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strict";Object.defineProperty(t,"__esModule",{value:!0}),t.avgPoolGrad=o,t.avgPoolGradConfig=void 0;var r=n(735534),a=n(468462);function o(e){const{inputs:t,backend:n,attrs:o}=e,{dy:s,input:i}=t,c=i;(0,a.assertNotComplex)([s,i],"avgPoolGrad");const{filterSize:l,strides:u,pad:d}=o,p=r.backend_util.computePool2DInfo(c.shape,l,u,1,d),h=p.strideHeight,f=p.strideWidth,m=p.filterHeight,g=p.filterWidth,y=p.dilationHeight,v=p.dilationWidth,b=p.effectiveFilterHeight,x=p.effectiveFilterWidth,w=x-1-p.padInfo.left,k=b-1-p.padInfo.top,_=(0,r.buffer)(c.shape,"float32"),C=1/(m*g),P=n.data.get(s.dataId).values,O=(0,r.buffer)(s.shape,"float32",P);for(let e=0;e<p.batchSize;++e)for(let t=0;t<p.inChannels;++t)for(let n=0;n<p.inHeight;++n)for(let r=0;r<p.inWidth;++r){const a=n-k,o=r-w;let s=0;for(let n=0;n<b;n+=y){const r=(a+n)/h;if(!(r<0||r>=p.outHeight||Math.floor(r)!==r))for(let n=0;n<x;n+=v){const a=(o+n)/f;if(a<0||a>=p.outWidth||Math.floor(a)!==a)continue;s+=O.get(e,r,a,t)}}_.set(s*C,e,n,r,t)}return n.makeTensorInfo(_.shape,_.dtype,_.values)}t.avgPoolGradConfig={kernelName:r.AvgPoolGrad,backendName:"cpu",kernelFunc:o}},418026:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.batchMatMul=s,t.batchMatMulConfig=void 0;var r=n(735534),a=n(468462),o=n(434731);function s(e){const{inputs:t,backend:n,attrs:s}=e,{a:i,b:c}=t,{transposeA:l,transposeB:u}=s;(0,a.assertNotComplex)([i,c],"matMul");const d=i.shape.length,p=c.shape.length,h=l?i.shape[d-2]:i.shape[d-1],f=u?c.shape[p-1]:c.shape[p-2],m=l?i.shape[d-1]:i.shape[d-2],g=u?c.shape[p-2]:c.shape[p-1],y=i.shape.slice(0,-2),v=c.shape.slice(0,-2),b=r.util.sizeFromShape(y),x=r.util.sizeFromShape(v),w=r.broadcast_util.assertAndGetBroadcastShape(i.shape.slice(0,-2),c.shape.slice(0,-2)).concat([m,g]);r.util.assert(h===f,(()=>"Error in matMul: inner shapes (".concat(h,") and (")+"".concat(f,") of Tensors with shapes ").concat(i.shape," and ")+"".concat(c.shape," and transposeA=").concat(l)+" and transposeB=".concat(u," must 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n.disposeIntermediateTensorInfo(C),n.disposeIntermediateTensorInfo(P),n.makeTensorInfo(w,G.dtype,G.values)}t.batchMatMulConfig={kernelName:r.BatchMatMul,backendName:"cpu",kernelFunc:s}},337306:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.batchNorm=o,t.batchNormConfig=void 0;var r=n(735534),a=n(468462);function o(e){const{inputs:t,backend:n,attrs:o}=e,{x:s,scale:i,offset:c,mean:l,variance:u}=t;r.util.assert(l.shape.length===u.shape.length,(()=>"Batch normalization gradient requires mean and variance to have equal ranks.")),r.util.assert(null==c||l.shape.length===c.shape.length,(()=>"Batch normalization gradient requires mean and offset to have equal ranks.")),r.util.assert(null==i||l.shape.length===i.shape.length,(()=>"Batch normalization gradient requires mean and scale to have equal ranks.")),(0,a.assertNotComplex)([s,l,u,i,c],"batchNorm");let{varianceEpsilon:d}=o;null==d&&(d=.001);const p=n.data.get(s.dataId).values,h=n.data.get(l.dataId).values,f=n.data.get(u.dataId).values,m=i?n.data.get(i.dataId).values:new Float32Array([1]),g=c?n.data.get(c.dataId).values:new Float32Array([0]),y=new Float32Array(p.length),v=g.length,b=m.length,x=f.length,w=h.length;let k=0,_=0,C=0,P=0;for(let e=0;e<p.length;++e)y[e]=g[k++]+(p[e]-h[_++])*m[C++]/Math.sqrt(f[P++]+d),k>=v&&(k=0),_>=w&&(_=0),C>=b&&(C=0),P>=x&&(P=0);return n.makeTensorInfo(s.shape,s.dtype,y)}t.batchNormConfig={kernelName:r.FusedBatchNorm,backendName:"cpu",kernelFunc:o}},239847:(e,t,n)=>{"use strict";

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1 i++) {\n          inIdx = srcIdx;\n          ").concat(_,"\n          vec4 candidate = ").concat(C,";\n          bvec4 nan = isnan(candidate);\n          bvec4 replace = bvec4(\n            vec4(").concat(k,"(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,\n                           replace.y  ? candidate.y : bestValue.y,\n                           replace.z  ? candidate.z : bestValue.z,\n                           replace.w  ? candidate.w : bestValue.w);\n          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n          srcIdx++;\n        }\n        setOutput(bestIndex);\n      }\n    ")}}},77764:(e,t)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.AvgPool3DBackpropProgram=t.AvgPool2DBackpropProgram=void 0;t.AvgPool2DBackpropProgram=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;const t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,o=e.dilationHeight,s=e.dilationWidth,i=e.effectiveFilterHeight,c=e.effectiveFilterWidth,l=i-1-e.padInfo.top,u=c-1-e.padInfo.left,d=1/(t*n);this.userCode="\n      const ivec2 pads = ivec2(".concat(l,", ").concat(u,");\n      const float avgMultiplier = float(").concat(d,");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ").concat(i,";\n            wR += ").concat(o,") {\n          float dyR = float(dyRCorner + wR) / ").concat(r,".0;\n\n          if (dyR < 0.0 || dyR >= ").concat(e.outHeight,".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ").concat(c,";\n            wC+= ").concat(s,") {\n            float dyC = float(dyCCorner + wC) / ").concat(a,".0;\n\n            if (dyC < 0.0 || dyC >= ").concat(e.outWidth,".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    ")}};t.AvgPool3DBackpropProgram=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;const t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,o=e.strideHeight,s=e.strideWidth,i=e.dilationDepth,c=e.dilationHeight,l=e.dilationWidth,u=e.effectiveFilterDepth,d=e.effectiveFilterHeight,p=e.effectiveFilterWidth,h=u-1-e.padInfo.front,f=d-1-e.padInfo.top,m=p-1-e.padInfo.left,g=1/(t*n*r);this.userCode="\n      const ivec3 pads = ivec3(".concat(h,", ").concat(f,", ").concat(m,");\n      const float avgMultiplier = float(").concat(g,");\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ").concat(u,";\n            wD += ").concat(i,") {\n          float dyD = float(dyDCorner + wD) / ").concat(a,".0;\n\n          if (dyD < 0.0 || dyD >= ").concat(e.outDepth,".0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ").concat(d,";\n              wR += ").concat(c,") {\n            float dyR = float(dyRCorner + wR) / ").concat(o,".0;\n\n            if (dyR < 0.0 || dyR >= ").concat(e.outHeight,".0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ").concat(p,";\n                wC += ").concat(l,") {\n              float dyC = float(dyCCorner + wC) / ").concat(s,".0;\n\n              if (dyC < 0.0 || dyC >= ").concat(e.outWidth,".0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n              dotProd += dyValue * avgMultiplier;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ")}}}

1,473002:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.MathBackendWebGL=t.EPSILON_FLOAT32=t.EPSILON_FLOAT16=void 0,t.getBinaryCache=S,n(640571);var r=n(735534),a=n(821551),o=n(430729),s=n(258587),i=n(929414),c=n(567312),l=n(497365),u=n(769672),d=n(80567),p=P(n(965438)),h=n(509570),f=n(446060),m=n(769801),g=P(n(845772)),y=g,v=n(463574),b=P(n(934500)),x=b,w=n(721797),k=n(59579),_=P(n(519617));function C(e){if("function"!=typeof WeakMap)return null;var t=new WeakMap,n=new WeakMap;return(C=function(e){return e?n:t})(e)}function P(e,t){if(!t&&e&&e.__esModule)return e;if(null===e||"object"!=typeof e&&"function"!=typeof e)return{default:e};var n=C(t);if(n&&n.has(e))return n.get(e);var r={__proto__:null},a=Object.defineProperty&&Object.getOwnPropertyDescriptor;for(var o in e)if("default"!==o&&{}.hasOwnProperty.call(e,o)){var s=a?Object.getOwnPropertyDescriptor(e,o):null;s&&(s.get||s.set)?Object.defineProperty(r,o,s):r[o]=e[o]}return r.default=e,n&&n.set(e,r),r}const O=r.kernel_impls.whereImpl,N=t.EPSILON_FLOAT32=1e-7,I=t.EPSILON_FLOAT16=1e-4,T={};function S(e){return e in T||(T[e]={}),T[e]}const E=(0,r.env)().getNumber("CPU_HANDOFF_SIZE_THRESHOLD");class M extends r.KernelBackend{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!(0,r.env)().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(null!=e){if(e instanceof d.GPGPUContext)t=e;else{const n=(0,a.getWebGLContext)((0,r.env)().getNumber("WEBGL_VERSION"),e);t=new d.GPGPUContext(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{const e=(0,a.getWebGLContext)((0,r.env)().getNumber("WEBGL_VERSION"));t=new d.GPGPUContext(e),this.binaryCache=S((0,r.env)().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new v.TextureManager(this.gpgpu),this.numMBBeforeWarning=null==(0,r.env)().global.screen?1024:(0,r.env)().global.screen.height*(0,r.env)().global.screen.width*window.devicePixelRatio*600/1024/1024,this.texData=new r.DataStorage(this,(0,r.engine)())}nextDataId(){return M.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if(((0,r.env)().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||(0,r.env)().getBool("DEBUG"))&&this.checkNumericalProblems(e),"complex64"===n&&null!=e)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");const a={id:this.nextDataId()};return this.texData.set(a,{shape:t,dtype:n,values:e,usage:g.TextureUsage.UPLOAD,refCount:1}),a}refCount(e){if(this.texData.has(e)){return this.texData.get(e).refCount}return 0}incRef(e){this.texData.get(e).refCount++}decRef(e){if(this.texData.has(e)){this.texData.get(e).refCount--}}move(e,t,n,a,o){if((0,r.env)().getBool("DEBUG")&&this.checkNumericalProblems(t),"complex64"===a)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:a,values:t,usage:g.TextureUsage.UPLOAD,refCount:o})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){const t=this.texData.get(e),{values:n,dtype:a,complexTensorInfos:o,slice:s,shape:i,isPacked:c}=t;if(null!=s){let t;t=c?new w.UnaryOpPackedProgram(i,x.CLONE):new b.UnaryOpProgram(i,x.CLONE);const n=this.runWebGLProgram(t,[{dataId:e,shape:i,dtype:a}],a),r=this.readSync(n.dataId);return this.disposeIntermediateTensorInfo(n),r}if(null!=n)return this.convertAndCacheOnCPU(e);if("string"===a)return n;const l=null!=this.activeTimers;let u,d;if(l&&(u=r.util.now()),"complex64"===a){const e=this.readSync(o.real.dataId),t=this.readSync(o.imag.dataId);d=r.backend_util.mergeRealAndImagArrays(e,t)}else d=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=r.util.now()-u),this.convertAndCacheOnCPU(e,d)}async read(e){if(this.pendingRead.has(e)){const t=this.pendingRead.get(e);return new Promise((e=>t.push(e)))}const t=this.texData.get(e),{values:n,shape:a,slice:o,dtype:s,complexTensorInfos:i,isPacked:c}=t;if(null!=o){let t;t=c?new w.UnaryOpPackedProgram(a,x.CLONE):new b.UnaryOpProgram(a,x.CLONE);const n=this.runWebGLProgram(t,[{dataId:e,shape:a,dtype:s}],s),r=this.read(n.dataId);return this.disposeIntermediateTensorInfo(n),r}if(null!=n)return this.convertAndCacheOnCPU(e);if((0,r.env)().getBool("DEBUG")&&!(0,r.env)().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&2===(0,r.env)().getNumber("WEBGL_VERSION"))throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l,u,d=null;if("complex64"!==s&&(0,r.env)().get("WEBGL_BUFFER_SUPPORTED")){l=this.decode(e);const t=this.texData.get(l.dataId);d=this.gpgpu.createBufferFromTexture(t.texture.texture,...y.getDenseTexShape(a))}if(this.pendingRead.set(e,[]),"complex64"!==s&&await this.gpgpu.createAndWaitForFence(),"complex64"===s){const e=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),t=e[0],n=e[1];u=r.backend_util.mergeRealAndImagArrays(t,n)}else if(null==d)u=this.getValuesFromTexture(e);else{const e=r.util.sizeFromShape(a);u=this.gpgpu.downloadFloat32MatrixFromBuffer(d,e)}if(null!=l&&this.disposeIntermediateTensorInfo(l),null!=d){const e=this.gpgpu.gl;_.callAndCheck(e,(()=>e.deleteBuffer(d)))}const p=this.convertAndCacheOnCPU(e,u),h=this.pendingRead.get(e);return this.pendingRead.delete(e),h.forEach((e=>e(p))),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&(0,r.engine)().removeDataId(e,this),this.pendingDeletes--),p}readToGPU(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};const n=this.texData.get(e),{values:a,shape:o,slice:s,dtype:i,isPacked:c,texture:l}=n;if("complex64"===i)throw new Error("Does not support reading texture for complex64 dtype.");if(null!=s){let n;n=c?new w.UnaryOpPackedProgram(o,x.CLONE):new b.UnaryOpProgram(o,x.CLONE);const r=this.runWebGLProgram(n,[{dataId:e,shape:o,dtype:i}],i),a=this.readToGPU(r,t);return this.disposeIntermediateTensorInfo(r),a}if(null==l)throw null!=a?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");const u=this.decode(e,t.customTexShape),d=(0,r.engine)().makeTensorFromDataId(u.dataId,u.shape,u.dtype),p=this.texData.get(u.dataId);return Object.assign({tensorRef:d},p.texture)}bufferSync(e){const t=this.readSync(e.dataId);let n=t;if("string"===e.dtype)try{n=t.map((e=>r.util.decodeString(e)))}catch(e){throw new Error("Failed to decode encoded string bytes into utf-8")}return(0,r.buffer)(e.shape,e.dtype,n)}checkNumericalProblems(e){if(null!=e)for(let t=0;t<e.length;t++){const n=e[t];if(!_.canBeRepresented(n)){if((0,r.env)().getBool("WEBGL_RENDER_FLOAT32_CAPABLE"))throw Error("The value ".concat(n," cannot be represented with your ")+"current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'");throw Error("The value ".concat(n," cannot be represented on this device."))}}}getValuesFromTexture(e){const{shape:t,dtype:n,isPacked:a}=this.texData.get(e),o=r.util.sizeFromShape(t);if((0,r.env)().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){const n=this.decode(e),r=this.texData.get(n.dataId),a=this.gpgpu.downloadMatrixFromPackedTexture(r.texture.texture,...y.getDenseTexShape(t)).subarray(0,o);return this.disposeIntermediateTensorInfo(n),a}const s=(0,r.env)().getBool("WEBGL_PACK")&&!0===a,l=s?_.getShapeAs3D(t):t,u=s?new c.EncodeFloatPackedProgram(l):new i.EncodeFloatProgram(l),d=this.runWebGLProgram(u,[{shape:l,dtype:n,dataId:e}],"float32"),p=this.texData.get(d.dataId),h=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(p.texture.texture,p.texShape[0],p.texShape[1]).subarray(0,o);return this.disposeIntermediateTensorInfo(d),h}timerAvailable(){return(0,r.env)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){const t=this.activeTimers,n=[];let a=!1;null==this.programTimersStack?(this.programTimersStack=n,a=!0):this.activeTimers.push(n),this.activeTimers=n,e();const o=r.util.flatten(this.activeTimers.map((e=>e.query))).filter((e=>null!=e)),s=r.util.flatten(this.activeTimers.map((e=>e.name))).filter((e=>null!=e));this.activeTimers=t,a&&(this.programTimersStack=null);const i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>

1{if((0,r.env)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){const e=await Promise.all(o);i.kernelMs=r.util.sum(e),i.getExtraProfileInfo=()=>e.map(((e,t)=>({name:s[t],ms:e}))).map((e=>"".concat(e.name,": ").concat(e.ms))).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return(0,r.env)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:r.util.now(),endMs:null}}endTimer(e){return(0,r.env)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=r.util.now(),e)}async getQueryTime(e){if((0,r.env)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);const t=e;return t.endMs-t.startMs}disposeData(e){let t=arguments.length>1&&void 0!==arguments[1]&&arguments[1];if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);const{complexTensorInfos:n}=this.texData.get(e);return null!=n&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){const{texture:t,dtype:n,texShape:r,usage:a,isPacked:o,slice:s}=this.texData.get(e),i=s&&s.origDataId||e,c=this.dataRefCount.get(i);c>1?this.dataRefCount.set(i,c-1):(this.dataRefCount.delete(i),null!=t&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(t,r,a,o)));const l=this.texData.get(e);l.texture=null,l.texShape=null,l.isPacked=!1,l.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:E;return(0,r.env)().getBool("WEBGL_CPU_FORWARD")&&e.every((e=>null==this.texData.get(e.dataId).texture&&r.util.sizeFromShape(e.shape)<t))}getGPGPUContext(){return this.gpgpu}where(e){r.backend_util.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");const t=e.dataSync();return O(e.shape,t)}packedUnaryOp(e,t,n){const a=new w.UnaryOpPackedProgram(e.shape,t),o=this.compileAndRun(a,[e],n);return(0,r.engine)().makeTensorFromDataId(o.dataId,o.shape,o.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&"complex64"!==e.dtype){const t=(0,h.simpleAbsImplCPU)(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,t)}if((0,r.env)().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,x.ABS,e.dtype);const t=new b.UnaryOpProgram(e.shape,x.ABS),n=this.compileAndRun(t,[e]);return(0,r.engine)().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let a;if("string"===t&&null!=n&&n.length>0&&r.util.isString(n[0])){const o=n.map((e=>r.util.encodeString(e)));a=this.write(o,e,t)}else a=this.write(n,e,t);return this.texData.get(a).usage=null,{dataId:a,shape:e,dtype:t}}makeOutput(e,t,n){const{dataId:a}=this.makeTensorInfo(e,t,n);return(0,r.engine)().makeTensorFromDataId(a,e,t,this)}unpackTensor(e){const t=new k.UnpackProgram(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){const t=new f.PackProgram(e.shape);return this.runWebGLProgram(t,[e],e.dtype,null,!0)}packedReshape(e,t){const n=[_.getBatchDim(e.shape),..._.getRowsCols(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},a=[_.getBatchDim(t),..._.getRowsCols(t)],o=new m.ReshapePackedProgram(a,n),s=[n],i=this.runWebGLProgram(o,[r],e.dtype,s,!0);return{dataId:i.dataId,shape:t,dtype:i.dtype}}decode(e,t){const n=this.texData.get(e),{isPacked:a,shape:i,dtype:c}=n;if(null!=t){const e=r.util.sizeFromShape(i),n=t[0]*t[1]*4;r.util.assert(e<=n,(()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data."))}const l=_.getShapeAs3D(i);let u;u=a?new s.DecodeMatrixPackedProgram(l):new o.DecodeMatrixProgram(l);const d=[null!=t?t:y.getDenseTexShape(l)];return{dtype:c,shape:i,dataId:this.runWebGLProgram(u,[{shape:l,dtype:c,dataId:e}],c,d,!0,t).dataId}}runWebGLProgram(e,t,n,a){let o=arguments.length>4&&void 0!==arguments[4]&&arguments[4],s=arguments.length>5?arguments[5]:void 0;const i=this.makeTensorInfo(e.outputShape,n),c=this.texData.get(i.dataId);if(e.packedOutput&&(c.isPacked=!0),e.outPackingScheme===y.PackingScheme.DENSE){const t=null!=s?s:y.getDenseTexShape(e.outputShape);c.texShape=t.map((e=>2*e))}if(null!=e.outTexUsage&&(c.usage=e.outTexUsage),0===r.util.sizeFromShape(i.shape))return c.values=r.util.getTypedArrayFromDType(i.dtype,0),i;const l=[],u=t.map((t=>{if("complex64"===t.dtype)throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let n=this.texData.get(t.dataId);

