>1]|_[A+2>>1]<<16,v[n>>1]=R,v[n+2>>1]=R>>>16,v[n+4>>1]=t,v[n+6>>1]=t>>>16,t=_[A+60>>1]|_[A+62>>1]<<16,R=_[A+56>>1]|_[A+58>>1]<<16,v[n+56>>1]=R,v[n+58>>1]=R>>>16,v[n+60>>1]=t,v[n+62>>1]=t>>>16,t=_[A+52>>1]|_[A+54>>1]<<16,R=_[A+48>>1]|_[A+50>>1]<<16,v[n+48>>1]=R,v[n+50>>1]=R>>>16,v[n+52>>1]=t,v[n+54>>1]=t>>>16,t=_[A+44>>1]|_[A+46>>1]<<16,R=_[A+40>>1]|_[A+42>>1]<<16,v[n+40>>1]=R,v[n+42>>1]=R>>>16,v[n+44>>1]=t,v[n+46>>1]=t>>>16,t=_[A+36>>1]|_[A+38>>1]<<16,R=_[A+32>>1]|_[A+34>>1]<<16,v[n+32>>1]=R,v[n+34>>1]=R>>>16,v[n+36>>1]=t,v[n+38>>1]=t>>>16,t=_[A+28>>1]|_[A+30>>1]<<16,R=_[A+24>>1]|_[A+26>>1]<<16,v[n+24>>1]=R,v[n+26>>1]=R>>>16,v[n+28>>1]=t,v[n+30>>1]=t>>>16,t=_[A+20>>1]|_[A+22>>1]<<16,R=_[A+16>>1]|_[A+18>>1]<<16,v[n+16>>1]=R,v[n+18>>1]=R>>>16,v[n+20>>1]=t,v[n+22>>1]=t>>>16,t=_[A+12>>1]|_[A+14>>1]<<16,A=_[A+8>>1]|_[A+10>>1]<<16,v[n+8>>1]=A,v[n+10>>1]=A>>>16,v[n+12>>1]=t,v[n+14>>1]=t>>>16,g[m+177904|0]=0,m=-32768|_[n>>1],v[n>>1]=m,t=n)),e[P+4>>2]=t,v[P>>1]=M||50,v[P+2>>1]=16384|_[P+2>>1],v[t>>1]=16384|m,m=e[P+12>>2],A=d[m+17|0],n=e[32972],e[n+132>>2]&&(g[t+39|0]=d[m+39|0]-4);r:if(_e){if(2048&h){A=(F(A,31&yt)>>>0)/30|0,e[n+132>>2]||(n=d[t+17|0])&&(A=v[102896+(((0|(A=(A<<6>>>0)/(n>>>0)|0))>=199?199:A)<<1)>>1],g[t+18|0]=(0|F(A,d[t+18|0]))/512,g[t+19|0]=(0|F(A,d[t+19|0]))/512,g[t+20|0]=(0|F(A,d[t+20|0]))/512,g[t+21|0]=(0|F(A,d[t+21|0]))/512,g[t+22|0]=(0|F(A,d[t+22|0]))/512,g[t+23|0]=(0|F(A,d[t+23|0]))/512,g[t+24|0]=(0|F(A,d[t+24|0]))/512,g[t+25|0]=(0|F(A,d[t+25|0]))/512),gt(t,be,it,Wt,ee,Pt,$e,w);break r}if(gt(t,be,it,Wt,ee,Pt,$e,w),e[e[32972]+132>>2]||!(A=d[t+17|0]))break r;A=v[102896+(((0|(A=(ht<<7>>>0)/(A>>>0)|0))>=199?199:A)<<1)>>1],g[t+18|0]=(0|F(A,d[t+18|0]))/512,g[t+19|0]=(0|F(A,d[t+19|0]))/512,g[t+20|0]=(0|F(A,d[t+20|0]))/512,g[t+21|0]=(0|F(A,d[t+21|0]))/512,g[t+22|0]=(0|F(A,d[t+22|0]))/512,g[t+23|0]=(0|F(A,d[t+23|0]))/512,g[t+24|0]=(0|F(A,d[t+24|0]))/512,g[t+25|0]=(0|F(A,d[t+25|0]))/512}else if(n=e[n+132>>2],8&w){if(n||!(n=d[t+17|0]))break r;A=((16320&F(A,48))>>>0)/(n>>>0)|0,A=v[102896+((A>>>0>=199?199:A)<<1)>>1],g[t+18|0]=(0|F(A,d[t+18|0]))/512,g[t+19|0]=(0|F(A,d[t+19|0]))/512,g[t+20|0]=(0|F(A,d[t+20|0]))/512,g[t+21|0]=(0|F(A,d[t+21|0]))/512,g[t+22|0]=(0|F(A,d[t+22|0]))/512,g[t+23|0]=(0|F(A,d[t+23|0]))/512,g[t+24|0]=(0|F(A,d[t+24|0]))/512,g[t+25|0]=(0|F(A,d[t+25|0]))/512}else n||(A=d[t+17|0])&&(A=v[102896+(((A=1792/(A>>>0)|0)>>>0>=199?199:A)<<1)>>1],g[t+18|0]=(0|F(A,d[t+18|0]))/512,g[t+19|0]=(0|F(A,d[t+19|0]))/512,g[t+20|0]=(0|F(A,d[t+20|0]))/512,g[t+21|0]=(0|F(A,d[t+21|0]))/512,g[t+22|0]=(0|F(A,d[t+22|0]))/512,g[t+23|0]=(0|F(A,d[t+23|0]))/512,g[t+24|0]=(0|F(A,d[t+24|0]))/512,g[t+25|0]=(0|F(A,d[t+25|0]))/512);if(!(8&w))break t;A=2816,(0|(n=v[t+4>>1]))<300||(A=2560,n>>>0<400||(A=n>>>0<500?2304:2048)),e[36436]=A}4&w&&(v[t>>1]=32|_[t>>1]),2&w&&(v[t>>1]=16|_[t>>1])}64&w&&Kr(20,0),t=M&w<<27>>31}else t=0;m=t+e[36422]|0,e[36422]=m,A=e[ae+12>>2]}if((0|(n=A-1|0))<=0)h=0;else{if(t=0,M=0,h=0,A-2>>>0>=3)for($e=-4&n,ee=0;h=(((v[(w=M<<3)+P>>1]+h|0)+v[P+(8|w)>>1]|0)+v[P+(16|w)>>1]|0)+v[P+(24|w)>>1]|0,M=M+4|0,(0|$e)!=(0|(ee=ee+4|0)););if(w=3&n)for(;h=v[P+(M<<3)>>1]+h|0,M=M+1|0,(0|w)!=(0|(t=t+1|0)););}if(t=A,(w=e[r+20>>2])&&(t=n,M=w+e[34460]|0,(ee=d[M+2|0])&&(_e=_[M+4>>1],v[P+(n<<3)>>1]=d[M+20|0],w=1,t=A,(0|ee)!=1))){if($e=M+4|0,_e&=1,Pt=1&(M=ee-1|0),(0|ee)!=2)for(Wt=-2&M,ee=0;it=$e+(w<<6)|0,ht=$e+F(w,44)|0,yt=d[(be=_e?it:ht)+16|0],e[4+(M=P+(t<<3)|0)>>2]=be,v[M>>1]=yt,v[M+2>>1]=_[be>>1],it=d[16+(be=_e?it- -64|0:ht+44|0)|0],e[M+12>>2]=be,v[M+8>>1]=it,v[M+10>>1]=_[be>>1],w=w+2|0,t=t+2|0,(0|Wt)!=(0|(ee=ee+2|0)););Pt&&(w=$e+(_e?w<<6:F(w,44))|0,ee=d[w+16|0],e[4+(M=P+(t<<3)|0)>>2]=w,v[M>>1]=ee,v[M+2>>1]=_[w>>1],t=t+1|0)}e:if(!((0|h)<=0)){t:{switch(gA-1|0){case 1:if(w=(0|(w=(e[r+44>>2]+m|0)-45|0))<=10?10:w,8&d[0|y]&&(w=w+(d[e[36128]+14|0]<<1)|0),(0|n)<=0)break e;if(y=(w<<8)/(0|h)|0,M=0,(0|A)!=2)for(A=-2&n,w=0;v[(h=(m=M<<3)+P|0)>>1]=(0|F(y,v[h>>1]))/256,v[(m=P+(8|m)|0)>>1]=(0|F(y,v[m>>1]))/256,M=M+2|0,(0|A)!=(0|(w=w+2|0)););if(!(1&n))break e;v[(A=P+(M<<3)|0)>>1]=(0|F(y,v[A>>1]))/256;break e;case 0:if(e[r>>2]!=1||(0|(w=e[r+44>>2]))>129)break t;v[P>>1]=(0|F(w,v[P>>1]))/130;break t}(0|(w=e[r+44>>2]))<=0||(m=(w-h|0)+m|0,e[36422]=m)}if(!(!m|(0|n)<=0)){if(y=(h+m<<8)/(0|h)|0,M=0,(0|A)!=2)for(A=-2&n,w=0;v[(h=(m=M<<3)+P|0)>>1]=(0|F(y,v[h>>1]))/256,v[(m=P+(8|m)|0)>>1]=(0|F(y,v[m>>1]))/256,M=M+2|0,(0|A)!=(0|(w=w+2|0)););1&n&&(v[(A=P+(M<<3)|0)>>1]=(0|F(y,v[A>>1]))/256)}}if(e[l+108>>2]=t,oe=ae+16|0,P){if((0|(t=e[r+16>>2]))!=e[36438]&&(e[36438]=t,A=216192+(e[50758]<<4)|0,e[A>>2]=14,e[A+4>>2]=t,t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0),M=(t=e[e[32972]+132>>2])?1:3,A=e[P+4>>2],e[r+28>>2]|!d[145748]||(g[145748]=0,M=t?2:4),(n=e[36426])&&(!((2&(t=_[n>>1]))>>>1|d[n+16|0]<2)|16&t||(m=216192+(e[36439]<<4)|0,e[m+12>>2]=A,8&t&&(t=(0|(t=e[44469]+1|0))<=169?t:0,e[44469]=t,(t=177888+(w=t<<6)|0)&&(y=_[A+4>>1]|_[A+6>>1]<<16,h=_[A>>1]|_[A+2>>1]<<16,v[t>>1]=h,v[t+2>>1]=h>>>16,v[t+4>>1]=y,v[t+6>>1]=y>>>16,y=_[A+60>>1]|_[A+62>>1]<<16,h=_[A+56>>1]|_[A+58>>1]<<16,v[t+56>>1]=h,v[t+58>>1]=h>>>16,v[t+60>>1]=y,v[t+62>>1]=y>>>16,y=_[A+52>>1]|_[A+54>>1]<<16,h=_[A+48>>1]|_[A+50>>1]<<16,v[t+48>>1]=h,v[t+50>>1]=h>>>16,v[t+52>>1]=y,v[t+54>>1]=y>>>16,y=_[A+44>>1]|_[A+46>>1]<<16,h=_[A+40>>1]|_[A+42>>1]<<16,v[t+40>>1]=h,v[t+42>>1]=h>>>16,v[t+44>>1]=y,v[t+46>>1]=y>>>16,y=_[A+36>>1]|_[A+38>>1]<<16,h=_[A+32>>1]|_[A+34>>1]<<16,v[t+32>>1]=h,v[t+34>>1]=h>>>16,v[t+36>>1]=y,v[t+38>>1]=y>>>16,y=_[A+28>>1]|_[A+30>>1]<<16,h=_[A+24>>1]|_[A+26>>1]<<16,v[t+24>>1]=h,v[t+26>>1]=h>>>16,v[t+28>>1]=y,v[t+30>>1]=y>>>16,y=_[A+20>>1]|_[A+22>>1]<<16,h=_[A+16>>1]|_[A+18>>1]<<16,v[t+16>>1]=h,v[t+18>>1]=h>>>16,v[t+20>>1]=y,v[t+22>>1]=y>>>16,y=_[A+12>>1]|_[A+14>>1]<<16,h=_[A+8>>1]|_[A+10>>1]<<16,v[t+8>>1]=h,v[t+10>>1]=h>>>16,v[t+12>>1]=y,v[t+14>>1]=y>>>16,g[w+177904|0]=0,v[t>>1]=32768|_[t>>1]),v[8+(w=w+177888|0)>>1]=_[n+8>>1],g[w+21|0]=d[n+21|0],v[w+10>>1]=_[n+10>>1],g[w+22|0]=d[n+22|0],v[w+12>>1]=_[n+12>>1],g[w+23|0]=d[n+23|0],v[w+14>>1]=_[n+14>>1],g[w+24|0]=d[n+24|0],g[w+25|0]=d[n+25|0],e[m+12>>2]=t))),(0|gA)!=2|d[Ve+11|0]!=2||(CA(),e[36427]=e[50758]),!((0|(Ve=e[l+108>>2]))<2)){for(t=e[36433],y=(F(256-t|0,ue)+(t<<8)|0)/256|0,t=e[36432],m=(F(256-t|0,ue)+(t<<8)|0)/256|0,h=e[50754],t=0,n=1;w=_[2+(ae=(P+(n<<3)|0)-8|0)>>1],w=(0|F((0|F(h,v[ae>>1]))/1e3|0,4&w?m:16384&w?y:ue))/256|0,e[(n<<2)+l>>2]=w,t=t+w|0,(0|Ve)!=(0|(n=n+1|0)););if(!((0|t)<=0|(0|t)>=(0|cA)|(0|Ve)<2)){if(n=1,y=1&(w=Ve-1|0),(0|Ve)!=2)for(m=-2&w,ue=0;e[(w=(n<<2)+l|0)>>2]=(0|F(e[w>>2],cA))/(0|t),e[w+4>>2]=(0|F(e[w+4>>2],cA))/(0|t),n=n+2|0,(0|m)!=(0|(ue=ue+2|0)););y&&(e[(n=(n<<2)+l|0)>>2]=(0|F(e[n>>2],cA))/(0|t))}if(ue=0,!((0|Ve)<2))for(Ve=gA+256|0,n=1;t=e[4+(P+(n<<3)|0)>>2],!(w=e[r+28>>2])|128&d[0|A]||(e[36422]=0,ja(w,Ve,0,h=e[r>>2],0,y=(y=e[r+32>>2])?(y<<5)/100|0:32),g[145748]=1,e[r+28>>2]=0),(0|i)<0||(i=64&d[0|A]?6:i,(e[l+108>>2]-1|0)==(0|n)&&(i=(w=i)|(3840&(i=e[36436])?i:0))),w=e[(n<<2)+l>>2],e[36440]=w+e[36440],e[36441]=w+e[36441],w?(y=e[50758],e[36439]=y,(0|i)>=0&&(e[(y=216192+(y<<4)|0)>>2]=M,e[y+12>>2]=t,e[y+8>>2]=A,e[y+4>>2]=w+(i<<16),A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0),e[36426]=t,ue=w+ue|0
2810):e[36426]=0,A=t,(0|(n=n+1|0))<e[l+108>>2];);}!e[36438]|(0|gA)==1||(e[36438]=0,t=216192+(e[50758]<<4)|0,e[t>>2]=14,e[t+4>>2]=0,t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0)}}oe=l+112|0}function Ft(t,A,r,n){var i,l=0,h=0,m=0,w=0,M=0,y=0,P=0,R=0,ee=0,ae=0,ue=0,be=0,_e=0,Ve=0,$e=0,it=0,ht=0,yt=0,Pt=0,Wt=0,cA=0,gA=0;oe=i=oe-1856|0,e[i+164>>2]=0,r?R=e[r>>2]:IA(r=i- -64|0,0,96),e[33264]=0,e[i+1824>>2]=0,e[i+1828>>2]=0,e[i+1832>>2]=0,e[i+1836>>2]=0,g[i+1616|0]=0,g[i+992|0]=0,g[i+1200|0]=0,g[i+784|0]=0;e:if(e[t+688>>2]){for(e[i+1840>>2]=A,d[0|(l=A)]==32&&(l=A+1|0,e[i+1840>>2]=l),ee=i+416|1,yt=i+1844|1,Wt=i+1848|1,cA=i+1852|1,e[i+1820>>2]=l,or(i+168|0,l),l=e[i+1820>>2];(32|d[0|l])!=32;)l=or(i+164|0,l)+e[i+1820>>2]|0,e[i+1820>>2]=l,be=be+1|0;$A(i+256|0,A,Pt=(0|(m=l-A|0))>=159?159:m),!(it=4194304&R)|(0|be)!=1?(Ve=((0|(l=e[47202]))==36)<<2,(0|be)==1|(0|l)!=36||(l=e[i+1840>>2]-1|0,e[i+1840>>2]=l,g[0|l]=95,Ve=0,h=!!(0|Hr(t,i+1840|0,i+1616|0,i+1832|0,0,r)),l=e[47202])):(h=1,or(i+172|0,l+1|0),Wr(e[i+172>>2])&&(0|Wa(e[i+164>>2]))==(0|Wa(e[i+172>>2]))&&(h=0),Ve=(0|(l=e[47202]))==36?4:h,h=0);t:{r:{a:{n:{c:if(16&l)ae=15&l,Ve=0;else{if(_e=1,h||(_e=!!(0|Hr(t,i+1840|0,i+1616|0,i+1832|0,2,r))),50331648&(l=e[i+1832>>2])&&(m=e[i+1820>>2],d[m+1|0]==46&&(g[m+1|0]=32,l=e[i+1832>>2])),536870912&l){if(!n)break e;BA(n,e[i+1840>>2]);break e}if(8192&l|!(128&l)|_e)n=e[33264];else if(l=e[i+1840>>2],e[i+1820>>2]=l,!((0|(n=e[33264]))<=0))for(h=0;d[0|l]==32&&(g[0|l]=45,h=h+1|0,l=e[i+1820>>2],n=e[33264]),l=l+1|0,e[i+1820>>2]=l,(0|n)>(0|h););o:if(!(n|(0|be)!=1)&&(m=or(i+576|0,P=e[i+1840>>2]),d[m+P|0]==32)){h=i+1408|0,n=P;f:{l:{i:{for(;;){h:if(Wr(e[i+576>>2])){b:{if(d[1+(w=n+m|0)|0]==46){ae=0;m:switch(d[(l=m+2|0)+n|0]-32|0){case 0:break b;case 7:break m;default:break h}if(ae=1,m=l,d[w+3|0]==115)break b;break h}if(ae=1,(0|y)<=0)break i}if(!((0|m)<=0)){if(ue=3&m,M=0,m>>>0<4)l=0;else for($e=-4&m,l=0,w=0;g[0|h]=d[n+l|0],g[h+1|0]=d[(1|l)+n|0],g[h+2|0]=d[(2|l)+n|0],g[h+3|0]=d[(3|l)+n|0],l=l+4|0,h=h+4|0,(0|$e)!=(0|(w=w+4|0)););if(ue)for(;g[0|h]=d[n+l|0],l=l+1|0,h=h+1|0,(0|ue)!=(0|(M=M+1|0)););}if(y=y+1|0,ae)n=n+m|0;else if(m=or(i+576|0,n=3+(n+m|0)|0),d[n+m|0]==32)continue}break}if(!((0|y)<2)){(m=(m=h-(l=i+1408|0)|0)+(l=$A(P,l,m))|0)>>>0<n>>>0&&IA(m,32,(i+1408|0)+n-(l+h)|0),e[33264]=(y<<1)-2,e[i+1836>>2]=0;break l}}if(!y)break o;if(e[i+1832>>2]=0,e[i+1836>>2]=0,!e[33264])break f}e[i+1832>>2]=128}Ve=1}if(d[i+1616|0]==21){BA(189088,i+1616|0),l=0;break e}if(gA=d[i+1833|0],h=1,!_e){if(e[i+168>>2]-48>>>0<10){if(kA(t,84174,189088),l=0,d[189088]==21)break e;if(!(!(128&d[t+109|0])|32&d[r+2|0])){g[189088]=21,g[189089]=0;break e}h=!!(0|mA(t,e[i+1840>>2],i+1616|0,i+1832|0,r,0))}else h=0;if(!(h|(3&R)==2)&&(16777216&(n=e[t+104>>2])||(h=0,!(!(33554432&n)|!(1&R))))&&(16&R||(h=0,!(1&g[r+13|0])))){$e=e[i+1840>>2],l=0,y=0,w=0,oe=ue=oe-224|0,g[0|(M=i+1616|0)]=0,e[ue+216>>2]=0,e[ue+220>>2]=0;o:if(!(g[$e-2|0]-48>>>0<10|(1&g[0|r]?0:2&d[t+107|0])||(n=d[0|(m=$e+1|0)],(!(2561&_[t+106>>1])|!(1&g[r+2|0]))&(0|n)==32))){if((0|(y=d[0|$e]))!=32){for(ae=32767,n=0;;){if(!(l=Wn(101868,y<<24>>24,8))){y=0;break o}if(P=0,(0|(l=e[(l<<2)-305584>>2]))==(0|n)&&!((0|(P=w+1|0))<=2)){y=0;break o}f:{l:{i:{h:{if(!((0|n)<2)){if((0|n)==10|(0|n)==100)break h;if(!((0|n)>(0|l))){y=0;break o}}if(!n)break l;if((0|n)<(0|l))break i;break l}if((0|n)>=(0|l))break l}if(y=0,(0|ht)%10|(0|F(n,10))<(0|l))break o;l=l-n|0,ae=n;break f}if((0|l)>=(0|ae)){y=0;break o}ht=n+ht|0}if(y=d[0|m],m=h=m+1|0,n=l,w=P,(0|y)==32)break}n=d[0|h]}else h=m;if((n<<24>>24)-48>>>0<10)y=0;else if((0|(n=l+ht|0))<e[t+120>>2])y=0;else if((0|n)>e[t+116>>2])y=0;else if(kA(t,85600,ue+176|0),l=M,4&d[t+107|0]||(l=BA(M,l=ue+176|0)+wA(l)|0),e[ue+4>>2]=e[t+140>>2],e[ue>>2]=n,uA(ue+16|0,85839,ue),y=0,d[0|h]!=46){Bs(t,$e,h,r,1)&&(e[r>>2]=32768|e[r>>2]),m=0;f:if(8&d[t+107|0]){if(w=e[r>>2],e[t+212>>2]==26741){if(32768&w)break f;if(!(16384&w))break o;m=1,w=0;l:{i:switch(d[0|h]-97|0){case 0:case 4:break i;default:break l}i:{h:{b:{m:switch((ae=d[h+1|0])-116|0){case 6:break l;case 1:case 2:case 3:case 4:case 5:break h;case 0:break m;default:break b}if(d[h+2|0]!=116)break i;break l}if((0|ae)==32)break l}if(!((0|n)%1e3|0)&&(0|ae)==108)break l}w=1}if(w)break f;break o}e[r>>2]=32768|w}e[(n=t+8232|0)>>2]=0,e[n+4>>2]=0,mA(t,ue+16|2,l,ue+216|0,r,m),y=1,4&d[t+107|0]&&gs(M,ue+176|0)}}oe=ue+224|0,y?(e[i+1832>>2]=8192|e[i+1832>>2],h=1):h=0}}if(ae=_e?Ve:32&gA?1:Ve,Ve=0,!(!(1&R)|(0|be)<2)&&aa(e[i+168>>2])){o:{if(1&g[188785]){if(!(!(n=8192&(l=e[i+1832>>2]))|h))break o;Ve=n>>>2^2048;break c}if(h)break n;l=e[i+1832>>2]}if(!(128&l|be>>>0>3)&&!((0|(l=e[t+8220>>2]))<4)&&(n=1,(0|l)>=e[t+8216>>2]))break t}}if(M=0,(0|ae)<=0)break a;n=ae;break t}if((0|(n=ae))>0)break t;M=0,P=0,w=0,y=0;break r}if(h)P=0,w=0,y=0;else{n=e[i+1840>>2],e[i+1820>>2]=n,l=999,y=0,P=0,ee=0;a:{n:{c:{for(;;){o:{f:{l:{if(l-1>>>0>=2){if((0|be)<2||(or(i+1408|0,n),(0|(l=e[i+1408>>2]))<577&e[t+600>>2]>0)||(l=Wa(l),(e[l+4>>2]!=e[t+600>>2]?l:0)|e[t+40>>2]==1))break l;switch(l=d[0|n],e[i+1408>>2]=l<<24>>24,l-32|0){default:if(!l)break l;break;case 0:case 7:break l;
2810case 1:case 2:case 3:case 4:case 5:case 6:}M=or(i+1408|0,n),_e=9;i:{h:{b:{m:if(-33&(l=e[i+1408>>2])){for(h=0,w=0;;){C:{p:{if((0|l)==39){if((0|P)>0|(0|h)>1)break m;if(w=h?w:39,e[t+40>>2]!=3)break p;break C}w=h?w:l}h=h+1|0}if(!Ho(t,l)){if((0|(l=e[i+1408>>2]))!=39&&!aa(l))break l;if(M=or(i+1408|0,n+M|0)+M|0,-33&(l=e[i+1408>>2]))continue;break m}break}if((0|h)<=2)break b;_e=h}else w=0;if((0|(l=e[t+40>>2]))!=2)break h;oe=l=oe-208|0,g[0|l]=0,h=d[0|(m=n-1|0)],g[0|m]=32,n=zA(t,n,l,200,0,-2147483648,0),g[0|m]=h,oe=l+208|0,n=!n|(32768&n)>>>15;break i}l=e[t+40>>2],_e=h}n=(g[t+168|0]+1|0)<(_e-((0|l)==(0|w))|0)}if(!n)break l;n=e[i+1820>>2]}if(d[0|n]!=39)break f;y=67108864,Ve=0}if(M=0,PA(t,i+992|0,0,P),n=e[i+1820>>2],(0|(l=d[0|n]))!=32)break o;P=0,w=0;break r}if(Ve=0,n=Tt(t,n,i+992|0,1&(ee|=(0|P)>0))+e[i+1820>>2]|0,e[i+1820>>2]=n,d[i+992|0]==21)break c;for(P=P+1|0,h=0;h=(l=h)+1|0,d[n+l|0]!=32;);y=67108864;continue}break}if(!d[i+992|0]|(0|l)==39||(g[n-1|0]=32,n=e[i+1820>>2]),m=zA(t,n,i+1616|0,200,i+784|0,R,i+1832|0),(0|(n=d[i+1616|0]))==21){BA(189088,i+1616|0),l=0;break e}if(!(n|d[i+784|0])&&(or(i+1408|0,e[i+1820>>2]),(0|be)==1&&(Wr(e[i+1408>>2])||Jn(e[i+1408>>2])))){uo(t,e[i+1820>>2],i+1616|0,ae)&&BA(189088,i+1616|0),l=0;break e}e[i+172>>2]=g[e[i+1820>>2]-1|0];o:if(1024&m)for($e=i+176|1,ae=0,l=1,ee=0,P=0;;){if((_e=131072&m)|!(1&l)||(g[i+1408|0]=0,!(n=zA(t,e[i+1820>>2],i+1408|0,200,i+576|0,805306368|R,i+1832|0)))){2048&m&&(e[t+8184>>2]=1),g[e[i+1820>>2]-1|0]=e[i+172>>2];f:{l:{i:{h:if(_e){if(g[i+176|0]=0,l=e[i+1820>>2],n=1,w=63&m){if(ue=1&m,P=w-1|0,h=0,(0|w)!=1)for(ht=w-ue|0,w=0;M=l,e[i+1820>>2]=l+1,g[0|(it=(i+176|0)+n|0)]=(0|h)!=(0|P)?d[0|l]:0,l=l+2|0,e[i+1820>>2]=l,g[it+1|0]=(0|P)!=(1|h)?d[M+1|0]:0,h=h+2|0,n=n+2|0,(0|ht)!=(0|(w=w+2|0)););ue&&(w=l+1|0,e[i+1820>>2]=w,g[(i+176|0)+n|0]=(0|h)!=(0|P)?d[0|l]:0,n=n+1|0,l=w)}g[(i+176|0)+n|0]=0}else{if(l=e[i+1820>>2],!(w=15&m))break i;if(n=0,h=w,M=3&m)for(;l=l+1|0,e[i+1820>>2]=l,(192&d[0|l])==128||(h=h-1|0,(0|M)!=(0|(n=n+1|0))););if(w>>>0<4)break h;for(;;)if(l=l+1|0,e[i+1820>>2]=l,(192&d[0|l])!=128){for(;l=l+1|0,e[i+1820>>2]=l,(192&d[0|l])==128;);for(;l=l+1|0,e[i+1820>>2]=l,(192&d[0|l])==128;);for(;l=l+1|0,e[i+1820>>2]=l,(192&d[0|l])==128;);if(n=(0|h)>4,h=h-4|0,!n)break}}if(n=l-1|0,e[i+172>>2]=g[0|n],g[0|n]=32,n=R|=8388608,!_e)break l;if(vr(i+576|0,i+784|0,12),e[i+1852>>2]=$e,n=BA(i+1200|0,l=i+1616|0),Hr(t,i+1852|0,l,i+1832|0,0,r)&&BA(n,i+1616|0),!(32&d[i+1833|0]))break f;g[0|n]=0,uo(t,e[i+1852>>2],n,1);break f}n=l-1|0,e[i+172>>2]=g[0|n],g[0|n]=32,n=8388608|R}R=n,gs(i+1200|0,i+784|0)}if(g[i+784|0]=0,w=1,n=Hr(t,i+1820|0,i+1616|0,i+1824|0,1024,r),e[i+1832>>2]||(l=e[i+1828>>2],e[i+1832>>2]=e[i+1824>>2],e[i+1836>>2]=l,w=ee),n){M=0,P=m;break a}if(M=zA(t,e[i+1820>>2],i+1616|0,200,i+784|0,8404992&R,i+1832|0),l=1,P=m,ee=w,d[i+1616|0]==21){g[e[i+1820>>2]-1|0]=e[i+172>>2],BA(189088,i+1616|0),l=0;break e}}else l=i+416|0,ZA(t,e[i+1820>>2],n,l),M=zA(t,e[i+1820>>2],i+1616|0,200,i+784|0,268435456|R,i+1832|0),$A(e[i+1820>>2],l,wA(l)),1024&M||(BA(i+1616|0,i+1408|0),l=BA(i+784|0,i+576|0),8&d[188788]&&(TA(w=l,l=i+576|0),m=e[47195],e[i+48>>2]=l,ss(m,85205,i+48|0)),M=n),l=0;if(n=(h=1024&M)>>>10|0,ae>>>0>48)break o;if(ae=ae+1|0,m=M,!h)break}else M=m,P=0,ee=0,n=0;if(n|!M)break n;for(m=BA(i+1408|0,i+1616|0),h=ZA(t,e[i+1820>>2],M,i+416|0),w=ee,l=M;;){o:{if(g[i+1616|0]=0,d[i+1200|0]){if(g[e[i+1820>>2]-1|0]=e[i+172>>2],n=Hr(t,i+1840|0,i+1616|0,i+1824|0,h,r),g[e[i+1820>>2]-1|0]=32,d[i+1616|0]==21){t=i+416|0,$A(e[i+1820>>2],t,wA(t)),BA(189088,i+1616|0),l=0;break e}if(e[i+1832>>2]||(ee=e[i+1828>>2],e[i+1832>>2]=e[i+1824>>2],e[i+1836>>2]=ee),n){g[i+1200|0]=0;break o}w=e[i+1824>>2]?1:w}if(n=Hr(t,i+1820|0,i+1616|0,i+1824|0,h,r),d[i+1616|0]==21){t=i+416|0,$A(e[i+1820>>2],t,wA(t)),BA(189088,i+1616|0),l=0;break e}if(e[i+1832>>2]||(ee=e[i+1828>>2],e[i+1832>>2]=e[i+1824>>2],e[i+1836>>2]=ee),!n)if(16384&l)BA(i+1616|0,m);else{R|=h<<11&8192|l<<9&134217728;f:if(524288&l){if(ee=BA(i+576|0,n=i+784|0),l=zA(t,e[i+1820>>2],i+1616|0,200,n,R,i+1832|0),gs(n,ee),n=0,!l){l=0;break f}if(1024&l)break f;n=1,h=ZA(t,e[i+1820>>2],l,0)}else l=0,zA(t,e[i+1820>>2],i+1616|0,200,0,R,i+1832|0),n=0;if(d[i+1616|0]==21){BA(189088,i+1616|0),t=i+416|0,$A(e[i+1820>>2],t,wA(t)),g[e[i+1820>>2]-1|0]=e[i+172>>2],l=0;break e}if(n)continue}}break}65536&M||(Li(t,i+1616|0,200,i+784|0),g[i+784|0]=0),n=i+416|0,$A(e[i+1820>>2],n,wA(n));break a}BA(189088,t=i+992|0),l=!ra(1|t,84744,3)<<12;break e}M=0,w=ee}g[e[i+1820>>2]-1|0]=e[i+172>>2]}}if(n=e[i+164>>2],e[i+1852>>
vendor: 11,313 bytes, line 2810
28102]=8026656,e[i+1848>>2]=8022304,e[i+1844>>2]=7566112,4&R){r:{if((l=255&n)&&((0|l)==102||(yt=Wt,Ko(n<<24>>24))))break r;yt=cA}zA(t,yt,189088,200,0,0,0)}for(n=0,l=i+1200|0,ee=d[i+784|0];;){r:{a:{n:switch(0|(m=d[0|l])){case 0:break r;case 6:case 7:break n;default:break a}n=m}l=l+1|0;continue}break}r:if(n|w){if(e[t+32>>2]|65536&P){for(h=0,bA(t,i+1616|0,i+1832|0,3,0),l=i+1200|0;;){switch(d[0|l]){case 6:h&&(g[0|l]=5),h=1;default:l=l+1|0;continue;case 0:}break}e[i+24>>2]=i+1616,e[i+20>>2]=i+1200,e[i+16>>2]=i+992,An(189088,200,85233,i+16|0),g[189287]=0,bA(t,189088,i+1832|0,-1,0);break r}e[i+8>>2]=i+1616,e[i+4>>2]=i+1200,e[i>>2]=i+992,An(189088,200,85233,i),g[189287]=0,bA(t,189088,i+1832|0,-1,0)}else bA(t,n=i+1616|0,i+1832|0,-1,!!(0|ee)<<1),e[i+40>>2]=n,e[i+36>>2]=i+1200,e[i+32>>2]=i+992,An(189088,200,85233,i+32|0),g[189287]=0;d[i+784|0]&&(n=wA(189088),g[983+(i-n|0)|0]=0,BA(n+189088|0,i+784|0)),16&(n=R|Ve)&&(e[i+1832>>2]=-268435457&e[i+1832>>2]);r:if(!(128&n)|!(16&d[t+14|0]))if(3072&n){if(vt(t,6),!(2048&n))break r;e[i+1832>>2]=268435456|e[i+1832>>2]}else 16&d[F(e[33264],12)+r|0]&&(1536&(r=e[i+1832>>2])?vt(t,4):2048&r&&vt(t,3));else vt(t,3);8192&M&&(e[t+8192>>2]=2,e[t+8184>>2]=2);r:{if(8&(r=e[i+1836>>2]))e[t+8184>>2]=0,e[t+8188>>2]=3,n=t+8196|0;else if(1&r)e[t+8192>>2]=0,e[t+8184>>2]=2,n=t+8196|0;else if(2&r)e[t+8192>>2]=2,e[t+8184>>2]=0,e[t+8188>>2]=0,n=t+8196|0;else{if(!(4&r))break r;e[t+8184>>2]=0,e[t+8192>>2]=0,e[t+8196>>2]=2,n=t+8188|0}e[n>>2]=0}!d[e[i+1820>>2]]|256&r||((0|(r=e[t+8184>>2]))>0&&(e[t+8184>>2]=r-1),(0|(r=e[t+8192>>2]))>0&&(e[t+8192>>2]=r-1),(0|(r=e[t+8196>>2]))>0&&(e[t+8196>>2]=r-1),(0|(r=e[t+8188>>2]))<=0||(e[t+8188>>2]=r-1)),(0|be)!=1|e[t+212>>2]!=25966||!aa(e[i+168>>2])|e[i+168>>2]==105||(e[i+1832>>2]=16777216|e[i+1832>>2]);r:if(2&d[t+68|0]&&98304&(n=e[i+1832>>2])&&!((0|(r=wA(189088)-1|0))<=0))for(l=0;;){if(t=l+1|0,d[l+189088|0]==6){r=g[0|(t=t+189088|0)];a:{if(65536&n){if((0|Us(69))==(0|r)?(be=Us(101),g[0|t]=be):be=d[0|t],h=111,(0|Us(79))==be<<24>>24)break a;break r}if((0|Us(101))==(0|r)?(be=Us(69),g[0|t]=be):be=d[0|t],h=79,(0|Us(111))!=be<<24>>24)break r}g[0|t]=Us(h);break r}if((0|r)==(0|(l=t)))break}t=e[i+1832>>2],$A(A,i+256|0,Pt),l=t|y;break e}if(l=0,g[i+1616|0]=0,uo(t,e[i+1840>>2],i+1616|0,n)){if(m=BA(189088,i+1616|0),!it){if(n=e[i+164>>2],e[i+1408>>2]=8026656,e[i+576>>2]=8022304,e[i+416>>2]=7566112,4&R){A=i+576|1,r=i+1408|1;t:{if((l=255&n)&&((0|l)==102||(ee=A,Ko(n<<24>>24))))break t;ee=r}zA(t,ee,m,200,0,0,0)}l=128&e[i+1832>>2]}}else l=((0|be)>1)<<12}else g[189088]=0;return oe=i+1856|0,l}function dA(t,A,r,n,i,l,h,m){var w,M,y=0,P=0,R=0,ee=0,ae=0,ue=0,be=0,_e=0,Ve=0,$e=0,it=0,ht=0,yt=0,Pt=0,Wt=0,cA=0,gA=0,SA=0,VA=0,qA=0,Cr=0,Yr=0,Xr=0,ls=0,as=0,xs=0,Ms=0,zs=0,Os=0,ga=0,na=0,ha=0,pa=0,Gn=0,Fn=0,Ln=0,Vn=0,yo=0,to=0,Ao=0,Qi=0;oe=w=oe-384|0,M=e[A>>2];e:{t:{r:{a:{if(i){if(d[0|i]!=7)break a;e[A>>2]=(n||1)+M;break r}e[l>>2]=0,e[A>>2]=M+1;break e}Cr=86135,Os=268435456&h,ga=134217728&h,na=8388608&h,ha=16384&h,pa=8192&h,ls=r-1|0,as=n-r|0,Gn=2&h,Fn=128&h,Ln=h>>>31|0,Vn=-2147483648&h,xs=w+96|1;a:for(;;)for(e[w+268>>2]=0,be=(R=e[A>>2])+n|0,Wt=-2,yt=-6,r=i,cA=Vn,SA=0,Yr=0,Pt=1,y=0,gA=0,qA=0;;){ee=R,_e=y;n:{c:{o:{f:{l:{i:{h:{b:{m:{C:{p:{x:{w:{B:{F:{Y:{for(;$e=r,r=r+1|0,!((P=d[0|$e])>>>0>9);)switch(ae=r,0|P){case 0:if(!(r=Ms)){Ms=0,r=86135;break F}for(;;){P=1;R:{M:switch(0|(y=d[0|r])){case 0:case 3:break Y;case 5:break M;default:break R}P=2}r=(r+P|0)+(((0|y)==9)<<1)|0}case 1:if(qA=1,!Ln)continue;break l;case 2:qA=2;continue;case 4:Ms=r;continue;case 5:r=$e+2|0,y=e[t+320>>2];R:{if((R=d[$e+1|0])>>>0>=32){if(!(y>>>R-32&1))break R;break l}if(!(y>>>R&1))break l}Pt=Pt+1|0;continue;case 9:r=$e+3|0;continue;case 8:qA=1,Yr=1,cA=0;break;case 3:break B}ae=0,y=_e,R=ee;R:switch(0|qA){case 0:M:{y:{if((0|(y=d[0|be]))!=(0|P)){if((0|y)!=69)break l;if((0|P)==101)break y;break l}if(ae=0,(192&P)==128)break M}ae=21}be=be+1|0,SA=SA+1|0;break n;case 1:break w;case 2:break R;default:break C}if(e[w+264>>2]=e[w+268>>2],!d[be-1|0])break l;yt=(0|(y=yt+6|0))>=19?19:y,ht=be+1|0,it=or(w+268|0,be),Ve=d[0|be],ae=20,y=_e;R:{M:switch(P-11|0){case 6:r=$e+2|0,y=e[w+268>>2],R=g[$e+1|0];y:if(P=e[604+(((R=((0|R)<65?191:-65)+R|0)<<2)+t|0)>>2])y=!!(0|rs(P,y));else{if((0|R)>7)break l;G:{if((0|(P=e[t+600>>2]))>0){if((y=y-P|0)-1>>>0<255)break G;break l}if((P=y-192|0)>>>0<=413){y=d[344+(d[P+94240|0]+t|0)|0]&1<<R;break y}if(y>>>0>255)break l}y=d[344+(t+y|0)|0]&1<<R}if(!y)break l;be=be+it|0,ae=((0|R)==2?19:20)-yt|0;break n;case 7:if(r=$e+2|0,y=g[$e+1|0],!(y=e[4788+((((0|y)<65?191:-65)+y<<2)+t|0)>>2]))break l;y:for(;;){if((0|(ae=d[0|y]))==7)break l;if((0|ae)==126){ae=20-yt|0;break n}G:if(Ve){if(ue=be,P=y,(0|ae)==(0|Ve))for(;;){if((0|(ae=d[0|(P=P+1|0)]))!=(0|(y=d[0|(ue=ue+1|0)])))break G;if(!y)break}}else P=y,ue=be;if(ae){for(;;)if(R=d[0|P],P=y=P+1|0,!R)continue y}break}if((0|(y=ue-be|0))<0)break l;be=y+be|0,ae=20-yt|0;break n;case 14:P=e[w+268>>2];y:{G:if(y=e[t+604>>2])y=!!(0|rs(y,P));else{S:{if((0|(y=e[t+600>>2]))>0){if((ue=P-y|0)-1>>>0<255)break S;break y}if((y=P-192|0)>>>0<=413){y=1&g[344+(d[y+94240|0]+t|0)|0];break G}if(ue=P,P>>>0>255)break c}y=1&g[344+(t+ue|0)|0]}if(y)break l;P=e[w+268>>2]}if(!ga)break c;if((0|P)==32)break l;break c;case 4:if((y=e[w+268>>2])-48>>>0<10|y-2406>>>0<10)break c;if(!d[t+170|0])break l;ae=20-yt|0;break n;case 5:if(_i(e[w+268>>2]))break l;break o;case 0:if(e[w+268>>2]==e[w+264>>2])break o;break l;case 17:r=$e+2|0,y=32768,ae=0;y:{G:switch((P=d[$e+1|0])-1|0){case 0:break C;case 1:break G;default:break y}if(ae=1,y=_e,!na)break C;break l}if((0|(y=240&P))==16){if(ae=23,y=_e,m>>>(15&P)&16384)break C;break l}if((0|P)!=3&(0|y)!=32||($A(y=w+96|0,ls,R=1+(e[A>>2]+(SA+as|0)|0)|0),g[0|(y=y+R|0)]=32,g[y+1|0]=0,e[33265]=0,e[33266]=0,e[w+16>>2]=xs,Hr(t,w+16|0,w+272|0,133060,0,0),ae=23,!((0|P)!=3|(0|(ue=e[33265]))>=0|16384&e[33266])))break n;if(y=_e,R=ee,ue>>>(15&P)&16384)break C;break l;case 34:y:{if(y=Ve-32|0){if((0|y)==13)break y;break l}if(!ha)break l}ue=22-yt|0;break f;case 10:if(y=1,d[0|r]==21)break R;break i;case 18:if((0|(ue=e[w+268>>2]))!=32)for(P=be+it|0;;){y:{G:if(y=e[t+632>>2])y=!!(0|rs(y,ue));else{S:{if((0|(y=e[t+600>>2]))>0){if((ue=ue-y|0)-1>>>0<255)break S;break y}if((y=ue-192|0)>>>0<=413){y=128&d[344+(d[y+94240|0]+t|0)|0];break G}if(ue>>>0>255)break y}y=128&d[344+(t+ue|0)|0]}if(y)break l}if(P=or(w+268|0,P)+P|0,(0|(ue=e[w+268>>2]))==32)break}ue=19-yt|0;break f;case 49:break h;case 2:break b;case 3:break m;case 1:break C;case 13:break p;case 12:break M;default:break x}or(w+272|0,r),it=-1;M:if((0|(y=e[w+272>>2]))!=(0|(P=e[w+268>>2])))if(-33&P)for(;;){Ve=be,it=-1;y:if((0|y)==18&&(y=g[$e+2|0],ae=e[4788+((((0|y)<65?191:-65)+y<<2)+t|0)>>2])){for(;;){if((0|(R=d[0|ae]))==7)break y;if((0|R)==126){it=0;break y}G:if((0|(be=d[0|Ve]))==(0|R)){if(y=Ve,P=ae,be)for(;;){if((0|(R=d[0|(P=P+1|0)]))!=(0|(ae=d[0|(y=y+1|0)])))break G;if(!ae)break}}else P=ae,y=Ve;if(!R){y=y-Ve|0;break}for(;y=d[0|P],P=ae=P+1|0,y;);}it=y}if(R=or(w+268|0,Ve),(0|(y=e[w+272>>2]))==(0|(P=e[w+268>>2]))|!(-33&P))break M;if(be=R+Ve|0,(0|it)!=-1)break}else Ve=be;else Ve=be;be=(0|y)==(0|P)||(0|it)>=0?Ve:ht,ae=0;break n}for(;y=y+1|0,d[0|(r=r+1|0)]==21;);break i}r=r+1|0}ae=$e}if(!(d[ee-1|0]!=32&Yr|cA||((0|(P=Yr?Pt+4|0:Pt))>=(0|Xr)&&(yo=gA,to=_e,Xr=P,Ao=SA,Cr=r),!(8&e[47197])|Os|(0|P)<=0))){for(TA(r,$e=w+272|0),ht=e[47195],R=w+16|0,r=0,Ve=0,cA=0,gA=0,oe=be=oe-496|0,g[be+80|0]=0,(0|n)>0?($A(be+288|0,M,n),_e=n):_e=0,g[_e+(y=be+288|0)|0]=0,ee=wA(y)+y|0,it=(0|h)<0;;){if(ue=d[0|i],_e=i,i=i+1|0,ue>>>0>9)for(;;){B:{F:{Y:{R:{M:{y:switch((y=255&ue)-14|0){case 4:break Y;case 3:break R;case 0:break M;case 14:break y;default:break F}if(_e=_e+2|0,ue=32,!it&(0|(i=d[0|i]))==1)break B;g[0|ee]=36,BA(y=ee+1|0,i=sa(128960,i)),ee=wA(i)+y|0;break B}y=d[_e+2|0],ue=d[0|i],e[be+36>>2]=127&d[_e+3|0],e[be+32>>2]=4&y?80:83,uA(be+48|0,85131,be+32|0),1&y&&(i=wA(i=be+48|0)+i|0,g[0|i]=101,g[i+1|0]=0),2&(i=127&y)&&(y=wA(y=be+48|0)+y|0,g[0|y]=105,g[y+1|0]=0),4&i&&(y=wA(y=be+48|0)+y|0,g[0|y]=112,g[y+1|0]=0),8&i&&(y=wA(y=be+48|0)+y|0,g[0|y]=118,g[y+1|0]=0),16&i&&(y=wA(y=be+48|0)+y|0,g[0|y]=100,g[y+1|0]=0),32&i&&(y=wA(y=be+48|0)+y|0,g[0|y]=102,g[y+1|0]=0),i>>>0>=64&&(i=wA(i=be+48|0)+i|0,g[0|i]=113,g[i+1|0]=0),1&ue&&(i=wA(i=be+48|0)+i|0,g[0|i]=116,g[i+1|0]=0),_e=_e+4|0,ee=BA(ee,i=be+48|0)+wA(i)|0,ue=32;break B}_e=_e+2|0,ue=d[g[0|i]+93871|0];break B}i=g[0|i],g[0|ee]=76,y=((i=i+((0|i)<65?191:-65)|0)>>>0)/10|0,g[ee+1|0]=y+48,ue=i-F(y,10)|48,(0|gA)==1&&(g[0|ee]=ue,ue=76),_e=_e+2|0,ee=ee+2|0;break B}ue=y>>>0<=31?d[y+93904|0]:(0|y)==32?95:ue,_e=i}if(g[0|ee]=ue,i=_e+1|0,ee=ee+1|0,!((ue=d[0|_e])>>>0>=10))break}y=1;B:switch(0|ue){case 1:y=r;case 8:g[0|ee]=0,ee=be+80|0,r=y,gA=1;continue;case 2:g[0|ee]=0,_e=wA(y=be+288|0)+y|0,ee=d[84899]|d[84900]<<8,g[0|_e]=ee,g[_e+1|0]=ee>>>8,gA=2,g[_e+2|0]=d[84901],ee=wA(y)+y|0;continue;case 5:Ve=g[0|i],i=_e+2|0;continue;case 9:cA=(d[0|i]+F(d[_e+2|0],255)|0)-256|0,i=_e+3|0;continue;case 0:case 3:break B;default:continue}break}if(g[0|ee]=0,ee=R,(0|cA)>0&&(e[be+16>>2]=cA,uA(R,85581,be+16|0),ee=R+7|0),(0|Ve)>0&&(e[be>>2]=Ve,uA(ee,85694,be),ee=wA(ee)+ee|0),1&((0|(i=wA(be+80|0)))>0|r)){1&r&&(g[0|ee]=95,ee=ee+1|0);B:if(!((0|(_e=i-1|0))<0|ee>>>0>=R>>>0))for(;;){if(g[0|ee]=d[(be+80|0)+_e|0],ee=ee+1|0,(0|_e)<=0)break B;if(_e=_e-1|0,!(ee>>>0<R>>>0))break}g[0|ee]=41,g[ee+1|0]=32,ee=ee+2|0}g[0|ee]=0,g[(r=be+288|0)+((R+3|0)-ee|0)|0]=0,gs(ee,r),(0|(r=wA(R)))<=7&&(IA(r+R|0,32,8-r|0),r=8),g[r+R|0]=0,oe=be+496|0,e[w+4>>2]=R,e[w>>2]=(0|n)>1?P+35|0:P,e[w+8>>2]=$e,ss(ht,89088,w)}r=ae;break l}if(!d[0|ee])break l;Wt=(0|(y=Wt+2|0))>=19?19:y,or(w+264|0,ee),y=$s(w+268|0,R=ee-1|0),Ve=d[0|R],ht=R;w:{B:{F:{Y:{R:{M:switch(P-10|0){case 13:if(y=(0|(VA=d[0|r]))==(0|(P=d[0|ee])),it=-1,(0|P)==32|(0|P)==(0|VA))break Y;if(P)break R;break F;case 7:r=$e+2|0,ae=e[w+268>>2],ee=g[$e+1|0];y:if(P=e[604+(((ee=((0|ee)<65?191:-65)+ee|0)<<2)+t|0)>>2])P=!!(0|rs(P,ae));else{if((0|ee)>7)break l;G:{if((0|(P=e[t+600>>2]))>0){if((ae=ae-P|0)-1>>>0<255)break G;break l}if((P=ae-192|0)>>>0<=413){P=d[344+(d[P+94240|0]+t|0)|0]&1<<ee;break y}if(ae>>>0>255)break l}P=d[344+(t+ae|0)|0]&1<<ee}if(!P)break l;R=1+(R-y|0)|0,ae=((0|ee)==2?19:20)-Wt|0;break w;case 8:if(r=$e+2|0,y=g[$e+1|0],!(y=e[4788+((((0|y)<65?191:-65)+y<<2)+t|0)>>2]))break l;y:{for(;;){if((0|(Ve=d[0|y]))==7)break l;if((0|Ve)==126){it=0;break y}P=R;G:{S:{if((0|($e=(it=wA(y))-1|0))>0)for(P=ee-it|0,ae=0,ue=R;;){if(!d[0|(ue=ue-1|0)])break S;if((0|$e)==(0|(ae=ae+1|0)))break}X:if(!((0|(ae=d[0|P]))!=(0|Ve)|!ae))for(;;){if((0|(Ve=d[0|(y=y+1|0)]))!=(0|(ae=d[0|(P=P+1|0)])))break X;if(!ae)break}if(!Ve)break G}for(P=y;ae=d[0|P],P=y=P+1|0,ae;);continue}break}if((0|it)<0)break l}ae=20-yt|0,R=1+(R-it|0)|0;break w;case 15:P=e[w+268>>2];y:{G:if(ee=e[t+604>>2])ee=!!(0|rs(ee,P));else{S:{if((0|(ee=e[t+600>>2]))>0){if((P=P-ee|0)-1>>>0<255)break S;break y}if((ee=P-192|0)>>>0<=413){ee=1&g[344+(d[ee+94240|0]+t|0)|0];break G}if(P>>>0>255)break y}ee=1&g[344+(t+P|0)|0]}if(ee)break l}ae=20-Wt|0,R=1+(R-y|0)|0;break w;case 1:if(e[w+268>>2]!=e[w+264>>2])break l;ae=21-Wt|0,R=1+(R-y|0)|0;break w;case 5:if(!((ee=e[w+268>>2])-48>>>0<10|ee-2406>>>0<10))break l;ae=21-Wt|0,R=1+(R-y|0)|0;break w;case 6:if(_i(e[w+268>>2]))break l;ae=21-yt|0,R=1+(R-y|0)|0;break w;case 18:if(r=$e+2|0,!((0|(P=d[$e+1|0]))==3|(240&P)==32)||($A(y=w+96|0,ls,R=1+(e[A>>2]+(SA+as|0)|0)|0),g[0|(y=y+R|0)]=32,g[y+1|0]=0,e[33265]=0,e[33266]=0,e[w+16>>
28102]=xs,Hr(t,w+16|0,w+272|0,133060,0,0),ae=23,!((0|P)!=3|(0|(ue=e[33265]))>=0|16384&e[33266])))break n;if(y=_e,R=ee,ue>>>(15&P)&16384)break C;break l;case 11:if(P=1,d[0|r]==21)for(;P=P+1|0,d[0|(r=r+1|0)]==21;);if(e[t+8208>>2]<(0|P))break l;ae=18+(P-Wt|0)|0;break w;case 0:if(ae=19,y=_e,R=ee,e[t+8212>>2]>0)break C;break l;case 19:if(ae=3,(0|(ue=e[w+268>>2]))==32)break w;for(P=1+(R-y|0)|0;;){y:{G:if(y=e[t+632>>2])y=!!(0|rs(y,ue));else{S:{if((0|(y=e[t+600>>2]))>0){if((ue=ue-y|0)-1>>>0<255)break S;break y}if((y=ue-192|0)>>>0<=413){y=128&d[344+(d[y+94240|0]+t|0)|0];break G}if(ue>>>0>255)break y}y=128&d[344+(t+ue|0)|0]}if(y)break l}if(P=P-$s(w+268|0,P-1|0)|0,(0|(ue=e[w+268>>2]))==32)break}break w;case 16:if(ae=1,y=_e,R=ee,e[t+8184>>2])break C;break l;case 9:if(ae=1,y=_e,R=ee,Gn)break C;break l;case 36:for(;;){if(ae=50,y=_e,!(ee=(255&Ve)-32|0))break l;if((0|ee)==14)break C;Ve=d[0|(ht=ht-1|0)]}case 35:break M;default:break B}M:{if(y=Ve-32|0){if((0|y)==13)break M;break l}if(!Fn)break l}ae=22-yt|0;break w}R:{for(;;){it=-1,ue=ee,ee=ee-1|0;M:if((0|VA)==18&&(y=g[$e+2|0],ae=e[4788+((((0|y)<65?191:-65)+y<<2)+t|0)>>2]))for(Qi=ue+1|0;;){if((0|(ht=d[0|ae]))==7){it=-1;break M}if((0|ht)==126){it=0;break M}P=ue;y:{if((0|(zs=(it=wA(ae))-1|0))>0)for(P=Qi-it|0,Ve=0,y=ue;;){if(!d[0|(y=y-1|0)])break y;if((0|zs)==(0|(Ve=Ve+1|0)))break}G:if(!((0|(y=d[0|P]))!=(0|ht)|!y))for(;;){if((0|(ht=d[0|(ae=ae+1|0)]))!=(0|(y=d[0|(P=P+1|0)])))break G;if(!y)break}if(!ht)break M}for(P=ae;y=d[0|P],P=ae=P+1|0,y;);}if(y=(0|(P=d[0|ee]))==(0|VA),(0|P)==32|(0|P)==(0|VA))break R;if(!P){ee=ue;break F}if((0|it)!=-1)break}ee=ue;break F}ee=ue}R=y?ee:R}ae=0,R=(0|it)<0?R:ee+1|0;break w}if((0|P)!=(0|Ve))break l;ae=4,(0|P)!=32&&(ae=(192&P)!=128?21-Wt|0:0)}y=_e;break C}if((0|P)!=(0|Ve))break l;ue=(192&P)!=128?21-yt|0:0;break f}if(ae=1,pa)break l}Pt=ae+Pt|0;continue}if(y=g[$e+1|0],_e=d[$e+3|0],R=d[$e+2|0],1&!(e[t+8208>>2]|4&R)&g[t+84|0])break l;r=$e+4|0,ue=0,_e=127&_e|(127&R)<<8|y<<16;break f}b:if(!(ht>>>0<=(P=e[A>>2]+n|0)>>>0)){for(;;){if(d[0|P]!=101){if(y=P>>>0<be>>>0,P=P+1|0,y)continue;break b}break}ue=0,gA=P;break f}ue=0;break f}ae=-20;break n}if(Ve=0,(0|(P=e[w+268>>2]))!=32)for(ue=be+it|0,ae=0;;){if(!ae){i:{h:if(R=e[t+632>>2])P=!!(0|rs(R,P));else{b:{if((0|(R=e[t+600>>2]))>0){if((P=P-R|0)-1>>>0<255)break b;break i}if((R=P-192|0)>>>0<=413){P=128&d[344+(d[R+94240|0]+t|0)|0];break h}if(P>>>0>255)break i}P=128&d[344+(t+P|0)|0]}P&&(Ve=Ve+1|0)}P=e[w+268>>2]}i:if(R=e[t+632>>2])ae=!!(0|rs(R,P));else{h:{if((0|(R=e[t+600>>2]))>0){if(ae=0,(P=P-R|0)-1>>>0<255)break h;break i}if((R=P-192|0)>>>0<=413){ae=128&d[344+(d[R+94240|0]+t|0)|0];break i}if(ae=0,P>>>0>255)break i}ae=128&d[344+(t+P|0)|0]}if(ue=or(w+268|0,ue)+ue|0,(0|(P=e[w+268>>2]))==32)break}if(!((0|y)>(0|Ve))){ue=18+(y-yt|0)|0;break f}}for(;y=d[0|r],r=i=r+1|0,y;);if(d[0|i]!=7)continue a;if(t=n+Ao|0,e[A>>2]=e[A>>2]+(t||1),Xr)break t;break r}y=_e,be=ht,R=ee,Pt=ue+Pt|0;continue}be=be+it|0,ae=21-yt|0;break n}be=be+it|0,ae=20-yt|0}y=_e,R=ee,Pt=ae+Pt|0}}Cr=86135}e[l+12>>2]=yo,e[l+8>>2]=to,e[l+4>>2]=Cr,e[l>>2]=Xr}oe=w+384|0}function eA(t,A){var r,n=0,i=0,l=0,h=0,m=0,w=0,M=0,y=0,P=0,R=0,ee=0,ae=0,ue=0,be=0,_e=0,Ve=0,$e=0,it=0,ht=0,yt=0,Pt=0,Wt=0,cA=0,gA=0,SA=0,VA=0,qA=0,Cr=0,Yr=0,Xr=0;oe=r=oe-1168|0,e[r+928>>2]=0,e[r+932>>2]=0,e[r+920>>2]=0,e[r+924>>2]=0,e[r+912>>2]=0,e[r+916>>2]=0,e[r+904>>2]=0,e[r+908>>2]=0,e[r+896>>2]=0,e[r+900>>2]=0;e:{t:{if(t){if(d[0|t]|8&A)break t;break e}if(!(8&A))break e}if(vr(r+1088|0,t,40),16&A){if((0|Na(BA(r+704|0,t)))<=0)break e;ue=8&A}else(ue=8&A)|d[r+1088|0]||(v[r+1088>>1]=d[85055]|d[85056]<<8,g[r+1090|0]=d[85057]),e[r+496>>2]=137584,e[r+500>>2]=47,e[r+504>>2]=47,uA(n=r+512|0,85286,r+496|0),e[r+484>>2]=r+1088,e[r+480>>2]=n,uA(n=r+704|0,85425,r+480|0),(0|Na(n))>0||(e[r+468>>2]=47,e[r+472>>2]=47,e[r+464>>2]=137584,uA(n=r+512|0,85648,r+464|0),e[r+452>>2]=r+1088,e[r+448>>2]=n,uA(r+704|0,85425,r+448|0));if(i=ue?86012:85055,!(be=la(r+704|0,85712))){if(n=0,3&A)break e;i=(0|di(n=r+1088|0))<0?i:n}if((ae=2&A)||(n=e[47192])&&(Mn(n),e[47192]=0),$e=BA(r+992|0,i),Ve=BA(r+944|0,i),ae?((n=an(200992,43))&&(g[0|n]=0),e[r+432>>2]=t+3,uA(t=r+704|0,86030,r+432|0),gs(200992,t)):(e[32972]=199592,vr(200992,t,40),g[201088]=0,g[201040]=0,e[50299]=200992,e[50298]=201088,e[50297]=201040),nr(ae),be){for(ht=e[30450],yt=r+548|0,Pt=r+544|0,Wt=r+540|0,cA=r+536|0,gA=r+532|0,SA=r+528|0,VA=12|(t=r+512|0),qA=8|t,Cr=4|t;_r(r+704|0,190,be);){t=r+704|0;t:{if(d[r+704|0]!=35){r:if(!((0|(t=wA(r+704|0)-1|0))<=0))for(;;){if(!((0|(i=g[0|(n=(r+704|0)+t|0)]))==32|i-9>>>0<5))break r;if(g[0|n]=0,!((0|(t=t-1|0))>0))break}if(!(t=Dr(r+704|0)))break t}g[0|t]=0}t=r+704|0;t:if(n=d[r+704|0])for(;;){if((0|(n=n<<24>>24))==32|n-9>>>0<5)break t;if(!(n=d[0|(t=t+1|0)]))break}if(g[0|t]=0,d[r+704|0])if(t=t+1|0,n=ti(129744,r+704|0)){l=0,oe=i=oe-416|0;t:if(h=e[47192]){switch(n-19|0){case 16:if(e[i+32>>2]=i+412,(0|nA(t,84249,i+32|0))!=1)break t;e[h+324>>2]=e[i+412>>2];break t;case 8:Bi(t,h+320|0,27);break t;case 2:if(e[i+48>>2]=188784,nA(t,84249,i+48|0),!(t=d[188784]))break t;e[h+152>>2]=t;break t;case 11:if(d[0|t]){for(m=e[30450];;)if(n=t,t=t+1|0,!((0|(l=g[0|n]))==32|l-9>>>0<5)){for(l=Pn(n),e[i+412>>2]=l,(0|l)>0&&(l>>>0<=31?e[h+104>>2]=e[h+104>>2]|1<<l:l>>>0<=63?e[h+108>>2]=e[h+108>>
vendor: 8,491 bytes, line 2810
28102]|1<<l-32:(e[i+64>>2]=l,ss(m,84700,i- -64|0)),n=t);n=(t=n)+1|0,(l=g[0|t])-48>>>0<10|(32|l)-97>>>0<26;);if(!l)break}}8&(t=e[h+104>>2])&&(e[h+124>>2]=46,e[h+128>>2]=44),4&t&&(e[h+124>>2]=0);break t;default:if((65280&n)!=256)break t;e[i+16>>2]=24+(h+((255&n)<<2)|0),nA(t,84249,i+16|0);break t;case 1:e[i+144>>2]=h,e[i+148>>2]=h+4,nA(t,85642,i+144|0);break t;case 3:if(n=0,IA(l=i+160|0,0,240),e[i+132>>2]=i+360,e[i+128>>2]=i+320,e[i+124>>2]=i+280,e[i+120>>2]=i+240,e[i+116>>2]=i+200,e[i+112>>2]=l,l=nA(t,85037,i+112|0),e[i+412>>2]=l,e[h+152>>2]=0,(0|l)<=0)break t;for(y=e[30450];;){r:if(Zr(m=(i+160|0)+F(n,40)|0,85301)){a:{if((0|(M=e[34454]))>0)for(ee=e[34455],t=0;;){if(!Zr(m,ee+F(t,68)|0))break a;if((0|M)==(0|(t=t+1|0)))break}e[i+96>>2]=m,ss(y,85562,i+96|0),l=e[i+412>>2];break r}g[156+(n+h|0)|0]=t}if(!((0|l)>(0|(n=n+1|0))))break}break t;case 9:e[i+88>>2]=h+20,e[i+84>>2]=h+16,e[i+80>>2]=h+8,nA(t,84778,i+80|0);break t;case 10:Bi(t,h+12|0,29);break t;case 5:if((0|(y=oo(t,i+160|0)))<=0)break t;if(n=0,t=0,y>>>0>=4)for(ee=-4&y,m=h+304|0;M=i+160|0,v[m+(t<<1)>>1]=e[M+(t<<2)>>2],v[m+((w=1|t)<<1)>>1]=e[M+(w<<2)>>2],v[m+((w=2|t)<<1)>>1]=e[M+(w<<2)>>2],v[m+((w=3|t)<<1)>>1]=e[M+(w<<2)>>2],t=t+4|0,(0|ee)!=(0|(l=l+4|0)););if(!(l=3&y))break t;for(;v[304+(h+(t<<1)|0)>>1]=e[(i+160|0)+(t<<2)>>2],t=t+1|0,(0|l)!=(0|(n=n+1|0)););break t;case 6:if((0|(y=oo(t,i+160|0)))<=0)break t;if(n=0,t=0,y>>>0>=4)for(ee=-4&y,m=h+296|0;M=i+160|0,g[t+m|0]=e[M+(t<<2)>>2],g[(w=1|t)+m|0]=e[M+(w<<2)>>2],g[(w=2|t)+m|0]=e[M+(w<<2)>>2],g[(w=3|t)+m|0]=e[M+(w<<2)>>2],t=t+4|0,(0|ee)!=(0|(l=l+4|0)););if(!(l=3&y))break t;for(;g[296+(t+h|0)|0]=e[(i+160|0)+(t<<2)>>2],t=t+1|0,(0|l)!=(0|(n=n+1|0)););break t;case 7:if((0|(y=oo(t,i+160|0)))<=0)break t;if(n=0,t=0,y>>>0>=4)for(ee=-4&y,m=h+304|0;w=M=m+(t<<1)|0,P=_[M>>1],M=i+160|0,v[w>>1]=P+_[M+(t<<2)>>1],v[(P=m+((w=1|t)<<1)|0)>>1]=_[P>>1]+_[M+(w<<2)>>1],v[(P=m+((w=2|t)<<1)|0)>>1]=_[P>>1]+_[M+(w<<2)>>1],v[(P=m+((w=3|t)<<1)|0)>>1]=_[P>>1]+_[M+(w<<2)>>1],t=t+4|0,(0|ee)!=(0|(l=l+4|0)););if(!(l=3&y))break t;for(;v[304+(m=h+(t<<1)|0)>>1]=_[m+304>>1]+_[(i+160|0)+(t<<2)>>1],t=t+1|0,(0|l)!=(0|(n=n+1|0)););break t;case 4:g[h+169|0]=1;break t;case 0:}g[h+208|0]=1}else e[i>>2]=sa(129568,n),ss(e[30450],89101,i);oe=i+416|0}else{switch(ti(131904,r+704|0)-1|0){case 1:if(ae||(g[r+1040|0]=0,e[r+512>>2]=5,e[r+32>>2]=r+1040,e[r+36>>2]=r+512,nA(t,86237,r+32|0),e[r+1040>>2]==1769103734&e[r+1044>>2]==7630433))continue;if((t=wA(r+1040|0)+2|0)>>>0<99-_e>>>0&&(g[0|(n=_e+201088|0)]=e[r+512>>2],BA(n+1|0,r+1040|0),_e=t+_e|0),!Yr){if(t=0,(n=m=r+1040|0)||(n=e[57150])){if(t=86875,e[24+(i=oe-32|0)>>2]=0,e[i+28>>2]=0,e[i+16>>2]=0,e[i+20>>2]=0,e[i+8>>2]=0,e[i+12>>2]=0,e[i>>2]=0,e[i+4>>2]=0,h=0,l=d[86875])if(d[86876]){for(;e[(h=i+(l>>>3&28)|0)>>2]=e[h>>2]|1<<l,l=d[t+1|0],t=t+1|0,l;);t:if(l=d[0|(t=n)])for(;;){if(!(e[i+(l>>>3&28)>>2]>>>l&1))break t;if(l=d[t+1|0],t=t+1|0,!l)break}h=t-n|0}else{for(t=n;i=t,t=t+1|0,d[0|i]==(0|l););h=i-n|0}if(d[0|(t=h+n|0)]){n=86875,oe=l=oe-32|0,i=g[86875];t:if(d[86876]&&i){if(IA(l,0,32),i=d[86875])for(;e[(h=l+(i>>>3&28)|0)>>2]=e[h>>2]|1<<i,i=d[n+1|0],n=n+1|0,i;);if(n=t,i=d[0|t])for(;;){if(e[l+(i>>>3&28)>>2]>>>i&1)break t;if(i=d[n+1|0],n=n+1|0,!i)break}}else n=Un(t,i);oe=l+32|0,d[0|(n=(n-t|0)+t|0)]?(e[57150]=n+1,g[0|n]=0):e[57150]=0}else e[57150]=0,t=0}n=BA($e,t),BA(Ve,t),di(BA(r+896|0,t)),e[47192]=st(n),vr(e[32972]+40|0,m,20)}Yr=1;continue;case 0:if(ae)continue;for(;n=t,t=t+1|0,(0|(i=g[0|n]))==32|i-9>>>0<5;);vr(201040,n,40);continue;case 2:e[r+1152>>2]=0,n=r+512|0,e[r+48>>2]=n,e[r+52>>2]=r+1152,nA(t,86237,r+48|0),g[201200]=ti(132112,n),g[201201]=e[r+1152>>2];continue;case 4:e[r+64>>2]=Ve,nA(t,86939,r- -64|0);continue;case 3:e[r+80>>2]=r+896,nA(t,86939,r+80|0);continue;case 8:if(e[r+1152>>2]=100,e[r+1164>>2]=100,e[r+1148>>2]=100,e[r+112>>2]=r+1144,e[r+1144>>2]=0,e[r+96>>2]=r+512,e[r+100>>2]=r+1152,e[r+104>>2]=r+1164,e[r+108>>2]=r+1148,(0|nA(t,91156,r+96|0))<2||(t=e[r+512>>2])>>>0>8||((0|(n=e[r+1152>>2]))>=0&&(i=e[32972]+(t<<1)|0,n=he(R=2.56001*+(0|n))<2147483648?~~R:-2147483648,v[i+236>>1]=n,v[i+164>>1]=n),(0|(n=e[r+1164>>2]))>=0&&(i=e[32972]+(t<<1)|0,n=he(R=2.56001*+(0|n))<2147483648?~~R:-2147483648,v[i+254>>1]=n,v[i+182>>1]=n),(0|(i=e[r+1148>>2]))<0?n=e[32972]:(m=(n=e[32972])+(t<<1)|0,i=he(R=2.56001*+(0|i))<2147483648?~~R:-2147483648,v[m+200>>1]=i),v[218+((t<<1)+n|0)>>1]=e[r+1144>>2],t))continue;v[n+200>>1]=(0|F(v[n+200>>1],105))/100;continue;case 9:if(e[r+132>>2]=r+696,e[r+128>>2]=r+700,(0|nA(t,87106,r+128|0))!=2)continue;if(t=e[32972],n=e[r+700>>2],e[t+64>>2]=(n<<12)-36864,e[t+68>>2]=F(e[r+696>>2]-n|0,108),he(R=256*(+(n-82|0)/82*.25+1))<2147483648){e[t+116>>2]=~~R;continue}e[t+116>>2]=-2147483648;continue;case 35:Xr||di(r+896|0),e[r+1164>>2]=0,g[r+1156|0]=d[91267],e[r+1152>>2]=d[91263]|d[91264]<<8|d[91265]<<16|d[91266]<<24,e[r+144>>2]=r+1164,e[r+148>>2]=r+512,e[r+152>>2]=r+1152,(0|nA(t,91302,r+144|0))<2|e[49848]>59||(t=_o(r+512|0))&&(g[F(e[49848],3)+199408|0]=t,t=_o(r+1152|0),n=e[49848],i=F(n,3)+199408|0,g[i+1|0]=t,e[49848]=n+1,g[i+2|0]=e[r+1164>>2]),Xr=1;continue;case 10:e[r+1140>>2]=0,n=e[32972],e[n+100>>2]=0,e[r+164>>2]=n+100,e[r+160>>2]=n+96,nA(t,87106,r+160|0);continue;case 11:if(e[r+176>>2]=r+1140,(0|nA(t,87268,r+176|0))!=1)continue;e[e[32972]+88>>2]=e[r+1140>>2]<<5;continue;case 12:if(e[r+192>>2]=r+1140,(0|nA(t,87268,r+192|0))!=1)continue;e[e[32972]+92>>2]=e[r+1140>>2];continue;case 13:if(e[r+208>>2]=r+1140,(0|nA(t,87268,r+208|0))!=1)continue;n=e[32972],(0|(t=e[r+1140>>2]))>=5&&(e[n+108>>2]=1,e[r+1140>>2]=4,t=4),e[n+104>>2]=t+1;continue;case 14:for(e[r+552>>2]=-1,e[r+556>>2]=-1,e[r+544>>2]=-1,e[r+548>>2]=-1,e[r+536>>2]=-1,e[r+540>>2]=-1,e[r+528>>2]=-1,e[r+532>>2]=-1,e[r+240>>2]=SA,e[r+244>>2]=gA,e[r+248>>2]=cA,e[r+252>>2]=Wt,e[r+256>>2]=Pt,e[r+260>>2]=yt,e[r+520>>2]=-1,e[r+524>>2]=-1,e[r+512>>2]=-1,e[r+516>>2]=-1,e[r+228>>2]=Cr,e[r+232>>2]=qA,e[r+236>>2]=VA,e[r+224>>2]=r+512,nA(t,84222,r+224|0),ee=e[32972],t=0,l=e[r+516>>2],i=0;;){if(n=i,h=l,m=t,(0|(i=e[(l=(t<<=2)+(r+512|0)|0)>>2]))==-1&&(i=8e3,e[l>>2]=8e3,m&&(e[(r+512|0)+(4|t)>>2]=e[508+(t+r|0)>>2])),l=e[(r+512|0)+(4|t)>>2],!((0|n)>=(0|(i=(0|i)/8|0))||(0|(M=i-n|0))<=0||(y=n+1|0,t=n,1&M&&(g[344+(n+ee|0)|0]=(0|h)>=255?255:h,t=y),(0|i)==(0|y))))for(y=l-h|0;P=ee+344|0,w=h+((0|F(y,t-n|0))/(0|M)|0)|0,g[P+t|0]=(0|w)>=255?255:w,w=h+((0|F(y,(it=t+1|0)-n|0))/(0|M)|0)|0,g[P+it|0]=(0|w)>=255?255:w,(0|i)!=(0|(t=t+2|0)););if(t=m+2|0,!(m>>>0<10))break}continue;case 15:if(e[r+272>>2]=r+1140,(0|nA(t,87268,r+272|0))!=1)continue;e[e[32972]+112>>2]=(e[r+1140>>2]<<6)/100;continue;case 16:n=e[32972],e[(i=n+300|0)>>2]=0,e[i+4>>2]=0,e[(l=n+292|0)>>2]=0,e[l+4>>2]=0,e[(h=n+284|0)>>2]=0,e[h+4>>2]=0,e[(m=n+276|0)>>2]=0,e[m+4>>2]=0,e[r+316>>2]=n+304,e[r+312>>2]=i,e[r+308>>2]=n+296,e[r+304>>2]=l,e[r+300>>2]=n+288,e[r+296>>2]=h,e[r+292>>2]=n+280,e[r+288>>2]=m,n=nA(t,84553,r+288|0),t=e[32972],e[t+272>>2]=n,e[t+276>>2]=0-e[t+276>>2],e[t+284>>2]=0-e[t+284>>2],e[t+292>>2]=0-e[t+292>>2],e[t+300>>2]=0-e[t+300>>2];continue;case 17:n=e[32972],e[(i=n+336|0)>>2]=0,e[i+4>>2]=0,e[(l=n+328|0)>>2]=0,e[l+4>>2]=0,e[(h=n+320|0)>>2]=0,e[h+4>>2]=0,e[(m=n+312|0)>>2]=0,e[m+4>>2]=0,e[r+348>>2]=n+340,e[r+344>>2]=i,e[r+340>>2]=n+332,e[r+336>>2]=l,e[r+332>>2]=n+324,e[r+328>>2]=h,e[r+324>>2]=n+316,e[r+320>>2]=m,t=nA(t,84553,r+320|0),e[e[32972]+308>>2]=t;continue;case 36:n=e[32972],e[r+352>>2]=n+120,e[r+356>>2]=n+124,e[r+1140>>2]=nA(t,87106,r+352|0);continue;case 33:e[r+368>>2]=e[32972]+84,nA(t,87268,r+368|0),Ra(3);continue;case 31:n=e[32972],e[(i=n+156|0)>>2]=0,e[i+4>>2]=0,e[(l=n+148|0)>>2]=0,e[l+4>>2]=0,e[(h=n+140|0)>>2]=0,e[h+4>>2]=0,e[(m=n+132|0)>>2]=0,e[m+4>>2]=0,e[r+412>>2]=n+160,e[r+408>>2]=i,e[r+404>>2]=n+152,e[r+400>>2]=l,e[r+396>>2]=n+144,e[r+392>>2]=h,e[r+388>>2]=n+136,e[r+384>>2]=m,nA(t,84553,r+384|0),t=e[32972],e[t+152>>2]=e[t+152>>2]-40;continue;case 32:e[r+416>>2]=145740,nA(t,87268,r+416|0),Ra(3);continue;case 6:case 7:continue}e[r+16>>2]=r+704,ss(ht,87359,r+16|0)}}fa(be)}t:{if((t=e[47192])|ae){if(ae)break t}else t=st($e),e[47192]=t;r:{if(!ue){if((0|(t=di(r+896|0)))<0&&(e[r>>2]=r+896,ss(e[30450],87567,r),t=0),e[e[32972]+60>>2]=t,n=e[47192],e[n+292>>2]=t,qs(n,Ve,4&A),d[132848])break r;Mn(e[47192]),n=0;break e}e[e[32972]+60>>2]=0,e[t+292>>2]=0}g[_e+201088|0]=0}n=e[32972]}return oe=r+1168|0,n}function nA(t,A,r){var n,i,l,h=0,m=0,w=0,M=0,y=0,P=0,R=0,ee=0,ae=0,ue=0,be=0,_e=0,Ve=0,$e=0,it=0,ht=0,yt=0,Pt=0,Wt=0;oe=l=oe-16|0,e[l+12>>2]=r,oe=h=oe-144|0,n=IA(h,0,144),e[n+76>>2]=-1,e[n+44>>2]=t,e[n+32>>2]=18,e[n+84>>2]=t,h=A,yt=r,t=0,oe=i=oe-304|0;
2810e:{t:{r:if(e[n+4>>2]||(Di(n),e[n+4>>2])){if(!(A=d[0|h]))break e;a:{n:{c:{o:{for(;;){f:{l:if((0|(A&=255))==32|A-9>>>0<5){for(;A=h,h=h+1|0,(0|(r=d[A+1|0]))==32|r-9>>>0<5;);for(ua(n,0,0);(0|(r=e[n+4>>2]))==e[n+104>>2]?r=oA(n):(e[n+4>>2]=r+1,r=d[0|r]),(0|r)==32|r-9>>>0<5;);h=e[n+4>>2],(0|(r=e[n+116>>2]))>0|(0|r)>=0&&(h=h-1|0,e[n+4>>2]=h),h=r=h-e[n+44>>2]|0,y=_e+e[n+124>>2]|0,y=(m=r>>31)+((r=ae+e[n+120>>2]|0)>>>0<ae>>>0?y+1|0:y)|0,_e=(ae=r+h|0)>>>0<r>>>0?y+1|0:y}else{i:{h:{b:{if(d[0|h]==37){if((0|(A=d[h+1|0]))==42)break b;if((0|A)!=37)break h}if(ua(n,0,0),d[0|h]!=37)(0|(A=e[n+4>>2]))==e[n+104>>2]?A=oA(n):(e[n+4>>2]=A+1,A=d[0|A]);else{for(;(0|(A=e[n+4>>2]))==e[n+104>>2]?A=oA(n):(e[n+4>>2]=A+1,A=d[0|A]),(0|A)==32|A-9>>>0<5;);h=h+1|0}if(d[0|h]!=(0|A)){if((0|(r=e[n+116>>2]))>0|(0|r)>=0&&(e[n+4>>2]=e[n+4>>2]-1),(0|A)>=0||(w=0,Pt))break e;break r}M=(m=A=e[n+4>>2]-e[n+44>>2]|0)>>31,A=_e+e[n+124>>2]|0,w=((r=ae+e[n+120>>2]|0)>>>0<ae>>>0?A+1|0:A)+M|0,_e=(ae=r+m|0)>>>0<r>>>0?w+1|0:w,A=h;break l}P=0,A=h+2|0;break i}d[h+2|0]!=36|A-48>>>0>=10?(P=e[yt>>2],yt=yt+4|0,A=h+1|0):(A=d[h+1|0]-48|0,e[12+(r=oe-16|0)>>2]=yt,A=(A>>>0>1?(A<<2)-4|0:0)+yt|0,e[r+8>>2]=A+4,P=e[A>>2],A=h+3|0)}if(R=0,h=0,d[0|A]-48>>>0<10)for(;h=(d[0|A]+F(h,10)|0)-48|0,r=d[A+1|0],A=A+1|0,r-48>>>0<10;);(0|(ue=d[0|A]))==109&&(ee=0,R=!!(0|P),ue=d[A+1|0],t=0,A=A+1|0),A=(r=A)+1|0,m=3,w=R;i:{h:switch(ue-65|0){case 39:m=r+2|0,A=(r=d[r+1|0]==104)?m:A,m=r?-2:-1;break i;case 43:m=r+2|0,A=(r=d[r+1|0]==108)?m:A,m=r?3:1;break i;case 51:case 57:m=1;break i;case 11:m=2;break i;case 41:break i;case 0:case 2:case 4:case 5:case 6:case 18:case 23:case 26:case 32:case 34:case 35:case 36:case 37:case 38:case 40:case 45:case 46:case 47:case 50:case 52:case 55:break h;default:break a}m=0,A=r}w=m,it=(m=(47&(r=d[0|A]))==3)?1:w;i:if((0|($e=m?32|r:r))!=91){h:{if((0|$e)!=110){if((0|$e)!=99)break h;h=(0|h)<=1?1:h;break i}s0(P,it,ae,_e);break l}for(ua(n,0,0);(0|(r=e[n+4>>2]))==e[n+104>>2]?r=oA(n):(e[n+4>>2]=r+1,r=d[0|r]),(0|r)==32|r-9>>>0<5;);r=e[n+4>>2],(0|(m=e[n+116>>2]))>0|(0|m)>=0&&(r=r-1|0,e[n+4>>2]=r),m=r=r-e[n+44>>2]|0,y=_e+e[n+124>>2]|0,_e=(w=r>>31)+((r=ae+e[n+120>>2]|0)>>>0<ae>>>0?y+1|0:y)|0,_e=(ae=r+m|0)>>>0<r>>>0?_e+1|0:_e}if(be=h,ua(n,h,Ve=h>>31),(0|(r=e[n+4>>2]))==e[n+104>>2]){if((0|oA(n))<0)break n}else e[n+4>>2]=r+1;(0|(r=e[n+116>>2]))>0|(0|r)>=0&&(e[n+4>>2]=e[n+4>>2]-1),r=16;i:{h:{b:{m:{switch($e-88|0){default:if((r=$e-65|0)>>>0>6|!(1<<r&113))break i;case 9:case 13:case 14:case 15:if(jt(i+8|0,n,it,0),r=e[n+4>>2]-e[n+44>>2]|0,e[n+120>>2]!=(0-r|0)|e[n+124>>2]!=(0-((r>>31)+!!(0|r)|0)|0))break b;break c;case 3:case 11:case 27:if((16|$e)==115){if(IA(i+32|0,-1,257),g[i+32|0]=0,(0|$e)!=115)break h;g[i+65|0]=0,g[i+46|0]=0,v[i+42>>1]=0,v[i+44>>1]=0;break h}IA(i+32|0,M=(0|(m=d[A+1|0]))==94,257),g[i+32|0]=0,r=M?A+2|0:A+1|0;C:{p:{x:{if((0|(A=d[(M?2:1)+A|0]))!=45){if((0|A)==93)break x;m=(0|m)!=94,A=r;break C}m=(0|m)!=94,g[i+78|0]=m;break p}m=(0|m)!=94,g[i+126|0]=m}A=r+1|0}for(;;){if((0|(r=d[0|A]))==45){if(r=45,!(!(M=d[A+1|0])|(0|M)==93)){if(w=A+1|0,M>>>0<=(A=d[A-1|0])>>>0)r=M;else for(;g[(A=A+1|0)+(i+32|0)|0]=m,(r=d[0|w])>>>0>A>>>0;);A=w}}else{if(!r)break n;if((0|r)==93)break h}g[33+(r+i|0)|0]=m,A=A+1|0}case 23:r=8;break m;case 12:case 29:r=10;break m;case 1:case 2:case 4:case 5:case 6:case 7:case 8:case 10:case 16:case 18:case 19:case 20:case 21:case 22:case 25:case 26:case 28:case 30:case 31:break i;case 0:case 24:case 32:break m;case 17:}r=0}M=0,y=0,m=0,w=0,ue=0,oe=ht=oe-16|0;m:if((0|r)!=1&r>>>0<=36){for(;(0|(h=e[n+4>>2]))==e[n+104>>2]?h=oA(n):(e[n+4>>2]=h+1,h=d[0|h]),(0|h)==32|h-9>>>0<5;);C:{p:switch(h-43|0){case 0:case 2:break p;default:break C}ue=(0|h)==45?-1:0,(0|(h=e[n+4>>2]))==e[n+104>>2]?h=oA(n):(e[n+4>>2]=h+1,h=d[0|h])}C:{p:{x:{w:{if(!(!!(0|r)&(0|r)!=16|(0|h)!=48)){if((0|(h=e[n+4>>2]))==e[n+104>>2]?h=oA(n):(e[n+4>>2]=h+1,h=d[0|h]),(-33&h)==88){if(r=16,(0|(h=e[n+4>>2]))==e[n+104>>2]?h=oA(n):(e[n+4>>2]=h+1,h=d[0|h]),d[h+121329|0]<16)break x;(0|(r=e[n+116>>2]))>0|(0|r)>=0&&(e[n+4>>2]=e[n+4>>2]-1),ua(n,0,0);break m}if(r)break w;r=8;break x}if(!((r=r||10)>>>0>d[h+121329|0])){(0|(r=e[n+116>>2]))>0|(0|r)>=0&&(e[n+4>>2]=e[n+4>>2]-1),ua(n,0,0),e[56798]=28;break m}}if((0|r)==10){if((m=h-48|0)>>>0<=9){for(r=0;w=(r=F(r,10)+m|0)>>>0<429496729,(0|(h=e[n+4>>2]))==e[n+104>>2]?h=oA(n):(e[n+4>>2]=h+1,h=d[0|h]),w&(m=h-48|0)>>>0<=9;);M=r}w:if(!(m>>>0>9)){for(r=rr(M,0,10,0),w=fe;;){if(y=w,w=(0|(y=(M=r+m|0)>>>0<m>>>0?y+1|0:y))==429496729&M>>>0>=2576980378|y>>>0>429496729,(0|(r=e[n+4>>2]))==e[n+104>>2]?h=oA(n):(e[n+4>>2]=r+1,h=d[0|r]),w|(m=h-48|0)>>>0>9)break w;if(r=rr(M,y,10,0),!((0|(w=fe))==-1&~m>>>0>=r>>>0|(0|w)!=-1))break}r=10;break p}if(r=10,m>>>0<=9)break p;break C}}if(r-1&r){if((w=d[h+121329|0])>>>0<r>>>0){for(;M=(m=F(r,m)+w|0)>>>
vendor: 12,568 bytes, line 2810
28100<119304647,(0|(h=e[n+4>>2]))==e[n+104>>2]?h=oA(n):(e[n+4>>2]=h+1,h=d[0|h]),M&(w=d[h+121329|0])>>>0<r>>>0;);M=m}if(r>>>0<=w>>>0)break p;for(;;){if(m=rr(M,y,r,0),(0|(be=fe))==-1&~(w&=255)>>>0<m>>>0||(y=be,y=(M=m+w|0)>>>0<w>>>0?y+1|0:y,(0|(h=e[n+4>>2]))==e[n+104>>2]?h=oA(n):(e[n+4>>2]=h+1,h=d[0|h]),r>>>0<=(w=d[h+121329|0])>>>0))break p;if(Ns(ht,r,0,0,0,M,y,0,0),e[ht+8>>2]|e[ht+12>>2])break}}else{if(be=g[84400+(F(r,23)>>>5&7)|0],(m=d[h+121329|0])>>>0<r>>>0){for(;M=(w=w<<be|m)>>>0<134217728,(0|(h=e[n+4>>2]))==e[n+104>>2]?h=oA(n):(e[n+4>>2]=h+1,h=d[0|h]),M&(m=d[h+121329|0])>>>0<r>>>0;);M=w}if(!(r>>>0<=m>>>0||(Ve=31&be,(63&be)>>>0>=32?(w=0,Ve=-1>>>Ve|0):Ve=(w=-1>>>Ve|0)|(1<<Ve)-1<<32-Ve,!w&M>>>0>Ve>>>0)))for(;;){if(Wt=255&m,m=M,h=31&be,(63&be)>>>0>=32?(y=m<<h,h=0):(y=(1<<h)-1&m>>>32-h|y<<h,h=m<<h),M=Wt|h,(0|(h=e[n+4>>2]))==e[n+104>>2]?h=oA(n):(e[n+4>>2]=h+1,h=d[0|h]),r>>>0<=(m=d[h+121329|0])>>>0)break p;if(!((0|w)==(0|y)&M>>>0<=Ve>>>0|w>>>0>y>>>0))break}}}if(!(d[h+121329|0]>=r>>>0)){for(;(0|(h=e[n+4>>2]))==e[n+104>>2]?h=oA(n):(e[n+4>>2]=h+1,h=d[0|h]),d[h+121329|0]<r>>>0;);e[56798]=68,ue=0,M=-1,y=-1}}(0|(r=e[n+116>>2]))>0|(0|r)>=0&&(e[n+4>>2]=e[n+4>>2]-1),M=(r=M^ue)-ue|0,y=((h=ue>>31)^y)-((r>>>0<ue>>>0)+h|0)|0}else e[56798]=28;if(oe=ht+16|0,r=e[n+4>>2]-e[n+44>>2]|0,e[n+120>>2]==(0-r|0)&e[n+124>>2]==(0-((r>>31)+!!(0|r)|0)|0))break c;if(!(!P|(0|$e)!=112)){e[P>>2]=M;break i}s0(P,it,M,y);break i}if(!P)break i;h=e[i+16>>2],r=e[i+20>>2],m=e[i+8>>2],R=e[i+12>>2];b:switch(0|it){case 0:oe=y=oe-32|0;m:if(0|(w=(M=2147483647&r)-1065418752|0)>>>0<(be=M-1082064896|0)>>>0){if(M=(33554431&r)<<7|h>>>25,be=w=0,!(!w&(0|(h&=33554431))==16777216?!(m|R):!w&h>>>0<16777216)){w=M+1073741825|0;break m}if(w=M+1073741824|0,16777216^h|m|R|be)break m;w=(1&M)+w|0}else(!h&(0|M)==2147418112?!(m|R):M>>>0<2147418112)?(w=2139095040,M>>>0>1082064895||(w=0,(M=M>>>16|0)>>>0<16145||(ea(y+16|0,m,R,h,w=65535&r|65536,M-16129|0),Da(y,m,R,h,w,16257-M|0),h=e[y+8>>2],w=(33554431&e[y+12>>2])<<7|h>>>25,M=e[y>>2]|!!(e[y+16>>2]|e[y+24>>2]|e[y+20>>2]|e[y+28>>2]),R=e[y+4>>2],(!(m=0)&(0|(h&=33554431))==16777216?!(M|R):!m&h>>>0<16777216)?16777216^h|M|m|R||(w=(1&w)+w|0):w=w+1|0))):w=4194303&((33554431&r)<<7|h>>>25)|2143289344;oe=y+32|0,e[P>>2]=-2147483648&r|w;break i;case 1:S[P>>3]=Pa(m,R,h,r);break i;case 2:break b;default:break i}e[P>>2]=m,e[P+4>>2]=R,e[P+8>>2]=h,e[P+12>>2]=r;break i}m=(ht=(0|$e)!=99)?31:h+1|0;h:if((0|it)!=1){if(R){if(h=0,!(r=aA(m)))break o;for(;;){for(t=r;;){if((0|(r=e[n+4>>2]))==e[n+104>>2]?r=oA(n):(e[n+4>>2]=r+1,r=d[0|r]),!d[33+(r+i|0)|0]){m=0,ee=t;break h}if(g[t+h|0]=r,(0|m)==(0|(h=h+1|0)))break}if(w=1,!(r=ds(t,m=m<<1|1)))break}ee=t,t=0;break a}if(h=0,P)for(;;){if((0|(t=e[n+4>>2]))==e[n+104>>2]?t=oA(n):(e[n+4>>2]=t+1,t=d[0|t]),!d[33+(t+i|0)|0]){m=0,ee=t=P;break h}g[h+P|0]=t,h=h+1|0}for(;(0|(t=e[n+4>>2]))==e[n+104>>2]?t=oA(n):(e[n+4>>2]=t+1,t=d[0|t]),d[33+(t+i|0)|0];);t=0,ee=0,m=0}else{if(r=P,R&&!(r=aA(m<<2)))break o;for(e[i+296>>2]=0,e[i+300>>2]=0,h=0;;){t=r;b:{for(;;){if((0|(r=e[n+4>>2]))==e[n+104>>2]?r=oA(n):(e[n+4>>2]=r+1,r=d[0|r]),!d[33+(r+i|0)|0])break b;g[i+27|0]=r,M=i+28|0,r=e[(w=(r=i+296|0)||228604)>>2];m:{C:{p:{x:{if(!(ee=i+27|0)){if(r)break x;r=0;break m}if(!r){if((0|(y=(r=d[0|ee])<<24>>24))>=0){M&&(e[M>>2]=r),r=!!(0|y);break m}if(!e[e[56841]>>2]){if(r=1,!M)break p;e[M>>2]=57343&y,r=1;break m}if((r=r-194|0)>>>0>50)break x;r=e[124752+(r<<2)>>2];break C}if(ue=1,!(((it=(y=d[0|ee])>>>3|0)-16|(r>>26)+it)>>>0>7))for(;;){if(ue=ue-1|0,(0|(r=y-128|r<<6))>=0){e[w>>2]=0,M&&(e[M>>2]=r),r=1-ue|0;break m}if(!ue)break C;if((192&(y=d[0|(ee=ee+1|0)]))!=128)break}}e[w>>2]=0,e[56798]=25,r=-1}break m}e[w>>2]=r,r=-2}if((0|r)!=-2){if(ee=0,(0|r)==-1)break n;if(t&&(e[(h<<2)+t>>2]=e[i+28>>2],h=h+1|0),!(!R|(0|h)!=(0|m)))break}}if(w=1,r=ds(t,(m=m<<1|1)<<2))continue;break a}break}if(ee=0,m=t,i+296|0&&e[i+296>>2])break n}if(r=e[n+4>>2],(0|(M=e[n+116>>2]))>0|(0|M)>=0&&(r=r-1|0,e[n+4>>2]=r),r=(M=r-e[n+44>>2]|0)+e[n+120>>2]|0,y=e[n+124>>2]+(M>>31)|0,!((y=r>>>0<M>>>0?y+1|0:y)|r)|!(ht|(0|r)==(0|be)&(0|y)==(0|Ve)))break f;R&&(e[P>>2]=t),(0|$e)!=99&&(m&&(e[(h<<2)+m>>2]=0),ee?g[h+ee|0]=0:ee=0),t=m}h=r=e[n+4>>2]-e[n+44>>2]|0,w=_e+e[n+124>>2]|0,_e=(m=r>>31)+((r=ae+e[n+120>>2]|0)>>>0<ae>>>0?w+1|0:w)|0,_e=(ae=r+h|0)>>>0<r>>>0?_e+1|0:_e,Pt=!!(0|P)+Pt|0}if(h=A+1|0,A=d[A+1|0])continue;break e}break}t=m;break c}w=1,ee=0,t=0;break a}w=R;break t}w=R}if(Pt)break t}Pt=-1}w&&(EA(ee),EA(t))}return oe=i+304|0,oe=n+144|0,oe=l+16|0,Pt}function aA(t){var A,r=0,n=0,i=0,l=0,h=0,m=0,w=0,M=0,y=0,P=0;oe=A=oe-16|0;e:{t:{r:{a:{n:{c:{o:{f:{l:{if((t|=0)>>>0<=244){if(3&(r=(m=e[57152])>>>(n=(w=t>>>0<11?16:t+11&-8)>>>3|0)|0)){r=228648+(t=(n=n+(1&~r)|0)<<3)|0,i=e[t+228656>>2],(0|r)!=(0|(t=e[i+8>>2]))?(e[t+12>>2]=r,e[r+8>>2]=t):e[57152]=Ks(-2,n)&m,t=i+8|0,r=n<<3,e[i+4>>2]=3|r,e[4+(r=r+i|0)>>2]=1|e[r+4>>2];break e}if((P=e[57154])>>>0>=w>>>0)break l;if(r){r=228648+(t=(i=vo(0-(t=(0-(t=2<<n)|t)&r<<n)&t))<<3)|0,l=e[t+228656>>2],(0|r)!=(0|(t=e[l+8>>2]))?(e[t+12>>2]=r,e[r+8>>2]=t):(m=Ks(-2,i)&m,e[57152]=m),e[l+4>>2]=3|w,i=(t=i<<3)-w|0,e[4+(n=l+w|0)>>2]=1|i,e[t+l>>2]=i,P&&(r=228648+(-8&P)|0,h=e[57157],(t=1<<(P>>>3))&m?t=e[r+8>>2]:(e[57152]=t|m,t=r),e[r+8>>2]=h,e[t+12>>2]=h,e[h+12>>2]=r,e[h+8>>2]=t),t=l+8|0,e[57157]=n,e[57154]=i;break e}if(!(y=e[57153]))break l;for(n=e[228912+(vo(0-y&y)<<2)>>2],h=(-8&e[n+4>>2])-w|0,r=n;(t=e[r+16>>2])||(t=e[r+20>>2]);)h=(i=(r=(-8&e[t+4>>2])-w|0)>>>0<h>>>0)?r:h,n=i?t:n,r=t;if(M=e[n+24>>2],(0|(i=e[n+12>>2]))!=(0|n)){t=e[n+8>>2],e[t+12>>2]=i,e[i+8>>2]=t;break t}if(!(t=e[(r=n+20|0)>>2])){if(!(t=e[n+16>>2]))break f;r=n+16|0}for(;l=r,i=t,(t=e[(r=t+20|0)>>2])||(r=i+16|0,t=e[i+16>>2]););e[l>>2]=0;break t}if(w=-1,!(t>>>0>4294967231)&&(w=-8&(t=t+11|0),y=e[57153])){h=0-w|0,m=0,w>>>0<256||(m=31,w>>>0>16777215||(m=62+((w>>>38-(t=ce(t>>>8|0))&1)-(t<<1)|0)|0));i:{h:{if(r=e[228912+(m<<2)>>2])for(t=0,n=w<<((0|m)!=31?25-(m>>>1|0):0);;){if(!((l=(-8&e[r+4>>2])-w|0)>>>0>=h>>>0||(i=r,h=l,l))){h=0,t=r;break h}if(l=e[r+20>>2],r=e[16+((n>>>29&4)+r|0)>>2],t=l?(0|l)==(0|r)?t:l:t,n<<=1,!r)break}else t=0;if(!(t|i)){if(i=0,!(t=(0-(t=2<<m)|t)&y))break l;t=e[228912+(vo(t&0-t)<<2)>>2]}if(!t)break i}for(;h=(n=(r=(-8&e[t+4>>2])-w|0)>>>0<h>>>0)?r:h,i=n?t:i,t=(r=e[t+16>>2])||e[t+20>>2];);}if(!(!i|e[57154]-w>>>0<=h>>>0)){if(m=e[i+24>>2],(0|i)!=(0|(n=e[i+12>>2]))){t=e[i+8>>2],e[t+12>>2]=n,e[n+8>>2]=t;break r}if(!(t=e[(r=i+20|0)>>2])){if(!(t=e[i+16>>2]))break o;r=i+16|0}for(;l=r,n=t,(t=e[(r=t+20|0)>>2])||(r=n+16|0,t=e[n+16>>2]););e[l>>2]=0;break r}}}if((t=e[57154])>>>0>=w>>>0){i=e[57157],(r=t-w|0)>>>0>=16?(e[4+(n=i+w|0)>>2]=1|r,e[t+i>>2]=r,e[i+4>>2]=3|w):(e[i+4>>2]=3|t,e[4+(t=t+i|0)>>2]=1|e[t+4>>2],n=0,r=0),e[57154]=r,e[57157]=n,t=i+8|0;break e}if((M=e[57155])>>>0>w>>>0){r=M-w|0,e[57155]=r,t=(n=e[57158])+w|0,e[57158]=t,e[t+4>>2]=1|r,e[n+4>>2]=3|w,t=n+8|0;break e}if(t=0,y=w+47|0,e[57270]?n=e[57272]:(e[57273]=-1,e[57274]=-1,e[57271]=4096,e[57272]=4096,e[57270]=A+12&-16^1431655768,e[57275]=0,e[57263]=0,n=4096),(r=(l=y+n|0)&(h=0-n|0))>>>0<=w>>>0||(i=e[57262])&&i>>>0<(m=(n=e[57260])+r|0)>>>0|n>>>0>=m>>>0)break e;l:{if(!(4&d[229052])){i:{h:{b:{m:{if(i=e[57158])for(t=229056;;){if((n=e[t>>2])>>>0<=i>>>0&i>>>0<n+e[t+4>>2]>>>0)break m;if(!(t=e[t+8>>2]))break}if((0|(n=za(0)))==-1||(m=r,(t=(i=e[57271])-1|0)&n&&(m=(r-n|0)+(t+n&0-i)|0),m>>>0<=w>>>0)||(i=e[57262])&&i>>>0<(h=(t=e[57260])+m|0)>>>0|t>>>0>=h>>>0)break i;if((0|n)!=(0|(t=za(m))))break b;break l}if((0|(n=za(m=h&l-M)))==(e[t>>2]+e[t+4>>2]|0))break h;t=n}if((0|t)==-1)break i;if(w+48>>>0<=m>>>0){n=t;break l}if((0|za(n=(n=e[57272])+(y-m|0)&0-n))==-1)break i;m=n+m|0,n=t;break l}if((0|n)!=-1)break l}e[57263]=4|e[57263]}if((0|(n=za(r)))==-1|(0|(t=za(0)))==-1|t>>>0<=n>>>0||(m=t-n|0)>>>0<=w+40>>>0)break a}t=e[57260]+m|0,e[57260]=t,t>>>0>re[57261]&&(e[57261]=t);l:{if(l=e[57158]){for(t=229056;;){if(((i=e[t>>2])+(r=e[t+4>>2])|0)==(0|n))break l;if(!(t=e[t+8>>2]))break}break c}for((t=e[57156])>>>0<=n>>>0&&t||(e[57156]=n),t=0,e[57265]=m,e[57264]=n,e[57160]=-1,e[57161]=e[57270],e[57267]=0;r=228648+(i=t<<3)|0,e[i+228656>>2]=r,e[i+228660>>2]=r,(0|(t=t+1|0))!=32;);r=(i=m-40|0)-(t=n+8&7?-8-n&7:0)|0,e[57155]=r,t=t+n|0,e[57158]=t,e[t+4>>2]=1|r,e[4+(n+i|0)>>2]=40,e[57159]=e[57274];break n}if(8&d[t+12|0]|i>>>0>l>>>0|n>>>0<=l>>>0)break c;e[t+4>>2]=r+m,n=(t=l+8&7?-8-l&7:0)+l|0,e[57158]=n,t=(r=e[57155]+m|0)-t|0,e[57155]=t,e[n+4>>2]=1|t,e[4+(r+l|0)>>2]=40,e[57159]=e[57274];break n}i=0;break t}n=0;break r}re[57156]>n>>>0&&(e[57156]=n),r=n+m|0,t=229056;c:{o:{f:{l:{i:{h:{for(;;){if((0|r)!=e[t>>2]){if(t=e[t+8>>2])continue;break h}break}if(!(8&d[t+12|0]))break i}for(t=229056;;){if((r=e[t>>2])>>>0<=l>>>0&&(h=r+e[t+4>>2]|0)>>>0>l>>>0)break l;t=e[t+8>>2]}}if(e[t>>2]=n,e[t+4>>2]=e[t+4>>2]+m,e[4+(y=(n+8&7?-8-n&7:0)+n|0)>>2]=3|w,t=(m=r+(r+8&7?-8-r&7:0)|0)-(M=w+y|0)|0,(0|l)==(0|m)){e[57158]=M,t=e[57155]+t|0,e[57155]=t,e[M+4>>2]=1|t;break o}if(e[57157]==(0|m)){e[57157]=M,t=e[57154]+t|0,e[57154]=t,e[M+4>>2]=1|t,e[t+M>>2]=t;break o}if((3&(h=e[m+4>>2]))==1){l=-8&h;i:if(h>>>0<=255){if(i=e[m+8>>2],r=h>>>3|0,(0|(n=e[m+12>>2]))==(0|i)){e[57152]=e[57152]&Ks(-2,r);break i}e[i+12>>2]=n,e[n+8>>2]=i}else{if(w=e[m+24>>2],(0|m)==(0|(n=e[m+12>>2])))if((r=e[(h=m+20|0)>>2])||(r=e[(h=m+16|0)>>2])){for(;i=h,(r=e[(h=(n=r)+20|0)>>2])||(h=n+16|0,r=e[n+16>>2]););e[i>>2]=0}else n=0;else r=e[m+8>>2],e[r+12>>2]=n,e[n+8>>2]=r;if(w){i=e[m+28>>2];h:{if(e[(r=228912+(i<<2)|0)>>2]==(0|m)){if(e[r>>2]=n,n)break h;e[57153]=e[57153]&Ks(-2,i);break i}if(e[w+(e[w+16>>2]==(0|m)?16:20)>>2]=n,!n)break i}e[n+24>>2]=w,(r=e[m+16>>2])&&(e[n+16>>2]=r,e[r+24>>2]=n),(r=e[m+20>>2])&&(e[n+20>>2]=r,e[r+24>>2]=n)}}h=e[4+(m=l+m|0)>>2],t=t+l|0}if(e[m+4>>2]=-2&h,e[M+4>>2]=1|t,e[t+M>>2]=t,t>>>0<=255){r=228648+(-8&t)|0,(n=e[57152])&(t=1<<(t>>>3))?t=e[r+8>>2]:(e[57152]=t|n,t=r),e[r+8>>2]=M,e[t+12>>2]=M,e[M+12>>2]=r,e[M+8>>2]=t;break o}if(h=31,t>>>0<=16777215&&(h=62+((t>>>38-(r=ce(t>>>8|0))&1)-(r<<1)|0)|0),e[M+28>>2]=h,e[M+16>>2]=0,e[M+20>>2]=0,r=228912+(h<<2)|0,(i=e[57153])&(n=1<<h)){for(h=t<<((0|h)!=31?25-(h>>>1|0):0),n=e[r>>2];;){if(r=n,(-8&e[n+4>>2])==(0|t))break f;if(n=h>>>29|0,h<<=1,!(n=e[16+(i=(4&n)+r|0)>>2]))break}e[i+16>>2]=M}else e[57153]=n|i,e[r>>2]=M;e[M+24>>2]=r,e[M+12>>2]=M,e[M+8>>2]=M;break o}for(r=(i=m-40|0)-(t=n+8&7?-8-n&7:0)|0,e[57155]=r,t=t+n|0,e[57158]=t,e[t+4>>2]=1|r,e[4+(n+i|0)>>2]=40,e[57159]=e[57274],e[(i=(t=(h+(h-39&7?39-h&7:0)|0)-47|0)>>>0<l+16>>>0?l:t)+4>>2]=27,t=e[57267],e[i+16>>2]=e[57266],e[i+20>>2]=t,t=e[57265],e[i+8>>2]=e[57264],e[i+12>>2]=t,e[57266]=i+8,e[57265]=m,e[57264]=n,e[57267]=0,t=i+24|0;e[t+4>>2]=7,r=t+8|0,t=t+4|0,r>>>0<h>>>0;);if((0|i)==(0|l))break n;if(e[i+4>>2]=-2&e[i+4>>2],h=i-l|0,e[l+4>>2]=1|h,e[i>>2]=h,h>>>0<=255){r=228648+(-8&h)|0,(n=e[57152])&(t=1<<(h>>>3))?t=e[r+8>>2]:(e[57152]=t|n,t=r),e[r+8>>2]=l,e[t+12>>2]=l,e[l+12>>2]=r,e[l+8>>2]=t;break n}if(t=31,h>>>0<=16777215&&(t=62+((h>>>38-(t=ce(h>>>8|0))&1)-(t<<1)|0)|0),e[l+28>>2]=t,e[l+16>>2]=0,e[l+20>>2]=0,r=228912+(t<<2)|0,(i=e[57153])&(n=1<<t)){for(t=h<<((0|t)!=31?25-(t>>>1|0):0),i=e[r>>2];;){if((0|h)==(-8&e[(r=i)+4>>2]))break c;if(n=t>>>29|0,t<<=1,!(i=e[16+(n=(4&n)+r|0)>>2]))break}e[n+16>>2]=l}else e[57153]=n|i,e[r>>2]=l;e[l+24>>2]=r,e[l+12>>2]=l,e[l+8>>2]=l;break n}t=e[r+8>>2],e[t+12>>2]=M,e[r+8>>2]=M,e[M+24>>2]=0,e[M+12>>2]=r,e[M+8>>2]=t}t=y+8|0;break e}t=e[r+8>>2],e[t+12>>2]=l,e[r+8>>2]=l,e[l+24>>2]=0,e[l+12>>2]=r,e[l+8>>2]=t}if(!((t=e[57155])>>>0<=w>>>0)){r=t-w|0,e[57155]=r,t=(n=e[57158])+w|0,e[57158]=t,e[t+4>>2]=1|r,e[n+4>>2]=3|w,t=n+8|0;break e}}e[56798]=48,t=0;break e}r:if(m){r=e[i+28>>2];a:{if(e[(t=228912+(r<<2)|0)>>2]==(0|i)){if(e[t>>2]=n,n)break a;y=Ks(-2,r)&y,e[57153]=y;break r}if(e[m+(e[m+16>>2]==(0|i)?16:20)>>2]=n,!n)break r}e[n+24>>2]=m,(t=e[i+16>>2])&&(e[n+16>>2]=t,e[t+24>>2]=n),(t=e[i+20>>2])&&(e[n+20>>2]=t,e[t+24>>2]=n)}r:if(h>>>0<=15)t=h+w|0,e[i+4>>2]=3|t,e[4+(t=t+i|0)>>2]=1|e[t+4>>2];else if(e[i+4>>2]=3|w,e[4+(l=i+w|0)>>2]=1|h,e[l+h>>2]=h,h>>>0<=255)r=228648+(-8&h)|0,(n=e[57152])&(t=1<<(h>>>3))?t=e[r+8>>2]:(e[57152]=t|n,t=r),e[r+8>>2]=l,e[t+12>>2]=l,e[l+12>>2]=r,e[l+8>>2]=t;else{t=31,h>>>0<=16777215&&(t=62+((h>>>38-(t=ce(h>>>8|0))&1)-(t<<1)|0)|0),e[l+28>>2]=t,e[l+16>>2]=0,e[l+20>>2]=0,r=228912+(t<<2)|0;a:{if((n=1<<t)&y){for(t=h<<((0|t)!=31?25-(t>>>1|0):0),w=e[r>>2];;){if((-8&e[(r=w)+4>>2])==(0|h))break a;if(n=t>>>29|0,t<<=1,!(w=e[16+(n=(4&n)+r|0)>>2]))break}e[n+16>>2]=l}else e[57153]=n|y,e[r>>2]=l;e[l+24>>2]=r,e[l+12>>2]=l,e[l+8>>2]=l;break r}t=e[r+8>>2],e[t+12>>2]=l,e[r+8>>2]=l,e[l+24>>2]=0,e[l+12>>2]=r,e[l+8>>2]=t}t=i+8|0;break e}t:if(M){r=e[n+28>>2];r:{if(e[(t=228912+(r<<2)|0)>>2]==(0|n)){if(e[t>>2]=i,i)break r;e[57153]=Ks(-2,r)&y;break t}if(e[M+(e[M+16>>2]==(0|n)?16:20)>>
28102]=i,!i)break t}e[i+24>>2]=M,(t=e[n+16>>2])&&(e[i+16>>2]=t,e[t+24>>2]=i),(t=e[n+20>>2])&&(e[i+20>>2]=t,e[t+24>>2]=i)}h>>>0<=15?(t=h+w|0,e[n+4>>2]=3|t,e[4+(t=t+n|0)>>2]=1|e[t+4>>2]):(e[n+4>>2]=3|w,e[4+(i=n+w|0)>>2]=1|h,e[i+h>>2]=h,P&&(r=228648+(-8&P)|0,l=e[57157],(t=1<<(P>>>3))&m?t=e[r+8>>2]:(e[57152]=t|m,t=r),e[r+8>>2]=l,e[t+12>>2]=l,e[l+12>>2]=r,e[l+8>>2]=t),e[57157]=i,e[57154]=h),t=n+8|0}return oe=A+16|0,0|t}function bA(t,A,r,n,i){var l,h=0,m=0,w=0,M=0,y=0,P=0,R=0,ee=0,ae=0,ue=0,be=0,_e=0,Ve=0,$e=0,it=0,ht=0,yt=0;oe=l=oe-544|0,IA(l+320|0,0,100),IA(l+208|0,0,100),M=r?e[r>>2]:M,ae=e[t+12>>2],y=e[36115];e:{t:{r:{a:{for(;;){if(h=(0|(h=d[A+m|0]))>=(0|y)?13:h,g[l+m|0]=h,!h){h=m;break a}if(w=(0|y)<=(0|(w=d[(h=1|m)+A|0]))?13:w,g[h+l|0]=w,!w)break a;if((0|(m=m+2|0))==200)break}m=198,be=d[l+199|0];break r}if(!h)break t;be=d[l+(m=h-1|0)|0],(0|h)!=1&&(m=h-2|0)}if(it=8&M,e[l+536>>2]=(it>>>3|0?3:7)&M,M=d[l+m|0],y=1,R=(0|(P=$a(t,l,l+432|0,l+540|0,l+536|0,1)))>=0?P:0,Ve=d[0|l])for(m=l,h=Ve;h=e[144464+((255&h)<<2)>>2],d[h+11|0]==2&&(1048576&(h=e[h+4>>2])||(w=(2097152&h)>>>21|0,w|=h=d[e[144464+(d[0|(ee=m+1|0)]<<2)>>2]+10|0]==12,g[(l+208|0)+y|0]=w,ue=e[144464+(d[(h?2:1)+m|0]<<2)>>2],w=d[ue+11|0]-10>>>0<4294967289|!(32&d[ue+6|0])&d[e[144464+(d[(h?3:2)+m|0]<<2)>>2]+11|0]==2?w:w?2:1,m=h?ee:m,g[(l+320|0)+y|0]=w,y=y+1|0)),h=d[0|(m=m+1|0)];);m=r=r?R:P;r:{a:{n:{c:{o:{f:{l:{i:{h:{b:{m:{C:switch(e[t+8>>2]-1|0){case 11:if((0|(r=e[l+540>>2]))<2)break h;if(m=1,R=1&(h=r-1|0),(0|r)!=2)break m;y=0;break b;case 8:if((0|(h=e[l+540>>2]))<2)break a;if(y=3&(w=h-1|0),m=1,h-2>>>0>=3)for(R=-4&w,w=0;M=g[0|(h=(l+432|0)+m|0)],g[0|h]=(0|M)<0?4:M,M=g[h+1|0],g[h+1|0]=(0|M)<0?4:M,M=g[h+2|0],g[h+2|0]=(0|M)<0?4:M,M=h,h=g[h+3|0],g[M+3|0]=(0|h)<0?4:h,m=m+4|0,(0|R)!=(0|(w=w+4|0)););if(!y)break a;for(h=0;w=g[0|(M=(l+432|0)+m|0)],g[0|M]=(0|w)<0?4:w,m=m+1|0,(0|y)!=(0|(h=h+1|0)););break a;case 7:if(!d[l+322|0]|g[l+321|0]>0)break a;case 0:if(e[l+536>>2]|e[l+540>>2]<3)break a;if(e[l+536>>2]=2,m=4,r)break r;g[l+434|0]=4;break r;case 1:if(e[l+536>>2])break r;p:{x:{if((0|(y=e[l+540>>2]))>=3){r=y-2|0,e[l+536>>2]=r,m=r;w:if(512&ae&&(h=e[144464+(be<<2)>>2],(0|(w=d[h+11|0]))!=2)){m=e[h>>2],h=l;B:{F:{if((0|(R=e[t+212>>2]))!=26977){if((0|R)!=24942)break F;switch(m-110|0){case 0:case 5:if(m=r,d[e[144464+(M<<2)>>2]+11|0]==2)break w}m=y-1|0;break B}if((0|m)==115&&(m=r,d[e[144464+(M<<2)>>2]+11|0]==2))break w;m=y-1|0;break B}F:{Y:{if((0|m)==115){if(m=r,(0|(w=d[e[144464+(M<<2)>>2]+11|0]))!=8)break Y;break w}if((0|w)!=8)break F;w=d[e[144464+(M<<2)>>2]+11|0]}if(m=r,(255&w)==2)break w}m=y-1|0}e[h+536>>2]=m}if(524288&ae&&(g[(h=y-1|0)+(y=l+208|0)|0]<=g[r+y|0]||(e[l+536>>2]=h,m=h)),d[(l+432|0)+m|0]>1){h=m;break p}if(h=2,r=m-1|0,m>>>0>=2)break x;e[l+536>>2]=m+1;break p}r=1}h=r,e[l+536>>2]=h}if(m=4,g[0|(r=(l+432|0)+h|0)]>=0||g[(h=(l+432|0)+h|0)-1|0]>=4&g[h+1|0]>3)break r;g[0|r]=4;break r;case 2:if(e[l+536>>2])break r;for(r=(m=e[l+540>>2])-1|0,r&=r>>31;;){if((0|(m=m-1|0))<=0)break n;if(!(g[0|(h=(l+432|0)+m|0)]>=0))break}e[l+536>>2]=m,m=4,g[0|h]=4;break r;case 3:if(e[l+536>>2])break a;if(h=(0|(m=e[l+540>>2]-3|0))<=1?1:m,e[l+536>>2]=h,m=4,r)break r;g[h+(l+432|0)|0]=4;break r;case 4:if(e[l+536>>2])break r;if(h=(r=e[l+540>>2])-3|0,e[l+536>>2]=h,(0|r)<=15){p:{switch(d[e[144464+(be<<2)>>2]+11|0]-2|0){case 0:h=g[r+94176|0];break p;case 2:h=g[r+94192|0];break p}h=g[r+94160|0]}e[l+536>>2]=h}m=4,g[(l+432|0)+h|0]=4;break r;case 5:if(e[l+536>>2])break r;if(y=-1,w=0,!((0|(r=(h=e[l+540>>2])-1|0))<2)){if(m=1,ee=1&h,(0|h)!=3)for(ue=(-2&h)-4|0,h=0;g[(l+432|0)+m|0]<0&&(w=(M=(0|(R=g[(l+320|0)+m|0]))<(0|y))?w:m,y=M?y:R),g[(M=m+1|0)+(l+432|0)|0]<0&&(w=(R=(0|($e=g[M+(l+320|0)|0]))<(0|y))?w:M,y=R?y:$e),m=m+2|0,M=(0|h)!=(0|ue),h=h+2|0,M;);!ee|g[(l+432|0)+m|0]>=0||(y=(h=(0|(M=g[(l+320|0)+m|0]))<(0|y))?y:M,w=h?w:m)}e[l+536>>2]=w,d[r+(l+320|0)|0]!=2|(0|y)>1?(0|y)>0||(w=1,e[l+536>>2]=1):(e[l+536>>2]=r,w=r),m=4,g[(l+432|0)+w|0]=4;break r;case 14:break c;case 12:break o;case 6:break C;default:break r}if(e[l+536>>2])break r;y=(r=e[l+540>>2])-1|0,e[l+536>>2]=y;C:if(!((0|r)<2))for(m=1;;){if(d[(l+432|0)+m|0]==1){y=m-1|0,e[l+536>>2]=y;break C}if((0|r)==(0|(m=m+1|0)))break}m=4,g[(l+432|0)+y|0]=4;break r}for(ee=-2&h,y=0,w=0;h=d[0|(ue=(M=l+432|0)+m|0)],_e=ue,$e=g[(ue=l+208|0)+m|0]>0,g[0|_e]=$e||(0|h)==4?3:h,ht=(0|(M=d[0|(_e=(h=m+1|0)+M|0)]))==4?3:M,M=g[h+ue|0]>0,g[0|_e]=M?3:ht,y=M?h:$e?m:y,m=m+2|0,(0|ee)!=(0|(w=w+2|0)););}if(R&&(M=(0|(h=d[0|(w=(l+432|0)+m|0)]))==4?3:h,h=g[(l+208|0)+m|0]>0,g[0|w]=h?3:M,y=h?m:y),h=e[l+536>>2])break f;if((0|y)>0){e[l+536>>2]=y,h=y;break f}if((0|r)<6)break i;h=r-3|0;break l}if(h=e[l+536>>2])break f}h=r-1|0}e[l+536>>2]=h}m=4,g[(l+432|0)+h|0]=4;break r}if(e[l+536>>2])break r;h=1,e[l+536>>
28102]=1,d[l+209|0]|e[l+540>>2]<3|g[l+210|0]<=0||(h=2,e[l+536>>2]=2),m=4,g[l+432|h]=4;break r}if(e[l+536>>2]||(0|(h=e[l+540>>2]))<3)break a;if(IA(l+432|1,0,h-1|0),e[l+536>>2]=2,r||(g[l+434|0]=4),m=4,h>>>0<4)break r;g[431+(h+l|0)|0]=3;break r}e[l+536>>2]=r,m=4;break r}m=r}!(256&ae)|2&i||(0|(r=e[l+540>>2]))<3|(0|P)>2||d[0|(h=(r=r+(l+432|0)|0)-1|0)]!=4|d[e[144464+(be<<2)>>2]+11|0]!=2||(g[0|h]=1,g[r-2|0]=4);r:{a:{if(it)P=e[l+540>>2];else{if(r=g[l+433|0],!(!(4096&ae)|(0|(P=e[l+540>>2]))!=3)){if((0|r)==4){g[l+434|0]=3;break a}if(d[l+434|0]==4){g[l+433|0]=3;break a}}if(!(!(8192&ae)|(0|r)>=0|(0|P)<4|g[l+434|0]<4)){g[l+433|0]=3;break a}}if(w=0,(0|P)<2)break r}for(y=(0|m)<4?4:3,ue=128&ae,$e=64&ae,_e=32&ae,R=P-1|0,ht=16&ae,yt=!(32768&ae),be=0,M=0,m=1;;){a:{n:if((0|(h=g[0|(ee=(l+432|0)+m|0)]))>=0)w=y;else{w=3;c:{o:if(!(!(!ht|(0|y)>3)&(0|m)==(0|R))){if(!(1&(be|yt)))break c;if(!(g[431+(l+m|0)|0]>1)){if((0|(h=g[(r=m+1|0)+(l+432|0)|0]))>=2){if((0|y)!=4||(w=4,h>>>0>=3))break o}else if(!(!_e|(0|y)!=3)){y=3;break a}if(!$e|m>>>0<2||(w=d[(l+320|0)+m|0]))break c;if((0|R)>(0|(h=m))){for(;;){if(g[(l+320|0)+h|0]>0)break a;if((0|R)==(0|(h=h+1|0)))break}if(w)break c}if(g[r+(l+320|0)|0]<=0)break c;break a}w=y}h=d[0|ee];break n}g[0|ee]=y,be=1,w=3,h=y}n:{if(h<<24>>24>=4){if(r=M||m,!M|!ue)break n;g[0|ee]=3}y=w;break a}y=w,M=r}if(w=1,(0|P)==(0|(m=m+1|0)))break}}if(n=!it|(0|n)>=0?n:e[((0|P)<3?16:20)+t>>2],y=0,h=0,w){if(be=3&(r=P-1|0),w=0,P-2>>>0<3)m=1;else for(it=-4&r,m=1,M=0;h=(r=(0|(h=(ee=(0|(h=(R=(0|(h=(P=(0|(R=g[(r=l+432|0)+m|0]))<(0|h))?h:R))>(0|(ee=g[(ue=m+1|0)+r|0])))?h:ee))>(0|(_e=g[($e=m+2|0)+r|0])))?h:_e))>(0|(ht=g[(_e=m+3|0)+r|0])))?h:ht,y=r?ee?R?P?y:m:ue:$e:_e,m=m+4|0,(0|it)!=(0|(M=M+4|0)););if(be)for(;h=(r=(0|(M=g[(l+432|0)+m|0]))<(0|h))?h:M,y=r?y:m,m=m+1|0,(0|be)!=(0|(w=w+1|0)););}if((0|n)<0?n=h:(0|n)<=(0|h)&(0|h)>4||(g[(l+432|0)+y|0]=n),be=A+197|0,P=1,!(1&i)&&(r=e[144464+(Ve<<2)>>2])){if(h=l,!((0|(w=d[r+11|0]))!=1&(0|Ve)!=15))for(;r=d[0|(h=h+1|0)],(0|(w=d[e[144464+(r<<2)>>2]+11|0]))==1|(0|r)==15;);!(48&(r=e[t+4>>2]))|(0|w)!=2||(g[0|A]=(32&r)>>>5|0&&g[l+433|0]>3?11:23,A=A+1|0)}r:if(!(A>>>0>=be>>>0))for(R=65536&ae,ee=2&ae,it=4&ae,m=l;;){if(!(h=d[0|m]))break r;if(i=m,m=m+1|0,r=e[144464+(h<<2)>>2]){a:{n:{switch(d[r+11|0]){case 0:e[t+8200>>2]=0;break a;case 2:if(!(16&d[r+6|0]))break n}if(d[0|m]!=20)break a}if((0|(Ve=e[l+540>>2]))<(0|P))break e;M=g[0|(ae=(l+432|0)+P|0)],e[t+8200>>2]=M;n:{c:{if(!((0|(r=M))>1)){if(w=Ve-1|0,!(!it|(0|P)<2|(0|n)<2)&&(r=0,(0|w)==(0|P)))break c;if(r=1,!((0|P)==1|ee|(Ve-2|0)==(0|P)&g[w+(l+432|0)|0]<2|(0|w)==(0|P)||g[431+(l+P|0)|0]>=0&&(r=M,R))){r=0,g[0|ae]=0;break c}}if(r&&(0|r)<2)break n}g[0|A]=d[r+94151|0],A=A+1|0,M=g[0|ae]}w=(0|n)>(0|M),d[0|m]==12&&1&(Ve=e[t+28>>2])&&(m=(16&Ve?(0|y)!=(0|P):(0|r)<4)?i+2|0:m),n=w?n:M,P=P+1|0}if((0|h)!=1&&(g[0|A]=h,A=A+1|0),!(A>>>0<be>>>0))break}}g[0|A]=0}return void(oe=l+544|0)}me(86136,86634,1353,94208),W()}function vA(t,A,r,n,i,l,h,m,w){var M,y=0,P=0,R=0,ee=0,ae=0,ue=0,be=0,_e=0,Ve=0,$e=0,it=0,ht=0,yt=0,Pt=0,Wt=0,cA=0,gA=0,SA=0,VA=0,qA=0,Cr=0,Yr=0,Xr=0,ls=0,as=0,xs=0,Ms=0,zs=0,Os=0,ga=0;oe=M=oe-336|0,ee=m,ue=65535&w,ae=n,R=65535&i,it=-2147483648&(i^w);e:{if(!((Ve=w>>>16&32767)-32767>>>0>4294934529&(be=i>>>16&32767)-32767>>>0>=4294934530)){if(!(!n&(0|(y=2147483647&i))==2147418112?!(A|r):y>>>0<2147418112)){_e=n,it=32768|i;break e}if(!(!m&(0|(i=2147483647&w))==2147418112?!(l|h):i>>>0<2147418112)){_e=m,it=32768|w,A=l,r=h;break e}if(!(A|n|2147418112^y|r)){if(!(l|m|2147418112^i|h)){A=0,r=0,it=2147450880;break e}it|=2147418112,A=0,r=0;break e}if(!(l|m|2147418112^i|h)){A=0,r=0;break e}if(!(A|n|r|y)){_e=(A=!(l|m|i|h))?0:_e,it=A?2147450880:it,A=0,r=0;break e}if(!(l|m|i|h)){it|=2147418112,A=0,r=0;break e}(0|y)==65535|y>>>0<65535&&(w=n=!(R|ae),y=n?A:ae,m=n<<=6,ea(M+320|0,A,r,ae,R,(n=n+((0|(w=ce(w?r:R)))==32?ce(y)+32|0:w)|0)-15|0),ht=16-n|0,ae=e[M+328>>2],R=e[M+332>>2],r=e[M+324>>2],A=e[M+320>>2]),i>>>0>65535||(m=n=!(ee|ue),w=n?l:ee,i=n<<=6,ea(M+304|0,l,h,ee,ue,(n=n+((0|(m=ce(m?h:ue)))==32?ce(w)+32|0:m)|0)-15|0),ht=(n+ht|0)-16|0,ee=e[M+312>>2],ue=e[M+316>>2],l=e[M+304>>2],h=e[M+308>>2])}if(gA=i=65536|ue,SA=ee,y=i<<15|(n=ee)>>>17,Ns(M+288|0,n=i=n<<15|h>>>17,m=y,0,0,w=0-n|0,y=1963258675-(y+!!(0|n)|0)|0,0,0),Ns(M+272|0,0-(n=e[M+296>>2])|0,0-(e[M+300>>2]+!!(0|n)|0)|0,0,0,w,y,0,0),Ns(M+256|0,w=(n=e[M+280>>2])<<1|e[M+276>>2]>>>31,n=e[M+284>>2]<<1|n>>>31,0,0,i,m,0,0),Ns(M+240|0,w,n,0,0,0-(y=e[M+264>>2])|0,0-(e[M+268>>2]+!!(0|y)|0)|0,0,0),Ns(M+224|0,y=(w=e[M+248>>2])<<1|e[M+244>>2]>>>31,n=e[M+252>>2]<<1|w>>>31,0,0,i,m,0,0),Ns(M+208|0,y,n,0,0,0-(w=e[M+232>>2])|0,0-(e[M+236>>2]+!!(0|w)|0)|0,0,0),Ns(M+192|0,w=(n=e[M+216>>2])<<1|e[M+212>>2]>>>31,n=e[M+220>>2]<<1|n>>>31,0,0,i,m,0,0),Ns(M+176|0,w,n,0,0,0-(y=e[M+200>>2])|0,0-(e[M+204>>2]+!!(0|y)|0)|0,0,0),Ns(M+160|0,w=i,n=m,0,0,m=(ee=(i=e[M+184>>2])<<1|e[M+180>>2]>>>31)-1|0,i=(e[M+188>>2]<<1|i>>>31)-!ee|0,0,0),Ns(M+144|0,l<<15,h<<15|l>>>17,0,0,n=m,i,0,0),$e=M+112|0,Wt=e[M+168>>2],m=e[M+172>>2],P=(ee=e[M+160>>2])+(w=e[M+152>>2])|0,y=(ue=e[M+164>>2])+e[M+156>>2]|0,w=y=w>>>0>P>>>0?y+1|0:y,y=(y=(0|ue)==(0|y)&P>>>0<ee>>>0|y>>>0<ue>>>0)>>>0>(ue=y+Wt|0)>>>0?m+1|0:m,Ns($e,n,i,0,0,0-(m=(ee=!w&P>>>0>1|!!(0|w))+ue|0)|0,0-(!!(0|m)+(y=ee>>>0>m>>>0?y+1|0:y)|0)|0,0,0),Ns(M+128|0,1-P|0,0-((P>>>0>1)+w|0)|0,0,0,n,i,0,0),ls=(be-Ve|0)+ht|0,$e=i=e[M+116>>2],ee=(n=e[M+112>>2])<<1,be=y=i<<1|n>>>31,n=y,cA=m=e[M+140>>2],n=n+(y=m<<1|(i=e[M+136>>2])>>>31)|0,i=n=(m=(w=i<<1|e[M+132>>2]>>>31)+ee|0)>>>0<w>>>0?n+1|0:n,Pt=n=n-(m>>>0<13927)|0,Wt=n,as=y=65536|R,xs=ae,zs=(n=ae)<<1,Os=y=y<<1|n>>>
vendor: 5,592 bytes, line 2810
281031,qA=y,Cr=n=rr(Pt,P=0,y,0),yt=y=fe,ht=A<<1,Ve=n=r<<1|A>>>31,ue=y=0,Pt=(0|i)==(0|Pt)&(w=m-13927|0)>>>0<m>>>0|i>>>0>Pt>>>0,i=(0|i)==(0|be)&m>>>0<ee>>>0|i>>>0<be>>>0,n=e[M+120>>2],y=m=e[M+124>>2]<<1|n>>>31,y=(n=(P=cA>>>31|0)+(n=n<<1|$e>>>31)|0)>>>0<P>>>0?y+1|0:y,P=(m=n)>>>0>(n=n+i|0)>>>0?y+1|0:y,P=(i=n)>>>0>(n=n+Pt|0)>>>0?P+1|0:P,i=n-1|0,y=rr(Ve,ue,Pt=P-!n|0,be=0),m=fe+yt|0,$e=(0|yt)==(0|(m=(n=y+Cr|0)>>>0<y>>>0?m+1|0:m))&n>>>0<Cr>>>0|m>>>0<yt>>>0,cA=i,i=rr(i,y=0,VA=(Ms=r>>>31|0)|ae<<1,yt=0),y=fe+m|0,P=0,ee=y=i>>>0>(ae=i+n|0)>>>0?y+1|0:y,P=(i=n=(0|y)==(0|m)&n>>>0>ae>>>0|m>>>0>y>>>0)>>>0>(n=n+$e|0)>>>0?1:P,i=rr(qA,ue,Pt,be),y=fe+P|0,$e=n=i+n|0,n=n>>>0<i>>>0?y+1|0:y,i=rr(qA,ue,cA,yt),R=fe,m=i,i=rr(VA,yt,Pt,be),y=fe+R|0,i=y=i>>>0>(P=m+i|0)>>>0?y+1|0:y,n=n+(y=(0|R)==(0|y)&m>>>0>P>>>0|y>>>0<R>>>0)|0,$e=R=$e+i|0,R=n=R>>>0<i>>>0?n+1|0:n,y=P+ee|0,i=y=(n=(i=0)+ae|0)>>>0<i>>>0?y+1|0:y,m=(0|y)==(0|ee)&n>>>0<ae>>>0|y>>>0<ee>>>0,y=R,P=m,Yr=m=m+$e|0,P=y=P>>>0>m>>>0?y+1|0:y,$e=n,R=n,ae=i,Cr=w,n=rr(w,0,VA,yt),m=fe,i=n,w=rr(Wt,_e,Ve,_e),y=fe+m|0,w=(0|m)==(0|(y=(n=n+w|0)>>>0<w>>>0?y+1|0:y))&n>>>0<i>>>0|m>>>0>y>>>0,m=y,i=rr(cA,yt,Xr=-2&ht,0),y=fe+y|0,i=y=i>>>0>(ee=i+n|0)>>>0?y+1|0:y,n=(0|y)==(0|m)&n>>>0>ee>>>0|m>>>0>y>>>0,m=0,n=((w=n+w|0)>>>0<n>>>0?1:m)+ae|0,y=P,w=n=(R=w+R|0)>>>0<w>>>0?n+1|0:n,m=n=(0|n)==(0|ae)&R>>>0<$e>>>0|n>>>0<ae>>>0,ga=n=n+Yr|0,$e=y=m>>>0>n>>>0?y+1|0:y,n=rr(qA,ue,Cr,_e),Yr=fe,qA=n,m=rr(Pt,be,Xr,_e),y=fe+Yr|0,ae=n=n+m|0,P=n+(ue=rr(Wt,_e,VA,yt))|0,n=(m=n>>>0<m>>>0?y+1|0:y)+fe|0,n=P>>>0<ue>>>0?n+1|0:n,be=P,ue=P+(y=rr(Ve,_e,cA,yt))|0,P=fe+n|0,VA=(0|n)==(0|(P=y>>>0>ue>>>0?P+1|0:P))&ue>>>0<be>>>0|n>>>0>P>>>0,y=((n=(n=(n=(0|n)==(0|m)&ae>>>0>be>>>0|n>>>0<m>>>0)+(y=(0|m)==(0|Yr)&ae>>>0<qA>>>0|m>>>0<Yr>>>0)|0)+VA|0)|(cA=0))+w|0,ae=y=(m=P)>>>0>(be=m+R|0)>>>0?y+1|0:y,n=(0|w)==(0|y)&R>>>0>be>>>0|w>>>0>y>>>0,y=$e,m=n,cA=n=n+ga|0,w=y=m>>>0>n>>>0?y+1|0:y,n=rr(Wt,_e,Xr,_e),Wt=fe,R=n,m=rr(Ve,_e,Cr,_e),y=fe+Wt|0,m=(0|(y=(n=n+m|0)>>>0<m>>>0?y+1|0:y))==(0|Wt)&n>>>0<R>>>0|y>>>0<Wt>>>0,R=y,n=y+ee|0,y=(m|($e=0))+i|0,R=(0|i)==(0|(y=n>>>0<R>>>0?y+1|0:y))&n>>>0<ee>>>0|i>>>0>y>>>0,P=(m=y)+(y=ue)|0,y=0,y=((i=n=(0|m)==(0|(P=(ee=(ue=0)+n|0)>>>0<ue>>>0?P+1|0:P))&n>>>0>ee>>>0|m>>>0>P>>>0)>>>0>(n=n+R|0)>>>0?1:y)+ae|0,P=w,i=y=(i=n)>>>0>(n=n+be|0)>>>0?y+1|0:y,w=P=(w=m=(0|ae)==(0|y)&n>>>0<be>>>0|y>>>0<ae>>>0)>>>0>(m=m+cA|0)>>>0?P+1|0:P,(0|P)==131071|P>>>0<131071?(xs=zs|Ms,as=yt|Os,Ns(M+80|0,n,i,m,w,l,h,SA,gA),ue=P=e[M+84>>2],y=A<<17,ae=(r=(ee=0)-(R=e[M+88>>2])|0)-(P=!!(P|(A=e[M+80>>2])))|0,R=(y-(e[M+92>>2]+(R>>>0>ee>>>0)|0)|0)-(r>>>0<P>>>0)|0,ee=0-A|0,ue=0-(!!(0|A)+ue|0)|0,A=ls+16382|0):(Ns(M+96|0,n=(1&i)<<31|n>>>1,i=m<<31|i>>>1,m=(1&w)<<31|m>>>1,w=w>>>1|0,l,h,SA,gA),Ve=ae=e[M+100>>2],ae=(ee=0-(ht=e[M+104>>2])|0)-(R=!!(ae|(P=e[M+96>>2])))|0,R=((A<<16)-(e[M+108>>2]+(ue>>>0<ht>>>0)|0)|0)-(R>>>0>ee>>>0)|0,ee=0-P|0,ue=0-(!!(0|P)+Ve|0)|0,ht=A,Ve=r,A=ls+16383|0),(0|A)>=32767)it|=2147418112,A=0,r=0;else{if((0|A)>0)P=R<<1|ae>>>31,ae=ae<<1|ue>>>31,R=P,ht=m,Ve=65535&w|A<<16,P=ue<<1|ee>>>31,w=ee<<1;else{if((0|A)<=-113){A=0,r=0;break e}Da(M- -64|0,n,i,m,w,1-A|0),ea(M+48|0,ht,Ve,xs,as,A+112|0),Ns(M+32|0,l,h,SA,gA,n=e[M+64>>2],i=e[M+68>>2],ht=e[M+72>>2],Ve=e[M+76>>2]),A=e[M+40>>2],ee=(r=e[M+56>>2])-(ae=A<<1|(P=e[M+36>>2])>>>31)|0,R=e[M+60>>2]-((e[M+44>>2]<<1|A>>>31)+(r>>>0<ae>>>0)|0)|0,y=(A=e[M+32>>2])<<1,ae=ee-(A=(0|(m=P<<1|A>>>31))==(0|(w=e[M+52>>2]))&y>>>0>(r=e[M+48>>2])>>>0|m>>>0>w>>>0)|0,R=R-(A>>>0>ee>>>0)|0,P=w-((r>>>0<y>>>0)+m|0)|0,w=r-y|0}A=w,Ns(M+16|0,l,h,SA,gA,3,0,0,0),Ns(M,l,h,SA,gA,5,0,0,0),m=P+(r=0)|0,m=w>>>0>(A=A+(y=1&n)|0)>>>0?m+1|0:m,w=A,h=(0|h)==(0|m)&A>>>0>l>>>0|h>>>0<m>>>0,P=R,A=(0|r)==(0|m)&A>>>0<y>>>0|r>>>0>m>>>0,y=i,r=y=(r=A=(A=(0|(P=A>>>0>(l=A+ae|0)>>>0?P+1|0:P))==(0|gA))&(0|l)==(0|SA)?h:A&l>>>0>SA>>>0|P>>>0>gA>>>0)>>>0>(A=A+n|0)>>>0?y+1|0:y,n=(0|i)==(0|y)&A>>>0<n>>>0|i>>>0>y>>>0,y=Ve,y=(i=n)>>>0>(n=n+ht|0)>>>0?y+1|0:y,h=n,R=(0|(i=e[M+20>>2]))==(0|m)&re[M+16>>2]<w>>>0|i>>>0<m>>>0,i=e[M+28>>2],i=y>>>0<2147418112&((0|(n=e[M+24>>2]))==(0|l)&(0|i)==(0|P)?R:(0|i)==(0|P)&n>>>0<l>>>0|i>>>0<P>>>0),n=r,y=(r=A=(0|r)==(0|(n=(R=i)>>>0>(i=A+i|0)>>>0?n+1|0:n))&A>>>0>i>>>0|r>>>0>n>>>0)>>>0>(A=A+h|0)>>>0?y+1|0:y,h=A,m=(0|(r=e[M+4>>2]))==(0|m)&re[M>>2]<w>>>0|r>>>0<m>>>0,r=e[M+12>>2],r=P=(r=A=y>>>0<2147418112&((0|(A=e[M+8>>2]))==(0|l)&(0|r)==(0|P)?m:(0|r)==(0|P)&A>>>0<l>>>0|r>>>0<P>>>0))>>>0>(A=A+i|0)>>>0?n+1|0:n,i=(0|n)==(0|P)&A>>>0<i>>>0|n>>>0>P>>>0,n=y,l=i,_e|=i=i+h|0,it|=n=l>>>0>i>>>0?n+1|0:n}}e[t>>2]=A,e[t+4>>2]=r,e[t+8>>2]=_e,e[t+12>>2]=it,oe=M+336|0}function Gt(t,A,r,n,i){var l,h,m,w=0,M=0,y=0,P=0,R=0,ee=0,ae=0,ue=0,be=0,_e=0,Ve=0,$e=0,it=0,ht=0,yt=0,Pt=0,Wt=0,cA=0,gA=0,SA=0,VA=0;if(oe=h=oe-80|0,m=e[r+8>>2],!i|!_[r+4>>1]||(e[i+8>>2]=0),l=IA(n,0,152),e[l+44>>2]=d[m+14|0],e[l+40>>2]=d[m+15|0],n=_[m+8>>1]){for(n=e[34459]+(n<<1)|0,_e=256&A,Ve=r+32|0,ht=r-32|0,yt=r- -64|0,Pt=r+96|0,Wt=r+-64|0,cA=r-28|0,gA=1&A,SA=r-24|0,$e=e[30450];;){w=15&(R=(A=_[n>>1])>>>8|0);e:{t:{r:{a:{n:{c:{o:{f:{l:{i:{h:{b:switch(0|(y=A>>>12|0)){case 10:break o;case 9:break f;case 6:break l;case 2:case 3:break i;case 1:break h;case 0:break b;case 11:case 12:case 13:case 14:case 15:break c;default:break n}M=255&A;b:{m:{C:switch(0|w){case 13:if(M)break m;w=n,A=0;break b;case 0:switch(w=n,M-1|0){case 1:break e;case 0:break t}e[h+20>>2]=n0(h+75|0,e[m>>2]),e[h+16>>2]=A,ss($e,85851,h+16|0);break e;case 5:if(d[e[144464+(d[r+34|0]<<2)>>2]+11|0]!=2)break e;e[l+20>>2]=M;break e;case 12:break C;default:break a}e[l+44>>2]=e[l+44>>2]+(A<<24>>31&-256|M);break e}g[l+132|0]=d[n+3|0],w=n+2|0,g[l+133|0]=d[0|w],A=2,M>>>0<3||(g[l+134|0]=d[n+5|0],w=n+4|0,g[l+135|0]=d[0|w],A=4,M>>>