1if(null==n.texture){if(!e.packedInputs&&r.util.sizeFromShape(t.shape)<=(0,r.env)().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:t.shape,texData:null,isUniform:!0,uniformValues:n.values};e.packedInputs&&(n.isPacked=!0,n.shape=t.shape)}if(this.uploadToGPU(t.dataId),!!n.isPacked!=!!e.packedInputs)t=n.isPacked?this.unpackTensor(t):this.packTensor(t),l.push(t),n=this.texData.get(t.dataId);else if(n.isPacked&&!_.isReshapeFree(n.shape,t.shape)){const e=t,r=t.shape;t.shape=n.shape,t=this.packedReshape(t,r),l.push(t),n=this.texData.get(t.dataId),e.shape=r}return{shape:t.shape,texData:n,isUniform:!1}}));this.uploadToGPU(i.dataId);const d={shape:i.shape,texData:c,isUniform:!1},h=p.makeShaderKey(e,u,d),f=this.getAndSaveBinary(h,(()=>p.compileProgram(this.gpgpu,e,u,d))),m=null!=this.activeTimers;let g;m&&(g=this.startTimer()),p.runProgram(this.gpgpu,f,u,d,a),l.forEach((e=>this.disposeIntermediateTensorInfo(e))),m&&(g=this.endTimer(g),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(g)}));const v=(0,r.env)().get("WEBGL_FLUSH_THRESHOLD");if(v>0){const e=r.util.now();e-this.lastGlFlushTime>v&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=e)}if(!(0,r.env)().getBool("WEBGL_LAZILY_UNPACK")&&c.isPacked&&!1===o){const e=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),e}return i}compileAndRun(e,t,n,r){let a=arguments.length>4&&void 0!==arguments[4]&&arguments[4];n=n||t[0].dtype;return this.runWebGLProgram(e,t,n,r,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){if(!this.disposed){if(!(0,r.env)().getBool("IS_TEST")){Object.keys(this.binaryCache).forEach((e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}))}this.textureManager.dispose(),null!=this.canvas&&"undefined"!=typeof HTMLCanvasElement&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0}}floatPrecision(){return null==this.floatPrecisionValue&&(this.floatPrecisionValue=(0,r.tidy)((()=>{if(!(0,r.env)().get("WEBGL_RENDER_FLOAT32_ENABLED")){const e=(0,r.env)().getBool("DEBUG");(0,r.env)().set("DEBUG",!1);const t=this.abs((0,r.scalar)(1e-8)).dataSync()[0];if((0,r.env)().set("DEBUG",e),t>0)return 32}return 16}))),this.floatPrecisionValue}epsilon(){return 32===this.floatPrecision()?N:I}uploadToGPU(e){const t=this.texData.get(e),{shape:n,dtype:a,values:o,texture:s,usage:i,isPacked:c}=t;if(null!=s)return;const d=null!=this.activeTimers;let p;d&&(p=r.util.now());let h=t.texShape;if(null==h&&(h=_.getTextureShapeFromLogicalShape(n,c),t.texShape=h),null!=o){const e=_.getShapeAs3D(n);let s,i=h[1],f=h[0];const m=o instanceof Uint8Array||o instanceof Uint8ClampedArray;!c&&m||([i,f]=y.getPackedMatrixTextureShapeWidthHeight(h[0],h[1])),s=c?new u.EncodeMatrixPackedProgram(e,m):new l.EncodeMatrixProgram(e,m);const v=m?[f,i]:h,b=this.makeTensorInfo(v,a),x=this.texData.get(b.dataId);x.usage=m?g.TextureUsage.PIXELS:g.TextureUsage.UPLOAD,x.texShape=v,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),i,f,o);const w=[[f,i]],k=!0,C=this.runWebGLProgram(s,[b],a,w,k),P=this.texData.get(C.dataId);t.texture=P.texture,t.texShape=P.texShape,t.isPacked=P.isPacked,t.usage=P.usage,this.disposeIntermediateTensorInfo(b),this.texData.delete(C.dataId),t.values=null,d&&(this.uploadWaitMs+=r.util.now()-p)}else{const e=this.acquireTexture(h,i,a,c);t.texture=e}}convertAndCacheOnCPU(e,t){const n=this.texData.get(e),{dtype:r}=n;return this.releaseGPUData(e),null!=t&&(n.values=function(e,t){if("float32"===t||"complex64"===t)return e;if("int32"===t||"bool"===t){const n="int32"===t?new Int32Array(e.length):new Uint8Array(e.length);for(let t=0;t<n.length;++t)n[t]=Math.round(e[t]);return n}throw new Error("Unknown dtype ".concat(t))}(t,r)),n.values}acquireTexture(e,t,n,r){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>1024*this.numMBBeforeWarning*1024){const e=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn("High memory usage in GPU: ".concat(e," MB, ")+"most likely due to a memory leak")}return this.textureManager.acquireTexture(e,t,r)}computeBytes(e,t){return e[0]*e[1]*r.util.bytesPerElement(t)}}

1t.MathBackendWebGL=M,M.nextDataId=0},108244:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0});var r={webgl:!0,version_webgl:!0};Object.defineProperty(t,"version_webgl",{enumerable:!0,get:function(){return s.version}}),t.webgl=void 0;var a=n(735534),o=n(473002),s=n(319902),i=n(929267);Object.keys(i).forEach((function(e){"default"!==e&&"__esModule"!==e&&(Object.prototype.hasOwnProperty.call(r,e)||e in t&&t[e]===i[e]||Object.defineProperty(t,e,{enumerable:!0,get:function(){return i[e]}}))})),a.device_util.isBrowser()&&(0,a.registerBackend)("webgl",(()=>new o.MathBackendWebGL),2);t.webgl={forceHalfFloat:i.forceHalfFloat}},716833:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.BatchNormProgram=void 0;var r=n(735534);t.BatchNormProgram=class{constructor(e,t,n,a,o,s){this.outputShape=[],this.variableNames=["x","mean","variance"],r.backend_util.assertAndGetBroadcastShape(e,t),r.backend_util.assertAndGetBroadcastShape(e,n);let i="0.0";null!=a&&(r.backend_util.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let c="1.0";null!=o&&(r.backend_util.assertAndGetBroadcastShape(e,o),this.variableNames.push("scale"),c="getScaleAtOutCoords()"),this.outputShape=e,this.userCode="\n      void main() {\n        float x = getXAtOutCoords();\n        float mean = getMeanAtOutCoords();\n        float variance = getVarianceAtOutCoords();\n        float offset = ".concat(i,";\n        float scale = ").concat(c,";\n        float inv = scale * inversesqrt(variance + float(").concat(s,"));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    ")}}},577245:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.BatchNormPackedProgram=void 0;var r=n(735534);t.BatchNormPackedProgram=class{constructor(e,t,n,a,o,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],r.backend_util.assertAndGetBroadcastShape(e,t),r.backend_util.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";null!=a&&(r.backend_util.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let c="vec4(1.0)";null!=o&&(r.backend_util.assertAndGetBroadcastShape(e,o),this.variableNames.push("scale"),c="getScaleAtOutCoords()"),this.outputShape=e,this.userCode="\n      void main() {\n        vec4 offset = ".concat(i,";\n        vec4 scale = ").concat(c,";\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4(").concat(s,"));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    ")}}},434595:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.COMPLEX_MULTIPLY=t.BinaryOpComplexProgram=void 0;var r=n(735534);t.COMPLEX_MULTIPLY={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"};t.BinaryOpComplexProgram=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=r.backend_util.assertAndGetBroadcastShape(t,n),this.userCode="\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        ".concat(e,"\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    ")}}},386960:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.SQUARED_DIFFERENCE=t.CHECK_NAN_SNIPPET=t.BinaryOpProgram=void 0;var r=n(735534),a=n(965438);t.CHECK_NAN_SNIPPET="\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n",t.SQUARED_DIFFERENCE="return (a - b) * (a - b);";t.BinaryOpProgram=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=r.backend_util.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=(0,a.useShapeUniforms)(this.outputShape.length),this.userCode="\n      float binaryOperation(float a, float b) {\n        ".concat(e,"\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    ")}}}

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1Object.defineProperty(t,"__esModule",{value:!0}),t.Conv3DDerInputProgram=t.Conv3DDerFilterProgram=t.Conv2DDerInputProgram=t.Conv2DDerFilterProgram=void 0;t.Conv2DDerFilterProgram=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,o="channelsLast"===e.dataFormat;this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ".concat(e.batchSize,"; b++) {\n          for (int yR = 0; yR < ").concat(e.outHeight,"; yR++) {\n            int xR = wR + yR * ").concat(t," - ").concat(r,";\n\n            if (xR < 0 || xR >= ").concat(e.inHeight,") {\n              continue;\n            }\n\n            for (int yC = 0; yC < ").concat(e.outWidth,"; yC++) {\n              int xC = wC + yC * ").concat(n," - ").concat(a,";\n\n              if (xC < 0 || xC >= ").concat(e.inWidth,") {\n                continue;\n              }\n\n              if (").concat(o,") {\n                float dyValue = getDy(b, yR, yC, d2);\n                float xValue = getX(b, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              } else {\n                float dyValue = getDy(b, d2, yR, yC);\n                float xValue = getX(b, d1, xR, xC);\n                dotProd += (xValue * dyValue);\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ")}};t.Conv2DDerInputProgram=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,o="channelsLast"===e.dataFormat,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,c=o?1:2,l=o?2:3,u=o?3:1;this.userCode="\n      const ivec2 pads = ivec2(".concat(s,", ").concat(i,");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[").concat(u,"];\n\n        ivec2 dyCorner = ivec2(coords[").concat(c,"], coords[").concat(l,"]) - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ").concat(t,"; wR++) {\n          float dyR = float(dyRCorner + wR) / ").concat(r,".0;\n\n          if (dyR < 0.0 || dyR >= ").concat(e.outHeight,".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ").concat(t," - 1 - wR;\n\n          for (int wC = 0; wC < ").concat(n,"; wC++) {\n            float dyC = float(dyCCorner + wC) / ").concat(a,".0;\n\n            if (dyC < 0.0 || dyC >= ").concat(e.outWidth,".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ").concat(n," - 1 - wC;\n\n            for (int d2 = 0; d2 < ").concat(e.outChannels,"; d2++) {\n\n              if (").concat(o,") {\n                float xValue = getDy(batch, idyR, idyC, d2);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              } else {\n                float xValue = getDy(batch, d2, idyR, idyC);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ")}};t.Conv3DDerFilterProgram=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.padInfo.front,o=e.padInfo.top,s=e.padInfo.left;this.userCode="\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ".concat(e.batchSize,"; b++) {\n          for (int yF = 0; yF < ").concat(e.outDepth,";

1 yF++) {\n            int xF = wF + yF * ").concat(t," - ").concat(a,";\n\n            if (xF < 0 || xF >= ").concat(e.inDepth,") {\n              continue;\n            }\n\n            for (int yR = 0; yR < ").concat(e.outHeight,"; yR++) {\n              int xR = wR + yR * ").concat(n," - ").concat(o,";\n\n              if (xR < 0 || xR >= ").concat(e.inHeight,") {\n                continue;\n              }\n\n              for (int yC = 0; yC < ").concat(e.outWidth,"; yC++) {\n                int xC = wC + yC * ").concat(r," - ").concat(s,";\n\n                if (xC < 0 || xC >= ").concat(e.inWidth,") {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ")}};t.Conv3DDerInputProgram=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,o=e.strideHeight,s=e.strideWidth,i=t-1-e.padInfo.front,c=n-1-e.padInfo.top,l=r-1-e.padInfo.left;this.userCode="\n      const ivec3 pads = ivec3(".concat(i,", ").concat(c,", ").concat(l,");\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ").concat(t,"; wF++) {\n          float dyF = float(dyFCorner + wF) / ").concat(a,".0;\n\n          if (dyF < 0.0 || dyF >= ").concat(e.outDepth,".0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = ").concat(t," - 1 - wF;\n\n          for (int wR = 0; wR < ").concat(n,"; wR++) {\n            float dyR = float(dyRCorner + wR) / ").concat(o,".0;\n\n            if (dyR < 0.0 || dyR >= ").concat(e.outHeight,".0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = ").concat(n," - 1 - wR;\n\n            for (int wC = 0; wC < ").concat(r,"; wC++) {\n              float dyC = float(dyCCorner + wC) / ").concat(s,".0;\n\n              if (dyC < 0.0 || dyC >= ").concat(e.outWidth,".0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = ").concat(r," - 1 - wC;\n\n              for (int d2 = 0; d2 < ").concat(e.outChannels,"; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ")}}},804605:(e,t)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.DepthwiseConv2DDerInputProgram=t.DepthwiseConv2DDerFilterProgram=void 0;t.DepthwiseConv2DDerFilterProgram=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * ".concat(o," + dm;\n\n        float dotProd = 0.0;\n\n        // TO DO: Vec4 over the batch size\n        for (int b = 0; b < ").concat(e.batchSize,"; b++) {\n          for (int yR = 0; yR < ").concat(e.outHeight,"; yR++) {\n            int xR = wR + yR * ").concat(t," - ").concat(r,";\n\n            if (xR < 0 || xR >= ").concat(e.inHeight,") {\n              continue;\n            }\n\n            for (int yC = 0; yC < ").concat(e.outWidth,"; yC++) {\n              int xC = wC + yC * ").concat(n," - ").concat(a,";\n\n              if (xC < 0 || xC >= ").concat(e.inWidth,") {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ")}};t.DepthwiseConv2DDerInputProgram=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,o=t-1-e.padInfo.top,s=n-1-e.padInfo.left,i=e.outChannels/e.inChannels;this.userCode="\n      const ivec2 pads = ivec2(".concat(o,", ").concat(s,");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < ").concat(t,"; wR++) {\n          float dyR = float(dyRCorner + wR) / ").concat(r,".0;\n\n          if (dyR < 0.0 || dyR >= ").concat(e.outHeight,".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ").concat(t," - 1 - wR;\n\n          for (int wC = 0; wC < ").concat(n,"; wC++) {\n            float dyC = float(dyCCorner + wC) / ").concat(a,".0;\n\n            if (dyC < 0.0 || dyC >= ").concat(e.outWidth,".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ").concat(n," - 1 - wC;\n\n            // TO DO: Vec4 over the channelMul\n            for (int dm = 0; dm < ").concat(i,"; dm++) {\n              int d2 = d1 * ").concat(i," + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ")}}},811213:(e,t)=>{"use strict";

1Object.defineProperty(t,"__esModule",{value:!0}),t.Conv3DProgram=t.Conv2DProgram=void 0;t.Conv2DProgram=class{constructor(e){let t=arguments.length>1&&void 0!==arguments[1]&&arguments[1],n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:null,r=arguments.length>3&&void 0!==arguments[3]&&arguments[3],a=arguments.length>4&&void 0!==arguments[4]&&arguments[4];this.variableNames=["x","W"],this.outputShape=e.outShape;const o=e.padInfo.top,s=e.padInfo.left,i=e.strideHeight,c=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,d=e.filterHeight,p=e.filterWidth,h=4*Math.floor(e.inChannels/4),f=e.inChannels%4,m="channelsLast"===e.dataFormat,g=m?1:2,y=m?2:3,v=m?3:1;let b="",x="";n&&(b=r?"float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ".concat(n,"\n        }"):a?"float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ".concat(n,"\n        }"):"\n          float activation(float x) {\n            ".concat(n,"\n          }\n        "),x="result = activation(result);");const w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode="\n      ".concat(b,"\n\n      const ivec2 strides = ivec2(").concat(i,", ").concat(c,");\n      const ivec2 pads = ivec2(").concat(o,", ").concat(s,");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[").concat(v,"];\n\n        ivec2 xRCCorner =\n            ivec2(coords[").concat(g,"], coords[").concat(y,"]) * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ").concat(d,"; wR++) {\n          int xR = xRCorner + wR * ").concat(l,";\n\n          if (xR < 0 || xR >= ").concat(e.inHeight,") {\n            continue;\n          }\n\n          for (int wC = 0; wC < ").concat(p,"; wC++) {\n            int xC = xCCorner + wC * ").concat(u,";\n\n            if (xC < 0 || xC >= ").concat(e.inWidth,") {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < ").concat(h,"; d1 += 4) {\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              if (").concat(m,") {\n                vec4 xValues = vec4(\n                  getX(batch, xR, xC, d1),\n                  getX(batch, xR, xC, d1 + 1),\n                  getX(batch, xR, xC, d1 + 2),\n                  getX(batch, xR, xC, d1 + 3)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec4 xValues = vec4(\n                  getX(batch, d1, xR, xC),\n                  getX(batch, d1 + 1, xR, xC),\n                  getX(batch, d1 + 2, xR, xC),\n                  getX(batch, d1 + 3, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n\n            if (").concat(1===f,") {\n\n              if (").concat(m,") {\n                dotProd +=\n                    getX(batch, xR, xC, ").concat(h,") *\n                    getW(wR, wC, ").concat(h,", d2);\n              } else {\n                dotProd +=\n                    getX(batch, ").concat(h,", xR, xC) *\n                    getW(wR, wC, ").concat(h,", d2);\n              }\n\n            } else if (").concat(2===f,") {\n              vec2 wValues = vec2(\n                getW(wR, wC, ").concat(h,", d2),\n                getW(wR, wC, ").concat(h," + 1, d2)\n              );\n\n              if (").concat(m,") {\n                vec2 xValues = vec2(\n                  getX(batch, xR, xC, ").concat(h,"),\n                  getX(batch, xR, xC, ").concat(h," + 1)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec2 xValues = vec2(\n                  getX(batch, ").concat(h,", xR, xC),\n                  getX(batch, ").concat(h," + 1, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            } else if (").concat(3===f,") {\n              vec3 wValues = vec3(\n                getW(wR, wC, ").concat(h,", d2),\n                getW(wR, wC, ").concat(h," + 1, d2),\n                getW(wR, wC, ").concat(h," + 2, d2)\n              );\n\n              if (").concat(m,") {\n                vec3 xValues = vec3(\n                  getX(batch, xR, xC, ").

1concat(h,"),\n                  getX(batch, xR, xC, ").concat(h," + 1),\n                  getX(batch, xR, xC, ").concat(h," + 2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec3 xValues = vec3(\n                  getX(batch, ").concat(h,", xR, xC),\n                  getX(batch, ").concat(h," + 1, xR, xC),\n                  getX(batch, ").concat(h," + 2, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            }\n          }\n        }\n\n        float result = dotProd;\n        ").concat(w,"\n        ").concat(x,"\n        setOutput(result);\n      }\n    ")}};t.Conv3DProgram=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;const t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,a=e.strideDepth,o=e.strideHeight,s=e.strideWidth,i=e.dilationDepth,c=e.dilationHeight,l=e.dilationWidth,u=e.filterDepth,d=e.filterHeight,p=e.filterWidth,h=4*Math.floor(e.inChannels/4),f=e.inChannels%4;this.userCode="\n      const ivec3 strides = ivec3(".concat(a,", ").concat(o,", ").concat(s,");\n      const ivec3 pads = ivec3(").concat(t,", ").concat(n,", ").concat(r,");\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ").concat(u,"; wF++) {\n          int xF = xFCorner + wF * ").concat(i,";\n\n          if (xF < 0 || xF >= ").concat(e.inDepth,") {\n            continue;\n          }\n\n          for (int wR = 0; wR < ").concat(d,"; wR++) {\n            int xR = xRCorner + wR * ").concat(c,";\n\n            if (xR < 0 || xR >= ").concat(e.inHeight,") {\n              continue;\n            }\n\n            for (int wC = 0; wC < ").concat(p,"; wC++) {\n              int xC = xCCorner + wC * ").concat(l,";\n\n              if (xC < 0 || xC >= ").concat(e.inWidth,") {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < ").concat(h,"; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if (").concat(1===f,") {\n                dotProd +=\n                  getX(batch, xF, xR, xC, ").concat(h,") *\n                  getW(wF, wR, wC, ").concat(h,", d2);\n              } else if (").concat(2===f,") {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, ").concat(h,"),\n                  getX(batch, xF, xR, xC, ").concat(h," + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, ").concat(h,", d2),\n                  getW(wF, wR, wC, ").concat(h," + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if (").concat(3===f,") {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, ").concat(h,"),\n                  getX(batch, xF, xR, xC, ").concat(h," + 1),\n                  getX(batch, xF, xR, xC, ").concat(h," + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, ").concat(h,", d2),\n                  getW(wF, wR, wC, ").concat(h," + 1, d2),\n                  getW(wF, wR, wC, ").concat(h," + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ")}}},73168:(e,t,n)=>{"use strict";

1Object.defineProperty(t,"__esModule",{value:!0}),t.DepthwiseConv2DProgram=void 0;var r=n(965438);t.DepthwiseConv2DProgram=class{constructor(e){let t=arguments.length>1&&void 0!==arguments[1]&&arguments[1],n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:null,a=arguments.length>3&&void 0!==arguments[3]&&arguments[3],o=arguments.length>4&&void 0!==arguments[4]&&arguments[4];this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=(0,r.useShapeUniforms)(this.outputShape.length);const s=e.filterHeight,i=e.filterWidth,c=e.outChannels/e.inChannels;let l="",u="";n&&(l=a?"float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ".concat(n,"\n        }"):o?"float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ".concat(n,"\n        }"):"\n          float activation(float x) {\n            ".concat(n,"\n          }\n        "),u="result = activation(result);");const d=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),o&&this.variableNames.push("leakyreluAlpha"),this.userCode="\n      ".concat(l,"\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ").concat(c,";\n        int q = d2 - d1 * ").concat(c,";\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < ").concat(s,"; wR++) {\n          int xR = xRCorner + wR * dilations[0];\n\n          if (xR < 0 || xR >= inDims[0]) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ").concat(i,"; wC++) {\n            int xC = xCCorner + wC * dilations[1];\n\n            if (xC < 0 || xC >= inDims[1]) {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n\n        float result = dotProd;\n        ").concat(d,"\n        ").concat(u,"\n        setOutput(result);\n      }\n    ")}}},318263:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.DepthwiseConvPacked2DProgram=void 0;var r=n(735534),a=n(965438);t.DepthwiseConvPacked2DProgram=class{constructor(e){let t=arguments.length>1&&void 0!==arguments[1]&&arguments[1],n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:null,o=arguments.length>3&&void 0!==arguments[3]&&arguments[3],s=arguments.length>4&&void 0!==arguments[4]&&arguments[4];this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=(0,a.useShapeUniforms)(this.outputShape.length);const i=e.outChannels/e.inChannels,c=e.padInfo.left,l=e.strideWidth,u=e.dilationWidth,d=e.filterHeight,p=e.filterWidth,h=p;let f="\n      int xR; int xC; int xCOffset;\n      vec4 wTexel; vec4 previous; vec4 final;";for(let e=0;e<p;e++)f+="\n          vec4 xTexelC".concat(2*e,";\n          int xTexelC").concat(2*e,"Ready;\n          vec4 xTexelC").concat(2*e+1,";\n          int xTexelC").concat(2*e+1,"Ready;\n          vec4 xC").concat(e,";");f+="\n    for (int r = 0; r < ".concat(d,"; r++) {\n      ");for(let e=0;e<p;e++)f+="\n          xTexelC".concat(2*e," = vec4(0.0);\n          xTexelC").concat(2*e,"Ready = 0;\n          xTexelC").concat(2*e+1," = vec4(0.0);\n          xTexelC").concat(2*e+1,"Ready = 0;\n          xC").concat(e," = vec4(0.0);");f+="\n        xR = xRCorner + r * dilations[0];\n        if (xR >=0 && xR < inDims[0]) {\n      ";for(let e=0;e<(h+1)/2;e++){const t=2*e;if(f+="\n          xC = xCCorner + ".concat(t*u,";\n          "),1===l){if(t<p&&(c%2==1?(f+="\n                xCOffset = xC + 1;\n                if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC".concat(t,"Ready == 0) {\n                  xTexelC").concat(t," = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC").concat(t,".zw = vec2(0.0);\n                  }\n                  xTexelC").concat(t,"Ready = 1;\n                }\n              "),f+=1===u&&t>0?"\n                xC".concat(t," = vec4(xTexelC").concat(t-2,".zw, xTexelC").concat(t,".xy);\n                "):"\n                  xCOffset = xC + 1 - 2;\n\n                  if (xCOffset >= 0 && xCOffset < inDims[1]) {\n                    previous = getX(batch, xR, xCOffset, d1);\n\n                    // Need to manually clear unused channels in case\n                    // we're reading from recycled texture.\n                    if (xCOffset + 1 >= inDims[1]) {\n                      previous.zw = vec2(0.0);\n                    }\n\n                    xC".concat(t," = vec4(previous.zw, xTexelC").concat(t,".xy);\n                  } else {\n                    xC").concat(t," = vec4(0.0, 0.0, xTexelC").concat(t,".xy);\n                  }\n                  ")):f+="\n                if (xC >= 0 && xC < inDims[1] && xTexelC".concat(t,"Ready == 0) {\n                  xTexelC").concat(t," = getX(batch, xR, xC, d1);\n                  if (xC + 1 >= inDims[1]) {\n                    xTexelC").concat(t,".zw = vec2(0.0);\n                  }\n                  xTexelC").concat(t,"Ready = 1;\n                }\n\n                xC").concat(t," = xTexelC").concat(t,";\n                "),t+1<p)){const e=c%2==0?r.util.nearestLargerEven(u):u;u%2==0&&c%2==1||u%2!=0&&c%2!=1?(f+="\n                  xCOffset = xC + imod(pads[1], 2) + ".concat(e,";\n\n                  if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC").concat(t+1,"Ready == 0) {\n                    xTexelC").concat(t+1," = getX(batch, xR, xCOffset, d1);\n\n                    // Need to manually clear unused channels in case\n                    // we're reading from recycled texture.\n                    if (xCOffset + 1 >= inDims[1]) {\n                      xTexelC").concat(t+1,".zw = vec2(0.0);\n                    }\n                    xTexelC").concat(t+1,"Ready = 1;\n                  }\n                  "),u>1&&(f+="\n                    xCOffset -= 2;\n                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC".concat(t,"Ready == 0) {\n                      xTexelC").concat(t," = getX(batch, xR, xCOffset, d1);\n                      xTexelC").concat(t,"Ready = 1;\n                    }\n                    ")),f+="\n                  xC".concat(t+1," = vec4(xTexelC").concat(t,".zw, xTexelC").concat(t+1,".xy);\n                  ")):f+=1===e?"\n                    xC".concat(t+1," = xTexelC").concat(t,";\n                    "):"\n                    xCOffset = xC + ".concat(e,";\n\n                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC").concat(t+1,"Ready == 0) {\n                      xTexelC").concat(t+1," = getX(batch, xR, xCOffset, d1);\n                      if (xCOffset + 1 >= inDims[1]) {\n                        xTexelC").concat(t+1,".zw = vec2(0.0);\n                      }\n                      xTexelC").concat(t+1,"Ready = 1;\n                    }\n\n                    xC").concat(t+1," = xTexelC").concat(t+1,";\n                    ")}}else t<p&&(c%2==1?(f+="\n                xCOffset = xC + 1 - 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This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),null!=this.outputTexture&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");const e=this.gl;i.callAndCheck(e,(()=>e.finish())),i.callAndCheck(e,(()=>e.bindFramebuffer(e.FRAMEBUFFER,null))),i.callAndCheck(e,(()=>e.deleteFramebuffer(this.framebuffer))),i.callAndCheck(e,(()=>e.bindBuffer(e.ARRAY_BUFFER,null))),i.callAndCheck(e,(()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null))),i.callAndCheck(e,(()=>e.deleteBuffer(this.indexBuffer))),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),o.createFloat32MatrixTexture(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),o.createFloat16MatrixTexture(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),o.createUnsignedBytesMatrixTexture(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),o.uploadPixelDataToTexture(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,r){this.throwIfDisposed(),o.uploadDenseMatrixToTexture(this.gl,e,t,n,r,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),o.createFloat16PackedMatrixTexture(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),o.createPackedMatrixTexture(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(i.unbindColorTextureFromFramebuffer(this.gl,this.framebuffer),this.outputTexture=null),i.callAndCheck(this.gl,(()=>this.gl.deleteTexture(e)))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,(()=>o.downloadByteEncodedFloatMatrixFromOutputTexture(this.gl,t,n,this.textureConfig)))}downloadPackedMatrixFromBuffer(e,t,n,r,a,s){return o.downloadPackedMatrixFromBuffer(this.gl,e,t,n,r,a,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return o.downloadFloat32MatrixFromBuffer(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);const r=o.createBufferFromOutputTexture(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r}createAndWaitForFence(){const e=this.createFence(this.gl);

1return this.pollFence(e)}createFence(e){let t,n;if((0,r.env)().getBool("WEBGL_FENCE_API_ENABLED")){const r=e,a=r.fenceSync(r.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{const e=r.clientWaitSync(a,0,0);return e===r.ALREADY_SIGNALED||e===r.CONDITION_SATISFIED},t=a}else(0,r.env)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,(0,r.env)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,(()=>o.downloadMatrixFromPackedOutputTexture(this.gl,t,n)))}createProgram(e){this.throwIfDisposed();const t=this.gl;null==this.vertexShader&&(this.vertexShader=o.createVertexShader(t));const n=i.createProgram(t);return i.callAndCheck(t,(()=>t.attachShader(n,this.vertexShader))),i.callAndCheck(t,(()=>t.attachShader(n,e))),i.linkProgram(t,n),this.debug&&i.validateProgram(t,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=o.bindVertexProgramAttributeStreams(t,this.program,this.vertexBuffer)),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),null!=e&&i.callAndCheck(this.gl,(()=>this.gl.deleteProgram(e)))}setProgram(e){this.throwIfDisposed(),this.program=e,null!=this.program&&this.debug&&i.validateProgram(this.gl,this.program),i.callAndCheck(this.gl,(()=>this.gl.useProgram(e)))}getUniformLocation(e,t){let n=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];return this.throwIfDisposed(),n?i.getProgramUniformLocationOrThrow(this.gl,e,t):i.getProgramUniformLocation(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),i.callAndCheck(this.gl,(()=>this.gl.getAttribLocation(e,t)))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),i.bindTextureToProgramUniformSampler(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();const[r,a]=s.getPackedMatrixTextureShapeWidthHeight(t,n);this.setOutputMatrixTextureDriver(e,r,a)}setOutputMatrixWriteRegion(e,t,n,r){this.setOutputMatrixWriteRegionDriver(n,e,r,t)}setOutputPackedMatrixWriteRegion(e,t,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){null!=this.program&&i.validateProgram(this.gl,this.program),i.validateFramebuffer(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();const e=this.gl;this.debug&&this.debugValidate(),i.callAndCheck(e,(()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0)))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),i.callAndCheck(this.gl,(()=>this.gl.finish()))}getQueryTimerExtension(){return null==this.disjointQueryTimerExtension&&(this.disjointQueryTimerExtension=i.getExtensionOrThrow(this.gl,2===(0,r.env)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(2===(0,r.env)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){const e=this.gl,t=this.getQueryTimerExtensionWebGL2(),n=e.createQuery();return e.beginQuery(t.TIME_ELAPSED_EXT,n),n}const e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(2===(0,r.env)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){const e=this.gl,t=this.getQueryTimerExtensionWebGL2();return void e.endQuery(t.TIME_ELAPSED_EXT)}const e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await r.util.repeatedTry((()=>this.disposed||this.isQueryAvailable(e,(0,r.env)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")))),this.getQueryTime(e,(0,r.env)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(0===t)return null;if(2===t){const t=this.gl;return t.getQueryParameter(e,t.QUERY_RESULT)/1e6}{const t=this.getQueryTimerExtensionWebGL1();return t.getQueryObjectEXT(e,t.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(0===t)return!0;

1if(2===t){const t=this.gl,n=this.getQueryTimerExtensionWebGL2(),r=t.getQueryParameter(e,t.QUERY_RESULT_AVAILABLE);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),r&&!this.disjoint}{const t=this.getQueryTimerExtensionWebGL1(),n=t.getQueryObjectEXT(e,t.QUERY_RESULT_AVAILABLE_EXT);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(t.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise((t=>{this.addItemToPoll((()=>e.isFencePassed()),(()=>t()))}))}pollItems(){const e=u(this.itemsToPoll.map((e=>e.isDoneFn)));for(let t=0;t<=e;++t){const{resolveFn:e}=this.itemsToPoll[t];e()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1||r.util.repeatedTry((()=>(this.pollItems(),0===this.itemsToPoll.length)))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),i.bindColorTextureToFramebuffer(this.gl,e,this.framebuffer),this.debug&&i.validateFramebuffer(this.gl)}unbindTextureToFrameBuffer(){null!=this.outputTexture?(i.bindColorTextureToFramebuffer(this.gl,this.outputTexture,this.framebuffer),this.debug&&i.validateFramebuffer(this.gl)):i.unbindColorTextureFromFramebuffer(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);const n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();const r=this.gl;i.bindColorTextureToFramebuffer(r,e,this.framebuffer),this.debug&&i.validateFramebuffer(r),this.outputTexture=e,i.callAndCheck(r,(()=>r.viewport(0,0,t,n))),i.callAndCheck(r,(()=>r.scissor(0,0,t,n)))}setOutputMatrixWriteRegionDriver(e,t,n,r){this.throwIfDisposed(),i.callAndCheck(this.gl,(()=>this.gl.scissor(e,t,n,r)))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(null==this.program)throw new Error("No GPU program is currently set.")}}},965438:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.compileProgram=function(e,t,n,s){const i=n.map(((e,n)=>{const r={logicalShape:e.shape,texShape:e.isUniform?null:e.texData.texShape,isUniform:e.isUniform,isPacked:!e.isUniform&&e.texData.isPacked,flatOffset:null};return null!=e.texData&&null!=e.texData.slice&&e.texData.slice.flatOffset>0&&(r.flatOffset=e.texData.slice.flatOffset),{name:t.variableNames[n],shapeInfo:r}})),c=i.map((e=>e.shapeInfo)),l={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},u=a.makeShader(i,l,t),d=(0,o.createFragmentShader)(e.gl,u),p=e.createProgram(d);let h=null;const f=e.getUniformLocation(p,"NAN",!1);1===(0,r.env)().getNumber("WEBGL_VERSION")&&(h=e.getUniformLocation(p,"INFINITY",!1));const m=!1,g={},y={},v={};for(let n=0;n<t.variableNames.length;n++){const r=t.variableNames[n];g[r]=e.getUniformLocation(p,r,m),g["offset".concat(r)]=e.getUniformLocation(p,"offset".concat(r),m),t.enableShapeUniforms&&(y["".concat(r,"Shape")]=e.getUniformLocation(p,"".concat(r,"Shape"),m),v["".concat(r,"TexShape")]=e.getUniformLocation(p,"".concat(r,"TexShape"),m))}let b,x,w;t.enableShapeUniforms&&(b=e.getUniformLocation(p,"outShape",m),w=e.getUniformLocation(p,"outShapeStrides",m),x=e.getUniformLocation(p,"outTexShape",m));const k=[];

1t.customUniforms&&t.customUniforms.forEach(((t,n)=>{k[n]=e.getUniformLocation(p,t.name,m)}));return{program:t,fragmentShader:d,source:u,webGLProgram:p,uniformLocations:g,customUniformLocations:k,inShapeInfos:c,outShapeInfo:l,infLoc:h,nanLoc:f,inShapesLocations:y,inTexShapesLocations:v,outShapeLocation:b,outShapeStridesLocation:w,outTexShapeLocation:x}},t.makeShaderKey=function(e,t,n){let o="";t.concat(n).forEach((t=>{const s=null!=t.texData&&null!=t.texData.slice&&t.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!t.isUniform){const i=t.texData.texShape,{useSqueezeShape:c,uniformShape:l,keptDims:u}=a.getUniformInfoFromShape(e.packedInputs,t.shape,i);let d="",p="",h="";if(1===l.length&&e.packedInputs){const e=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)];d="".concat(e[0]>1,"_").concat(e[1]>1)}else if(2!==l.length||e.packedInputs){if(l.length>2&&!e.packedInputs){const e=r.util.computeStrides(l);h="".concat(e[0]===i[1],"_").concat(e[e.length-1]===i[1])}}else p="".concat(l[0]>1,"_").concat(l[1]>1);const f=t.shape.length,m=2===l.length&&r.util.arraysEqual(t.shape,i),g=1===r.util.sizeFromShape(t.shape),y=r.backend_util.getBroadcastDims(t.shape,n.shape),v=!e.packedInputs&&f===n.shape.length&&r.util.arraysEqual(i,n.texData.texShape),b=e.packedInputs||l.length>2?"":"".concat(i[0]>1,"_").concat(i[1]>1);o+="".concat(f,"_").concat(v,"_").concat(c?u:"","_").concat(l.length,"_").concat(g,"_").concat(y,"_").concat(m,"_").concat(d,"_").concat(p,"_").concat(h,"_").concat(b,"_").concat(s)}else{const e=t.isUniform?"uniform":t.texData.texShape;o+="".concat(t.shape,"_").concat(e,"_").concat(s)}}));const s=e.userCode;let i=e.constructor.name;return i+="_"+o+"_"+s+"".concat((0,r.env)().getNumber("WEBGL_VERSION")),i},t.runProgram=function(e,t,n,o,s){t.program.enableShapeUniforms||(i(t.inShapeInfos,n),i([t.outShapeInfo],[o]));const c=o.texData.texture,l=o.texData.texShape;o.texData.isPacked?e.setOutputPackedMatrixTexture(c.texture,l[0],l[1]):e.setOutputMatrixTexture(c.texture,l[0],l[1]);e.setProgram(t.webGLProgram),1===(0,r.env)().getNumber("WEBGL_VERSION")&&null!==t.infLoc&&e.gl.uniform1f(t.infLoc,1/0);null!==t.nanLoc&&e.gl.uniform1f(t.nanLoc,NaN);n.forEach(((n,o)=>{const s=t.program.variableNames[o],i=t.uniformLocations[s],c=t.uniformLocations["offset".concat(s)],l=t.inShapesLocations["".concat(s,"Shape")],u=t.inTexShapesLocations["".concat(s,"TexShape")];if(l){const{uniformShape:r}=a.getUniformInfoFromShape(t.program.packedInputs,n.shape,n.texData.texShape);switch(r.length){case 1:e.gl.uniform1iv(l,new Int32Array(r));break;case 2:e.gl.uniform2iv(l,new Int32Array(r));break;case 3:e.gl.uniform3iv(l,new Int32Array(r));break;case 4:e.gl.uniform4iv(l,new Int32Array(r))}}if(u&&e.gl.uniform2i(u,n.texData.texShape[0],n.texData.texShape[1]),null!=i)if(n.isUniform)if(r.util.sizeFromShape(n.shape)<2)e.gl.uniform1f(i,n.uniformValues[0]);else{let t=n.uniformValues;t instanceof Float32Array||(t=new Float32Array(t)),e.gl.uniform1fv(i,t)}else null!=n.texData.slice&&null!=c&&e.gl.uniform1i(c,n.texData.slice.flatOffset),e.setInputMatrixTexture(n.texData.texture.texture,i,o)}));const u=t.outShapeLocation;if(u)switch(o.shape.length){case 1:e.gl.uniform1iv(u,new Int32Array(o.shape));break;case 2:e.gl.uniform2iv(u,new Int32Array(o.shape));break;case 3:e.gl.uniform3iv(u,new Int32Array(o.shape));break;case 4:e.gl.uniform4iv(u,new Int32Array(o.shape))}if(t.outShapeStridesLocation){const n=r.util.computeStrides(o.shape);switch(o.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(n));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(n));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(n))}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,o.texData.texShape[0],o.texData.texShape[1]);

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1,362114:(e,t)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.LRNProgram=void 0;t.LRNProgram=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[];const o=t,s=e[3]-1;let i;this.outputShape=e;const c="float(".concat(n,") + float(").concat(r,") * sum");i=.5===a?"inversesqrt(".concat(c,")"):1===a?"1.0/(".concat(c,")"):"exp(log(".concat(c,") * float(-").concat(a,"));"),this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -".concat(o,"; j <= ").concat(o,"; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  ").concat(s,") {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * ").concat(i,";\n        setOutput(val);\n      }\n    ")}}},111759:(e,t)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.LRNGradProgram=void 0;t.LRNGradProgram=class{constructor(e,t,n,r,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=a,this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < ".concat(this.depth,"; ++d) {\n          int depthBegin = int(max(0.0, float(d - ").concat(t,")));\n          int depthEnd = int(min(float(").concat(this.depth,"),\n              float(d + ").concat(t," + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = ").concat(this.depth,";\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float(").concat(r,") * norm + float(").concat(n,");\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float(").concat(r,")\n                * float(").concat(a,")\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * ").concat(a,");\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    ")}}},525504:(e,t)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.LRNPackedProgram=void 0;t.LRNPackedProgram=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;const o=t,s=e[3]-1;let i;this.outputShape=e;const c="float(".concat(n,") + float(").concat(r,") * sum");i=.5===a?"inversesqrt(".concat(c,")"):1===a?"1.0/(".concat(c,")"):"exp(log(".concat(c,") * float(-").concat(a,"));"),this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords.x;\n        int r = coords.y;\n        int c = coords.z;\n        int d = coords.w;\n\n        bool hasNextCol = d < ".concat(this.outputShape[3],";\n        bool hasNextRow = c < ").concat(this.outputShape[2],";\n\n        vec4 sum = vec4(0.);\n        vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n        vec4 xAtOutputCoords = vec4(\n          getChannel(xFragAtOutputCoords, vec2(c, d)),\n          hasNextCol ?\n            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n          hasNextRow ?\n            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n        );\n\n        int firstChannel = d - ").concat(o,";\n        vec2 cache = vec2(0.);\n        if(firstChannel >= 0){\n          vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n            if(hasNextRow){\n              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n            }\n        }

1\n\n        ivec2 depth = ivec2(d, d + 1);\n        for (int j = - ").concat(o,"; j <= ").concat(o,"; j++) {\n          ivec2 idx = depth + j;\n          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(").concat(s,"));\n\n          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n          if(depthInRange || depthPlusOneInRange){\n            vec4 z = vec4(0.);\n            vec4 xFragAtCurrentDepth;\n            z.xz = cache.xy;\n            if(depthPlusOneInRange && hasNextCol){\n              xFragAtCurrentDepth = idx.y != d ?\n                getX(b, r, c, idx.y) : xFragAtOutputCoords;\n              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n              if(hasNextRow){\n                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n              }\n            }\n            cache.xy = z.yw;\n            sum += z * z;\n          }\n        }\n        vec4 result = xAtOutputCoords * ").concat(i,";\n        setOutput(result);\n      }\n    ")}}},862242:(e,t)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.MaxPool3DBackpropProgram=t.MaxPool2DBackpropProgram=void 0;t.MaxPool2DBackpropProgram=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;const t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,a=e.effectiveFilterHeight,o=e.effectiveFilterWidth,s=a-1-e.padInfo.top,i=o-1-e.padInfo.left,c=a*o-1;this.userCode="\n      const ivec2 pads = ivec2(".concat(s,", ").concat(i,");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ").concat(a,";\n          wR += ").concat(r,") {\n          float dyR = float(dyRCorner + wR) / ").concat(t,".0;\n\n          if (dyR < 0.0 || dyR >= ").concat(e.outHeight,".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ").concat(o,"; wC++) {\n            float dyC = float(dyCCorner + wC) / ").concat(n,".0;\n\n            if (dyC < 0.0 || dyC >= ").concat(e.outWidth,".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = ").concat(c," - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * ").concat(o," + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    ")}};t.MaxPool3DBackpropProgram=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;const t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,o=e.dilationHeight,s=e.dilationWidth,i=e.effectiveFilterDepth,c=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=i-1-e.padInfo.front,d=c-1-e.padInfo.top,p=l-1-e.padInfo.left,h=i*c*l-1;this.userCode="\n      const ivec3 pads = ivec3(".concat(u,", ").concat(d,", ").concat(p,");\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ").concat(i,";\n           wD += ").concat(a,") {\n          float dyD = float(dyDCorner + wD) / ").concat(t,".0;\n\n          if (dyD < 0.0 || dyD >= ").concat(e.outDepth,".0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ").concat(c,";\n              wR += ").concat(o,") {\n            float dyR = float(dyRCorner + wR) / ").concat(n,".0;\n\n            if (dyR < 0.0 || dyR >= ").concat(e.outHeight,".0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ").concat(l,";\n                wC += ").concat(s,") {\n              float dyC = float(dyCCorner + wC) / ").concat(r,".0;\n\n              if (dyC < 0.0 || dyC >= ").concat(e.outWidth,".0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n              int maxPosValue = ").concat(h," -\n                  int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n              // Get the current value, check it against the value from the\n              // position matrix.\n              int curPosValue =\n                  wD * ").concat(c," * ").concat(l," +\n                  wR * ").concat(l," + wC;\n              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n              dotProd += dyValue * mask;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ")}}}