28100<5||(g[l+136|0]=d[n+7|0],w=n+6|0,g[l+137|0]=d[0|w],A=6,M>>>0<7||(g[l+138|0]=d[n+9|0],w=n+8|0,g[l+139|0]=d[0|w],A=8,M>>>0<9||(g[l+140|0]=d[n+11|0],w=n+10|0,g[l+141|0]=d[0|w],A=10,M>>>0<11||(g[l+142|0]=d[n+13|0],w=n+12|0,g[l+143|0]=d[0|w],A=12,M>>>0<13||(g[l+144|0]=d[n+15|0],w=n+14|0,g[l+145|0]=d[0|w],A=14,M>>>0<15||(g[l+146|0]=d[n+17|0],w=n+16|0,g[l+147|0]=d[0|w],A=16)))))))}g[132+(A+l|0)|0]=0,M=ae;break t}if(!t|w>>>0>7||(y=r,d[e[144464+(d[r+2|0]<<2)>>2]+11|0]!=2&&(y=Ve,d[e[144464+(d[r+34|0]<<2)>>2]+11|0]!=2))||!(1&(w=e[t+56>>2]))&&16&d[0|r])break e;M=15&d[y+3|0],M=2&w&&d[y+6|0]<=M>>>0?4:M;h:{b:{m:{C:switch((w=7&R)-3|0){case 1:break b;case 0:break C;default:break m}if(M>>>0>3)break h;break e}if(e[102832+(w<<2)>>2]>(0|M))break h;break e}if(d[y+6|0]>M>>>0)break e}e[l+8>>2]=255&A,M=1;break r}if((57344&A)==8192){for(VA=e[32972],R=1,it=0;;){P=255&A,w=(ee=4095&A)>>>8|0;i:if(ee>>>0<=3583){(0|(M=(w>>>0)%7|0))==6&&(M=_[n+2>>1]),be=0,w=r;h:{b:{m:switch(0|M){case 6:if(y=0,_[r+36>>1]|_[r+68>>1])break i;case 3:w=yt;break b;case 9:if(y=0,_[r+36>>1]|_[r+68>>1])break i;if(w=Pt,!_[r+100>>1])break h;break i;case 7:if(y=0,_[r+36>>1])break i;for(M=1;;){if(d[e[144464+(d[2+(w=(M<<5)+r|0)|0]<<2)>>2]+11|0]==2)break b;if(_[4+(((M=M+1|0)<<5)+r|0)>>1])break}break i;case 5:if(y=0,_[r+4>>1])break i;case 0:be=1,w=ht;break b;case 4:if(y=0,_[r+36>>1])break i;case 2:w=Ve;break b;case 8:if(y=0,!i)break i;if(be=1,e[(w=i)+8>>2])break h;break i;case 10:break m;default:break b}if(y=0,_[r+4>>1]|_[cA>>1])break i;be=1,w=Wt;break h}b:switch(0|M){case 0:case 5:break b;default:break h}w=(d[w+2|0]==1?-32:0)+w|0}if(_e?(M=e[144464+(d[w+2|0]<<2)>>2],e[w+8>>2]=M):M=e[w+8>>2],ee>>>0<=1791){if(y=1,e[e[144464+(P<<2)>>2]>>2]==e[M>>2])break i;if(!(!be|d[M+11|0]!=2)){y=(0|P)==d[M+13|0];break i}y=(0|P)==d[M+12|0];break i}P=31&ee,y=0;h:switch(ee>>>5&7){case 0:y=(0|P)==d[M+11|0];break i;case 1:y=(0|P)==(15&_[M+6>>1]);break i;case 2:y=e[M+4>>2]>>>P&1;break i;case 4:break h;default:break i}h:switch(0|P){case 0:case 1:case 2:case 3:case 4:if(d[e[144464+(d[w+2|0]<<2)>>2]+11|0]!=2){if(d[e[144464+(d[w+34|0]<<2)>>2]+11|0]!=2)break i;w=w+32|0}M=15&d[w+3|0],M=!t|!(2&d[t+56|0])?M:d[w+6|0]<=M>>>0?4:M;b:{switch(P-3|0){case 1:y=d[w+6|0]<=M>>>0;break i;case 0:if(y=1,M>>>0<=3)break b;break i}if(y=1,e[102832+(P<<2)>>2]>(0|M))break i}y=0;break i;case 17:if(!d[M+11|0]){y=1;break i}y=(32&d[r+1|0])>>>5|0;break i;case 18:y=_[w+4>>1]!=0;break i;case 19:if(y=1,_[w+36>>1])break i;y=!d[e[w+40>>2]+11|0];break i;case 9:if(_[w+4>>1])break i;for(;;){if(y=!!(0|(M=12&d[w-29|0])),M)break i;if(_[4+(w=w-32|0)>>1])break}break i;case 10:y=d[M+11|0]!=2;break i;case 11:for(;;){if(y=!!(0|(M=_[w+36>>1])),M)break i;if(M=w,w=w+32|0,d[e[M+40>>2]+11|0]==2)break}break i;case 12:if(y=1,(254&d[M+11|0])==2)break i;y=(16&d[M+4|0])>>>4|0;break i;case 13:for(;y=(d[e[w+8>>2]+11|0]==2)+y|0,M=_[w+4>>1],w=w-32|0,!M;);y=(0|y)==1;break i;case 14:for(;y=(d[e[w+8>>2]+11|0]==2)+y|0,M=_[w+4>>1],w=w-32|0,!M;);y=(0|y)==2;break i;case 16:break h;default:break i}y=(16&d[0|w])>>>4|0}else if(y=0,(0|w)==15){h:switch(P-1|0){case 0:y=gA;break i;case 1:break h;default:break i}y=e[VA+132>>2]!=0}i:if(1970>>>(w=(M=65535&A)>>>12|0)&1)w=g[w+102848|0];else{switch(0|w){case 0:if(w=1,(3840&M)!=3328)break i;w=1+(1+(255&M)>>>1|0)|0;break i;case 6:w=(M>>>9&7)-5>>>0<2?12:1;break i;case 2:case 3:w=(0|(w=3840&M))==3328||(0|w)==1536?2:1;break i}w=4,(M=_[n+4>>1])>>>0>61439||(w=(0|M)==2?3:2)}if(n=((w=_[(n=(w<<1)+n|0)>>1]==3)<<1)+n|0,w^=y,R=it?w|R:w&R,it=4096&A,(57344&(A=_[n>>1]))!=8192)break}if(!(1&R))if((63488&A)!=26624){i:if(1970>>>(w=A>>>12|0)&1)w=g[w+102848|0];else{switch(0|w){case 0:if(w=1,(3840&A)!=3328)break i;w=1+(1+(255&A)>>>1|0)|0;break i;case 6:w=(A>>>9&7)-5>>>0<2?12:1;break i;case 2:case 3:w=(0|(A&=3840))==3328||(0|A)==1536?2:1;break i}w=4,(A=_[n+4>>1])>>>0>61439||(w=(0|A)==2?3:2)}n=(((65024&_[(A=(w<<1)+n|0)>>1])==24576)<<1)+A|0}else n=((255&A)<<1)+n|0}w=n-2|0,M=ae;break t}l:switch(w>>>1|0){case 0:n=(((255&A)<<1)+n|0)-2|0;break e;case 5:e[l>>2]=2|e[l>>2],((A=d[e[r+40>>2]+12|0])-28&255)>>>0<=5&&(w=_[4+(A=((A<<2)+n|0)-112|0)>>1],A=_[A+2>>1],e[l+96>>2]=A>>>4<<24>>24,e[l+76>>2]=(15&A)<<18|w<<2),n=n+24|0;break e;case 6:break l;default:break e}((A=d[e[SA>>2]+13|0])-28&255)>>>0<=5&&(w=_[4+(A=((A<<2)+n|0)-112|0)>>1],A=_[A+2>>1],e[l+100>>2]=A>>>4<<24>>24,e[l+80>>2]=(15&A)<<18|w<<2),n=n+24|0;break e}A=_[(n=n+2|0)>>1]|A<<16&983040;f:switch(w-1|0){case 0:if((0|ue)>9)break e;e[(h+32|0)+(ue<<2)>>2]=n,n=(e[34459]+(A<<1)|0)-2|0,ue=ue+1|0;break e;case 1:e[l+124>>2]=A;break e;case 2:break f;default:break e}e[l+128>>2]=A;break e}e[108+(w=(((0|w)!=1)<<3)+l|0)>>2]=_[n+2>>1]|(255&A)<<16,A=_[n+4>>1]<<16,n=n+6|0,e[w+112>>2]=A|_[n>>1];break e}if(M=_[(w=n+2|0)>>1],ee=A>>>4|0,e[88+(R=((P=y-11|0)<<2)+l|0)>>2]=255&ee,e[R+68>>2]=A<<18&3932160|M<<2,(0|(n=_[n+4>>1]))==2){n=w;break e}if(M=A>>>0<=53247?n>>>0>61439?2:1:ae-((0|P)==4)|0,y-13>>>0>1)break t;e[R+88>>2]=ee<<24>>24;break t}e[h+4>>2]=n0(h+75|0,e[m>>2]),e[h>>2]=A,ss($e,85851,h);break e}e[4+((w<<2)+l|0)>>2]=M,M=_e&&(0|w)==1?1:ae}w=n}(0|M)!=1|(0|ue)<=0?(n=w,ae=M):(n=e[(h+32|0)+((ue=ue-1|0)<<2)>>2],ae=0)}if(n=n+2|0,(0|ae)==1)break}!i|d[r+17|0]!=2||(t=e[r+4>>2],e[i>>2]=e[r>>2],e[i+4>>2]=t,t=e[r+28>>2],e[i+24>>2]=e[r+24>>2],e[i+28>>2]=t,t=e[r+20>>2],e[i+16>>2]=e[r+16>>2],e[i+20>>2]=t,t=e[r+12>>2],e[i+8>>2]=e[r+8>>2],e[i+12>>2]=t),g[r+23|0]=e[l+44>>
28102],(t=e[l+68>>2])?(e[r+24>>2]=t,t=l+88|0):(e[r+24>>2]=e[l+72>>2],t=l+92|0),e[r+28>>2]=e[t>>2]}oe=h+80|0}function mA(t,A,r,n,i,l){var h,m=0,w=0,M=0,y=0,P=0,R=0,ee=0,ae=0,ue=0,be=0,_e=0,Ve=0,$e=0,it=0,ht=0,yt=0,Pt=0,Wt=0;oe=h=oe-848|0;e:if(!(!e[t+104>>2]|8&d[i+2|0]|e[47202]==193)){for(e[n>>2]=0,e[33272]=0,e[33274]=l,g[h+192|0]=0,e[33273]=h+192;y=(l=y)+1|0,g[0|(Ve=A+l|0)]-48>>>0<10;);e[56798]=0,yt=h+188|0,R=-2147483648,oe=_e=oe-16|0;t:if(m=d[0|A]){P=A;r:{for(;;){if(!((0|(m=m<<24>>24))==32|m-9>>>0<5))break r;if(m=d[P+1|0],P=P+1|0,!m)break}break t}r:switch((m=d[0|P])-43|0){case 0:case 2:break r;default:break t}ue=(0|m)==45?-1:0,P=P+1|0}else P=A;for(;m=-48,(((ee=g[0|P])-48&255)>>>0<10||(m=-87,(ee-97&255)>>>0<26||(m=-55,!((ee-65&255)>>>0>25))))&&!((0|(ee=m+ee|0))>=10);)Ns(_e,10,0,0,0,ae,be,0,0),m=1,e[_e+8>>2]|e[_e+12>>2]||(it=rr(ae,be,10,0),(0|(ht=fe))==-1&~ee>>>0<it>>>0||(m=ht,be=(ae=ee+it|0)>>>0<ee>>>0?m+1|0:m,$e=1,m=M)),P=P+1|0,M=m;yt&&(e[yt>>2]=$e?P:A);t:{r:{if(M)e[56798]=68,ae=-2147483648,be=0;else if(!be&ae>>>0<2147483648)break r;if(!ue){e[56798]=68,R=2147483647;break t}if(!(!be&ae>>>0<=2147483648)){e[56798]=68;break t}}R=(ue^ae)-ue|0}if(oe=_e+16|0,ue=R,!(e[56798]|e[h+188>>2]==(0|A))){t:{r:{a:{if(!(!((0|(ee=64&d[t+109|0]?4:3))!=(0|l)|e[t+124>>2]!=g[A-2|0])&g[A-3|0]-48>>>0<10)){n:{if(e[t+124>>2]!=32){if(!(16&d[t+105|0]))break a;if((0|l)==3)break n;break a}if((0|l)!=3)break a}if(4&d[i+2|0]|g[A-2|0]-48>>>0>=10)break a}g[133104]=0,g[h+288|0]=0,Pt=1;break r}if(g[133104]=0,e[33275]=0,g[h+288|0]=0,Wt=1,$e=0,d[0|A]==48)break t}$e=Bs(t,A,Ve,i,0)}d[0|Ve]!=46|g[A+y|0]-48>>>0<10|1&g[i+13|0]|g[Ve+2|0]-48>>>0<10||(g[0|Ve]=0);t:if(!$e||(it=1,e[t+212>>2]==26741)){if(R=h+256|0,e[h+844>>2]=R,w=y,64&d[i+1|0]&&(g[h+256|0]=45,R=h+256|1,e[h+844>>2]=R,w=l+2|0),P=d[A+w|0]){for(;!((255&P)==32|(0|w)>28)&&(m=e[h+844>>2],e[h+844>>2]=m+1,g[0|m]=P,P=d[(w=w+1|0)+A|0]););R=e[h+844>>2]}if(g[0|R]=0,it=1,m=g[h+256|0]){if(!(M=e[t+136>>2])||Zr(h+256|0,M)){if(m-48>>>0<10||(e[h+176>>2]=h+256,uA(m=h+800|0,88653,h+176|0),!kA(t,m,133104)))break t;e[n>>2]=128|e[n>>2],e[h+160>>2]=h+256,uA(m=h+800|0,88773,h+160|0),kA(t,m,133116),it=0}$e=2}}yt=e[i>>2],g[h+352|0]=0,g[h+624|0]=0;t:{r:{a:if(!(!Wt|d[0|A]!=48||(0|(m=g[A+1|0]))==32|(0|m)==e[t+128>>2])){n:{if((0|l)==2){if(d[A+3|0]!=58|g[A+5|0]-48>>>0>=10||!((0|(m=g[A+7|0]))==32|m-9>>>0<5))break n;break a}if((0|l)>3)break r}if(d[0|A]==48&&!((0|(m=l-1|0))<=0))for(w=0;;){if(kA(t,88875,wA(M=h+288|0)+M|0),d[(w=w+1|0)+A|0]!=48)break a;if(!((0|m)>(0|w)))break}}a:{n:{if(!((0|(m=g[0|Ve]))==32&&16&d[t+105|0])){if(ht=2,ae=l+2|0,(0|m)==e[t+124>>2])break n;be=1,R=0,m=0;break a}ht=1,ae=l+2|0}if(4&d[i+14|0])R=1,m=0,be=1;else for(m=0,P=1,w=l,be=1;;){M=m,m=P,P=(_e=w+ht|0)+A|0,w=0;n:{for(;;){if(R=1,!(g[w+P|0]-48>>>0>=10)){if((0|ee)!=(0|(w=w+1|0)))continue;break n}break}m=M;break a}if(g[P+ee|0]-48>>>0<10){m=M;break a}if(w=0,g[P-1|0]-48>>>0<10){m=M;break a}n:{for(;;){if(d[(w+_e|0)+A|0]==48){if((0|ee)!=(0|(w=w+1|0)))continue;break n}break}be=0}if((0|(M=g[(w=ee+_e|0)+A|0]))!=e[t+124>>2]&(!(16&d[t+105|0])|(0|M)!=32))break a;if(ae=w+2|0,4&d[2+(F(P=m+1|0,12)+i|0)|0])break}}w=!ue;a:if(!(!be|!(64&d[1+(F(m,12)+i|0)|0])|e[t+212>>2]!=26741)){n:switch(d[0|(M=A+ae|0)]-97|0){case 0:case 4:break n;default:break a}n:{c:{o:{f:switch((P=d[M+1|0])-116|0){case 6:break a;case 1:case 2:case 3:case 4:case 5:break c;case 0:break f;default:break o}if(d[M+2|0]!=116)break n;break a}if((0|P)==32)break a}if(!(!!((0|ue)%1e3|0)&(0|m)!=1)&&(0|P)==108)break a}e[33274]=1|e[33274]}ae=32768&yt,w&=Pt;a:if(e[t+128>>2]!=g[0|Ve]|g[A+y|0]-48>>>0>=10){n:{if(!w){if(w=0,M=1,!((0|m)>0&R))break n;ue=(y=QA(t,ue,m,be,h+624|0))?0:ue,w=!!(0|y),P=0;break a}w=1,ue=0,e[33275]==1&&(e[h+144>>2]=m+1,uA(y=h+800|0,89026,h+144|0),kA(t,y,h+688|0)||(e[h+128>>2]=m,uA(y=h+800|0,89026,h+128|0),kA(t,y,h+624|0)))}M=1,P=0}else kA(t,88882,h+624|0),M=0,P=256;R=ae?2:$e;a:{if(m|d[h+624|0]|d[0|Ve]!=46){if(m)break a}else kA(t,89192,h+624|0);if(e[h+844>>2]=A,g[A+1|0]-48>>>0<10)for(;y=e[h+844>>2],e[h+844>>2]=y+1,g[y+2|0]-48>>>0<10;);if(g[e[h+844>>2]-1|0]-48>>>0>=10||(e[h+416>>2]=e[h+844>>2]-1,Hr(t,h+416|0,h+192|0,n,4,i)&&(e[33272]=2)),d[h+192|0]|d[e[h+844>>2]]==48||Hr(t,h+844|0,h+192|0,n,4,i)&&(e[33272]=1),Wt){if(!R&M&&(e[h+112>>2]=ue,uA(i=h+800|0,89214,h+112|0),kA(t,i,r)))break t;if(1&g[t+110|0]){for(y=A;(32|d[0|y])!=32;)y=y+1|0;e[h+416>>2]=y,d[y+1|0]==37&&(kA(t,89328,r),i=wA(r),g[e[h+416>>2]+1|0]=32,r=r+i|0)}}}
vendor: 29,464 bytes, line 2810
2810Vs(t,ue,h+416|0,w,m,R|P|Pt),!(2&d[t+109|0])|(0|m)<=0?(e[h+60>>2]=15,e[h- -64>>2]=h+624,e[h+56>>2]=h+416,e[h+52>>2]=h+352,e[h+48>>2]=h+288,uA(r,89415,h+48|0)):(e[h+88>>2]=15,e[h+96>>2]=h+416,e[h+92>>2]=h+352,e[h+84>>2]=h+624,e[h+80>>2]=h+288,uA(r,89346,h+80|0));a:if(!M)for(;;){for(l=l+1|0,M=0;M=(y=M)+1|0,g[(m=l+y|0)+A|0]-48>>>0<10;);i=2;n:{c:{o:{f:{l:{i:switch((M=57344&e[t+104>>2])-8192>>>13|0){case 6:break c;case 2:break o;case 0:case 4:case 5:break f;case 1:break l;case 3:break i;default:break n}i=5}if((0|(M=d[0|(w=A+l|0)]))==48)for(;kA(t,88875,m=h+688|0),gs(r,m),y=y-1|0,(0|(M=d[0|(w=(l=l+1|0)+A|0)]))==48;);if((0|i)<(0|y)|(M<<24>>24)-48>>>0>=10)break n;i=h+688|0,Vs(t,Pn(w),i,0,0,0),gs(r,i),l=l+y|0;break n}if(Vs(t,Pn(i=A+l|0),h+416|0,0,0,0),!((0|M)==8192&d[0|i]!=48)){if(e[h+16>>2]=y,uA(i=h+800|0,89508,h+16|0),!kA(t,i,h+688|0))break n;gs((0|M)==49152?r:h+416|0,h+688|0)}gs(r,h+416|0),l=m;break n}if((0|y)>4||d[0|(i=A+l|0)]==48)break n;Vs(t,l=Pn(i),i=h+688|0,0,0,0),gs(r,i),l=m;break n}if(!((0|y)<=1))for(;;){if(e[h+32>>2]=g[A+l|0],uA(i=h+800|0,89575,h+32|0),!kA(t,i,h+688|0))break n;if(gs(r,h+688|0),l=l+1|0,!((0|(y=y-1|0))>1))break}}n:if(!((M=d[0|(y=A+l|0)])-48>>>0>=10||wA(r)>>>0>=190))for(;;){if(i=h+688|0,Vr(t,g[0|y]-48|0,0,2,i),m=wA(r),e[h>>2]=15,e[h+4>>2]=i,uA(r+m|0,89594,h),(M=d[0|(y=(l=l+1|0)+A|0)])-48>>>0>=10)break n;if(!(wA(r)>>>0<=189))break}if(kA(t,89678,h+688|0)&&gs(r,h+688|0),e[t+128>>2]!=(0|M)|g[1+(A+l|0)|0]-48>>>0>=10)break a;kA(t,88882,i=h+688|0),gs(r,i)}if(!(i=d[0|r])|(0|i)==21||(i=or(h+184|0,A=1+(A+l|0)|0),l=e[h+184>>2],!(2&d[t+106|0])|(0|l)!=32||(or(h+184|0,A+i|0),l=e[h+184>>2]),aa(l)|be||(t=wA(r)+r|0,g[0|t]=11,g[t+1|0]=0)),e[n>>2]=-2147483648|e[n>>2],e[33275]=e[33275]-1,w=1,it)break e;e[33264]=1;break e}e[n>>2]=-129&e[n>>2],w=0;break e}w=1}}return oe=h+848|0,w}function AA(t,A,r,n){var i,l,h=0,m=0,w=0,M=0,y=0,P=0,R=0,ee=0,ae=0,ue=0,be=0,_e=0,Ve=0,$e=0,it=0,ht=0,yt=0,Pt=0,Wt=0,cA=0,gA=0;if(oe=i=oe-416|0,$e=_[r+8>>1],ae=d[r+10|0],64&(l=e[r>>2])){for(e[r>>2]=-65&l,g[199388]=1,w=e[47202],R=e[49846],y=e[47352],P=e[47351],M=e[47350];;){h=(m=e[198304+(P<<2)>>2])>>8;e:{t:switch((31&m)-9|0){case 0:w=h;break e;case 4:R=h;break e;case 3:break t;default:break e}y=m>>>0>=256?h+y|0:0}if(!(!(128&m)&(0|M)>(0|(P=P+1|0))))break}e[47352]=y,e[47351]=P,e[49846]=R,e[47202]=w}R=0;e:if(!((0|(h=e[49572]))>997)){if(1048576&l||!d[0|A])d[199388]&&(e[49572]=h+1,g[199388]=0,e[(t=190288+(h<<3)|0)>>2]=983042,e[t+4>>2]=0),g[189088]=0;else if(!((0|h)>990)){(0|(h=e[t+8224>>2]))>0&&(e[t+8224>>2]=h-1),w=512&l?n:n+4|0,y=(240&e[47202])==16,(_e=2&l)&&((0|(m=e[47200]))<3||(0|(h=e[47350]))>243||(d[199388]&&(e[(P=198300+(h<<2)|0)>>2]=-129&e[P>>2]),e[47350]=h+1,g[199388]=1,it=(0|m)==3?20:m,e[198304+(h<<2)>>2]=it<<8|193)),M=y?w:n;t:{r:{if(!(8&l)){for(;n=d[(h=ee)+A|0],g[h+(i+240|0)|0]=n,223&n&&(ee=h+1|0,h>>>0<160););if(g[i+66|0]=0,ee=i- -64|2,R=lA(e[47192],A,r,ee),e[i+412>>2]=R,!(4096&R))break r;$A(A,i+240|0,h);break e}a:if(ra(A,87276,3))Rn(A,189088,i+240|0);else{if(P=0,223&(h=d[0|(ee=A+3|0)]))for(;g[(i+240|0)+P|0]=wr(h<<24>>24),P=P+1|0,223&(h=d[0|(ee=ee+1|0)]););g[(n=i+240|0)+P|0]=0,h=0;n:if(!((0|(r=e[34461]))<=0)){for(;;){if(!Zr(n,F(h,44)+137856|0)){e[34457]=h;break n}if((0|r)==(0|(h=h+1|0)))break}h=r}if((0|(r=(0|r)==(0|h)?-1:h))<=0)break a;Ia(r),g[189090]=0,g[189089]=r,g[189088]=21}R=-2147483648,e[i+412>>2]=-2147483648,n=-1;break t}if(!(!(8388608&R)|8&d[r+12|0])){for(oe=y=oe-208|0,P=e[t+60>>2],m=A;n=m,m=m+1|0,d[0|n]!=32;);or(y+204|0,m);r:if(aa(e[y+204>>2])){w=BA(y,189088),m=(!(256&P)|(be=Ft(t,m,r+12|0,0))>>>15)&!(67108864&be)&d[189088]!=21;a:{n:{if(512&P){if(!(m&!(16&d[r+12|0])))break n;break a}if(m)break a}BA(189088,w);break r}if(g[0|n]=45,e[r>>2]=-2&e[r>>2],R=0,m=189088,ue=Ft(e[47192],A,r,0),e[i+412>>2]=ue,!((0|P)<=0)&&(w=d[189088])){for(;R=(d[e[144464+(w<<2)>>2]+11|0]==2)+R|0,w=d[0|(m=m+1|0)];);if(!((31&P)>=(0|R))){g[0|n]=32,e[i+412>>2]=Ft(e[47192],A,r,0);break r}}e[i+412>>2]=128|(ue||be),e[33264]=1}oe=y+208|0}if(n=-1,d[189088]==21){if(P=BA(i+16|0,132848),y=e[i+412>>2],m=$A(A,i+240|0,h),w=i- -64|1,(0|(n=lo(d[189089]?189089:87315,188772,189296)))<0||(e[r>>2]=4194304|e[r>>2],d[i+66|0]?(v[i+64>>1]=8192,y=Ft(e[47193],w,r,0)):y=lA(e[47193],m,r,ee)),d[189088]==21&&(h=$A(m,i+240|0,h),(0|(n=lo(d[189089]?189089:87315,188772,189296)))<0||(e[r>>2]=4194304|e[r>>2],d[i+66|0]?(v[i+64>>1]=8192,y=Ft(e[47193],w,r,0)):y=lA(e[47193],h,r,ee)),R=4096,d[189088]==21))break e;e[i+412>>2]=y,(0|n)>=0||(g[189090]=0,v[94544]=3341,(0|n)==-1&&(BA(132848,P),Ia(e[e[32972]+60>>2]),n=e[e[32972]+60>>2]))}R=e[i+412>>2],128&l||(M=268435456&R&&(0|M)<=1?1:M,!(256&R)|528&l|e[t+8224>>2]|2&d[r-11|0]||(e[t+8224>>2]=3,M=(0|M)<=4?4:M)),M=(0|M)<=0&&e[49846]>2?1:M}if(r=d[199388],!((0|M)<=0|(0|(ee=e[49572]))>990)){e[49572]=ee+1,m=1&r,r=0,v[(h=190288+(ee<<3)|0)>>1]=m?2:0,g[h+7|0]=0,g[h+3|0]=0,m=M>>>0>1,g[h+2|0]=m?9:11,v[h+4>>1]=0,e[t+8236>>2]=0;t:if(!(!(h=m?M-2|0:0)|(0|(ee=e[49572]))>990))for(w=t+8236|0;;){if(e[49572]=ee+1,v[(m=190288+(ee<<3)|0)>>1]=0,g[m+7|0]=0,g[m+3|0]=0,y=h>>>0>1,g[m+2|0]=y?9:11,v[m+4>>1]=0,e[w>>2]=0,ee=e[49572],(0|(h=y?h-2|0:0))<=0)break t;if(!((0|ee)<991))break}e[t+8228>>2]=0,e[t+8232>>2]=0}g[199388]=1&r,!_e|e[47200]!=1||(e[49572]=ee+2,g[199388]=0,v[(h=190288+(ee<<3)|0)>>1]=1&r?2:0,g[h+7|0]=0,v[h+2>>1]=10,v[h+4>>1]=0,v[h+12>>1]=0,e[h+8>>2]=1179648,g[h+15|0]=0,1&l&&Wr(g[A+1|0])&&(r=d[199388],g[199388]=0,A=e[49572],e[49572]=A+2,v[(A=190288+(A<<3)|0)>>1]=r?2:0,g[A+7|0]=0,v[A+2>>1]=10,v[A+4>>1]=0,v[A+12>>1]=0,e[A+8>>2]=1179648,g[A+15|0]=0)),h=ae>>>0<31;t:if(!((0|n)<0))if(A=e[49572],r=d[190290+((M=A-1|0)<<3)|0],d[189088]!=9|d[189089]!=21)(0|r)!=21&&(m=d[199388],g[199388]=0,g[7+(r=190288+(A<<3)|0)|0]=0,v[r+2>>1]=21,v[r+4>>1]=0,v[r>>1]=m?2:0,M=A),e[49572]=M+1,g[190295+(M<<3)|0]=n;else{if((0|r)!=21)break t;e[49572]=M}A=2047&$e,r=(h?ae:31)<<11,y=(ht=128&l)?d[e[144464+(d[189088]<<2)>>2]+11|0]?189088:189089:189088,(M=d[0|y])|!(1&g[199388])||(M=23,g[0|y]=23,g[y+1|0]=0),yt=A|r,w=e[49572];t:if(M)if((0|w)>994)A=0;else for(cA=((-1610612736&R)==-2147483648)<<4,$e=yt+1|0,gA=t+8233|0,A=0,m=1,be=0,Pt=1,ae=-1,_e=-1,h=0;;){P=y+1|0;r:{if((0|(ue=255&M))!=255){if(r=e[144464+(ue<<2)>>2])break r;e[i>>2]=ue,oe=r=oe-16|0,e[r+12>>2]=i,wa(132552,87474,i),oe=r+16|0,w=e[49572]}if(!(M=d[0|P]))break t;if(y=P,(0|w)<995)continue;break t}r:if((0|(Ve=255&M))!=21)if((0|(y=d[r+11|0]))!=1){switch(r=$e,Ve-12|0){case 8:g[3+(r=190288+((ae=w-1|0)<<3)|0)|0]=m,v[r>>1]=4|_[r>>1],r=h;break r;case 0:v[(r=190280+(w<<3)|0)>>1]=8|_[r>>1],r=h;break r;case 10:R|=16384,e[i+412>>2]=R,r=h;break r;case 3:break r}ue=d[199388],g[199388]=0,g[7+(r=190288+(Ve=w<<3)|0)|0]=0,g[r+2|0]=M,v[r+4>>1]=h,h=(ue?2:0)|cA,v[r>>1]=h,(0|y)==2?((0|m)>=4&&(g[189076]=1),(0|ae)<0||(0|(A=w-1|0))!=(0|ae)&&(g[190291+(A<<3)|0]=m),v[r>>1]=4|h,Wt=(A=(0|m)>(0|_e))?w:Wt,_e=A?m:_e,h=1,be&&(g[r+7|0]=be),ae=w,be=0,A=m):(!Pt|!(64&d[0|gA])||(v[r>>1]=8|h),h=m),w=w+1|0,e[49572]=w,g[Ve+190291|0]=A,r=0,Pt=0,m=h}else{if(!_[r+8>>1]){m=d[r+14|0],r=h;break r}if((0|ae)<0){r=h,be=ue;break r}g[190295+(ae<<3)|0]=M,r=h}else e[(r=190288+(w<<3)|0)>>2]=1376256,v[r+4>>1]=0,g[r+7|0]=d[y+1|0],e[49572]=w+1,Ia(d[y+1|0]),P=y+2|0,w=e[49572],r=h;if(!(M=d[0|P]))break t;if(y=P,h=r,!((0|w)<995))break}else A=0;131072&l&&(r=w+1|0,e[49572]=r,m=d[199388],g[199388]=0,g[7+(h=190288+(w<<3)|0)|0]=0,v[h+2>>1]=27,v[h+4>>1]=0,v[h>>1]=m?2:0,w=r),ht||(v[190292+(ee<<3)>>1]=yt),e[t+8228>>2]=0,d[e[144464+(d[190282+(w<<3)|0]<<2)>>2]+11|0]!=2|(0|A)<4||(e[t+8228>>2]=1),(0|n)>=0&&(BA(132848,i+16|0),Ia(e[e[32972]+60>>2]),r=d[199388],g[199388]=0,n=e[49572],v[(A=190288+(n<<3)|0)>>1]=r?2:0,v[A+2>>1]=21,v[A+4>>1]=0,g[A+7|0]=e[e[32972]+60>>2],w=n+1|0,e[49572]=w),(0|it)>0&&(g[199388]=0,e[49572]=w+1,e[(A=190288+(w<<3)|0)>>2]=655362,r=e[47350],e[47350]=r+1,g[A+7|0]=0,v[A+4>>1]=0,e[198304+(r<<2)>>2]=it<<8|225),1024&R&&(v[(A=190288+(Wt<<3)|0)>>1]=64|_[A>>1]),e[t+8232>>2]=R}}return oe=i+416|0,R}function gr(t,A,r,n,i){var l=0,h=0,m=0,w=0,M=0,y=0,P=0,R=0,ee=0,ae=0,ue=0,be=0,_e=0,Ve=0,$e=0,it=0,ht=0,yt=0,Pt=0,Wt=0,cA=0,gA=0,SA=0,VA=0;e:if(A){if(_e=i<<4,h=(A=e[33268])+r|0,!((0|A)<=0||(l=d[0|(w=_e+129360|0)],P=d[w+1|0]-l<<8,P=(0|A)!=1?(0|P)/(0|A)|0:P,(0|r)>=(0|h))))for(R=P>>>0>255,ue=P>>>8|0,l<<=8,w=0-P>>>8|0,it=(0|P)<=0,A=r;it?(M=(y=(y=(0|(m=(0|(m=e[101024+(g[F(A,6)+t|0]<<2)>>2]))<(0|w)?w:m))>=18?18:m)+(m=(0|(m=(0|(l=l+P|0))/256|0))>0?m:0)|0)>>>0>=254?254:y,m=m>>>0>=254?254:m):(m=F(A,6)+t|0,g[m+2|0]=R|d[m+2|0],m=(y=(0|(m=(0|l)/256|0))>0?m:0)>>>0>=254?254:y,M=(y=y+ue|0)>>>0>=254?254:y,l=l+P|0),y=F(A,6)+t|0,g[y+5|0]=m,g[y+4|0]=M,(0|h)!=(0|(A=A+1|0)););if(2&d[188785]?(w=e[33271],e[33270]=w):w=e[33270],ue=(R=d[3+(l=129360+(A=i<<4)|0)|0])-(m=d[l+2|0])<<8,it=e[l+4>>2],(0|r)>0?(yt=101056,Ve=5,y=(0|ue)/(d[A+129368|0]-1|0)|0):(yt=e[12+(A=A+129360|0)>>2],Ve=d[A+10|0],y=0),(0|h)<(0|w)){for(P=(0|r)<=0,be=((A=ue>>31)^ue)-A|0,ee=129360+(i<<4)|0,m<<=8,ht=R<<8,R=0;;){t:{if(M=F(h,6)+t|0,!(!(1&P)&(0|(l=g[0|M]))<4)){r=0,A=h;r:if(1&((0|l)==5|P)){for(;(0|(y=g[F(A,6)+t|0]))<=6&&(r=((0|y)>3)+r|0,(0|w)!=(0|(A=A+1|0))););if(ae=0,(0|($e=(0|(A=d[ee+8|0]))>(0|r)?r:A))<2){y=0,R=m;break r}y=(0|ue)/($e-1|0)|0,R=m}else(0|$e)>0?R=y+R|0:(R=ht+(F(be,g[ae+yt|0])>>6)|0,(0|Ve)>(0|(ae=ae+1|0))||(yt=e[ee+12>>2],ae=0));if($e=$e-1|0,!((0|l)<4)){g[0|M]=6,r=(A=(0|(A=(0|R)/256|0))>0?A:0)+(((r=(l=e[it+(l<<2)>>2])>>31)^l)-r|0)|0;break t}}(0|l)!=3?(A=(0|R)/256|0,(63&d[M-6|0])>>>0>=3?(r=(l=e[it+(l<<2)>>2])>>31,r=(A=(0|(A=A-g[ee+9|0]|0))>0?A:0)+((r^l)-r|0)|0):r=(A=(0|A)>0?A:0)+(((r=(l=e[it+(l<<2)>>2])>>31)^l)-r|0)|0):r=(A=(0|(A=(0|R)/256|0))>0?A:0)+(((r=(l=e[it+12>>2])>>31)^l)-r|0)|0}if(g[M+5|0]=A>>>0>=254?254:A,P=0,A=(0|r)>0?r:0,g[M+4|0]=A>>>0>=254?254:A,g[M+2|0]=d[M+2|0]|l>>>31,(0|w)==(0|(h=h+1|0)))break}h=w}if(!(d[133068]||((268435455&i)==3&&(A=F(h,6)+t|0,g[A+2|0]=2|d[A+2|0]),e[33269]?(A=d[5+(l=100768+(i<<4)|0)|0],m=l+3|0,r=d[l+4|0]-A|0):(A=d[2+(r=100768+(i<<4)|0)|0],m=_e+100768|0,r=d[r+1|0]-A|0),l=F(h,6)+t|0,g[l+5|0]=A>>>0>=254?254:A,g[l+2|0]=d[l+2|0]|r>>>31,A=(y=A)+(((A=r>>31)^r)-A|0)|0,g[l+4|0]=A>>>0>=254?254:A,r=F(w,6)+t|0,g[r+1|0]=d[0|m],A=h+1|0,d[0|r]==4&&(g[0|r]=6),(0|(r=n-A|0))<=0||(i=d[12+(h=100768+(i<<4)|0)|0],h=d[h+13|0]-i<<8,h=(0|r)!=1?(0|h)/(0|r)|0:h,(0|A)>=(0|n)))))for(y=h>>>0>255,P=h>>>8|0,r=i<<8,i=0-h>>>8|0,R=(0|h)<=0;R?(m=(w=(m=(0|(l=(0|i)>(0|(l=e[101024+(g[F(A,6)+t|0]<<2)>>2]))?i:l))>=18?18:l)+(l=(0|(l=(0|(r=r+h|0))/256|0))>0?l:0)|0)>>>0>=254?254:w,w=l>>>0>=254?254:l):(l=F(A,6)+t|0,g[l+2|0]=y|d[l+2|0],w=(l=(0|(l=(0|r)/256|0))>0?l:0)>>>0>=254?254:l,m=(l=l+P|0)>>>0>=254?254:l,r=r+h|0),l=F(A,6)+t|0,g[l+5|0]=w,g[l+4|0]=m,(0|n)!=(0|(A=A+1|0)););}else{if(A=(Ve=e[34455])+F(i,68)|0,h=d[A+24|0],w=d[A+25|0],l=(A=e[33268])+r|0,!((0|A)<=0||(M=w-h<<8,M=(0|A)!=1?(0|M)/(0|A)|0:M,(0|r)>=(0|l))))for(y=M>>>0>255,P=M>>>8|0,A=h<<8,w=0-M>>>8|0,R=(0|M)<=0;R?(h=(h=(ue=(0|(h=(0|(h=e[101024+(g[F(r,6)+t|0]<<2)>>2]))<(0|w)?w:h))>=18?18:h)+(m=(0|(h=(0|(A=A+M|0))/256|0))>0?h:0)|0)>>>0>=254?254:h,m=m>>>0>=254?254:m):(h=F(r,6)+t|0,g[h+2|0]=y|d[h+2|0],m=(h=(0|(h=(0|A)/256|0))>0?h:0)>>>0>=254?254:h,h=(h=h+P|0)>>>0>=254?254:h,A=A+M|0),ue=F(r,6)+t|0,g[ue+5|0]=m,g[ue+4|0]=h,(0|l)!=(0|(r=r+1|0)););2&d[188785]?(r=e[33271],e[33270]=r):r=e[33270],w=r,A=Ve+F(i,68)|0;t:if((0|(P=d[A+33|0]))!=255)for(;;){if((0|(w=w-1|0))<(0|l)){w=r;break t}if(!(g[F(w,6)+t|0]<4))break}if((0|r)>(0|l)){for(gA=((h=(Wt=(R=d[A+32|0])-(M=d[0|(m=A+31|0)])<<8)>>31)^Wt)-h|0,ht=(0|(A=d[0|(y=A+30|0)]))==255,ue=P<<8,it=M<<8,SA=R<<8,_e=Ve+F(i,68)|0,VA=(0|A)!=255,R=0,P=0,A=1,M=0;;){t:{if(ee=F(l,6)+t|0,!(!(1&A)&(0|(be=g[0|ee]))<4)){r:if(1&((0|be)==5|A)){a:if(VA){if(Pt=1,A=0,h=y,!((0|w)<=(0|(M=l+1|0)))){for(;;){if(h=y,(0|(P=g[F(M,6)+t|0]))>6)break a;if(A=((0|P)>3)+A|0,(0|w)==(0|(M=M+1|0)))break}h=y}}else{if(A=0,M=l,h=m,(0|w)<=(0|l))break a;for(;;){if(h=m,(0|(P=g[F(M,6)+t|0]))>6)break a;if(A=((0|P)>3)+A|0,(0|w)==(0|(M=M+1|0)))break}h=m}if(P=d[0|h]<<8,cA=0,(0|(M=(0|A)<(0|(h=d[_e+34|0]))?A:h))<2){yt=0;break r}yt=(0|Wt)/(M-1|0)|0}else(0|l)!=(0|w)?Pt?(ht=1,M=M+1|0,Pt=0,P=it):(0|M)>0?(P=P+yt|0,Pt=0):(Pt=0,P=(F(g[16+(_e+cA|0)|0],gA)>>6)+SA|0,cA=(0|(A=cA+1|0))<d[_e+35|0]?A:0):(ht=2,P=ue);if(M=M-1|0,!((0|be)<4)){A=h=l+1|0;r:if(!((0|r)<=(0|l)))for(;;){if(g[F(A,6)+t|0]>1)break r;if(!((0|r)>=(0|(A=A+1|0))))break}g[0|ee]=6,g[ee+1|0]=d[_e+26|0],R=0,ae=d[_e+27|0],l=(0|(l=(0|P)/256|0))>0?l:0,g[ee+5|0]=l>>>0>=254?254:l,l=l+ae|0,g[ee+4|0]=l>>>0>=254?254:l,$e=A-h|0;break t}}if((0|be)>=2){A=h=l+1|0;r:if(!((0|r)<=(0|l)))for(;;){if(g[F(A,6)+t|0]>1)break r;if(!((0|r)>=(0|(A=A+1|0))))break}R=0,l=(0|(l=(0|P)/256|0))>0?l:0,g[ee+5|0]=l>>>0>=254?254:l,ae=e[100976+(be<<2)>>2],g[ee+2|0]=d[ee+2|0]|ae>>>31,l=(0|(l=(be=l)+(((l=ae>>31)^ae)-l|0)|0))>0?l:0,g[ee+4|0]=l>>>0>=254?254:l,$e=A-h|0}else(0|$e)<=1?(ae=g[36+(_e+ht|0)|0],A=0):(ae=g[36+(A=_e+ht|0)|0],A=(g[A+39|0]-ae|0)/($e-1|0)|0),h=e[100976+(be<<2)>>2],g[ee+2|0]=d[ee+2|0]|h>>>31,A=(0|(A=(((0|P)/256|0)+ae|0)+F(A,R)|0))>0?A:0,g[ee+5|0]=A>>>0>=254?254:A,A=(0|(A=A+(((be=h)^(h>>=31))-h|0)|0))>0?A:0,g[ee+4|0]=A>>>0>=254?254:A,R=R+1|0,h=l+1|0}if(A=0,(0|r)==(0|(l=h)))break}l=r}if(d[133068]||(h=F(l,6)+t|0,e[33269]?(m=Ve+F(i,68)|0,A=d[m+47|0],w=d[m+46|0]-A|0,m=m+45|0):(m=Ve+F(i,68)|0,A=d[m+44|0],w=d[m+43|0]-A|0,m=m+42|0),m=d[0|m],y=(((y=w>>31)^w)-y|0)+A|0,g[h+4|0]=y>>>0>=254?254:y,g[h+5|0]=A>>>0>=254?254:A,A=F(l,6)+t|0,g[A+2|0]=d[A+2|0]|w>>>31,r=F(r,6)+t|0,g[r+1|0]=m,A=l+1|0,d[0|r]==4&&(g[0|r]=6),(0|(r=n-A|0))<=0)||(h=Ve+F(i,68)|0,i=d[h+48|0],h=d[h+49|0]-i<<8,h=(0|r)!=1?(0|h)/(0|r)|0:h,(0|A)>=(0|n)))break e;for(y=h>>>0>255,P=h>>>8|0,r=i<<8,i=0-h>>>8|0,R=(0|h)<=0;R?(m=(w=(m=(0|(l=(0|i)>(0|(l=e[101024+(g[F(A,6)+t|0]<<2)>>2]))?i:l))>=18?18:l)+(l=(0|(l=(0|(r=r+h|0))/256|0))>0?l:0)|0)>>>0>=254?254:w,w=l>>>0>=254?254:l):(l=F(A,6)+t|0,g[l+2|0]=y|d[l+2|0],w=(l=(0|(l=(0|r)/256|0))>0?l:0)>>>0>=254?254:l,m=(l=l+P|0)>>>0>=254?254:l,r=r+h|0),l=F(A,6)+t|0,g[l+5|0]=w,g[l+4|0]=m,(0|n)!=(0|(A=A+1|0)););}}function CA(){var t,A=0,r=0,n=0,i=0,l=0,h=0,m=0,w=0,M=0,y=0,P=0,R=0,ee=0,ae=0,ue=0;if((0|(t=e[36455]))!=(0|(w=e[36454]))){e:if(!((0|(ee=e[36427]))<0|(0|w)==(0|ee))){r=P=e[8+(216192+((M=ee)<<4)|0)>>2];t:{for(;;){if((n=e[(h=216192+((M=(0|(n=M-1|0))<0?169:n)<<4)|0)>>2])-5>>>0<2)break t;r:{if((0|n)<=4){if(e[h+12>>2]!=(0|r))break t;if(n=_[h+4>>1],e[h+12>>2]=P,r=e[h+8>>2],16&(A=_[r>>1]))break r;for(ue=32&A?(F(n,12)>>>0)/10|0:n,i=0,n=r,m=0;;){A=_[r>>1];a:if(!(m>>>0<3&&8&A)){if(A=A<<16>>16,(0|(ae=(y=v[2+((R=m<<1)+r|0)>>1])-(l=v[2+(R=P+R|0)>>1])|0))>(0|(y=(0|F(ue,(0|F(e[200944+(m<<2)>>2],(0|ae)>0?y+(l<<1)|0:(y<<1)+l|0))/3e3|0))/256|0)))i||((0|A)<0?n=r:(n=0,A=(0|(A=e[44469]+1|0))<=169?A:0,e[44469]=A,(A=177888+(i=A<<6)|0)&&(n=_[r+4>>1]|_[r+6>>1]<<16,l=_[r>>1]|_[r+2>>1]<<16,v[A>>1]=l,v[A+2>>1]=l>>>16,v[A+4>>1]=n,v[A+6>>1]=n>>>16,n=_[r+60>>1]|_[r+62>>1]<<16,l=_[r+56>>1]|_[r+58>>1]<<16,v[A+56>>1]=l,v[A+58>>1]=l>>>16,v[A+60>>1]=n,v[A+62>>1]=n>>>16,n=_[r+52>>1]|_[r+54>>1]<<16,l=_[r+48>>1]|_[r+50>>1]<<16,v[A+48>>1]=l,v[A+50>>1]=l>>>16,v[A+52>>1]=n,v[A+54>>1]=n>>>16,n=_[r+44>>1]|_[r+46>>1]<<16,l=_[r+40>>1]|_[r+42>>1]<<16,v[A+40>>1]=l,v[A+42>>1]=l>>>16,v[A+44>>1]=n,v[A+46>>1]=n>>>16,n=_[r+36>>1]|_[r+38>>1]<<16,l=_[r+32>>1]|_[r+34>>1]<<16,v[A+32>>1]=l,v[A+34>>1]=l>>>16,v[A+36>>1]=n,v[A+38>>1]=n>>>16,n=_[r+28>>1]|_[r+30>>1]<<16,l=_[r+24>>1]|_[r+26>>1]<<16,v[A+24>>1]=l,v[A+26>>1]=l>>>16,v[A+28>>1]=n,v[A+30>>1]=n>>>16,n=_[r+20>>1]|_[r+22>>1]<<16,l=_[r+16>>1]|_[r+18>>1]<<16,v[A+16>>1]=l,v[A+18>>1]=l>>>16,v[A+20>>1]=n,v[A+22>>1]=n>>>16,n=_[r+12>>1]|_[r+14>>1]<<16,l=_[r+8>>1]|_[r+10>>1]<<16,v[A+8>>1]=l,v[A+10>>1]=l>>>16,v[A+12>>1]=n,v[A+14>>1]=n>>>16,g[i+177904|0]=0,v[A>>1]=32768|_[A>>1],n=A))),A=y+_[R+2>>1]|0;else{if((0-y|0)<=(0|ae))break a;i||((0|A)<0?n=r:(n=0,A=(0|(A=e[44469]+1|0))<=169?A:0,e[44469]=A,(A=177888+(i=A<<6)|0)&&(n=_[r+4>>1]|_[r+6>>1]<<16,l=_[r>>1]|_[r+2>>1]<<16,v[A>>1]=l,v[A+2>>1]=l>>>16,v[A+4>>1]=n,v[A+6>>1]=n>>>16,n=_[r+60>>1]|_[r+62>>1]<<16,l=_[r+56>>1]|_[r+58>>1]<<16,v[A+56>>1]=l,v[A+58>>1]=l>>>16,v[A+60>>1]=n,v[A+62>>1]=n>>>16,n=_[r+52>>1]|_[r+54>>1]<<16,l=_[r+48>>1]|_[r+50>>1]<<16,v[A+48>>1]=l,v[A+50>>1]=l>>>16,v[A+52>>1]=n,v[A+54>>1]=n>>>16,n=_[r+44>>1]|_[r+46>>1]<<16,l=_[r+40>>1]|_[r+42>>1]<<16,v[A+40>>1]=l,v[A+42>>1]=l>>>16,v[A+44>>1]=n,v[A+46>>1]=n>>>16,n=_[r+36>>1]|_[r+38>>1]<<16,l=_[r+32>>1]|_[r+34>>1]<<16,v[A+32>>1]=l,v[A+34>>1]=l>>>16,v[A+36>>1]=n,v[A+38>>1]=n>>>16,n=_[r+28>>1]|_[r+30>>1]<<16,l=_[r+24>>1]|_[r+26>>1]<<16,v[A+24>>1]=l,v[A+26>>1]=l>>>16,v[A+28>>1]=n,v[A+30>>1]=n>>>16,n=_[r+20>>1]|_[r+22>>1]<<16,l=_[r+16>>1]|_[r+18>>1]<<16,v[A+16>>1]=l,v[A+18>>1]=l>>>16,v[A+20>>1]=n,v[A+22>>1]=n>>>16,n=_[r+12>>1]|_[r+14>>1]<<16,l=_[r+8>>1]|_[r+10>>1]<<16,v[A+8>>1]=l,v[A+10>>1]=l>>>16,v[A+12>>1]=n,v[A+14>>1]=n>>>16,g[i+177904|0]=0,v[A>>1]=32768|_[A>>1],n=A))),A=_[R+2>>1]-y|0}i=1,v[2+((m<<1)+n|0)>>1]=A,e[h+8>>2]=n}if((0|(m=m+1|0))==6)break}P=n}if((0|w)!=(0|M))continue;break t}break}P=r}for(n=0;;){if((r=e[(M=216192+(ee<<4)|0)>>2])-5>>>0<2)break e;if((0|r)<=4){if(r=e[M+8>>2],A=_[M+4>>1],n){if((0|r)!=(0|n))break e;e[M+8>>2]=P}else P=r;if(16&(n=_[P>>1]))break e;for(R=32&n?(F(A,6)>>>0)/5|0:A,i=0,r=n=e[M+12>>2],m=0;;){t:{r:if((0|(w=(h=v[2+((A=m<<1)+n|0)>>1])-(A=v[2+(y=A+P|0)>>1])|0))>(0|(h=(0|F(R,(0|F(e[200944+(m<<2)>>2],(0|w)>0?h+(A<<1)|0:(h<<1)+A|0))/3e3|0))/256|0))){if(!i){if(v[n>>1]<0){r=n,A=A+h|0;break r}r=(0|(r=e[44469]+1|0))<=169?r:0,e[44469]=r,i=_[n+20>>1]|_[n+22>>1]<<16,A=16+(r=177888+(r<<6)|0)|0,w=_[n+16>>1]|_[n+18>>1]<<16,v[A>>1]=w,v[A+2>>1]=w>>>16,v[A+4>>1]=i,v[A+6>>1]=i>>>16,A=_[n+4>>1]|_[n+6>>1]<<16,i=_[n>>1]|_[n+2>>1]<<16,v[r>>1]=i,v[r+2>>1]=i>>>16,v[r+4>>1]=A,v[r+6>>1]=A>>>16,A=_[n+12>>1]|_[n+14>>1]<<16,i=_[n+8>>1]|_[n+10>>1]<<16,v[r+8>>1]=i,v[r+10>>1]=i>>>16,v[r+12>>1]=A,v[r+14>>1]=A>>>16,A=_[n+28>>1]|_[n+30>>1]<<16,i=_[n+24>>1]|_[n+26>>1]<<16,v[r+24>>1]=i,v[r+26>>1]=i>>>16,v[r+28>>1]=A,v[r+30>>1]=A>>>16,A=_[n+36>>1]|_[n+38>>1]<<16,i=_[n+32>>1]|_[n+34>>1]<<16,v[r+32>>1]=i,v[r+34>>1]=i>>>16,v[r+36>>1]=A,v[r+38>>1]=A>>>16,A=_[n+44>>1]|_[n+46>>1]<<16,i=_[n+40>>1]|_[n+42>>1]<<16,v[r+40>>1]=i,v[r+42>>1]=i>>>16,v[r+44>>1]=A,v[r+46>>1]=A>>>16,A=_[n+52>>1]|_[n+54>>1]<<16,i=_[n+48>>1]|_[n+50>>1]<<16,v[r+48>>1]=i,v[r+50>>1]=i>>>16,v[r+52>>1]=A,v[r+54>>1]=A>>>16,A=_[n+60>>1]|_[n+62>>1]<<16,i=_[n+56>>1]|_[n+58>>1]<<16,v[r+56>>1]=i,v[r+58>>1]=i>>>16,v[r+60>>1]=A,v[r+62>>1]=A>>>16,g[r+16|0]=0,v[r>>1]=32768|_[r>>1],A=_[y+2>>1]}A=A+h|0}else{if((0|w)>=(0-h|0))break t;i||(v[n>>1]<0?r=n:(r=(0|(r=e[44469]+1|0))<=169?r:0,e[44469]=r,i=_[n+20>>1]|_[n+22>>1]<<16,A=16+(r=177888+(r<<6)|0)|0,w=_[n+16>>1]|_[n+18>>1]<<16,v[A>>1]=w,v[A+2>>1]=w>>>16,v[A+4>>1]=i,v[A+6>>1]=i>>>16,A=_[n+4>>1]|_[n+6>>1]<<16,i=_[n>>1]|_[n+2>>1]<<16,v[r>>1]=i,v[r+2>>1]=i>>>16,v[r+4>>1]=A,v[r+6>>1]=A>>>16,A=_[n+12>>1]|_[n+14>>1]<<16,i=_[n+8>>1]|_[n+10>>1]<<16,v[r+8>>1]=i,v[r+10>>1]=i>>>16,v[r+12>>1]=A,v[r+14>>1]=A>>>16,A=_[n+28>>1]|_[n+30>>1]<<16,i=_[n+24>>1]|_[n+26>>1]<<16,v[r+24>>1]=i,v[r+26>>1]=i>>>16,v[r+28>>1]=A,v[r+30>>1]=A>>>16,A=_[n+36>>1]|_[n+38>>1]<<16,i=_[n+32>>1]|_[n+34>>1]<<16,v[r+32>>1]=i,v[r+34>>1]=i>>>16,v[r+36>>1]=A,v[r+38>>1]=A>>>16,A=_[n+44>>1]|_[n+46>>1]<<16,i=_[n+40>>1]|_[n+42>>1]<<16,v[r+40>>1]=i,v[r+42>>1]=i>>>16,v[r+44>>1]=A,v[r+46>>1]=A>>>16,A=_[n+52>>1]|_[n+54>>1]<<16,i=_[n+48>>1]|_[n+50>>1]<<16,v[r+48>>1]=i,v[r+50>>1]=i>>>16,v[r+52>>1]=A,v[r+54>>1]=A>>>16,A=_[n+60>>1]|_[n+62>>1]<<16,i=_[n+56>>1]|_[n+58>>1]<<16,v[r+56>>1]=i,v[r+58>>1]=i>>>16,v[r+60>>1]=A,v[r+62>>1]=A>>>16,g[r+16|0]=0,v[r>>1]=32768|_[r>>1],A=_[y+2>>1])),A=A-h|0}i=1,v[2+((m<<1)+r|0)>>1]=A,e[M+12>>2]=r}if((0|(m=m+1|0))==6)break}P=r}if((0|t)==(0|(ee=(0|(r=ee+1|0))<=169?r:0)))break}}e[36454]=t}}function NA(t,A){var r,n,i=0,l=0,h=0,m=0,w=0,M=0,y=0,P=0,R=0,ee=0,ae=0,ue=0,be=0,_e=0,Ve=0,$e=0,it=0,ht=0,yt=0,Pt=0,Wt=0,cA=0,gA=0;oe=r=oe-48|0,V(+t),l=0|G(1),i=0|G(0),n=l;e:{t:{r:{if((w=2147483647&l)>>>0<=1074752122){if((1048575&l)==598523)break r;if(w>>>0<=1073928572){if((0|n)>0|(0|n)>=0){m=(t+=-1.5707963267341256)+-6077100506506192e-26,S[A>>3]=m,S[A+8>>3]=t-m-6077100506506192e-26,l=1;break e}m=(t+=1.5707963267341256)+6077100506506192e-26,S[A>>3]=m,S[A+8>>3]=t-m+6077100506506192e-26,l=-1;break e}if((0|n)>0|(0|n)>=0){m=(t+=-3.1415926534682512)+-12154201013012384e-26,S[A>>3]=m,S[A+8>>3]=t-m-12154201013012384e-26,l=2;break e}m=(t+=3.1415926534682512)+12154201013012384e-26,S[A>>3]=m,S[A+8>>3]=t-m+12154201013012384e-26,l=-2;break e}if(w>>>0<=1075594811){if(w>>>0<=1075183036){if((0|w)==1074977148)break r;if((0|n)>0|(0|n)>=0){m=(t+=-4.712388980202377)+-18231301519518578e-26,S[A>>3]=m,S[A+8>>3]=t-m-18231301519518578e-26,l=3;break e}m=(t+=4.712388980202377)+18231301519518578e-26,S[A>>3]=m,S[A+8>>3]=t-m+18231301519518578e-26,l=-3;break e}if((0|w)==1075388923)break r;if((0|n)>0|(0|n)>=0){m=(t+=-6.2831853069365025)+-2430840202602477e-25,S[A>>3]=m,S[A+8>>3]=t-m-2430840202602477e-25,l=4;break e}m=(t+=6.2831853069365025)+2430840202602477e-25,S[A>>3]=m,S[A+8>>3]=t-m+2430840202602477e-25,l=-4;break e}if(w>>>0>1094263290)break t}i=(it=(m=t+-1.5707963267341256*(P=.6366197723675814*t+6755399441055744-6755399441055744))-(ee=6077100506506192e-26*P))<-.7853981633974483,l=he(P)<2147483648?~~P:-2147483648,i?(l=l-1|0,ee=6077100506506192e-26*(P+=-1),m=t+-1.5707963267341256*P):it>.7853981633974483&&(l=l+1|0,ee=6077100506506192e-26*(P+=1),m=t+-1.5707963267341256*P),t=m-ee,S[A>>3]=t,V(+t),i=0|G(1),G(0),((h=w>>>20|0)-(i>>>20&2047)|0)<17||(ee=m,t=(m-=t=6077100506303966e-26*P)-(ee=20222662487959506e-37*P-(ee-m-t)),S[A>>3]=t,V(+t),i=0|G(1),G(0),(h-(i>>>20&2047)|0)<50||(ee=m,t=(m-=t=20222662487111665e-37*P)-(ee=84784276603689e-45*P-(ee-m-t)),S[A>>3]=t)),S[A+8>>3]=m-t-ee;break e}if(w>>>0>=2146435072)t-=t,S[A>>3]=t,S[A+8>>3]=t,l=0;else{for(D(0,0|i),D(1,1048575&n|1096810496),t=+Q(),l=0,i=1;h=(r+16|0)+(l<<3)|0,m=+(0|(l=he(t)<2147483648?~~t:-2147483648)),S[h>>3]=m,t=16777216*(t-m),l=1,h=i,i=0,h;);for(S[r+32>>3]=t,l=2;l=(i=l)-1|0,S[(r+16|0)+(i<<3)>>3]==0;);if(ht=r+16|0,h=0,oe=M=oe-560|0,w=F($e=(0|(w=((l=(w>>>20|0)-1046|0)-3|0)/24|0))>0?w:0,-24)+l|0,((ae=e[28105])+(y=(_e=i+1|0)-1|0)|0)>=0)for(l=ae+_e|0,i=$e-y|0;S[(M+320|0)+(h<<3)>>3]=(0|i)<0?0:+e[112432+(i<<2)>>2],i=i+1|0,(0|l)!=(0|(h=h+1|0)););for(be=w-24|0,l=0,h=(0|ae)>0?ae:0,ue=(0|_e)<=0;;){if(ue)t=0;else for(R=l+y|0,i=0,t=0;t=S[(i<<3)+ht>>3]*S[(M+320|0)+(R-i<<3)>>3]+t,(0|_e)!=(0|(i=i+1|0)););if(S[(l<<3)+M>>3]=t,i=(0|l)==(0|h),l=l+1|0,i)break}cA=47-w|0,yt=48-w|0,gA=w-25|0,l=ae;t:{for(;;){if(t=S[(l<<3)+M>>3],i=0,h=l,!(R=(0|l)<=0))for(;ue=(M+480|0)+(i<<2)|0,y=he(m=5960464477539063e-23*t)<2147483648?~~m:-2147483648,y=he(t=-16777216*(m=+(0|y))+t)<2147483648?~~t:-2147483648,e[ue>>2]=y,t=S[((h=h-1|0)<<3)+M>>3]+m,(0|l)!=(0|(i=i+1|0)););t=fi(t,be),t+=-8*Ce(.125*t),t-=+(0|(ue=he(t)<2147483648?~~t:-2147483648));r:{a:{n:{if(Pt=(0|be)<=0){if(be)break n;y=e[476+((l<<2)+M|0)>>2]>>23}else Ve=h=(l<<2)+M|0,h=(y=e[h+476>>2])-((i=y>>yt)<<yt)|0,e[Ve+476>>2]=h,ue=i+ue|0,y=h>>cA;if((0|y)<=0)break r;break a}if(y=2,!(t>=.5)){y=0;break r}}if(i=0,h=0,!R)for(;Wt=e[(Ve=(M+480|0)+(i<<2)|0)>>2],R=16777215,h||(R=16777216,Wt)?(e[Ve>>2]=R-Wt,h=1):h=0,(0|l)!=(0|(i=i+1|0)););a:if(!Pt){i=8388607;n:switch(0|gA){case 1:i=4194303;break;case 0:break n;default:break a}e[476+(R=(l<<2)+M|0)>>2]=e[R+476>>2]&i}ue=ue+1|0,(0|y)==2&&(t=1-t,y=2,h&&(t-=fi(1,be)))}if(t!=0)break;if(h=0,!((0|ae)>=(0|(i=l)))){for(;h=e[(M+480|0)+((i=i-1|0)<<2)>>2]|h,(0|i)>(0|ae););if(h){for(w=be;w=w-24|0,!e[(M+480|0)+((l=l-1|0)<<2)>>2];);break t}}for(i=1;h=i,i=i+1|0,!e[(M+480|0)+(ae-h<<2)>>2];);for(h=l+h|0;;){if(y=l+_e|0,l=l+1|0,S[(M+320|0)+(y<<3)>>3]=e[112432+($e+l<<2)>>2],i=0,t=0,(0|_e)>0)for(;t=S[(i<<3)+ht>>3]*S[(M+320|0)+(y-i<<3)>>3]+t,(0|_e)!=(0|(i=i+1|0)););if(S[(l<<3)+M>>3]=t,!((0|l)<(0|h)))break}l=h}(t=fi(t,24-w|0))>=16777216?(be=(M+480|0)+(l<<2)|0,i=he(m=5960464477539063e-23*t)<2147483648?~~m:-2147483648,h=he(t=-16777216*+(0|i)+t)<2147483648?~~t:-2147483648,e[be>>2]=h,l=l+1|0):(i=he(t)<2147483648?~~t:-2147483648,w=be),e[(M+480|0)+(l<<2)>>2]=i}if(t=fi(1,w),!((0|l)<0)){for(i=l;h=i,S[(i<<3)+M>>3]=t*+e[(M+480|0)+(i<<2)>>2],i=i-1|0,t*=5960464477539063e-23,h;);if(R=0,!((0|l)<0))for(w=(0|ae)>0?ae:0,h=l;;){for(be=w>>>0<R>>>0?w:R,ae=l-h|0,i=0,t=0;t=S[115200+(i<<3)>>3]*S[(i+h<<3)+M>>3]+t,_e=(0|i)!=(0|be),i=i+1|0,_e;);if(S[(M+160|0)+(ae<<3)>>3]=t,h=h-1|0,i=(0|l)!=(0|R),R=R+1|0,!i)break}}if(t=0,(0|l)>=0)for(i=l;h=i,i=i-1|0,t+=S[(M+160|0)+(h<<3)>>3],h;);if(S[r>>3]=y?-t:t,t=S[M+160>>3]-t,i=1,(0|l)>0)for(;t+=S[(M+160|0)+(i<<3)>>3],h=(0|i)!=(0|l),i=i+1|0,h;);S[r+8>>3]=y?-t:t,oe=M+560|0,l=7&ue,t=S[r>>3],(0|n)<0?(S[A>>3]=-t,S[A+8>>3]=-S[r+8>>3],l=0-l|0):(S[A>>3]=t,S[A+8>>3]=S[r+8>>3])}}return oe=r+48|0,l}function tA(){var t=0,A=0,r=0,n=0,i=0,l=0,h=0,m=0,w=0,M=0,y=0,P=0,R=0,ee=0,ae=0,ue=0,be=0,_e=0,Ve=0,$e=0,it=0,ht=0,yt=0,Pt=0,Wt=0,cA=0,gA=0,SA=0,VA=0,qA=0,Cr=0,Yr=0,Xr=0,ls=0,as=0,xs=0,Ms=0,zs=0,Os=0,ga=0,na=0,ha=0,pa=0,Gn=0,Fn=0,Ln=0;e[55925]=0,t=e[56772],e[56772]=t+1,r=e[55961],R=e[55922],ae=Vi(39.89822670059037*(A=+(0|t))),M=Vi(22.30530784048753*A),A=+(0|R)/50*(+(0|r)/100)*(Vi(14.765485471872028*A)+(ae+M))*10,t=he(A)<2147483648?~~A:-2147483648,e[56607]=t+e[56607];e:if(!(e[55923]<=0)){for(;;){for(r=rr(e[33209],0,1103515245,0),t=fe,t=Cn(r=r+12345|0,t=r>>>0<12345?t+1|0:t),e[33209]=t,t=8191+((t>>>0)%16383|0)|0,e[55929]=t,qA=+(0|t),A=.75*S[28387]+qA,S[28387]=A,Cr=(0|(t=e[55924]))>(0|(yt=e[55928]))?.5*A:A,as=(i=.033*+e[56652])>0?1-i:1,r=e[56650]<<2,cA=(0|(Wt=e[55921]))==1&&(0|r)>263?263:r,xs=111136+((Yr=e[56651])<<1)|0,Ms=111136+((gA=e[56658])<<1)|0,zs=(0|gA)>0,Xr=e[55918],Os=F(Xr,40),ga=e[55925],na=S[27967],ls=e[56607],A=S[27979],be=S[27969],m=S[27970],SA=e[56780],$e=e[56654],P=S[27976],ee=S[27972],_e=S[28388],Ve=S[28389],R=e[55927],n=S[28383],y=e[55926],it=e[55956],ha=S[27975],M=S[28131],l=S[28130],h=S[28123],ue=S[28121],w=S[28122],Pt=S[28120],ht=S[28119],pa=S[28129],Gn=S[28128],Fn=S[28127],Ln=Aa(0*S[27968]),VA=0;;){ae=l;t:{r:switch(Wt-1|0){case 0:_e=0,l=ue*h,h=w,w=n=l+(ht*(_e=(0|t)<=2?S[111312+(t<<3)>>3]:_e)+Pt*h);break t;case 1:if(n=0,(0|t)>=(0|R)){Ve=0;break t}l=S[27965]-S[27966],S[27965]=l,n=.028*(Ve=l+Ve);break t;case 2:if(!y){it=100,n=0;break t}it=100,r=he(l=+(0|t)/+(0|y)*100)<2147483648?~~l:-2147483648,n=+v[110928+((0|r)%100<<1)>>1],n=A*((+v[110928+((r+1|0)%100<<1)>>1]-n)*(l-+(0|r))+n);break t;case 3:break r;default:break t}y?(it=256,r=he(l=+(0|t)/+(0|y)*256)<2147483648?~~l:-2147483648,n=+v[111344+((0|r)%256<<1)>>1],n=A*((+v[111344+((r+1|0)%256<<1)>>1]-n)*(l-+(0|r))+n)):(it=256,n=0)}if((0|t)>=(0|y)&&((0|ls)>0?(t=(0|Os)/(0|ls)|0,P=0,ee=0,ee=gA>>>0<=87?.001*+v[Ms>>1]:ee,P=Yr>>>0<=87?.001*+v[xs>>1]*.1:P,yt=t>>zs,R=(0|(r=(y=(t-1|0)<=(0|cA))?t-2|0:cA))<=40?40:r,l=+v[111776+((R=y||(0|r)<40?R:cA)<<1)>>1],S[27966]=l,ue=l,l=+(0|R),S[27965]=ue*l*.333,$e=(0|(r=t-R|0))>(0|$e)?$e:r,SA=0-(r=(0|SA)<0?0-$e|0:$e)|0,ht=(l*=.00833)*l,ht*=1-(Pt=(ue=(l=fr(na*+((0|Xr)/(0|R)|0)))*Ln)+ue)-(ue=l*-l),t=(0|(y=t+r|0))!=4):(e[55930]=0,e[55931]=0,e[55932]=0,e[55933]=0,yt=4,P=0,ee=0,y=4,t=0),m=(t|=!ga)?i:m,be=t?as:be,t=0),t=t+1|0,l=n=pa*M+(Fn*n+Gn*ae),M=ae,(0|(VA=VA+1|0))==4)break}if(e[55926]=y,e[55956]=it,e[55924]=t,S[28383]=n,e[55927]=R,S[28389]=Ve,S[28388]=_e,S[27972]=ee,S[27976]=P,e[55928]=yt,e[56654]=$e,e[56780]=SA,S[27970]=m,S[27969]=be,S[28122]=w,S[28120]=Pt,S[28119]=ht,S[28123]=h,S[28121]=ue,S[28131]=M,S[28130]=n,(0|Wt)==5&&(n=6e3*((A=+(0|t)/+(0|y))+A+-1),S[28383]=n),A=n*be+S[28384]*m,S[28383]=A,S[28384]=A,(0|t)<(0|R)&&(A=P*qA+A,S[28383]=A),m=Cr*S[27974],ae=A*S[27973]+m,M=0,e[55916]!=2&&(M=S[27987],w=S[27986],S[27987]=w,A=m+A*ee,S[27986]=A,m=S[28059],h=S[28058],S[28059]=h,A=m*S[28057]+(S[28055]*(M*S[27985]+(S[27983]*A+w*S[27984]))+h*S[28056]),S[28058]=A,M=S[28049],m=S[28051],w=S[28048],h=S[28047],i=S[28050],S[28051]=i,A=M*m+(h*A+w*i),S[28050]=A,M=S[28043],m=S[28041],w=S[28040],h=S[28039],i=S[28042],S[28043]=i,A=m*M+(h*A+w*i),S[28042]=A,M=S[28035],m=S[28033],w=S[28032],h=S[28031],i=S[28034],S[28035]=i,A=m*M+(h*A+w*i),S[28034]=A,M=S[28027],m=S[28025],w=S[28024],h=S[28023],i=S[28026],S[28027]=i,A=m*M+(h*A+w*i),S[28026]=A,M=S[28019],m=S[28017],w=S[28016],h=S[28015],i=S[28018],S[28019]=i,A=m*M+(h*A+w*i),S[28018]=A,M=S[28011],m=S[28009],w=S[28008],h=S[28007],i=S[28010],S[28011]=i,A=m*M+(h*A+w*i),S[28010]=A,M=S[28003],m=S[28001],w=S[28e3],h=S[27999],i=S[28002],S[28003]=i,A=m*M+(h*A+w*i),S[28002]=A,M=S[27995],m=S[27993],w=S[27992],h=S[27991],i=S[27994],S[27995]=i,M=m*M+(h*A+w*i),S[27994]=M),A=S[28385],S[28385]=ae,m=S[28075],w=S[28074],S[28075]=w,h=S[28067],i=S[28066],S[28067]=i,m=m*S[28073]+(S[28071]*ae+w*S[28072]),S[28074]=m,w=h*S[28065]+(S[28063]*ae+i*S[28064]),S[28066]=w,h=S[28081],i=S[28083],l=S[28079],n=S[28080],P=S[28082],S[28083]=P,ae=h*i+(l*(A=ha*Cr+ae-A)+n*P),S[28082]=ae,h=S[28091],i=S[28089],l=S[28087],n=S[28088],P=S[28090],S[28091]=P,h=i*h+(l*A+n*P),S[28090]=h,i=S[28099],l=S[28097],n=S[28095],P=S[28096],ee=S[28098],S[28099]=ee,i=l*i+(n*A+P*ee),S[28098]=i,l=S[28107],n=S[28105],P=S[28103],ee=S[28104],be=S[28106],S[28107]=be,l=n*l+(P*A+ee*be),S[28106]=l,n=S[28115],P=S[28113],ee=S[28111],be=S[28112],_e=S[28114],S[28115]=_e,n=P*n+(ee*A+be*_e),S[28114]=n,P=S[28139],ee=S[28137],be=S[28136],_e=S[28135],Ve=S[27971],ue=S[28138],S[28139]=ue,A=ee*P+(_e*(A*Ve-(n-(l-(i-(h-(ae-(M+m+w)))))))+be*ue),S[28138]=A,A=S[27977]*(A*+e[50779]),n=+(0|(t=he(A)<2147483648?~~A:-2147483648)),(0|(t=e[50776]))<e[50773]&&(y=t+1|0,r=e[50772],(R=e[50774])?(e[50776]=y,t=F(R,g[t+r|0])):(R=d[t+r|0],r=g[r+y|0],e[50776]=t+2,t=R|r<<8),n+=+((0|F(e[50775],(0|F(t,e[50780]))/1024|0))/40|0)),(0|(t=e[55962]))<=63&&(e[55962]=t+1,n=n*+(0|t)*.015625),(0|(t=e[55963]))<=0||(t=t-1|0,e[55963]=t,n=n*+(0|t)*.015625,t||(e[55962]=0)),r=(t=e[51293])+1|0,e[51293]=r,y=F(v[205184+(t<<1)>>1],e[50755])>>8,t=he(n)<2147483648?~~n:-2147483648,(0|r)>=5500&&(e[51293]=0),r=e[51290],e[51290]=r+1,t=(0|(t=(0|(t=t+y|0))<=-32768?-32768