1,796183:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.MeanProgram=void 0;var r=n(735534);t.MeanProgram=class{constructor(e,t){this.variableNames=["x"];const{windowSize:n,batchSize:a,inSize:o,outSize:s}=e;this.outputShape=[a,s];const i=4*Math.floor(n/4),c=n%4;let l="sumValue += dot(values, ones);";if(null!=t){const e=1/t;l="sumValue += dot(values * ".concat(r.util.isInt(e)?e.toPrecision(2):e,", ones);")}let u="";o%n>0&&(u="\n        if (inIdx < 0 || inIdx >= ".concat(o,") {\n          return 0.0;\n        }\n      ")),this.userCode="\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ".concat(u,"\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ").concat(n,";\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ").concat(i,"; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ").concat(l,"\n        }\n\n        int inIdx = inOffset + ").concat(i,";\n        if (").concat(1===c,") {\n          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n          ").concat(l,"\n        } else if (").concat(2===c,") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1), 0.0, 0.0);\n\n          ").concat(l,"\n        } else if (").concat(3===c,") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2), 0.0);\n\n          ").concat(l,"\n        }\n        setOutput(sumValue);\n      }\n    ")}}},528893:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.MirrorPadProgram=void 0;var r=n(41990);t.MirrorPadProgram=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map(((t,n)=>t[0]+e[n]+t[1]));const a=e.length,o=(0,r.getCoordsDataType)(a),s=t.map((e=>e[0])).join(","),i=t.map(((t,n)=>t[0]+e[n])).join(","),c=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a),l="reflect"===n?0:1;this.userCode=1!==a?"\n      ".concat(o," start = ").concat(o,"(").concat(s,");\n      ").concat(o," end = ").concat(o,"(").concat(i,");\n\n      void main() {\n        ").concat(o," outC = getOutputCoords();\n        for (int i = 0; i < ").concat(a,"; i++) {\n          if (outC[i] < start[i]) {\n            outC[i] = start[i] * 2 - outC[i] - ").concat(l,";\n          } else if(outC[i] >= end[i]) {\n            outC[i] = (end[i] - 1) * 2 - outC[i] + ").concat(l,";\n          }\n        }\n        ").concat(o," coords = outC - start;\n        setOutput(getX(").concat(c,"));\n      }\n    "):"\n        int start = ".concat(s,";\n        int end = ").concat(i,";\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start) {\n            outC = start * 2 - outC - ").concat(l,";\n          } else if(outC >= end) {\n            outC = (end - 1) * 2 - outC + ").concat(l,";\n          }\n          setOutput(getX(outC - start));\n        }\n      ")}}},878805:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.MirrorPadPackedProgram=void 0;var r=n(530675),a=n(41990);t.MirrorPadPackedProgram=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map(((t,n)=>t[0]+e[n]+t[1]));const o=e.length,s=(0,a.getCoordsDataType)(o),i=t.map((e=>e[0])).join(","),c=t.map(((t,n)=>t[0]+e[n])).join(","),l=(0,r.getChannels)("rc",o),u=(0,r.getChannels)("source",o),d="".concat(l[o-1]," < ").concat(this.outputShape[o-1]),p=1===o?"source":"vec2(".concat(u.slice(-2).join(),")"),h="reflect"===n?0:1;let f="";if(1===o){const e="\n        ".concat(s," source = rc;\n        if (source < start) {\n          source = start * 2 - source - ").concat(h,";\n        } else if (source >

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1,235023:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.PadPackedProgram=void 0;var r=n(530675),a=n(41990);t.PadPackedProgram=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map(((t,n)=>t[0]+e[n]+t[1]));const o=e.length,s=(0,a.getCoordsDataType)(o),i=t.map((e=>e[0])).join(","),c=t.map(((t,n)=>t[0]+e[n])).join(","),l=(0,r.getChannels)("rc",o),u=(0,r.getChannels)("source",o),d="".concat(l[o-1]," < ").concat(this.outputShape[o-1]),p=1===o?"source":"vec2(".concat(u.slice(-2).join(),")"),h=["".concat(s," rc = outputLoc;"),"".concat(l[o-1]," += 1;\n       if(").concat(d,") {\n      "),1===o?"":"}\n       rc = outputLoc;\n       ".concat(l[o-2]," += 1;\n       if(").concat(l[o-2]," < ").concat(this.outputShape[o-2],") {"),1===o?"":"  ".concat(l[o-1]," += 1;\n         if(").concat(d,") {")],f=1===o?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))";let m="";for(let e=0,t=1===o?2:4;e<t;e++)m+="\n        ".concat(h[e],"\n        if (").concat(f,") {\n          result[").concat(e,"] = float(value);\n        } else {\n          ").concat(s," source = rc - start;\n          result[").concat(e,"] = getChannel(getX(").concat(u.join(),"), ").concat(p,");\n        }\n      ");m+=1===o?"} ":"}}",this.userCode="\n      const ".concat(s," start = ").concat(s,"(").concat(i,");\n      const ").concat(s," end = ").concat(s,"(").concat(c,");\n\n      void main() {\n        ").concat(s," outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ").concat(m,"\n        setOutput(result);\n      }\n    ")}}},19718:(e,t)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.Pool3DProgram=t.Pool2DProgram=void 0;t.Pool2DProgram=class{constructor(e,t,n){let r=arguments.length>3&&void 0!==arguments[3]&&arguments[3],a=arguments.length>4&&void 0!==arguments[4]&&arguments[4];if(this.variableNames=["x"],"avg"===t&&n)throw new Error("Cannot compute positions for average pool.");const o=e.filterWidth,s=e.strideHeight,i=e.strideWidth,c=e.dilationHeight,l=e.dilationWidth,u=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;const f="avg"===t,m="((batch  * ".concat(e.inHeight," + xR) * ").concat(e.inWidth," + xC) * ").concat(e.inChannels," + d"),g="(xR * ".concat(e.inWidth," + xC) * ").concat(e.inChannels," + d");let y="0.0";if(f||(y="-1.0 / 1e-20"),n){const t=">=";return void(this.userCode="\n        const ivec2 strides = ivec2(".concat(s,", ").concat(i,");\n        const ivec2 pads = ivec2(").concat(p,", ").concat(h,");\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < ").concat(u,";\n              wR += ").concat(c,") {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ").concat(e.inHeight,") {\n              continue;\n            }\n\n            for (int wC = 0; wC < ").concat(d,";\n                wC += ").concat(l,") {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= ").concat(e.inWidth,") {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value ").concat(t," currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = ").concat(r?a?m:g:"wR * ".concat(d," + wC"),";\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      "))}let v="".concat(t,"(").concat(t,"(").concat(t,"(")+"minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";"avg"===t&&(v="avgValue / count");const b=4*Math.floor(o/4),x=o%4,w="\n      if (".concat(f,") {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ").concat("max","(values, minMaxValue);\n      }\n    ");this.userCode="\n      const ivec2 strides = ivec2(".concat(s,", ").concat(i,");\n      const ivec2 pads = ivec2(").concat(p,", ").concat(h,");\n      const float initializationValue = ").concat(y,";\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= ").concat(e.inWidth,") {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(").concat(y,");\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < ").concat(u,";\n            wR += ").concat(c,") {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= ").concat(e.inHeight,") {\n            continue;\n          }\n\n          for (int wC = 0; wC < ").concat(b,"; wC += 4) {\n            int xC = xCCorner + wC * ").concat(l,";\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ").concat(l,", d),\n              getValue(batch, xR, xC + 2 * ").concat(l,", d),\n              getValue(batch, xR, xC + 3 * ").concat(l,", d)\n            );\n\n            ").concat(w,"\n          }\n\n          int xC = xCCorner + ").concat(b,";\n          if (").concat(1===x,") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            ").concat(w,"\n          } else if (").concat(2===x,") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ").concat(l,", d),\n              initializationValue,\n              initializationValue\n            );\n\n            ").concat(w,"\n          } else if (").concat(3===x,") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ").concat(l,", d),\n              getValue(batch, xR, xC + 2 * ").concat(l,", d),\n              initializationValue\n            );\n\n            ").concat(w,"\n          }\n        }\n        setOutput(").concat(v,");\n      }\n    ")}};t.Pool3DProgram=class{constructor(e,t,n){let r=arguments.length>3&&void 0!==arguments[3]&&arguments[3],a=arguments.length>4&&void 0!==arguments[4]&&arguments[4];if(this.variableNames=["x"],"avg"===t&&n)throw new Error("Cannot compute positions for average pool.");const o=e.filterWidth,s=e.strideDepth,i=e.strideHeight,c=e.strideWidth,l=e.dilationDepth,u=e.dilationHeight,d=e.dilationWidth,p=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;const v="avg"===t;let b="0.0";if(v||(b="-1.0 / 1e-20"),n){const t=">=";return void(this.userCode="\n        const ivec3 strides =\n            ivec3(".concat(s,", ").concat(i,", ").concat(c,");\n        const ivec3 pads = ivec3(").concat(m,", ").concat(g,", ").concat(y,");\n\n        void main() {\n          ivec5 coords = getOutputCoords();\n          int batch = coords.x;\n          int ch = coords.u;\n\n          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n          int xDCorner = xCorner.x;\n          int xRCorner = xCorner.y;\n          int xCCorner = xCorner.z;\n\n          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n\n          for (int wD = 0; wD < ").concat(p,";\n              wD += ").concat(l,") {\n            int xD = xDCorner + wD;\n\n            if (xD < 0 || xD >= ").concat(e.inDepth,") {\n              continue;\n            }\n\n            for (int wR = 0; wR < ").concat(h,";\n                wR += ").concat(u,") {\n              int xR = xRCorner + wR;\n\n              if (xR < 0 || xR >= ").concat(e.inHeight,") {\n                continue;\n              }\n\n              for (int wC = 0; wC < ").concat(f,";\n                  wC += ").concat(d,") {\n                int xC = xCCorner + wC;\n\n                if (xC < 0 || xC >= ").concat(e.inWidth,") {\n                  continue;\n                }\n\n                float value = getX(batch, xD, xR, xC, ch);\n\n                // If a min / max value has already been found, use it. If not,\n                // use the current value.\n                float currMinMaxValue = mix(\n                    value, minMaxValue, minMaxValueFound);\n                if (value ").concat(t," currMinMaxValue) {\n                  minMaxValue = value;\n                  minMaxValueFound = 1.0;\n                  minMaxPosition = ").concat(r?a?"(((batch * ".concat(e.inDepth," + xD) * ").concat(e.inHeight," + xR) * ").concat(e.inWidth," + xC) * ").concat(e.inChannels," + ch"):"((xD * ".concat(e.inHeight," + xR) * ").concat(e.inWidth," + xC) * ").concat(e.inChannels," + ch"):"wD * ".concat(h," * ").concat(f," +\n                      wR * ").concat(f," + wC"),";\n                }\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      "))}let x="".concat(t,"(").concat(t,"(").concat(t,"(")+"minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";"avg"===t&&(x="avgValue / count");const w=4*Math.floor(o/4),k=o%4,_="\n      if (".concat(v,") {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ").concat("max","(values, minMaxValue);\n      }\n    ");this.userCode="\n      const ivec3 strides =\n        ivec3(".concat(s,", ").concat(i,", ").concat(c,");\n      const ivec3 pads = ivec3(").concat(m,", ").concat(g,", ").concat(y,");\n      const float initializationValue = ").concat(b,";\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xD, int xR, int xC, int ch) {\n        if (xC < 0 || xC >= ").concat(e.inWidth,") {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xD, xR, xC, ch);\n      }\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xDCorner = xCorner.x;\n        int xRCorner = xCorner.y;\n        int xCCorner = xCorner.z;\n\n        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(").concat(b,");\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wD = 0; wD < ").concat(p,";\n            wD += ").concat(l,") {\n          int xD = xDCorner + wD;\n\n          if (xD < 0 || xD >= ").concat(e.inDepth,") {\n            continue;\n          }\n\n          for (int wR = 0; wR < ").concat(h,";\n            wR += ").concat(u,") {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ").concat(e.inHeight,") {\n              continue;\n            }\n\n            for (int wC = 0; wC < ").concat(w,"; wC += 4) {\n              int xC = xCCorner + wC * ").concat(d,";\n\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ").concat(d,", ch),\n                getValue(batch, xD, xR, xC + 2 * ").concat(d,", ch),\n                getValue(batch, xD, xR, xC + 3 * ").concat(d,", ch)\n              );\n\n              ").concat(_,"\n            }\n\n            int xC = xCCorner + ").concat(w,";\n            if (").concat(1===k,") {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                initializationValue,\n                initializationValue,\n                initializationValue\n              );\n\n              ").concat(_,"\n            } else if (").concat(2===k,") {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ").concat(d,", ch),\n                initializationValue,\n                initializationValue\n              );\n\n              ").concat(_,"\n            } else if (").concat(3===k,") {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ").concat(d,", ch),\n                getValue(batch, xD, xR, xC + 2 * ").concat(d,", ch),\n                initializationValue\n              );\n\n              ").concat(_,"\n            }\n          }\n          setOutput(").concat(x,");\n        }\n      }\n    ")}}},316187:(e,t)=>{"use strict";

1Object.defineProperty(t,"__esModule",{value:!0}),t.ReduceProgram=void 0;t.ReduceProgram=class{constructor(e,t){this.variableNames=["x"];const{windowSize:n,batchSize:r,inSize:a,outSize:o}=e;this.outputShape=[r,o];let s="0.0",i="";"prod"===t?s="1.0":"min"===t?(s="1.0 / 1e-20",i="min"):"max"===t&&(s="-1.0 / 1e-20",i="max");let c="".concat(t,"(").concat(t,"(").concat(t,"(")+"minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";"sum"===t?c="sumValue":"prod"===t?c="prodValue":"all"===t?c="allValue":"any"===t&&(c="anyValue");const l=4*Math.floor(n/4),u=n%4;let d="\n      if (".concat("sum"===t,") {\n        sumValue += dot(values, ones);\n      } else if (").concat("prod"===t,") {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = ").concat(i,"(values, minMaxValue);\n        if (").concat("min"===t," || ").concat("max"===t,") {\n          minMaxValue = ").concat(i,"(values, minMaxValue);\n          bvec4 isNaN = isnan(values);\n          if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {\n            minMaxValue = vec4(NAN);\n          }\n        }\n      }\n    "),p="vec4";"all"===t?(s="1.0",d="\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      ",p="bvec4"):"any"===t&&(s="0.0",d="\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      ",p="bvec4");let h="";a%n>0&&(h="\n        if (inIdx < 0 || inIdx >= ".concat(a,") {\n          return initializationValue;\n        }\n      ")),this.userCode="\n      const float initializationValue = ".concat(s,";\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ").concat(h,"\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ").concat(n,";\n\n        vec4 minMaxValue = vec4(").concat(s,");\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < ").concat(l,"; i += 4) {\n          int inIdx = inOffset + i;\n          ").concat(p," values = ").concat(p,"(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ").concat(d,"\n        }\n\n        int inIdx = inOffset + ").concat(l,";\n        if (").concat(1===u,") {\n          ").concat(p," values = ").concat(p,"(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          ").concat(d,"\n        } else if (").concat(2===u,") {\n          ").concat(p," values = ").concat(p,"(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          ").concat(d,"\n        } else if (").concat(3===u,") {\n          ").concat(p," values = ").concat(p,"(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          ").concat(d,"\n        }\n        setOutput(").concat(c,");\n      }\n    ")}}},461396:(e,t,n)=>{"use strict";var r=n(735534),a=n(843625),o=n(354418),s=n(243635),i=n(901347),c=n(281243),l=n(963064),u=n(258469),d=n(459182),p=n(100747),h=n(706479),f=n(21068),m=n(969205),g=n(764110),y=n(451439),v=n(476755),b=n(244728),x=n(902565),w=n(976689),k=n(593372),_=n(795397),C=n(188244),P=n(816749),O=n(18827),N=n(846504),I=n(682879),T=n(733326),S=n(680518),E=n(63255),M=n(10266),A=n(555022),D=n(505759),R=n(67774),F=n(96365),j=n(932997),z=n(605516),L=n(690426),V=n(373619),G=n(592334),B=n(250714),W=n(796260),U=n(975169),q=n(312887),H=n(947041),K=n(914173),X=n(612330),Y=n(308838),Z=n(426273),J=n(278663),Q=n(212138),$=n(158489),ee=n(498948),te=n(319903),ne=n(538596),re=n(802175),ae=n(949315),oe=n(395779),se=n(531075),ie=n(684087),ce=n(754224),le=n(694524),ue=n(938357),de=n(116222),pe=n(990931),he=n(444678),fe=n(752555),me=n(885909),ge=n(927043),ye=n(750257),ve=n(944951),be=n(859559),xe=n(506418),we=n(728223),ke=n(934104),_e=n(410636),Ce=n(417271),Pe=n(23909),Oe=n(763052),Ne=n(906064),Ie=n(681681),Te=n(936422),Se=n(682055),Ee=n(190033),Me=n(619366),Ae=n(870728),De=n(197339),Re=n(442304),Fe=n(752485),je=n(105474),ze=n(921460),Le=n(738768),Ve=n(605051),Ge=n(724776),Be=n(293696),We=n(462585),Ue=n(640261),qe=n(620241),He=n(466426),Ke=n(810077),Xe=n(258832),Ye=n(964426),Ze=n(42529),Je=n(157649),Qe=n(731579),$e=n(880024),et=n(519925),tt=n(441488),nt=n(554300),rt=n(641596),at=n(750888),ot=n(104492),st=n(526560),it=n(723295),ct=n(2883),lt=n(580983),ut=n(489291),dt=n(589283),pt=n(144638),ht=n(989210),ft=n(236758),mt=n(778450),gt=n(997608),yt=n(966369),vt=n(726602),bt=n(316428),xt=n(23778),wt=n(322919),kt=n(167112),_t=n(860890),Ct=n(923574),Pt=n(777660),Ot=n(804156),Nt=n(819636),It=n(336963),Tt=n(789276),St=n(607665),Et=n(866168),Mt=n(964298),At=n(543760),Dt=n(67020),Rt=n(939131),Ft=n(24347),jt=n(496301),zt=n(58527),Lt=n(152730),Vt=n(195801),Gt=n(933154),Bt=n(831058),Wt=n(147967),Ut=n(528643),qt=n(727168),Ht=n(571784),Kt=n(926749),

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10?"if(thisRC.y < rows && thisRC.z < cols){":"","\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[").concat(e,"] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        ").concat(e>0?"}":"","\n      ")}this.userCode="\n      ".concat(function(e,t){const n=t?a.getLogicalCoordinatesFromFlatIndexByUniform(["r","c","d"],"inputShape"):a.getLogicalCoordinatesFromFlatIndex(["r","c","d"],e);return"\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      ".concat(n,"\n      return ivec3(r, c, d);\n    }\n  ")}(t,this.enableShapeUniforms),"\n      ").concat(this.enableShapeUniforms?a.getFlatIndexFrom3DOutput():a.getFlatIndexFrom3D(e),"\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = ").concat(this.enableShapeUniforms?"outShape[1]":e[1],";\n        int cols = ").concat(this.enableShapeUniforms?"outShape[2]":e[2],";\n\n        ").concat(n,"\n\n        setOutput(result);\n      }\n    ")}}},63622:(e,t)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.ResizeBilinearBackpropProgram=void 0;t.ResizeBilinearBackpropProgram=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;const[,r,a]=t,[,o,s]=e,i=[n&&o>1?r-1:r,n&&s>1?a-1:a],c=[n&&o>1?o-1:o,n&&s>1?s-1:s],l=i[0]/c[0],u=i[1]/c[1],d=1/l,p=1/u,h=2*Math.ceil(d)+2,f=2*Math.ceil(p)+2;this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(".concat(l,");\n        const float widthScale = float(").concat(u,");\n\n        const float invHeightScale = float(").concat(d,");\n        const float invWidthScale = float(").concat(p,");\n\n        const int winHeight = int(").concat(h,");\n        const int winWidth = int(").concat(f,");\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ").concat(o,") {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ").concat(s,") {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), ").concat(r-1,".0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), ").concat(a-1,".0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    ")}}},881898:(e,t)=>

1{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.ResizeBilinearProgram=void 0;t.ResizeBilinearProgram=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];const[o,s,i,c]=e;this.outputShape=[o,t,n,c];const l=[r&&t>1?s-1:s,r&&n>1?i-1:i],u=[r&&t>1?t-1:t,r&&n>1?n-1:n];let d;d=a?"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":"vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode="\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ".concat(l[0]/u[0],",\n          ").concat(l[1]/u[1],");\n      const vec2 inputShapeRC = vec2(").concat(s,".0, ").concat(i,".0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = ").concat(d,";\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    ")}}},425419:(e,t)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.ResizeBilinearPackedProgram=void 0;t.ResizeBilinearPackedProgram=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];const[o,s,i,c]=e;this.outputShape=[o,t,n,c];const l=[r&&t>1?s-1:s,r&&n>1?i-1:i],u=[r&&t>1?t-1:t,r&&n>1?n-1:n];let d;d=a?"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":"vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode="\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ".concat(l[0]/u[0],",\n          ").concat(l[1]/u[1],",\n          ").concat(l[1]/u[1],");\n      const vec3 inputShapeRC = vec3(").concat(s,".0, ").concat(i,".0,\n                                     ").concat(i,".0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = ").concat(d,";\n\n        // Compute the four integer indices.\n        ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n        ivec3 sourceCeilRC = ivec3(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ").concat(c-1,";\n        bool hasNextRow = coords.z < ").concat(n-1,";\n\n        // In parallel, construct four corners for all four components in\n        // packed 2x2 cell.\n        vec4 topLeft = vec4(\n          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 bottomLeft = vec4(\n          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getA

1Value(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 topRight = vec4(\n          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec4 bottomRight = vec4(\n          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n        vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n        vec4 newValue = mix(top, bottom, fracRC.x);\n\n        setOutput(newValue);\n      }\n    ")}}},174583:(e,t)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.ResizeNearestNeigborBackpropProgram=void 0;t.ResizeNearestNeigborBackpropProgram=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;const[,r,a]=t,[,o,s]=e,i=[n&&o>1?r-1:r,n&&s>1?a-1:a],c=[n&&o>1?o-1:o,n&&s>1?s-1:s],l=i[0]/c[0],u=i[1]/c[1],d=1/l,p=1/u,h=2*Math.ceil(d)+2,f=2*Math.ceil(p)+2;this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(".concat(l,");\n        const float widthScale = float(").concat(u,");\n\n        const float invHeightScale = float(").concat(d,");\n        const float invWidthScale = float(").concat(p,");\n\n        const int winHeight = int(").concat(h,");\n        const int winWidth = int(").concat(f,");\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ").concat(o,") {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ").concat(s,") {\n              continue;\n            }\n\n            float sourceFracRow =\n              float(").concat(i[0],") *\n                (float(dyR) / float(").concat(c[0],"));\n\n            float sourceFracCol =\n                float(").concat(i[1],") *\n                  (float(dyC) / float(").concat(c[1],"));\n\n            int sourceNearestRow = int(min(\n                float(int(").concat(r,") - 1),\n                ").concat(n," ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int(").concat(a,") - 1),\n                ").concat(n," ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    ")}}},583929:(e,t)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.ResizeNearestNeighborProgram=void 0;t.ResizeNearestNeighborProgram=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];const[o,s,i,c]=e;this.outputShape=[o,t,n,c];const l=[r&&t>1?s-1:s,r&&n>1?i-1:i],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],d=r?"0.5":"0.0";let p;p=a?"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":"vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode="\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ".concat(l[0]/u[0],",\n          ").concat(l[1]/u[1],");\n      const vec2 inputShapeRC = vec2(").concat(s,".0, ").concat(i,".0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = ").concat(p,";\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ").concat(d,")));\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    ")}}},210949:(e,t)=>

1{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.ResizeNearestNeighborPackedProgram=void 0;t.ResizeNearestNeighborPackedProgram=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];const[o,s,i,c]=e;this.outputShape=[o,t,n,c];const l=[r&&t>1?s-1:s,r&&n>1?i-1:i],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],d=r?"0.5":"0.0";let p;p=a?"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":"vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode="\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ".concat(l[0]/u[0],",\n          ").concat(l[1]/u[1],",\n          ").concat(l[1]/u[1],");\n      const vec3 inputShapeRC = vec3(").concat(s,".0, ").concat(i,".0,\n                                     ").concat(i,".0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = ").concat(p,";\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec3 sourceNearestRC = ivec3(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ").concat(d,")));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ").concat(c-1,";\n        bool hasNextRow = coords.z < ").concat(n-1,";\n\n        vec4 newValue = vec4(\n          getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n          hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n        setOutput(newValue);\n      }\n    ")}}},241467:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.ReverseProgram=void 0;var r=n(41990);t.ReverseProgram=class{constructor(e,t){this.variableNames=["x"];const n=e.length;if(n>4)throw new Error("WebGL backend: Reverse of rank-".concat(n," tensor is not yet supported"));if(this.outputShape=e,1===n)return void(this.userCode="\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX(".concat(e[0]," - coord - 1));\n        }\n      "));const a=e.map(((n,r)=>(n=>-1!==t.indexOf(n)&&1!==e[n]?"".concat(e[n]," - coords[").concat(n,"] - 1"):"coords[".concat(n,"]"))(r))).join(","),o=(0,r.getCoordsDataType)(n);this.userCode="\n      void main() {\n        ".concat(o," coords = getOutputCoords();\n        setOutput(getX(").concat(a,"));\n      }\n    ")}}},941661:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.ReversePackedProgram=void 0;var r=n(530675),a=n(41990);t.ReversePackedProgram=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;const n=e.length;if(n>4)throw new Error("WebGL backend: Reverse of rank-".concat(n," tensor is not yet supported"));this.outputShape=e;const o=(0,r.getChannels)("rc",n),s="".concat(o[n-1]," + 1 < ").concat(this.outputShape[n-1]),i="".concat(o[n-2]," + 1 < ").concat(this.outputShape[n-2]),c=(0,a.getCoordsDataType)(n);function l(n){const r=e.map(((r,a)=>function(n,r){return-1!==t.indexOf(n)&&1!==e[n]?"".concat(e[n]," - ").concat(r[n]," - 1"):"".concat(r[n])}(a,n))),a=r.join(","),o=r.slice(-2).join(",");return"getChannel(getX(".concat(a,"), vec2(").concat(o,"))")}this.userCode=1===n?"\n        void main(){\n          int rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = getChannel(getX(".concat(e[0]," - rc - 1),\n            ").concat(e[0]," - rc - 1);\n          if(").concat(s,"){\n              result.g = getChannel(getX(").concat(e[0]," - (rc  + 1) - 1),\n                ").concat(e[0]," - (rc  + 1) - 1);\n          }\n          setOutput(result);\n        }\n      "):"\n        void main() {\n          ".concat(c," rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = ").concat(function(e){return l(e)}(o.slice()),";\n          if(").concat(s,"){\n            result.g = ").concat(function(e){return e[n-1]="("+e[n-1]+" + 1)",l(e)}(o.slice()),";\n          }\n          if(").concat(i,") {\n            result.b = ").concat(function(e){return e[n-2]="("+e[n-2]+" + 1)",l(e)}(o.slice()),";\n            if(").concat(s,") {\n              result.a = ").concat(function(e){return e[n-1]="("+e[n-1]+" + 1)",e[n-2]="("+e[n-2]+" + 1)",l(e)}(o.slice()),";\n            }\n          }\n          setOutput(result);\n        }\n    ")}}},601928:(e,t)=>

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n=o.getLogicalCoordinatesFromFlatIndex(["r","c","d","d2","d3"],e);return"\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(".concat(t[0],",\n                             ").concat(t[1],"));\n\n      int index = resTexRC.x * ").concat(t[1]," + resTexRC.y;\n\n      ").concat(n,"\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  ")}(e,t);case 6:return function(e,t){const n=o.getLogicalCoordinatesFromFlatIndex(["r","c","d","d2","d3","d4"],e);return"\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(".concat(t[0],", ").concat(t[1],"));\n      int index = resTexRC.x * ").concat(t[1]," + resTexRC.y;\n\n      ").concat(n,"\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  ")}(e,t);default:throw new Error("".concat(e.length,"-D output sampling is not yet supported"))}}(t.logicalShape,v,n.enableShapeUniforms),k=function(e){return"\n    void setOutput(float val) {\n      ".concat(e.output," = vec4(val, 0, 0, 0);\n    }\n  ")}(b));n.packedInputs&&(_+=h);return[_,x,k,f,w,m,n.userCode].join("\n")},t.squeezeInputInfo=v;var r=n(735534),a=n(540236),o=function(e,t){if(!t&&e&&e.__esModule)return e;if(null===e||"object"!=typeof e&&"function"!=typeof e)return{default:e};var n=s(t);if(n&&n.has(e))return n.get(e);var r={__proto__:null},a=Object.defineProperty&&Object.getOwnPropertyDescriptor;for(var o in e)if("default"!==o&&{}.hasOwnProperty.call(e,o)){var i=a?Object.getOwnPropertyDescriptor(e,o):null;i&&(i.get||i.set)?Object.defineProperty(r,o,i):r[o]=e[o]}return r.default=e,n&&n.set(e,r),r}(n(270327));function s(e){if("function"!=typeof WeakMap)return null;var t=new WeakMap,n=new WeakMap;return(s=function(e){return e?n:t})(e)}const{getBroadcastDims:i}=r.backend_util;function c(e){let t=arguments.length>1&&void 0!==arguments[1]&&arguments[1];const n=e.shapeInfo.logicalShape;switch(n.length){case 0:return function(e,t){const n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return"float ".concat(r,"() {return ").concat(n,";}");const[a,o]=e.shapeInfo.texShape;if(1===a&&1===o)return"\n      float ".concat(r,"() {\n        return sampleTexture(").concat(n,", halfCR);\n      }\n    ");const s=f(n);if(t)return"\n    float ".concat(r,"() {\n      vec2 uv = uvFromFlat(").concat(n,"TexShape[0], ").concat(n,"TexShape[1], ").concat(s,");\n      return sampleTexture(").concat(n,", uv);\n    }\n  ");const[i,c]=e.shapeInfo.texShape;return"\n    float ".concat(r,"() {\n      vec2 uv = uvFromFlat(").concat(i,", ").concat(c,", ").concat(s,");\n      return sampleTexture(").concat(n,", uv);\n    }\n  ")}(e,t);case 1:return function(e,t){const n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return"\n      float ".concat(r,"(int index) {\n        ").concat(m(e),"\n      }\n    ");const a=e.shapeInfo.texShape,o=a[0],s=a[1];if(1===s&&1===o)return"\n      float ".concat(r,"(int index) {\n        return sampleTexture(").concat(n,", halfCR);\n      }\n    ");const i=f(n);if(1===s)return t?"\n      float ".concat(r,"(int index) {\n        vec2 uv = vec2(0.5, (float(index + ").concat(i,") + 0.5) / float(").concat(n,"TexShape[0]));\n        return sampleTexture(").concat(n,", uv);\n      }\n    "):"\n      float ".concat(r,"(int index) {\n        vec2 uv = vec2(0.5, (float(index + ").concat(i,") + 0.5) / ").concat(o,".0);\n        return sampleTexture(").concat(n,", uv);\n      }\n    ");if(1===o)return t?"\n      float ".concat(r,"(int index) {\n        vec2 uv = vec2((float(index + ").concat(i,") + 0.5) / float(").concat(n,"TexShape[1]), 0.5);\n        return sampleTexture(").concat(n,", uv);\n      }\n    "):"\n      float ".concat(r,"(int index) {\n        vec2 uv = vec2((float(index + ").concat(i,") + 0.5) / ").concat(s,".0, 0.5);\n        return sampleTexture(").concat(n,", uv);\n      }\n    ");if(t)return"\n    float ".concat(r,"(int index) {\n      vec2 uv = uvFromFlat(").concat(n,"TexShape[0], ").concat(n,"TexShape[1], index + ").concat(i,");\n      return sampleTexture(").concat(n,", uv);\n    }\n  ");return"\n    float ".concat(r,"(int index) {\n      vec2 uv = uvFromFlat(").concat(o,", ").concat(s,", index + ").concat(i,");\n      return sampleTexture(").concat(n,", uv);\n    }\n  ")}(e,t);case 2:return function(e,t){const n=e.shapeInfo.logicalShape,a=e.name,o="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape;if(null!=s&&r.util.arraysEqual(n,s)){if(t)return"\n      float ".concat(o,"(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(").concat(a,"TexShape[1], ").concat(a,"TexShape[0]);\n        return sampleTexture(").concat(a,", uv);\n      }\n    ");const e=s[0],n=s[1];return"\n    float ".concat(o,"(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(").concat(n,".0, ").concat(e,".0);\n      return sampleTexture(").concat(a,", uv);\n    }\n  ")}const{newShape:i,keptDims:l}=r.util.squeezeShape(n),u=i;if(u.length<n.length){const n=v(e,u),r=["row","col"];return"\n      ".concat(c(n,t),"\n      float ").concat(o,"(int row, int col) {\n        return ").concat(o,"(").concat(b(r,l),");\n      }\n    ")}if(e.shapeInfo.isUniform)return"\n      float ".concat(o,"(int row, int col) {\n        int index = round(dot(vec2(row, col), vec2(").concat(n[1],", 1)));\n        ").concat(m(e),"\n      }\n    ");const d=s[0],p=s[1],h=f(a);if(1===p)return t?"\n      float ".concat(o,"(int row, int col) {\n        float index = dot(vec3(row, col, ").concat(h,"), vec3(").concat(a,"Shape[1], 1, 1));\n        vec2 uv = vec2(0.5, (index + 0.5) / float(").concat(a,"TexShape[0]));\n        return sampleTexture(").concat(a,", uv);\n      }\n    "):"\n    float ".concat(o,"(int row, int col) {\n      float index = dot(vec3(row, col, ").concat(h,"), vec3(").concat(n[1],", 1, 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / ").concat(d,".0);\n      return sampleTexture(").concat(a,", uv);\n    }\n  ");if(1===d)return t?"\n      float ".concat(o,"(int row, int col) {\n        float index = dot(vec3(row, col, ").concat(h,"), vec3(").concat(a,"Shape[1], 1, 1));\n        vec2 uv = vec2((index + 0.5) / float(").concat(a,"TexShape[1]), 0.5);\n        return sampleTexture(").concat(a,", uv);\n      }\n    "):"\n    float ".concat(o,"(int row, int col) {\n      float index = dot(vec3(row, col, ").concat(h,"), vec3(").concat(n[1],", 1, 1));\n      vec2 uv = vec2((index + 0.5) / ").concat(p,".0, 0.5);\n      return sampleTexture(").concat(a,", uv);\n    }\n  ");if(t)return"\n      float ".concat(o,"(int row, int col) {\n        // Explicitly use integer operations as dot() only w

1orks on floats.\n        int index = row * ").concat(a,"Shape[1] + col + ").concat(h,";\n        vec2 uv = uvFromFlat(").concat(a,"TexShape[0], ").concat(a,"TexShape[1], index);\n        return sampleTexture(").concat(a,", uv);\n      }\n    ");return"\n  float ".concat(o,"(int row, int col) {\n    // Explicitly use integer operations as dot() only works on floats.\n    int index = row * ").concat(n[1]," + col + ").concat(h,";\n    vec2 uv = uvFromFlat(").concat(d,", ").concat(p,", index);\n    return sampleTexture(").concat(a,", uv);\n  }\n")}(e,t);case 3:return function(e,t){const n=e.shapeInfo.logicalShape,a=e.name,o="get"+a.charAt(0).toUpperCase()+a.slice(1),s=n[1]*n[2],i=n[2],{newShape:l,keptDims:u}=r.util.squeezeShape(n),d=l;if(d.length<n.length){const n=v(e,d),r=["row","col","depth"];return"\n        ".concat(c(n,t),"\n        float ").concat(o,"(int row, int col, int depth) {\n          return ").concat(o,"(").concat(b(r,u),");\n        }\n      ")}if(e.shapeInfo.isUniform)return"\n      float ".concat(o,"(int row, int col, int depth) {\n        int index = round(dot(vec3(row, col, depth),\n                          vec3(").concat(s,", ").concat(i,", 1)));\n        ").concat(m(e),"\n      }\n    ");const p=e.shapeInfo.texShape,h=p[0],g=p[1],y=e.shapeInfo.flatOffset;if(g===s&&null==y)return t?"\n      float ".concat(o,"(int row, int col, int depth) {\n        int stride1 = ").concat(a,"Shape[2];\n        float texR = float(row);\n        float texC = dot(vec2(col, depth), vec2(stride1, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(").concat(a,"TexShape[1], ").concat(a,"TexShape[0]);\n        return sampleTexture(").concat(a,", uv);\n      }\n    "):"\n        float ".concat(o,"(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2(").concat(i,", 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(").concat(g,".0, ").concat(h,".0);\n          return sampleTexture(").concat(a,", uv);\n        }\n      ");if(g===i&&null==y)return t?"\n      float ".concat(o,"(int row, int col, int depth) {\n        float texR = dot(vec2(row, col), vec2(").concat(a,"Shape[1], 1));\n        float texC = float(depth);\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(").concat(a,"TexShape[1], ").concat(a,"TexShape[0]);\n        return sampleTexture(").concat(a,", uv);\n      }\n    "):"\n    float ".concat(o,"(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2(").concat(n[1],", 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(").concat(g,".0, ").concat(h,".0);\n      return sampleTexture(").concat(a,", uv);\n    }\n  ");const x=f(a);if(t)return"\n    float ".concat(o,"(int row, int col, int depth) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int stride0 = ").concat(a,"Shape[1] * ").concat(a,"Shape[2];\n      int stride1 = ").concat(a,"Shape[2];\n      int index = row * ").concat(s," + col * ").concat(i," + depth + ").concat(x,";\n      vec2 uv = uvFromFlat(").concat(a,"TexShape[0], ").concat(a,"TexShape[1], index);\n      return sampleTexture(").concat(a,", uv);\n    }\n    ");return"\n      float ".concat(o,"(int row, int col, int depth) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ").concat(s," + col * ").concat(i," + depth + ").concat(x,";\n        vec2 uv = uvFromFlat(").concat(h,", ").concat(g,", index);\n        return sampleTexture(").concat(a,", uv);\n      }\n  ")}(e,t);case 4:return function(e,t){const n=e.shapeInfo.logicalShape,a=e.name,o="get"+a.charAt(0).toUpperCase()+a.slice(1),s=n[3],i=n[2]*s,l=n[1]*i,{newShape:u,keptDims:d}=r.util.squeezeShape(n);if(u.length<n.length){const n=v(e,u),r=["row","col","depth","depth2"];return"\n      ".concat(c(n,t),"\n      float ").concat(o,"(int row, int col, int depth, int depth2) {\n        return ").concat(o,"(").concat(b(r,d),");\n      }\n    ")}if(e.shapeInfo.isUniform)return"\n      float ".concat(o,"(int row, int col, int depth, int depth2) {\n        int index = round(dot(vec4(row, col, depth, depth2),\n                          vec4(").concat(l,", ").concat(i,", ").concat(s,", 1)));\n        ").concat(m(e),"\n      }\n    ");const p=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,g=h[0],y=h[1],x="int str

1ide2 = ".concat(a,"Shape[3];"),w="int stride1 = ".concat(a,"Shape[2] * stride2;"),k="int stride0 = ".concat(a,"Shape[1] * stride1;");if(y===l&&null==p)return t?"\n      float ".concat(o,"(int row, int col, int depth, int depth2) {\n        ").concat(x,"\n        ").concat(w,"\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(stride1, stride2, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(").concat(a,"TexShape[1], ").concat(a,"TexShape[0]);\n        return sampleTexture(").concat(a,", uv);\n      }\n    "):"\n      float ".concat(o,"(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(").concat(i,", ").concat(s,", 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(").concat(y,".0, ").concat(g,".0);\n        return sampleTexture(").concat(a,", uv);\n      }\n    ");if(y===s&&null==p)return t?"\n      float ".concat(o,"(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(").concat(a,"Shape[1] * ").concat(a,"Shape[2], ").concat(a,"Shape[2], 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(").concat(a,"TexShape[1], ").concat(a,"TexShape[0]);\n        return sampleTexture(").concat(a,", uv);\n      }\n    "):"\n      float ".concat(o,"(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(").concat(n[1]*n[2],", ").concat(n[2],", 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(").concat(y,".0, ").concat(g,".0);\n        return sampleTexture(").concat(a,", uv);\n      }\n    ");const _=f(a);if(t)return"\n    float ".concat(o,"(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      ").concat(x,"\n      ").concat(w,"\n      ").concat(k,"\n      int index = row * stride0 + col * stride1 +\n          depth * stride2 + depth2;\n      vec2 uv = uvFromFlat(").concat(a,"TexShape[0], ").concat(a,"TexShape[1], index + ").concat(_,");\n      return sampleTexture(").concat(a,", uv);\n    }\n  ");return"\n    float ".concat(o,"(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ").concat(l," + col * ").concat(i," +\n          depth * ").concat(s," + depth2;\n      vec2 uv = uvFromFlat(").concat(g,", ").concat(y,", index + ").concat(_,");\n      return sampleTexture(").concat(a,", uv);\n    }\n  ")}(e,t);case 5:return function(e){const t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),o=t[4],s=t[3]*o,i=t[2]*s,l=t[1]*i,{newShape:u,keptDims:d}=r.util.squeezeShape(t);if(u.length<t.length){const t=v(e,u),n=["row","col","depth","depth2","depth3"];return"\n      ".concat(c(t),"\n      float ").concat(a,"(int row, int col, int depth, int depth2, int depth3) {\n        return ").concat(a,"(").concat(b(n,d),");\n      }\n    ")}if(e.shapeInfo.isUniform)return"\n      float ".concat(a,"(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(").concat(l,", ").concat(i,", ").concat(s,", ").concat(o,")) +\n          depth3;\n        ").concat(m(e),"\n      }\n    ");const p=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,g=h[0],y=h[1];if(y===l&&null==p)return"\n      float ".concat(a,"(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n                         vec4(").concat(i,", ").concat(s,", ").concat(o,", 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(").concat(y,".0, ").concat(g,".0);\n        return sampleTexture(").concat(n,", uv);\n      }\n    ");if(y===o&&null==p)return"\n      float ".concat(a,"(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot

1(\n          vec4(row, col, depth, depth2),\n          vec4(").concat(t[1]*t[2]*t[3],",\n               ").concat(t[2]*t[3],", ").concat(t[3],", 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(").concat(y,".0, ").concat(g,".0);\n        return sampleTexture(").concat(n,", uv);\n      }\n    ");const x=f(n);return"\n    float ".concat(a,"(int row, int col, int depth, int depth2, int depth3) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ").concat(l," + col * ").concat(i," + depth * ").concat(s," +\n          depth2 * ").concat(o," + depth3 + ").concat(x,";\n      vec2 uv = uvFromFlat(").concat(g,", ").concat(y,", index);\n      return sampleTexture(").concat(n,", uv);\n    }\n  ")}(e);case 6:return function(e){const t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:o,keptDims:s}=r.util.squeezeShape(t);if(o.length<t.length){const t=v(e,o),n=["row","col","depth","depth2","depth3","depth4"];return"\n      ".concat(c(t),"\n      float ").concat(a,"(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return ").concat(a,"(").concat(b(n,s),");\n      }\n    ")}const i=t[5],l=t[4]*i,u=t[3]*l,d=t[2]*u,p=t[1]*d;if(e.shapeInfo.isUniform)return"\n      float ".concat(a,"(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        int index = round(dot(\n          vec4(row, col, depth, depth2),\n          vec4(").concat(p,", ").concat(d,", ").concat(u,", ").concat(l,")) +\n          dot(\n            vec2(depth3, depth4),\n            vec2(").concat(i,", 1)));\n        ").concat(m(e),"\n      }\n    ");const h=e.shapeInfo.flatOffset,g=e.shapeInfo.texShape,y=g[0],x=g[1];if(x===p&&null==h)return"\n      float ".concat(a,"(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n          vec4(").concat(d,", ").concat(u,", ").concat(l,", ").concat(i,")) +\n               float(depth4);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(").concat(x,".0, ").concat(y,".0);\n        return sampleTexture(").concat(n,", uv);\n      }\n    ");if(x===i&&null==h)return"\n      float ".concat(a,"(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(vec4(row, col, depth, depth2),\n          vec4(").concat(t[1]*t[2]*t[3]*t[4],",\n               ").concat(t[2]*t[3]*t[4],",\n               ").concat(t[3]*t[4],",\n               ").concat(t[4],")) + float(depth3);\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(").concat(x,".0, ").concat(y,".0);\n        return sampleTexture(").concat(n,", uv);\n      }\n    ");const w=f(n);return"\n    float ".concat(a,"(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ").concat(p," + col * ").concat(d," + depth * ").concat(u," +\n          depth2 * ").concat(l," + depth3 * ").concat(i," + depth4 + ").concat(w,";\n      vec2 uv = uvFromFlat(").concat(y,", ").concat(x,", index);\n      return sampleTexture(").concat(n,", uv);\n    }\n  ")}(e);default:throw new Error("".concat(n.length,"-D input sampling")+" is not yet supported")}}function l(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return function(e){const t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=(0,a.getGlslDifferences)();return"\n    vec4 ".concat(n,"() {\n      return ").concat(r.texture2D,"(").concat(t,", halfCR);\n    }\n  ")}(e);case 1:return function(e,t){const n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=e.shapeInfo.texShape,s=(0,a.getGlslDifferences)();if(t)return"\n    vec4 ".concat(r,"(int index) {\n      ivec2 packedTexShape = ivec2(ceil(float(").concat(n,"TexShape[0]) / 2.0), ceil(float(").concat(n,"TexShape[1]) / 2.0));\n      vec2 uv = packedUVfrom1D(\n        packedTexShape[0], packedTexShape[1], index);\n      return ").concat(s.texture2D,"(").concat(n,", uv);\n    }\n  ");const i=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];return"\n    vec4 ".concat(r,"(int index) {\n      vec2 uv = packedUVfrom1D(\n        ").concat(i[0],", ").concat(i[1],", index);\n      return ").concat(s.texture2D,"(").concat(n,", uv);\n    }\n  ")}(e,t);case 2:return function(e,t){const n=e.shapeInfo.logicalShape,o=e.name,s="get"+o.charAt(0).toUpperCase()+o.slice(1),i=e.shapeInfo.texShape,c=i[0],l=i[1],u=(0,a.getGlslDifferences)();if(null!=i&&r.util.arraysEqual(n,i))return t?"\n      vec4 ".concat(s,"(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(").concat(o,"TexShape[1], ").concat(o,"TexShape[0]);\n\n        return ").concat(u.texture2D,"(").concat(o,", uv);\n      }\n    "):"\n      vec4 ".concat(s,"(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(").concat(l,".0, ").concat(c,".0);\n\n        return ").concat(u.texture2D,"(").concat(o,", uv);\n      }\n    ");if(t)return"\n    vec4 ".concat(s,"(int row, int col) {\n      ivec2 packedTexShape = ivec2(ceil(float(").concat(o,"TexShape[0]) / 2.0), ceil(float(").concat(o,"TexShape[1]) / 2.0));\n      int valuesPerRow = int(ceil(float(").concat(o,"Shape[1]) / 2.0));\n      vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, c

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Within each group, we compare each pair at\n         // positions i and i+inc. To decide whether an element at position i\n         // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n         // inc, it is in the first half of the group, we denote it as x0,\n         // otherwise we denote it as x1.\n         // For example, as shown in the Bitonic top K paper referenced above,\n         // Figure5(a) shows that element[1] is in the\n         // second half of the group when group size is 2, but it is in the\n         // first half of the group when group size is 4.\n\n         bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;\n         int i = isFirstInPair ? elemIdx : elemIdx - inc;\n\n         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n         int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));\n         float x0 = i0 < n ? getX(batch, i0) : negativeInf;\n         float x1 = i1 < n ? getX(batch, i1) : negativeInf;\n\n         // Denotes which direction indices are in (ascending or descending).\n         bool reverse = imod(elemIdx, 2 * dir) >= dir;\n         bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n         if (reverse == isGreater) { // Elements in opposite order of direction\n           int iTemp = i0;\n           i0 = i1;\n           i1 = iTemp;\n         }\n         if (isFirstInPair) {\n            setOutput(float(i0));\n         } else {\n            setOutput(float(i1));\n         }\n       }\n     "}};t.MergeProgram=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode="\n    void main() {\n         // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...\n         ivec2 coords = getOutputCoords();\n         int batch = coords[0];\n         int elemIdx = coords[1];\n\n         // The output size is half of the previous size.\n         // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _ (k=4),\n         // we only need to output the indices at positions |, the indices at\n         // positions _ can be thrown away, see Figure5(b) After Phase 2\n         // (Merge phase) in the Bitonic Top K paper referenced above.\n         // For example, the paper shows we only need to output the orange bars.\n         // The output sequence should look like this | | | | | | | |.\n         // Because the sequence is halved, to map the output index back\n         // to the previous sequence to find the corresponding value,\n         // we need to double the index. When we double the index,\n         // we basically interpolate a position, so 2i looks like\n         // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position\n         // of each 2k positions by - elemIdx % k. E.g. for output at\n         // index 4,5,6,7, we want to get the corresponding element at\n         // original index 8,9,10,11, for output at index 8,9,10,11,\n         // we want to get the corresponding element at original index\n         // 16,17,18,19, so on and so forth.\n\n         int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));\n         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n         int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));\n\n         float x0 = getX(batch, i0);\n         float x1 = i1 < n ? getX(batch, i1) : x0;\n\n         setOutput(x0 >= x1 ? float(i0) : float(i1));\n       }\n     "}}},508334:(e,t)=>{"use strict";

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result;\n",t.SIGMOID="return 1.0 / (1.0 + exp(-1.0 * x));";t.UnaryOpPackedProgram=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=(0,r.useShapeUniforms)(this.outputShape.length),this.userCode="\n      vec4 unaryOperation(vec4 x) {\n        ".concat(t,"\n      }\n\n      void main() {\n        vec4 x = getAAtOutCoords();\n        vec4 y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    ")}}},59579:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.UnpackProgram=void 0;var r=n(965438),a=n(530675),o=n(41990);t.UnpackProgram=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=(0,r.useShapeUniforms)(this.outputShape.length);const 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1Object.defineProperty(t,"__esModule",{value:!0}),t.ExecutionContext=void 0;t.ExecutionContext=class{constructor(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{},t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{},r=arguments.length>3&&void 0!==arguments[3]?arguments[3]:{};this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=r,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){const e=[];for(let t=0;t<this.contexts.length-1;t++){const n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map((e=>0===e.id&&0===e.iterationId?"":"".concat(e.frameName,"-").concat(e.iterationId))).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(!(this.contexts&&this.contexts.length>1))throw new Error("Cannot exit frame, the context is empty");this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift()}nextIteration(){if(!(this.contexts&&this.contexts.length>0))throw new Error("Cannot increase frame iteration, the context is empty");{this.contexts=this.contexts.slice(),this.lastId++;const e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(const t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(const t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}}},897628:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.GraphExecutor=void 0;var r=n(735534),a=n(598453),o=n(347750),s=n(658558),i=n(530539);class c{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,null!=e.functions&&Object.keys(e.functions).forEach((t=>{this._functionExecutorMap[t]=new c(e.functions[t],this)}))}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){const t=Object.keys(e).map((t=>e[t].map((e=>e.id))));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map((e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0})))}get outputs(){return this._outputs.map((e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0})))}get inputNodes(){return this._inputs.map((e=>e.signatureKey||e.name))}get outputNodes(){return this._outputs.map((e=>{const t=e.signatureKey||e.name;return e.defaultOutput?"".concat(t,":").concat(e.defaultOutput):t}))}get functions(){return Object.keys(this._functions).reduce(((e,t)=>(e[t]=this._functions[t].signature,e)),{})}getCompilationKey(e,t){const n=e.map((e=>e.name)).sort(),r=t.map((e=>e.name)).sort();return n.join(this.SEPERATOR)+"--"+r.join(this.SEPERATOR)}compile(e,t){const n=(0,i.getExecutionSubgraph)(e,t,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:a,syncInputs:o}=n;if(null!=a)throw new Error("This execution contains the node '".concat(a.name,"', which has ")+"the dynamic op '".concat(a.op,"'. Please use ")+"model.executeAsync() instead. Alternatively, to avoid the "+"dynamic ops, specify the inputs [".concat(o,"]"));if(r.length>0){const n=t.map((e=>e.name)),a=Object.keys(e);throw new Error("Cannot compute the outputs [".concat(n,"] from the provided inputs ")+"[".concat(a,"]. Missing the following inputs: [").concat(r,"]"))}return(0,i.getNodesInTopologicalOrder)(this.graph,this.weightMap,n)}

1execute(e,t){e=this.mapInputs(e);const n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);const i=n.map((e=>this.graph.nodes[(0,a.parseNodeName)(e)[0]])),c=t.map((e=>(0,a.parseNodeName)(e)[0]));let l=c.map((e=>this.graph.nodes[e]));this.resetIntermediateTensors(),0===l.length&&(l=this._outputs);const u=this.getCompilationKey(i,l);let d=this.compiledMap.get(u);null==d&&(d=this.compile(e,l),this.compiledMap.set(u,d));const p={},h={};return(0,r.tidy)((()=>{const n=new s.ExecutionContext(this.weightMap,p,h,this.functionExecutorMap),i=Object.assign({},this.weightMap);Object.keys(e).forEach((t=>{const[n,r]=(0,a.parseNodeName)(t),o=[];o[r]=e[t],i[n]=o}));const l=this.getFrozenTensorIds(i),u={};for(let e=0;e<d.length;e++){const t=d[e];if(!i[t.name]){const e=(0,o.executeOp)(t,i,n,this._resourceManager);if(r.util.isPromise(e))throw new Error("The execution of the op '".concat(t.op,"' returned a promise. ")+"Please use model.executeAsync() instead.");i[t.name]=e,this.checkTensorForDisposal(t.name,t,i,n,l,c,u)}}return null==this.parent&&n.dispose(l),t.map((e=>(0,a.getTensor)(e,i,n)))}))}getFrozenTensorIds(e){const t=[].concat.apply([],Object.keys(e).map((t=>e[t])).map((e=>e.map((e=>e.id)))));return new Set(t)}checkTensorForDisposal(e,t,n,r,o,s,i){"control"!==t.category&&-1===s.indexOf(e)&&(n[e].forEach((e=>{null!=e&&(i[e.id]=(i[e.id]||0)+t.children.length)})),t.inputs.forEach((e=>{if("control"!==e.category){const s=(0,a.getTensorsForCurrentContenxt)(e.name,n,r);null!=s&&s.forEach((e=>{if(e&&!e.kept&&!o.has(e.id)){const n=i[e.id];if(1===n){if(this.keepTensorForDebug){const[n,o]=(0,a.getNodeNameAndIndex)(t.name,r);this.intermediateTensors[n]||(this.intermediateTensors[n]=[]),this.intermediateTensors[n][o]=e}else e.dispose();delete i[e.id]}else null!=n&&i[e.id]--}}))}})))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){this.intermediateTensors&&(Object.keys(this.intermediateTensors).forEach((e=>this.intermediateTensors[e].forEach((e=>e.dispose())))),this.disposeTensorsMap())}disposeTensorsMap(){this.tensorsMap&&Object.keys(this.tensorsMap).forEach((e=>{this.tensorsMap[e].forEach((e=>{!e||e.kept||e.isDisposed||this.keepIds.has(e.id)||e.dispose()}))}))}getIntermediateTensors(){return this.tensorsMap}resetIntermediateTensors(){for(const e in this.intermediateTensors)this.intermediateTensors[e].forEach((e=>e.dispose())),delete this.intermediateTensors[e]}async _executeAsync(e,t){let n=arguments.length>2&&void 0!==arguments[2]&&arguments[2],o=arguments.length>3&&void 0!==arguments[3]?arguments[3]:{},i=arguments.length>4&&void 0!==arguments[4]?arguments[4]:{};n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepTensorForDebug=(0,r.env)().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(e){console.warn(e.message)}this.resetIntermediateTensors();const c=new s.ExecutionContext(this.weightMap,o,i,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,c,t,n);const l=t.map((e=>(0,a.getTensor)(e,this.tensorsMap,c))),u=l.map((e=>e.id)),d=Object.keys(e).map((t=>e[t].id));return this.keepIds=new Set([...u,...d,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),null==this.parent&&c.dispose(this.keepIds),l}async executeFunctionAsync(e,t,n){const r=e.reduce(((e,t,n)=>(e[this.inputs[n].name]=t,e)),{});return this._executeAsync(r,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,r){const o=Object.keys(e),s=o.map((e=>this.graph.nodes[(0,a.parseNodeName)(e)[0]])),c=n.map((e=>(0,a.parseNodeName)(e)[0]));let l=c.map((e=>this.graph.nodes[e]));0===l.length&&(l=this._outputs);const{usedNodes:u,missingInputs:d,dynamicNode:p,syncInputs:h}=(0,i.getExecutionSubgraph)(e,l,this.weightMap,this._initNodes),f=[...s,...this.graph.weights,...this._initNodes||[]].map((e=>({node:e,contexts:t.currentContext}))),m=Object.assign({},this.weightMap);Object.keys(e).forEach((t=>{const[n,r]=(0,a.parseNodeName)(t),o=[];o[r]=e[t],m[n]=o}));const g={},y=this.getFrozenTensorIds(m),v={};for(;f.length>0;){const e=this.processStack(s,f,t,m,v,y,c,g,u);await Promise.all(e)}null!=p||r||console.warn("This model execution did not contain any no

1des with control flow or dynamic output shapes. You can use model.execute() instead.");const b=l.filter((e=>!(0,i.isControlFlow)(e)&&!(0,a.getTensor)(e.name,m,t))).map((e=>e.name));if(b.length>0){let e="";throw null!=p&&(e="Alternatively, to avoid the dynamic ops, use model.execute() "+"and specify the inputs [".concat(h,"]")),new Error("Cannot compute the outputs [".concat(b,"] from the provided ")+"inputs [".concat(o,"]. Consider providing the following inputs: ")+"[".concat(d,"]. ").concat(e))}return m}processStack(e,t,n,s,i,c,l,u,d){const p=[];for(;t.length>0;){const e=t.pop();n.currentContext=e.contexts;let h="";if("Enter"===e.node.op&&(0,a.getParamValue)("isConstant",e.node,s,n)&&([h]=(0,a.getNodeNameAndIndex)(e.node.name,n)),null==s[e.node.name]){const f=(0,o.executeOp)(e.node,s,n,this._resourceManager);h||([h]=(0,a.getNodeNameAndIndex)(e.node.name,n));const m=n.currentContext;r.util.isPromise(f)?p.push(f.then((r=>(s[h]=r,n.currentContext=m,this.checkTensorForDisposal(h,e.node,s,n,c,l,u),this.processChildNodes(e.node,t,n,s,i,d),r)))):(s[h]=f,this.checkTensorForDisposal(h,e.node,s,n,c,l,u),this.processChildNodes(e.node,t,n,s,i,d))}else this.processChildNodes(e.node,t,n,s,i,d)}return p}processChildNodes(e,t,n,r,o,s){e.children.forEach((e=>{const[i]=(0,a.getNodeNameAndIndex)(e.name,n);!o[i]&&s.has(e.name)&&("Merge"===e.op?e.inputNames.some((e=>!!(0,a.getTensor)(e,r,n)))&&(o[i]=!0,t.push({contexts:n.currentContext,node:e})):e.inputNames.every((e=>!!(0,a.getTensor)(e,r,n)))&&(o[i]=!0,t.push({contexts:n.currentContext,node:e})))}))}dispose(){Object.keys(this.weightMap).forEach((e=>this.weightMap[e].forEach((e=>e.dispose()))))}checkInputShapeAndType(e){Object.keys(e).forEach((t=>{const n=e[t],[o]=(0,a.parseNodeName)(t),s=this.graph.nodes[o];if(s.attrParams.shape&&s.attrParams.shape.value){const e=s.attrParams.shape.value,t=e.length===n.shape.length&&n.shape.every(((t,n)=>-1===e[n]||e[n]===t));r.util.assert(t,(()=>"The shape of dict['".concat(s.name,"'] provided in ")+"model.execute(dict) must be [".concat(e,"], but was ")+"[".concat(n.shape,"]")))}s.attrParams.dtype&&s.attrParams.dtype.value&&r.util.assert(n.dtype===s.attrParams.dtype.value,(()=>"The dtype of dict['".concat(s.name,"'] provided in ")+"model.execute(dict) must be "+"".concat(s.attrParams.dtype.value,", but was ").concat(n.dtype)))}))}mapInputs(e){const t={};for(const n in e)if(null!=this._signature&&null!=this._signature.inputs&&null!=this._signature.inputs[n]){t[this._signature.inputs[n].name]=e[n]}else t[n]=e[n];return t}checkInputs(e){const t=Object.keys(e).filter((e=>{const[t]=(0,a.parseNodeName)(e);return null==this.graph.nodes[t]}));if(t.length>0)throw new Error("The dict provided in model.execute(dict) has "+"keys: [".concat(t,"] that are not part of graph"))}mapOutputs(e){return e.map((e=>{if(null!=this._signature&&null!=this._signature.outputs&&null!=this._signature.outputs[e]){return this._signature.outputs[e].name}return e}),{})}checkOutputs(e){e.forEach((e=>{const[t]=(0,a.parseNodeName)(e);if(!this.graph.nodes[t])throw new Error("The output '".concat(e,"' is not found in the graph"))}))}}t.GraphExecutor=c},75342:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.TFHUB_SEARCH_PARAM=t.GraphModel=t.DEFAULT_MODEL_NAME=void 0,t.loadGraphModel=async function(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};if(null==e)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");null==t&&(t={});t.fromTFHub&&null==e.load&&(e.endsWith("/")||(e+="/"),e="".concat(e).concat(c).concat(i));const n=new l(e,t);return await n.load(),n};var r=n(735534),a=n(691966),o=n(897628),s=n(624976);const i=t.TFHUB_SEARCH_PARAM="?tfjs-format=file",c=t.DEFAULT_MODEL_NAME="model.json";class l{constructor(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};this.modelUrl=e,this.loadOptions=t,this.version="n/a",null==t&&(this.loadOptions={}),this.resourceManager=new s.ResourceManager}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){const e=this.modelUrl;if(null!=e.load)this.handler=e;else if(null!=this.loadOptions.requestInit)this.handler=r.io.browserHTTPRequest

1(e,this.loadOptions);else{const t=r.io.getLoadHandlers(e,this.loadOptions);if(0===t.length)t.push(r.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error("Found more than one (".concat(t.length,") load handlers for ")+"URL '".concat([e],"'"));this.handler=t[0]}}async load(){if(this.findIOHandler(),null==this.handler.load)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;const t=this.artifacts.modelTopology;let n;n=null!=this.artifacts.userDefinedMetadata&&null!=this.artifacts.userDefinedMetadata.signature?this.artifacts.userDefinedMetadata.signature:this.artifacts.signature,this.signature=n,this.version="".concat(t.versions.producer,".").concat(t.versions.minConsumer);const s=r.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new o.GraphExecutor(a.OperationMapper.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),this.executor.resourceManager=this.resourceManager,null!=e.modelInitializer&&null!=e.modelInitializer.node){const t=a.OperationMapper.Instance.transformGraph(e.modelInitializer);this.initializer=new o.GraphExecutor(t),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if("string"==typeof e){const t=r.io.getSaveHandlers(e);if(0===t.length)throw new Error("Cannot find any save handlers for URL '".concat(e,"'"));if(t.length>1)throw new Error("Found more than one (".concat(t.length,") save handlers for ")+"URL '".concat(e,"'"));e=t[0]}if(null==e.save)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof r.Tensor||Array.isArray(e)))return e;if((e=Array.isArray(e)?e:[e]).length!==this.inputNodes.length)throw new Error("Input tensor count mismatch,"+"the graph model has ".concat(this.inputNodes.length," placeholders, ")+"while there are ".concat(e.length," input tensors."));return this.inputNodes.reduce(((t,n,r)=>(t[n]=e[r],t)),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce(((t,n)=>(t[n]=[e[n]],t)),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}}t.GraphModel=l}