2810:t))>=32767?32767:t,g[0|r]=t,r=e[51290],e[51290]=r+1,g[0|r]=t>>>8,y=(r=e[51292])+1|0,e[51292]=y,v[205184+(r<<1)>>1]=t,(0|y)>=5500&&(e[51292]=0),y=1,e[56606]=e[56606]+1,re[54046]<e[51290]+2>>>0)break e;if(t=e[55925]+1|0,e[55925]=t,!((0|t)<e[55923]))break}y=0}return y}function iA(t,A,r,n,i,l,h){var m,w,M,y=0,P=0,R=0,ee=0,ae=0,ue=0,be=0,_e=0,Ve=0,$e=0,it=0,ht=0,yt=0,Pt=0,Wt=0,cA=0,gA=0;oe=m=oe-80|0,e[m+76>>2]=A,M=m+55|0,w=m+56|0;e:{t:{r:{a:{n:for(;;){if(R=A,(2147483647^_e)<(0|y))break a;_e=y+_e|0;c:{o:{f:{if(P=d[0|(y=R)])for(;;){l:{i:if(A=255&P){if((0|A)!=37)break l;for(P=y;;){if(d[P+1|0]!=37){A=P;break i}if(y=y+1|0,ae=d[P+2|0],P=A=P+2|0,(0|ae)!=37)break}}else A=y;if((0|(y=y-R|0))>(0|(Wt=2147483647^_e)))break a;if(t&&Hs(t,R,y),y)continue n;e[m+76>>2]=A,y=A+1|0,Ve=-1,d[A+2|0]!=36|g[A+1|0]-48>>>0>=10||(Ve=g[A+1|0]-48|0,ht=1,y=A+3|0),e[m+76>>2]=y,ue=0;i:if((A=(P=g[0|y])-32|0)>>>0>31)be=y;else if(be=y,75913&(A=1<<A))for(;;){if(be=y+1|0,e[m+76>>2]=be,ue|=A,(A=(P=g[y+1|0])-32|0)>>>0>=32)break i;if(y=be,!(75913&(A=1<<A)))break}i:if((0|P)!=42){if((0|($e=ji(m+76|0)))<0)break a;P=e[m+76>>2]}else{if(d[be+2|0]!=36|g[be+1|0]-48>>>0>=10){if(ht)break f;if(P=be+1|0,!t){e[m+76>>2]=P,ht=0,$e=0;break i}A=e[r>>2],e[r>>2]=A+4,ht=0,A=e[A>>2]}else e[((g[be+1|0]<<2)+i|0)-192>>2]=10,P=be+3|0,ht=1,A=e[((g[be+1|0]<<3)+n|0)-384>>2];if(e[m+76>>2]=P,$e=A,(0|A)>=0)break i;$e=0-$e|0,ue|=8192}if(y=0,ee=-1,d[0|P]==46)if(d[P+1|0]!=42)e[m+76>>2]=P+1,ee=ji(m+76|0),A=e[m+76>>2],Pt=1;else{if(d[P+3|0]!=36|g[P+2|0]-48>>>0>=10){if(ht)break f;A=P+2|0,ee=0,t&&(P=e[r>>2],e[r>>2]=P+4,ee=e[P>>2])}else e[((g[P+2|0]<<2)+i|0)-192>>2]=10,A=P+4|0,ee=e[((g[P+2|0]<<3)+n|0)-384>>2];e[m+76>>2]=A,Pt=~ee>>>31|0}else A=P,Pt=0;for(;;){if(it=y,be=28,ae=A,(y=g[0|A])-123>>>0<4294967238)break r;if(A=ae+1|0,!((y=d[123983+(y+F(it,58)|0)|0])-1>>>0<8))break}e[m+76>>2]=A;i:{h:{if((0|y)!=27){if(!y)break r;if((0|Ve)>=0){e[(Ve<<2)+i>>2]=y,y=e[4+(P=(Ve<<3)+n|0)>>2],e[m+64>>2]=e[P>>2],e[m+68>>2]=y;break h}if(!t)break c;jr(m- -64|0,y,r,h);break i}if((0|Ve)>=0)break r}if(y=0,!t)continue n}P=-65537&ue,ue=8192&ue?P:ue,Ve=0,yt=84065,be=w;i:{h:{b:{m:{C:{p:{x:{w:{B:{F:{Y:{R:{M:{y:{G:{switch(y=g[0|ae],(y=it&&(15&y)==3?-33&y:y)-88|0){case 11:break i;case 9:case 13:case 14:case 15:break h;case 27:break x;case 12:case 17:break F;case 23:break Y;case 0:case 32:break R;case 24:break M;case 22:break y;case 29:break G;case 1:case 2:case 3:case 4:case 5:case 6:case 7:case 8:case 10:case 16:case 18:case 19:case 20:case 21:case 25:case 26:case 28:case 30:case 31:break o}switch(y-65|0){case 0:case 4:case 5:case 6:break h;case 2:break C;case 1:case 3:break o}if((0|y)==83)break p;break o}P=e[m+64>>2],ae=e[m+68>>2],yt=84065;break B}y=0;y:switch(255&it){case 0:case 1:case 6:e[e[m+64>>2]>>2]=_e;continue n;case 2:R=e[m+64>>2],e[R>>2]=_e,e[R+4>>2]=_e>>31;continue n;case 3:v[e[m+64>>2]>>1]=_e;
vendor: 24,247 bytes, line 2810
2810continue n;case 4:g[e[m+64>>2]]=_e;continue n;case 7:break y;default:continue n}R=e[m+64>>2],e[R>>2]=_e,e[R+4>>2]=_e>>31;continue n}ee=ee>>>0<=8?8:ee,ue|=8,y=120}if(R=w,cA=32&y,(P=e[m+64>>2])|(ae=e[m+68>>2]))for(;g[0|(R=R-1|0)]=cA|d[124512+(15&P)|0],gA=!ae&P>>>0>15|!!(0|ae),it=ae,ae=ae>>>4|0,P=(15&it)<<28|P>>>4,gA;);if(!(e[m+64>>2]|e[m+68>>2])|!(8&ue))break w;yt=84065+(y>>>4|0)|0,Ve=2;break w}if(y=w,ae=R=e[m+68>>2],R|(P=e[m+64>>2]))for(;g[0|(y=y-1|0)]=7&P|48,it=!ae&P>>>0>7|!!(0|ae),ae=(R=ae)>>>3|0,P=(7&R)<<29|P>>>3,it;);if(R=y,!(8&ue))break w;ee=(0|(y=w-R|0))<(0|ee)?ee:y+1|0;break w}P=e[m+64>>2],ae=y=e[m+68>>2],(0|y)<0?(ae=R=0-(ae+!!(0|P)|0)|0,P=0-P|0,e[m+64>>2]=P,e[m+68>>2]=R,Ve=1,yt=84065):2048&ue?(Ve=1,yt=84066):yt=(Ve=1&ue)?84067:84065}R=fn(P,ae,w)}if((0|ee)<0&&Pt)break a;if(ue=Pt?-65537&ue:ue,!(ee|!!((y=e[m+64>>2])|(P=e[m+68>>2])))){R=w,ee=0;break o}ee=(0|(y=!(y|P)+(w-R|0)|0))<(0|ee)?ee:y;break o}if(be=(y=(y=Wn(R=(y=e[m+64>>2])||84639,0,ae=ee>>>0>=2147483647?2147483647:ee))?y-R|0:ae)+R|0,(0|ee)>=0){ue=P,ee=y;break o}if(ue=P,ee=y,d[0|be])break a;break o}if(ee){P=e[m+64>>2];break m}y=0,Ws(t,32,$e,0,ue);break b}e[m+12>>2]=0,e[m+8>>2]=e[m+64>>2],P=m+8|0,e[m+64>>2]=P,ee=-1}y=0;m:{for(;;){if(!(R=e[P>>2]))break m;if(!((R=(0|(ae=ui(m+4|0,R)))<0)|ae>>>0>ee-y>>>0)){if(P=P+4|0,ee>>>0>(y=y+ae|0)>>>0)continue;break m}break}if(R)break t}if(be=61,(0|y)<0)break r;if(Ws(t,32,$e,y,ue),y)for(be=0,P=e[m+64>>2];;){if(!(R=e[P>>2])||(be=(R=ui(m+4|0,R))+be|0)>>>0>y>>>0)break b;if(Hs(t,m+4|0,R),P=P+4|0,!(y>>>0>be>>>0))break}else y=0}Ws(t,32,$e,y,8192^ue),y=(0|y)<(0|$e)?$e:y;continue n}if((0|ee)<0&&Pt)break a;if(be=61,(0|(y=0|LA[0|l](t,S[m+64>>3],$e,ee,ue,y)))>=0)continue n;break r}g[m+55|0]=e[m+64>>2],ee=1,R=M,ue=P;break o}P=d[y+1|0],y=y+1|0}if(t)break e;if(!ht)break c;for(y=1;;){if(t=e[(y<<2)+i>>2]){if(jr((y<<3)+n|0,t,r,h),_e=1,(0|(y=y+1|0))!=10)continue;break e}break}if(_e=1,y>>>0>=10)break e;for(;;){if(e[(y<<2)+i>>2])break f;if((0|(y=y+1|0))==10)break}break e}be=28;break r}if((0|(P=(0|ee)>(0|(ae=be-R|0))?ee:ae))>(2147483647^Ve))break a;if(be=61,(0|Wt)<(0|(y=(0|(ee=P+Ve|0))<(0|$e)?$e:ee)))break r;Ws(t,32,y,ee,ue),Hs(t,yt,Ve),Ws(t,48,y,ee,65536^ue),Ws(t,48,P,ae,0),Hs(t,R,ae),Ws(t,32,y,ee,8192^ue);continue}break}_e=0;break e}be=61}e[56798]=be}_e=-1}return oe=m+80|0,_e}function qt(t,A,r,n,i,l,h,m,w){var M,y,P,R=0,ee=0,ae=0,ue=0,be=0,_e=0,Ve=0,$e=0,it=0,ht=0,yt=0,Pt=0,Wt=0,cA=0,gA=0,SA=0,VA=0,qA=0,Cr=0,Yr=0,Xr=0,ls=0,as=0,xs=0,Ms=0,zs=0,Os=0,ga=0,na=0,ha=0,pa=0;oe=M=oe-96|0,yt=65535&w,be=-2147483648&(i^w),SA=Ve=65535&i;e:{if(!((y=w>>>16&32767)-32767>>>0>4294934529&(P=i>>>16&32767)-32767>>>0>=4294934530)){if(R=n,!(!n&(0|($e=it=2147483647&i))==2147418112?!(A|r):$e>>>0<2147418112)){ae=n,be=32768|i;break e}if(!(!(i=m)&(0|(_e=it=2147483647&w))==2147418112?!(l|h):_e>>>0<2147418112)){ae=m,be=32768|w,A=l,r=h;break e}if(!(A|R|2147418112^$e|r)){if(!(i|l|h|_e)){be=2147450880,A=0,r=0;break e}be|=2147418112,A=0,r=0;break e}if(!(i|l|2147418112^_e|h)){if(i=A|R,n=r|$e,A=0,r=0,!(n|i)){be=2147450880;break e}be|=2147418112;break e}if(!(A|R|r|$e)){A=0,r=0;break e}if(!(i|l|h|_e)){A=0,r=0;break e}(0|$e)==65535|$e>>>0<65535&&(it=(R=!(n|Ve))<<6,i=ce(w=R?A:n)+32|0,ea(M+80|0,A,r,n,Ve,(w=it+((0|(w=ce(R?r:Ve)))==32?i:w)|0)-15|0),ht=16-w|0,n=e[M+88>>2],SA=e[M+92>>2],r=e[M+84>>2],A=e[M+80>>2]),_e>>>0>65535||(Ve=(w=!(m|yt))<<6,R=ce(i=w?l:m)+32|0,ea(M- -64|0,l,h,m,yt,(i=Ve+((0|(i=ce(w?h:yt)))==32?R:i)|0)-15|0),ht=16+(ht-i|0)|0,m=e[M+72>>2],yt=e[M+76>>2],l=e[M+64>>2],h=e[M+68>>2])}if(i=l,l=h<<15|l>>>17,zs=r,it=rr(Pt=-32768&(w=i<<15),i=0,r,0),gA=i=fe,Os=l,$e=A,A=rr(l,0,A,0),l=fe+i|0,r=A>>>0>(w=A+it|0)>>>0?l+1|0:l,R=0,A=rr($e,ee,Pt,ee),i=(l=w)+fe|0,Ve=i=A>>>0>(_e=R+A|0)>>>0?i+1|0:i,ga=(0|l)==(0|i)&R>>>0>_e>>>0|i>>>0<l>>>0,na=n,Wt=rr(Pt,ee,n,0),ha=fe,A=rr(zs,ee,Os,ee),R=fe+ha|0,R=A>>>0>(cA=A+Wt|0)>>>0?R+1|0:R,A=yt<<15|m>>>17,n=rr(VA=m<<15|h>>>17,0,$e,ee),l=fe+R|0,Cr=l=n>>>0>(qA=n+cA|0)>>>0?l+1|0:l,l=(n=(0|r)==(0|gA)&w>>>0<it>>>0|r>>>0<gA>>>0)+l|0,yt=l=r>>>0>(Yr=r+qA|0)>>>0?l+1|0:l,h=Yr,r=l,SA=rr(Pt,ee,Xr=65536|SA,ue),pa=fe,n=rr(na,ae,Os,ee),i=fe+pa|0,w=i=n>>>0>(ls=n+SA|0)>>>0?i+1|0:i,A=rr(as=-2147483648|A,0,$e,ee),l=fe+i|0,l=A>>>0>(xs=A+ls|0)>>>0?l+1|0:l,A=rr(VA,ae,zs,ee),Ms=l,l=l+fe|0,it=A>>>0>(gA=A+xs|0)>>>0?l+1|0:l,i=r+gA|0,l=Pt=(A=0)>>>0>($e=A+h|0)>>>0?i+1|0:i,r=(A=$e+ga|0)>>>0<$e>>>0?l+1|0:l,ht=((P+y|0)+ht|0)-16383|0,n=rr(as,ae,zs,ee),m=fe,i=rr(Xr,ae,Os,ee),l=fe+m|0,ue=(0|m)==(0|(l=i>>>0>(h=i+n|0)>>>0?l+1|0:l))&n>>>0>h>>>0|l>>>0<m>>>0,m=l,i=rr(VA,ae,na,ae),l=fe+l|0,i=l=(n=i+h|0)>>>0<i>>>0?l+1|0:l,h=(0|l)==(0|m)&n>>>0<h>>>0|l>>>0<m>>>0,l=0,l=(m=h)>>>0>(h=h+ue|0)>>>0?1:l,m=h,h=rr(as,ae,Xr,ae),l=fe+l|0,ga=m=m+h|0,h=h>>>0>m>>>0?l+1|0:l,m=n,ee=i,i=(0|R)==(0|ha)&Wt>>>0>cA>>>0|R>>>0<ha>>>0,l=0,i=((ue=R=(0|R)==(0|Cr)&cA>>>0>qA>>>0|R>>>0>Cr>>>0)>>>0>(R=i+R|0)>>>0?1:l)+ee|0,l=h,ue=i=(n=n+R|0)>>>0<R>>>0?i+1|0:i,Wt=n,i=n=(0|i)==(0|ee)&n>>>0<m>>>0|i>>>0<ee>>>0,R=n=n+ga|0,h=l=i>>>0>n>>>0?l+1|0:l,i=rr(VA,ae,Xr,ae),ee=fe,n=rr(as,ae,na,ae),l=fe+ee|0,n=l=n>>>0>(m=n+i|0)>>>0?l+1|0:l,i=(l=(0|ee)==(0|l)&i>>>0>m>>>0|l>>>0<ee>>>0)+h|0,h=i=n>>>0>(cA=n+R|0)>>>0?i+1|0:i,l=m+ue|0,i=l=(n=(i=0)+Wt|0)>>>0<i>>>0?l+1|0:l,m=(0|ue)==(0|l)&n>>>0<Wt>>>0|l>>>0<ue>>>0,l=h,l=(h=m+(ee=cA)|0)>>>0<m>>>0?l+1|0:l,Wt=h,m=n,R=i,i=(n=(n=(n=(0|w)==(0|pa)&SA>>>0>ls>>>0|w>>>0<pa>>>0)+(w=(0|w)==(0|Ms)&ls>>>0>xs>>>0|w>>>0>Ms>>>0)|0)+(i=(0|it)==(0|Ms)&gA>>>0<xs>>>0|it>>>0<Ms>>>0)|0)+R|0,l=h=l,R=h=(m=(0|(i=(n=w=(ue=it)+m|0)>>>0<ue>>>0?i+1|0:i))==(0|R)&m>>>0>n>>>0|i>>>0<R>>>0)+Wt|0,h=l=m>>>0>h>>>0?l+1|0:l,m=n,l=0,w=i,i=i+((ee=ue=(0|yt)==(0|Pt)&$e>>>0<Yr>>>0|yt>>>0>Pt>>>0)>>>0>(ue=ue+((0|yt)==(0|Cr)&qA>>>0>Yr>>>0|yt>>>0<Cr>>>0)|0)>>>0?1:l)|0,l=h,w=l=(h=m=(0|(i=(n=n+ue|0)>>>0<ue>>>0?i+1|0:i))==(0|w)&n>>>0<m>>>0|i>>>0<w>>>0)>>>0>(m=m+R|0)>>>0?l+1|0:l,65536&l?ht=ht+1|0:(R=Ve>>>31|0,l=w<<1|m>>>31,m=m<<1|i>>>31,w=l,l=i<<1|n>>>31,n=n<<1|r>>>31,i=l,l=Ve<<1|_e>>>31,_e<<=1,Ve=l,l=r<<1|A>>>31,A=A<<1|R,r=l|(h=0)),(0|ht)>=32767)be|=2147418112,A=0,r=0;else{t:{if((0|ht)<=0){if((h=1-ht|0)>>>0<=127){ea(M+48|0,_e,Ve,A,r,l=ht+127|0),ea(M+32|0,n,i,m,w,l),Da(M+16|0,_e,Ve,A,r,h),Da(M,n,i,m,w,h),_e=e[M+32>>2]|e[M+16>>2]|!!(e[M+48>>2]|e[M+56>>2]|e[M+52>>2]|e[M+60>>2]),Ve=e[M+36>>2]|e[M+20>>2],A=e[M+40>>2]|e[M+24>>2],r=e[M+44>>2]|e[M+28>>2],n=e[M>>2],i=e[M+4>>2],h=e[M+8>>2],l=e[M+12>>2];break t}A=0,r=0;break e}h=m,l=65535&w|ht<<16}ae|=h,be|=l,(!A&(0|r)==-2147483648?!(_e|Ve):(0|r)>0|(0|r)>=0)?A|_e|-2147483648^r|Ve?(A=n,r=i):(R=be,be=(n=(0|(l=i))==(0|(r=(r=A=1&n)>>>0>(A=A+n|0)>>>0?l+1|0:l))&A>>>0<n>>>0|r>>>0<l>>>0)>>>0>(ae=n+ae|0)>>>0?R+1|0:R):(n=(0|i)==(0|(r=(A=n+1|0)?i:i+1|0))&A>>>0<n>>>0|r>>>0<i>>>0,i=be,be=(ae=n+ae|0)>>>0<n>>>0?i+1|0:i)}}e[t>>2]=A,e[t+4>>2]=r,e[t+8>>2]=ae,e[t+12>>2]=be,oe=M+96|0}function zA(t,A,r,n,i,l,h){var m,w=0,M=0,y=0,P=0,R=0,ee=0,ae=0,ue=0,be=0,_e=0,Ve=0,$e=0,it=0,ht=0,yt=0;oe=m=oe-480|0,e[m+476>>2]=0,e[m+456>>2]=0,e[m+460>>2]=0,e[m+448>>2]=0,e[m+452>>2]=0,e[m+440>>2]=0,e[m+444>>2]=0,e[m+432>>2]=0,e[m+436>>2]=0,w=0;e:if(e[t+684>>2]){for(ae=h?e[h>>2]:ae;y=d[A+w|0],g[(m+112|0)+w|0]=y,M=w+1|0,y&&(y=w>>>0<158,w=M,y););if(g[M+(m+112|0)|0]=0,!((_e=268435456&l)|!(8&e[47197]))){M=0;t:if(223&(y=d[0|A]))for(w=0;;){if(g[(m+272|0)+w|0]=y,!(223&(y=d[(M=w+1|0)+A|0])))break t;if(P=w>>>0<118,w=M,!P)break}g[(w=m+272|0)+M|0]=0,e[m+48>>2]=w,ss(e[47195],(0|l)>=0?87019:86877,m+48|0)}e[m+464>>2]=A,e[t+8208>>2]=0,e[t+8212>>2]=0,i&&(g[0|i]=0);t:{r:if(223&(w=d[0|A]))for(Ve=536870912&l,$e=4096&l,it=m+105|0,M=A,y=0;;){P=or(m+476|0,M),be=!!(0|Wr(e[m+476>>2]))+be|0,ee=d[7668+(w=(R=255&w)+t|0)|0];a:if(!((ue=e[m+476>>2])-48>>>0<10|ue-2406>>>0<10)|(be?d[t+170|0]:0)){n:if((y=e[m+476>>2]-e[t+600>>2]|0)>>>0>127||!(y=e[6192+((y<<2)+t|0)>>2])){if(ee){for(ue=5168+((R<<2)+t|0)|0,ee=ee+(w=d[w+7924|0])|0,ht=R|d[M+1|0]<<8,y=0;e[7184+(M=(w<<2)+t|0)>>2]==(0|ht)&&(e[m+472>>2]=e[m+464>>2],dA(t,m+472|0,A,2,e[M+6704>>2],m+432|0,l,ae),(0|(M=e[m+432>>2]))>0&&(M=M+35|0,e[m+432>>2]=M),y=1,dA(t,m+464|0,A,1,e[ue>>2],m+448|0,l,ae),e[m+448>>2]>(0|M)||(M=e[m+444>>2],e[m+456>>2]=e[m+440>>2],e[m+460>>2]=M,M=e[m+436>>2],e[m+448>>2]=e[m+432>>2],e[m+452>>2]=M,e[m+464>>2]=e[m+472>>2])),ee>>>0>(w=w+1|0)>>>0;);if(y)break n}c:{o:{if(!(w=e[5168+((R<<2)+t|0)>>2])){if(dA(t,m+464|0,A,0,e[t+5168>>2],m+448|0,l,ae),e[m+448>>2])break c;if(16&d[188808])break o;if(y=or(m+468|0,R=(M=e[m+464>>2])-1|0),w=e[m+468>>2],!(e[t+600>>2]<=0|(0|w)>577)){if(_i(w)){e[m+32>>2]=21,uA(r,87049,m+32|0);break t}w=e[m+468>>2]}if((0|w)==57384&&((0|(ee=e[t+92>>2]))<=e[47352]||(e[47352]=ee)),pi(w)&&((0|(w=e[t+72>>2]))<=e[47352]||(e[47352]=w)),!((ee=(w=e[m+468>>2])-192|0)>>>0>413)&&(ee=d[ee+94240|0])&&(y=y-1|0,!(d[M-2|0]==32&d[y+M|0]==32))){for(e[m+472>>2]=R,g[0|R]=ee;P=d[(w=M)+y|0],g[0|w]=P,M=w+1|0,(0|P)!=32;);if((0|y)>0&&IA(w,32,y),e[t+24>>2]&&!((0|Uo(94222,e[m+468>>2]))<=0)){e[m+464>>2]=R,y=0;break a}y=0,g[0|r]=0,e[m+464>>2]=A,e[t+8208>>2]=0,e[t+8212>>2]=0;break a}if(!(w=Wa(w))||(0|(M=e[w+4>>2]))==e[t+600>>2])break o;if((0|M)==e[t+188>>2]){e[m+4>>2]=mo(m- -64|0,e[t+192>>2]),e[m>>2]=21,uA(r,87218,m);break t}if(!(4&d[w+16|0]))break o;e[m+20>>2]=mo(m- -64|0,e[w+12>>2]),e[m+16>>2]=21,uA(r,87218,m+16|0);break t}if(dA(t,m+464|0,A,1,w,m+448|0,l,ae),e[m+448>>2])break c}o:if(!((w=e[m+476>>2])-768>>>0<112)){if(Wr(w)){if(g[(P+e[m+464>>2]|0)-1|0]<33&(0|be)<=1)break o;if(g[0|r]=0,!h)break r;e[h>>2]=4096|e[h>>2];break r}ir(t,e[m+476>>2],-1,m+272|0,0),d[m+272|0]&&(e[m+448>>2]=1,e[m+452>>2]=m+272)}e[m+464>>2]=(P+e[m+464>>2]|0)-1;break n}e[t+288>>2]=0}else dA(t,m+464|0,A,P,y,m+448|0,l,ae);if(M=(w=e[m+452>>2])||86135,e[m+452>>2]=M,y=0,!(e[m+448>>2]<=0)){if(w=1|e[m+456>>2],(0|l)<0)break e;if(!(d[0|M]!=21|$e)){BA(r,M);break t}if(!(!(8&e[47197])|_e))n:if(w=e[47195],(0|(P=e[w+76>>2]))>=0&(!P|e[56823]!=(-1073741825&P)))R=e[(P=w+76|0)>>2],e[P>>2]=R||1073741823,e[w+80>>2]==10||(0|(R=e[w+20>>2]))==e[w+16>>2]?Ri(w):(e[w+20>>2]=R+1,g[0|R]=10),e[P>>2]=0;else{if(e[w+80>>2]!=10&&(0|(P=e[w+20>>2]))!=e[w+16>>2]){e[w+20>>2]=P+1,g[0|P]=10;break n}Ri(w)}if(P=-32769&(w=e[m+456>>2]),e[m+456>>2]=P,!(!i|!P|(1024&w?Ve:0))){t=e[m+464>>2],BA(i,M),w=P|(yt=(r=t)-$A(A,t=m+112|0,wA(t))|0,(1151&w)==1024?yt:0);break e}(w=e[m+460>>2])&&(g[0|w]=69),Li(t,r,n,M)}}else g[m+104|0]=95,$A(it,M,P),w=1,g[105+(m+P|0)|0]=0,kA(t,m+104|0,m- -64|0),y-1>>>0<=4294967293&&(w=wA(w=m- -64|0)+w|0,g[0|w]=11,g[w+1|0]=0,w=0),Li(t,r,n,m- -64|0),e[m+464>>2]=M+P,y=w;if(M=e[m+464>>2],!(223&(w=d[0|M])))break}$A(A,t=m+112|0,wA(t))}w=0}return oe=m+480|0,w}function Fr(t,A){var r,n=0,i=0,l=0,h=0,m=0,w=0,M=0,y=0,P=0,R=0,ee=0,ae=0,ue=0,be=0,_e=0,Ve=0,$e=0,it=0,ht=0,yt=0,Pt=0,Wt=0;oe=r=oe-2976|0,e[A>>2]=1,n=e[t+20>>2],e[(m=r+2960|0)>>2]=e[t+16>>2],e[m+4>>2]=n,n=e[t+12>>2],e[(m=r+2952|0)>>2]=e[t+8>>2],e[m+4>>2]=n,n=e[t+4>>2],e[r+2944>>2]=e[t>>2],e[r+2948>>2]=n,e[50303]||_A();e:if(t=e[r+2948>>2],d[0|t]&&t||((t=e[r+2944>>2])||(t=(t=e[r+2952>>2])||85055,e[r+2944>>2]=t),vr(n=r+80|0,t,60),vn(n,0),!(l=Js(201216,n))||(e[r+2948>>2]=e[l+4>>2]+1,d[r+2958|0]|d[r+2956|0]|d[r+2957|0]))){be=r+1536|0,oe=ae=oe-336|0;t:if(!(!(t=e[4+(ue=r+2944|0)>>2])|!d[0|t])){if((0|(_e=wA(t)))>=0){for(n=_e>>>0>=79?79:_e,y=1;t=wr(g[e[ue+4>>2]+i|0]),g[(ae+256|0)+i|0]=t,y=((255&t)==45)+y|0,t=(0|n)!=(0|i),i=i+1|0,t;);if((0|y)!=1)break t}y=1}if((0|(P=e[50303]))<=0)e[be>>2]=0,t=0;else{for(m=(0|y)>=0;;){M=e[201216+(it<<2)>>2];t:if(ra(e[M+8>>2],88032,3)){if((t=e[ue+4>>2])&&ra(t,91687,3)){if(m){t=100;r:if(y){if(n=0,i=e[M+4>>2],!(Ve=d[0|i])){if(!ra(ae+256|0,90013,9))break r;break t}for(;;){for(ht=1,w=i+1|0,$e=1,yt=0,i=0;(0|i)<(0|_e)&&(0|(t=g[(ae+256|0)+i|0]))!=45||(t=0),yt=((R=(0|(ee=d[i+w|0]))==45)&!!(0|($e=(R?0:ee)<<24>>24==(0|t)?$e:0)))+yt|0,i=i+1|0,ht=R+ht|0,ee;);if(i=i+w|0,(R=$e+yt|0)&&(n=(0|(t=F((w=(0|(t=y-R|0))<=0?5:5-t|0)-((0|(t=ht-R|0))>0?t:0)|0,100)-(Ve<<24>>24<<1)|0))>(0|n)?t:n),!(Ve=d[0|i]))break}if(!(t=n))break t}(n=e[ue>>2])&&(t=Zr(n,e[M>>2])?Zr(n,e[M+8>>2])?t:t+400|0:t+500|0),((i=d[ue+12|0])-1&255)>>>0>1||((n=d[M+12|0])-1&255)>>>0>1||(t=(0|n)!=(0|i)?t-50|0:t+50|0),i=d[ue+13|0],t=d[M+12|0]!=2|i>>>0>12?t:d[M+13|0]>12?t+5|0:t,(n=d[M+13|0])&&((n=((i?F(i,100):3e3)>>>0)/(n>>>0)|0)>>>0<=99&&(n=1e4/(n>>>0)|0),t=(w=t)+((t=5-(((n-100&65535)>>>0)/10|0)|0)>>31&t)|0,t=i?t+10|0:t),t=(0|t)<=1?1:t}else{if(ra(e[M+8>>2],ae+256|0,_e))break t;t=100}e[be+(h<<2)>>2]=M,e[M+16>>2]=t}else e[be+(h<<2)>>2]=M;h=h+1|0}if((0|P)==(0|(it=it+1|0)))break}e[be+(h<<2)>>2]=0,t=0,h&&(vi(be,h,8),t=h)}if(oe=ae+336|0,y=t,t||(e[A>>2]=0,t=Js(201216,85055),e[r+1536>>2]=t,y=!!(0|t)),A=d[r+2957|0],m=2,(0|(t=d[r+2956|0]))!=2&&(m=2,(A-1&255)>>>0<12||(Wt=(0|t)!=1,m=(0|t)==1)),P=(h=e[132136+(m<<2)>>2])+(ee=A>>>0<60)|0,t=0,(0|y)>0)for(n=0;;){l=e[(r+1536|0)+(Pt<<2)>>2];t:{r:{a:{if(Wt){if(ee||(A=0,n))break r}else{if(A=d[l+12|0],n|ee)break a;A=(0|A)!=(0|m)}if(i=0,A|d[l+13|0]<60)break t;break r}if((0|A)!=(0|m)){i=n;break t}}e[(r+80|0)+(n<<2)>>2]=l,i=n+1|0}t:if(d[l+15|0]){if(R=0,A=t,n=i,!((0|t)>11))for(;;){if((i=d[0|P])||(P=h,i=d[0|h]),t=e[l+12>>2],w=F(A,24)+202624|0,e[w+8>>2]=e[l+8>>2],e[w+12>>2]=t,t=e[l+4>>2],e[w>>2]=e[l>>2],e[w+4>>2]=t,t=e[l+20>>2],e[w+16>>2]=e[l+16>>2],e[w+20>>2]=t,g[w+14|0]=i,e[(r+80|0)+(n<<2)>>2]=w,P=P+1|0,n=n+1|0,t=A+1|0,(R=R+1|0)>>>0>=d[l+15|0])break t;if(i=(0|A)<11,A=t,!i)break}}else n=i;if((0|(Pt=Pt+1|0))==(0|y))break}else{if(!l)break e;n=0}t:if(!(!(i=d[0|P])|(0|t)>=12))for(;;){if(A=e[l+12>>2],h=F(t,24)+202624|0,e[h+8>>2]=e[l+8>>2],e[h+12>>2]=A,A=e[l+4>>2],e[h>>2]=e[l>>2],e[h+4>>2]=A,A=e[l+20>>2],e[h+16>>2]=e[l+16>>2],e[h+20>>2]=A,g[h+14|0]=i,e[(r+80|0)+(n<<2)>>2]=h,n=n+1|0,!(i=d[0|(P=P+1|0)]))break t;if(A=(0|t)<11,t=t+1|0,!A)break}n?(t=e[(r+80|0)+(d[r+2958|0]%(0|n)<<2)>>2],(A=d[t+14|0])?(g[202976]=0,e[r+48>>2]=47,uA(r+2971|0,91351,r+48|0),g[r+2971|0]=0,A>>>0<=9?(e[r+20>>2]=A,e[r+16>>2]=r+2971,uA(202976,91378,r+16|0)):(e[r+36>>2]=A-10,e[r+32>>2]=r+2971,uA(202976,91503,r+32|0)),t=e[t+8>>2],e[r+4>>2]=202976,e[r>>2]=t,t=202912,uA(202912,87760,r)):t=e[t+8>>2]):t=0}else{if(t=e[l+8>>2],!d[202976])break e;e[r+64>>2]=t,e[r+68>>2]=202976,t=202912,uA(202912,87760,r- -64|0)}return oe=r+2976|0,t}function Vr(t,A,r,n,i){var l,h,m=0,w=0,M=0,y=0,P=0,R=0,ee=0;oe=l=oe-464|0,g[l+432|0]=0,g[l+368|0]=0,g[l+304|0]=0,g[l+292|0]=0,P=(0|A)/10|0,m=e[33273];e:{if(!(h=2&n)|e[33272]!=2){ee=32&n?113:111,y=1&n,R=A-F(P,10)|0;t:{r:{a:{n:{c:{o:{f:{l:if(d[0|m])m=0;else{i:{if(8&n){if(e[l+288>>2]=A,uA(w=l+452|0,91198,l+288|0),m=kA(t,w,l+304|0)){w=0;break l}e[l+272>>2]=A,uA(w=l+452|0,91314,l+272|0),m=kA(t,w,l+304|0),w=0}else{if(!y)break i;if(M=BA(l+432|0,133104),4&n){if(e[l+260>>2]=ee,e[l+256>>2]=A,uA(w=l+452|0,91324,l+256|0),m=kA(t,w,l+304|0),d[133116]&&m)break f;if(w=m,m)break l}e[l+244>>2]=ee,e[l+240>>2]=A,uA(w=l+452|0,91384,l+240|0),w=m=kA(t,w,l+304|0)}if(m)break l}i:{if(h){if(!(1&g[133096]))break i;e[l+208>>2]=A,uA(m=l+452|0,91498,l+208|0),m=kA(t,m,l+304|0)}else M=e[t+108>>2],e[l+224>>2]=A,uA(m=l+452|0,(0|r)>=2?91700:(262144&M)>>>18|0?91534:91700,l+224|0),m=kA(t,m,l+304|0);if(m)break l}!y|!(32&d[t+109|0])?(e[l+192>>2]=A,uA(m=l+452|0,91766,l+192|0),m=kA(t,m,l+304|0)):m=0}if(!(16&n)|(0|A)>9)break c;m=w;break o}if(BA(M,133116),!(16&n)|(0|A)>9)break n}kA(t,88875,l+368|0);break r}if(!m)break a;m=w}g[l+368|0]=0;break r}a:if(y&&(e[l+180>>2]=ee,e[l+176>>2]=P,uA(m=l+452|0,91846,l+176|0),kA(t,m,l+368|0))){if(w=1,!R|!(16&d[t+109|0]))break a;gs(l+368|0,133104)}else w||(e[l+160>>2]=P,uA(w=l+452|0,512&n?91936:92016,l+160|0),kA(t,w,l+368|0),w=0);if(m=R,d[l+368|0]||(m=R,16&d[t+106|0]&&(e[l+144>>2]=254&P,uA(m=l+452|0,92016,l+144|0),kA(t,m,l+368|0),m=(0|A)%20|0)),g[l+304|0]=0,P=m,(0|m)<=0)m=w;else{if(h&&(m=e[33273],d[0|m])){BA(l+304|0,m),g[l+432|0]=0,M=y;break t}if(M=0,8&n&&(e[l+128>>2]=P,uA(n=l+452|0,91314,l+128|0),M=kA(t,n,l+304|0)),!y|16&d[t+104|0]||(e[l+116>>2]=ee,e[l+112>>2]=P,uA(n=l+452|0,91384,l+112|0),w=(M=kA(t,n,l+304|0))?1:w),m=w,!M){a:{if(!h|!(1&e[33274])){if(!(16&d[t+104|0])&&h)break a;w=e[t+108>>2],e[l+96>>2]=P,uA(n=l+452|0,(0|r)>=2?91700:(262144&w)>>>18|0?91534:91700,l+96|0),r=kA(t,n,l+304|0)}else e[l+80>>2]=P,uA(r=l+452|0,91498,l+80|0),r=kA(t,r,l+304|0);if(r)break r}e[l+64>>2]=P,uA(r=l+452|0,91766,l- -64|0),kA(t,r,l+304|0)}}}M=y,d[l+432|0]|m|!y||((0|A)<20|(16&d[t+104|0]?0:R)||(kA(t,92162,l+432|0),M=1,!d[l+432|0]))&&(kA(t,92205,l+432|0),M=1)}if(!(!(r=g[l+304|0])|!(48&(A=e[t+104>>2]))|!d[l+368|0])){if(kA(t,90824,l+292|0),!M|!(8&d[t+109|0])||(g[l+292|0]=0),16&d[t+104|0]){e[l+28>>2]=l+432,e[l+24>>2]=l+368,e[l+20>>2]=l+292,e[l+16>>2]=l+304,uA(i,91059,l+16|0),n=1;break e}e[l+12>>2]=l+432,e[l+8>>2]=l+304,e[l+4>>2]=l+292,e[l>>2]=l+368,uA(i,91059,l),n=1;break e}512&A&&(!r|(0|(A=wA(l+368|0)-1|0))<0||(m=d[e[144464+(g[0|(A=A+(l+368|0)|0)]<<2)>>2]+11|0]!=2,(0|(n=d[e[144464+(r<<2)>>2]+11|0]))==1&&(n=d[e[144464+(g[l+305|0]<<2)>>2]+11|0]),m|(255&n)!=2||(g[0|A]=0))),!(8&d[t+110|0])|!d[l+432|0]?(e[l+56>>2]=l+432,e[l+52>>2]=l+304,e[l+48>>2]=l+368,uA(i,92282,l+48|0)):(e[l+36>>2]=l+304,e[l+32>>2]=l+368,(0|(A=uA(i,90368,l+32|0)))>0&&(A=d[e[144464+(d[(r=A-1|0)+i|0]<<2)>>2]+11|0]==2?r:A),BA(A+i|0,l+432|0))}else BA(i,m);n=0}e:if(268435456&(t=e[t+104>>2])){if((0|wA(i))<=0)break e;for(A=0,t=0;d[0|(r=t+i|0)]==6&&(A&&(g[0|r]=5),A=1),t=t+1|0,(0|wA(i))>(0|t););}else if(256&t&&(A=0,!((0|(t=(y=wA(i))-1|0))<0))){if(t)for(R=-2&y,m=0;d[0|(w=t+i|0)]==6?(r=1,A&&(g[0|w]=5)):r=A,d[0|(w=w-1|0)]==6?(A=1,r&&(g[0|w]=5)):A=r,t=t-2|0,(0|R)!=(0|(m=m+2|0)););1&y&&(!A|d[0|(t=t+i|0)]!=6||(g[0|t]=5))}return oe=l+464|0,n}function Tt(t,A,r,n){var i,l,h=0,m=0,w=0,M=0,y=0,P=0,R=0;if(oe=i=oe-352|0,g[i+304|0]=0,g[i+224|0]=0,g[i+64|0]=0,w=e[e[47192]+292>>2],R=or(i+348|0,A),(1048320&(h=e[i+348>>2]))==57344&&(h&=255,e[i+348>>2]=h),2&n&&$n(h)&&kA(t,85437,i+304|0),h=Za(e[i+348>>2],t),e[i+348>>2]=h,P=1&n,ir(t,h,g[0|(l=A+R|0)],i+224|0,P),!(A=d[i+224|0])){e:if((A=Jn(e[i+348>>2]))&&(e[i+348>>2]=16383&A,4&n)){switch(1073741823&(A>>=14)){case 0:case 3:break e}kA(t,A=e[131232+(A<<2)>>2],i+304|0),d[i+304|0]||(g[i+306|0]=Ji(84744),y=A,A=i+304|3,kA(e[47194],y,A),d[i+307|0]&&(v[i+304>>1]=5385,A=wA(A)+(i+304|0)|0,g[A+5|0]=0,g[A+4|0]=w,g[A+3|0]=21))}ir(t,e[i+348>>2],g[0|l],i+224|0,P),A=d[i+224|0]}e:{t:{if(A&=255){if((0|A)!=21)break t;BA(r,i+224|0),R=0;break e}if(A=1632,!((0|(h=e[i+348>>2]))<1632)){for(M=103360;;){if((0|h)>=(A+10|0)){if(!(A=e[(M=M+4|0)>>2]))break t;if((0|A)<=(0|h))continue;break t}break}(0|(A=48+(h-A|0)|0))<=0||ir(t,A,0,i+224|0,P)}}t:{r:{a:{n:{if(A=Wa(e[i+348>>2])){if(h=e[A+4>>2],!A|1&(M=e[A+16>>2])||(m=e[47192],e[m+600>>2]==(0|h)|e[m+188>>2]==(0|h)|e[m+184>>2]==(0|h)||(g[i+144|0]=0,kA(m,e[A>>2],i- -64|0)?(0|(m=e[47192]))!=(0|t)&&(w=e[t+292>>2],BA(i+144|0,i- -64|0),g[i+66|0]=e[m+292>>2]):(g[i+66|0]=Ji(84744),kA(e[47194],e[A>>2],i+144|0)),d[i+144|0]&&(v[i+64>>1]=5385,BA(3|(m=i- -64|0),y=i+144|0),m=wA(y)+m|0,g[m+5|0]=0,g[m+4|0]=w,g[m+3|0]=21))),d[i+224|0])break t;if(!h||(w=e[47192],e[w+188>>2]!=(0|h)))break n;A=e[w+192>>2];break r}if(d[i+224|0])break t;M=0,h=0;break a}if((A=e[A+12>>2])&&!(2&M))break r}A=25966}if((e[t+212>>2]==(0|A)&(0|A)!=27503||(g[i+226|0]=Ji(mo(i+47|0,A)),(A=e[47194])&&((0|(w=e[i+348>>2]))>55215||(0|(m=w-44032|0))<0?ir(A,w,g[0|l],i+224|3,P):(g[i+52|0]=32,A=y=i+53|0,w-50500>>>0>=588&&(A=_s(4352+((m>>>0)/588|0)|0,y)+y|0),_s(4449+(((w=(m>>>0)/28|0)>>>0)%21|0)|0,A),_s(4519+(m-F(w,28)|0)|0,A+3|0),g[A+6|0]=32,g[A+7|0]=0,g[i+227|0]=0,A=i+224|3,zA(e[47194],y,A,77,0,0,0),bA(e[47194],A,0,-1,0)),A=i+224|3,d[i+227|0]==21&&(g[i+226|0]=Ji(i+224|4),ir(e[47194],e[i+348>>2],g[0|l],A,P)),Ia(e[e[32972]+60>>2]),d[i+227|0]&&(v[i+224>>1]=5385,A=wA(A)+(i+224|0)|0,g[A+3|0]=21,P=e[t+292>>2],g[A+5|0]=0,g[A+4|0]=P)),!d[i+224|0]))&&(16&M||(aa(e[i+348>>2])&&kA(e[47192],85683,i+224|0),d[i+224|0]||(Ts(e[i+348>>2])||kA(e[47192],85778,i+224|0),d[i+224|0]||Rn(85992,i+224|0,0))),!(8&M)||4&n)){if(A=e[i+348>>2],(0|h)!=10240?(e[i+32>>2]=A,uA(i+52|0,86013,i+32|0)):(h=i+52|0,1&A&&(g[i+52|0]=49,h=i+53|0),2&A&&(g[0|h]=50,h=h+1|0),4&A&&(g[0|h]=51,h=h+1|0,A=e[i+348>>2]),8&A&&(g[0|h]=52,h=h+1|0,A=e[i+348>>2]),16&A&&(g[0|h]=53,h=h+1|0,A=e[i+348>>2]),32&A&&(g[0|h]=54,h=h+1|0,A=e[i+348>>2]),64&A&&(g[0|h]=55,h=h+1|0,A=e[i+348>>2]),128&A&&(g[0|h]=56,h=h+1|0),g[0|h]=0),A=i+224|0,M=d[i+52|0])for(h=i+52|0;A=wA(A)+A|0,g[0|A]=23,A=A+1|0,ir(e[47192],M<<24>>24,0,A,1),(n=d[0|A])&&(0|n)!=21||(0|(n=g[0|h]))<97||Rn(e[130860+((255&n)<<2)>>2],A,0),M=d[0|(h=h+1|0)];);A=wA(A)+A|0,g[0|A]=9,g[A+1|0]=0}}A=wA(r),2&d[t+144|0]?(e[i+16>>2]=255,e[i+28>>2]=i+304,e[i+24>>2]=i+224,e[i+20>>2]=i- -64,uA(i+144|0,86210,i+16|0)):(e[i>>2]=255,e[i+12>>2]=i+224,e[i+8>>2]=i+304,e[i+4>>2]=i- -64,uA(i+144|0,86210,i)),wA(i+144|0)+A>>>0>199||BA(A+r|0,i+144|0)}return oe=i+352|0,R}function MA(t,A,r,n,i,l,h){var m,w=0,M=0,y=0,P=0,R=0,ee=0,ae=0,ue=0,be=0,_e=0,Ve=0,$e=0,it=0,ht=0,yt=0,Pt=0,Wt=0,cA=0,gA=0,SA=0,VA=0,qA=0;oe=m=oe-528|0,_e=h?e[h>>2]:0,Pt=e[i+4>>2];e:{t:{if(e[t+220>>2]>0){vr(M=m+352|0,A,160),oe=ee=oe-176|0,ue=1-(ae=e[t+220>>2])|0,be=e[t+224>>2],Ve=e[t+216>>2],P=M;r:{a:{for(;;){if($e=or(ee+172|0,P),w=e[ee+172>>2]){if((0|w)<(0|ae)|(0|w)>(0|Ve))break a;if(be){if((0|(w=g[be+(w-ae|0)|0]))<=0)break a}else w=w+ue|0;if(P=P+$e|0,g[R+ee|0]=w,w=160,(0|(R=R+1|0))!=160)continue}else w=R;break}if(be=0,g[w+ee|0]=0,ue=g[0|ee],e[ee+172>>2]=ue,ue){for(Wt=2+(Ve-ae|0)|0,ae=w=ee;;){Ve=w+1|0;n:{if(($e=e[t+8180>>2])&&(R=0,!((0|(P=v[$e>>1]))>(0|(ht=(g[0|Ve]<<8)+ue|0)))))for(;;){if((0|P)==(0|ht)){ue=R+Wt|0,e[ee+172>>2]=ue,w=w+2|0;break n}if(!((0|ht)>=(0|(P=v[$e+((R=R+1|0)<<1)>>1]))))break}w=Ve}if(it=63&ue|it<<6,(0|(R=be+6|0))<8?be=R:(be=be-2|0,g[0|ae]=it>>be,ae=ae+1|0),ue=g[0|w],e[ee+172>>2]=ue,!ue)break}(0|be)<=0||(g[0|ae]=it<<8-be,ae=ae+1|0)}else ae=ee;g[0|ae]=0,$A(M,ee,w=ae-ee|0),ae=64|w;break r}ae=wA(M)}oe=ee+176|0,ee=M}else ae=wA(A),ee=A;if(M=d[0|ee]){for(w=0,R=ee;y=1023&(y=(y<<3)+M|0)^y>>>8,w=w+1|0,M=d[0|(R=R+1|0)];);w=w+y&1023}else w=0;if(y=e[692+((w<<2)+t|0)>>2]){if(w=d[0|y])break t;w=0;break e}if(w=0,!i)break e;e[i>>2]=0;break e}for($e=1073741824&Pt,ht=2048&l,Pt=512&_e,Wt=65536&_e,cA=1&_e,gA=2&_e,_e=8&l,SA=1024&l,Ve=4&l,VA=63&ae,qA=t+8233|0;;){l=(255&w)+y|0;t:{r:if((127&(w=d[y+1|0]))==(0|ae)&&!ra(ee,y+2|0,VA)){y=2+((63&w)+y|0)|0;a:{if(w<<24>>24<0)be=0,g[0|n]=0;else{if((0|(be=wA(y)))>=160)break a;BA(n,y),y=1+(y+be|0)|0}if(M=0,l>>>0<=y>>>0)w=r,P=0;else{R=0,P=0;n:{for(;;){y=(w=y)+1|0;c:if((w=d[0|w])>>>0>=100){if(ue=e[t+320>>2],w>>>0>=132){R|=ue>>>w-132&1;break c}R|=!(ue>>>w-100&1)}else{if(w>>>0>=81){ue=w-80|0,it=l-y|0;o:if(h)for(w=0;;){if(yt=F(w,12)+h|0,!d[yt+10|0])break o;if(R=!!(12&d[yt+1|0])|R,yt=(0|w)!=(0|ue),w=w+1|0,!yt)break}if(go(r,y,it)|1&R)break r;e[33264]=ue,w=r+it|0,P|=128,y=l;break n}w>>>0>=65?(P=15&w|-16&P,P=12&~w?P:512|P):w>>>0>=32?M|=1<<w-32:P|=1<<w}if(!(l>>>0>y>>>0))break}if(w=r,1&R)break t}if(65536&M&&!Ve||SA&&49152&M)break t}if(Ve&&(16384&M||!_e&&32768&M)||(gA?0:512&M)|(cA?0:1024&M)|(Wt?0:33554432&P)||!(!(131072&M)|re[e[47192]+8204>>2]<=w>>>0|$e)|(Pt?0:262144&M)|(8&d[e[47192]+8242|0]?0:8192&M)||16&M&&(!e[t+8184>>2]&(!_e|!e[t+8192>>2])||!(!_e|e[t+212>>2]!=25966)&&2097152&e[t+8232>>2])||(e[t+8188>>2]?0:64&M)|(!e[t+8196>>2]|ht?32&M:0)||!(!(65536&P)|e[t+212>>2]!=26741|128&d[0|qA])|(e[47192]!=(0|t)?524288&M:0))break t;n:{c:{o:{if(!i){if(!be)break o;break n}if(e[i+4>>2]=M,e[i>>2]=1073741824|P,be)break c}if(w=0,!(8&d[188788]))break e;Hi(i,t=m+272|0),e[m>>2]=A,e[m+4>>2]=t,ss(e[47195],89330,m);break e}e[i>>2]=-1073741824|P}if(8&d[188788]&&(TA(n,m- -64|0),d[e[47192]+172|0]==(P>>>29&1)&&(!h|!(128&P)?(e[m+48>>2]=A,ss(e[47195],89426,m+48|0)):($A(t=m+352|0,n=r,r=w-r|0),g[351+(r+m|0)|0]=0,e[m+32>>2]=A,e[m+36>>2]=t,ss(e[47195],89397,m+32|0)),Hi(i,t=m+272|0),A=e[47195],e[m+16>>2]=m- -64,e[m+20>>2]=t,ss(A,89534,m+16|0))),d[or(m- -64|0,ee)+ee|0]|!i||Wr(e[m+64>>2]))break e;e[i>>2]=134217728|e[i>>2];break e}me(89236,86634,2467,94846),W()}y=l}if(!(w=d[0|y]))break}w=0}return oe=m+528|0,w}function QA(t,A,r,n,i){var l,h=0,m=0;oe=l=oe-304|0,g[l+278|0]=0;e:{if((0|A)>0&&(1&n&&(2&n&&(e[l+164>>2]=r,e[l+160>>2]=A,uA(h=l+290|0,89701,l+160|0),h=kA(t,h,l+224|0))||1&g[133096]&&(e[l+148>>2]=r,e[l+144>>2]=A,uA(h=l+290|0,89757,l+144|0),h=kA(t,h,l+224|0))||(e[l+132>>2]=r,e[l+128>>2]=A,uA(h=l+290|0,89894,l+128|0),h=kA(t,h,l+224|0)))||(e[l+116>>2]=r,e[l+112>>2]=A,uA(h=l+290|0,89974,l+112|0),h=kA(t,h,l+224|0))))break e;if((0|(m=(0|A)%100|0))>=20&&kA(t,90022,l+278|0),1&n){if(2&n){h=m-11|0;t:{r:{a:switch((448&e[e[47192]+108>>2])-64>>>6|0){case 0:if(h>>>0<9)break r;if(n=90418,(0|(h=(0|A)%10|0))==1)break t;if(h-2>>>0>=3)break r;n=90453;break t;case 1:if(A-2>>>0>=3)break r;n=90453;break t;case 2:if(h>>>0<9|((0|A)%10|0)-2>>
2810>0>=3)break r;n=90453;break t;case 3:if(n=90508,h>>>0<9)break t;n=(n=(0|A)%10|0)?(0|n)==1?90453:90586:90508;break t;case 4:break a;default:break r}if(!(h>>>0<9)){if(n=90537,(0|(h=(0|A)%10|0))==1)break t;if(!(h-2>>>0>=3)){n=90453;break t}}}n=90586}if(e[l+100>>2]=r,e[l+96>>2]=n,uA(n=l+290|0,90058,l+96|0),h=0,kA(t,n,l+224|0))break e}if(n=m-11|0,1&g[133096]){t:{r:{a:switch((448&e[e[47192]+108>>2])-64>>>6|0){case 0:if(n>>>0<9)break r;if(h=90418,(0|(m=(0|A)%10|0))==1)break t;if(m-2>>>0>=3)break r;h=90453;break t;case 1:if(A-2>>>0>=3)break r;h=90453;break t;case 2:if(n>>>0<9|((0|A)%10|0)-2>>>0>=3)break r;h=90453;break t;case 3:if(h=90508,n>>>0<9)break t;h=(h=(0|A)%10|0)?(0|h)==1?90453:90586:90508;break t;case 4:break a;default:break r}if(!(n>>>0<9)){if(h=90537,(0|(m=(0|A)%10|0))==1)break t;if(!(m-2>>>0>=3)){h=90453;break t}}}h=90586}if(e[l+84>>2]=r,e[l+80>>2]=h,uA(m=l+290|0,90110,l+80|0),h=0,kA(t,m,l+224|0))break e}t:{r:{a:switch((448&e[e[47192]+108>>2])-64>>>6|0){case 0:if(n>>>0<9)break r;if(h=90418,(0|(m=(0|A)%10|0))==1)break t;if(m-2>>>0>=3)break r;h=90453;break t;case 1:if(A-2>>>0>=3)break r;h=90453;break t;case 2:if(n>>>0<9|((0|A)%10|0)-2>>>0>=3)break r;h=90453;break t;case 3:if(h=90508,n>>>0<9)break t;h=(h=(0|A)%10|0)?(0|h)==1?90453:90586:90508;break t;case 4:break a;default:break r}if(!(n>>>0<9)){if(h=90537,(0|(m=(0|A)%10|0))==1)break t;if(!(m-2>>>0>=3)){h=90453;break t}}}h=90586}if(e[l+68>>2]=r,e[l+64>>2]=h,uA(m=l+290|0,90139,l- -64|0),h=0,kA(t,m,l+224|0))break e}else n=m-11|0;t:{r:{a:switch((448&e[e[47192]+108>>2])-64>>>6|0){case 0:if(n>>>0<9)break r;if(h=90418,(0|(n=(0|A)%10|0))==1)break t;if(n-2>>>0>=3)break r;h=90453;break t;case 1:if(A-2>>>0>=3)break r;h=90453;break t;case 2:if(n>>>0<9|((0|A)%10|0)-2>>>0>=3)break r;h=90453;break t;case 3:if(h=90508,n>>>0<9)break t;h=(n=(0|A)%10|0)?(0|n)==1?90453:90586:90508;break t;case 4:break a;default:break r}if(!(n>>>0<9)){if(h=90537,(0|(n=(0|A)%10|0))==1)break t;if(!(n-2>>>0>=3)){h=90453;break t}}}h=90586}e[l+52>>2]=r,e[l+48>>2]=h,uA(n=l+290|0,90218,l+48|0),h=0,kA(t,n,l+224|0)||((0|r)<4||(e[l+32>>2]=r-1,uA(n=l+290|0,89026,l+32|0),kA(t,n,l+176|0)||(kA(t,90273,l+224|0),e[33275]=3)),d[l+224|0]||(e[l+16>>2]=A,uA(n=l+290|0,90303,l+16|0),(h=kA(t,n,l+224|0))||kA(t,90347,l+224|0),e[33275]=2))}return e[l+4>>2]=l+224,e[l>>2]=l+278,uA(i,90368,l),oe=l+304|0,!((0|A)!=1|(0|r)!=1)&&(A=1,32&d[t+106|0])||(A=h),A}function ZA(t,A,r,n){var i,l=0,h=0,m=0,w=0,M=0,y=0,P=0;oe=i=oe+-64|0,v[i+48>>1]=0,e[i+40>>2]=0,e[i+44>>2]=0,e[i+32>>2]=0,e[i+36>>2]=0,e[i+24>>2]=0,e[i+28>>2]=0,e[i+16>>2]=0,e[i+20>>2]=0,e[i+8>>2]=0,e[i+12>>2]=0,e[i>>2]=0,e[i+4>>2]=0,l=A;e:{for(;;){t:{r:{if((0|(h=d[0|l]))!=69){if((0|h)!=32)break r;if(n&&(g[$A(h=n,A,n=(0|(n=l-A|0))>=159?159:n)+n|0]=0),n=63&r)break t;break e}g[0|l]=101}l=l+1|0;continue}break}if(1&r){t:if((l=l-1|0)>>>0<A>>>0)h=n;else for(h=n;;){if((192&d[0|l])!=128)break t;if(h=h+1|0,!((l=l-1|0)>>>0>=A>>>0))break}m=n-1|0}else m=n,h=n;if((0|n)!=1)for(;;){n=m;t:if(!((l=l-1|0)>>>0<A>>>0))for(;;){if((192&d[0|l])!=128)break t;if(h=h+1|0,!((l=l-1|0)>>>0>=A>>>0))break}t:if(!((l=l-1|0)>>>0<A>>>0))for(;;){if((192&d[0|l])!=128)break t;if(h=h+1|0,!((l=l-1|0)>>>0>=A>>>0))break}if(m=n-2|0,!((0|n)>2))break}if((0|h)<=0)m=0;else{if(w=3&(m=(n=(A=h-1|0)>>>0>=48?48:A)+1|0),A=0,h=0,n>>>0>=3)for(P=-4&m,n=0;M=l+h|0,g[h+i|0]=d[0|M],g[0|M]=32,M=(y=1|h)+l|0,g[i+y|0]=d[0|M],g[0|M]=32,M=(y=2|h)+l|0,g[i+y|0]=d[0|M],g[0|M]=32,M=(y=3|h)+l|0,g[i+y|0]=d[0|M],g[0|M]=32,h=h+4|0,(0|P)!=(0|(n=n+4|0)););if(w)for(;n=l+h|0,g[h+i|0]=d[0|n],g[0|n]=32,h=h+1|0,(0|w)!=(0|(A=A+1|0)););}}if(g[i+m|0]=0,w=65520&r,!(512&r)|d[0|(n=l-1|0)]!=105||(g[0|n]=121),h=4|w,256&r){e:{t:{r:{if((0|(A=e[t+212>>2]))!=25966){if((0|A)!=28268)break r;if(g[0|n]<0||128&(A=g[0|(m=l-2|0)]))break e;if(w=e[t+632>>2])A=!!(0|rs(w,A));else{if((0|(w=e[t+600>>2]))>0&&(A=A-w|0)-1>>>0>254)break e;A=128&d[344+(t+A|0)|0]}if(!A)break e;if(A=g[0|n],w=e[t+612>>2])A=!!(0|rs(w,A));else{a:{if((0|(w=e[t+600>>2]))>0){if((A=A-w|0)-1>>>0<255)break a;break e}if((0|A)<0)break e}A=4&d[344+(t+A|0)|0]}if(!A)break e;A=g[l-3|0];a:{if(w=e[t+632>>2])A=!!(0|rs(w,A));else{n:{if((0|(w=e[t+600>>2]))>0){if((A=A-w|0)-1>>>0<255)break n;break a}if((0|A)<0)break a}A=128&d[344+(t+A|0)|0]}if(A)break e}g[0|l]=d[0|n],g[0|n]=d[0|m],g[l+1|0]=32;break e}if(m=g[l-2|0],A=e[t+632>>2])A=!!(0|rs(A,m));else{a:{if((0|(A=e[t+600>>2]))>0){if((m=m-A|0)-1>>>0<255)break a;break t}if((0|m)<0)break t}A=128&d[344+(t+m|0)|0]}if(!A)break t;if(m=g[0|n],A=e[t+608>>2])A=!!(0|rs(A,m));else{if((0|(A=e[t+600>>2]))>0){if((m=m-A|0)-1>>>0>=255)break t}else if((0|m)<0)break t;A=2&d[344+(t+m|0)|0]}if(!A)break t;h=ra(87771,l-3|0,3)?20|w:h;break e}h=e[t+204>>2]?20|w:h;break e}(d[0|n]==99||(m=d[1+(A=l-2|0)|0]<<8,(d[0|A]|m)==29554|(m|d[0|A])==29289||(d[0|(A=l-2|0)]|d[A+1|0]<<8)==29301||!ra(88115,l-3|0,3)||(d[0|(A=l-2|0)]|d[A+1|0]<<8)==29550|d[0|n]==117||!ra(88384,l-5|0,5)||(d[0|(A=l-4|0)]|d[A+1|0]<<8|d[A+2|0]<<16|d[A+3|0]<<24)==1735287154||(d[0|A]|d[A+1|0]<<8|d[A+2|0]<<16|d[A+3|0]<<24)==1735549292))&&(h=20|w)}16&h&&(_s(e[t+204>>2],l),8&d[188788]&&bn(88683,6,e[47195]))}return e[t+8184>>2]|!(2048&r)||(e[t+8184>>2]=1),_[i>>1]!=115&&ra(i,88850,3)||(h|=8),oe=i- -64|0,d[0|i]==39?65531&h:h}function FA(t,A,r,n,i,l,h,m,w){var M,y=0,P=0,R=0,ee=0,ae=0,ue=0,be=0,_e=0,Ve=0,$e=0;oe=M=oe-112|0,y=2147483647&w;e:{if(R=!(A|r),(n|(P=2147483647&i)?P-2147418112>>>0<2147549184:R)||!(!m&(0|(ae=y-2147418112|0))==-2147418112?l|h:(0|ae)==-2147418112&!!(0|m)|ae>>>0>2147549184)){if(!(!n&(0|P)==2147418112?R:P>>>0<2147418112)){m=n,w=32768|i,l=A,h=r;break e}if(!(!m&(0|y)==2147418112?!(l|h):y>>>0<2147418112)){w|=32768;break e}if(!(A|n|2147418112^P|r)){ee=n,m=(n=!(A^l|n^m|r^h|i^w^-2147483648))?0:ee,w=n?2147450880:i,l=n?0:A,h=n?0:r;break e}if(!(l|m|2147418112^y|h))break e;if(!(A|n|r|P)){if(l|m|h|y)break e;l&=A,h&=r,m&=n,w&=i;break e}if(!(l|m|h|y)){l=A,h=r,m=n,w=i;break e}}P=(R=_e=(ee=(0|y)==(0|P))&(0|n)==(0|m)?(0|r)==(0|h)&A>>>0<l>>>0|r>>>0<h>>>0:ee&n>>>0<m>>>0|y>>>0>P>>>0)?l:A,ae=R?h:r,Ve=ee=R?w:i,R=R?m:n,be=65535&ee,n=_e?n:m,$e=i=_e?i:w,ee=i>>>16&32767,(ue=Ve>>>16&32767)||(w=i=!(R|be),y=i?P:R,m=i<<=6,ea(M+96|0,P,ae,R,be,(i=i+((0|(w=ce(w?ae:be)))==32?ce(y)+32|0:w)|0)-15|0),R=e[M+104>>2],be=e[M+108>>2],ae=e[M+100>>2],ue=16-i|0,P=e[M+96>>2]),l=_e?A:l,h=_e?r:h,m=n,w=65535&$e,ee||(i=A=!(m|w),y=A?l:m,r=A<<=6,ea(M+80|0,l,h,m,w,(A=A+((0|(i=ce(i?h:w)))==32?ce(y)+32|0:i)|0)-15|0),ee=16-A|0,m=e[M+88>>2],w=e[M+92>>2],h=e[M+84>>2],l=e[M+80>>2]),r=w<<3|m>>>29,A=m<<3|h>>>29,r|=524288,m=R<<3|ae>>>29,w=be<<3|R>>>29,_e=Ve^$e,i=h<<3|l>>>29,n=l<<3,(0|ee)!=(0|ue)&&((l=ue-ee|0)>>>0>127?(A=0,r=0,i=0,n=1):(ea(M- -64|0,n,i,A,r,128-l|0),Da(M+48|0,n,i,A,r,l),A=e[M+56>>2],r=e[M+60>>2],i=e[M+52>>2],n=e[M+48>>2]|!!(e[M+64>>2]|e[M+72>>2]|e[M+68>>2]|e[M+76>>
vendor: 15,024 bytes, line 2810