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Make ")+"sure to await tf.ready() or await tf.setBackend() before calling other methods");if(null==this.backendInstance){const{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error("The highest priority backend '".concat(e,"' has not yet been ")+"initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods");this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry)){if(!(e in this.registryFactory))return null;{const{asyncInit:t}=this.initializeBackend(e);if(t)return null}}return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:1;return e in this.registryFactory?(c.warn("".concat(e," backend was already registered. 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1shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){const r=this.backend.numDataIds();let a=0;n.forEach((e=>{a+="complex64"===e.dtype?3:1}));const o=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],s=r-t-a-o;if(s>0)throw new Error("Backend '".concat(this.backendName,"' has an internal memory leak ")+"(".concat(s," data ids) after running '").concat(e,"'"))}runKernelFunc(e){let t,n=[];const r=this.isTapeOn(),a=this.state.numBytes,o=this.state.numTensors;let s,c;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0),null==this.backendName&&this.backend;const l=y(e)?e.kernelName:null!=this.state.activeScope?this.state.activeScope.name:"";if(y(e)){const{kernelName:t,inputs:a,attrs:o}=e;null==this.backendName&&this.backend;const l=(0,i.getKernel)(t,this.backendName);f.assert(null!=l,(()=>"Cannot find registered kernel '".concat(t,"' for backend '").concat(this.backendName,"'"))),s=()=>{const e=this.backend.numDataIds();c=l.kernelFunc({inputs:a,attrs:o,backend:this.backend});const s=Array.isArray(c)?c:[c];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(t,e,s);const i=s.map((e=>{if(null!=e.rank)return e;const{dataId:t,shape:n,dtype:r}=e;return this.makeTensorFromDataId(t,n,r)}));if(r){const e=this.getTensorsForGradient(t,a,i);n=this.saveTensorsForBackwardMode(e)}return i}}else{const{forwardFunc:t}=e,a=e=>{r&&(n=e.map((e=>this.keep(this.clone(e)))))};s=()=>{const e=this.backend.numDataIds();c=this.tidy((()=>t(this.backend,a)));const n=Array.isArray(c)?c:[c];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,e,n),n}}const{inputs:u,attrs:d}=e,p=y(e)?null:e.backwardsFunc;let h;return this.scopedRun((()=>this.state.kernelDepth++),(()=>this.state.kernelDepth--),(()=>{this.ENV.getBool("DEBUG")||this.state.profiling?(h=this.profiler.profileKernel(l,u,(()=>s())),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(h),t=h.outputs):t=s()})),r&&this.addTapeNode(l,u,t,p,n,d),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-a,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-o,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map((e=>null!=u[e]?u[e].shape:null)),outputShapes:t.map((e=>e.shape)),kernelTimeMs:h.timeMs,extraInfo:h.extraInfo}),Array.isArray(c)?t:t[0]}saveTensorsForBackwardMode(e){return e.map((e=>this.keep(this.clone(e))))}getTensorsForGradient(e,t,n){const r=(0,i.getGradient)(e);if(null!=r){const e=r.inputsToSave||[],a=r.outputsToSave||[];let o;r.saveAllInputs?(f.assert(Array.isArray(t),(()=>"saveAllInputs is true, expected inputs to be an array.")),o=Object.keys(t).map((e=>t[e]))):o=e.map((e=>t[e]));const s=n.filter(((e,t)=>a[t]));return o.concat(s)}return[]}makeTensor(e,t,n,r){if(null==e)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",r=r||this.backend;let a=e;"string"===n&&f.isString(e[0])&&(a=e.map((e=>f.encodeString(e))));const o=r.write(a,t,n),s=new d.Tensor(t,n,o,this.nextTensorId());if(this.trackTensor(s,r),"string"===n){const e=this.state.tensorInfo.get(o),t=(0,h.bytesFromStringArray)(a);this.state.numBytes+=t-e.bytes,e.bytes=t}return s}makeTensorFromDataId(e,t,n,r){n=n||"float32";const a=new d.Tensor(t,n,e,this.nextTensorId());return this.trackTensor(a,r),a}makeVariable(e){let t=!(arguments.length>1&&void 0!==arguments[1])||arguments[1],n=arguments.length>2?arguments[2]:void 0,r=arguments.length>3?arguments[3]:void 0;n=n||this.nextVariableId().toString(),null!=r&&r!==e.dtype&&(e=e.cast(r));const a=new d.Variable(e,t,n,this.nextTensorId());if(null!=this.state.registeredVariables[a.name])throw new Error("Variable with name ".concat(a.name," was already registered"));return this.state.registeredVariables[a.name]=a,this.incRef(a,this.backend),a}trackTensor(e,t){this.state.numTensors++,"string"===e.dtype&&this.state.numStringTensors++;let n=0;"complex64"!==e.dtype&&"string"!==e.dtype&&(n=e.size*f.bytesPerElement(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof d.Variable||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;const t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,"string"===e.dtype&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),"complex64"!==e.dtype&&"string"!==e.dtype){const t=e.size*f.bytesPerElement

1(e.dtype);this.state.numBytes-=t}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(const e in this.state.registeredVariables){const t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),null!=this.state.registeredVariables[e.name]&&delete this.state.registeredVariables[e.name]}memory(){const e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,null==e.reasons&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;const t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map((e=>e.totalBytesSnapshot))),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(const e of this.state.activeProfile.kernels)e.kernelTimeMs=await e.kernelTimeMs,e.extraInfo=await e.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&0===this.state.kernelDepth}addTapeNode(e,t,n,r,

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1return e!==t&&t.dispose(),o}},627896:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.zeros=function e(t){let n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:"float32";if("complex64"===n){const n=e(t,"float32"),r=e(t,"float32");return(0,o.complex)(n,r)}const s=(0,a.makeZerosTypedArray)((0,a.sizeFromShape)(t),n);return r.ENGINE.makeTensor(s,t,n)};var r=n(90337),a=n(904737),o=n(291158)},166119:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.zerosLike=void 0;var r=n(90337),a=n(536217),o=n(188695),s=n(930684);t.zerosLike=(0,s.op)({zerosLike_:function(e){const t={x:(0,o.convertToTensor)(e,"x","zerosLike")};return r.ENGINE.runKernel(a.ZerosLike,t)}})},538885:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.AdadeltaOptimizer=void 0;var r=n(90337),a=n(271725),o=n(353632),s=n(67282),i=n(295702),c=n(899090),l=n(985987),u=n(166119),d=n(39037),p=n(524823);class h extends p.Optimizer{constructor(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:null;super(),this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],null==n&&(this.epsilon=r.ENGINE.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{const d=r.ENGINE.registeredVariables[t],p=!1;null==this.accumulatedGrads[n]&&(this.accumulatedGrads[n]={originalName:"".concat(t,"/accum_grad"),variable:(0,a.tidy)((()=>(0,u.zerosLike)(d).variable(p)))}),null==this.accumulatedUpdates[n]&&(this.accumulatedUpdates[n]={originalName:"".concat(t,"/accum_var"),variable:(0,a.tidy)((()=>(0,u.zerosLike)(d).variable(p)))});const h=Array.isArray(e)?e[n].tensor:e[t];if(null==h)return;const f=this.accumulatedGrads[n].variable,m=this.accumulatedUpdates[n].variable;(0,a.tidy)((()=>{const e=(0,o.add)((0,i.mul)(f,this.rho),(0,i.mul)((0,l.square)(h),1-this.rho)),t=(0,i.mul)((0,s.div)((0,c.sqrt)((0,o.add)(m,this.epsilon)),(0,c.sqrt)((0,o.add)(f,this.epsilon))),h),n=(0,o.add)((0,i.mul)(m,this.rho),(0,i.mul)((0,l.square)(t),1-this.rho));f.assign(e),m.assign(n);const r=(0,o.add)((0,i.mul)(t,-this.learningRate),d);d.assign(r)}))})),this.incrementIterations()}dispose(){null!=this.accumulatedUpdates&&((0,a.dispose)(this.accumulatedGrads.map((e=>e.variable))),(0,a.dispose)(this.accumulatedUpdates.map((e=>e.variable))))}async getWeights(){const e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map((e=>({name:e.originalName,tensor:e.variable}))))}async setWeights(e){const t=(e=await this.extractIterations(e)).length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map((e=>({originalName:e.name,variable:e.tensor.variable(n)}))),this.accumulatedUpdates=e.slice(t,2*t).map((e=>({originalName:e.name,variable:e.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}}t.AdadeltaOptimizer=h,h.className="Adadelta",(0,d.registerClass)(h)},499678:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.AdagradOptimizer=void 0;var r=n(90337),a=n(271725),o=n(353632),s=n(67282),i=n(970061),c=n(295702),l=n(435438),u=n(985987),d=n(39037),p=n(524823);class h extends p.Optimizer{constructor(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:.1;super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{const d=r.ENGINE.registeredVariables[t];if(null==this.accumulatedGrads[n]){const e=!1;this.accumulatedGrads[n]={originalName:"".concat(t,"/accumulator"),variable:(0,a.tidy)((()=>(0,i.fill)(d.shape,this.initialAccumulatorValue).variable(e)))}}const p=Array.isArray(e)?e[n].tensor:e[t];if(null==p)return;const h=this.accumulatedGrads[n].variable;(0,a.tidy)((()=>{const e=(0,o.add)(h,(0,u.square)(p));h.assign(e);const t=(0,o.add)((0,c.mul)((0,s.div)(p,(0,l.sqrt)((0,o.add)(e,r.ENGINE.backend.epsilon()))),-this.learningRate),d);d.assign(t)}))})),this.incrementIterations()}dispose(){null!=this.accumulatedGrads&&(0,a.dispose)(this.accumulatedGrads.map((e=>e.variable)))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map((e=>({name:e.originalName,tensor:e.variable}

1))))}async setWeights(e){e=await this.extractIterations(e);this.accumulatedGrads=e.map((e=>({originalName:e.name,variable:e.tensor.variable(false)})))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}}t.AdagradOptimizer=h,h.className="Adagrad",(0,d.registerClass)(h)},947007:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.AdamOptimizer=void 0;var r=n(90337),a=n(271725),o=n(353632),s=n(67282),i=n(295702),c=n(216556),l=n(454179),u=n(435438),d=n(985987),p=n(702222),h=n(166119),f=n(39037),m=n(524823);class g extends m.Optimizer{constructor(e,t,n){let o=arguments.length>3&&void 0!==arguments[3]?arguments[3]:null;super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=o,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],(0,a.tidy)((()=>{this.accBeta1=(0,l.scalar)(t).variable(),this.accBeta2=(0,l.scalar)(n).variable()})),null==o&&(this.epsilon=r.ENGINE.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map((e=>e.name)):Object.keys(e);(0,a.tidy)((()=>{const n=(0,p.sub)(1,this.accBeta1),c=(0,p.sub)(1,this.accBeta2);t.forEach(((t,l)=>{const p=r.ENGINE.registeredVariables[t],f=!1;null==this.accumulatedFirstMoment[l]&&(this.accumulatedFirstMoment[l]={originalName:"".concat(t,"/m"),variable:(0,a.tidy)((()=>(0,h.zerosLike)(p).variable(f)))}),null==this.accumulatedSecondMoment[l]&&(this.accumulatedSecondMoment[l]={originalName:"".concat(t,"/v"),variable:(0,a.tidy)((()=>(0,h.zerosLike)(p).variable(f)))});const m=Array.isArray(e)?e[l].tensor:e[t];if(null==m)return;const g=this.accumulatedFirstMoment[l].variable,y=this.accumulatedSecondMoment[l].variable,v=(0,o.add)((0,i.mul)(g,this.beta1),(0,i.mul)(m,1-this.beta1)),b=(0,o.add)((0,i.mul)(y,this.beta2),(0,i.mul)((0,d.square)(m),1-this.beta2)),x=(0,s.div)(v,n),w=(0,s.div)(b,c);g.assign(v),y.assign(b);const k=(0,o.add)((0,i.mul)((0,s.div)(x,(0,o.add)((0,u.sqrt)(w),this.epsilon)),-this.learningRate),p);p.assign(k)})),this.accBeta1.assign((0,i.mul)(this.accBeta1,this.beta1)),this.accBeta2.assign((0,i.mul)(this.accBeta2,this.beta2))})),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),null!=this.accumulatedFirstMoment&&(0,a.dispose)(this.accumulatedFirstMoment.map((e=>e.variable))),null!=this.accumulatedSecondMoment&&(0,a.dispose)(this.accumulatedSecondMoment.map((e=>e.variable)))}async getWeights(){const e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map((e=>({name:e.originalName,tensor:e.variable}))))}async setWeights(e){e=await this.extractIterations(e),(0,a.tidy)((()=>{this.accBeta1.assign((0,c.pow)(this.beta1,this.iterations_+1)),this.accBeta2.assign((0,c.pow)(this.beta2,this.iterations_+1))}));const t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map((e=>({originalName:e.name,variable:e.tensor.variable(n)}))),this.accumulatedSecondMoment=e.slice(t,2*t).map((e=>({originalName:e.name,variable:e.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}}t.AdamOptimizer=g,g.className="Adam",(0,f.registerClass)(g)},119673:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.AdamaxOptimizer=void 0;var r=n(90337),a=n(271725),o=n(438415),s=n(353632),i=n(67282),c=n(720036),l=n(295702),u=n(454179),d=n(702222),p=n(166119),h=n(39037),f=n(524823);class m extends f.Optimizer{constructor(e,t,n){let o=arguments.length>3&&void 0!==arguments[3]?arguments[3]:null,s=arguments.length>4&&void 0!==arguments[4]?arguments[4]:0;super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=o,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],(0,a.tidy)((()=>{this.iteration=(0,u.scalar)(0).variable(),this.accBeta1=(0,u.scalar)(t).variable()})),null==o&&(this.epsilon=r.ENGINE.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map((e=>e.name)):Object.keys(e);(0,a.tidy)((()=>{const n=(0,d.sub)(1,this.accBeta1),a=(0,i.div)(-this.learningRate,(0,s.add)((0,l.mul)(this.iteration,this.decay),1));t.forEach(((t,u)=>{const d=r.ENGINE.registeredVariables[t],h=!1;null==this.accumulatedFirstMoment[u]&&(this.accumulatedFirstMoment[u]={originalName:"".concat(t,"/m"),variable:(0,p.zerosLike)(d).variable(h)}),null==this.accumulatedWeightedInfNorm[u]&&(this.accumulatedWeightedInfNorm[u]={originalName:"".concat(t,"/v"),variable:(0,p.zerosLike)(d).variable(h)});const f=Array.isArray(e)?e[u].tensor:e[t];if(null==f)return;const m=this.accumulatedFirstMoment[u].variable,g=this.accumulatedWeightedInfNorm[u].variable,y=(0,s.add)((0,l.mul)(m,this.beta1),(0,l.mul)(f,1-this.beta1)),v=(0,l.mul)(g,this.beta2),b=(0,o.abs)(f),x=(0,c.maximum)(v,b);m.assign(y),g.assign(x);const w=(0,s.add)((0,l.mul)((0,i.div)(a,n),(0,i.div)(y,(0,s.add)(x,this.epsilon))),d);d.assign(w)})),this.iteration.assign((0,s.add)(this.iteration,1)),this.accBeta1.assign((0,l.mul)(this.accBeta1,this.beta1))})),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),null!=this.accumulatedFirstMoment&&(0,a.dispose)(this.accumulatedFirstMoment.map((e=>e.variable))),null!=this.accumulatedWeightedInfNorm&&(0,a.dispose)(this.accumulatedWeightedInfNorm.map((e=>e.variable)))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}}t.AdamaxOptimizer=m,m.className="Adamax",(0,h.registerClass)(m)},81424:(e,t,n)=>{"use strict";

1Object.defineProperty(t,"__esModule",{value:!0}),t.MomentumOptimizer=void 0;var r=n(90337),a=n(271725),o=n(353632),s=n(295702),i=n(454179),c=n(166119),l=n(39037),u=n(475231);class d extends u.SGDOptimizer{constructor(e,t){let n=arguments.length>2&&void 0!==arguments[2]&&arguments[2];super(e),this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=(0,i.scalar)(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{const i=r.ENGINE.registeredVariables[t];if(null==this.accumulations[n]){const e=!1;this.accumulations[n]={originalName:"".concat(t,"/momentum"),variable:(0,a.tidy)((()=>(0,c.zerosLike)(i).variable(e)))}}const l=this.accumulations[n].variable,u=Array.isArray(e)?e[n].tensor:e[t];null!=u&&(0,a.tidy)((()=>{let e;const t=(0,o.add)((0,s.mul)(this.m,l),u);e=this.useNesterov?(0,o.add)((0,s.mul)(this.c,(0,o.add)(u,(0,s.mul)(t,this.m))),i):(0,o.add)((0,s.mul)(this.c,t),i),l.assign(t),i.assign(e)}))})),this.incrementIterations()}dispose(){this.m.dispose(),null!=this.accumulations&&(0,a.dispose)(this.accumulations.map((e=>e.variable)))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map((e=>({name:e.originalName,tensor:e.variable}))))}async setWeights(e){e=await this.extractIterations(e);this.accumulations=e.map((e=>({originalName:e.name,variable:e.tensor.variable(false)})))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}}t.MomentumOptimizer=d,d.className="Momentum",(0,l.registerClass)(d)},524823:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.Optimizer=void 0;var r=n(271725),a=n(931564),o=n(899090),s=n(39037);class i extends s.Serializable{minimize(e){let t=arguments.length>1&&void 0!==arguments[1]&&arguments[1],n=arguments.length>2?arguments[2]:void 0;const{value:a,grads:o}=this.computeGradients(e,n);if(null!=n){const e=n.map((e=>({name:e.name,tensor:o[e.name]})));this.applyGradients(e)}else this.applyGradients(o);return(0,r.dispose)(o),t?a:(a.dispose(),null)}get iterations(){return null==this.iterations_&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return(0,a.variableGrads)(e,t)}dispose(){null!=this.iterations_&&(0,r.dispose)(this.iterations_)}async saveIterations(){return null==this.iterations_&&(this.iterations_=0),{name:"iter",tensor:(0,o.scalar)(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for this optimizer class "+"".concat(this.getClassName()))}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}}t.Optimizer=i,Object.defineProperty(i,Symbol.hasInstance,{value:e=>null!=e.minimize&&null!=e.computeGradients&&null!=e.applyGradients})},472732:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.OptimizerConstructors=void 0;var r=n(538885),a=n(499678),o=n(947007),s=n(119673),i=n(81424),c=n(452951),l=n(475231);t.OptimizerConstructors=class{static sgd(e){return new l.SGDOptimizer(e)}static momentum(e,t){let n=arguments.length>2&&void 0!==arguments[2]&&arguments[2];return new i.MomentumOptimizer(e,t,n)}static rmsprop(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:.9,n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:0,r=arguments.length>3&&void 0!==arguments[3]?arguments[3]:null,a=arguments.length>4&&void 0!==arguments[4]&&arguments[4];return new c.RMSPropOptimizer(e,t,n,r,a)}static adam(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:.001,t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:.9,n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:.999,r=arguments.length>3&&void 0!==arguments[3]?arguments[3]:null;return new o.AdamOptimizer(e,t,n,r)}static adadelta(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:.001,t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:.95,n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:null;return new r.AdadeltaOptimizer(e,t,n)}static adamax(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:.002,t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:.9,n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:.999,r=arguments.length>3&&void 0!==arguments[3]?arguments[3]:null,a=arguments.length>4&&void 0!==arguments[4]?arguments[4]:0;return new s.AdamaxOptimizer(e,t,n,r,a)}static adagrad(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:.1;return new a.AdagradOptimizer(e,t)}}},452951:(e,t,n)=>{"use strict";

1Object.defineProperty(t,"__esModule",{value:!0}),t.RMSPropOptimizer=void 0;var r=n(90337),a=n(271725),o=n(353632),s=n(67282),i=n(295702),c=n(435438),l=n(985987),u=n(702222),d=n(166119),p=n(39037),h=n(524823);class f extends h.Optimizer{constructor(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:.9,n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:0,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:null,o=arguments.length>4&&void 0!==arguments[4]&&arguments[4];if(super(),this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=a,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=o,null==a&&(this.epsilon=r.ENGINE.backend.epsilon()),null==e)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{const p=r.ENGINE.registeredVariables[t],h=!1;null==this.accumulatedMeanSquares[n]&&(this.accumulatedMeanSquares[n]={originalName:"".concat(t,"/rms"),variable:(0,a.tidy)((()=>(0,d.zerosLike)(p).variable(h)))}),null==this.accumulatedMoments[n]&&(this.accumulatedMoments[n]={originalName:"".concat(t,"/momentum"),variable:(0,a.tidy)((()=>(0,d.zerosLike)(p).variable(h)))}),null==this.accumulatedMeanGrads[n]&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:"".concat(t,"/mg"),variable:(0,a.tidy)((()=>(0,d.zerosLike)(p).variable(h)))});const f=Array.isArray(e)?e[n].tensor:e[t];if(null==f)return;const m=this.accumulatedMeanSquares[n].variable,g=this.accumulatedMoments[n].variable;(0,a.tidy)((()=>{const e=(0,o.add)((0,i.mul)(m,this.decay),(0,i.mul)((0,l.square)(f),1-this.decay));if(this.centered){const t=this.accumulatedMeanGrads[n].variable,r=(0,o.add)((0,i.mul)(t,this.decay),(0,i.mul)(f,1-this.decay)),a=(0,s.div)((0,i.mul)(f,this.learningRate),(0,c.sqrt)((0,u.sub)(e,(0,o.add)((0,l.square)(r),this.epsilon)))),d=(0,o.add)((0,i.mul)(g,this.momentum),a);m.assign(e),t.assign(r),g.assign(d);const h=(0,u.sub)(p,d);p.assign(h)}else{const e=(0,o.add)((0,i.mul)(m,this.decay),(0,i.mul)((0,l.square)(f),1-this.decay)),t=(0,o.add)((0,i.mul)(g,this.momentum),(0,s.div)((0,i.mul)(f,this.learningRate),(0,c.sqrt)((0,o.add)(e,this.epsilon))));m.assign(e),g.assign(t);const n=(0,u.sub)(p,t);p.assign(n)}}))})),this.incrementIterations()}dispose(){null!=this.accumulatedMeanSquares&&(0,a.dispose)(this.accumulatedMeanSquares.map((e=>e.variable))),null!=this.accumulatedMeanGrads&&this.centered&&(0,a.dispose)(this.accumulatedMeanGrads.map((e=>e.variable))),null!=this.accumulatedMoments&&(0,a.dispose)(this.accumulatedMoments.map((e=>e.variable)))}async getWeights(){const e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map((e=>({name:e.originalName,tensor:e.variable}))))}async setWeights(e){e=await this.extractIterations(e);const t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map((e=>({originalName:e.name,variable:e.tensor.variable(n)}))),this.accumulatedMoments=e.slice(t,2*t).map((e=>({originalName:e.name,variable:e.tensor.variable(n)}))),this.centered&&(this.accumulatedMeanGrads=e.slice(2*t,3*t).map((e=>({originalName:e.name,variable:e.tensor.variable(n)}))))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}}t.RMSPropOptimizer=f,f.className="RMSProp",(0,p.registerClass)(f)},475231:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.SGDOptimizer=void 0;var r=n(90337),a=n(271725),o=n(353632),s=n(295702),i=n(454179),c=n(39037),l=n(524823);class u extends l.Optimizer{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{const i=Array.isArray(e)?e[n].tensor:e[t];if(null==i)return;const c=r.ENGINE.registeredVariables[t];(0,a.tidy)((()=>{const e=(0,o.add)((0,s.mul)(this.c,i),c);c.assign(e)}))})),this.incrementIterations()}setLearningRate(e){this.learningRate=e,null!=this.c&&this.c.dispose(),this.c=(0,a.keep)((0,i.scalar)(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(0!==(e=await this.extractIterations(e)).length)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}}t.SGDOptimizer=u,u.className="SGD",(0,c.registerClass)(u)}