28102]))),R=n,y=i,ee=m,be=524288|w,i=ae<<3|P>>>29,ae=P<<3,P=i;t:if((0|_e)<0){if(l=0,h=0,m=0,w=0,!(R^ae|A^ee|y^P|r^be))break e;if(n=ae-R|0,i=P-((R>>>0>ae>>>0)+y|0)|0,m=(l=ee-A|0)-(h=(0|y)==(0|P)&R>>>0>ae>>>0|y>>>0>P>>>0)|0,w=A=(be-((A>>>0>ee>>>0)+r|0)|0)-(l>>>0<h>>>0)|0,A>>>0>524287)break t;l=A=!(m|w),h=A?n:m,r=A<<=6,ea(M+32|0,n,i,m,w,A=(A=A+((0|(l=ce(l?i:w)))==32?ce(h)+32|0:l)|0)-12|0),ue=ue-A|0,m=e[M+40>>2],w=e[M+44>>2],n=e[M+32>>2],i=e[M+36>>2]}else i=y+P|0,l=(0|y)==(0|(i=(n=R+ae|0)>>>0<ae>>>0?i+1|0:i))&n>>>0<R>>>0|i>>>0<y>>>0,y=r+be|0,y=(A=A+ee|0)>>>0<ee>>>0?y+1|0:y,1048576&(w=(m=A+l|0)>>>0<A>>>0?y+1|0:y)&&(n=1&R|(1&i)<<31|n>>>1,i=m<<31|i>>>1,ue=ue+1|0,m=(1&w)<<31|m>>>1,w=w>>>1|0);if(r=0,P=-2147483648&Ve,(0|ue)>=32767)m=r,w=2147418112|P,l=0,h=0;else if(ee=0,(0|ue)>0?ee=ue:(ea(M+16|0,n,i,m,w,ue+127|0),Da(M,n,i,m,w,1-ue|0),n=e[M>>2]|!!(e[M+16>>2]|e[M+24>>2]|e[M+20>>2]|e[M+28>>2]),i=e[M+4>>2],m=e[M+8>>2],w=e[M+12>>2]),ae=7&n,n=(0|(A=m<<29|i>>>3))==(0|(h=(n=(7&i)<<29|n>>>3)>>>0>(l=(ae>>>0>4)+n|0)>>>0?A+1|0:A))&n>>>0>l>>>0|A>>>0>h>>>0,A=r|(7&w)<<29|m>>>3,w=P|w>>>3&65535|ee<<16,w=A>>>0>(m=n+A|0)>>>0?w+1|0:w,(0|ae)!=4){if(!ae)break e}else y=h+(A=0)|0,w=(A=(0|A)==(0|(h=(n=l)>>>0>(l=l+(r=1&l)|0)>>>0?y+1|0:y))&r>>>0>l>>>0|A>>>0>h>>>0)>>>0>(m=A+m|0)>>>0?w+1|0:w}e[t>>2]=l,e[t+4>>2]=h,e[t+8>>2]=m,e[t+12>>2]=w,oe=M+112|0}function EA(t){var A=0,r=0,n=0,i=0,l=0,h=0,m=0;e:if(t|=0){l=(n=t-8|0)+(t=-8&(A=e[t-4>>2]))|0;t:if(!(1&A)){if(!(3&A)||(n=n-(A=e[n>>2])|0)>>>0<re[57156])break e;if(t=t+A|0,e[57157]==(0|n)){if(!(3&~(A=e[l+4>>2])))return e[57154]=t,e[l+4>>2]=-2&A,e[n+4>>2]=1|t,void(e[t+n>>2]=t)}else{if(A>>>0<=255){if(i=e[n+8>>2],A=A>>>3|0,(0|(r=e[n+12>>2]))==(0|i)){e[57152]=e[57152]&Ks(-2,A);break t}e[i+12>>2]=r,e[r+8>>2]=i;break t}if(m=e[n+24>>2],(0|n)==(0|(A=e[n+12>>2])))if((r=e[(i=n+20|0)>>2])||(r=e[(i=n+16|0)>>2])){for(;h=i,(r=e[(i=(A=r)+20|0)>>2])||(i=A+16|0,r=e[A+16>>2]););e[h>>2]=0}else A=0;else r=e[n+8>>2],e[r+12>>2]=A,e[A+8>>2]=r;if(!m)break t;i=e[n+28>>2];r:{if(e[(r=228912+(i<<2)|0)>>2]==(0|n)){if(e[r>>2]=A,A)break r;e[57153]=e[57153]&Ks(-2,i);break t}if(e[m+(e[m+16>>2]==(0|n)?16:20)>>2]=A,!A)break t}if(e[A+24>>2]=m,(r=e[n+16>>2])&&(e[A+16>>2]=r,e[r+24>>2]=A),!(r=e[n+20>>2]))break t;e[A+20>>2]=r,e[r+24>>2]=A}}if(!(n>>>0>=l>>>0)&&1&(A=e[l+4>>2])){t:{if(!(2&A)){if(e[57158]==(0|l)){if(e[57158]=n,t=e[57155]+t|0,e[57155]=t,e[n+4>>2]=1|t,e[57157]!=(0|n))break e;return e[57154]=0,void(e[57157]=0)}if(e[57157]==(0|l))return e[57157]=n,t=e[57154]+t|0,e[57154]=t,e[n+4>>2]=1|t,void(e[t+n>>2]=t);t=(-8&A)+t|0;r:if(A>>>0<=255){if(i=e[l+8>>2],A=A>>>3|0,(0|(r=e[l+12>>2]))==(0|i)){e[57152]=e[57152]&Ks(-2,A);break r}e[i+12>>2]=r,e[r+8>>2]=i}else{if(m=e[l+24>>2],(0|l)==(0|(A=e[l+12>>2])))if((r=e[(i=l+20|0)>>2])||(r=e[(i=l+16|0)>>2])){for(;h=i,(r=e[(i=(A=r)+20|0)>>2])||(i=A+16|0,r=e[A+16>>2]););e[h>>2]=0}else A=0;else r=e[l+8>>2],e[r+12>>2]=A,e[A+8>>2]=r;if(m){i=e[l+28>>2];a:{if(e[(r=228912+(i<<2)|0)>>2]==(0|l)){if(e[r>>2]=A,A)break a;e[57153]=e[57153]&Ks(-2,i);break r}if(e[m+(e[m+16>>2]==(0|l)?16:20)>>2]=A,!A)break r}e[A+24>>2]=m,(r=e[l+16>>2])&&(e[A+16>>2]=r,e[r+24>>2]=A),(r=e[l+20>>2])&&(e[A+20>>2]=r,e[r+24>>2]=A)}}if(e[n+4>>2]=1|t,e[t+n>>2]=t,e[57157]!=(0|n))break t;return void(e[57154]=t)}e[l+4>>2]=-2&A,e[n+4>>2]=1|t,e[t+n>>2]=t}if(t>>>0<=255)return A=228648+(-8&t)|0,(r=e[57152])&(t=1<<(t>>>3))?t=e[A+8>>2]:(e[57152]=t|r,t=A),e[A+8>>2]=n,e[t+12>>2]=n,e[n+12>>2]=A,void(e[n+8>>2]=t);i=31,t>>>0<=16777215&&(i=62+((t>>>38-(A=ce(t>>>8|0))&1)-(A<<1)|0)|0),e[n+28>>2]=i,e[n+16>>2]=0,e[n+20>>2]=0,h=228912+(i<<2)|0;t:{r:{if((r=e[57153])&(A=1<<i)){for(i=t<<((0|i)!=31?25-(i>>>1|0):0),A=e[h>>2];;){if(r=A,(-8&e[A+4>>2])==(0|t))break r;if(A=i>>>29|0,i<<=1,!(A=e[16+(h=r+(4&A)|0)>>2]))break}e[h+16>>2]=n,e[n+24>>2]=r}else e[57153]=A|r,e[h>>2]=n,e[n+24>>2]=h;e[n+12>>2]=n,e[n+8>>2]=n;break t}t=e[r+8>>2],e[t+12>>2]=n,e[r+8>>2]=n,e[n+24>>2]=0,e[n+12>>2]=r,e[n+8>>2]=t}t=e[57160]-1|0,e[57160]=t||-1}}}function Rr(t,A,r){var n,i=0,l=0,h=0,m=0,w=0,M=0,y=0,P=0,R=0,ee=0,ae=0,ue=0;oe=n=oe-176|0;e:{if(32&A)ee=r-((0|r)>1)|0;else{l=Tr(t,93302);t:{r:{a:{if((0|A)!=2){if(l)break a;A=0;break e}if(ee=r+1|0,P=56+(ae=F(r,76)+133152|0)|0,R=Tr(t,89360),h=Tr(t,93318),m=Tr(t,93426),w=Tr(t,93499),l)break r;break t}ee=r+1|0,P=56+(ae=F(r,76)+133152|0)|0,w=0}for(t=0,M=(0|(i=e[l-4>>2]))!=34?(0|i)==39?i:0:i,ue=F(r,76)+133208|0;;){if(i=t,!(t=e[l>>2]))break t;r:{if(!M){if((0|t)==32|t-9>>>0<5)break t;if((0|t)!=47)break r;break t}if((0|i)!=92&&(0|t)==(0|M))break t}if(l=l+4|0,!((0|(y=_s(t,y+ue|0)+y|0))<16))break}}M=0,g[y+P|0]=0,y=F(r,76)+133168|0,l=0;t:if(R)for(t=0,P=(0|(i=e[R-4>>2]))!=34?(0|i)==39?i:0:i;;){if(i=t,!(t=e[R>>2]))break t;r:{if(!P){if((0|t)==32|t-9>>>0<5)break t;if((0|t)!=47)break r;break t}if((0|i)!=92&&(0|t)==(0|P))break t}if(R=R+4|0,!((0|(l=_s(t,l+y|0)+l|0))<36))break}if(g[l+y|0]=0,!(!h|e[h>>2]-48>>>0>=10)){for(;M=(e[h>>2]+F(M,10)|0)-48|0,e[(h=h+4|0)>>2]-48>>>0<10;);(0|M)<=0||(M=M-1|0)}if(i=F(r,76)+133152|0,e[i+4>>2]=M,t=0,l=0,!(!m|e[m>>2]-48>>>0>=10))for(;l=(e[m>>2]+F(l,10)|0)-48|0,e[(m=m+4|0)>>2]-48>>>0<10;);e[i+12>>2]=l,l=F(r,76)+133152|0;t:{r:if(w){for(;r=g[t+93099|0],(i=e[(t<<2)+w>>2])&&(t=t+1|0,(0|r)==(0|i)););a:{n:switch(i-34|0){case 0:case 5:break n;default:break a}if(!r){t=0;break t}}for(t=0;r=g[t+93116|0],(i=e[(t<<2)+w>>2])&&(t=t+1|0,(0|r)==(0|i)););a:{n:switch(i-34|0){case 0:case 5:break n;default:break a}if(!r){t=1;break t}}for(t=0;r=g[t+93197|0],(i=e[(t<<2)+w>>2])&&(t=t+1|0,(0|r)==(0|i)););a:switch(i-34|0){case 0:case 5:break a;default:break r}if(!r){t=2;break t}}t=3}e[l+8>>2]=e[131156+(t<<3)>>2],e[ae>>2]=A}if(BA(137776,133168),r=BA(n+96|0,133208),g[n+157|0]=e[33291],g[n+156|0]=e[33290],t=e[33289],e[n+152>>2]=0,g[n+158|0]=t,(0|ee)>0)for(h=0;;){if(m=1,A=F(h,76)+133152|0,d[0|(t=A+16|0)]&&Js(0,t)&&(BA(137776,t),m=0,g[0|r]=0,g[n+158|0]=0,v[n+156>>1]=0),d[0|(t=A+56|0)]){w=BA(r,t),i=e[33679];t:if(d[0|(t=i)])for(;;){if(!Zr(t=t+1|0,w)){BA(w,i+1|0);break t}if(t=1+(wA(t)+t|0)|0,!d[0|t])break}m&&(g[137776]=0)}if((t=e[A+8>>2])&&(g[n+156|0]=t),(t=e[A+12>>2])&&(g[n+157|0]=t),(t=e[A+4>>2])&&(g[n+158|0]=t),(0|ee)==(0|(h=h+1|0)))break}e[n+148>>2]=r,e[n+144>>2]=137776,(t=Fr(n+144|0,n+172|0))?an(t,43)||(A=d[n+156|0],!d[134672]|((0|A)!=d[134724]?A:0)||(e[n>>2]=t,e[n+4>>2]=134672,uA(A=n+16|0,93533,n),t=137776,vr(137776,A,40))):t=92003,A=0,Zr(t,134784)&&(BA(134784,t),A=131072)}return oe=n+176|0,A}function Er(t,A){var r,n=0,i=0,l=0,h=0,m=0;r=t+A|0;e:{t:if(!(1&(n=e[t+4>>2]))){if(!(3&n))break e;A=(n=e[t>>2])+A|0;r:{if((0|(t=t-n|0))!=e[57157]){if(n>>>0<=255){if(l=e[t+8>>2],n=n>>>3|0,(0|(i=e[t+12>>2]))!=(0|l))break r;e[57152]=e[57152]&Ks(-2,n);break t}if(m=e[t+24>>2],(0|(n=e[t+12>>2]))==(0|t))if((i=e[(l=t+20|0)>>2])||(i=e[(l=t+16|0)>>2])){for(;h=l,(i=e[(l=(n=i)+20|0)>>2])||(l=n+16|0,i=e[n+16>>2]););e[h>>2]=0}else n=0;else i=e[t+8>>2],e[i+12>>2]=n,e[n+8>>2]=i;if(!m)break t;l=e[t+28>>2];a:{if(e[(i=228912+(l<<2)|0)>>2]==(0|t)){if(e[i>>2]=n,n)break a;e[57153]=e[57153]&Ks(-2,l);break t}if(e[m+(e[m+16>>2]==(0|t)?16:20)>>2]=n,!n)break t}if(e[n+24>>2]=m,(i=e[t+16>>2])&&(e[n+16>>2]=i,e[i+24>>2]=n),!(i=e[t+20>>2]))break t;e[n+20>>2]=i,e[i+24>>2]=n;break t}if(3&~(n=e[r+4>>2]))break t;return e[57154]=A,e[r+4>>2]=-2&n,e[t+4>>2]=1|A,void(e[r>>2]=A)}e[l+12>>2]=i,e[i+8>>2]=l}t:{if(!(2&(n=e[r+4>>2]))){if(e[57158]==(0|r)){if(e[57158]=t,A=e[57155]+A|0,e[57155]=A,e[t+4>>2]=1|A,e[57157]!=(0|t))break e;return e[57154]=0,void(e[57157]=0)}if(e[57157]==(0|r))return e[57157]=t,A=e[57154]+A|0,e[57154]=A,e[t+4>>2]=1|A,void(e[t+A>>2]=A);A=(-8&n)+A|0;r:if(n>>>0<=255){if(l=e[r+8>>2],n=n>>>3|0,(0|(i=e[r+12>>2]))==(0|l)){e[57152]=e[57152]&Ks(-2,n);break r}e[l+12>>2]=i,e[i+8>>2]=l}else{if(m=e[r+24>>2],(0|r)==(0|(n=e[r+12>>2])))if((l=e[(i=r+20|0)>>2])||(l=e[(i=r+16|0)>>2])){for(;h=i,(l=e[(i=(n=l)+20|0)>>2])||(i=n+16|0,l=e[n+16>>2]););e[h>>2]=0}else n=0;else i=e[r+8>>2],e[i+12>>2]=n,e[n+8>>2]=i;if(m){l=e[r+28>>2];a:{if(e[(i=228912+(l<<2)|0)>>2]==(0|r)){if(e[i>>2]=n,n)break a;e[57153]=e[57153]&Ks(-2,l);break r}if(e[m+(e[m+16>>2]==(0|r)?16:20)>>2]=n,!n)break r}e[n+24>>2]=m,(i=e[r+16>>2])&&(e[n+16>>2]=i,e[i+24>>2]=n),(i=e[r+20>>2])&&(e[n+20>>2]=i,e[i+24>>2]=n)}}if(e[t+4>>2]=1|A,e[t+A>>2]=A,e[57157]!=(0|t))break t;return void(e[57154]=A)}e[r+4>>2]=-2&n,e[t+4>>2]=1|A,e[t+A>>2]=A}if(A>>>0<=255)return n=228648+(-8&A)|0,(i=e[57152])&(A=1<<(A>>>3))?A=e[n+8>>2]:(e[57152]=A|i,A=n),e[n+8>>2]=t,e[A+12>>2]=t,e[t+12>>2]=n,void(e[t+8>>2]=A);l=31,A>>>0<=16777215&&(l=62+((A>>>38-(n=ce(A>>>8|0))&1)-(n<<1)|0)|0),e[t+28>>2]=l,e[t+16>>2]=0,e[t+20>>2]=0,h=228912+(l<<2)|0;t:{if((i=e[57153])&(n=1<<l)){for(l=A<<((0|l)!=31?25-(l>>>1|0):0),n=e[h>>2];;){if(i=n,(-8&e[n+4>>2])==(0|A))break t;if(n=l>>>29|0,l<<=1,!(n=e[16+(h=i+(4&n)|0)>>2]))break}e[h+16>>2]=t,e[t+24>>2]=i}else e[57153]=n|i,e[h>>2]=t,e[t+24>>2]=h;return e[t+12>>2]=t,void(e[t+8>>2]=t)}A=e[i+8>>2],e[A+12>>2]=t,e[i+8>>2]=t,e[t+24>>2]=0,e[t+12>>2]=i,e[t+8>>2]=A}}function Dr(t){var A=0,r=0,n=0,i=0,l=0,h=0,m=0,w=0,M=0,y=0,P=0,R=0,ee=0;if(!(A=g[88105]))return t;if(t=an(t,A)){if(!d[88106])return t;if(d[t+1|0]){if(!d[88107]){i=!!(0|(r=d[t+1|0]));e:if(r&&(0|(A=r|d[0|t]<<8))!=(0|(m=d[88106]|d[88105]<<8)))for(r=t+1|0;;){if(i=!!(0|(n=d[(t=r)+1|0])),!n)break e;if(r=t+1|0,(0|m)==(0|(A=n|A<<8&65280)))break}return i?t:0}if(d[t+2|0]){if(!d[88108]){r=t+2|0,i=!!(0|(A=d[t+2|0]));e:if(A&&(0|(A=d[t+1|0]<<16|d[0|t]<<24|A<<8))!=(0|(m=d[88106]<<16|d[88105]<<24|d[88107]<<8)))for(;;){if(t=r+1|0,i=!!(0|(n=d[r+1|0])),!n)break e;if(r=t,(0|m)==(0|(A=(A|n)<<8)))break}else t=r;return i?t-2|0:0}if(d[t+3|0]){if(!d[88109]){r=t+3|0,i=!!(0|(A=d[t+3|0]));e:if(A&&(0|(A=A|d[t+1|0]<<16|d[0|t]<<24|d[t+2|0]<<8))!=(0|(m=(t=d[88105]|d[88106]<<8|d[88107]<<16|d[88108]<<24)<<24|(65280&t)<<8|t>>>8&65280|t>>>24)))for(;;){if(t=r+1|0,i=!!(0|(n=d[r+1|0])),!n)break e;if(r=t,(0|m)==(0|(A=n|A<<8)))break}else t=r;return i?t-3|0:0}m=t,oe=w=oe-1056|0,e[(t=w+1048|0)>>2]=0,e[t+4>>2]=0,e[(t=w+1040|0)>>2]=0,e[t+4>>2]=0,e[w+1032>>2]=0,e[w+1036>>2]=0,e[w+1024>>2]=0,e[w+1028>>2]=0;e:{t:{r:{a:{if(A=d[88105]){for(;;){if(!d[l+m|0])break t;if(l=l+1|0,e[((255&A)<<2)+w>>2]=l,e[(t=(w+1024|0)+(A>>>3&28)|0)>>2]=e[t>>2]|1<<A,!(A=d[l+88105|0]))break}if(t=1,M=-1,l>>>0>1)break a}else M=-1,t=1;h=-1,r=1;break r}for(n=1,A=1;;){a:if((0|(h=d[88105+(A+M|0)|0]))!=(0|(i=d[t+88105|0])))i>>>0<h>>>0?(n=t-M|0,r=t,A=1):(M=r,r=r+1|0,n=1,A=1);else{if((0|A)==(0|n)){r=r+n|0,A=1;break a}A=A+1|0}if(!(l>>>0>(t=r+A|0)>>>0))break}if(r=1,h=-1,l>>>0<=1)t=n;else{for(t=0,i=1,A=1;;){a:if((0|(P=d[88105+(A+h|0)|0]))!=(0|(y=d[r+88105|0])))y>>>0>P>>>0?(i=r-h|0,t=r,A=1):(h=t,t=t+1|0,i=1,A=1);else{if((0|A)==(0|i)){t=t+i|0,A=1;break a}A=A+1|0}if(!(l>>>0>(r=t+A|0)>>>0))break}t=n,r=i}}for(A=t,ra(88105,(n=(t=h+1>>>0>M+1>>>0)?r:A)+88105|0,P=(y=t?h:M)+1|0)?(n=((t=~y+l|0)>>>0<y>>>0?y:t)+1|0,i=0):i=l-n|0,ee=l-1|0,R=63|l,h=0,t=m;;){if(!(m-t>>>0>=l>>>0))if(r=Wn(m,0,R)){if(m=r,r-t>>>0<l>>>0)break t}else m=m+R|0;r=d[t+ee|0],A=l;r:{if(e[(w+1024|0)+(r>>>3&28)>>2]>>>r&1)if((0|(r=e[(r<<2)+w>>2]))==(0|l)){a:{if(M=d[(r=(A=P)>>>0>h>>>0?A:h)+88105|0])for(;;){if(d[t+r|0]!=(255&M))break a;if(!(M=d[88105+(r=r+1|0)|0]))break}for(;;){if(A>>>0<=h>>>0)break e;if(d[88105+(A=A-1|0)|0]!=d[t+A|0])break}A=n,h=i;break r}A=r-y|0}else A=(r=l-r|0)>>>0>h>>>0?r:h;h=0}t=t+A|0}}t=0}oe=w+1056|0,r=t}}}}return r}function Vs(t,A,r,n,i,l){var h,m,w,M,y=0,P=0,R=0;if(oe=h=oe-560|0,g[h+448|0]=0,g[h+144|0]=0,g[h+120|0]=0,w=34&l,m=A-F(P=(0|A)/100|0,100)|0,1&(M=64&d[t+106|0]?(0|A)>999|l:0)|(0|A)>99){e:{t:{r:{if(!(!w|m)){if(!kA(t,90606,h+304|0))break r;break e}if(m)break t}if(kA(t,90691,h+304|0))break e}kA(t,90725,h+304|0)}y=l,(0|A)<1e3||(y=l,!(8&d[t+105|0])|A-2e3>>>0<4294967196&&(g[h+208|0]=0,QA(t,y=(P>>>0)/10|0,n=16384&e[t+108>>2]?0:i+1|0,!((A>>>0)%1e3|0)|w,h+272|0)||Vr(t,y,i,e[t+212>>2]==28012?520:(0|n)<4?(e[t+108>>2]>>>n&1)<<3:0,h+208|0),2&d[t+109|0]?(e[h+108>>2]=15,e[h+100>>2]=15,e[h+104>>2]=h+208,e[h+96>>2]=h+272,uA(h+144|0,90761,h+96|0)):(e[h+92>>2]=15,e[h+84>>2]=15,e[h+88>>2]=h+272,e[h+80>>2]=h+208,uA(h+144|0,90761,h+80|0)),n=1,1&(!!(0|(P=P-F(y,10)|0))|M)||(g[h+304|0]=0),y=1|l)),g[h+208|0]=0;e:if(!(~M&(0|P)<=0))if(!(4&d[t+106|0])|!(1&y|d[h+144|0])||kA(t,90824,h+120|0),!w|(16&d[t+109|0]?0:m)||(e[h+64>>2]=P,uA(n=h+548|0,90875,h- -64|0),R=kA(t,n,h+208|0),!(4096&e[t+108>>2])|(0|m)<=0||gs(h+208|0,133104)),n=1,1&(~M|!!(0|P))){t:{r:{a:{n:{if(!(131072&e[t+108>>2])||1&y|(0|P)!=1){if(m|R||(e[h+48>>2]=P,uA(n=h+548|0,90985,h+48|0),R=kA(t,n,h+208|0)),R||(e[h+32>>2]=P,uA(n=h+548|0,91027,h+32|0),kA(t,n,h+208|0)))break n;if((0|P)!=1)break r;break a}if(!R)break a}g[h+304|0]=0;break t}if(n=1,4&d[t+105|0])break e}Vr(t,P,i,0,h+208|0)}n=1}else kA(t,88875,h+208|0);e[h+28>>2]=h+304,e[h+24>>2]=h+208,e[h+20>>2]=h+120,e[h+16>>2]=h+144,uA(h+448|0,91059,h+16|0)}else y=l;g[h+132|0]=0;e:{t:{if((0|m)>0)16&d[t+109|0]&&2&y||(!(1&y)|i&&(0|A)<=100||!(64&(n=e[t+104>>2]))&(!(8388608&n)|m>>>0>9)||kA(t,90824,h+132|0),!(1&y|d[h+144|0])|!(524288&e[t+104>>2])|P||kA(t,90824,h+132|0)),g[h+336|0]=0;else if(g[h+336|0]=0,!m&n)break t;if(i?(l=e[t+108>>2],A=(0|i)<4?(l>>>i&1)<<3:0):(n=w?3:2,A=32&l|((0|A)<100?1&y?n:4|n:n),l=e[t+108>>2]),A=(0|i)==1&&e[t+212>>2]==28012?520|A:A,1048576&l&&(n=16|A,A=(0|P)>0||1&y?n:A),!Vr(t,m,i,256&y|A,h+336|0)|!(128&d[t+104|0]))break e;g[h+132|0]=0;break e}d[133104]&&((0|(t=wA(h+448|0)))<=0||d[447+(t=t+h|0)|0]==10&&(g[t+447|0]=0),BA(h+336|0,133104))}e[h+8>>2]=15,e[h+12>>2]=h+336,e[h+4>>2]=h+132,e[h>>2]=h+448,uA(r,91101,h),oe=h+560|0}function nr(t){var A,r,n,i=0,l=0,h=0,m=0,w=0,M=0,y=0,P=0,R=0,ee=0,ae=0,ue=0;for(i=e[32972],e[i+64>>2]=290816,e[i+68>>2]=4104,e[i+96>>2]=0,e[i+100>>2]=0,e[i+120>>2]=90,e[i+124>>2]=100,e[i+112>>2]=64,e[i+116>>2]=256,e[i+104>>2]=5,e[i+108>>2]=0,e[i+84>>2]=100,e[i+88>>2]=64,l=e[50754],e[i+132>>2]=0,e[i+136>>2]=0,e[i+128>>2]=l,e[i+140>>2]=0,e[i+144>>2]=0,e[i+148>>2]=0,e[i+152>>2]=0,e[i+156>>2]=0,e[i+160>>2]=0,e[36435]=450,e[i+92>>2]=2,e[50870]=0,e[50871]=0,e[50872]=0,e[50873]=0,e[50880]=0,e[50881]=0,e[50882]=0,e[50883]=0,e[50890]=0,e[50891]=0,e[50892]=0,e[50893]=0,h=-3.141592653589793/+e[50754],S[25429]=h,w=-2*h,S[25430]=w,A=(h=fr(200*h))*-h,S[25434]=A,S[25439]=A,S[25444]=A,h=(w=h*Aa(2e3*w))+w,S[25433]=h,S[25438]=h,S[25443]=h,w=1-h-A,S[25432]=w,S[25437]=w,S[25442]=w,e[50902]=0,e[50903]=0,e[50900]=0,e[50901]=0,S[25449]=A,S[25448]=h,e[50912]=0,e[50913]=0,S[25447]=w,e[50910]=0,e[50911]=0,S[25454]=A,S[25453]=h,e[50922]=0,e[50923]=0,S[25452]=w,e[50920]=0,e[50921]=0,S[25459]=A,S[25458]=h,e[50932]=0,e[50933]=0,S[25457]=w,e[50930]=0,e[50931]=0,S[25464]=A,S[25463]=h,S[25462]=w,e[50942]=0,e[50943]=0,e[50940]=0,e[50941]=0,S[25469]=A,S[25468]=h,S[25467]=w,e[50952]=0,e[50953]=0,e[50950]=0,e[50951]=0,S[25474]=A,S[25473]=h,S[25472]=w,r=e[32972],l=0;v[236+(M=(l<<1)+r|0)>>1]=256,v[M+164>>1]=256,i=d[l+105376|0]<<1,v[M+254>>1]=i,v[M+182>>1]=i,v[M+200>>1]=d[l+105385|0]<<1,e[308+(i=(m=l<<2)+r|0)>>2]=e[m+105408>>2],e[i+272>>2]=0,v[M+218>>1]=0,e[m+200944>>2]=(0|F(e[m+105456>>2],22050))/e[50754],(0|(l=l+1|0))!=9;);for(ee=e[32961],l=0,n=r+344|0;;){if(i=y,P=ee,M=l,(0|(y=e[(l=131840+(m=l<<2)|0)>>2]))==-1&&(y=8e3,e[l>>2]=8e3,M&&(e[131840+(4|m)>>2]=e[m+131836>>2])),ee=e[131840+(4|m)>>2],!((0|i)>
2810=(0|(y=(0|y)/8|0))||(0|(R=y-i|0))<=0||(m=i+1|0,l=i,1&R&&(g[344+(i+r|0)|0]=(0|P)>=255?255:P,l=m),(0|m)==(0|y))))for(ae=ee-P|0;m=((0|F(l-i|0,ae))/(0|R)|0)+P|0,g[l+n|0]=(0|m)>=255?255:m,ue=((0|F((m=l+1|0)-i|0,ae))/(0|R)|0)+P|0,g[m+n|0]=(0|ue)>=255?255:ue,(0|y)!=(0|(l=l+2|0)););if(l=M+2|0,!(M>>>0<10))break}l=e[32972],e[l+80>>2]=232,e[l+72>>2]=256,e[l+76>>2]=238,t||(e[49848]=0),v[l+200>>1]=(0|F(v[l+200>>1],105))/100}function qs(t,A,r){var n,i=0,l=0,h=0,m=0,w=0,M=0,y=0,P=0;oe=n=oe-288|0,(0|A)!=132848&&Ds(132848,A,40),(0|(i=t+228|0))!=(0|A)&&Ds(i,A,40),e[n+88>>2]=A,e[n+84>>2]=47,e[n+80>>2]=137584,uA(i=n+96|0,84089,n+80|0),l=Na(i),(i=e[t+688>>2])&&(EA(i),e[t+688>>2]=0),i=la(n+96|0,84577);e:if((0|l)>0&&i)if(r=aA(l),e[t+688>>2]=r,r)if(M=Mi(r,l,i),fa(i),M>>>0<=1032)e[n+16>>2]=n+96,ss(e[30450],85164,n+16|0),r=2;else if(i=e[t+688>>2],r=e[i+4>>2],!((0|(l=e[i>>2]))!=1024|(0|r)<=0)&(0|r)<134217729){i=r+i|0,e[t+684>>2]=i,P=IA(t+5168|0,0,1024),IA(t+7664|0,0,260),IA(t+7924|0,255,256),IA(t+4788|0,0,380),IA(t+6192|0,0,512);t:if((0|(r=d[0|i]))!=7)for(;;){if((0|(l=255&r))!=6){if(!l)break t;l=e[t+684>>2],e[n+72>>2]=r<<24>>24,e[n+64>>2]=132848,e[n+68>>2]=i-l,ss(e[30450],88950,n- -64|0);break}r:{a:{n:{c:{o:switch((l=d[0|(r=i+1|0)])-18|0){case 0:break c;case 2:break o;default:break n}for(r=4+(-4&r)|0,e[t+180>>2]=r;r=(i=r)+1|0,!ko(i););for(;l=d[0|i],r=i,i=i+1|0,(0|l)!=7;);break r}if(r=i+3|0,(0|(i=((0|(i=g[i+2|0]))<65?191:-65)+i|0))>94)break a;e[4788+((i<<2)+t|0)>>2]=r;break a}switch(r=1+((m=wA(r))+r|0)|0,0|m){case 1:e[5168+((l<<2)+t|0)>>2]=r;break a;case 0:e[P>>2]=r;break a}m=d[i+2|0],(0|l)!=1?(i=e[t+7664>>2],d[0|(y=7924+(h=t+l|0)|0)]==255&&(g[0|y]=i),g[0|(h=h+7668|0)]=d[0|h]+1,e[6704+(h=(i<<2)+t|0)>>2]=r,e[t+7664>>2]=i+1,e[h+7184>>2]=l|m<<8):e[6188+((m<<2)+t|0)>>2]=r}if(d[0|r]!=7)for(;r=1+(wA(r)+r|0)|0,d[0|r]!=7;);}r=d[0|(i=r+1|0)]}for(i=e[t+688>>2]+8|0;;){for(e[692+((r=w<<2)+t|0)>>2]=i;l=d[0|i];)i=i+l|0;for(i=i+1|0,e[692+((4|r)+t|0)>>2]=i;r=d[0|i];)i=r+i|0;if(i=i+1|0,(0|(w=w+2|0))==1024)break}r=0,(0|(t=e[t+324>>2]))<=0|t>>>0<=M>>>0||(e[n+48>>2]=A,ss(e[30450],85519,n+48|0))}else e[n+40>>2]=r,e[n+36>>2]=l,e[n+32>>2]=n+96,ss(e[30450],85349,n+32|0),r=2;else fa(i),r=3;else{if(r||(e[n>>2]=n+96,ss(e[30450],84963,n)),r=1,!i)break e;fa(i)}return oe=n+288|0,r}function xa(t){var A,r=0,n=0;for(A=IA(t+344|0,0,256),g[t+364|0]=1,g[t+356|0]=1,g[t+357|0]=1,g[t+358|0]=1,g[t+359|0]=1,g[t+360|0]=1,g[t+361|0]=1,g[t+362|0]=1,g[t+363|0]=1,g[t+348|0]=1,g[t+349|0]=1,g[t+350|0]=1,g[t+351|0]=1,g[t+352|0]=1,g[t+353|0]=1,g[t+354|0]=1,g[t+355|0]=1,g[t+431|0]=3,g[t+429|0]=3,g[t+430|0]=3,g[t+406|0]=3,g[t+407|0]=3,g[t+408|0]=3,g[t+409|0]=3,g[t+410|0]=3,g[t+411|0]=3,g[t+412|0]=3,g[t+413|0]=3,g[t+414|0]=3,g[t+415|0]=3,g[t+416|0]=3,g[t+417|0]=3,g[t+418|0]=3,g[t+419|0]=3,g[t+420|0]=3,g[t+421|0]=3,g[t+440|0]=3,g[t+441|0]=3,g[t+442|0]=3,g[t+443|0]=3,n=21;g[344+(r=t+n|0)|0]=4|d[r+344|0],(0|(r=n+1|0))!=58;)g[0|(r=r+A|0)]=4|d[0|r],g[2+(r=n+A|0)|0]=4|d[r+2|0],g[r+3|0]=4|d[r+3|0],n=n+4|0;g[t+346|0]=4|d[t+346|0],g[t+347|0]=4|d[t+347|0],g[t+432|0]=4|d[t+432|0],g[t+433|0]=4|d[t+433|0],g[t+434|0]=4|d[t+434|0],g[t+435|0]=4|d[t+435|0],g[t+436|0]=4|d[t+436|0],g[t+437|0]=4|d[t+437|0],g[t+438|0]=4|d[t+438|0],g[t+439|0]=4|d[t+439|0],g[t+467|0]=4|d[t+467|0],g[t+468|0]=4|d[t+468|0],g[t+470|0]=4|d[t+470|0],g[t+471|0]=4|d[t+471|0],g[t+348|0]=64|d[t+348|0],g[t+349|0]=64|d[t+349|0],g[t+350|0]=64|d[t+350|0],g[t+351|0]=64|d[t+351|0],g[t+352|0]=64|d[t+352|0],g[t+353|0]=64|d[t+353|0],g[t+354|0]=64|d[t+354|0],g[t+355|0]=64|d[t+355|0],g[t+356|0]=64|d[t+356|0],g[t+357|0]=64|d[t+357|0],g[t+358|0]=64|d[t+358|0],g[t+359|0]=64|d[t+359|0],g[t+360|0]=64|d[t+360|0],g[t+361|0]=64|d[t+361|0],g[t+362|0]=64|d[t+362|0],g[t+363|0]=64|d[t+363|0],g[t+364|0]=64|d[t+364|0],g[t+406|0]=64|d[t+406|0],g[t+407|0]=64|d[t+407|0],g[t+408|0]=64|d[t+408|0],g[t+409|0]=64|d[t+409|0],g[t+410|0]=64|d[t+410|0],g[t+411|0]=64|d[t+411|0],g[t+412|0]=64|d[t+412|0],g[t+413|0]=64|d[t+413|0],g[t+414|0]=64|d[t+414|0],g[t+415|0]=64|d[t+415|0],g[t+416|0]=64|d[t+416|0],g[t+417|0]=64|d[t+417|0],g[t+418|0]=64|d[t+418|0],g[t+419|0]=64|d[t+419|0],g[t+420|0]=64|d[t+420|0],g[t+440|0]=64|d[t+440|0],g[t+441|0]=64|d[t+441|0],g[t+429|0]=64|d[t+429|0],g[t+430|0]=64|d[t+430|0],g[t+431|0]=64|d[t+431|0],g[t+442|0]=64|d[t+442|
vendor: 16,143 bytes, line 2810
28100],g[t+443|0]=64|d[t+443|0],e[t+40>>2]=1,e[t+204>>2]=e[t+600>>2]+77}function Ja(t,A,r,n,i,l,h,m,w){var M,y=0,P=0,R=0,ee=0,ae=0,ue=0,be=0,_e=0,Ve=0,$e=0;oe=M=oe-128|0;e:{t:{if(Yt(l,h,m,w,0,0,0,0)){P=65535&w;r:a:{if((0|(ee=w>>>16&32767))!=32767){if(y=4,ee)break a;y=l|m|h|P?3:2;break r}y=!(l|m|h|P)}if((0|(ae=32767&(Ve=i>>>16|0)))!=32767&&y)break t}qt(M+16|0,A,r,n,i,l,h,m,w),vA(M,A=e[M+16>>2],n=e[M+20>>2],i=e[M+24>>2],r=e[M+28>>2],A,n,i,r),n=e[M+8>>2],i=e[M+12>>2],m=e[M>>2],w=e[M+4>>2];break e}if(ee=m,(0|Yt(A,r,y=n,ue=2147483647&i,l,h,m,R=2147483647&w))<=0){if(Yt(A,r,y,ue,l,h,ee,R)){m=A,w=r;break e}qt(M+112|0,A,r,n,i,0,0,0,0),n=e[M+120>>2],i=e[M+124>>2],m=e[M+112>>2],w=e[M+116>>2]}else{if(be=w>>>16&32767,ae?(w=r,m=A):(qt(M+96|0,A,r,y,ue,0,0,0,1081540608),y=e[M+104>>2],ue=m=e[M+108>>2],ae=(m>>>16|0)-120|0,w=e[M+100>>2],m=e[M+96>>2]),be||(qt(M+80|0,l,h,ee,R,0,0,0,1081540608),ee=e[M+88>>2],R=l=e[M+92>>2],be=(l>>>16|0)-120|0,h=e[M+84>>2],l=e[M+80>>2]),_e=ee,$e=65535&R|65536,ue=65535&ue|65536,(0|ae)>(0|be)){for(;;){if(ee=(R=y-_e|0)-(P=(0|h)==(0|w)&l>>>0>m>>>0|h>>>0>w>>>0)|0,(0|(P=(ue-((y>>>0<_e>>>0)+$e|0)|0)-(P>>>0>R>>>0)|0))>0|(0|P)>=0){if(y=m,!((m=m-l|0)|ee|(w=w-((l>>>0>y>>>0)+h|0)|0)|P)){qt(M+32|0,A,r,n,i,0,0,0,0),n=e[M+40>>2],i=e[M+44>>2],m=e[M+32>>2],w=e[M+36>>2];break e}P=P<<1|ee>>>31,y=ee<<1|w>>>31}else P=ue<<1|y>>>31,y=y<<1|w>>>31;if(ue=P,P=w<<1|m>>>31,m<<=1,w=P,!((0|(ae=ae-1|0))>(0|be)))break}ae=be}if(ee=(R=y-_e|0)-(P=(0|h)==(0|w)&l>>>0>m>>>0|h>>>0>w>>>0)|0,R=P=(ue-((y>>>0<_e>>>0)+$e|0)|0)-(P>>>0>R>>>0)|0,(0|P)<0)ee=y,R=ue;else if(y=m,!((m=m-l|0)|ee|(w=w-((l>>>0>y>>>0)+h|0)|0)|R)){qt(M+48|0,A,r,n,i,0,0,0,0),n=e[M+56>>2],i=e[M+60>>2],m=e[M+48>>2],w=e[M+52>>2];break e}if((0|R)==65535|R>>>0<65535)for(;A=w>>>31|0,ae=ae-1|0,ue=w<<1|m>>>31,m<<=1,w=ue,r=A,A=R<<1|ee>>>31,ee=r|ee<<1,R=A,A>>>0<65536;);A=32768&Ve,(0|ae)<=0?(qt(M- -64|0,m,w,ee,65535&R|(A|ae+120)<<16,0,0,0,1065811968),n=e[M+72>>2],i=e[M+76>>2],m=e[M+64>>2],w=e[M+68>>2]):(n=ee,i=65535&R|(A|ae)<<16)}}e[t>>2]=m,e[t+4>>2]=w,e[t+8>>2]=n,e[t+12>>2]=i,oe=M+128|0}function ma(t,A,r){var n,i,l=0,h=0,m=0,w=0,M=0,y=0,P=0,R=0,ee=0;if(oe=n=oe-1040|0,(0|(h=Yo(t,589824,0)))>=0&&((l=a0(1,2072))?e[l+8>>2]=h:(Pe(0|h),l=0)),i=l,l){e:if(l=Xn(i))for(ee=e[30450],R=(n+96|0)+A|0;;){if((0|(h=e[50303]))>=348){e[n+4>>2]=350,e[n>>2]=h+1,ss(ee,91860,n);break e}if(d[l+19|0]!=46)if(e[n+88>>2]=l+19,e[n+84>>2]=47,e[n+80>>2]=t,uA(l=n+96|0,91924,n+80|0),(0|(l=Na(l)))!=-31){if(!((0|l)<=0)&&(P=la(n+96|0,85712))){m=0,g[n+832|0]=0,g[n+752|0]=0,e[n+360>>2]=0,e[n+356>>2]=4,M=0;t:for(;;){for(y=299-M|0;_r(n+912|0,120,P);){l=n+912|0;r:{if(d[n+912|0]!=35){a:if(!((0|(l=wA(n+912|0)-1|0))<=0))for(;;){if(!((0|(h=g[0|(w=(n+912|0)+l|0)]))==32|h-9>>>0<5))break a;if(g[0|w]=0,!((0|(l=l-1|0))>0))break}if(!(l=Dr(n+912|0)))break r}g[0|l]=0}l=n+912|0;r:if(h=d[n+912|0])for(;;){if(Ts(h<<24>>24))break r;if(!(h=d[0|(l=l+1|0)]))break}if(g[0|l]=0,d[n+912|0]){l=l+1|0;r:switch(ti(131904,n+912|0)-1|0){case 0:for(;h=l,l=l+1|0,(0|(w=g[0|h]))==32|w-9>>>0<5;);vr(n+832|0,h,80);continue;case 1:if(g[n+672|0]=0,e[n+364>>2]=5,h=n+672|0,e[n+16>>2]=h,e[n+20>>2]=n+364,nA(l,86237,n+16|0),(h=wA(h)+2|0)>>>0>=y>>>0)continue;g[0|(l=(n+368|0)+M|0)]=e[n+364>>2],BA(l+1|0,n+672|0),m=m+1|0,M=h+M|0;continue t;case 2:if(e[n+52>>2]=n+360,e[n+48>>2]=n+752,nA(l,86237,n+48|0),!r)continue;e[n+32>>2]=R,ss(ee,92042,n+32|0);continue;case 5:break r;default:continue}e[n+64>>2]=n+356,nA(l,87268,n- -64|0)}}break}g[(n+368|0)+M|0]=0,h=ti(132112,n+752|0),m?(w=wA(R)+M|0,y=$A((m=a0(28+(wA(n+832|0)+w|0)|0,1))+24|0,n+368|0,l=M+1|0),e[m+4>>2]=y,l=BA(l+y|0,R),e[m>>2]=l,e[m+8>>2]=l,d[n+832|0]&&(e[m>>2]=BA(2+(w+y|0)|0,n+832|0)),l=e[n+360>>2],g[m+14|0]=0,g[m+12|0]=h,g[m+13|0]=l,g[m+15|0]=e[n+356>>2],fa(P),l=e[50303],e[50303]=l+1,e[201216+(l<<2)>>2]=m):fa(P)}}else ma(n+96|0,A,r);if(!(l=Xn(i)))break}Dn(e[i+8>>2]),EA(i)}oe=n+1040|0}function Qa(t,A){var r,n=0,i=0,l=0,h=0,m=0,w=0,M=0,y=0,P=0;oe=r=oe-352|0;e:if(t||(t=e[136284+(A<<4)>>2])){d[0|t]!=47&&(e[r+12>>2]=t,e[r+4>>2]=47,e[r+8>>2]=47,e[r>>2]=137584,uA(t=r+16|0,84114,r)),g[r+240|0]=0;t:{r:{if(n=la(t,84577)){if((0|Io(n,20))==-1)break t;if(l=Ca(n),i=Ca(n),m=Ca(n),!((0|l)!=65537|(0|i)!=e[50754])&(0|m)==i<<1)break r;fa(n),v[r+256>>1]=d[84864]|d[84865]<<8,t=d[84852]|d[84853]<<8|d[84854]<<16|d[84855]<<24,e[r+240>>2]=d[84848]|d[84849]<<8|d[84850]<<16|d[84851]<<24,e[r+244>>2]=t,t=d[84860]|d[84861]<<8|d[84862]<<16|d[84863]<<24,e[r+248>>2]=d[84856]|d[84857]<<8|d[84858]<<16|d[84859]<<24,e[r+252>>2]=t,oe=l=oe-16|0;a:{if((n=wA(t=r+240|0))>>>0>=6&&!ra(m=(t+n|0)-6|0,84274,6)){for(M=100;;){for(w=0,oe=n=oe-16|0,d[227196]||(g[227197]=Ue(),g[227196]=1),P=+Ge(),he(h=P/1e3)<9223372036854776e3?(y=he(h)>=1?~~(h>0?ge(Ce(23283064365386963e-26*h),4294967295):Fe(23283064365386963e-26*(h-+(~~h>>>0>>>0))))>>>0:0,i=~~h>>>0):(y=-2147483648,i=0),e[n>>2]=i,e[n+4>>2]=y,h=1e3*(P-(+(rr(i,y,1e3,0)>>>0)+4294967296*+(0|fe)))*1e3,i=he(h)<2147483648?~~h:-2147483648,e[n+8>>2]=i,i=m+(n>>>4|0)^F(e[n+8>>2],65537);g[m+w|0]=65+(15&i|i<<1&32),i=i>>>5|0,(0|(w=w+1|0))!=6;);if(oe=n+16|0,e[l>>2]=384,(0|(n=Yo(t,194,l)))>=0)break a;if(M=M-1|0,e[56798]!=20||!M)break}$A(m,84274,6)}else e[56798]=28;n=-1}oe=l+16|0,(0|n)<0||Dn(n)}if(!(n=la(t,84577))){t=On(0,e[56798],t);break e}}if((0|(l=Na(t)))<0){fa(n),t=On(0,0-l|0,t);break e}if((0|Io(n,0))==-1){A=e[56798],fa(n),t=On(0,A,t);break e}if(!(i=ds(e[(m=136280+(A<<4)|0)>>2],l))){fa(n),t=48;break e}if((0|Mi(i,l,n))!=(0|l)){A=e[56798],fa(n),d[r+240|0]&&Co(r+240|0),EA(i),t=On(0,A,t);break e}fa(n),d[r+240|0]&&Co(r+240|0),e[136276+(A<<4)>>2]=(d[i+40|0]|d[i+41|0]<<8|d[i+42|0]<<16|d[i+43|0]<<24)/2,e[m>>2]=i,t=0;break e}A=e[56798],fa(n),t=On(0,A,t)}else t=28;return oe=r+352|0,t}function $a(t,A,r,n,i,l){var h,m=0,w=0,M=0,y=0,P=0,R=0,ee=0,ae=0,ue=0,be=0,_e=0,Ve=0,$e=0,it=0;for(g[0|r]=1,h=1&l,$e=1,ue=-1,be=-1,y=1,l=A;;){it=Ve-2|0,_e=P,ee=be;e:{t:{for(;;){if(!(R=d[0|l])){be=ee,P=_e;break e}if(l=l+1|0,ae=e[144464+(R<<2)>>2]){r:{if((0|(m=d[ae+11|0]))!=1){if(16&d[ae+6|0]|(0|m)!=2)break r;g[0|(P=r+y|0)]=ue,m=(0|ue)<4|(0|ee)>(0|ue),!(2&d[ae+4|0])|!h|(0|ue)>=0||(g[0|P]=1),P=m?_e:y,be=m?ee:ue,ue=-1,y=y+1|0;break t}if(!_[ae+8>>1]){m=0,M=y;a:if((0|R)!=8)m=d[ae+14|0],e[i>>2]&&m>>>0>=4||(ee=(0|m)<(0|ee)?ee:m,ue=m);else{for(;;){if(R=m,M=M-1|0,e[i>>2]|(0|M)<=0||(0|(ae=g[0|(w=r+M|0)]))>3)break a;if(m=R+1|0,!(ae>>>0<2))break}if(g[0|w]=4,_e=(0|ee)<4?M:_e,ee=(0|ee)<=4?4:ee,M>>>0<2)break a;if(ae=3&(M=~R+Ve|0),m=1,it-R>>>0>=3)for(R=-4&M,M=0;d[0|(w=r+m|0)]==4&&(g[0|w]=3),d[w+1|0]==4&&(g[w+1|0]=3),d[w+2|0]==4&&(g[w+2|0]=3),d[w+3|0]==4&&(g[w+3|0]=3),m=m+4|0,(0|R)!=(0|(M=M+4|0)););if(w=0,!ae)break a;for(;d[0|(R=r+m|0)]==4&&(g[0|R]=3),m=m+1|0,(0|ae)!=(0|(w=w+1|0)););}if($e)continue;break e}}break}}be=ee,P=_e,(0|R)==20&&(g[r+y|0]=h&&(0|ue)<0?1:ue,y=y+1|0)}if(g[0|A]=R,Ve=y-1|0,A=A+1|0,$e=(0|y)<99)continue}break}g[r+y|0]=1,g[0|A]=0;e:if((0|(A=e[i>>2]))>0)be=4,(0|A)>=(0|y)&&(e[i>>2]=Ve,A=Ve),g[A+r|0]=4,P=e[i>>2];else if((0|be)==5&&(be=4,!((0|y)<2))){if(l=1,ee=1&(A=y-1|0),(0|y)!=2)for(_e=-2&A,M=0;;){w=4;t:{r:{a:switch(d[0|(A=(m=l)+r|0)]-4|0){case 1:break r;case 0:break a;default:break t}w=2&d[t+14|0]?1:3,m=P}g[0|A]=w,P=m}w=4;t:{r:{a:switch(d[0|(A=(m=l+1|0)+r|0)]-4|0){case 1:break r;case 0:break a;default:break t}w=2&d[t+14|0]?1:3,m=P}g[0|A]=w,P=m}if(l=l+2|0,(0|_e)==(0|(M=M+2|0)))break}if(ee){w=4;t:{r:switch(d[0|(A=r+l|0)]-4|0){case 1:break t;case 0:break r;default:break e}w=2&d[t+14|0]?1:3,l=P}g[0|A]=w,P=l}}return e[i>>2]=P,e[n>>2]=y,be}function ds(t,A){var r,n,i=0,l=0,h=0,m=0,w=0,M=0,y=0,P=0,R=0;if(!t)return aA(A);if(A>>>0>=4294967232)return e[56798]=48,0;r=A>>>0<11?16:A+11&-8,h=-8&(n=e[4+(m=t-8|0)>>2]);e:if(3&n){w=h+m|0;t:if(h>>>0>=r>>>0){if((l=h-r|0)>>>0<16)break t;e[m+4>>2]=1&n|r|2,e[4+(i=m+r|0)>>2]=3|l,e[w+4>>2]=1|e[w+4>>2],Er(i,l)}else if(e[57158]!=(0|w))if(e[57157]!=(0|w)){if(2&(l=e[w+4>>2])||(y=h+(-8&l)|0)>>>0<r>>>0)break e;R=y-r|0;r:if(l>>>0<=255){if(h=e[w+8>>2],i=l>>>3|0,(0|(l=e[w+12>>2]))==(0|h)){e[57152]=e[57152]&Ks(-2,i);break r}e[h+12>>2]=l,e[l+8>>2]=h}else{if(P=e[w+24>>2],(0|(M=e[w+12>>2]))==(0|w))if((i=e[(h=w+20|0)>>2])||(i=e[(h=w+16|0)>>2])){for(;l=h,M=i,(i=e[(h=i+20|0)>>2])||(h=M+16|0,i=e[M+16>>2]););e[l>>2]=0}else M=0;else i=e[w+8>>2],e[i+12>>2]=M,e[M+8>>2]=i;if(P){l=e[w+28>>2];a:{if(e[(i=228912+(l<<2)|0)>>2]==(0|w)){if(e[i>>2]=M,M)break a;e[57153]=e[57153]&Ks(-2,l);break r}if(e[(e[P+16>>2]==(0|w)?16:20)+P>>2]=M,!M)break r}e[M+24>>2]=P,(i=e[w+16>>2])&&(e[M+16>>2]=i,e[i+24>>2]=M),(i=e[w+20>>2])&&(e[M+20>>2]=i,e[i+24>>2]=M)}}R>>>0<=15?(e[m+4>>2]=1&n|y|2,e[4+(i=m+y|0)>>2]=1|e[i+4>>2]):(e[m+4>>2]=1&n|r|2,e[4+(l=m+r|0)>>2]=3|R,e[4+(i=m+y|0)>>2]=1|e[i+4>>2],Er(l,R))}else{if((l=h+e[57154]|0)>>>0<r>>>0)break e;(i=l-r|0)>>>0>=16?(e[m+4>>2]=1&n|r|2,e[4+(h=m+r|0)>>2]=1|i,e[(l=l+m|0)>>2]=i,e[l+4>>2]=-2&e[l+4>>2]):(e[m+4>>2]=l|1&n|2,e[4+(i=l+m|0)>>2]=1|e[i+4>>2],i=0,h=0),e[57157]=h,e[57154]=i}else{if((h=h+e[57155]|0)>>>0<=r>>>0)break e;e[m+4>>2]=1&n|r|2,i=h-r|0,e[4+(l=m+r|0)>>2]=1|i,e[57155]=i,e[57158]=l}i=m}else{if(r>>>0<256||h>>>0>=r+4>>>0&&(i=m,h-r>>>0<=e[57272]<<1>>>0))break e;i=0}return i?i+8|0:(m=aA(A))?($A(m,t,A>>>0>(i=(3&(i=e[t-4>>2])?-4:-8)+(-8&i)|0)>>>0?i:A),EA(t),m):0}function en(){var t,A,r=0,n=0;return t=Ka(12),e[t>>2]=22050,A=Ka(432),e[(n=A)+4>>2]=0,e[n+8>>2]=0,e[n>>2]=132304,e[n+32>>2]=0,e[n+12>>2]=0,e[n+16>>2]=0,e[n+20>>2]=0,e[n+24>>2]=0,IA(n+40|0,0,376),e[n+420>>2]=0,e[n+424>>2]=-1,g[n+416|0]=1,r=IA(Ka(408),0,408),e[n+28>>2]=r,g[r+8|0]=1,e[t+4>>2]=n,r=Ka(1096),e[r+8>>2]=22050,e[r+4>>2]=22050,e[r>>2]=132352,e[r+64>>2]=22050,e[r+56>>2]=0,e[r+60>>2]=0,e[r+32>>2]=0,e[r+36>>2]=0,e[r+24>>2]=22050,e[r+16>>2]=0,e[r+20>>2]=0,e[r+40>>2]=0,e[r+44>>2]=0,g[r+48|0]=0,e[r+128>>2]=0,e[r+132>>2]=0,v[r+96>>1]=0,e[r+72>>2]=22050,e[r+136>>2]=0,e[r+140>>2]=0,v[r+168>>1]=0,e[r+144>>2]=22050,e[r+200>>2]=0,e[r+204>>2]=0,e[r+208>>2]=0,e[r+212>>2]=0,e[r+216>>2]=22050,v[r+240>>1]=0,e[r+280>>2]=0,e[r+284>>2]=0,e[r+272>>2]=0,e[r+276>>2]=0,e[r+288>>2]=22050,v[r+312>>1]=0,e[r+344>>2]=0,e[r+348>>2]=0,e[r+352>>2]=0,e[r+356>>2]=0,e[r+360>>2]=22050,v[r+384>>1]=0,e[r+416>>2]=0,e[r+420>>2]=0,e[r+424>>2]=0,e[r+428>>2]=0,e[r+432>>2]=22050,v[r+456>>1]=0,e[r+488>>2]=0,e[r+492>>2]=0,e[r+496>>2]=0,e[r+500>>2]=0,e[r+504>>2]=22050,v[r+528>>1]=1,e[r+560>>2]=0,e[r+564>>2]=0,e[r+568>>2]=0,e[r+572>>2]=0,v[r+600>>1]=0,e[r+576>>2]=22050,e[r+640>>2]=0,e[r+644>>2]=0,e[r+632>>2]=0,e[r+636>>2]=0,v[r+680>>1]=0,e[r+656>>2]=22050,e[r+648>>2]=22050,e[r+720>>2]=0,e[r+724>>2]=0,e[r+712>>2]=0,e[r+716>>2]=0,v[r+752>>1]=0,e[r+728>>2]=22050,e[r+792>>2]=0,e[r+796>>2]=0,e[r+784>>2]=0,e[r+788>>2]=0,v[r+824>>1]=0,e[r+800>>2]=22050,e[r+864>>2]=0,e[r+868>>2]=0,e[r+856>>2]=0,e[r+860>>2]=0,e[r+872>>2]=22050,v[r+896>>1]=0,e[r+936>>2]=0,e[r+940>>2]=0,e[r+928>>2]=0,e[r+932>>2]=0,v[r+968>>1]=0,e[r+944>>2]=22050,e[r+1008>>2]=0,e[r+1012>>2]=0,e[r+1e3>>2]=0,e[r+1004>>2]=0,v[r+1040>>1]=0,e[r+1016>>2]=22050,e[r+1088>>2]=0,e[(n=r+1080|0)>>2]=0,e[n+4>>2]=0,e[(n=r+1072|0)>>2]=0,e[n+4>>2]=0,e[t+8>>2]=r,LA[e[e[r>>2]+4>>2]](r,A),t}function oa(t,A,r){var n=0,i=0,l=0,h=0,m=0,w=0,M=0,y=0,P=0,R=0,ee=0;e:if(P=e[50759]){if(n=e[50980],i=F(n,80)+222176|0,(0|(w=(0|(i=(0|(i=(e[i+12>>2]+e[i>>2]|0)/(0|t)|0))<(0|(y=((0|F(e[50754],19))/40<<16)/(0|t)|0))?i:y))>=399?399:i))>=0&&(IA(A,0,4+(w<<2)|0),n=e[50980]),(0|n)>=0)for(R=e[50801];;){if(l=F(h,80)+222176|0,e[l+4>>2]&&(m=e[l>>2])){if(ee=m+e[l+12>>2]|0,n=1+((m-e[l+8>>2]|0)/(0|t)|0)|0,(0|m)>(0|(n=F(i=(0|n)<=1?1:n,t))))for(;e[(M=(i<<2)+A|0)>>2]=e[M>>2]+F(e[l+4>>2],d[((m-n|0)/(e[l+8>>2]>>8)|0)+R|0]),i=i+1|0,(0|m)>(0|(n=t+n|0)););if(!((0|n)>=(0|ee)))for(;e[(M=(i<<2)+A|0)>>2]=e[M>>2]+F(e[l+4>>2],d[((n-m|0)/(e[l+12>>2]>>8)|0)+R|0]),i=i+1|0,(0|ee)>(0|(n=t+n|0)););}if(!((0|(h=h+1|0))<=e[50980]))break}if(i=1,!((0|(l=65536e3/(0|t)|0))<=0||(0|(n=F(e[55565],10)))<=0))for(l=(0|n)/(0|l)|0;e[(m=(i<<2)+A|0)>>2]=e[m>>2]+n,i=i+1|0,(0|(n=n-l|0))>0;);if((0|h)<=8)for(;i=203216+(n=h<<2)|0,l=F(h,80)+222176|0,m=e[l+4>>2]>>14,e[i>>2]=(0|F(F(m,m),5))/2,r?n=e[n+203264>>2]:(m=n+203264|0,n=e[l>>2]/(0|t)|0,e[m>>2]=n),(0|n)>=(0|y)&&(e[i>>2]=0),(0|(h=h+1|0))!=9;);if(i=0,(0|w)>=0)for(n=0;l=e[(h=(n<<2)+A|0)>>2]>>15,l=F(l,l)>>8,e[h>>2]=l,(0|i)<=524287999&&(e[h>>2]=F(l,d[344+((i>>19)+P|0)|0])>>13),i=t+i|0,h=(0|n)!=(0|w),n=n+1|0,h;);if(e[A+4>>2]=(0|F(e[A+4>>2],d[203300]?6:10))/8,1&r)for(t=e[50826],n=1;;){if(e[203312+(r=n<<2)>>2]=e[A+r>>2]-e[t+r>>2]>>3,(0|(r=n+1|0))==30)break e;e[203312+(r<<=2)>>2]=e[A+r>>2]-e[t+r>>2]>>3,n=n+2|0}}else w=1;return w}function Yn(t,A,r,n){var i,l=0,h=0;oe=i=oe-176|0,g[0|t]=0,v[i+80>>1]=24320,e[i+104>>2]=0,e[i+108>>2]=0,g[82+(_s(r,l=i+80|2)+i|0)|0]=0;e:{t:{if(!n){if(n=i+80|1,e[i+12>>2]=n,Hr(A,i+12|0,i+16|0,i+104|0,0,0)||(e[i+12>>2]=l,Hr(A,i+12|0,i+16|0,i+104|0,0,0)||(g[i+81|0]=32,zA(A,l,i+16|0,60,0,0,0))),(r=d[i+16|0])&&(0|r)!=21)break t;r:{if(e[A+212>>2]!=25966){if(lo(85719,188772,189296),g[i+81|0]=95,e[i+12>>2]=n,Hr(e[47193],i+12|0,i+16|0,i+104|0,0,0)||(e[i+12>>2]=l,Hr(e[47193],i+12|0,i+16|0,i+104|0,0,0)),d[i+16|0])break r;Ia(e[e[32972]+60>>2]),r=d[i+16|0]}if(255&r)break t;A=d[87124]|d[87125]<<8|d[87126]<<16|d[87127]<<24,r=d[87120]|d[87121]<<8|d[87122]<<16|d[87123]<<24,g[0|t]=r,g[t+1|0]=r>>>8,g[t+2|0]=r>>>16,g[t+3|0]=r>>>24,g[t+4|0]=A,g[t+5|0]=A>>>8,g[t+6|0]=A>>>16,g[t+7|0]=A>>>24,g[t+16|0]=d[87136],A=d[87132]|d[87133]<<8|d[87134]<<16|d[87135]<<24,r=d[87128]|d[87129]<<8|d[87130]<<16|d[87131]<<24,g[t+8|0]=r,g[t+9|0]=r>>>8,g[t+10|0]=r>>>16,g[t+11|0]=r>>>24,g[t+12|0]=A,g[t+13|0]=A>>>8,g[t+14|0]=A>>>16,g[t+15|0]=A>>>24;break e}n=i+16|0,l=i+104|0,oe=r=oe-112|0,(h=e[47193])?(bA(h,n,l,-1,0),TA(l=n,n=r+48|0),A=e[A+212>>2],g[r+43|0]=A>>>24,g[0|(h=(l=r+43|0)+(A>>>0>16777215)|0)]=A>>>16,g[0|(h=h+!!(16711680&A)|0)]=A>>>8,g[0|(h=h+!!(65280&A)|0)]=A,g[h+!!(255&A)|0]=0,e[r+16>>2]=85719,e[r+24>>2]=l,e[r+20>>2]=n,uA(t,85662,r+16|0)):(bA(A,n,l,-1,0),TA(n,A=r+48|0),e[r>>2]=A,uA(t,85451,r)),oe=r+112|0,Ia(e[e[32972]+60>>2]);break e}if(e[i+12>>2]=l,Hr(A,i+12|0,i+16|0,i+104|0,0,0),!d[i+16|0])break e}bA(r=A,A=i+16|0,i+104|0,-1,0),TA(r=A,A=i+112|0),e[i>>2]=A,uA(t,85451,i)}return oe=i+176|0,t}function Ma(t,A){var r,n=0,i=0,l=0,h=0,m=0,w=0,M=0,y=0,P=0,R=0,ee=0,ae=0,ue=0,be=0,_e=0,Ve=0,$e=0,it=0;if(oe=r=oe-112|0,e[r+72>>2]=-1,e[(n=r- -64|0)>>2]=-1,e[n+4>>2]=-1,e[r+56>>2]=-1,e[r+60>>2]=-1,e[r+48>>2]=-1,e[r+52>>2]=-1,e[r+40>>2]=-1,e[r+44>>2]=-1,e[r+32>>2]=-1,e[r+36>>2]=-1,e[r+24>>2]=-1,e[r+28>>2]=-1,e[r+16>>2]=-1,e[r+20>>2]=-1,(0|A)>0){for(l=e[r+72>>2],h=e[r+68>>2],m=e[r+64>>2],w=e[r+60>>2],M=e[r+56>>2],y=e[r+52>>2],P=e[r+48>>2],R=e[r+44>>2],ee=e[r+40>>2],ae=e[r+36>>2],ue=e[r+32>>2],be=e[r+28>>2],_e=e[r+24>>2],Ve=e[r+20>>2],$e=e[r+16>>2];i=l,l=(0|(l=e[60+(n=134912+(it<<6)|0)>>2]))<0?i:l,i=h,h=(0|(h=e[n+56>>2]))<0?i:h,i=m,m=(0|(m=e[n+52>>2]))<0?i:m,i=w,w=(0|(w=e[n+48>>2]))<0?i:w,i=M,M=(0|(M=e[n+44>>2]))<0?i:M,i=y,y=(0|(y=e[n+40>>2]))<0?i:y,i=P,P=(0|(P=e[n+36>>2]))<0?i:P,i=R,R=(0|(R=e[n+32>>2]))<0?i:R,i=ee,ee=(0|(ee=e[n+28>>2]))<0?i:ee,i=ae,ae=(0|(ae=e[n+24>>2]))<0?i:ae,i=ue,ue=(0|(ue=e[n+20>>2]))<0?i:ue,i=be,be=(0|(be=e[n+16>>2]))<0?i:be,i=_e,_e=(0|(_e=e[n+12>>2]))<0?i:_e,i=Ve,Ve=(0|(Ve=e[n+8>>2]))<0?i:Ve,$e=(0|(n=e[n+4>>2]))<0?$e:n,(0|(it=it+1|0))!=(0|A););e[r+72>>2]=l,e[r+68>>2]=h,e[r+64>>2]=m,e[r+60>>2]=w,e[r+56>>2]=M,e[r+52>>2]=y,e[r+48>>2]=P,e[r+44>>2]=R,e[r+40>>2]=ee,e[r+36>>2]=ae,e[r+32>>2]=ue,e[r+28>>2]=be,e[r+24>>2]=_e,e[r+20>>2]=Ve,e[r+16>>2]=$e}for(A=0;;){if((0|(n=e[(l=A<<2)+(r+16|0)>>2]))!=e[(l=l+134848|0)>>2]){g[r+80|0]=0;e:{t:switch(A-1|0){case 4:e[47201]=n-1;break e;case 5:e[47200]=n;break e;case 0:case 1:case 2:case 3:case 11:break t;default:break e}e[r+4>>2]=n,e[r>>2]=1,e[r+8>>2]=g[A+102812|0],uA(r+80|0,91942,r)}e[l>>2]=n,n=r+80|0,BA(e[t>>2]+189424|0,n),e[t>>2]=e[t>>2]+wA(n)}if((0|(A=A+1|0))==15)break}oe=r+112|0}function Br(t){var A,r,n,i=0,l=0,h=0,m=0;for(i=17,e[t+328>>2]=17,e[t+224>>2]=0,e[t+216>>2]=1105,e[t+220>>2]=1072,e[t+600>>2]=1056,e[t+8180>>2]=105296,h=IA(t+344|0,0,256),g[t+393|0]=1,g[t+365|0]=1,g[t+360|0]=1,g[t+545|0]=1,g[t+529|0]=1,g[t+391|0]=1,g[t+379|0]=1,g[t+374|0]=1,g[t+489|0]=1,g[t+487|0]=1,g[t+398|0]=1,g[t+387|0]=1,g[t+388|0]=2,g[t+389|0]=1,g[t+390|0]=1,g[t+385|0]=2,g[t+383|0]=2,g[t+368|0]=1,g[t+369|0]=2,l=104224;g[0|(i=i+h|0)]=4|d[0|i],i=h+d[l+1|0]|0,g[0|i]=4|d[0|i],i=h+d[l+2|0]|0,g[0|i]=4|d[0|i],i=d[0|(l=l+3|0)],(0|l)!=104251;);
2810g[t+386|0]=8|d[t+386|0],g[t+382|0]=8|d[t+382|0],g[t+384|0]=8|d[t+384|0],g[t+369|0]=16|d[t+369|0],g[t+370|0]=16|d[t+370|0],g[t+371|0]=16|d[t+371|0],l=d[t+361|0],h=d[t+362|0],i=d[t+363|0],m=d[t+364|0],A=d[t+366|0],r=d[t+367|0],g[t+372|0]=16|d[t+372|0],g[t+373|0]=16|d[t+373|0],g[t+375|0]=16|d[t+375|0],g[t+376|0]=16|d[t+376|0],g[t+377|0]=16|d[t+377|0],g[t+378|0]=16|d[t+378|0],g[t+380|0]=16|d[t+380|0],g[t+381|0]=16|d[t+381|0],g[t+383|0]=16|d[t+383|0],g[t+385|0]=16|d[t+385|0],n=d[t+388|0],g[t+367|0]=48|r,g[t+366|0]=40|A,g[t+364|0]=48|m,g[t+363|0]=48|i,g[t+362|0]=48|h,g[t+361|0]=48|l,g[t+388|0]=80|n,l=d[t+390|0],h=d[t+391|0],i=d[t+393|0],g[t+360|0]=128|d[t+360|0],m=d[t+365|0],g[t+393|0]=192|i,g[t+365|0]=128|m,g[t+368|0]=128|d[t+368|0],g[t+374|0]=128|d[t+374|0],g[t+379|0]=128|d[t+379|0],g[t+387|0]=128|d[t+387|0],i=d[t+389|0],g[t+391|0]=192|h,g[t+390|0]=192|l,g[t+389|0]=128|i,g[t+529|0]=128|d[t+529|0],g[t+545|0]=128|d[t+545|0],g[t+489|0]=128|d[t+489|0],g[t+487|0]=128|d[t+487|0],g[t+398|0]=128|d[t+398|0]}function Hr(t,A,r,n,i,l){var h,m=0,w=0,M=0,y=0,P=0,R=0;oe=h=oe-192|0,m=P=e[A>>2];e:{t:{for(;w=1,(0|(M=g[0|m]))>=0||(w=2,M>>>0<4294967264||(w=M>>>0<4294967280?3:4)),!(d[0|(M=w+m|0)]!=32|d[M+1|0]!=46);){if(y-160>>>0<4294967135)break t;$A((R=h+32|0)+y|0,m,w),g[(w=w+y|0)+R|0]=46,m=M+3|0,y=w+1|0}if(y){for(w=0;M=w,w=w+1|0,223&d[m+M|0];);if(!(1+(R=M+y|0)>>>0>160)&&($A((w=h+32|0)+y|0,m,M),g[w+R|0]=0,MA(t,w,m,r,n,i,l))){e[n>>2]=128|e[n>>2],e[33264]=y,t=1;break e}}}for(m=0;;){if(P=(w=P)+1|0,223&(w=d[0|w]))if(!m|(0|w)!=46|g[31+(m+h|0)|0]-48>>>0>=10){if(g[(h+32|0)+m|0]=w,w=159,(0|(m=m+1|0))!=159)continue}else w=m;else w=m;break}g[(m=h+32|0)+w|0]=0,m=MA(t,m,P,r,n,i,l);t:if(8&d[n+3|0]){if(!Zr(r,M=t+268|0)){if(M=e[t+288>>2]+1|0,e[t+288>>2]=M,(0|M)<4)break t;g[0|r]=0;break t}vr(M,r,20),e[t+288>>2]=1}else e[t+288>>2]=0;t:{if(!m){if(m=0,8&d[n+5|0]&&(M=or(h+28|0,m=h+32|d[h+32|0]==95),_t(t,e[h+28>>2],r),m=m+M|0),!(w>>>0<2|m)){if(g[0|r]=0,!(16&i&&d[0|(m=31+(w+h|0)|0)]==101)&&(!(4096&i)||d[0|(m=(w=(h+32|0)+w|0)-1|0)]!=d[w-2|0]))break t;g[0|m]=0,m=MA(t,h+32|0,P,r,n,i,l)}if(!m)break t}if(y=e[n>>2],d[t+172|0]&&(y^=536870912,e[n>>2]=y),t=1,!(536870912&y))break e;2&i&&(v[66448]=8192,e[h+16>>2]=r,uA(132898,87470,h+16|0),t=e[A>>2],e[A>>2]=132898,8&d[188788]&&($A(A=h+32|0,n=t,t=m-t|0),g[t+A|0]=0,e[h+4>>2]=132898,t=e[47195],e[h>>2]=A,ss(t,87652,h)))}g[0|r]=0,t=0}return oe=h+192|0,t}function cn(t,A){var r=0,n=0,i=0;r=31&t;e:{t:{r:{if((0|(t&=96))==96)t=-1;else{if((0|t)!=64)break r;t=1}if(r>>>0>=15)break e;A=e[203136+(r<<2)>>2]+F(t,A)|0;break t}if(r>>>0>=15)break e}t=e[105616+(n=r<<2)>>2],e[n+203136>>2]=(0|A)>=0?(0|t)>(0|A)?A:t:0}e:{t:{r:{a:{n:switch(r-1|0){case 5:if(!(t=e[50759]))break t;e[54728]=e[50982],A=e[50979],r=e[50978],IA(205184,0,11e3),e[51293]=0,A=(r=(i=(0|(n=e[50789]))>0)?130:(0|r)>=5499?5499:r)?i?n:(0|A)>=100?100:A:0,e[50755]=A,r=(0|F(r,e[50754]))/1e3|0,e[51292]=r,e[54729]=(0|A)>20?r<<1:A?r:0,e[33037]=(0|F(500-A|0,(0|F(d[e[50797]+105596|0],(0|F(e[50787],55))/100|0))/16|0))/500;break a;case 0:break n;case 2:case 12:break e;case 4:break r;default:break t}if(!(t=e[50759]))break t}return A=256,(0|(r=(0|(r=e[50785]))>=101?101:r))>=51&&(A=256+(((F(r,25)-1250&65535)>>>0)/50|0)|0),v[t+164>>1]=(0|F(v[t+236>>1],A))/256,v[t+166>>1]=(0|F(v[t+238>>1],A))/256,v[t+168>>1]=(0|F(v[t+240>>1],A))/256,v[t+170>>1]=(0|F(v[t+242>>1],A))/256,v[t+172>>1]=(0|F(v[t+244>>1],A))/256,v[t+174>>1]=(0|F(v[t+246>>1],A))/256,t=e[50790],v[102e3]=(0|F(v[102036],F(t,-3)+256|0))/256,void(v[101999]=(0|F(v[102035],F(t,-6)+256|0))/256)}e[50759]&&(e[54728]=e[50982],t=e[50979],A=e[50978],IA(205184,0,11e3),e[51293]=0,t=(A=(n=(0|(r=e[50789]))>0)?130:(0|A)>=5499?5499:A)?n?r:(0|t)>=100?100:t:0,e[50755]=t,A=(0|F(A,e[50754]))/1e3|0,e[51292]=A,e[54729]=(0|t)>20?A<<1:t?A:0,e[33037]=(0|F(500-t|0,(0|F(d[e[50797]+105596|0],(0|F(e[50787],55))/100|0))/16|0))/500)}return}e[33037]=(0|F(d[e[50797]+105596|0],(0|F(e[50787],55))/100|0))/16}function Rn(t,A,r){var n=0,i=0,l=0,h=0,m=0,w=0,M=0,y=0,P=0,R=0;r&&(e[r>>2]=0);e:{t:if(!((0|(n=g[0|t]))<0)){for(;;){if((0|(l=255&n))==32|l-9>>>0<5){if((0|(n=g[0|(t=t+1|0)]))>=0)continue;break t}break}if(!(255&n))break e}for(;;){if((0|(n=w=255&n))==32|n-9>>>0<5)break e;if((0|w)!=124||(0|(n=d[0|(l=t+1|0)]))==124){t:{if((0|(P=e[36115]))>=2){for(n=1,l=-1,M=0;;){r:if(!(!(y=e[144464+(n<<2)>>2])|d[y+11|0]==15)){m=e[y>>2];a:{n:{if(w>>>0>=33){if(R=0,h=0,(255&m)==(0|w)&&(h=1,(i=d[t+1|0])>>>0<33|(0|i)!=(m>>>8&255)||(h=2,(i=d[t+2|0])>>>0<33|(0|i)!=(m>>>16&255)||(h=(i=(i=d[t+3|0])>>>0>32&(0|i)==(m>>>24|0))?4:3,R=0-i|0))),(0|l)>=(0|h))break r;if(i=4,!(1&R))break n;break a}if(h=0,(0|l)>=0)break r}if(m>>>((i=h)<<3)&255)break r}M=d[y+10|0],l=i}if((0|P)==(0|(n=n+1|0)))break}if(M)break t}return r&&or(r,t),void(g[0|A]=0)}g[0|A]=M,t=((0|l)<=1?1:l)+t|0,A=l=A+1|0;
vendor: 8,586 bytes, line 2810
2810t:if((0|M)==21){r:if((0|(i=d[0|t]))==32|i-9>>>0<5)n=l;else if(n=l,i)for(;;){if(g[0|n]=wr(i),n=n+1|0,(0|(i=d[0|(t=t+1|0)]))==32|i-9>>>0<5)break r;if(!i)break}if(g[0|n]=0,!i){if(A=n,Zr(l,85593))break t;return void(g[0|l]=0)}g[0|n]=124,A=n+1|0}n=d[0|t]}else t=l;if(!(255&n))break}}g[0|A]=0}function Oi(t,A){var r=0,n=0,i=0,l=0,h=0;e:{t:{r:{a:{n:switch((0|(r=e[t+4>>2]))==e[t+104>>2]?r=oA(t):(e[t+4>>2]=r+1,r=d[0|r]),r-43|0){case 0:case 2:break n;default:break a}if(l=(0|r)==45,h=!A,(0|(r=e[t+4>>2]))==e[t+104>>2]?r=oA(t):(e[t+4>>2]=r+1,r=d[0|r]),h|(A=r-58|0)>>>0>4294967285)break r;if(e[t+116>>2]<0)break t;e[t+4>>2]=e[t+4>>2]-1;break t}A=r-58|0}if(!(A>>>0<4294967286)){if((A=r-48|0)>>>0<10){for(;i=(0|(n=(n=F(n,10)+r|0)-48|0))<214748364,(0|(A=e[t+4>>2]))==e[t+104>>2]?r=oA(t):(e[t+4>>2]=A+1,r=d[0|A]),i&(A=r-48|0)>>>0<=9;);i=n>>31}r:if(!(A>>>0>=10))for(;;){if(A=(n=rr(n,i,10,0))+r|0,r=fe,r=A>>>0<n>>>0?r+1|0:r,n=A-48|0,i=r-(A>>>0<48)|0,(0|(A=e[t+4>>2]))==e[t+104>>2]?r=oA(t):(e[t+4>>2]=A+1,r=d[0|A]),(A=r-48|0)>>>0>9)break r;if(!(n>>>0<2061584302&(0|i)<=21474836|(0|i)<21474836))break}if(A>>>0<10)for(;(0|(A=e[t+4>>2]))==e[t+104>>2]?A=oA(t):(e[t+4>>2]=A+1,A=d[0|A]),A-48>>>0<10;);(0|(A=e[t+116>>2]))>0|(0|A)>=0&&(e[t+4>>2]=e[t+4>>2]-1),t=n,n=l?0-t|0:t,i=l?0-(!!(0|t)+i|0)|0:i;break e}}if(i=-2147483648,!(e[t+116>>2]<0))return e[t+4>>2]=e[t+4>>2]-1,fe=-2147483648,0}return fe=i,n}function Ra(t){var A=0,r=0,n=0,i=0;if(e[36432]=110,e[36433]=100,e[36434]=450,e[36430]=5,A=e[203136+((0|t)==2?32:8)>>2],n=e[32972],(0|(r=e[n+84>>2]))>0&&(A=(0|F(A,r))/100|0),r=(0|A)>=359?359:A,r=(0|(A=(0|A)>=450?450:A))>399?6:(0|A)>379?7:d[((0|r)<=80?80:r)+101856|0],1&t&&(e[32526]=(0|F(r,e[n+72>>2]))/256,e[32527]=(0|F(r,e[n+76>>2]))/256,e[32528]=(0|F(r,e[n+80>>2]))/256,r>>>0>7||(i=r-1|0,e[32528]=i,e[32526]=r,e[32527]=i)),2&t){t=e[n+72>>2];e:{t:{r:{a:{n:{c:{o:{f:{if((0|A)>=351)n=A-350|0,e[36432]=85-(((255&n)>>>0)/3|0)&255,n=60-(n>>>3|0)|0;else{if((0|A)<251)break f;n=A-250|0,e[36432]=110-(n>>>2|0),n=110-(n>>>1|0)|0}if(e[36433]=n,t=(0|F(t,r))/256|0,e[36431]=110+((0|F(t,150))/128|0),A>>>0<=349)break o;if(r=A-350|0,e[36431]=d[r+102224|0],A>>>0<390)break n;if(e[36434]=450+((A+112<<24>>24)/-2<<24>>24),A>>>0<441)break c;e[36434]=860-A,t=12;break t}t=(0|F(t,r))/256|0,e[36431]=(0|A)>=170?110+((0|F(t,150))/128|0)|0:128+((t<<7)/130|0)|0}t=(t<<8)/115|0;break t}if(t=12,A>>>0>430)break t;if(t=13,A>>>0<=400)break a;break t}if(t=(t<<8)/115|0,e[36428]=t,A>>>0<375)break r}t=14;break t}if((0|A)<351)break e;t=d[r+102336|0]}e[36428]=t}e[36429]=(0|t)<=16?16:t}}function jn(t,A,r){var n,i,l,h;n=.000244140625*+e[50767],S[r>>3]=n,S[r+40>>3]=.015625*+e[t+112>>2],S[r+48>>3]=.015625*+e[t+276>>2],S[r+56>>3]=.00390625*+(0|F(v[t+166>>1],v[A+4>>1]))+ +v[t+220>>1],S[r+64>>3]=.00390625*+(0|F(v[t+168>>1],v[A+6>>1]))+ +v[t+222>>1],S[r+72>>3]=.00390625*+(0|F(v[t+170>>1],v[A+8>>1]))+ +v[t+224>>1],S[r+80>>3]=.00390625*+(0|F(v[t+172>>1],v[A+10>>1]))+ +v[t+226>>1],S[r+88>>3]=.00390625*+(0|F(v[t+174>>1],v[A+12>>1]))+ +v[t+228>>1],i=v[t+230>>1],l=v[t+176>>1],h=v[A+14>>1],e[r+112>>2]=0,e[r+116>>2]=1080623104,e[r+104>>2]=0,e[r+108>>2]=1081032704,S[r+96>>3]=.00390625*+(0|F(l,h))+ +(0|i),d[A+40|0]?(e[r+184>>2]=0,e[r+188>>2]=1072693248,S[r+104>>3]=d[A+40|0]<<1):(e[r+184>>2]=0,e[r+188>>2]=0),S[r+120>>3]=.00390625*+v[t+202>>1]*+(d[A+35|0]<<1),S[r+128>>3]=.00390625*+v[t+204>>1]*+(d[A+36|0]<<1),S[r+136>>3]=.00390625*+v[t+206>>1]*+(d[A+37|0]<<1),A=d[A+38|0],t=v[t+208>>1],e[r+176>>2]=0,e[r+180>>2]=1079574528,e[r+160>>2]=0,e[r+164>>2]=1083129856,e[r+152>>2]=0,e[r+156>>2]=1083129856,e[r+352>>2]=0,e[r+356>>2]=1072693248,e[r+168>>2]=0,e[r+172>>2]=1079574528,S[r+144>>3]=.00390625*+(0|t)*+(A<<1),t=e[50779],S[r+368>>3]=n,S[r+360>>3]=+(0|t)/100*3}function Tn(t){var A=0;Je(t,ta(t));e:{t:{r:{a:{n:{c:{o:{f:{l:{i:{if((0|(t=-1048576&fe))<268435455|(0|t)<=268435455){h:{b:{if((0|t)<33554431|(0|t)<=33554431){if((0|t)<8388607|(0|t)<=8388607){if(A=524328,!0&(0|t)==-2147483648)break e;if(0|(0|t)!=-2143289344)break t;return 557096}if(!0&(0|t)==8388608)break b;if(0|(0|t)!=16777216)break t;return 524358}if((0|t)>71303167)break h;if(!0&(0|t)==33554432)break r;if(0|(0|t)!=67108864)break t}return 266270}if(!0&(0|t)==71303168)break i;if(!0&(0|t)==134217728)break a;if(0|(0|t)!=138412032)break t;return 294942}if((0|t)<542113791|(0|t)<=542113791){if((0|t)<536870911|(0|t)<=536870911){if(!0&(0|t)==268435456)break n;if(0|(0|t)!=272629760)break t;return 299028}if(!0&(0|t)==536870912)break o;if(!0&(0|t)==538968064)break c;if(0|(0|t)!=541065216)break t;return 569389}if((0|t)<1075838975|(0|t)<=1075838975){if(!0&(0|t)==542113792)break i;if(0|(0|t)!=1073741824)break t;return 532520}if(!0&(0|t)==1075838976)break f;if(!0&(0|t)==1077936128)break l;if(0|(0|t)!=1078984704)break t}return 299038}return 565288}return 1581096}return 536621}return 1585197}return 266260}return 262174}return 2396190}A=16384}return A}function ja(t,A,r,n,i,l){var h,m,w=0,M=0,y=0,P=0;if(m=8388607&t,M=e[34456],w=d[0|(t=m+M|0)]|d[t+1|0]<<8){h=!(y=d[t+2|0]),t=e[36434]<<h,(0|r)<=0?r=w:(r=(0|F(e[50754],r))/1e3<<h,t=(0|t)<(0|(P=(0|F(r,t))/(0|w)|0))?P:t),i=(0|i)>0?(0|F(r,i))/256|0:r,r=(0|F(i,e[36431]))/256|0,r=(0|t)<(0|(r=(4&n)>>>2|0&&(0|r)>(0|i)?i:r))?r:t,y||(w=w>>>1|0,r=(0|r)/2|0);e:if(!((0|l)<0)){if(n=m+4|0,256&A)t=e[50758],e[36439]=t,e[(A=216192+(t<<4)|0)>>2]=7,e[A+8>>2]=n+M,e[A+4>>2]=w<<16|r,l=y|l<<8;else{if(t=e[50758],e[36439]=t,e[(t=216192+(t<<4)|0)>>2]=6,l=y|l<<8,e[t+12>>2]=l,e[t+8>>2]=n+M,M=t,t=F(A=w>>>2|0,3),i=(0|r)>(0|w),e[M+4>>2]=i?t:r,M=e[50758]+1|0,e[50758]=(0|M)<=169?M:0,(0|t)<(0|(r=i?r-t|0:0)))for(i=A<<1,y=n+(y?A:i)|0;A=e[50758],e[36439]=A,e[(A=216192+(A<<4)|0)>>2]=6,e[A+4>>2]=i,e[A+12>>2]=l,e[A+8>>2]=y+e[34456],A=e[50758]+1|0,e[50758]=(0|A)<=169?A:0,(0|t)<(0|(r=r-i|0)););if((0|r)<=0)break e;t=e[50758],e[36439]=t,e[(A=216192+(t<<4)|0)>>2]=6,e[A+4>>2]=r,e[A+8>>2]=e[34456]+(n+(w-r<<h)|0)}e[12+(216192+(t<<4)|0)>>2]=l,t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0}}}function $A(t,A,r){var n,i=0,l=0;if(r>>>0>=512)return ot(0|t,0|A,0|r),t;n=t+r|0;e:if(3&(t^A))if(n>>>0<4)r=t;else if((i=n-4|0)>>>0<t>>>0)r=t;else for(r=t;g[0|r]=d[0|A],g[r+1|0]=d[A+1|0],g[r+2|0]=d[A+2|0],g[r+3|0]=d[A+3|0],A=A+4|0,i>>>0>=(r=r+4|0)>>>0;);else{t:if(3&t)if(r)for(r=t;;){if(g[0|r]=d[0|A],A=A+1|0,!(3&(r=r+1|0)))break t;if(!(r>>>0<n>>>0))break}else r=t;else r=t;if(!((i=-4&n)>>>0<64||(l=i+-64|0)>>>0<r>>>0))for(;e[r>>2]=e[A>>2],e[r+4>>2]=e[A+4>>2],e[r+8>>2]=e[A+8>>2],e[r+12>>2]=e[A+12>>2],e[r+16>>2]=e[A+16>>2],e[r+20>>2]=e[A+20>>2],e[r+24>>2]=e[A+24>>2],e[r+28>>2]=e[A+28>>2],e[r+32>>2]=e[A+32>>2],e[r+36>>2]=e[A+36>>2],e[r+40>>2]=e[A+40>>2],e[r+44>>2]=e[A+44>>2],e[r+48>>2]=e[A+48>>2],e[r+52>>2]=e[A+52>>2],e[r+56>>2]=e[A+56>>2],e[r+60>>2]=e[A+60>>2],A=A- -64|0,l>>>0>=(r=r- -64|0)>>>0;);if(r>>>0>=i>>>0)break e;for(;e[r>>2]=e[A>>2],A=A+4|0,i>>>0>(r=r+4|0)>>>0;);}if(r>>>0<n>>>0)for(;g[0|r]=d[0|A],A=A+1|0,(0|n)!=(0|(r=r+1|0)););return t}function fr(t){var A=0,r=0,n=0,i=0,l=0,h=0,m=0,w=0;V(+t),A=0|G(1),G(0);e:{if((n=(A=A>>>20&2047)-969|0)>>>0<63)w=A;else{if((0|n)<0)return t+1;if(!(A>>>0<1033)){if(V(+t),n=0|G(1),r=0,!(0|G(0))&(0|n)==-1048576)break e;return A>>>0>=2047?t+1:(0|n)<0?(S[8+(A=oe-16|0)>>3]=12882297539194267e-247,12882297539194267e-247*S[A+8>>3]):(S[8+(A=oe-16|0)>>3]=3105036184601418e216,3105036184601418e216*S[A+8>>3])}}if(r=S[14409],l=(r=(t=(r=(i=S[14408]*t+r)-r)*S[14411]+(r*S[14410]+t))*t)*r*(t*S[14415]+S[14414]),r*=t*S[14413]+S[14412],V(+i),G(1),m=0|G(0),t=l+(r+(S[115376+(n=m<<4&2032)>>3]+t)),h=e[(n=n+115384|0)>>2],A=(m<<13)+(A=e[n+4>>2])|0,A=(n=(n=h)+(h=0)|0)>>>0<h>>>0?A+1|0:A,!w)return-2147483648&m?(D(0,0|n),D(1,A+1071644672|0),(t=(i=(r=+Q())*t)+r)<1&&(e[8+(A=oe-16|0)>>2]=0,e[A+12>>2]=1048576,S[A+8>>3]=22250738585072014e-324*S[A+8>>3],t=(t=(l=t+1)+(i+(r-t)+(t+(1-l)))+-1)==0?0:t),t*=22250738585072014e-324):(D(0,0|n),D(1,A-1058013184|0),t=5486124068793689e288*((r=+Q())*t+r)),t;D(0,0|n),D(1,0|A),r=(r=+Q())*t+r}return r}function la(t,A){var r,n=0,i=0,l=0,h=0;oe=r=oe-16|0;e:{if(an(84270,g[0|A])){if(i=2,an(A,43)||(i=d[0|A]!=114),i=an(A,120)?128|i:i,l=i=an(A,101)?524288|i:i,h=64|i,l=(0|(i=d[0|A]))==114?l:h,l=(0|i)==119?512|l:l,e[r>>2]=438,e[r+4>>2]=0,(t=0|we(-100,0|t,32768|((0|i)==97?1024|l:l),0|r))>>>0>=4294963201&&(e[56798]=0-t,t=-1),(0|t)<0)break e;oe=i=oe-32|0;t:{r:{if(an(84270,g[0|A])){if(n=aA(1176))break r}else e[56798]=28;A=0;break t}IA(n,0,144),an(A,43)||(e[n>>2]=d[0|A]==114?8:4),d[0|A]==97?(1024&(A=0|pe(0|t,3,0))||(A|=1024,e[i+16>>2]=A,e[i+20>>2]=A>>31,pe(0|t,4,i+16|0)),A=128|e[n>>2],e[n>>2]=A):A=e[n>>2],e[n+80>>2]=-1,e[n+48>>2]=1024,e[n+60>>2]=t,e[n+44>>2]=n+152,8&A||(e[i>>2]=i+24,e[i+4>>2]=0,0|at(0|t,21523,0|i)||(e[n+80>>
28102]=10)),e[n+40>>2]=10,e[n+36>>2]=11,e[n+32>>2]=12,e[n+12>>2]=13,d[227205]||(e[n+76>>2]=-1),e[n+56>>2]=e[56816],(A=e[56816])&&(e[A+52>>2]=n),e[56816]=n,A=n}if(oe=i+32|0,n=A)break e;Pe(0|t)}else e[56798]=28;n=0}return oe=r+16|0,n}function vi(t,A,r){var n,i=0,l=0,h=0,m=0,w=0;if(h=t,oe=n=oe-208|0,e[n+8>>2]=1,e[n+12>>2]=0,m=A<<2){for(e[n+16>>2]=4,e[n+20>>2]=4,A=4,i=4,l=2;t=A,A=(i+4|0)+A|0,e[(n+16|0)+(l<<2)>>2]=A,l=l+1|0,i=t,A>>>0<m>>>0;);if((t=(h+m|0)-4|0)>>>0<=h>>>0)l=0,A=1,t=0;else{for(l=1,A=1;3&~l?(re[(n+16|0)+((i=A-1|0)<<2)>>2]>=t-h>>>0?ks(h,r,n+8|0,A,0,n+16|0):Sn(h,r,A,n+16|0),(0|A)!=1?(Xi(n+8|0,i),A=1):(Xi(n+8|0,1),A=0)):(Sn(h,r,A,n+16|0),Ua(n+8|0,2),A=A+2|0),l=1|(i=e[n+8>>2]),e[n+8>>2]=l,t>>>0>(h=h+4|0)>>>0;);l=i>>>0>1,t=e[n+12>>2]!=0}if(ks(h,r,n+8|0,A,0,n+16|0),l|(0|A)!=1|t)for(;(0|A)<=1?(Ua(i=n+8|0,t=Wo(i)),l=e[n+8>>2],t=t+A|0):(Xi(i=n+8|0,2),e[n+8>>2]=7^e[n+8>>2],Ua(i,1),ks((w=h-4|0)-e[(m=n+16|0)+((t=A-2|0)<<2)>>2]|0,r,i,A-1|0,1,m),Xi(i,1),l=1|e[n+8>>2],e[n+8>>2]=l,ks(w,r,i,t,1,m)),A=t,h=h-4|0,e[n+12>>2]|(0|A)!=1|(0|l)!=1;);}oe=n+208|0}function Pa(t,A,r,n){var i,l=0,h=0,m=0;oe=i=oe-32|0,m=l=2147483647&n,h=l-1006698496|0;e:if(0|(l=l-1140785152|0)>>>0>h>>>0){if(l=r<<4|A>>>28,r=n<<4|r>>>28,(0|(A&=268435455))==134217728&!!(0|t)|A>>>0>134217728){h=r+1073741824|0,h=(l=l+1|0)?h:h+1|0;break e}if(h=r+1073741824|0,t|(0|A)!=134217728)break e;h=(t=1&l)>>>0>(l=t+l|0)>>>0?h+1|0:h}else(!r&(0|m)==2147418112?!(t|A):m>>>0<2147418112)?(l=0,h=2146435072,m>>>0>1140785151||(h=0,(m=m>>>16|0)>>>0<15249||(ea(i+16|0,t,A,r,l=65535&n|65536,m-15233|0),Da(i,t,A,r,l,15361-m|0),l=(A=e[i+8>>2])<<4,A=e[i+12>>2]<<4|A>>>28,r=e[i>>2],m=h=e[i+4>>2],l|=h>>>28,h=A,(0|(t=268435455&m))==134217728&!!(0|(A=r|!!(e[i+16>>2]|e[i+24>>2]|e[i+20>>2]|e[i+28>>2])))|t>>>0>134217728?h=(l=l+1|0)?h:h+1|0:A|(0|t)!=134217728||(h=(t=l)>>>0>(l=l+(1&l)|0)>>>0?h+1|0:h)))):(l=r<<4|A>>>28,h=524287&(t=n<<4|r>>>28)|2146959360);return oe=i+32|0,D(0,0|l),D(1,-2147483648&n|h),+Q()}function ci(t){var A,r=0,n=0,i=0,l=0,h=0;if(V(+t),h=0|G(1),i=0|G(0),(0|(l=h>>>20&2047))==2047)return(t*=1)/t;if(!(n=i<<1)&(0|(r=h<<1|i>>>31))==2145386496|r>>>0<2145386496)return!n&(0|r)==2145386496?0*t:t;if(l)r=1048575&h|1048576;else{if(l=0,n=i<<12,(0|(r=h<<12|i>>>20))>0|(0|r)>=0)for(;l=l-1|0,r=r<<1|n>>>31,n<<=1,(0|r)>0|(0|r)>=0;);n=31&(r=1-l|0),(63&r)>>>0>=32?(r=i<<n,i=0):(r=(1<<n)-1&i>>>32-n|h<<n,i<<=n)}if(n=i,(0|l)>1023){for(;;){if(!((0|(i=r+-1048576|0))<0||(r=i)|n))return 0*t;if(r=r<<1|n>>>31,n<<=1,!((0|(l=l-1|0))>1023))break}l=1023}if(!((0|(i=r+-1048576|0))<0||(r=i)|n))return 0*t;if((0|r)==1048575|r>>>0<1048575)for(;l=l-1|0,i=r>>>0<524288,r=r<<1|n>>>31,n<<=1,i;);return A=-2147483648&h,(0|l)>0?r=r+-1048576|l<<20:(i=1-l|0,h=r,l=n,n=31&i,(63&i)>>>0>=32?(r=0,n=h>>>n|0):(r=h>>>n|0,n=((1<<n)-1&h)<<32-n|l>>>n)),D(0,0|n),D(1,r|A),+Q()}function tn(t,A,r,n,i){var l,h=0,m=0;oe=l=oe-160|0;e:{t:{r:{a:switch((h=d[A+10|0])-15|0){case 6:break r;case 0:break a;default:break t}g[0|t]=0;break e}e[l>>2]=F(d[r+7|0],44)+137856,uA(t,86002,l),t=wA(t)+t|0;break e}if(n){if(g[l+140|0]=0,r?Gt(0,0,r,l+8|0,0):zn(h,l+8|0),r=l+140|0,h=d[l+140|0]){if((0|h)==32){g[0|t]=0;break e}224&(h=h<<24>>24)||(i&&(e[i>>2]=h),r=l+141|0)}if(!((0|(i=wA(r)))<=0)){t=BA(t,r)+i|0,g[0|t]=0;break e}}i=0;t:if(!(!(r=255&(h=e[A>>2]))|(0|r)==47)){if(n){if((0|(m=255&h))==95)break t;r:{a:{if((0|m)==35){if(m=3,d[A+11|0]!=2)break a;break t}if((m=r-32|0)>>>0>95)break r}r=_[93952+(m<<1)>>1]}i=_s(r,t)}else g[0|t]=h,i=1;for(;;){if(!(r=255&(h>>=8))|(0|r)==47)break t;if(n){if((0|r)==35&d[A+11|0]==2)break t;if(r-48>>>0<10)continue;(m=r-32|0)>>>0<=95&&(r=_[93952+(m<<1)>>1]),i=_s(r,t+i|0)+i|0}else g[t+i|0]=h,i=i+1|0}}g[0|(t=t+i|0)]=0}return oe=l+160|0,t}function Wa(t){var A,r=0;A=t,r=131280;e:{t:{if(!((0|t)<=1023||(r=131300,t>>>0<1328||(r=131320,t>>>0<1424||(r=131340,t>>>0<1536||(r=131360,t>>>0<1792||(r=131380,t>>>0<1872||(r=131400,t>>>0<2432||(r=131420,t>>>0<2560||(r=131440,t>>>0<2688||(r=131460,t>>>0<2816||(r=131480,t>>>0<2944||(r=131500,t>>>0<3072||(r=131520,t>>>0<3200||(r=131540,t>>>0<3328||(r=131560,t>>>0<3456||(r=131580,t>>>0<3584||(r=131600,t>>>0<3712||(r=131620,t>>>0<3840||(r=131640,t>>>0<4096||(r=131660,t>>>0<4256||(r=131680,t>>>0<4352||(r=131700,t>>>0<4608||(r=131720,t>>>0<5024||(r=131740,t>>>0<10496||(r=131760,t>>>0<12544||(r=131780,t>>>0<40960))))))))))))))))))))))))))){if(t>>>0>=55296)break t;r=131800}if((0|A)>=_[(t=r)+8>>1])break e}t=0}return t}function _t(t,A,r){var n,i=0,l=0,h=0,m=0;oe=n=oe-208|0,g[n+80|0]=0;e:{if((i=A-224|0)>>>0<=158)A=101072+(i<<1)|0;else{if((A=A-592|0)>>>0>88)break e;A=101392+(A<<1)|0}if(A=_[A>>1]){if(h=A<<16>>16,m=(i=63&A)>>>0>37?i+59|0:v[101584+(i<<1)>>1],i=A>>>6|0,(0|h)<0)i=59+(63&i)|0,A=A>>>12&7;else{if(!(l=31&i))break e;i=0,A=A>>>11&15}(l=kA(t,e[129920+(l<<3)>>2],n+112|0))&&Ro(t,m,n+176|0)&&(A&&4096&kA(t,e[129920+(A<<3)>>2],n+80|0)&&(r=wA(A=BA(r,n+80|0)),g[n+80|0]=0,r=A+r|0),i?(Ro(A=t,i,t=n+144|0),e[n+68>>2]=n+80,e[n- -64>>2]=t,e[n+60>>2]=6,e[n+52>>2]=23,e[n+56>>2]=n+176,e[n+48>>
vendor: 50,645 bytes, line 2810
28102]=n+112,uA(r,84101,n+48|0)):(0|h)<0?BA(r,n+176|0):1&e[t+144>>2]|4096&l?(e[n+36>>2]=23,e[n+40>>2]=6,e[n+44>>2]=n+176,e[n+32>>2]=n+112,uA(r,84430,n+32|0)):(e[n+16>>2]=23,e[n+8>>2]=23,e[n>>2]=4,e[n+12>>2]=n+112,e[n+4>>2]=n+176,uA(r,84802,n)))}}oe=n+208|0}function Ee(){da(),e[55928]=0,e[55926]=0,e[55927]=0,e[55924]=0,e[56244]=0,e[56245]=0,e[56246]=0,e[56247]=0,e[56260]=0,e[56261]=0,e[56262]=0,e[56263]=0,e[56276]=0,e[56277]=0,e[56278]=0,e[56279]=0,e[55974]=0,e[55975]=0,e[55972]=0,e[55973]=0,e[55988]=0,e[55989]=0,e[55990]=0,e[55991]=0,e[56004]=0,e[56005]=0,e[56006]=0,e[56007]=0,e[56020]=0,e[56021]=0,e[56022]=0,e[56023]=0,e[56036]=0,e[56037]=0,e[56038]=0,e[56039]=0,e[56052]=0,e[56053]=0,e[56054]=0,e[56055]=0,e[56068]=0,e[56069]=0,e[56070]=0,e[56071]=0,e[56086]=0,e[56087]=0,e[56084]=0,e[56085]=0,e[56102]=0,e[56103]=0,e[56100]=0,e[56101]=0,e[56118]=0,e[56119]=0,e[56116]=0,e[56117]=0,e[56134]=0,e[56135]=0,e[56132]=0,e[56133]=0,e[56150]=0,e[56151]=0,e[56148]=0,e[56149]=0,e[56166]=0,e[56167]=0,e[56164]=0,e[56165]=0,e[56182]=0,e[56183]=0,e[56180]=0,e[56181]=0,e[56198]=0,e[56199]=0,e[56196]=0,e[56197]=0,e[56214]=0,e[56215]=0,e[56212]=0,e[56213]=0,e[56230]=0,e[56231]=0,e[56228]=0,e[56229]=0}function qe(t,A){var r=0,n=0,i=0,l=0,h=0,m=0,w=0,M=0,y=0,P=0;e:{if((0|(l=e[t+4>>2]))==e[t>>2])if((h=e[t+8>>2])>>>0<(r=e[t+12>>2])>>>0)r=(i=(1+(r-h>>2)|0)/2<<2)+h|0,(0|l)!=(0|h)&&(sr(r=r-(n=h-l|0)|0,l,n),l=e[t+8>>2]),e[t+4>>2]=r,e[t+8>>2]=i+l;else{if((n=(0|r)==(0|l)?1:r-l>>1)>>>0>=1073741824)break e;if(y=(w=Ka(r=n<<2))+r|0,m=r=(n+3&-4)+w|0,(0|l)!=(0|h)){if(P=-4&(h=h-l|0),i=r,n=l,h=1+((M=h-4|0)>>>2|0)&7)for(m=0;e[i>>2]=e[n>>2],n=n+4|0,i=i+4|0,(0|h)!=(0|(m=m+1|0)););if(m=r+P|0,!(M>>>0<28))for(;e[i>>2]=e[n>>2],e[i+4>>2]=e[n+4>>2],e[i+8>>2]=e[n+8>>2],e[i+12>>2]=e[n+12>>2],e[i+16>>2]=e[n+16>>2],e[i+20>>2]=e[n+20>>2],e[i+24>>2]=e[n+24>>2],e[i+28>>2]=e[n+28>>2],n=n+32|0,(0|m)!=(0|(i=i+32|0)););}e[t+12>>2]=y,e[t+8>>2]=m,e[t+4>>2]=r,e[t>>2]=w,l&&(EA(l),r=e[t+4>>2])}else r=l;return e[r-4>>2]=e[A>>2],void(e[t+4>>2]=e[t+4>>2]-4)}rn(),W()}function ut(t,A,r){var n=0,i=0,l=0,h=0,m=0,w=0,M=0,y=0,P=0;e:{t:{r:{a:{n:{c:{o:{f:{l:{if(A){if(!r)break l;break f}return zt=(A=t)-F(t=(t>>>0)/(r>>>0)|0,r)|0,ke=0,fe=0,t}if(!t)break o;break c}if(!((n=r-1|0)&r))break n;l=0-(h=(ce(r)+33|0)-ce(A)|0)|0;break r}return zt=0,ke=A-F(t=(A>>>0)/0|0,0)|0,fe=0,t}if((n=32-ce(A)|0)>>>0<31)break a;break t}if(zt=t&n,ke=0,(0|r)==1)break e;return r=31&(n=vo(r)),(63&n)>>>0>=32?t=A>>>r|0:(i=A>>>r|0,t=((1<<r)-1&A)<<32-r|t>>>r),fe=i,t}h=n+1|0,l=63-n|0}if(n=31&(i=63&h),i>>>0>=32?(i=0,m=A>>>n|0):(i=A>>>n|0,m=((1<<n)-1&A)<<32-n|t>>>n),n=31&(l&=63),l>>>0>=32?(A=t<<n,t=0):(A=(1<<n)-1&t>>>32-n|A<<n,t<<=n),h)for(y=(0|(n=r-1|0))==-1?-1:0;w=i<<1|m>>>31,m=(i=m<<1|A>>>31)-(M=r&(l=y-(w+(i>>>0>n>>>0)|0)>>31))|0,i=w-(i>>>0<M>>>0)|0,A=A<<1|t>>>31,t=P|t<<1,P=w=1&l,h=h-1|0;);return zt=m,ke=i,fe=A<<1|t>>>31,w|t<<1}zt=t,ke=A,t=0,A=0}return fe=A,t}function mt(t,A){var r=0,n=0,i=0,l=0,h=0,m=0,w=0,M=0,y=0,P=0;e:{if((0|(r=e[t+8>>2]))==e[t+12>>2])if((n=e[t+4>>2])>>>0>(h=e[t>>2])>>>0)i=sr((l=(1+(n-h>>2)|0)/-2<<2)+n|0,n,r=r-n|0)+r|0,e[t+8>>2]=i,e[t+4>>2]=l+e[t+4>>2];else{if((l=(0|r)==(0|h)?1:r-h>>1)>>>0>=1073741824)break e;if(y=(m=Ka(i=l<<2))+i|0,i=l=(-4&l)+m|0,(0|r)!=(0|n)){if(P=-4&(r=r-n|0),M=1+((w=r-4|0)>>>2|0)&7)for(i=0,r=l;e[r>>2]=e[n>>2],n=n+4|0,r=r+4|0,(0|M)!=(0|(i=i+1|0)););else r=l;if(i=l+P|0,!(w>>>0<28))for(;e[r>>2]=e[n>>2],e[r+4>>2]=e[n+4>>2],e[r+8>>2]=e[n+8>>2],e[r+12>>2]=e[n+12>>2],e[r+16>>2]=e[n+16>>2],e[r+20>>2]=e[n+20>>2],e[r+24>>2]=e[n+24>>2],e[r+28>>2]=e[n+28>>2],n=n+32|0,(0|i)!=(0|(r=r+32|0)););}e[t+12>>2]=y,e[t+8>>2]=i,e[t+4>>2]=l,e[t>>2]=m,h&&(EA(h),i=e[t+8>>2])}else i=r;return e[i>>2]=e[A>>2],void(e[t+8>>2]=e[t+8>>2]+4)}rn(),W()}function vt(t,A){var r,n=0,i=0,l=0,h=0,m=0;i=189088,oe=r=oe-320|0,e[r+312>>2]=0,l=$a(t,h=BA(r+112|0,189088),r,r+316|0,r+312|0,0),n=e[r+316>>2];e:if((0|A)<=3){if((0|n)<2)break e;if(l=3&(A=n-1|0),t=1,n-2>>>0>=3)for(m=-4&A,A=0;g[0|(n=t+r|0)]>=4&&(g[0|n]=3),g[1+(n=t+r|0)|0]>=4&&(g[n+1|0]=3),g[n+2|0]>=4&&(g[n+2|0]=3),g[n+3|0]>=4&&(g[n+3|0]=3),t=t+4|0,(0|m)!=(0|(A=A+4|0)););if(!l)break e;for(A=0;g[0|(n=t+r|0)]>=4&&(g[0|n]=3),t=t+1|0,(0|l)!=(0|(A=A+1|0)););}else if(t=1,!((0|n)<=1)){for(;;){if((0|l)>g[0|(m=t+r|0)]){if((0|n)!=(0|(t=t+1|0)))continue;break e}break}g[0|m]=A}if(t=d[0|h])for(A=1;n=e[144464+((255&t)<<2)>>2],d[n+11|0]!=2|16&d[n+6|0]||(l=255&(n=g[A+r|0]),(0|n)<2&&l||(g[0|i]=d[l+94151|0],i=i+1|0,t=d[0|h]),A=A+1|0),g[0|i]=t,i=i+1|0,t=d[0|(h=h+1|0)];);g[0|i]=0,oe=r+320|0}function Rt(t){var A=0,r=0,n=0,i=0,l=0,h=0;r=A=e[(t|=0)>>2],e[t>>2]=A+1;e:{t:{r:{a:{n:{c:{o:switch(((l=d[0|A])>>>4|0)-8|0){case 0:case 1:case 2:case 3:break t;case 7:break n;case 6:break c;case 4:case 5:break o;default:break e}if((n=A+2|0)>>>0>=(i=e[t+4>>2])>>>0)break a;if(e[t>>2]=n,(192&(r=d[r+1|0]))!=128)break r;return 63&r|l<<6&1984}if((n=A+3|0)>>>0>=(i=e[t+4>>2])>>>0)break a;if(r=A+2|0,e[t>>2]=r,(192&(A=d[A+1|0]))!=128){n=r;break r}if(e[t>>2]=n,(192&(r=d[0|r]))!=128)break r;return 63&r|(63&A|l<<6&960)<<6}if(!((i=e[t+4>>2])>>>0<=(r=A+4|0)>>>0)){if(n=A+2|0,e[t>>2]=n,(192&(i=d[A+1|0]))!=128||(n=A+3|0,e[t>>2]=n,(192&(h=d[A+2|0]))!=128)||(e[t>>2]=r,A=d[0|n],n=r,(192&A)!=128))break r;return 0|((t=63&A|h<<6&4032|(63&i|l<<6&960)<<12)>>>0>=1114112?65533:t)}}e[t>>2]=i;break t}e[t>>2]=n-1}l=65533}return 0|l}function lA(t,A,r,n){var i,l,h=0,m=0,w=0,M=0,y=0,P=0;if(oe=i=oe-432|0,!(!n|!(536870912&(l=Ft(t,A,r,n))))&&(v[i+48>>1]=8192,n=BA(i+48|2,n),d[0|n])){for(w=i+224|0,h=1,M=200;;){if(or(i+44|0,n),A=$n(e[i+44>>2]),m=e[r>>2],A?(e[r>>2]=2|m,_s(wr(e[i+44>>2]),n)):e[r>>2]=-3&m,P=e[33264],Ft(t,n,r,0),1&h?(e[i+16>>2]=189088,m=An(w,M,84130,i+16|0)):(e[i+32>>2]=15,e[i+36>>2]=189088,m=An(w,M,84434,i+32|0)),h=(A=e[33264])+1|0,e[33264]=h,A>>>0<=2147483646){for(;;)if(A=n,n=n+1|0,(0|(y=g[0|A]))==32|y-9>>>0<5){for(;A=(n=A)+1|0,(0|(y=g[0|n]))==32|y-9>>>0<5;);if(h=h-1|0,e[33264]=h,!((0|h)>0))break}}if(w=w+m|0,e[33264]=P,!(d[0|n]&&(h=0,(0|(M=M-m|0))>1)))break}(i+224|0)!=(0|w)&&(e[i>>2]=i+224,An(189088,200,84130,i))}return oe=i+432|0,l}function gt(t,A,r,n,i,l,h,m){var w,M;w=e[32972],M=e[w+116>>2],v[t+8>>1]=_[t+8>>1]+l,l=32&m?0-l|0:l,v[t+10>>1]=l+_[t+10>>1],v[t+12>>1]=l+_[t+12>>1],n=(0|n)>(0|(l=((l=(0|F(A,M))/256|0)-(A=v[t+6>>1])|0)/2|0))?l:n,v[t+6>>1]=((0|r)<(0|n)?n:r)+A;e:{t:switch(i-1|0){case 0:A=(0|(A=235-(r=v[t+4>>1])|0))<=-100?-100:A,v[t+4>>1]=((0|A)>=-60?-60:A)+r;break e;case 1:A=(0|(A=(0|(A=235-(r=v[t+4>>1])|0))<=-300?-300:A))>=-150?-150:A,v[t+4>>1]=A+r,v[t+2>>1]=A+_[t+2>>1];break e;case 2:break t;default:break e}A=(0|(A=(0|(A=100-(r=v[t+4>>1])|0))<=-400?-400:A))>-300?-400:A,v[t+4>>1]=A+r,v[t+2>>1]=A+_[t+2>>1]}e[w+132>>2]||(g[t+20|0]=(F(d[t+20|0],h)>>>0)/100,g[t+21|0]=(F(d[t+21|0],h)>>>0)/100,g[t+22|0]=(F(d[t+22|0],h)>>>0)/100,g[t+23|0]=(F(d[t+23|0],h)>>>0)/100,g[t+24|0]=(F(d[t+24|0],h)>>>0)/100,g[t+25|0]=(F(d[t+25|0],h)>>>0)/100)}function pA(t){var A,r=0,n=0,i=0,l=0;oe=A=oe-48|0;e:{if(t){d[0|t]||(t=Nn(84285),d[0|t]&&t||(t=Nn(121696),d[0|t]&&t||(t=Nn(84614),d[0|t]&&t||(t=84891))));t:{for(;;){if(!(!(n=d[t+r|0])|(0|n)==47)){if(i=23,(0|(r=r+1|0))!=23)continue;break t}break}i=r}n=84891;t:{r:{if(r=d[0|t],(d[t+i|0]|(0|r)==46||(n=t,(0|r)==67))&&!d[n+1|0]||!Zr(n,84891)||!Zr(n,85136)){if(r=121652,d[n+1|0]==46)break r;t=0;break t}if(r=e[56851])for(;;){if(!Zr(n,r+8|0))break r;if(!(r=e[r+32>>2]))break}(t=aA(36))&&(r=e[30414],e[t>>2]=e[30413],e[t+4>>2]=r,$A(r=t+8|0,n,i),g[r+i|0]=0,e[t+32>>2]=e[56851],e[56851]=t),r=t||121652}t=r}if((0|t)==-1)break e;e[56809]=t}else t=e[56809];l=t?t+8|0:84309}return oe=A+48|0,l}function oA(t){var A=0,r=0,n=0,i=0,l=0,h=0,m=0,w=0,M=0;w=!!((A=e[t+112>>2])|(n=e[t+116>>2])),i=A,h=A=(l=e[t+4>>2])-(m=e[t+44>>2])|0,r=A+e[t+120>>2]|0,A=e[t+124>>2]+(A>>31)|0;e:{if(!(((0|(A=r>>>0<h>>>0?A+1|0:A))>=(0|n)&r>>>0>=i>>>0|(0|A)>(0|n))&w)){if((0|(w=Zi(t)))>=0)break e;l=e[t+4>>2],m=e[t+44>>2]}return e[t+112>>2]=-1,e[t+116>>2]=-1,e[t+104>>2]=l,n=(h=r)+(r=m-l|0)|0,A=(r>>31)+A|0,e[t+120>>2]=n,e[t+124>>2]=r>>>0>n>>>0?A+1|0:A,-1}return A=(n=r+1|0)?A:A+1|0,l=e[t+4>>2],m=e[t+8>>2],h=i=e[t+116>>2],i|(r=e[t+112>>2])&&(i=r-n|0,(0|(r=h-(A+(r>>>0<n>>>0)|0)|0))>=(0|(h=(M=m-l|0)>>31))&i>>>0>=M>>>0|(0|r)>(0|h)||(m=i+l|0)),e[t+104>>2]=m,n=(i=(r=e[t+44>>2])-l|0)+n|0,A=(i>>31)+A|0,e[t+120>>2]=n,e[t+124>>2]=n>>>0<i>>>0?A+1|0:A,r>>>0>=l>>>0&&(g[l-1|0]=w),w}function sr(t,A,r){var n=0,i=0;e:if((0|t)!=(0|A)){if(A-(i=t+r|0)>>>0<=0-(r<<1)>>>0)return $A(t,A,r);if(n=3&(t^A),t>>>0<A>>>0){if(n)n=t;else{if(3&t)for(n=t;;){if(!r)break e;if(g[0|n]=d[0|A],A=A+1|0,r=r-1|0,!(3&(n=n+1|0)))break}else n=t;if(!(r>>>0<=3))for(;e[n>>2]=e[A>>2],A=A+4|0,n=n+4|0,(r=r-4|0)>>>0>3;);}if(r)for(;g[0|n]=d[0|A],n=n+1|0,A=A+1|0,r=r-1|0;);}else{if(!n){if(3&i)for(;;){if(!r)break e;if(g[0|(n=(r=r-1|0)+t|0)]=d[A+r|0],!(3&n))break}if(!(r>>>0<=3))for(;e[(r=r-4|0)+t>>2]=e[A+r>>2],r>>>0>3;);}if(!r)break e;for(;g[(r=r-1|0)+t|0]=d[A+r|0],r;);}}return t}function jr(t,A,r,n){switch(A-9|0){case 0:return A=e[r>>2],e[r>>2]=A+4,void(e[t>>2]=e[A>>2]);case 6:return A=e[r>>2],e[r>>2]=A+4,A=v[A>>1],e[t>>2]=A,void(e[t+4>>2]=A>>31);case 7:return A=e[r>>2],e[r>>2]=A+4,e[t>>2]=_[A>>1],void(e[t+4>>2]=0);case 8:return A=e[r>>2],e[r>>2]=A+4,A=g[0|A],e[t>>2]=A,void(e[t+4>>2]=A>>31);case 9:return A=e[r>>2],e[r>>2]=A+4,e[t>>2]=d[0|A],void(e[t+4>>2]=0);case 16:return A=e[r>>2]+7&-8,e[r>>2]=A+8,void(S[t>>3]=S[A>>3]);case 17:LA[0|n](t,r);default:return;case 1:case 4:case 14:return A=e[r>>2],e[r>>2]=A+4,A=e[A>>2],e[t>>2]=A,void(e[t+4>>2]=A>>31);case 2:case 5:case 11:case 15:return A=e[r>>2],e[r>>2]=A+4,e[t>>2]=e[A>>2],void(e[t+4>>2]=0);case 3:case 10:case 12:case 13:}A=e[r>>2]+7&-8,e[r>>2]=A+8,r=e[A+4>>2],e[t>>2]=e[A>>2],e[t+4>>2]=r}function Gr(t,A,r,n,i,l){var h;oe=h=oe-80|0;e:if((0|l)>=16384){if(qt(h+32|0,A,r,n,i,0,0,0,2147352576),n=e[h+40>>2],i=e[h+44>>2],A=e[h+32>>2],r=e[h+36>>2],l>>>0<32767){l=l-16383|0;break e}qt(h+16|0,A,r,n,i,0,0,0,2147352576),l=((0|l)>=49149?49149:l)-32766|0,n=e[h+24>>2],i=e[h+28>>2],A=e[h+16>>2],r=e[h+20>>2]}else(0|l)>-16383||(qt(h- -64|0,A,r,n,i,0,0,0,7471104),n=e[h+72>>2],i=e[h+76>>2],A=e[h+64>>2],r=e[h+68>>2],l>>>0>4294934644?l=l+16269|0:(qt(h+48|0,A,r,n,i,0,0,0,7471104),l=((0|l)<=-48920?-48920:l)+32538|0,n=e[h+56>>2],i=e[h+60>>2],A=e[h+48>>2],r=e[h+52>>2]));qt(h,A,r,n,i,0,0,0,l+16383<<16),A=e[h+12>>2],e[t+8>>2]=e[h+8>>2],e[t+12>>2]=A,A=e[h+4>>2],e[t>>2]=e[h>>2],e[t+4>>2]=A,oe=h+80|0}function Or(t,A){var r,n,i=0;oe=r=oe+-64|0,i=e[t>>2],n=e[i-4>>2],i=e[i-8>>2],e[r+32>>2]=0,e[r+36>>2]=0,e[r+40>>2]=0,e[r+44>>2]=0,e[r+48>>2]=0,e[r+52>>2]=0,g[r+55|0]=0,g[r+56|0]=0,g[r+57|0]=0,g[r+58|0]=0,g[r+59|0]=0,g[r+60|0]=0,g[r+61|0]=0,g[r+62|0]=0,e[r+24>>2]=0,e[r+28>>2]=0,e[r+20>>2]=0,e[r+16>>2]=125084,e[r+12>>2]=t,e[r+8>>2]=A,t=t+i|0,i=0;e:if(Bn(n,A,0))e[r+56>>2]=1,LA[e[e[n>>2]+20>>2]](n,r+8|0,t,t,1,0),i=e[r+32>>2]==1?t:0;else{LA[e[e[n>>2]+24>>2]](n,r+8|0,t,1,0);t:switch(e[r+44>>2]){case 0:i=e[r+48>>2]==1&&e[r+36>>2]==1&&e[r+40>>2]==1?e[r+28>>2]:0;break e;case 1:break t;default:break e}e[r+32>>2]!=1&&e[r+48>>2]|e[r+36>>2]!=1|e[r+40>>2]!=1||(i=e[r+24>>2])}return oe=r- -64|0,i}function ir(t,A,r,n,i){var l,h,m=0;oe=l=oe-80|0,v[l+72>>1]=0,e[l+64>>2]=0,e[l+68>>2]=0,g[0|n]=0,m=_s(A,h=2|(m=l- -64|0))+m|0,g[m+2|0]=32;e:if((0|r)!=-1)A>>>0>=33&&!Ts(A)?(g[m+3|0]=(0|r)==32?32:31,g[l+65|0]=95,kA(t,l- -64|1,l+16|0)||(g[l+65|0]=32,kA(t,h,l+16|0)||zA(t,h,l+16|0,40,0,268435456,0)),d[l+16|0]||_t(t,A,l+16|0),A=BA(n,l+16|0),!(r=d[0|A])|(0|r)==21||(e[l+56>>2]=0,e[l+60>>2]=0,bA(t,A,l+56|0,-1,1&i))):(e[l>>2]=A,uA(A=l- -64|1,85485,l),kA(t,A,n));else{if(kA(t,h,n)||(g[l+65|0]=95,kA(t,l- -64|1,l+16|0)|e[t+212>>2]==25966))break e;Ji(85055),kA(e[47194],h,l+16|0)&&(g[0|n]=21,g[n+1|0]=0),Ia(e[e[32972]+60>>2])}oe=l+80|0}function IA(t,A,r){var n=0,i=0,l=0,h=0;if(r&&(g[0|t]=A,g[(n=t+r|0)-1|0]=A,!(r>>>0<3||(g[t+2|0]=A,g[t+1|0]=A,g[n-3|0]=A,g[n-2|0]=A,r>>>0<7||(g[t+3|0]=A,g[n-4|0]=A,r>>>0<9||(i=(n=0-t&3)+t|0,A=F(255&A,16843009),e[i>>2]=A,e[(r=(n=r-n&-4)+i|0)-4>>2]=A,n>>>0<9||(e[i+8>>2]=A,e[i+4>>2]=A,e[r-8>>2]=A,e[r-12>>2]=A,n>>>0<25||(e[i+24>>2]=A,e[i+20>>2]=A,e[i+16>>2]=A,e[i+12>>2]=A,e[r-16>>2]=A,e[r-20>>2]=A,e[r-24>>2]=A,e[r-28>>2]=A,(r=n-(h=4&i|24)|0)>>>0<32))))))))for(n=rr(A,0,1,1),l=fe,A=i+h|0;e[A+24>>2]=n,e[A+28>>2]=l,e[A+16>>2]=n,e[A+20>>2]=l,e[A+8>>2]=n,e[A+12>>2]=l,e[A>>2]=n,e[A+4>>2]=l,A=A+32|0,(r=r-32|0)>>>0>31;);return t}function _A(){var t,A=0,r=0,n=0,i=0,l=0;if(oe=t=oe-208|0,(0|(n=e[50303]))>0)for(;(i=e[(r=201216+(A<<2)|0)>>2])&&(EA(i),e[r>>2]=0),(0|n)!=(0|(A=A+1|0)););if(e[50303]=0,e[t+16>>2]=137584,e[t+20>>2]=47,uA(A=t+32|0,87827,t+16|0),ma(A,wA(A)+1|0,0),e[t+4>>2]=47,e[t>>2]=137584,uA(A,87933,t),ma(A,wA(A)+1|0,1),A=e[50303],e[201216+(r=A<<2)>>2]=0,r=ds(n=e[50741],r+4|0)){if(e[50741]=r,vi(201216,A,7),n=e[50741],r=0,A=e[50304])for(i=0;l=e[A+4>>2],d[0|l]&&Zr(l+1|0,86589)&&ra(e[A+8>>2],88032,3)&&(e[(r<<2)+n>>2]=A,r=r+1|0),A=e[201216+((i=i+1|0)<<2)>>2];);e[(r<<2)+n>>2]=0}return oe=t+208|0,n}function _r(t,A,r){var n=0,i=0,l=0,h=0;if(l=A-1|0,(0|A)>=2){A=t;e:{for(;;){t:{r:{if((0|(n=e[r+4>>2]))!=(0|(i=e[r+8>>2]))){if((h=Wn(n,10,i-n|0))?i=1+(h-(n=e[r+4>>2])|0)|0:(n=e[r+4>>2],i=e[r+8>>2]-n|0),$A(A,n,n=i>>>0<l>>>0?i:l),i=n+e[r+4>>2]|0,e[r+4>>2]=i,A=A+n|0,h||!(l=l-n|0))break t;if((0|i)!=e[r+8>>2]){e[r+4>>2]=i+1,n=d[0|i];break r}}if(!((0|(n=Zi(r)))>=0)){if(n=0,(0|t)==(0|A))break e;if(16&d[0|r])break t;break e}}if(g[0|A]=n,A=A+1|0,(255&n)!=10&&(l=l-1|0))continue}break}t?(g[0|A]=0,n=t):n=0}}else if(A=e[r+72>>2],e[r+72>>2]=A-1|A,!l)return g[0|t]=0,t;return n}function ba(t){var A=0,r=0,n=0,i=0,l=0,h=0,m=0,w=0;if(A=F(t,44),(0|(t=e[A+137896>>2]))>0&&ba(t-1|0),t=e[36115],!((0|(A=e[36+(r=A+137856|0)>>2]))<=0)){if(i=e[r+32>>2],m=1&A,(0|A)!=1)for(w=-2&A,r=0;A=d[10+(l=(n=r<<4)+i|0)|0],e[144464+(A<<2)>>2]=l,(0|t)>=(0|A)?A=t:IA(144464+((t=t+1|0)<<2)|0,0,A-t<<2),t=d[10+(n=(16|n)+i|0)|0],e[144464+(t<<2)>>2]=n,(0|t)<=(0|A)?t=A:IA(144464+((A=A+1|0)<<2)|0,0,t-A<<2),r=r+2|0,(0|w)!=(0|(h=h+2|0)););else r=0;m&&(A=d[10+(r=(r<<4)+i|0)|0],e[144464+(A<<2)>>2]=r,(0|t)>=(0|A)||(IA(144464+((t=t+1|0)<<2)|0,0,A-t<<2),t=A))}e[36115]=t}function Bs(t,A,r,n,i){var l,h=0,m=0,w=0;oe=l=oe-16|0;e:if(1&g[t+106|0]&&(h=d[0|r],!(!(1&g[n+2|0])&(0|h)!=46||256&(w=e[n+12>>2])|!(!(2&w)||i)||(or(l+12|0,(0|h)!=46?r:r+2|0),!(h=d[0|r])|!d[r+1|0])))){if(!(!(w=e[l+12>>2])|2&d[n+2|0])){if(!Wr(w))break e;h=d[0|r]}(0|h)==46&&(g[0|r]=32),m=2,e[t+212>>2]!=26741|i||(r=Wr(e[l+12>>2])?lA(t,r+2|0,0,0):0,128&d[t+8233|0]&&(!(!(i=e[l+12>>2])|2&d[n+2|0])&i-48>>>0>=10||(m=0)),m=32768&r?0:m,131072&r&&(m=163840&e[t+8232>>2]?34:d[A-2|0]!=45?m:0))}return oe=l+16|0,m}function ts(t,A,r,n,i){var l,h=0,m=0;if(oe=l=oe-208|0,e[l+204>>2]=r,IA(r=l+160|0,0,40),e[l+200>>2]=e[l+204>>2],(0|iA(0,A,l+200|0,l+80|0,r,n,i))<0)i=-1;else{e[t+76>>2],h=e[t>>2],e[t+72>>2]<=0&&(e[t>>2]=-33&h);e:{t:{if(e[t+48>>2]){if(e[t+16>>2])break t}else e[t+48>>2]=80,e[t+28>>2]=0,e[t+16>>2]=0,e[t+20>>2]=0,m=e[t+44>>2],e[t+44>>2]=l;if(r=-1,dn(t))break e}r=iA(t,A,l+200|0,l+80|0,l+160|0,n,i)}m&&(LA[e[t+36>>2]](t,0,0),e[t+48>>2]=0,e[t+44>>2]=m,e[t+28>>2]=0,A=e[t+20>>2],e[t+16>>2]=0,e[t+20>>2]=0,r=A?r:-1),A=t,t=e[t>>2],e[A>>2]=t|32&h,i=32&t?-1:r}return oe=l+208|0,i}function PA(t,A,r,n){var i,l=0,h=0,m=0,w=0,M=0,y=0,P=0;if(oe=i=oe-208|0,h=d[0|A])for(;g[l+i|0]=h,w=((255&h)==6&(0|m)!=21)+w|0,m=h<<24>>24,h=d[(l=l+1|0)+A|0];);if(g[l+i|0]=0,l=d[0|i])for(y=w-2|0,m=0,P=(0|n)<2,h=0;;){e:{t:if((255&l)!=6|P|(0|h)==21){if((0|(n=255&l))==255){if(!M|(0|r)<2)break e;n=r>>>0>2?11:(0|m)%3|0?23:11}h=n,n=m}else{if(l=m+1|0,d[t+169|0]){h=(0|l)>1?5:6,n=l;break t}if(h=6,n=w,(0|l)==(0|w))break t;h=(0|l)%3|0||(0|m)==(0|y)?5:6,n=l}m=n,g[0|A]=h,A=A+1|0}if(!(l=d[(M=M+1|0)+i|0]))break}(0|r)>=2&&(g[0|A]=11,A=A+1|0),g[0|A]=0,oe=i+208|0}function Kr(t,A){var r=0;r=0,t&&(r=e[50754],r=(t=(t=(0|F(e[145712+(A?12:((0|t)>199)<<2)>>2],t))/256|0)>>>0>(A=e[36430])>>>0?t:A)>>>0<=89999?(F(t,r)>>>0)/1e3|0:(F(t,(0|r)/25|0)>>>0)/40|0),(0|(t=e[36440]))<=0||(0|(A=e[36424]))<0||(e[4+(A=216192+(A<<4)|0)>>2]||(e[A+4>>2]=t),e[36440]=0),e[36426]=0,e[36439]=-1,e[36455]=e[50758],CA(),e[36427]=-1,t=216192+(e[50758]<<4)|0,e[t>>2]=5,e[t+4>>2]=r,t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0,e[36426]=0,e[36438]&&(e[36438]=0,t=216192+(e[50758]<<4)|0,e[t>>2]=14,e[t+4>>2]=0,t=e[50758]+1|0,e[50758]=(0|t)<=169?t:0)}function Yt(t,A,r,n,i,l,h,m){var w,M=0,y=0,P=0;M=1,w=y=2147483647&n;e:if(!((P=(0|y)==2147418112)&!r?t|A:P&!!(0|r)|y>>>0>2147418112)&&!((P=(0|(y=2147483647&m))==2147418112)&!h?i|l:P&!!(0|h)|y>>>0>2147418112)){if(!(t|i|r|h|A|l|y|w))return 0;if((0|(M=n&m))>0|(0|M)>=0){if(M=-1,(0|r)==(0|h)&(0|n)==(0|m)?(0|A)==(0|l)&t>>>0<i>>>0|A>>>0<l>>>0:r>>>0<h>>>0&(0|n)<=(0|m)|(0|n)<(0|m))break e;return!!(t^i|r^h|A^l|n^m)}M=-1,((0|r)==(0|h)&(0|n)==(0|m)?(0|A)==(0|l)&t>>>0>i>>>0|A>>>0>l>>>0:r>>>0>h>>>0&(0|n)>=(0|m)|(0|n)>(0|m))||(M=!!(t^i|r^h|A^l|n^m))}return M}function TA(t,A){var r=0,n=0,i=0,l=0;for(r=d[85836]|d[85837]<<8,g[0|A]=r,g[A+1|0]=r>>>8,g[A+2|0]=d[85838];;)if(i=d[0|t],t=r=t+1|0,(0|i)!=255){if(!i)break;if(!(n=e[144464+(i<<2)>>2]))continue;if(d[n+11|0]==1&&(l=d[n+14|0],!(_[n+8>>1]|l>>>0>4))){if(l>>>0<2)continue;g[0|A]=d[l+93943|0],A=A+1|0;continue}if(255&(t=e[n>>2]))for(;g[0|A]=t,A=A+1|0,n=65280&t,t=t>>>8|0,n;);if(t=r,(0|i)!=21||(32|(r=g[0|t]))-97>>>0>=26)continue;for(;g[0|A]=r,A=A+1|0,(32|(r=g[0|(t=t+1|0)]))-97>>>0<26;);}g[0|A]=0}function Js(t,A){var r,n,i=0,l=0,h=0;oe=r=oe-112|0,t||(e[50303]||_A(),t=201216),vr(i=r+16|0,A,40),e[r>>2]=47,e[r+4>>2]=i,uA(A=r- -64|0,87599,r),n=wA(A),h=-1;e:{t:{r:{if(l=e[t>>2]){A=0,i=-1;a:{for(;;){if(Zn(r+16|0,e[l>>2])){if(Zn(r+16|0,l=e[l+8>>2])?h=Zn(r- -64|0,l+(wA(l)-n|0)|0)?h:A:i=A,l=e[((A=A+1|0)<<2)+t>>2])continue;break a}break}if((0|A)>=0)break t;A=(0|i)<0?h:i;break r}if((0|(A=i))>=0)break r}A=h}if(i=0,(0|A)<0)break e}i=e[(A<<2)+t>>2]}return oe=r+112|0,i}function Ss(t,A){var r,n,i=0,l=0,h=0,m=0,w=0;oe=r=oe-16|0,V(+A),n=0|G(1),l=0|G(0),(0|(h=(i=2147483647&n)-1048576|0))==2145386495|h>>>0<2145386495?(m=l<<28,h=i>>>4|0,i=(15&i)<<28|l>>>4,l=h+1006632960|0):(0|i)==2146435072|i>>>0>2146435072?(m=l<<28,i=(15&n)<<28|l>>>4,l=n>>>4|2147418112):i|l?(ea(r,l,h=i,0,0,(i=i?ce(i):ce(l)+32|0)+49|0),w=e[r>>2],m=e[r+4>>2],h=15372-i<<16,i=e[r+8>>2],l=h|65536^e[r+12>>2]):(i=0,l=0),e[t>>2]=w,e[t+4>>2]=m,e[t+8>>2]=i,e[t+12>>2]=-2147483648&n|l,oe=r+16|0}function Ds(t,A,r){var n=0,i=0;e:{t:{r:{if(!(3&((i=t)^A))){n=!!(0|r);a:if(!(!(3&A)|!r))for(;;){if(n=d[0|A],g[0|i]=n,!n)break e;if(i=i+1|0,n=!!(0|(r=r-1|0)),!(3&(A=A+1|0)))break a;if(!r)break}if(!n)break t;if(!d[0|A])break e;if(!(r>>>0<4))for(;;){if(~(n=e[A>>2])&n-16843009&-2139062144)break r;if(e[i>>2]=n,i=i+4|0,A=A+4|0,!((r=r-4|0)>>>0>3))break}}if(!r)break t}for(;;){if(n=d[0|A],g[0|i]=n,!n)break e;if(i=i+1|0,A=A+1|0,!(r=r-1|0))break}}r=0}return IA(i,0,r),t}function ks(t,A,r,n,i,l){var h,m=0,w=0,M=0;oe=h=oe-240|0,m=e[r>>2],e[h+232>>2]=m,r=e[r+4>>2],e[h>>2]=t,e[h+236>>2]=r,M=1;e:{t:{r:{if(r|(0|m)!=1){for(m=t;;){if((0|mi(r=m-e[(w=(n<<2)+l|0)>>2]|0,t,A))<=0){r=m;break r}a:{if(!((0|n)<2|i)&&(i=e[w-8>>2],(0|mi(w=m-4|0,r,A))>=0||(0|mi(w-i|0,r,A))>=0))break a;if(e[(M<<2)+h>>2]=r,Ua(m=h+232|0,i=Wo(m)),M=M+1|0,n=n+i|0,i=0,m=r,e[h+236>>2]|e[h+232>>2]!=1)continue;break t}break}r=m;break t}r=t}if(i)break e}yn(h,M),Sn(r,A,n,l)}oe=h+240|0}function os(t,A){var r=0;e[4+(134912+(r=t<<2)|0)>>2]=A,e[r+136192>>2]=A,r=28;e:{t:{r:switch(t-1|0){case 0:e[50792]=A,e[50786]=A,Ra(3);break t;case 1:e[50787]=A,e[33037]=(0|F(d[e[50797]+105596|0],(0|F(e[50787],55))/100|0))/16;break t;case 2:t=(0|A)>=99?99:A,e[50785]=(0|t)>0?t:0;break t;case 3:e[50788]=(0|A)>=99?99:A;break t;case 12:e[47268]=A;break t;case 6:e[47205]=A;break t;case 9:break t;case 8:break r;default:break e}(t=255&A)&&(e[e[47192]+152>>2]=t),e[47196]=A}r=0}return r}function Tr(t,A){var r=0,n=0,i=0;e:if(e[t>>2])for(;;){if(Ts(e[t-4>>2])){if(n=0,(0|(r=g[0|A]))==e[t>>2])for(;(0|(r=g[(n=n+1|0)+A|0]))==e[(t=t+4|0)>>2];);if(!r){for(;A=t,t=t+4|0,Ts(e[A>>2]););for(i=A+((e[A>>2]==61)<<2)|0;i=(t=i)+4|0,Ts(e[t>>2]););switch((A=e[t>>2])-34|0){case 0:case 5:break e}return Ts(A)||e[t>>2]==47?102808:t}}if(!e[(t=t+4|0)>>2])break}return i}function As(t,A,r,n){var i,l,h=0,m=0,w=0;return!d[t+25|0]|S[t+8>>3]!=r|S[t+16>>3]!=n?(S[t+16>>3]=n,S[t+8>>3]=r,n=(h=fr(-3.141592653589793/(m=+e[t>>2])*n))*-h,S[t+48>>3]=n,h*=Aa(-6.283185307179586/m*r),h+=h,S[t+40>>3]=h,m=1-h-n,S[t+32>>3]=m,!(w=d[t+24|0])|r==0||(m=1/m,S[t+32>>3]=m,n*=r=-m,S[t+48>>3]=n,h*=r,S[t+40>>3]=h,w=1)):(w=d[t+24|0],n=S[t+48>>3],h=S[t+40>>3],m=S[t+32>>3]),g[t+25|0]=1,r=S[t+64>>3],i=S[t+56>>3],S[t+64>>3]=i,l=A,A=n*r+(m*A+h*i),S[t+56>>3]=w?l:A,A}function yi(t,A,r,n,i){var l,h=0,m=0,w=0,M=0;if((l=e[34388])&&!((0|(m=e[34436]))>=(e[34393]-2|0))){if(e[34436]=m+1,h=F(m,36)+l|0,e[h>>2]=t,e[h+4>>2]=e[34437],M=e[34438],e[h+12>>2]=A>>>24,e[h+8>>2]=16777215&A,e[h+24>>2]=M,A=e[50754],i=e[34439]+((i-e[34392]|0)/2|0)|0,e[h+20>>2]=i,A=he(w=1e3*+(0|i)/+(0|A))<2147483648?~~w:-2147483648,e[h+16>>2]=A,t-3>>>0<=1)return void(e[28+(F(m,36)+l|0)>>2]=e[33282]+r);A=28+(F(m,36)+l|0)|0,e[A>>2]=r,(0|t)==7&&(e[A+4>>2]=n)}}function Da(t,A,r,n,i,l){var h=0,m=0,w=0,M=0;64&l?(A=31&(r=l+-64|0),(63&r)>>>0>=32?(r=0,A=i>>>A|0):(r=i>>>A|0,A=((1<<A)-1&i)<<32-A|n>>>A),n=0,i=0):l&&(w=n,h=31&(m=64-l|0),(63&m)>>>0>=32?(m=w<<h,M=0):(m=(1<<h)-1&w>>>32-h|i<<h,M=w<<h),w=A,A=31&l,(63&l)>>>0>=32?(h=0,A=r>>>A|0):(h=r>>>A|0,A=((1<<A)-1&r)<<32-A|w>>>A),A|=M,r=h|m,h=n,n=31&l,(63&l)>>>0>=32?(m=0,n=i>>>n|0):(m=i>>>n|0,n=((1<<n)-1&i)<<32-n|h>>>n),i=m),e[t>>2]=A,e[t+4>>2]=r,e[t+8>>2]=n,e[t+12>>2]=i}function Qn(t){var A=0,r=0;if(!t){if(e[33174]&&(A=Qn(e[33174])),e[33136]&&(A=Qn(e[33136])|A),t=e[56816])for(;e[t+20>>2]!=e[t+28>>2]&&(A=Qn(t)|A),t=e[t+56>>2];);return A}return e[t+76>>2],e[t+20>>2]==e[t+28>>2]||(LA[e[t+36>>2]](t,0,0),e[t+20>>2])?((0|(A=e[t+8>>2]))!=(0|(r=e[t+4>>2]))&&(A=r-A|0,LA[e[t+40>>2]](t,A,A>>31,1)),A=0,e[t+28>>2]=0,e[t+16>>2]=0,e[t+20>>2]=0,e[t+4>>2]=0,e[t+8>>2]=0):A=-1,A}function $s(t,A){var r=0,n=0,i=0,l=0,h=0;if((192&(r=d[0|A]))==128)for(;(192&(r=d[0|(A=A-1|0)]))==128;);e:if(128&(r=r<<24>>24)){if(n=1,(0|(i=224&r))!=192)if((240&r)!=224){if(n=3,(248&r)!=240){r&=255,n=0;break e}}else n=2,h=1;r=d[n+93846|0]&r,(l=d[A+1|0])?(r=63&l|r<<6,(0|i)!=192&&((i=d[A+2|0])?(r=63&i|r<<6,h||((A=d[A+3|0])?r=63&A|r<<6:n=2)):n=1)):n=0}return e[t>>2]=r,n+1|0}function Ei(t,A,r,n){var i,l=0,h=0;return oe=i=oe-224|0,t?(e[i>>2]=137584,e[i+4>>2]=47,e[i+8>>2]=A,uA(A=i+16|0,85430,i),(0|(l=Na(A)))<0?A=On(n,0-l|0,i+16|0):(A=la(i+16|0,85659))?((h=e[t>>2])&&EA(h),l?(h=aA(l),e[t>>2]=h,h?(0|Mi(h,l,A))==(0|l)?(fa(A),A=0,r&&(e[r>>2]=l)):(r=e[56798],fa(A),EA(e[t>>2]),e[t>>2]=0,A=On(n,r,i+16|0)):(fa(A),A=48)):(A=0,e[t>>2]=0)):A=On(n,e[56798],i+16|0)):A=28,oe=i+224|0,A}function or(t,A){var r=0,n=0,i=0,l=0,h=0;if((192&(r=d[0|A]))==128)for(;(192&(r=d[0|(A=A+1|0)]))==128;);e:if(128&(r=r<<24>>24)){if(n=1,(0|(i=224&r))!=192)if((240&r)!=224){if(n=3,(248&r)!=240){r&=255,n=0;break e}}else n=2,h=1;r=d[n+93846|0]&r,(l=d[A+1|0])?(r=63&l|r<<6,(0|i)!=192&&((i=d[A+2|0])?(r=63&i|r<<6,h||((A=d[A+3|0])?r=63&A|r<<6:n=2)):n=1)):n=0}return e[t>>2]=r,n+1|0}function ea(t,A,r,n,i,l){var h=0,m=0,w=0;64&l?(n=A,A=31&(i=l+-64|0),(63&i)>>>0>=32?(i=n<<A,n=0):(i=(1<<A)-1&n>>>32-A|r<<A,n<<=A),A=0,r=0):l&&(h=n,n=31&l,(63&l)>>>0>=32?(m=h<<n,w=0):(m=(1<<n)-1&h>>>32-n|i<<n,w=h<<n),h=A,n=31&(i=64-l|0),(63&i)>>>0>=32?(i=0,A=r>>>n|0):(i=r>>>n|0,A=((1<<n)-1&r)<<32-n|h>>>n),n=w|A,i|=m,A=31&l,(63&l)>>>0>=32?(m=h<<A,A=0):(m=(1<<A)-1&h>>>32-A|r<<A,A=h<<A),r=m),e[t>>2]=A,e[t+4>>2]=r,e[t+8>>2]=n,e[t+12>>2]=i}function ta(t){var A=0;return t>>>0<=55295?A=d[e[125552+(t>>>6&67108860)>>2]+(255&t)|0]:(A=4,t>>>0<57344||(t>>>0<63488?A=3:t>>>0<=195327?A=d[e[126416+(t-63488>>>6&67108860)>>2]+(255&t)|0]:(A=2,t>>>0<917504||(t>>>0<=918015?A=d[e[128476+(t-917504>>>6&67108860)>>2]+(255&t)|0]:t>>>0<983040||(t>>>0<1048574?A=3:t>>>0<1048576||(A=3,t>>>0<1114110||(A=t>>>0<1114112?2:5))))))),255&A}function Fo(t,A){var r=0,n=0,i=0,l=0,h=0,m=0,w=0,M=0,y=0;if(!((0|(r=e[33709]))<=0)){if(i=(0|t)>31?t-32|0:t,t=0,r>>>0>=4)for(y=-4&r;m=2|t,w=1|t,n=e[134912+((h=3|t)<<6)>>2]==(0|i)?h:e[134912+(m<<6)>>2]==(0|i)?m:e[134912+(w<<6)>>2]==(0|i)?w:e[134912+(t<<6)>>2]==(0|i)?t:n,t=t+4|0,(0|y)!=(0|(l=l+4|0)););if(l=3&r)for(;n=e[134912+(t<<6)>>2]==(0|i)?t:n,t=t+1|0,(0|l)!=(0|(M=M+1|0)););(0|n)<=0||(e[33709]=n,r=n)}Ma(A,r)}function io(t){var A,r=0,n=0,i=0;for(oe=A=oe-96|0,vr(A,t,60),vn(A,1);i=wr(g[0|(n=A+r|0)]),g[0|n]=i,r=r+1|0,255&i;);e[A+92>>2]=0,e[A+84>>2]=0,e[A+88>>2]=0,e[A+76>>2]=0,e[A+80>>2]=0,e[A+72>>2]=t;e:{t:{r:{if(eA(A,1)){if(d[202976])break r;break t}if(e[50303]||_A(),r=268437247,!(t=Js(201216,A))||!eA(e[t+8>>2],0))break e;if(!d[202976])break t}eA(202976,2)}hn(e[32972]),e[A+76>>2]=e[32972]+40,xi(A+72|0,202976),r=0}return oe=A+96|0,r}function ui(t,A){if(!t)return 0;e:{t:{if(t){if(A>>>0<=127)break t;if(e[e[56841]>>2]){if(A>>>0<=2047){g[t+1|0]=63&A|128,g[0|t]=A>>>6|192,t=2;break 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r,n=0;oe=r=oe+-64|0,g[202976]=0,e[r+48>>2]=47,uA(r+59|0,91351,r+48|0),A||(g[r+59|0]=0);e:{t:{if(t&&(t=an(t,43))){if(g[0|t]=0,g[0|(t=t+1|0)]-48>>>0>=10)break t;n=Pn(t)}if((0|n)<=0)break e;if(n>>>0<=9){e[r+4>>2]=n,e[r>>2]=r+59,uA(202976,91378,r);break e}e[r+20>>2]=n-10,e[r+16>>2]=r+59,uA(202976,91503,r+16|0);break e}e[r+36>>2]=t,e[r+32>>2]=r+59,uA(202976,85425,r+32|0)}oe=r- -64|0}function Sa(t){var A,r=0,n=0,i=0;oe=A=oe-80|0,r=Fr(t,A+12|0),e[A+12>>2]?(vr(n=A+16|0,r,60),r=0,vn(n,1),!eA(n,0)|!d[202976]||eA(202976,2),hn(e[32972]),xi(t,86012)):r=268437247,oe=A+80|0;e:{t:{r:{a:{if((0|r)<=268437502){if(!r)break e;if((0|r)==268436479)break t;if((0|r)!=268437247)break a;return 2}if((0|r)==268437503|(0|r)==268437759)break r;if((0|r)==268439295)break e}return-1}return 2}i=1}return i}function Ns(t,A,r,n,i,l,h,m,w){var M,y,P;w=rr(A,r,m,w),m=fe,i=rr(n,i,l,h),n=fe+m|0,m=i>>>0>(w=i+w|0)>>>0?n+1|0:n,M=h,y=r,h=(r=rr(h,i=0,r,n=0))+w|0,w=fe+m|0,P=h,r=r>>>0>h>>>0?w+1|0:w,h=rr(l,0,A,0),m=fe,n=rr(l,w=0,y,n),l=fe+w|0,l=n>>>0>(m=m+n|0)>>>0?l+1|0:l,n=r,l=l>>>0>(w=l+P|0)>>>0?n+1|0:n,r=rr(A,0,M,i)+m|0,i=fe,m=(i=r>>>0<m>>>0?i+1|0:i)+w|0,w=l,e[t+8>>2]=m,e[t+12>>2]=i>>>0>m>>>0?w+1|0:w,e[t>>2]=h,e[t+4>>2]=r}function BA(t,A){var r=0,n=0;e:{if(3&((n=t)^A))r=d[0|A];else{if(3&A)for(;;){if(r=d[0|A],g[0|n]=r,!r)break e;if(n=n+1|0,!(3&(A=A+1|0)))break}if(!(~(r=e[A>>2])&r-16843009&-2139062144))for(;e[n>>2]=r,r=e[A+4>>2],n=n+4|0,A=A+4|0,!(r-16843009&~r&-2139062144););}if(g[0|n]=r,255&r)for(;r=d[A+1|0],g[n+1|0]=r,n=n+1|0,A=A+1|0,r;);}return t}function Aa(t){var A,r=0,n=0;oe=A=oe-16|0,V(+t),n=0|G(1),G(0);e:if((n&=2147483647)>>>0<=1072243195){if(r=1,n>>>0<1044816030)break e;r=qn(t,0)}else if(r=t-t,!(n>>>0>=2146435072)){switch(3&NA(t,A)){case 0:r=qn(S[A>>3],S[A+8>>3]);break e;case 1:r=-En(S[A>>3],S[A+8>>3],1);break e;case 2:r=-qn(S[A>>3],S[A+8>>3]);break e}r=En(S[A>>3],S[A+8>>3],1)}return oe=A+16|0,r}function Li(t,A,r,n){var i=0,l=0,h=0;if(!((wA(n)+wA(A)|0)>=(0|r))){for(h=e[36115],r=n;i=d[0|r];)if(r=r+1|0,!((0|i)>=(0|h))){e:{t:switch(i=e[144464+(i<<2)>>2],d[i+11|0]-1|0){case 1:break e;case 0:break t;default:continue}l=d[i+14|0]<4|l;continue}1&(d[i+4|0]>>>1|l)||(e[t+8212>>2]=e[t+8212>>2]+1),e[t+8208>>2]=e[t+8208>>2]+1,l=0}A&&gs(A,n)}}function Vi(t){var A,r=0;oe=A=oe-16|0,V(+t),r=0|G(1),G(0);e:if((r&=2147483647)>>>0<=1072243195){if(r>>>0<1045430272)break e;t=En(t,0,0)}else if(r>>>0>=2146435072)t-=t;else{switch(3&NA(t,A)){case 0:t=En(S[A>>3],S[A+8>>3],1);break e;case 1:t=qn(S[A>>3],S[A+8>>3]);break e;case 2:t=-En(S[A>>3],S[A+8>>3],1);break e}t=-qn(S[A>>3],S[A+8>>3])}return oe=A+16|0,t}function Go(t){var A=0;e[t+296>>2]=303173648,e[t+300>>2]=370677780,A=e[26341],e[t+304>>2]=e[26340],e[t+308>>2]=A,A=e[26343],e[t+312>>2]=e[26342],e[t+316>>2]=A,Br(t),e[t+56>>2]=2,e[t+36>>2]=3,e[t+40>>2]=1074,g[t+168|0]=5,e[t+132>>2]=32,e[t+104>>2]=1032,e[t+108>>2]=66,e[t+8>>2]=5,e[t+12>>2]=32,g[t+365|0]=64|d[t+365|0],g[t+368|0]=64|d[t+368|0],g[t+396|0]=64|d[t+396|0],g[t+399|0]=64|d[t+399|0]}function Kn(t,A,r){var n=0,i=0,l=0;e:{if(!(n=e[r+16>>2])){if(dn(r))break e;n=e[r+16>>2]}if(n-(l=e[r+20>>2])>>>0<A>>>0)return 0|LA[e[r+36>>2]](r,t,A);t:if(e[r+80>>2]<0)n=0;else{for(i=A;;){if(!(n=i)){n=0;break t}if(d[(i=n-1|0)+t|0]==10)break}if((i=0|LA[e[r+36>>2]](r,t,n))>>>0<n>>>0)break e;t=t+n|0,A=A-n|0,l=e[r+20>>2]}$A(l,t,A),e[r+20>>2]=e[r+20>>2]+A,i=A+n|0}return i}function Vo(t){var A,r=0,n=0;e:{if((0|(A=e[34064]))>0)for(;;){if((n=e[136284+(r<<4)>>2])&&!Zr(t,n)){if(e[136276+(r<<4)>>2])return r;if(n=-1,Qa(0,r))break e;return r}if((0|A)==(0|(r=r+1|0)))break}n=-1,Qa(t,A)||(r=ds(e[12+(136272+(e[34064]<<4)|0)>>2],wA(t)+1|0),n=e[34064],e[12+(136272+(n<<4)|0)>>2]=r,BA(r,t),e[34064]=n+1)}return n}function Hi(t,A){var r,n=0,i=0,l=0,h=0;for(oe=r=oe-16|0,g[0|A]=0,(n=15&e[t>>2])&&(A=(i=wA(A=BA(A,sa(128496,64|n))))+A|0),n=8;;){e:{t:{if(n>>>0<=29){if(e[t>>2]>>>n&1)break t;break e}if(!(e[t+4>>2]>>>n-32&1)|n>>>0<32)break e}(0|(i=(h=wA(l=sa(128496,n))+1|0)+i|0))>=80||(e[r>>2]=l,uA(A,84439,r),A=A+h|0)}if((0|(n=n+1|0))==64)break}oe=r+16|0}function Bi(t,A,r){var n,i=0,l=0,h=0;if(oe=n=oe-16|0,d[0|t]){for(h=e[30450];;)if(l=t,t=t+1|0,!((0|(i=g[0|l]))==32|i-9>>>0<5)){for((0|(i=Pn(l)))>0&&((0|i)<32?e[A>>2]=e[A>>2]|1<<i:(l=sa(129568,r),e[n+4>>2]=i,e[n>>2]=l,ss(h,84902,n)),l=t);l=(t=l)+1|0,(i=g[0|t])-48>>>0<10|(32|i)-97>>>0<26;);if(!i)break}}oe=n+16|0}function ca(t,A,r){var n=0,i=0,l=0,h=0;e:if(A&&!((0|(i=r-4|0))<=0))for(l=(0|(r=e[A-4>>2]))!=34?(0|r)==39?r:0:r,r=0;;){if(h=r,!(r=e[A>>2]))break e;t:{if(!l){if((0|r)==32|r-9>>>0<5)break e;if((0|r)!=47)break t;break e}if((0|h)!=92&&(0|r)==(0|l))break e}if(A=A+4|0,!((0|i)>(0|(n=_s(r,t+n|0)+n|0))))break}return g[t+n|0]=0,n}function Wr(t){var A=0;e:if(!aa(t)){A=0;t:if(!(t>>>0<768)){if(t-2305>>>0<=1270){if((124&t)>>>0<100)break e;if(A=1,Uo(93850,t))break t;return t-3450>>>0<6}if((0|t)==1541|t-1456>>>0<19|(0|t)==1648||(0|(A=-256&t))==10240|(0|A)==4352|t-3904>>>0<125|t>>>0<880)break e;A=1,t-1611>>>0<20||(A=t-12353>>>0<30400)}return A}return 1}function xi(t,A){var r=0;t?((r=e[t+4>>2])&&BA(133208,r),(r=e[t>>2])&&vr(133168,r,40),e[33289]=d[t+14|0],e[33291]=d[t+13|0],e[33290]=d[t+12|0],vr(134672,d[0|A]!=33|d[A+1|0]!=118?A:(d[A+2|0]==47?3:0)+A|0,40),t=e[50298],e[33678]=e[50297],e[33679]=t,t=e[50302],e[33682]=e[50301],e[33683]=t,t=e[50300],e[33680]=e[50299],e[33681]=t):IA(133152,0,76)}function Yi(t,A,r,n){g[t+53|0]=1;e:if(e[t+4>>2]==(0|r)){g[t+52|0]=1;t:{if(!(r=e[t+16>>2])){if(e[t+36>>2]=1,e[t+24>>2]=n,e[t+16>>2]=A,(0|n)!=1)break e;if(e[t+48>>2]==1)break t;break e}if((0|A)==(0|r)){if((0|(r=e[t+24>>2]))==2&&(e[t+24>>2]=n,r=n),e[t+48>>2]!=1)break e;if((0|r)==1)break t;break e}e[t+36>>2]=e[t+36>>2]+1}g[t+54|0]=1}}function Gs(t,A){var r=0,n=0,i=0,l=0,h=0;e:if(r=e[A>>2])for(;;){n=0;t:if(t){for(;h=g[n+r|0],(i=e[(n<<2)+t>>2])&&(n=n+1|0,(0|h)==(0|i)););r:switch(i-34|0){case 0:case 5:break r;default:break t}if(!h)break e}if(!(r=e[((l=l+1|0)<<3)+A>>2]))break}return e[4+((l<<3)+A|0)>>2]}function fi(t,A){e:if((0|A)>=1024){if(t*=898846567431158e293,A>>>0<2047){A=A-1023|0;break e}t*=898846567431158e293,A=((0|A)>=3069?3069:A)-2046|0}else(0|A)>-1023||(t*=2004168360008973e-307,A>>>0>4294965304?A=A+969|0:(t*=2004168360008973e-307,A=((0|A)<=-2960?-2960:A)+1938|0));return D(0,0),D(1,A+1023<<20),t*+Q()}function An(t,A,r,n){var i,l,h,m=0;return oe=l=oe-16|0,e[l+12>>2]=n,oe=i=oe-160|0,h=A?t:i+158|0,e[i+144>>2]=h,m=-1,t=A-1|0,e[i+148>>2]=t>>>0<=A>>>0?t:0,t=IA(i,0,144),e[t+76>>2]=-1,e[t+36>>2]=17,e[t+80>>2]=-1,e[t+44>>2]=t+159,e[t+84>>2]=t+144,(0|A)<0?e[56798]=61:(g[0|h]=0,m=ts(t,r,n,15,16)),oe=t+160|0,oe=l+16|0,m}function Zn(t,A){var r=0,n=0,i=0;e:if(r=d[0|t])for(;;){if(!(n=d[0|A])){i=r;break e}if((0|r)!=(0|n)&&(0|(n=r-65>>>0<26?32|r:r))!=(0|((r=d[0|A])-65>>>0<26?32|r:r))){i=d[0|t];break e}if(A=A+1|0,r=d[t+1|0],t=t+1|0,!r)break}return(i=(t=255&i)-65>>>0<26?32|t:t)-((t=d[0|A])-65>>>0<26?32|t:t)|0}function Mi(t,A,r){var n=0,i=0;if(n=e[r+72>>2],e[r+72>>2]=n-1|n,(0|(n=e[r+4>>2]))==(0|(i=e[r+8>>2]))?n=A:($A(t,n,n=A>>>0>(n=i-n|0)>>>0?n:A),e[r+4>>2]=n+e[r+4>>2],t=t+n|0,n=A-n|0),n)for(;;){if(Di(r)||!(i=0|LA[e[r+32>>2]](r,t,n)))return A-n|0;if(t=t+i|0,!(n=n-i|0))break}return A}function Pn(t){for(var A=0,r=0,n=0,i=0;t=(A=t)+1|0,(0|(r=g[0|A]))==32|r-9>>>0<5;);e:{t:{r:switch((r=g[0|A])-43|0){case 0:break t;case 2:break r;default:break e}i=1}r=g[0|t],A=t}if(r-48>>>0<10)for(;n=48+(F(n,10)-g[0|A]|0)|0,t=g[A+1|0],A=A+1|0,t-48>>>0<10;);return i?n:0-n|0}function oo(t,A){var r,n,i,l=0;return oe=r=oe-32|0,e[A>>2]=0,e[A+4>>2]=0,e[(l=n=A+24|0)>>2]=0,e[l+4>>2]=0,e[(l=i=A+16|0)>>2]=0,e[l+4>>2]=0,e[(l=A+8|0)>>2]=0,e[l+4>>2]=0,e[r+28>>2]=A+28,e[r+24>>2]=n,e[r+20>>2]=A+20,e[r+16>>2]=i,e[r+12>>2]=A+12,e[r+8>>2]=l,e[r+4>>2]=A+4,e[r>>2]=A,t=nA(t,84553,r),oe=r+32|0,t}function _o(t){var A=0,r=0,n=0;if((A=d[0|t])&&((r=d[t+1|0])?(r=A|r<<8,(A=d[t+2|0])&&(r|=A<<16,(t=d[t+3|0])&&(r|=t<<24))):r=A),(0|(A=e[36115]))>0)for(t=0;;){if(!(!(n=e[144464+(t<<2)>>2])|e[n>>2]!=(0|r)))return d[n+10|0];if((0|A)==(0|(t=t+1|0)))break}return 0}function Sn(t,A,r,n){var i,l=0,h=0,m=0,w=0,M=0;oe=i=oe-240|0,e[i>>2]=t,m=1;e:if(!((0|r)<2))for(l=t;;){if((0|mi(t,h=(l=l-4|0)-e[((w=r-2|0)<<2)+n>>2]|0,A))>=0&&(0|mi(t,l,A))>=0)break e;if(M=h,l=(h=(0|mi(h,l,A))>=0)?M:l,e[(m<<2)+i>>2]=l,m=m+1|0,!((0|(r=h?r-1|0:w))>1))break}yn(i,m),oe=i+240|0}function uA(t,A,r){var n,i,l,h=0;return oe=i=oe-16|0,e[i+12>>2]=r,oe=n=oe-160|0,$A(l=n+8|0,124528,144),e[n+52>>2]=t,e[n+28>>2]=t,h=(h=-2-t|0)>>>0>2147483647?2147483647:h,e[n+56>>2]=h,t=t+h|0,e[n+36>>2]=t,e[n+24>>2]=t,t=wa(l,A,r),h&&(A=e[n+28>>2],g[A-((0|A)==e[n+24>>2])|0]=0),oe=n+160|0,oe=i+16|0,t}function yn(t,A){var r,n=0,i=0,l=0,h=0,m=0;if(n=4,oe=r=oe-256|0,(0|A)>=2)for(e[(m=(A<<2)+t|0)>>2]=r;;){for(l=n>>>0>=256?256:n,$A(e[m>>2],e[t>>2],l),i=0;h=(i<<2)+t|0,i=i+1|0,$A(e[h>>2],e[(i<<2)+t>>2],l),e[h>>2]=e[h>>2]+l,(0|A)!=(0|i););if(!(n=n-l|0))break}oe=r+256|0}function kA(t,A,r){var n,i=0;return oe=n=oe-96|0,e[n+88>>2]=0,e[n+92>>2]=1073741824,e[n+84>>2]=A,A=Hr(t,n+84|0,r,n+88|0,2,0),536870912&(i=e[n+88>>2])?(A=e[47202],e[47202]=0,g[n+2|0]=32,v[n>>1]=8192,vr(i=3|n,e[n+84>>2],77),t=lA(t,i,0,0),BA(r,189088),e[47202]=A):t=A?i:0,oe=n+96|0,t}function lo(t,A,r){var n=0,i=0,l=0;i=di(t),n=e[A>>2];e:{t:if((0|i)>=0){if(n){if(!Zr(t,r))break t;(l=e[n+688>>2])&&EA(l),EA(n),e[A>>2]=0}e[A>>2]=st(t),t=BA(r,t),qs(r=e[A>>2],r+228|0,0)&&(Ia(e[e[32972]+60>>2]),g[0|t]=0,i=-1),n=e[A>>2],e[n+292>>2]=i}else if(!n)break e;g[n+268|0]=0}return i}function co(t){var A=0,r=0;return(0|(A=e[t+76>>2]))>=0&(!A|e[56823]!=(-1073741825&A))?(r=e[(A=t+76|0)>>2],e[A>>2]=r||1073741823,(0|(r=e[t+4>>2]))==e[t+8>>2]?t=Zi(t):(e[t+4>>2]=r+1,t=d[0|r]),e[A>>2]=0,t):(0|(A=e[t+4>>2]))!=e[t+8>>2]?(e[t+4>>2]=A+1,d[0|A]):Zi(t)}function ra(t,A,r){var n=0,i=0;e:{t:{if(r>>>0>=4){if(3&(t|A))break t;for(;;){if(e[t>>2]!=e[A>>2])break t;if(A=A+4|0,t=t+4|0,!((r=r-4|0)>>>0>3))break}}if(!r)break e}for(;;){if((0|(n=d[0|t]))==(0|(i=d[0|A]))){if(A=A+1|0,t=t+1|0,r=r-1|0)continue;break e}break}return n-i|0}return 0}function js(t,A){var r,n=0,i=0,l=0;oe=r=oe-16|0,A?(ea(r,i=((n=A>>31)^A)-n|0,0,0,0,(n=ce(i))+81|0),i=0+e[r+8>>2]|0,n=(65536^e[r+12>>2])+(16414-n<<16)|0,l=-2147483648&A|(n=i>>>0<l>>>0?n+1|0:n),n=e[r+4>>2],A=e[r>>2]):A=0,e[t>>2]=A,e[t+4>>2]=n,e[t+8>>2]=i,e[t+12>>2]=l,oe=r+16|0}function Nn(t){var A,r=0,n=0,i=0;if((0|(r=Un(t,61)))==(0|t))return 0;e:if(!d[(A=r-t|0)+t|0]&&(r=e[56800])&&(n=e[r>>2])){for(;;){if(go(t,n,A)||(n=e[r>>2]+A|0,d[0|n]!=61)){if(n=e[r+4>>2],r=r+4|0,n)continue;break e}break}i=n+1|0}return i}function di(t){var A=0,r=0;e:if(!((0|(r=e[34461]))<=0)){for(;;){if(!Zr(t,F(A,44)+137856|0)){e[34457]=A;break e}if((0|r)==(0|(A=A+1|0)))break}return-1}return(t=(0|A)==(0|r))?-1:((0|(t=t?-1:A))!=e[36114]&&(e[36115]=0,ba(t),e[36114]=t,e[36115]=e[36115]+1),A)}function uo(t,A,r,n){var i=0,l=0;e:if((32|d[0|A])!=32){if(i=((0|n)>2)<<1,l=Tt(t,A,r,i=(0|n)>1?4|i:i),d[0|r]!=21)for(i|=1,A=A+l|0,l=1;;){if((32|d[0|A])==32)break e;if(A=Tt(t,A,r,i)+A|0,l=l+1|0,d[0|r]==21)break}return BA(189088,r),0}return PA(t,r,n,l),A}function _s(t,A){var r,n=0,i=0,l=0;if(t>>>0<=127)return g[0|A]=t,1;if(t>>>0>=1114112)return g[0|A]=32,1;for(n=F(r=t>>>0<2048?1:t>>>0<65536?2:3,6),g[0|A]=d[r+93842|0]|t>>>n;n=n-6|0,g[(i=i+1|0)+A|0]=t>>>n&63|128,(0|(l=l+1|0))!=(0|r););return r+1|0}function Xn(t){var A=0,r=0;e:{if((0|(A=e[t+12>>2]))>=e[t+16>>2]){if(A=0,(0|(r=0|ze(e[t+8>>2],t+24|0,2048)))<=0){if(!r|(0|r)==-44)break e;return e[56798]=0-r,0}e[t+16>>2]=r}r=A,A=t+A|0,e[t+12>>2]=r+_[A+40>>1],r=e[A+36>>2],e[t>>2]=e[A+32>>2],e[t+4>>2]=r,A=A+24|0}return A}function Ho(t,A){var r,n=0;if(n=e[t+632>>2])return!!(0|rs(n,A));e:{t:{if((0|(r=e[t+600>>2]))>0){if(n=0,(A=A-r|0)-1>>>0<255)break t;break e}if((n=A-192|0)>>>0<=413)return 128&d[344+(d[n+94240|0]+t|0)|0];if(n=0,A>>>0>255)break e}n=128&d[344+(t+A|0)|0]}return n}function En(t,A,r){var n,i,l;return 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vendor: 59,320 bytes, line 2810
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vendor: 47,685 bytes, line 2810
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vendor: 13,201 bytes, line 2810
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P=8&i;if(ee=-1,(0|((ae=P|R)?2147483645:2147483646))<(0|R))break e;if(ue=1+(!!(0|ae)+R|0)|0,(0|(w=-33&l))!=70){if((it-(m=fn(((m=n>>31)^n)-m|0,0,it))|0)<=1)for(;g[0|(m=m-1|0)]=48,(it-m|0)<2;);if(g[0|($e=m-2|0)]=l,g[m-1|0]=(0|n)<0?45:43,(0|(m=it-$e|0))>(2147483647^ue))break e}else{if((2147483647^ue)<(0|n))break e;m=(0|n)>0?n:0}if((0|(n=m+ue|0))>(2147483647^Ve))break e;Ws(t,32,r,ue=n+Ve|0,i),Hs(t,Pt,Ve),Ws(t,48,r,ue,65536^i);r:{a:{n:{if((0|w)==70){for(n=8|(l=h+16|0),P=9|l,M=w=M>>>0>be>>>0?be:M;;){m=fn(e[M>>2],0,P);c:if((0|w)==(0|M))(0|m)==(0|P)&&(g[h+24|0]=48,m=n);else{if(h+16>>>0>=m>>>0)break c;for(;g[0|(m=m-1|0)]=48,h+16>>>0<m>>>0;);}if(Hs(t,m,P-m|0),!(be>>>0>=(M=M+4|0)>>>0))break}if(ae&&Hs(t,85998,1),(0|R)<=0|M>>>0>=y>>>0)break n;for(;;){if((m=fn(e[M>>2],0,P))>>>0>h+16>>>0)for(;g[0|(m=m-1|0)]=48,h+16>>>0<m>>>0;);if(Hs(t,m,(0|R)>=9?9:R),m=R-9|0,y>>>0<=(M=M+4|0)>>>0)break a;if(n=(0|R)>9,R=m,!n)break}break a}c:if(!((0|R)<0))for(be=M>>>0<y>>>0?y:M+4|0,n=8|(l=h+16|0),y=9|l,w=M;;){(0|y)==(0|(m=fn(e[w>>2],0,y)))&&(g[h+24|0]=48,m=n);o:if((0|w)==(0|M))Hs(t,m,1),m=m+1|0,P|R&&Hs(t,85998,1);else{if(h+16>>>0>=m>>>0)break o;for(;g[0|(m=m-1|0)]=48,h+16>>>0<m>>>0;);}if(Hs(t,m,(0|(l=y-m|0))>(0|R)?R:l),R=R-l|0,be>>>0<=(w=w+4|0)>>>0)break c;if(!((0|R)>=0))break}Ws(t,48,R+18|0,18,0),Hs(t,$e,it-$e|0);break r}m=R}Ws(t,48,m+9|0,9,0)}Ws(t,32,r,ue,8192^i),ee=(0|r)<(0|ue)?ue:r;break e}if(P=(l<<26>>31&9)+Pt|0,!(n>>>0>11)){for(m=12-n|0,_e=16;_e*=16,m=m-1|0;);A=d[0|P]!=45?A+_e-_e:-(_e+(-A-_e))}for((0|it)==(0|(m=fn(((m=e[h+44>>2])^(w=m>>31))-w|0,0,it)))&&(g[h+15|0]=48,m=h+15|0),be=2|Ve,M=32&l,w=e[h+44>>2],g[0|(R=m-2|0)]=l+15,g[m-1|0]=(0|w)<0?45:43,m=8&i,w=h+16|0;l=w,y=he(A)<2147483648?~~A:-2147483648,g[0|w]=M|d[y+124512|0],!((0|n)>0|m)&(A=16*(A-+(0|y)))==0|((w=l+1|0)-(h+16|0)|0)!=1||(g[l+1|0]=46,w=l+2|0),A!=0;);ee=-1,(2147483645-(l=(m=it-R|0)+be|0)|0)<(0|n)||(Ws(t,32,r,l=(n=!n||((M=w-(h+16|0)|0)-2|0)>=(0|n)?M=w-(h+16|0)|0:n+2|0)+l|0,i),Hs(t,P,be),Ws(t,48,r,l,65536^i),Hs(t,h+16|0,M),Ws(t,48,n-M|0,0,0),Hs(t,R,m),Ws(t,32,r,l,8192^i),ee=(0|r)<(0|l)?l:r)}else Ws(t,32,r,m=Ve+3|0,-65537&i),Hs(t,Pt,Ve),n=32&l,Hs(t,A!=A?n?85596:85774:n?85247:85460,3),Ws(t,32,r,m,8192^i),ee=(0|r)<(0|m)?m:r;return oe=h+560|0,0|ee},function(t,A){var r;t|=0,r=A|=0,A=e[A>>2]+7&-8,e[r>>2]=A+16,S[t>>3]=Pa(e[A>>2],e[A+4>>2],e[A+8>>2],e[A+12>>2])},function(t,A,r){A|=0,r|=0;var n,i,l=0,h=0;return n=e[84+(t|=0)>>2],h=e[n+4>>2],i=e[t+28>>2],(l=(l=e[t+20>>2]-i|0)>>>0>h>>>0?h:l)&&($A(e[n>>2],i,l),e[n>>2]=l+e[n>>2],h=e[n+4>>2]-l|0,e[n+4>>2]=h),l=e[n>>2],(h=r>>>0>h>>>0?h:r)&&($A(l,A,h),l=h+e[n>>2]|0,e[n>>2]=l,e[n+4>>2]=e[n+4>>2]-h),g[0|l]=0,A=e[t+44>>2],e[t+28>>2]=A,e[t+20>>2]=A,0|r},function(t,A,r){r|=0;var n,i,l=0;return $A(A|=0,n=e[84+(t|=0)>>2],r=r>>>0>(l=(i=Wn(n,0,l=r+256|0))?i-n|0:l)>>>0?l:r),A=n+l|0,e[t+84>>2]=A,e[t+8>>2]=A,e[t+4>>2]=r+n,0|r},function(t,A,r){A|=0,r|=0;var n,i=0,l=0,h=0;i=e[84+(t|=0)>>2],n=e[i>>2]?i:84412,i=0;e:if(e[t+48>>2])for(;;){if(!(l=e[(i<<2)+n>>2]))break e;if(g[e[t+44>>2]+i|0]=(0|l)>=128?64:l,!((i=i+1|0)>>>0<re[t+48>>2]))break}return l=e[t+44>>2],e[t+4>>2]=l,e[t+84>>2]=(i<<2)+n,e[t+8>>2]=i+l,!r|!i||(e[t+4>>2]=l+1,g[0|A]=d[0|l],h=1),0|h},Zo,Ga,i0,i0,function(t,A,r){r|=0;var n,i=0;return oe=n=oe+-64|0,i=1,Bn(t|=0,A|=0,0)||(i=0,A&&(i=0,(A=Or(A,125132))&&(IA(4|(i=n+8|0),0,52),e[n+56>>2]=1,e[n+20>>2]=-1,e[n+16>>2]=t,e[n+8>>2]=A,LA[e[e[A>>2]+28>>2]](A,i,e[r>>2],1),(0|(t=e[n+32>>2]))==1&&(e[r>>2]=e[n+24>>2]),i=(0|t)==1))),oe=n- -64|0,0|i},function(t,A,r,n,i,l){r|=0,n|=0,i|=0,l|=0,Bn(t|=0,e[8+(A|=0)>>2],l)&&Yi(A,r,n,i)},function(t,A,r,n,i){if(r|=0,n|=0,i|=0,Bn(t|=0,e[8+(A|=0)>>2],i))e[A+28>>2]==1|e[A+4>>2]!=(0|r)||(e[A+28>>2]=n);else e:if(Bn(t,e[A>>2],i)){if(!(e[A+16>>2]!=(0|r)&e[A+20>>2]!=(0|r))){if((0|n)!=1)break e;return void(e[A+32>>2]=1)}e[A+20>>2]=r,e[A+32>>2]=n,e[A+40>>2]=e[A+40>>2]+1,e[A+36>>2]!=1|e[A+24>>2]!=2||(g[A+54|0]=1),e[A+44>>2]=4}},function(t,A,r,n){r|=0,n|=0,Bn(t|=0,e[8+(A|=0)>>2],0)&&po(A,r,n)},Ga,function(t,A,r,n,i,l){r|=0,n|=0,i|=0,l|=0,Bn(t|=0,e[8+(A|=0)>>2],l)?Yi(A,r,n,i):(t=e[t+8>>2],LA[e[e[t>>2]+20>>2]](t,A,r,n,i,l))},function(t,A,r,n,i){if(r|=0,n|=0,i|=0,Bn(t|=0,e[8+(A|=0)>>2],i))e[A+28>>2]==1|e[A+4>>2]!=(0|r)||(e[A+28>>2]=n);else e:{if(Bn(t,e[A>>2],i)){if(!(e[A+16>>2]!=(0|r)&e[A+20>>2]!=(0|r))){if((0|n)!=1)break e;return void(e[A+32>>2]=1)}e[A+32>>2]=n;t:if(e[A+44>>2]!=4){if(v[A+52>>1]=0,t=e[t+8>>2],LA[e[e[t>>2]+20>>2]](t,A,r,r,1,i),d[A+53|0]){if(e[A+44>>2]=3,!d[A+52|0])break t;break e}e[A+44>>2]=4}if(e[A+20>>2]=r,e[A+40>>2]=e[A+40>>2]+1,e[A+36>>2]!=1|e[A+24>>2]!=2)break e;return void(g[A+54|0]=1)}t=e[t+8>>2],LA[e[e[t>>2]+24>>2]](t,A,r,n,i)}},function(t,A,r,n){r|=0,n|=0,Bn(t|=0,e[8+(A|=0)>>2],0)?po(A,r,n):(t=e[t+8>>2],LA[e[e[t>>2]+28>>2]](t,A,r,n))},Ga,function(t){return 84787},Ga,function(t){return 85058},Ga,function(t){return 84147},function(t){var A;return A=t|=0,t=e[t>>2],e[A>>2]=t+1,0|((0|(t=g[0|t]))<0?65533:255&t)},function(t,A,r,n,i,l){t|=0,A|=0,r|=0,n|=0,i|=0,l|=0;var h,m=0,w=0,M=0,y=0,P=0,R=0,ee=0,ae=0;if(h=Ka(408),e[h+4>>2]=n,e[h>>2]=r,A?($A(h+16|0,A,376),S[h+392>>3]=(S[A+368>>3]-S[A>>3])/+(r>>>0),A=0):A=1,e[h+400>>2]=i,g[h+8|0]=A,l){if(r=e[t+24>>2])for(A=e[t+20>>2],n=e[t+8>>2];(i=e[e[(A>>>8&16777212)+n>>2]+((1023&A)<<2)>>2])&&(EA(i),r=e[t+24>>2],n=e[t+8>>2],A=e[t+20>>2]),A=A+1|0,e[t+20>>2]=A,r=r-1|0,e[t+24>>2]=r,A>>>0>=2048&&(EA(e[n>>2]),n=e[t+8>>2]+4|0,e[t+8>>2]=n,A=e[t+20>>2]-1024|0,e[t+20>>2]=A,r=e[t+24>>2]),r;);A=e[t+28>>2],e[t+420>>2]=e[A>>2],(r=e[t+32>>2])&&(g[A+8|0]=d[r+8|0],$A(A+16|0,t+40|0,376),(A=e[t+32>>2])&&EA(A),e[t+32>>2]=0)}if(r=(n=e[t+24>>2])+e[t+20>>2]|0,i=e[t+12>>2],(0|r)==(0|((0|(A=e[t+8>>2]))!=(0|i)?(i-A<<8)-1:0))){oe=m=oe-32|0;e:{t:{r:{a:{if((A=e[16+(l=t+4|0)>>2])>>>0>=1024){if(e[l+16>>2]=A-1024,A=e[l+4>>2],R=e[A>>2],i=A+4|0,e[l+4>>2]=i,(0|(A=e[l+8>>2]))==e[l+12>>2])if((w=e[l>>2])>>>0<i>>>0)r=sr((n=(1+(i-w>>2)|0)/-2<<2)+i|0,i,A=A-i|0)+A|0,e[l+8>>2]=r,e[l+4>>2]=n+e[l+4>>2];else{if((r=(0|A)==(0|w)?1:A-w>>1)>>>0>=1073741824)break a;if(ee=(n=r<<2)+(M=Ka(n))|0,r=n=M+(-4&r)|0,(0|A)!=(0|i)){if(ae=-4&(A=A-i|0),P=1+((y=A-4|0)>>>2|0)&7)for(r=0,A=n;e[A>>2]=e[i>>2],i=i+4|0,A=A+4|0,(0|P)!=(0|(r=r+1|0)););else A=n;if(r=n+ae|0,!(y>>>0<28))for(;e[A>>2]=e[i>>2],e[A+4>>2]=e[i+4>>2],e[A+8>>2]=e[i+8>>2],e[A+12>>2]=e[i+12>>2],e[A+16>>2]=e[i+16>>2],e[A+20>>2]=e[i+20>>2],e[A+24>>2]=e[i+24>>2],e[A+28>>2]=e[i+28>>2],i=i+32|0,(0|r)!=(0|(A=A+32|0)););}e[l+12>>2]=ee,e[l+8>>2]=r,e[l+4>>2]=n,e[l>>2]=M,w&&(EA(w),r=e[l+8>>2])}else r=A;e[r>>2]=R,e[l+8>>2]=e[l+8>>2]+4;break e}if((w=(i=e[l+8>>2])-e[l+4>>2]>>2)>>>0<(n=(A=e[l+12>>2])-(r=e[l>>2])|0)>>2>>>0){if((0|A)!=(0|i)){e[m+8>>2]=Ka(4096),mt(l,m+8|0);break e}if(e[m+8>>2]=Ka(4096),qe(l,m+8|0),A=e[l+4>>2],R=e[A>>2],i=A+4|0,e[l+4>>2]=i,(0|(A=e[l+8>>2]))==e[l+12>>2])if((w=e[l>>2])>>>0<i>>>0)r=sr((n=(1+(i-w>>2)|0)/-2<<2)+i|0,i,A=A-i|0)+A|0,e[l+8>>2]=r,e[l+4>>2]=n+e[l+4>>2];else{if((r=(0|A)==(0|w)?1:A-w>>1)>>>0>=1073741824)break a;if(ee=(n=r<<2)+(M=Ka(n))|0,r=n=M+(-4&r)|0,(0|A)!=(0|i)){if(ae=-4&(A=A-i|0),P=1+((y=A-4|0)>>>2|0)&7)for(r=0,A=n;e[A>>2]=e[i>>2],i=i+4|0,A=A+4|0,(0|P)!=(0|(r=r+1|0)););else A=n;if(r=n+ae|0,!(y>>>0<28))for(;e[A>>2]=e[i>>2],e[A+4>>2]=e[i+4>>2],e[A+8>>2]=e[i+8>>2],e[A+12>>2]=e[i+12>>2],e[A+16>>2]=e[i+16>>2],e[A+20>>2]=e[i+20>>2],e[A+24>>2]=e[i+24>>2],e[A+28>>2]=e[i+28>>2],i=i+32|0,(0|r)!=(0|(A=A+32|0)););}e[l+12>>2]=ee,e[l+8>>2]=r,e[l+4>>2]=n,e[l>>2]=M,w&&(EA(w),r=e[l+8>>2])}else r=A;e[r>>2]=R,e[l+8>>2]=e[l+8>>2]+4;break e}if(e[m+24>>2]=l+12,!((A=(0|A)==(0|r)?1:n>>1)>>>0>=1073741824)){if(A=Ka(r=A<<2),e[m+8>>2]=A,n=A+(w<<2)|0,e[m+16>>2]=n,e[m+20>>2]=A+r,e[m+12>>2]=n,e[m+4>>2]=Ka(4096),mt(m+8|0,m+4|0),(0|(i=e[l+8>>2]))==e[l+4>>2]){A=i;break t}for(;qe(m+8|0,i=i-4|0),e[l+4>>2]!=(0|i););break r}}rn(),W()}A=e[l+8>>2]}r=e[l>>2],e[l>>2]=e[m+8>>2],e[m+8>>2]=r,e[l+4>>2]=e[m+12>>2],e[m+12>>2]=i,e[l+8>>2]=e[m+16>>2],e[m+16>>2]=A,n=e[l+12>>2],e[l+12>>2]=e[m+20>>2],e[m+20>>2]=n,(0|A)!=(0|i)&&(e[m+16>>2]=A+(3+(i-A|0)&-4)),r&&EA(r)}oe=m+32|0,r=(n=e[t+24>>2])+e[t+20>>2]|0,A=e[t+8>>2]}e[e[A+(r>>>8&16777212)>>2]+((1023&r)<<2)>>2]=h,e[t+24>>2]=n+1},function(t){var A=0,r=0,n=0,i=0,l=0,h=0,m=0,w=0,M=0;r=e[420+(t|=0)>>2]+1|0,e[t+420>>2]=r;e:{if(A=e[t+32>>2]){if(r>>>0>(m=e[A+4>>2])>>>0){(r=e[t+28>>2])&&(EA(r),A=e[t+32>>2]),e[t+32>>2]=0,e[t+28>>2]=A;break e}for(l=t+40|0,h=A+16|0,w=e[t+28>>2]+16|0,M=+(r>>>0)/+(m>>>0),A=0;;){if(n=S[(r=A<<3)+h>>3],i=S[r+w>>3],S[r+l>>3]=n==n?(n-i)*M+i:i,(0|(r=1|A))==47)break e;n=S[(r<<=3)+h>>3],i=S[r+w>>3],S[r+l>>3]=n==n?(n-i)*M+i:i,A=A+2|0}}if(A=e[t+28>>2],r>>>0>re[A>>2]){if(l=e[t+24>>2]){if(g[t+416|0]=0,h=e[t+8>>2],r=e[t+20>>2],A=e[e[h+(r>>>8&16777212)>>2]+((1023&r)<<2)>>2],e[t+32>>2]=A,e[t+24>>2]=l-1,r=r+1|0,e[t+20>>2]=r,r>>>0>=2048&&(EA(e[h>>2]),e[t+8>>2]=e[t+8>>2]+4,e[t+20>>2]=e[t+20>>2]-1024,A=e[t+32>>2]),d[A+8|0])$A(A+16|0,e[t+28>>2]+16|0,376),A=e[t+32>>2],e[A+368>>2]=0,e[A+372>>2]=0,n=S[t+40>>3],e[A+392>>2]=0,e[A+396>>2]=0,S[A+16>>3]=n;else if(r=e[t+28>>2],d[r+8|0]&&($A(r+16|0,A+16|0,376),A=e[t+28>>2],e[A+368>>2]=0,e[A+372>>2]=0,!(A=e[t+32>>2])))break e;(0|(r=e[A+400>>2]))!=-1&&(e[t+424>>2]=r),e[t+420>>2]=0,S[A+16>>3]=S[A+392>>3]*+re[A+4>>2]+S[A+16>>3];break e}g[t+416|0]=1}else n=S[A+392>>3]+S[t+40>>3],S[t+40>>3]=n,S[A+16>>3]=n}return 0|(d[t+416|0]?0:t+40)},function(t){return e[424+(t|=0)>>2]},function(t){var A=0;return e[(t|=0)>>2]=132304,(A=e[t+28>>2])&&EA(A),(A=e[t+32>>2])&&EA(A),Fi(t+4|0),0|t},function(t){var A=0;e[(t|=0)>>2]=132304,(A=e[t+28>>2])&&EA(A),(A=e[t+32>>2])&&EA(A),Fi(t+4|0),EA(t)},function(t,A,r){A|=0,r|=0;var n=0,i=0,l=0,h=0,m=0,w=0,M=0,y=0,P=0,R=0,ee=0,ae=0,ue=0,be=0,_e=0,Ve=0,$e=0,it=0,ht=0;if(!e[1088+(t|=0)>>2])return 0;e:if(A){for(M=t+648|0,w=t- -64|0;;){if(n=e[t+1088>>2],!(n=0|LA[e[e[n>>2]+4>>2]](n)))break e;if(i=ci(S[t+32>>3]+S[n+16>>3]/+e[t+24>>2]),S[t+32>>3]=i,i=Vi(6.283185307179586*i),i=ci(S[t+16>>3]+S[n>>3]*(.06*i*S[n+8>>3]+1)/+e[t+8>>2]),S[t+16>>3]=i,m=rr(e[56848],e[56849],1284865837,1481765933),l=fe,l=(m=m+1|0)?l:l+1|0,e[56848]=m,e[56849]=l,h=.75*S[t+40>>3]+ +(l>>>1|0)/2147483647,S[t+40>>3]=h,y=S[n+24>>3],l=i>=S[n+32>>3],g[t+48|0]=l,h=(R=y)*(y=.2*h),h=As(w+512|0,As(w+440|0,i=S[n+352>>3]*(y*S[n+48>>3]+S[n+40>>3]*(i+i+-1+(l?h:.01*h)))*.5,S[n+104>>3],S[n+168>>3]),S[n+112>>3],S[n+176>>3]),h=As(w+8|0,As(w+80|0,As(w+152|0,As(w+224|0,As(w+296|0,As(w+368|0,h==h?(h-i)*S[n+184>>3]+i:i,S[n+96>>3],S[n+160>>3]),S[n+88>>3],S[n+152>>3]),S[n+80>>3],S[n+144>>3]),S[n+72>>3],S[n+136>>3]),S[n+64>>3],S[n+128>>3]),S[n+56>>3],S[n+120>>3]),m=rr(e[56848],e[56849],1284865837,1481765933),l=fe,l=(m=m+1|0)?l:l+1|0,e[56848]=m,e[56849]=l,i=.75*S[t+56>>3]+ +(l>>>1|0)/2147483647,S[t+56>>3]=i,l=(P<<1)+r|0,y=As(M+8|0,i=S[n+352>>3]*(S[n+192>>3]*(.3*i))*.5,S[n+200>>3],S[n+248>>3]),ee=S[n+296>>3],ae=As(M+80|0,i,S[n+208>>3],S[n+256>>3]),ue=S[n+304>>3],be=As(M+152|0,i,S[n+216>>3],S[n+264>>3]),_e=S[n+312>>3],Ve=As(M+224|0,i,S[n+224>>3],S[n+272>>3]),$e=S[n+320>>3],it=As(M+296|0,i,S[n+232>>3],S[n+280>>3]),ht=S[n+328>>3],R=h,h=(As(M+368|0,i,S[n+240>>3],S[n+288>>3])-i)*S[n+336>>3]+(ht*(it-i)+($e*(Ve-i)+(_e*(be-i)+(ue*(ae-i)+(ee*(y-i)+0))))),i=(R+(i==i?(i-h)*S[n+344>>3]+h:h))*S[n+360>>3]*4e3,n=(0|(n=he(i)<2147483648?~~i:-2147483648))>=32e3?32e3:n,v[l>>1]=(0|n)<=-32e3?-32e3:n,(0|(P=P+1|0))==(0|A))break}P=A}return 0|(A>>>0>P>>>0?P:A)},function(t,A){A|=0,e[1088+(t|=0)>>2]=A},Zo,Ga,eo,function(t,A,r,n){return fe=0,0}],Ci.grow=function(t){var A=this.length;return this.length=this.length+t,A},Ci.set=function(t,A){this[t]=A},Ci.get=function(t){return this[t]},Ci);return{v:function(){var t,A=0;oe=t=oe-16|0,0|Bt(t+12|0,t+8|0)||(A=aA(4+(e[t+12>>2]<<2)|0),e[56800]=A,A&&(!(A=aA(e[t+8>>2]))||(e[e[56800]+(e[t+12>>2]<<2)>>2]=0,0|rt(e[56800],0|A)))&&(e[56800]=0)),oe=t+16|0,e[56841]=227236,e[56823]=42},w:Ti,x:wn,y:function(t,A){A|=0,e[(t|=0)>>2]=A},z:function(t,A){return A|=0,g[e[4+(t|=0)>>2]+A|0]},A:ka,B:Qo,C:function(t){return d[12+(t|=0)|0]},D:function(t,A){A|=0,g[12+(t|=0)|0]=A},E:function(t){return d[13+(t|=0)|0]},F:function(t,A){A|=0,g[13+(t|=0)|0]=A},G:function(t){return d[14+(t|=0)|0]},H:function(t,A){A|=0,g[14+(t|=0)|0]=A},I:function(t){return d[15+(t|=0)|0]},J:function(t,A){A|=0,g[15+(t|=0)|0]=A},K:Ai,L:function(t,A){A|=0,e[16+(t|=0)>>2]=A},M:In,N:function(t,A){A|=0,e[20+(t|=0)>>2]=A},O:Ti,P:wn,Q:Po,R:ka,S:bi,T:Ai,U:In,V:function(t){return e[24+(t|=0)>>2]},W:Ti,X:function(){var t,A=0,r=0,n=0,i=0,l=0,h=0,m=0,w=0,M=0,y=0,P=0,R=0,ee=0,ae=0,ue=0,be=0,_e=0,Ve=0,$e=0,it=0,ht=0,yt=0,Pt=0;if(t=Ka(20),e[t+16>>2]=0,e[t+8>>2]=175,e[t+12>>2]=50,!(A=e[33208])){oe=A=(oe=$e=oe-16|0)-80|0;e:{if((r=Nn(84292))&&(e[A+32>>2]=r,An(137584,160,85959,A+32|0),(0|Na(137584))==-31||(e[A+16>>2]=r,An(137584,160,86031,A+16|0),(0|Na(137584))==-31)))break e;(r=Nn(84619))&&(e[A>>2]=r,An(137584,160,85959,A),(0|Na(137584))==-31)||(r=d[84826]|d[84827]<<8|d[84828]<<16|d[84829]<<24,e[34396]=d[84822]|d[84823]<<8|d[84824]<<16|d[84825]<<24,e[34397]=r,v[68804]=d[84846]|d[84847]<<8,r=d[84842]|d[84843]<<8|d[84844]<<16|d[84845]<<24,e[34400]=d[84838]|d[84839]<<8|d[84840]<<16|d[84841]<<24,e[34401]=r,r=d[84834]|d[84835]<<8|d[84836]<<16|d[84837]<<24,e[34398]=d[84830]|d[84831]<<8|d[84832]<<16|d[84833]<<24,e[34399]=r)}if(oe=A+80|0,e[$e+12>>2]=0,i=$e+12|0,oe=m=oe-16|0,e[m+12>>2]=22050,pA(85144)||pA(85315)||pA(85473)||pA(85698),P=m+12|0,oe=l=oe-16|0,e[l+12>>2]=0,!((A=Ei(137832,84262,0,i))||(A=Ei(137836,84420,0,i))||(A=Ei(137840,84813,0,i))||(A=Ei(137820,85016,l+12|0,i))))if(e[34454]=re[l+12>>2]/68,r=e[34460],e[34456]=r,r&&(0|(M=d[0|r]|d[r+1|0]<<8|d[r+2|0]<<16|d[r+3|0]<<24))==83969){if(R=d[r+4|0]|d[r+5|0]<<8|d[r+6|0]<<16|d[r+7|0]<<24,r=e[34458],ae=d[0|r],e[34461]=ae,ae)for(A=r+4|0,M=0;n=F(M,44)+137856|0,i=d[0|A],e[n+36>>2]=i,e[n+40>>2]=d[A+1|0],h=d[A+8|0]|d[A+9|0]<<8|d[A+10|0]<<16|d[A+11|0]<<24,r=d[A+4|0]|d[A+5|0]<<8|d[A+6|0]<<16|d[A+7|0]<<24,g[0|n]=r,g[n+1|0]=r>>>8,g[n+2|0]=r>>>16,g[n+3|0]=r>>>24,g[n+4|0]=h,g[n+5|0]=h>>>8,g[n+6|0]=h>>>16,g[n+7|0]=h>>>24,h=d[A+16|0]|d[A+17|0]<<8|d[A+18|0]<<16|d[A+19|0]<<24,r=d[A+12|0]|d[A+13|0]<<8|d[A+14|0]<<16|d[A+15|0]<<24,g[n+8|0]=r,g[n+9|0]=r>>>8,g[n+10|0]=r>>>16,g[n+11|0]=r>>>24,g[n+12|0]=h,g[n+13|0]=h>>>8,g[n+14|0]=h>>>16,g[n+15|0]=h>>>24,h=d[A+24|0]|d[A+25|0]<<8|d[A+26|0]<<16|d[A+27|0]<<24,r=d[A+20|0]|d[A+21|0]<<8|d[A+22|0]<<16|d[A+23|0]<<24,g[n+16|0]=r,g[n+17|0]=r>>>8,g[n+18|0]=r>>>16,g[n+19|0]=r>>>24,g[n+20|0]=h,g[n+21|0]=h>>>8,g[n+22|0]=h>>>16,g[n+23|0]=h>>>24,h=d[A+32|0]|d[A+33|0]<<8|d[A+34|0]<<16|d[A+35|0]<<24,r=d[A+28|0]|d[A+29|0]<<8|d[A+30|0]<<16|d[A+31|0]<<24,g[n+24|0]=r,g[n+25|0]=r>>>8,g[n+26|0]=r>>>16,g[n+27|0]=r>>>24,g[n+28|0]=h,g[n+29|0]=h>>>8,g[n+30|0]=h>>>16,g[n+31|0]=h>>>24,r=A+36|0,e[n+32>>2]=r,A=r+(i<<4)|0,(0|ae)!=(0|(M=M+1|0)););(0|ae)<=e[34457]&&(e[34457]=0),A=0,P&&(e[P>>2]=R)}else e:{t:{if(i){if(r=e[i>>2]){EA(e[r+4>>2]),A=e[i>>2];break t}if(A=aA(16),e[i>>2]=A,A)break t;A=48}else A=268436223;break e}e[A>>2]=1,e[A+4>>2]=sn(137584),r=e[i>>2],e[r+12>>2]=83969,e[r+8>>2]=M,A=268436223}if(oe=l+16|0,r=A,!A){if(P=e[m+12>>2],e[50754]=P,e[50759]=0,e[50760]=134217728/(0|P),e[50762]=0,e[50763]=0,e[50765]=2147483647,e[50781]=100,e[50779]=32,e[50761]=(P<<6)/(0|P),A=e[26385],e[50784]=e[26384],e[50785]=A,A=e[26387],e[50786]=e[26386],e[50787]=A,A=e[26389],e[50788]=e[26388],e[50789]=A,A=e[26391],e[50790]=e[26390],e[50791]=A,A=e[26393],e[50792]=e[26392],e[50793]=A,A=e[26395],e[50794]=e[26394],e[50795]=A,A=e[26397],e[50796]=e[26396],e[50797]=A,e[50798]=e[26398],R=(0|(A=(0|(i=F(P,60)))/12800|0))>=128?128:A,e[50799]=R,e[50800]=(0|R)/2,!((0|P)==22050|(0|i)<12800)){if(P=1&(A=(0|R)<=1?1:R),_e=+(0|R),M=0,(0|R)>=2)for(R=2147483646&A,A=0;ue=M+132160|0,w=127*(1-Aa(6.283185307179586*+(0|M)/_e)),ee=he(w)<2147483648?~~w:-2147483648,g[0|ue]=ee,ue=132160+(i=1|M)|0,w=127*(1-Aa(6.283185307179586*+(0|i)/_e)),ee=he(w)<2147483648?~~w:-2147483648,g[0|ue]=ee,M=M+2|0,(0|R)!=(0|(A=A+2|0)););P&&(A=M+132160|0,w=127*(1-Aa(6.283185307179586*+(0|M)/_e)),ue=he(w)<2147483648?~~w:-2147483648,g[0|A]=ue)}if(e[50801]=105792,e[56797]=en(),e[55964]=38,e[55921]=1,e[55918]=22050,e[56606]=0,e[55960]=110928,e[55958]=0,e[55959]=1074266112,e[55956]=100,e[55922]=20,e[55923]=220,e[55916]=1,e[55917]=0,da(),e[56244]=0,e[56245]=0,e[55928]=0,e[55926]=0,e[55927]=0,e[55924]=0,e[56246]=0,e[56247]=0,e[56260]=0,e[56261]=0,e[56262]=0,e[56263]=0,e[56276]=0,e[56277]=0,e[56278]=0,e[56279]=0,e[55974]=0,e[55975]=0,e[55972]=0,e[55973]=0,_e=-3.141592653589793/+(0|(A=e[55918])),S[27967]=_e,i=(0|F(A,630))/1e4|0,e[55920]=i,A=(0|F(A,950))/1e4|0,e[55919]=A,w=-2*_e,S[27968]=w,_e=(yt=fr(_e*+(0|i)))*-yt,S[28129]=_e,w=yt*Aa(w*+(0|A)),w+=w,S[28128]=w,S[28127]=1-w-_e,e[55990]=0,e[55991]=0,e[55988]=0,e[55989]=0,e[56006]=0,e[56007]=0,e[56004]=0,e[56005]=0,e[56022]=0,e[56023]=0,e[56020]=0,e[56021]=0,e[56038]=0,e[56039]=0,e[56036]=0,e[56037]=0,e[56054]=0,e[56055]=0,e[56052]=0,e[56053]=0,e[56070]=0,e[56071]=0,e[56068]=0,e[56069]=0,e[56086]=0,e[56087]=0,e[56084]=0,e[56085]=0,e[56102]=0,e[56103]=0,e[56100]=0,e[56101]=0,e[56118]=0,e[56119]=0,e[56116]=0,e[56117]=0,e[56134]=0,e[56135]=0,e[56132]=0,e[56133]=0,e[56150]=0,e[56151]=0,e[56148]=0,e[56149]=0,e[56166]=0,e[56167]=0,e[56164]=0,e[56165]=0,e[56182]=0,e[56183]=0,e[56180]=0,e[56181]=0,e[56198]=0,e[56199]=0,e[56196]=0,e[56197]=0,e[56214]=0,e[56215]=0,e[56212]=0,e[56213]=0,e[56230]=0,e[56231]=0,e[56228]=0,e[56229]=0,e[56639]=59,e[56640]=59,e[56629]=0,e[56630]=59,e[56619]=89,e[56620]=160,e[56609]=280,e[56610]=688,e[56611]=1064,e[56621]=70,e[56631]=59,e[56612]=2806,e[56613]=3260,e[56622]=160,e[56623]=200,e[56632]=59,e[56633]=59,e[56641]=89,e[56642]=149,e[56643]=200,e[56644]=200,e[56634]=59,e[56635]=59,e[56624]=200,e[56625]=500,e[56614]=3700,e[56615]=6500,e[56645]=500,e[56646]=0,e[56616]=7e3,e[56626]=500,e[56636]=0,e[56647]=0,e[56637]=0,e[56627]=500,e[56617]=8e3,e[56669]=89,e[56648]=0,e[56638]=0,e[56628]=89,e[56618]=280,e[56657]=62,e[56655]=0,e[56656]=0,e[56653]=50,e[56654]=0,e[56651]=0,e[56652]=0,e[56649]=0,e[56650]=40,e[56607]=1e3,e[56608]=59,oe=l=oe-416|0,e[l+16>>2]=137584,e[l+20>>2]=47,e[l+24>>2]=85952,uA(A=l+240|0,85699,l+16|0),h=la(A,86034)){if(_r(l+240|0,170,h))for(P=5|(A=l+240|0),R=10|A;d[l+240|0]!=47&&(e[l+240>>2]!=1701736308?ra(l+240|0,86614,9)||(e[l+4>>2]=l+32,e[l>>2]=l+239,(0|nA(R,86829,l))==2&&(i=e[34064],e[(ae=136272+(i<<4)|0)>>2]=g[l+239|0],A=sn(l+32|0),e[34064]=i+1,e[ae+12>>2]=A,e[ae+4>>2]=0)):(oe=A=oe-48|0,e[32960]=-1,e[32961]=-1,e[32970]=-1,e[32971]=-1,e[32968]=-1,e[32969]=-1,e[32966]=-1,e[32967]=-1,e[32964]=-1,e[32965]=-1,e[32962]=-1,e[32963]=-1,e[A+36>>2]=131876,e[A+32>>2]=131872,e[A+28>>2]=131868,e[A+24>>2]=131864,e[A+20>>2]=131860,e[A+16>>2]=131856,e[A+12>>2]=131852,e[A+8>>2]=131848,e[A+4>>2]=131844,e[A>>2]=131840,nA(P,84222,A),oe=A+48|0)),_r(l+240|0,170,h););fa(h)}oe=l+416|0,e[50297]=0,e[50298]=0,e[50301]=0,e[50302]=0,e[50299]=0,e[50300]=0,xi(0,85698),e[36425]=0,e[36424]=0,e[36426]=0,e[36427]=-1,mn(),nr(0),n=e[25690],e[34062]=n,y=e[25689],l=e[25688],e[34060]=l,e[34061]=y,be=e[25687],h=e[25686],e[34058]=h,e[34059]=be,Ve=e[25685],ae=e[25684],e[34056]=ae,e[34057]=Ve,it=e[25683],P=e[25682],e[34054]=P,e[34055]=it,ht=e[25681],R=e[25680],e[34052]=R,e[34053]=ht,ee=e[25679],i=e[25678],e[34050]=i,e[34051]=ee,ue=e[25677],A=e[25676],e[34048]=A,e[34049]=ue,e[33729]=A,e[33730]=ue,e[33731]=i,e[33732]=ee,e[33733]=R,e[33734]=ht,e[33735]=P,e[33736]=it,e[33737]=ae,e[33738]=Ve,e[33739]=h,e[33740]=be,e[33741]=l,e[33742]=y,e[33743]=n,os(1,175),os(2,100),os(6,e[47200]),os(5,e[47201]),os(7,0),e[47198]=0,e[47197]=0,w=+Ge()/1e3,i=rr(A=he(w)<9223372036854776e3?~~w>>>0:0,0,1103515245,0),A=fe,A=(i=i+12345|0)>>>0<12345?A+1|0:A,e[33209]=Cn(i,A)}if(oe=m+16|0,r){ue=e[30450],it=e[$e+12>>2],m=48+(y=oe-560|0)|0,oe=n=(oe=y)-16|0;e:{switch(0|Ks(r-268435967|0,24)){case 0:vr(m,84133,512);break e;case 1:vr(m,84580,512);break e;case 2:vr(m,84747,512);break e;case 3:vr(m,85084,512);break e;case 4:vr(m,85251,512);break e;case 5:vr(m,85380,512);break e;case 6:vr(m,85607,512);break e;case 7:vr(m,85722,512);break e;case 8:vr(m,85913,512);break e;case 9:vr(m,86046,512);break e;case 10:vr(m,86153,512);break e;case 11:vr(m,86678,512);break e;case 12:vr(m,86773,512);break e;case 14:vr(m,86958,512);break e;case 15:vr(m,87071,512);break e}if(1879048192&r)e[n>>2]=r,An(m,512,87182,n);else{if(M=0,l=_[123728+((r>>>0<=153?r:0)<<1)>>1]+121804|0,r=e[e[56841]+20>>2]){ht=e[r+4>>2],be=e[r>>2],Ve=e[be>>2]+1794895138|0,ee=hi(e[be+8>>2],Ve),i=hi(e[be+12>>2],Ve),A=hi(e[be+16>>2],Ve);t:if(!(ht>>>2>>>0<=ee>>>0||3&(A|i)|(r=ht-(ee<<2)|0)>>>0<=i>>>0|A>>>0>=r>>>0))for(P=A>>>2|0,R=i>>>2|0;;){if(ae=hi(e[(r=((A=(i=(h=ee>>>1|0)+Pt|0)<<1)+R<<2)+be|0)>>2],Ve),(r=hi(e[r+4>>2],Ve))>>>0>=ht>>>0|ae>>>0>=ht-r>>>0|d[(r+ae|0)+be|0])break t;if(!(r=Zr(l,r+be|0))){if(A=hi(e[(r=(A+P<<2)+be|0)>>2],Ve),(r=hi(e[r+4>>2],Ve))>>>0>=ht>>>0|A>>>0>=ht-r>>>0)break t;M=d[(A+r|0)+be|0]?0:r+be|0;break t}if((0|ee)==1)break t;ee=(r=(0|r)<0)?h:ee-h|0,Pt=r?Pt:i}}if((r=wA(A=M||l))>>>0>=512){$A(m,A,511),g[m+511|0]=0;break e}$A(m,A,r+1|0)}}oe=n+16|0;e:if(it){t:switch(e[it>>2]){case 0:e[y+16>>2]=e[it+4>>2],e[y+20>>2]=y+48,ss(ue,87384,y+16|0);break e;case 1:break t;default:break e}A=e[it+12>>2],r=e[it+8>>2],e[y+36>>2]=e[it+4>>2],fe=r,e[y+40>>2]=A,e[y+44>>2]=fe,e[y+32>>2]=y+48,ss(ue,87521,y+32|0)}else e[y>>2]=y+48,ss(ue,87700,y);oe=y+560|0,(0|$e)!=-12&&(r=e[$e+12>>2])&&(EA(e[r+4>>2]),EA(e[$e+12>>2]),e[$e+12>>2]=0)}r=e[24806],e[34389]=0,e[32538]=r,r=(1e3+((r=F(e[50754],100))-((0|r)%1e3|0)|0)|0)/500|0,e[34390]=r,r=ds(e[34391],r),e[34392]=r,r&&(e[34391]=r,e[34393]=40,(r=ds(e[34388],1440))&&(e[34388]=r)),e[47198]=0,oe=$e+16|0,A=e[50754],e[33208]=A}return 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Me.ErrnoError(63);Jr.ops_table||(Jr.ops_table={dir:{node:{getattr:Jr.node_ops.getattr,setattr:Jr.node_ops.setattr,lookup:Jr.node_ops.lookup,mknod:Jr.node_ops.mknod,rename:Jr.node_ops.rename,unlink:Jr.node_ops.unlink,rmdir:Jr.node_ops.rmdir,readdir:Jr.node_ops.readdir,symlink:Jr.node_ops.symlink},stream:{llseek:Jr.stream_ops.llseek}},file:{node:{getattr:Jr.node_ops.getattr,setattr:Jr.node_ops.setattr},stream:{llseek:Jr.stream_ops.llseek,read:Jr.stream_ops.read,write:Jr.stream_ops.write,allocate:Jr.stream_ops.allocate,mmap:Jr.stream_ops.mmap,msync:Jr.stream_ops.msync}},link:{node:{getattr:Jr.node_ops.getattr,setattr:Jr.node_ops.setattr,readlink:Jr.node_ops.readlink},stream:{}},chrdev:{node:{getattr:Jr.node_ops.getattr,setattr:Jr.node_ops.setattr},stream:Me.chrdev_stream_ops}});var u=Me.createNode(c,a,s,o);return Me.isDir(u.mode)?(u.node_ops=Jr.ops_table.dir.node,u.stream_ops=Jr.ops_table.dir.stream,u.contents={}
2810):Me.isFile(u.mode)?(u.node_ops=Jr.ops_table.file.node,u.stream_ops=Jr.ops_table.file.stream,u.usedBytes=0,u.contents=null):Me.isLink(u.mode)?(u.node_ops=Jr.ops_table.link.node,u.stream_ops=Jr.ops_table.link.stream):Me.isChrdev(u.mode)&&(u.node_ops=Jr.ops_table.chrdev.node,u.stream_ops=Jr.ops_table.chrdev.stream),u.timestamp=Date.now(),c&&(c.contents[a]=u,c.timestamp=u.timestamp),u},getFileDataAsTypedArray:function(c){return c.contents?c.contents.subarray?c.contents.subarray(0,c.usedBytes):new Uint8Array(c.contents):new Uint8Array(0)},expandFileStorage:function(c,a){var s=c.contents?c.contents.length:0;if(!(s>=a)){a=Math.max(a,s*(s<1048576?2:1.125)>>>0),s!=0&&(a=Math.max(a,256));var o=c.contents;c.contents=new Uint8Array(a),c.usedBytes>0&&c.contents.set(o.subarray(0,c.usedBytes),0)}},resizeFileStorage:function(c,a){if(c.usedBytes!=a)if(a==0)c.contents=null,c.usedBytes=0;else{var s=c.contents;c.contents=new Uint8Array(a),s&&c.contents.set(s.subarray(0,Math.min(a,c.usedBytes))),c.usedBytes=a}},node_ops:{getattr:function(c){var a={};return a.dev=Me.isChrdev(c.mode)?c.id:1,a.ino=c.id,a.mode=c.mode,a.nlink=1,a.uid=0,a.gid=0,a.rdev=c.rdev,Me.isDir(c.mode)?a.size=4096:Me.isFile(c.mode)?a.size=c.usedBytes:Me.isLink(c.mode)?a.size=c.link.length:a.size=0,a.atime=new Date(c.timestamp),a.mtime=new Date(c.timestamp),a.ctime=new Date(c.timestamp),a.blksize=4096,a.blocks=Math.ceil(a.size/a.blksize),a},setattr:function(c,a){a.mode!==void 0&&(c.mode=a.mode),a.timestamp!==void 0&&(c.timestamp=a.timestamp),a.size!==void 0&&Jr.resizeFileStorage(c,a.size)},lookup:function(c,a){throw Me.genericErrors[44]},mknod:function(c,a,s,o){return Jr.createNode(c,a,s,o)},rename:function(c,a,s){if(Me.isDir(c.mode)){var o;try{o=Me.lookupNode(a,s)}catch{}if(o)for(var u in o.contents)throw new Me.ErrnoError(55)}delete c.parent.contents[c.name],c.parent.timestamp=Date.now(),c.name=s,a.contents[s]=c,a.timestamp=c.parent.timestamp,c.parent=a},unlink:function(c,a){delete c.contents[a],c.timestamp=Date.now()},rmdir:function(c,a){var s=Me.lookupNode(c,a);for(var o in s.contents)throw new Me.ErrnoError(55);delete c.contents[a],c.timestamp=Date.now()},readdir:function(c){var a=[".",".."];for(var s in c.contents)c.contents.hasOwnProperty(s)&&a.push(s);return a},symlink:function(c,a,s){var o=Jr.createNode(c,a,41471,0);return o.link=s,o},readlink:function(c){if(!Me.isLink(c.mode))throw new Me.ErrnoError(28);return c.link}},stream_ops:{read:function(c,a,s,o,u){var p=c.node.contents;if(u>=c.node.usedBytes)return 0;var C=Math.min(c.node.usedBytes-u,o);if(C>8&&p.subarray)a.set(p.subarray(u,u+C),s);else for(var b=0;b<C;b++)a[s+b]=p[u+b];return C},write:function(c,a,s,o,u,p){if(!o)return 0;var C=c.node;if(C.timestamp=Date.now(),a.subarray&&(!C.contents||C.contents.subarray)){if(p)return C.contents=a.subarray(s,s+o),C.usedBytes=o,o;if(C.usedBytes===0&&u===0)return C.contents=a.slice(s,s+o),C.usedBytes=o,o;if(u+o<=C.usedBytes)return C.contents.set(a.subarray(s,s+o),u),o}if(Jr.expandFileStorage(C,u+o),C.contents.subarray&&a.subarray)C.contents.set(a.subarray(s,s+o),u);else for(var b=0;b<o;b++)C.contents[u+b]=a[s+b];return C.usedBytes=Math.max(C.usedBytes,u+o),o},llseek:function(c,a,s){var o=a;if(s===1?o+=c.position:s===2&&Me.isFile(c.node.mode)&&(o+=c.node.usedBytes),o<0)throw new Me.ErrnoError(28);return o},allocate:function(c,a,s){Jr.expandFileStorage(c.node,a+s),c.node.usedBytes=Math.max(c.node.usedBytes,a+s)},mmap:function(c,a,s,o,u){if(!Me.isFile(c.node.mode))throw new Me.ErrnoError(43);var p,C,b=c.node.contents;if(2&u||b.buffer!==zi.buffer){if((s>0||s+a<b.length)&&(b=b.subarray?b.subarray(s,s+a):Array.prototype.slice.call(b,s,s+a)),C=!0,!(p=Vm()))throw new Me.ErrnoError(48);zi.set(b,p)}else C=!1,p=b.byteOffset;return{ptr:p,allocated:C}},msync:function(c,a,s,o,u){return Jr.stream_ops.write(c,a,0,o,s,!1),0}}},Me={root:null,mounts:[],devices:{},streams:[],nextInode:1,nameTable:null,currentPath:"/",initialized:!1,ignorePermissions:!0,ErrnoError:null,genericErrors:{},filesystems:null,syncFSRequests:0,lookupPath:(c,a={})=>{if(!(c=ec.resolve(c)))return{path:"",node:null};if((a=Object.assign({follow_mount:!0,recurse_count:0},a)).recurse_count>8)throw new Me.ErrnoError(32);for(var s=c.split("/").filter(E=>!!E),o=Me.root,u="/",p=0;p<s.length;p++){var C=p===s.length-1;if(C&&a.parent)break;
2810if(o=Me.lookupNode(o,s[p]),u=Rs.join2(u,s[p]),Me.isMountpoint(o)&&(!C||C&&a.follow_mount)&&(o=o.mounted.root),!C||a.follow)for(var b=0;Me.isLink(o.mode);){var k=Me.readlink(u);if(u=ec.resolve(Rs.dirname(u),k),o=Me.lookupPath(u,{recurse_count:a.recurse_count+1}).node,b++>40)throw new Me.ErrnoError(32)}}return{path:u,node:o}},getPath:c=>{for(var a;;){if(Me.isRoot(c)){var s=c.mount.mountpoint;return a?s[s.length-1]!=="/"?s+"/"+a:s+a:s}a=a?c.name+"/"+a:c.name,c=c.parent}},hashName:(c,a)=>{for(var s=0,o=0;o<a.length;o++)s=(s<<5)-s+a.charCodeAt(o)|0;return(c+s>>>0)%Me.nameTable.length},hashAddNode:c=>{var a=Me.hashName(c.parent.id,c.name);c.name_next=Me.nameTable[a],Me.nameTable[a]=c},hashRemoveNode:c=>{var a=Me.hashName(c.parent.id,c.name);if(Me.nameTable[a]===c)Me.nameTable[a]=c.name_next;else for(var s=Me.nameTable[a];s;){if(s.name_next===c){s.name_next=c.name_next;break}s=s.name_next}},lookupNode:(c,a)=>{var s=Me.mayLookup(c);if(s)throw new Me.ErrnoError(s,c);for(var o=Me.hashName(c.id,a),u=Me.nameTable[o];u;u=u.name_next){var p=u.name;if(u.parent.id===c.id&&p===a)return u}return Me.lookup(c,a)},createNode:(c,a,s,o)=>{var u=new Me.FSNode(c,a,s,o);return Me.hashAddNode(u),u},destroyNode:c=>{Me.hashRemoveNode(c)},isRoot:c=>c===c.parent,isMountpoint:c=>!!c.mounted,isFile:c=>(61440&c)==32768,isDir:c=>(61440&c)==16384,isLink:c=>(61440&c)==40960,isChrdev:c=>(61440&c)==8192,isBlkdev:c=>(61440&c)==24576,isFIFO:c=>(61440&c)==4096,isSocket:c=>!(49152&~c),flagModes:{r:0,"r+":2,w:577,"w+":578,a:1089,"a+":1090},modeStringToFlags:c=>{var a=Me.flagModes[c];if(a===void 0)throw new Error("Unknown file open mode: "+c);return a},flagsToPermissionString:c=>{var a=["r","w","rw"][3&c];return 512&c&&(a+="w"),a},nodePermissions:(c,a)=>Me.ignorePermissions||(!a.includes("r")||292&c.mode)&&(!a.includes("w")||146&c.mode)&&(!a.includes("x")||73&c.mode)?0:2,mayLookup:c=>Me.nodePermissions(c,"x")||(c.node_ops.lookup?0:2),mayCreate:(c,a)=>{try{return Me.lookupNode(c,a),20}catch{}return Me.nodePermissions(c,"wx")},mayDelete:(c,a,s)=>{var o;try{o=Me.lookupNode(c,a)}catch(p){return p.errno}var u=Me.nodePermissions(c,"wx");if(u)return u;if(s){if(!Me.isDir(o.mode))return 54;if(Me.isRoot(o)||Me.getPath(o)===Me.cwd())return 10}else if(Me.isDir(o.mode))return 31;return 0},mayOpen:(c,a)=>c?Me.isLink(c.mode)?32:Me.isDir(c.mode)&&(Me.flagsToPermissionString(a)!=="r"||512&a)?31:Me.nodePermissions(c,Me.flagsToPermissionString(a)):44,MAX_OPEN_FDS:4096,nextfd:(c=0,a=Me.MAX_OPEN_FDS)=>{for(var s=c;s<=a;s++)if(!Me.streams[s])return s;throw new Me.ErrnoError(33)},getStream:c=>Me.streams[c],createStream:(c,a,s)=>{Me.FSStream||(Me.FSStream=function(){this.shared={}},Me.FSStream.prototype={},Object.defineProperties(Me.FSStream.prototype,{object:{get:function(){return this.node},set:function(u){this.node=u}},isRead:{get:function(){return(2097155&this.flags)!=1}},isWrite:{get:function(){return!!(2097155&this.flags)}},isAppend:{get:function(){return 1024&this.flags}},flags:{get:function(){return this.shared.flags},set:function(u){this.shared.flags=u}},position:{get:function(){return this.shared.position},set:function(u){this.shared.position=u}}})),c=Object.assign(new Me.FSStream,c);var o=Me.nextfd(a,s);return c.fd=o,Me.streams[o]=c,c},closeStream:c=>{Me.streams[c]=null},chrdev_stream_ops:{open:c=>{var a=Me.getDevice(c.node.rdev);c.stream_ops=a.stream_ops,c.stream_ops.open&&c.stream_ops.open(c)},llseek:()=>{throw new Me.ErrnoError(70)}},major:c=>c>>8,minor:c=>255&c,makedev:(c,a)=>c<<8|a,registerDevice:(c,a)=>{Me.devices[c]={stream_ops:a}},getDevice:c=>Me.devices[c],getMounts:c=>{for(var a=[],s=[c];s.length;){var o=s.pop();a.push(o),s.push.apply(s,o.mounts)}return a},syncfs:(c,a)=>{typeof c=="function"&&(a=c,c=!1),Me.syncFSRequests++,Me.syncFSRequests>1&&I2("warning: "+Me.syncFSRequests+" FS.syncfs operations in flight at once, probably just doing extra work");var s=Me.getMounts(Me.root.mount),o=0;function u(C){return Me.syncFSRequests--,a(C)}function p(C){if(C)return p.errored?void 0:(p.errored=!0,u(C));++o>=s.length&&u(null)}s.forEach(C=>{if(!C.type.syncfs)return p(null);C.type.syncfs(C,c,p)})},mount:(c,a,s)=>{var o,u=s==="/",p=!s;if(u&&Me.root)throw new Me.ErrnoError(10);if(!u&&!p){var C=Me.lookupPath(s,{follow_mount:!1});if(s=C.path,o=C.node,Me.isMountpoint(o))throw new Me.ErrnoError(10);if(!Me.isDir(o.mode))throw new Me.ErrnoError(54)}var b={type:c,opts:a,mountpoint:s,mounts:[]},k=c.mount(b);return k.mount=b,b.root=k,u?Me.root=k:o&&(o.mounted=b,o.mount&&o.mount.mounts.push(b)),k},unmount:c=>{var a=Me.lookupPath(c,{follow_mount:!1});if(!Me.isMountpoint(a.node))throw new Me.ErrnoError(28);var s=a.node,o=s.mounted,u=Me.getMounts(o);Object.keys(Me.nameTable).forEach(C=>{for(var b=Me.nameTable[C];b;){var k=b.name_next;u.includes(b.mount)&&Me.destroyNode(b),b=k}}),s.mounted=null;var p=s.mount.mounts.indexOf(o);s.mount.mounts.splice(p,1)},lookup:(c,a)=>c.node_ops.lookup(c,a),mknod:(c,a,s)=>{var o=Me.lookupPath(c,{parent:!0}).node,u=Rs.basename(c);if(!u||u==="."||u==="..")throw new Me.ErrnoError(28);var p=Me.mayCreate(o,u);if(p)throw new Me.ErrnoError(p);if(!o.node_ops.mknod)throw new Me.ErrnoError(63);return o.node_ops.mknod(o,u,a,s)},create:(c,a)=>(a=a!==void 0?a:438,a&=4095,a|=32768,Me.mknod(c,a,0)),mkdir:(c,a)=>(a=a!==void 0?a:511,a&=1023,a|=16384,Me.mknod(c,a,0)),mkdirTree:(c,a)=>{for(var s=c.split("/"),o="",u=0;u<s.length;++u)if(s[u]){o+="/"+s[u];try{Me.mkdir(o,a)}catch(p){if(p.errno!=20)throw p}}},mkdev:(c,a,s)=>(s===void 0&&(s=a,a=438),a|=8192,Me.mknod(c,a,s)),symlink:(c,a)=>
2810{if(!ec.resolve(c))throw new Me.ErrnoError(44);var s=Me.lookupPath(a,{parent:!0}).node;if(!s)throw new Me.ErrnoError(44);var o=Rs.basename(a),u=Me.mayCreate(s,o);if(u)throw new Me.ErrnoError(u);if(!s.node_ops.symlink)throw new Me.ErrnoError(63);return s.node_ops.symlink(s,o,c)},rename:(c,a)=>{var s,o,u=Rs.dirname(c),p=Rs.dirname(a),C=Rs.basename(c),b=Rs.basename(a);if(s=Me.lookupPath(c,{parent:!0}).node,o=Me.lookupPath(a,{parent:!0}).node,!s||!o)throw new Me.ErrnoError(44);if(s.mount!==o.mount)throw new Me.ErrnoError(75);var k,E=Me.lookupNode(s,C),B=ec.relative(c,p);if(B.charAt(0)!==".")throw new Me.ErrnoError(28);if((B=ec.relative(a,u)).charAt(0)!==".")throw new Me.ErrnoError(55);try{k=Me.lookupNode(o,b)}catch{}if(E!==k){var G=Me.isDir(E.mode),D=Me.mayDelete(s,C,G);if(D)throw new Me.ErrnoError(D);if(D=k?Me.mayDelete(o,b,G):Me.mayCreate(o,b))throw new Me.ErrnoError(D);if(!s.node_ops.rename)throw new Me.ErrnoError(63);if(Me.isMountpoint(E)||k&&Me.isMountpoint(k))throw new Me.ErrnoError(10);if(o!==s&&(D=Me.nodePermissions(s,"w")))throw new Me.ErrnoError(D);Me.hashRemoveNode(E);try{s.node_ops.rename(E,o,b)}catch(Q){throw Q}finally{Me.hashAddNode(E)}}},rmdir:c=>{var a=Me.lookupPath(c,{parent:!0}).node,s=Rs.basename(c),o=Me.lookupNode(a,s),u=Me.mayDelete(a,s,!0);if(u)throw new Me.ErrnoError(u);if(!a.node_ops.rmdir)throw new Me.ErrnoError(63);if(Me.isMountpoint(o))throw new Me.ErrnoError(10);a.node_ops.rmdir(a,s),Me.destroyNode(o)},readdir:c=>{var a=Me.lookupPath(c,{follow:!0}).node;if(!a.node_ops.readdir)throw new Me.ErrnoError(54);return a.node_ops.readdir(a)},unlink:c=>{var a=Me.lookupPath(c,{parent:!0}).node;if(!a)throw new Me.ErrnoError(44);var s=Rs.basename(c),o=Me.lookupNode(a,s),u=Me.mayDelete(a,s,!1);if(u)throw new Me.ErrnoError(u);if(!a.node_ops.unlink)throw new Me.ErrnoError(63);if(Me.isMountpoint(o))throw new Me.ErrnoError(10);a.node_ops.unlink(a,s),Me.destroyNode(o)},readlink:c=>{var a=Me.lookupPath(c).node;if(!a)throw new Me.ErrnoError(44);if(!a.node_ops.readlink)throw new Me.ErrnoError(28);return ec.resolve(Me.getPath(a.parent),a.node_ops.readlink(a))},stat:(c,a)=>{var s=Me.lookupPath(c,{follow:!a}).node;if(!s)throw new Me.ErrnoError(44);if(!s.node_ops.getattr)throw new Me.ErrnoError(63);return s.node_ops.getattr(s)},lstat:c=>Me.stat(c,!0),chmod:(c,a,s)=>{var o;if(!(o=typeof c=="string"?Me.lookupPath(c,{follow:!s}).node:c).node_ops.setattr)throw new Me.ErrnoError(63);o.node_ops.setattr(o,{mode:4095&a|-4096&o.mode,timestamp:Date.now()})},lchmod:(c,a)=>{Me.chmod(c,a,!0)},fchmod:(c,a)=>{var s=Me.getStream(c);if(!s)throw new Me.ErrnoError(8);Me.chmod(s.node,a)},chown:(c,a,s,o)=>{var u;if(!(u=typeof c=="string"?Me.lookupPath(c,{follow:!o}).node:c).node_ops.setattr)throw new Me.ErrnoError(63);u.node_ops.setattr(u,{timestamp:Date.now()})},lchown:(c,a,s)=>{Me.chown(c,a,s,!0)},fchown:(c,a,s)=>{var o=Me.getStream(c);if(!o)throw new Me.ErrnoError(8);Me.chown(o.node,a,s)},truncate:(c,a)=>{if(a<0)throw new Me.ErrnoError(28);var s;if(!(s=typeof c=="string"?Me.lookupPath(c,{follow:!0}).node:c).node_ops.setattr)throw new Me.ErrnoError(63);if(Me.isDir(s.mode))throw new Me.ErrnoError(31);if(!Me.isFile(s.mode))throw new Me.ErrnoError(28);var o=Me.nodePermissions(s,"w");if(o)throw new Me.ErrnoError(o);s.node_ops.setattr(s,{size:a,timestamp:Date.now()})},ftruncate:(c,a)=>{var s=Me.getStream(c);if(!s)throw new Me.ErrnoError(8);if(!(2097155&s.flags))throw new Me.ErrnoError(28);Me.truncate(s.node,a)},utime:(c,a,s)=>{var o=Me.lookupPath(c,{follow:!0}).node;o.node_ops.setattr(o,{timestamp:Math.max(a,s)})},open:(c,a,s)=>{if(c==="")throw new Me.ErrnoError(44);var o;if(s=s===void 0?438:s,s=64&(a=typeof a=="string"?Me.modeStringToFlags(a):a)?4095&s|32768:0,typeof c=="object")o=c;else{c=Rs.normalize(c);try{o=Me.lookupPath(c,{follow:!(131072&a)}).node}catch{}}var u=!1;if(64&a)if(o){if(128&a)throw new Me.ErrnoError(20)}else o=Me.mknod(c,s,0),u=!0;if(!o)throw new Me.ErrnoError(44);if(Me.isChrdev(o.mode)&&(a&=-513),65536&a&&!Me.isDir(o.mode))throw new Me.ErrnoError(54);if(!u){var p=Me.mayOpen(o,a);if(p)throw new Me.ErrnoError(p)}512&a&&!u&&Me.truncate(o,0),a&=-131713;var C=Me.createStream({node:o,path:Me.getPath(o),flags:a,seekable:!0,position:0,stream_ops:o.stream_ops,ungotten:[],error:!1});return C.stream_ops.open&&C.stream_ops.open(C),!He.logReadFiles||1&a||(Me.readFiles||(Me.readFiles={}),c in Me.readFiles||(Me.readFiles[c]=1)),C},close:c=>{if(Me.isClosed(c))throw new Me.ErrnoError(8);c.getdents&&(c.getdents=null);try{c.stream_ops.close&&c.stream_ops.close(c)}catch(a){throw a}finally{Me.closeStream(c.fd)}c.fd=null},isClosed:c=>c.fd===null,llseek:(c,a,s)=>{if(Me.isClosed(c))throw new Me.ErrnoError(8);if(!c.seekable||!c.stream_ops.llseek)throw new Me.ErrnoError(70);if(s!=0&&s!=1&&s!=2)throw new Me.ErrnoError(28);return c.position=c.stream_ops.llseek(c,a,s),c.ungotten=[],c.position},read:(c,a,s,o,u)=>{if(o<0||u<0)throw new Me.ErrnoError(28);if(Me.isClosed(c))throw new Me.ErrnoError(8);if((2097155&c.flags)==1)throw new Me.ErrnoError(8);if(Me.isDir(c.node.mode))throw new Me.ErrnoError(31);if(!c.stream_ops.read)throw new Me.ErrnoError(28);var p=u!==void 0;if(p){if(!c.seekable)throw new Me.ErrnoError(70)}else u=c.position;var C=c.stream_ops.read(c,a,s,o,u);return p||(c.position+=C),C},write:(c,a,s,o,u,p)=>
2810{if(o<0||u<0)throw new Me.ErrnoError(28);if(Me.isClosed(c))throw new Me.ErrnoError(8);if(!(2097155&c.flags))throw new Me.ErrnoError(8);if(Me.isDir(c.node.mode))throw new Me.ErrnoError(31);if(!c.stream_ops.write)throw new Me.ErrnoError(28);c.seekable&&1024&c.flags&&Me.llseek(c,0,2);var C=u!==void 0;if(C){if(!c.seekable)throw new Me.ErrnoError(70)}else u=c.position;var b=c.stream_ops.write(c,a,s,o,u,p);return C||(c.position+=b),b},allocate:(c,a,s)=>{if(Me.isClosed(c))throw new Me.ErrnoError(8);if(a<0||s<=0)throw new Me.ErrnoError(28);if(!(2097155&c.flags))throw new Me.ErrnoError(8);if(!Me.isFile(c.node.mode)&&!Me.isDir(c.node.mode))throw new Me.ErrnoError(43);if(!c.stream_ops.allocate)throw new Me.ErrnoError(138);c.stream_ops.allocate(c,a,s)},mmap:(c,a,s,o,u)=>{if(2&o&&!(2&u)&&(2097155&c.flags)!=2)throw new Me.ErrnoError(2);if((2097155&c.flags)==1)throw new Me.ErrnoError(2);if(!c.stream_ops.mmap)throw new Me.ErrnoError(43);return c.stream_ops.mmap(c,a,s,o,u)},msync:(c,a,s,o,u)=>c.stream_ops.msync?c.stream_ops.msync(c,a,s,o,u):0,munmap:c=>0,ioctl:(c,a,s)=>{if(!c.stream_ops.ioctl)throw new Me.ErrnoError(59);return c.stream_ops.ioctl(c,a,s)},readFile:(c,a={})=>{if(a.flags=a.flags||0,a.encoding=a.encoding||"binary",a.encoding!=="utf8"&&a.encoding!=="binary")throw new Error('Invalid encoding type "'+a.encoding+'"');var s,o=Me.open(c,a.flags),u=Me.stat(c).size,p=new Uint8Array(u);return Me.read(o,p,0,u,0),a.encoding==="utf8"?s=g2(p,0):a.encoding==="binary"&&(s=p),Me.close(o),s},writeFile:(c,a,s={})=>{s.flags=s.flags||577;var o=Me.open(c,s.flags,s.mode);if(typeof a=="string"){var u=new Uint8Array(Pm(a)+1),p=cd(a,u,0,u.length);Me.write(o,u,0,p,void 0,s.canOwn)}else{if(!ArrayBuffer.isView(a))throw new Error("Unsupported data type");Me.write(o,a,0,a.byteLength,void 0,s.canOwn)}Me.close(o)},cwd:()=>Me.currentPath,chdir:c=>{var a=Me.lookupPath(c,{follow:!0});if(a.node===null)throw new Me.ErrnoError(44);if(!Me.isDir(a.node.mode))throw new Me.ErrnoError(54);var s=Me.nodePermissions(a.node,"x");if(s)throw new Me.ErrnoError(s);Me.currentPath=a.path},createDefaultDirectories:()=>{Me.mkdir("/tmp"),Me.mkdir("/home"),Me.mkdir("/home/web_user")},createDefaultDevices:()=>{Me.mkdir("/dev"),Me.registerDevice(Me.makedev(1,3),{read:()=>0,write:(c,a,s,o,u)=>o}),Me.mkdev("/dev/null",Me.makedev(1,3)),Cc.register(Me.makedev(5,0),Cc.default_tty_ops),Cc.register(Me.makedev(6,0),Cc.default_tty1_ops),Me.mkdev("/dev/tty",Me.makedev(5,0)),Me.mkdev("/dev/tty1",Me.makedev(6,0)),Me.mkdir("/dev/shm"),Me.mkdir("/dev/shm/tmp")},createSpecialDirectories:()=>{Me.mkdir("/proc");var c=Me.mkdir("/proc/self");Me.mkdir("/proc/self/fd"),Me.mount({mount:()=>{var a=Me.createNode(c,"fd",16895,73);return a.node_ops={lookup:(s,o)=>{var u=+o,p=Me.getStream(u);if(!p)throw new Me.ErrnoError(8);var C={parent:null,mount:{mountpoint:"fake"},node_ops:{readlink:()=>p.path}};return C.parent=C,C}},a}},{},"/proc/self/fd")},createStandardStreams:()=>{He.stdin?Me.createDevice("/dev","stdin",He.stdin):Me.symlink("/dev/tty","/dev/stdin"),He.stdout?Me.createDevice("/dev","stdout",null,He.stdout):Me.symlink("/dev/tty","/dev/stdout"),He.stderr?Me.createDevice("/dev","stderr",null,He.stderr):Me.symlink("/dev/tty1","/dev/stderr"),Me.open("/dev/stdin",0),Me.open("/dev/stdout",1),Me.open("/dev/stderr",1)},ensureErrnoError:()=>{Me.ErrnoError||(Me.ErrnoError=function(c,a){this.node=a,this.setErrno=function(s){this.errno=s},this.setErrno(c),this.message="FS error"},Me.ErrnoError.prototype=new Error,Me.ErrnoError.prototype.constructor=Me.ErrnoError,[44].forEach(c=>{Me.genericErrors[c]=new Me.ErrnoError(c),Me.genericErrors[c].stack="<generic error, no stack>"}))},staticInit:()=>{Me.ensureErrnoError(),Me.nameTable=new Array(4096),Me.mount(Jr,{},"/"),Me.createDefaultDirectories(),Me.createDefaultDevices(),Me.createSpecialDirectories(),Me.filesystems={MEMFS:Jr}},init:(c,a,s)=>{Me.init.initialized=!0,Me.ensureErrnoError(),He.stdin=c||He.stdin,He.stdout=a||He.stdout,He.stderr=s||He.stderr,Me.createStandardStreams()},quit:()=>{Me.init.initialized=!1;for(var c=0;c<Me.streams.length;c++){var a=Me.streams[c];a&&Me.close(a)}},getMode:(c,a)=>{var s=0;return c&&(s|=365),a&&(s|=146),s},findObject:(c,a)=>
2810{var s=Me.analyzePath(c,a);return s.exists?s.object:null},analyzePath:(c,a)=>{try{c=(o=Me.lookupPath(c,{follow:!a})).path}catch{}var s={isRoot:!1,exists:!1,error:0,name:null,path:null,object:null,parentExists:!1,parentPath:null,parentObject:null};try{var o=Me.lookupPath(c,{parent:!0});s.parentExists=!0,s.parentPath=o.path,s.parentObject=o.node,s.name=Rs.basename(c),o=Me.lookupPath(c,{follow:!a}),s.exists=!0,s.path=o.path,s.object=o.node,s.name=o.node.name,s.isRoot=o.path==="/"}catch(u){s.error=u.errno}return s},createPath:(c,a,s,o)=>{c=typeof c=="string"?c:Me.getPath(c);for(var u=a.split("/").reverse();u.length;){var p=u.pop();if(p){var C=Rs.join2(c,p);try{Me.mkdir(C)}catch{}c=C}}return C},createFile:(c,a,s,o,u)=>{var p=Rs.join2(typeof c=="string"?c:Me.getPath(c),a),C=Me.getMode(o,u);return Me.create(p,C)},createDataFile:(c,a,s,o,u,p)=>{var C=a;c&&(c=typeof c=="string"?c:Me.getPath(c),C=a?Rs.join2(c,a):c);var b=Me.getMode(o,u),k=Me.create(C,b);if(s){if(typeof s=="string"){for(var E=new Array(s.length),B=0,G=s.length;B<G;++B)E[B]=s.charCodeAt(B);s=E}Me.chmod(k,146|b);var D=Me.open(k,577);Me.write(D,s,0,s.length,0,p),Me.close(D),Me.chmod(k,b)}return k},createDevice:(c,a,s,o)=>{var u=Rs.join2(typeof c=="string"?c:Me.getPath(c),a),p=Me.getMode(!!s,!!o);Me.createDevice.major||(Me.createDevice.major=64);var C=Me.makedev(Me.createDevice.major++,0);return Me.registerDevice(C,{open:b=>{b.seekable=!1},close:b=>{o&&o.buffer&&o.buffer.length&&o(10)},read:(b,k,E,B,G)=>{for(var D=0,Q=0;Q<B;Q++){var V;try{V=s()}catch{throw new Me.ErrnoError(29)}if(V===void 0&&D===0)throw new Me.ErrnoError(6);if(V==null)break;D++,k[E+Q]=V}return D&&(b.node.timestamp=Date.now()),D},write:(b,k,E,B,G)=>{for(var D=0;D<B;D++)try{o(k[E+D])}catch{throw new Me.ErrnoError(29)}return B&&(b.node.timestamp=Date.now()),D}}),Me.mkdev(u,p,C)},forceLoadFile:c=>{if(c.isDevice||c.isFolder||c.link||c.contents)return!0;throw typeof XMLHttpRequest<"u"?new Error("Lazy loading should have been performed (contents set) in createLazyFile, but it was not. Lazy loading only works in web workers. Use --embed-file or --preload-file in emcc on the main thread."):new Error("Cannot load without read() or XMLHttpRequest.")},createLazyFile:(c,a,s,o,u)=>{function p(){this.lengthKnown=!1,this.chunks=[]}if(p.prototype.get=function(G){if(!(G>this.length-1||G<0)){var D=G%this.chunkSize,Q=G/this.chunkSize|0;return this.getter(Q)[D]}},p.prototype.setDataGetter=function(G){this.getter=G},p.prototype.cacheLength=function(){var G=new XMLHttpRequest;if(G.open("HEAD",s,!1),G.send(null),!(G.status>=200&&G.status<300||G.status===304))throw new Error("Couldn't load "+s+". Status: "+G.status);var D,Q=Number(G.getResponseHeader("Content-length")),V=(D=G.getResponseHeader("Accept-Ranges"))&&D==="bytes",W=(D=G.getResponseHeader("Content-Encoding"))&&D==="gzip",U=1048576;V||(U=Q);var H=this;H.setDataGetter(K=>{var J=K*U,g=(K+1)*U-1;if(g=Math.min(g,Q-1),H.chunks[K]===void 0&&(H.chunks[K]=((v,e)=>{if(v>e)throw new Error("invalid range ("+v+", "+e+") or no bytes requested!");if(e>Q-1)throw new Error("only "+Q+" bytes available! programmer error!");var d=new XMLHttpRequest;if(d.open("GET",s,!1),Q!==U&&d.setRequestHeader("Range","bytes="+v+"-"+e),d.responseType="arraybuffer",d.overrideMimeType&&d.overrideMimeType("text/plain; charset=x-user-defined"),d.send(null),!(d.status>=200&&d.status<300||d.status===304))throw new Error("Couldn't load "+s+". Status: "+d.status);return d.response!==void 0?new Uint8Array(d.response||[]):Lm(d.responseText||"",!0)})(J,g)),H.chunks[K]===void 0)throw new Error("doXHR failed!");return H.chunks[K]}),!W&&Q||(U=Q=1,Q=this.getter(0).length,U=Q,X1("LazyFiles on gzip forces download of the whole file when length is accessed")),this._length=Q,this._chunkSize=U,this.lengthKnown=!0},typeof XMLHttpRequest<"u"){if(!Rk)throw"Cannot do synchronous binary XHRs outside webworkers in modern browsers. Use --embed-file or --preload-file in emcc";var C=new p;Object.defineProperties(C,{length:{get:function(){return this.lengthKnown||this.cacheLength(),this._length}},chunkSize:{get:function(){return this.lengthKnown||this.cacheLength(),this._chunkSize}}});var b={isDevice:!1,contents:C}}else b={isDevice:!1,url:s};var k=Me.createFile(c,a,b,o,u);b.contents?k.contents=b.contents:b.url&&(k.contents=null,k.url=b.url),Object.defineProperties(k,{usedBytes:{get:function(){return this.contents.length}}});var E={};function B(G,D,Q,V,W){var U=G.node.contents;if(W>=U.length)return 0;var H=Math.min(U.length-W,V);if(U.slice)for(var K=0;K<H;K++)D[Q+K]=U[W+K];else for(K=0;K<H;K++)D[Q+K]=U.get(W+K);return H}
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c=="object"&&(c=c.ptr),qm(a,c)},Object.defineProperty(JA.prototype,"age",{get:JA.prototype.get_age,set:JA.prototype.set_age}),JA.prototype.get_variant=JA.prototype.get_variant=function(){var c=this.ptr;return Jm(c)},JA.prototype.set_variant=JA.prototype.set_variant=function(c){var a=this.ptr;c&&typeof c=="object"&&(c=c.ptr),$m(a,c)},Object.defineProperty(JA.prototype,"variant",{get:JA.prototype.get_variant,set:JA.prototype.set_variant}),JA.prototype.get_xx1=JA.prototype.get_xx1=function(){var c=this.ptr;return e3(c)},JA.prototype.set_xx1=JA.prototype.set_xx1=function(c){var a=this.ptr;c&&typeof c=="object"&&(c=c.ptr),t3(a,c)},Object.defineProperty(JA.prototype,"xx1",{get:JA.prototype.get_xx1,set:JA.prototype.set_xx1}),JA.prototype.get_score=JA.prototype.get_score=function(){var c=this.ptr;return A3(c)},JA.prototype.set_score=JA.prototype.set_score=function(c){var a=this.ptr;c&&typeof c=="object"&&(c=c.ptr),r3(a,c)},Object.defineProperty(JA.prototype,"score",{get:JA.prototype.get_score,set:JA.prototype.set_score}),JA.prototype.get_spare=JA.prototype.get_spare=function(){var c=this.ptr;return s3(c)},JA.prototype.set_spare=JA.prototype.set_spare=function(c){var a=this.ptr;c&&typeof c=="object"&&(c=c.ptr),a3(a,c)},Object.defineProperty(JA.prototype,"spare",{get:JA.prototype.get_spare,set:JA.prototype.set_spare}),JA.prototype.__destroy__=JA.prototype.__destroy__=function(){var c=this.ptr;n3(c)},ns.prototype=Object.create(xo.prototype),ns.prototype.constructor=ns,ns.prototype.__class__=ns,ns.__cache__={},He.espeak_EVENT=ns,ns.prototype.get_type=ns.prototype.get_type=function(){var c=this.ptr;return i3(c)},Object.defineProperty(ns.prototype,"type",{get:ns.prototype.get_type}),ns.prototype.get_unique_identifier=ns.prototype.get_unique_identifier=function(){var c=this.ptr;return o3(c)},Object.defineProperty(ns.prototype,"unique_identifier",{get:ns.prototype.get_unique_identifier}),ns.prototype.get_text_position=ns.prototype.get_text_position=function(){var c=this.ptr;return l3(c)},Object.defineProperty(ns.prototype,"text_position",{get:ns.prototype.get_text_position}),ns.prototype.get_length=ns.prototype.get_length=function(){var c=this.ptr;return c3(c)},Object.defineProperty(ns.prototype,"length",{get:ns.prototype.get_length}),ns.prototype.get_audio_position=ns.prototype.get_audio_position=function(){var c=this.ptr;return u3(c)},Object.defineProperty(ns.prototype,"audio_position",{get:ns.prototype.get_audio_position}),ns.prototype.get_sample=ns.prototype.get_sample=function(){var c=this.ptr;return f3(c)},Object.defineProperty(ns.prototype,"sample",{get:ns.prototype.get_sample}),ns.prototype.get_user_data=ns.prototype.get_user_data=function(){var c=this.ptr;return d3(c)},Object.defineProperty(ns.prototype,"user_data",{get:ns.prototype.get_user_data}),ns.prototype.__destroy__=ns.prototype.__destroy__=function(){var c=this.ptr;g3(c)},Lr.prototype=Object.create(xo.prototype),Lr.prototype.constructor=Lr,Lr.prototype.__class__=Lr,Lr.__cache__={},He.eSpeakNGWorker=Lr,Lr.prototype.synth_=Lr.prototype.synth_=function(c,a){var s=this.ptr;Ls.prepare(),c=c&&typeof c=="object"?c.ptr:mc(c),a&&typeof a=="object"&&(a=a.ptr),p3(s,c,a)},Lr.prototype.synth_ipa_=Lr.prototype.synth_ipa_=function(c,a){var s=this.ptr;return Ls.prepare(),c=c&&typeof c=="object"?c.ptr:mc(c),a=a&&typeof a=="object"?a.ptr:mc(a),m3(s,c,a)},Lr.prototype.getSizeOfEventStruct_=Lr.prototype.getSizeOfEventStruct_=function(){var c=this.ptr;return b3(c)},Lr.prototype.set_voice=Lr.prototype.set_voice=function(c,a,s,o,u){var p=this.ptr;return Ls.prepare(),c=c&&typeof c=="object"?c.ptr:mc(c),a=a&&typeof a=="object"?a.ptr:mc(a),s&&typeof s=="object"&&(s=s.ptr),o&&typeof o=="object"&&(o=o.ptr),u&&typeof u=="object"&&(u=u.ptr),s===void 0?C3(p,c,a):o===void 0?I3(p,c,a,s):u===void 0?w3(p,c,a,s,o):k3(p,c,a,s,o,u)},Lr.prototype.get_voices=Lr.prototype.get_voices=function(c){var a=this.ptr;return c&&typeof c=="object"&&(c=c.ptr),m2(v3(a,c),JA)},Lr.prototype.set_voices=Lr.prototype.set_voices=function(c,a){var s=this.ptr;Ls.prepare(),c&&typeof c=="object"&&(c=c.ptr),a&&typeof a=="object"&&(a=a.ptr),y3(s,c,a)},Object.defineProperty(Lr.prototype,"voices",{get:Lr.prototype.get_voices,set:Lr.prototype.set_voices}),Lr.prototype.get_samplerate=Lr.prototype.get_samplerate=function(){var c=this.ptr;return E3(c)},Object.defineProperty(Lr.prototype,"samplerate",{get:Lr.prototype.get_samplerate}),Lr.prototype.get_rate=Lr.prototype.get_rate=function(){var c=this.ptr;return B3(c)},Lr.prototype.set_rate=Lr.prototype.set_rate=function(c){var a=this.ptr;c&&typeof c=="object"&&(c=c.ptr),x3(a,c)},Object.defineProperty(Lr.prototype,"rate",{get:Lr.prototype.get_rate,set:Lr.prototype.set_rate}),Lr.prototype.get_pitch=Lr.prototype.get_pitch=function(){var c=this.ptr;return M3(c)},Lr.prototype.set_pitch=Lr.prototype.set_pitch=function(c){var a=this.ptr;c&&typeof c=="object"&&(c=c.ptr),D3(a,c)},Object.defineProperty(Lr.prototype,"pitch",{get:Lr.prototype.get_pitch,set:Lr.prototype.set_pitch}),Lr.prototype.__destroy__=Lr.prototype.__destroy__=function(){var c=this.ptr;G3(c)},function(){function c(){He.espeakEVENT_LIST_TERMINATED=_3(),He.espeakEVENT_WORD=T3(),He.espeakEVENT_SENTENCE=Q3(),He.espeakEVENT_MARK=P3(),He.espeakEVENT_PLAY=S3(),He.espeakEVENT_END=N3(),He.espeakEVENT_MSG_TERMINATED=z3(),He.espeakEVENT_PHONEME=O3(),He.espeakEVENT_SAMPLERATE=F3()}Nm?c():zm(c)}(),Lr.prototype.list_voices=function(){for(var c,a=[],s=this.get_voices(c=0);s.ptr!=0;s=this.get_voices(++c)){let b=function(k){for(var E="",B=k,G=s.get_languages(B++);G!=0;)E+=String.fromCharCode(G),G=s.get_languages(B++);return E};for(var o={name:s.get_name(),identifier:s.get_identifier(),languages:[]},u=0,p=s.get_languages(u);p!=0;
2810){var C={priority:p,name:b(++u)};o.languages.push(C),u+=C.name.length+1,p=s.get_languages(u)}a.push(o)}return a};var Zk=["list_terminated","word","sentence","mark","play","end","msg_terminated","phoneme","samplerate"];Lr.prototype.synthesize=function(c,a){var s=this.getSizeOfEventStruct_(),o=addFunction(function(u,p,C){for(var b=new Float32Array(2*p),k=0;k<p;k++)b[2*k]=Math.max(-1,Math.min(1,Wk(u+2*k,"i16")/32768)),b[2*k+1]=b[2*k];var E=[];for(u=C,ev=m2(u,ns);ev.get_type()!=He.espeakEVENT_LIST_TERMINATED;ev=m2(u+=s,ns))E.push({type:Zk[ev.get_type()],text_position:ev.get_text_position(),word_length:ev.get_length(),audio_position:ev.get_audio_position()});return a(b,E)?1:0});this.synth_(c,o),removeFunction(o)},Lr.prototype.synthesize_ipa=function(c,a){var s="espeak-ng-ipa-tmp-"+Math.random().toString().substring(2),o="",u=this.synth_ipa_(c,s);return u==0&&(o=Me.readFile(s,{encoding:"utf8"})),Me.unlink(s),{code:u,ipa:o}};const Y3=new Promise(c=>{He.calledRun?c(new He.eSpeakNGWorker):He.onRuntimeInitialized=()=>c(new He.eSpeakNGWorker)}),Xk=["en"],qk=Y3.then(c=>{const a=c.list_voices().map(({name:o,identifier:u,languages:p})=>({name:o,identifier:u,languages:p.filter(C=>Xk.includes(C.name.split("-")[0]))})).filter(o=>o.languages.length>0),s=new Set;for(const o of a){s.add(o.identifier);for(const u of o.languages)s.add(u.name)}return{voices:a,identifiers:s}});function Jk(c){if(c.includes("."))return c;if(c.includes(":")){let[p,C]=c.split(":").map(Number);return C===0?`${p} o'clock`:C<10?`${p} oh ${C}`:`${p} ${C}`}let a=parseInt(c.slice(0,4),10);if(a<1100||a%1e3<10)return c;let s=c.slice(0,2),o=parseInt(c.slice(2,4),10),u=c.endsWith("s")?"s":"";if(a%1e3>=100&&a%1e3<=999){if(o===0)return`${s} hundred${u}`;if(o<10)return`${s} oh ${o}${u}`}return`${s} ${o}${u}`}function $k(c){const a=c[0]==="$"?"dollar":"pound";if(isNaN(Number(c.slice(1))))return`${c.slice(1)} ${a}s`;if(!c.includes(".")){let p=c.slice(1)==="1"?"":"s";return`${c.slice(1)} ${a}${p}`}const[s,o]=c.slice(1).split("."),u=parseInt(o.padEnd(2,"0"),10);return`${s} ${a}${s==="1"?"":"s"} and ${u} ${c[0]==="$"?u===1?"cent":"cents":u===1?"penny":"pence"}`}function tv(c){let[a,s]=c.split(".");return`${a} point ${s.split("").join(" ")}`}const Av=new RegExp(`(\\s*[${fp=';:,.!?¡¿ââ¦"«»ââ(){}[]',fp.replace(/[.*+?^${}()|[\]\\]/g,"\\$&")}]+\\s*)+`,"g");var fp;async function rv(c,a="a",s=!0){s&&(c=function(b){return b.replace(/[ââ]/g,"'").replace(/«/g,"â").replace(/»/g,"â").replace(/[ââ]/g,'"').replace(/\(/g,"«").replace(/\)/g,"»").replace(/ã/g,", ").replace(/ã/g,". ").replace(/ï¼/g,"! ").replace(/ï¼/g,", ").replace(/ï¼/g,": ").replace(/ï¼/g,"; ").replace(/ï¼/g,"? ").replace(/[^\S \n]/g," ").replace(/ +/," ").replace(new RegExp("(?<=\\n) +(?=\\n)","g"),"").replace(/\bD[Rr]\.(?= [A-Z])/g,"Doctor").replace(/\b(?:Mr\.|MR\.(?= [A-Z]))/g,"Mister").replace(/\b(?:Ms\.|MS\.(?= [A-Z]))/g,"Miss").replace(/\b(?:Mrs\.|MRS\.(?= [A-Z]))/g,"Mrs").replace(/\betc\.(?! [A-Z])/gi,"etc").replace(/\b(y)eah?\b/gi,"$1e'a").replace(new RegExp("\\d*\\.\\d+|\\b\\d{4}s?\\b|(?<!:)\\b(?:[1-9]|1[0-2]):[0-5]\\d\\b(?!:)","g"),Jk).replace(new RegExp("(?<=\\d),(?=\\d)","g"),"").replace(/[$£]\d+(?:\.\d+)?(?: hundred| thousand| (?:[bm]|tr)illion)*\b|[$£]\d+\.\d\d?\b/gi,$k).replace(/\d*\.\d+/g,tv).replace(new RegExp("(?<=\\d)-(?=\\d)","g")," to ").replace(new RegExp("(?<=\\d)S","g")," S").replace(new RegExp("(?<=[BCDFGHJ-NP-TV-Z])'?s\\b","g"),"'S").replace(new RegExp("(?<=X')S\\b","g"),"s").replace(/(?:[A-Za-z]\.){2,} [a-z]/g,k=>k.replace(/\./g,"-")).replace(new RegExp("(?<=[A-Z])\\.(?=[A-Z])","gi"),"-").trim()}(c));const o=function(b,k){const E=[];let B=0;for(const G of b.matchAll(k)){const D=G[0];B<G.index&&E.push({match:!1,text:b.slice(B,G.index)}),D.length>0&&E.push({match:!0,text:D}),B=G.index+D.length}return B<b.length&&E.push({match:!1,text:b.slice(B)}),E}(c,Av),u=a==="a"?"en-us":"en";let C=(await Promise.all(o.map(async({match:b,text:k})=>b?k:(await(async(E,B="en-us")=>{var Q;const G=await Y3,{identifiers:D}=await qk;if(!D.has(B))throw new Error(`Invalid language identifier: "${B}". Should be one of: ${Array.from(D).toSorted().join(", ")}.`);
2810return G.set_voice(B),((Q=G.synthesize_ipa(E).ipa)==null?void 0:Q.split(` 2811`).filter(V=>V.length>0))??[]})(k,u)).join(" ")))).join("").replace(/kÉkËoËɹoÊ/g,"kËoÊkÉɹoÊ").replace(/kÉkËÉËɹÉÊ/g,"kËÉÊkÉɹÉÊ").replace(/ʲ/g,"j").replace(/r/g,"ɹ").replace(/x/g,"k").replace(/ɬ/g,"l").replace(new RegExp("(?<=[a-zɹË])(?=hËÊndɹɪd)","g")," ").replace(/ z(?=[;:,.!?¡¿ââ¦"«»ââ ]|$)/g,"z");return a==="a"&&(C=C.replace(new RegExp("(?<=nËaɪn)ti(?!Ë)","g"),"di")),C.trim()}var ju={};const Wu=Object.freeze({af_heart:{name:"Heart",language:"en-us",gender:"Female",traits:"â¤ï¸",targetQuality:"A",overallGrade:"A"},af_alloy:{name:"Alloy",language:"en-us",gender:"Female",targetQuality:"B",overallGrade:"C"},af_aoede:{name:"Aoede",language:"en-us",gender:"Female",targetQuality:"B",overallGrade:"C+"},af_bella:{name:"Bella",language:"en-us",gender:"Female",traits:"ð¥",targetQuality:"A",overallGrade:"A-"},af_jessica:{name:"Jessica",language:"en-us",gender:"Female",targetQuality:"C",overallGrade:"D"},af_kore:{name:"Kore",language:"en-us",gender:"Female",targetQuality:"B",overallGrade:"C+"},af_nicole:{name:"Nicole",language:"en-us",gender:"Female",traits:"ð§",targetQuality:"B",overallGrade:"B-"},af_nova:{name:"Nova",language:"en-us",gender:"Female",targetQuality:"B",overallGrade:"C"},af_river:{name:"River",language:"en-us",gender:"Female",targetQuality:"C",overallGrade:"D"},af_sarah:{name:"Sarah",language:"en-us",gender:"Female",targetQuality:"B",overallGrade:"C+"},af_sky:{name:"Sky",language:"en-us",gender:"Female",targetQuality:"B",overallGrade:"C-"},am_adam:{name:"Adam",language:"en-us",gender:"Male",targetQuality:"D",overallGrade:"F+"},am_echo:{name:"Echo",language:"en-us",gender:"Male",targetQuality:"C",overallGrade:"D"},am_eric:{name:"Eric",language:"en-us",gender:"Male",targetQuality:"C",overallGrade:"D"},am_fenrir:{name:"Fenrir",language:"en-us",gender:"Male",targetQuality:"B",overallGrade:"C+"},am_liam:{name:"Liam",language:"en-us",gender:"Male",targetQuality:"C",overallGrade:"D"},am_michael:{name:"Michael",language:"en-us",gender:"Male",targetQuality:"B",overallGrade:"C+"},am_onyx:{name:"Onyx",language:"en-us",gender:"Male",targetQuality:"C",overallGrade:"D"},am_puck:{name:"Puck",language:"en-us",gender:"Male",targetQuality:"B",overallGrade:"C+"},am_santa:{name:"Santa",language:"en-us",gender:"Male",targetQuality:"C",overallGrade:"D-"},bf_emma:{name:"Emma",language:"en-gb",gender:"Female",traits:"ðº",targetQuality:"B",overallGrade:"B-"},bf_isabella:{name:"Isabella",language:"en-gb",gender:"Female",targetQuality:"B",overallGrade:"C"},bm_george:{name:"George",language:"en-gb",gender:"Male",targetQuality:"B",overallGrade:"C"},bm_lewis:{name:"Lewis",language:"en-gb",gender:"Male",targetQuality:"C",overallGrade:"D+"},bf_alice:{name:"Alice",language:"en-gb",gender:"Female",traits:"ðº",targetQuality:"C",overallGrade:"D"},bf_lily:{name:"Lily",language:"en-gb",gender:"Female",traits:"ðº",targetQuality:"C",overallGrade:"D"},bm_daniel:{name:"Daniel",language:"en-gb",gender:"Male",traits:"ð¹",targetQuality:"C",overallGrade:"D"},bm_fable:{name:"Fable",language:"en-gb",gender:"Male",traits:"ð¹",targetQuality:"B",overallGrade:"C"}}),jf=new Map;async function sv(c){if(jf.has(c))return jf.get(c);const a=new Float32Array(await async function(s){if(ju!=null&&ju.readFile){const b=typeof __dirname<"u"?__dirname:import.meta.dirname,k=ju.resolve(b,`../voices/${s}.bin`),{buffer:E}=await ju.readFile(k);return E}const o=`https://huggingface.co/onnx-community/Kokoro-82M-v1.0-ONNX/resolve/main/voices/${s}.bin`;let u;try{u=await caches.open("kokoro-voices");const b=await u.match(o);if(b)return await b.arrayBuffer()}catch(b){console.warn("Unable to open cache",b)}const p=await fetch(o),C=await p.arrayBuffer();if(u)try{await u.put(o,new Response(C,{headers:p.headers}))}catch(b){console.warn("Unable to cache file",b)}return C}(c));return jf.set(c,a),a}class ud{constructor(a,s){this.model=a,this.tokenizer=s}static async from_pretrained(a,{dtype:s="fp32",device:o=null,progress_callback:u=null}={}){const p=Yk.from_pretrained(a,{progress_callback:u,dtype:s,device:o}),C=Vk.from_pretrained(a,{progress_callback:u}),b=await Promise.all([p,C]);return new ud(...b)}get voices(){return Wu}list_voices(){console.table(Wu)}_validate_voice(a){if(!Wu.hasOwnProperty(a))throw console.error(`Voice "${a}" not found. Available voices:`),console.table(Wu),new Error(`Voice "${a}" not found. Should be one of: ${Object.keys(Wu).join(", ")}.`)}async generate(a,{voice:s="af_heart",speed:o=1}={}
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