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Found: "+"".concat(this.outputs.map((e=>e.name)))),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(const e of this.outputs){const t=e.sourceLayer,n=e.nodeIndex,r=e.tensorIndex;this.outputLayers.push(t),this.outputLayersNodeIndices.push(n),this.outputLayersTensorIndices.push(r)}for(const e of this.inputs){const t=e.sourceLayer,n=e.nodeIndex,r=e.tensorIndex;i.assert(0===n,"input layer has >1 nodes"),i.assert(0===r,"input layer has >1 tensors"),this.inputLayers.push(t),this.inputLayersNodeIndices.push(n),this.inputLayersTensorIndices.push(r)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let t=0;t<this.inputLayers.length;t++){const n=this.inputLayers[t];if(!(n instanceof h.InputLayer))throw new TypeError("Input layers to a LayersModel must be InputLayer objects. "+"Received inputs: ".concat(e.inputs,". ")+"Input ".concat(t," (0-based) originates ")+"from layer type ".concat(n.getClassName(),"."));this.inputNames.push(n.name),this.feedInputShapes.push(n.batchInputShape),this.feedInputNames.push(n.name)}for(const e of this.outputLayers)this.outputNames.push(e.name);this.internalInputShapes=this.inputs.map((e=>e.shape)),this.internalOutputShapes=this.outputs.map((e=>e.shape));const t={},n={},r={},s={},c={},l=[],u=(e,t,n,r,a,s)=>{null!=r&&null!=a&&null!=s||(r=e.sourceLayer,a=e.nodeIndex,s=e.tensorIndex);const i=r.inboundNodes[a];if(-1!==n.indexOf(i))throw new o.RuntimeError("The tensor ".concat(e.name,' at layer "').concat(r.name,'" ')+"is part of a cycle.");if(-1!==t.indexOf(i))return;this.containerNodes.add(y.nodeKey(r,a)),r.id in c||(c[r.id]=Object.keys(c).length),-1===n.indexOf(i)&&n.push(i);const d=i.inboundLayers.length;for(let e=0;e<d;e++){const r=i.inputTensors[e],a=i.inboundLayers[e],o=i.nodeIndices[e],s=i.tensorIndices[e];u(r,t,n,a,o,s)}for(t.push(i);n.indexOf(i)>=0;)n.splice(n.indexOf(i),1);l.push(i)},d=[],p=[];for(const e of this.outputs)u(e,d,p);const m=l.slice().reverse();for(const e of m){n[e.id]=e,e.id in t||(t[e.id]=0);let a=t[e.id];const o=null==r[e.outboundLayer.id]?0:r[e.outboundLayer.id];a=Math.max(a,o),r[e.outboundLayer.id]=a,s[e.outboundLayer.id]=e.outboundLayer,t[e.id]=a;for(let r=0;r<e.inboundLayers.length;r++){const o=e.inboundLayers[r],s=e.nodeIndices[r],i=o.inboundNodes[s],c=null==t[i.id]?0:t[i.id];t[i.id]=Math.max(a+1,c),n[i.id]=i}}const g={};for(const e in t){const r=t[e];r in g||(g[r]=[]),g[r].push(n[e])}const v={};for(const e in r){const t=r[e];t in v||(v[t]=[]),v[t].push(s[e])}let b=Object.keys(v).map((e=>parseInt(e,10))).sort(i.reverseNumberCompare);this.layers=[];for(const e of b){const t=v[e];t.sort(((e,t)=>{const n=c[e.id],r=c[t.id];return n<r?-1:n>r?1:0}));for(const e of t)e instanceof y&&this.internalContainerRefs.push(e),this.layers.push(e)}this.layersByDepth=v,b=Object.keys(g).map((e=>parseInt(e,10))).sort(i.reverseNumberCompare);const x=this.inputs.slice(),w=[];for(const e of b)for(const t of g[e]){const e=t.outboundLayer;if(null!=e){for(const n of t.inputTensors)if(-1===x.indexOf(n))throw new o.RuntimeError("Graph disconnected: cannot obtain value for tensor ".concat(n)+' at layer "'.concat(e.name,'". ')+"The following previous layers were accessed without "+"issue: ".concat(w));for(const e of t.outputTensors)x.push(e);w.push(e.name)}}this.nodesByDepth=g;

1const k=this.layers.map((e=>e.name));for(const e of k){const t=k.filter((t=>t===e)).length;if(1!==t)throw new o.RuntimeError('The name "'.concat(e,'" is used ').concat(t," times ")+"in the model. All layer names should be unique. Layer names: "+JSON.stringify(k))}this.outboundNodes=[],this.inboundNodes=[],new f.Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map((e=>null)),outputMasks:this.outputs.map((e=>null)),inputShapes:this.inputs.map((e=>e.shape)),outputShapes:this.outputs.map((e=>e.shape))}),this.built=!0,this._refCount=1}assertNotDisposed(){if(0===this._refCount)throw new Error("Container '".concat(this.name,"' is already disposed."))}dispose(){this.assertNotDisposed();const e={refCountAfterDispose:null,numDisposedVariables:0};if(0==--this._refCount){for(const t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(const t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach((t=>{t._trainableWeights.forEach((t=>t.trainable=e))})),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new o.ValueError("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(const t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){const e=[];for(const t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){const t=[];for(const e of this.layers)t.push(...e.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e){let t=!(arguments.length>1&&void 0!==arguments[1])||arguments[1];const n={};let r=0;for(const e of this.layers)for(const t of e.weights){if(null!=n[t.originalName])throw new o.ValueError("Duplicate weight name: ".concat(t.originalName));n[t.originalName]=t,r++}const a=[];for(const r in e){let s=r;if(null==n[r]){const e=r.split("/");s=e.slice(0,-2).concat([e[e.length-1]]).join("/")}if(null!=n[s])a.push([n[s],e[r]]);else if(t)throw new o.ValueError("Provided weight data has no target variable: ".concat(r));delete n[s]}if(t){const e=[];for(const t in n)e.push(t);if(e.length>0)throw new o.ValueError("".concat(e.length," of ").concat(r," weights are not set: ")+"".concat(e))}(0,u.batchSetValue)(a)}updatedConfig(){const e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion="tfjs-layers ".concat(d.version),t.backend="TensorFlow.js",t}toJSON(e){let t=!(arguments.length>1&&void 0!==arguments[1])||arguments[1];const n=(0,c.convertTsToPythonic)(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return(0,r.tidy)((()=>{e=i.toList(e);const n=new p.FeedDict;for(let t=0;t<this.inputs.length;++t)n.add(this.inputs[t],e[t]);return(0,p.execute)(this.outputs,n,t)}))}computeMask(e,t){return(0,r.tidy)((()=>{let n;return e=i.toList(e),n=null==t?i.pyListRepeat(null,e.length):i.toList(t),this.runInternalGraph(e,n)[1]}))}computeOutputShape(e){const t=l.normalizeShapeList(e);if(t.length!==this.inputLayers.length)throw new o.ValueError("Invalid inputShape argument ".concat(e,": ")+"model has ".concat(this.inputLayers.length," tensor inputs."));const n={};for(let e=0;e<t.length;e++){const r=this.inputLayers[e],a=t[e];n[r.name+"_0_0"]=a}const r=Object.keys(this.nodesByDepth).map((e=>parseInt(e,10))).sort(i.reverseNumberCompare);if(r.length>1)for(const e of r){const t=this.nodesByDepth[e];for(const e of t){const t=e.outboundLayer;if(-1!==this.inputLayers.map((e=>e.id)).indexOf(t.id))continue;const r=[];for(let t=0;t<e.inboundLayers.length;t++){const a=e.inboundLayers[t],o=e.nodeIndices[t],s=e.tensorIndices[t],i=n["".concat(a.name,"_").concat(o,"_").concat(s)];r.push(i)}const a=t.computeOutputShape(i.singletonOrArray(r)),o=l.normalizeShapeList(a),s=t.inboundNodes.indexOf(e);for(let e=0;e<o.length;

1e++){n["".concat(t.name,"_").concat(s,"_").concat(e)]=o[e]}}}const a=[],s=[];for(let e=0;e<this.outputLayers.length;e++){const t=this.outputLayers[e],n=this.outputLayersNodeIndices[e],r=this.outputLayersTensorIndices[e],a="".concat(t.name,"_").concat(n,"_").concat(r);s.push(a)}for(let e=0;e<s.length;e++){const t=s[e];i.assert(t in n),a.push(n[t])}return i.singletonOrArray(a)}runInternalGraph(e,t){null==t&&(t=i.pyListRepeat(null,e.length));const n={};for(let r=0;r<this.inputs.length;++r){const a=this.inputs[r],o=e[r],s=t[r];n[a.id]=[o,s]}const r=Object.keys(this.nodesByDepth).map((e=>parseInt(e,10))).sort(i.reverseNumberCompare);for(const e of r){const t=this.nodesByDepth[e];for(const e of t){const t=e.outboundLayer,r=e.inputTensors,a=e.outputTensors,s=new Array;for(const e of r)e.id in n&&s.push(n[e.id]);if(s.length===r.length){let r,c,l,u,d={};if(null!=e.callArgs&&(d=e.callArgs),1===s.length){const[e,n]=s[0];null==d.mask&&(d.mask=n),l=i.toList(t.call(e,d)),u=i.toList(t.computeMask(e,n)),r=[e],c=[n]}else r=s.map((e=>e[0])),c=s.map((e=>e[1])),null==d.mask&&(d.mask=c),l=i.toList(t.call(r,d)),u=i.toList(t.computeMask(r,c));if(t.activityRegularizer)throw new o.NotImplementedError("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let e=0;e<a.length;++e){const t=a[e],r=l[e],o=u[e];n[t.id]=[r,o]}}}}const a=[],s=[],c=[];for(const e of this.outputs){i.assert(e.id in n,"Could not compute output ".concat(e.name," : ").concat(e.id));const[t,r]=n[e.id];c.push(t.shape),a.push(t),s.push(r)}return[a,s,c]}buildNodeConversionMap(e){const t={};let n;for(const e of this.layers){n=e instanceof y?1:0;for(let r=0;r<e.inboundNodes.length;r++){const a=y.nodeKey(e,r);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(null!=t){if(this.layers.length<=t)throw new o.ValueError("Was asked to retrieve layer at index ".concat(t,", but model only ")+"has ".concat(this.layers.length," layer(s)."));return this.layers[t]}if(null==e)throw new o.ValueError("Provide either a layer name or layer index");for(const t of this.layers)if(t.name===e)return t;throw new o.ValueError("No such layer: ".concat(e))}calculateLosses(){return(0,r.tidy)((()=>{const e=[];for(const t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){const r=y.nodeKey(t,n);this.containerNodes.has(r)&&e.push(...t.calculateLosses())}return e}))}getConfig(){const e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(const e of this.layers){const r=e.getClassName(),a=e.getConfig(),o=[];for(let n=0;n<e.inboundNodes.length;n++){const r=e.inboundNodes[n],a=y.nodeKey(e,n);let s={};if(this.containerNodes.has(a)){if(r.callArgs)try{JSON.stringify(r.callArgs),s=r.callArgs}catch(t){console.warn("Layer ".concat(e.name," was passed ")+"non-serializable keyword arguments: "+"".concat(r.callArgs,". They will not be included ")+"in the serialized model (and thus will be missing at deserialization time)."),s={}}if(r.inboundLayers.length>0){const e=[];for(let n=0;n<r.inboundLayers.length;n++){const a=r.inboundLayers[n],o=r.nodeIndices[n],i=r.tensorIndices[n];let c=t[y.nodeKey(a,o)];null==c&&(c=0),e.push([a.name,c,i,s])}o.push(e)}}}const s={};s.name=e.name,s.className=r,s.config=a,s.inboundNodes=o,n.push(s)}e.layers=n;const r=[];for(let e=0;e<this.inputLayers.length;e++){const n=this.inputLayers[e],a=this.inputLayersNodeIndices[e],o=y.nodeKey(n,a);if(!this.containerNodes.has(o))continue;let s=t[o];null==s&&(s=0);const i=this.inputLayersTensorIndices[e];r.push([n.name,s,i])}e.inputLayers=r;const a=[];for(let e=0;e<this.outputLayers.length;e++){const n=this.outputLayers[e],r=this.outputLayersNodeIndices[e],o=y.nodeKey(n,r);if(!this.containerNodes.has(o))continue;let s=t[o];null==s&&(s=0);const i=this.outputLayersTensorIndices[e];a.push([n.name,s,i])}return e.outputLayers=a,e}static fromConfig(e,t){let n=arguments.length>3&&void 0!==arguments[3]&&arguments[3];const r={},a={};function c(e,t){e.name in a?a[e.name].push(t):a[e.name]=[t]}function l(e,t){const n=[];let a;for(const o of t){const s=o[0],i=o[1],l=o[2];if(a=null==o[3]?{}:o[3],!(s in r))return void c(e,t);const u=r[s];if(u.inboundNodes.length<=i)return void c(e,t);const d=u.inboundNodes[i];n.push(d.outputTensors[l])}n.length>

10&&e.apply(i.singletonOrArray(n),a)}function u(e){const a=e.name,i=(0,s.deserialize)(e,null!=t.customObjects?t.customObjects:{});i.setFastWeightInitDuringBuild(n),r[a]=i;e.inboundNodes.forEach((e=>{if(!(e instanceof Array))throw new o.ValueError("Corrupted configuration, expected array for nodeData: ".concat(e));c(i,e)}))}const d=t.name,p=t.layers;for(const e of p)u(e);for(;!i.isObjectEmpty(a);)for(const e of p){const t=r[e.name];if(t.name in a){const e=a[t.name];delete a[t.name];for(const n of e)l(t,n)}}const h=[],f=[],m=t.inputLayers;for(const e of m){const t=e[0],n=e[1],a=e[2];i.assert(t in r);const o=r[t].inboundNodes[n].outputTensors;h.push(o[a])}const g=t.outputLayers;for(const e of g){const t=e[0],n=e[1],a=e[2];i.assert(t in r);const o=r[t].inboundNodes[n].outputTensors;f.push(o[a])}return new e({inputs:h,outputs:f,name:d})}get stateful(){if(this._stateful)throw new o.ValueError("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(const e of this.layers)if(e.stateful)return!0;return!1}resetStates(){(0,r.tidy)((()=>{this.layers.forEach((e=>{e.stateful&&e.resetStates()}))}))}}t.Container=y},179842:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.FeedDict=void 0,t.execute=function(e,t,n,a){const i=null!=n&&n.training,f=Array.isArray(e),m=f?e:[e],g=m.map((e=>e.name)),y=[],v=t.names();for(const e of g)-1!==v.indexOf(e)?y.push(t.getValue(e)):y.push(null);null!=a&&(a.maxNumTensors=-1/0,a.minNumTensors=1/0);const b=g.join(",")+"|"+t.names().join(",");let x,w;if(null==l[b]){const e=function(e,t){r.util.assert(null!=e&&e.length>0,(()=>"Expected at least one fetch, got none"));let n=[],a={};if(1===e.length){const r=p(e[0],t);n=r.sorted,a=r.recipientMap}else{const r=new Set;for(const o of e){const{sorted:e,recipientMap:s}=p(o,t);for(const t of e)r.has(t.name)||(n.push(t),r.add(t.name));for(const e in s)null==a[e]&&(a[e]=new Set),s[e].forEach((t=>a[e].add(t)))}}return{sorted:n,recipientCounts:d(a)}}(m,t);x=e.sorted,w=e.recipientCounts,l[b]=x,u[b]=w}x=l[b],w={},i||Object.assign(w,u[b]);const k=new c(t);for(let e=0;e<x.length;++e){if(null!=a){const e=(0,r.memory)().numTensors;e>a.maxNumTensors&&(a.maxNumTensors=e),e<a.minNumTensors&&(a.minNumTensors=e)}const c=x[e],l=c.sourceLayer;if(l instanceof s.InputLayer)continue;const u=[],d=[],p=[];let f=!1;for(const e of c.inputs){const n=k.getValue(e),r=k.getMask(e);u.push(n),d.push(r),null!=r&&(f=!0),i||(w[e.name]--,0!==w[e.name]||t.hasKey(e)||-1!==g.indexOf(e.name)||n.isDisposed||!0===e.sourceLayer.stateful||p.push(n))}f&&((n=n||{}).mask=d[0]);const m=(0,o.toList)(l.apply(u,n));let v=null;l.supportsMasking&&(v=l.computeMask(u,d));const b=h(c),_=Array.isArray(b)?b:[b];for(let e=0;e<_.length;++e){k.hasKey(_[e])||k.add(_[e],m[e],Array.isArray(v)?v[0]:v);const t=g.indexOf(_[e].name);-1!==t&&(y[t]=m[e])}i||(0,r.dispose)(p)}return k.disposeMasks(),f?y:y[0]},t.getTopologicalSortAndRecipientCountsForOneFetch=p;var r=n(735534),a=n(608012),o=n(986904),s=n(310750),i=n(840889);class c{constructor(e){if(this.id2Value={},this.id2Mask={},this.name2Id={},e instanceof c)for(const t in e.id2Value)this.id2Value[t]=e.id2Value[t],t in e.id2Mask&&(this.id2Mask[t]=e.id2Mask[t]);else{if(null==e)return;for(const t of e)this.add(t.key,t.value)}}add(e,t,n){if(null!=this.id2Value[e.id])throw new a.ValueError("Duplicate key: name=".concat(e.name,", id=").concat(e.id));return this.id2Value[e.id]=function(e,t){if(null==e.dtype||e.dtype===t.dtype)return t;try{return(0,r.cast)(t,e.dtype)}catch(n){throw new a.ValueError("The dtype of the feed (".concat(t.dtype,") can not be cast to the dtype ")+"of the key '".concat(e.name,"' (").concat(e.dtype,")."))}}(e,t),this.name2Id[e.name]=e.id,null!=n&&(this.id2Mask[e.id]=n),this}addFeed(e){this.add(e.key,e.value)}hasKey(e){return null!=this.id2Value[e.id]}names(){return Object.keys(this.name2Id)}getValue(e){if(e instanceof i.SymbolicTensor){if(null==this.id2Value[e.id])throw new a.ValueError("Nonexistent key: ".concat(e.name));return this.id2Value[e.id]}{const t=this.name2Id[e];if(null==t)throw new a.ValueError("Feed dict has no SymbolicTensor name: ".concat(e));return this.id2Value[t]}}getMask(e){if(e instanceof i.SymbolicTensor){if(null==this.id2Value[e.id])throw new a.ValueError("Nonexistent key: ".concat(e.name));return this.id2Mask[e.id]}{const t=this.name2Id[e];if(null==t)throw new a.ValueError("Feed dict has no SymbolicTensor name: ".concat(e));return this.id2Mask[t]}}disposeMasks(){null!=this.id2Mask&&(0,r.dispose)(this.id2Mask)}}t.FeedDict=c;const l={},u={};function d(e){const t={};for(const n in e)t[n]=e[n].size;return t}function p(e,t){const n=new Set,r=[],a={};for(const e of t.names())n.add(e);const o=[],s=[];for(o.push(e);o.length>0;){const e=o[o.length-1];if(n.has(e.name)){o.pop();continue}const t=s[s.length-1]===o.length-1;if(0===e.inputs.length||t)o.pop(),r.push(e),n.add(e.name),t&&s.pop();else{s.push(o.length-1);for(const t of e.inputs)null==a[t.name]&&(a[t.name]=new Set),a[t.name].add(e.name),n.has(t.name)||o.push(t)}}return{sorted:r,recipientMap:a}}function h(e){let t;

1if(1===e.sourceLayer.inboundNodes.length)t=e.sourceLayer.output;else{let n=null;for(let t=0;t<e.sourceLayer.inboundNodes.length;++t)for(const r of e.sourceLayer.inboundNodes[t].outputTensors)if(r.id===e.id){n=t;break}t=e.sourceLayer.getOutputAt(n)}return t}},310750:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.Input=function(e){if(null==e.batchShape&&null==e.shape)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");if(null!=e.batchShape&&null!=e.shape)throw new o.ValueError("Please provide either a `shape` or `batchShape` argument to Input, but not both.");let t=e.batchShape;null!=e.shape&&null==t&&(t=[null].concat(e.shape));let n=e.dtype;null==n&&(n="float32");const r=new i({batchInputShape:t,name:e.name,dtype:n,sparse:e.sparse});return r.inboundNodes[0].outputTensors[0]},t.InputLayer=void 0;var r=n(735534),a=n(284986),o=n(608012),s=n(840889);class i extends s.Layer{constructor(e){if(super({dtype:e.dtype,name:null!=e.name?e.name:(0,a.getUid)("input").toString()}),null==e.batchSize&&(e.batchSize=null),null==e.sparse&&(e.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=e.sparse,null!=e.inputShape&&null!=e.batchInputShape)throw new o.ValueError("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let t=e.batchInputShape;if(null==t){if(null==e.inputShape)throw new o.ValueError("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(null!=e.batchSize)throw new o.ValueError("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");const n=e.dtype||"float32";this.batchInputShape=t,this.dtype=n,this.inputSpec=[{shape:t}];const r=new s.SymbolicTensor(this.dtype,this.batchInputShape,this,[],{},this.name);r.nodeIndex=0,r.tensorIndex=0,new s.Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[r],outputTensors:[r],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new o.ValueError("Cannot pass any input to an "+"InputLayer's apply() method. InputLayer name: ".concat(this.name))}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}}t.InputLayer=i,i.className="InputLayer",r.serialization.registerClass(i)},840889:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.SymbolicTensor=t.Node=t.Layer=t.InputSpec=void 0,t.getSourceInputs=function e(t,n,r){(null==n||null!=r&&r>0)&&(n=t.sourceLayer,r=t.nodeIndex);if(0===n.inboundNodes.length)return[t];{const t=n.inboundNodes[r];if(0===t.inboundLayers.length)return t.inputTensors;{const n=[];for(let r=0;r<t.inboundLayers.length;r++){const a=t.inputTensors[r],o=t.inboundLayers[r],s=t.nodeIndices[r],i=e(a,o,s);for(const e of i)-1===n.indexOf(e)&&n.push(e)}return n}}};var r=n(735534),a=n(284986),o=n(545188),s=n(608012),i=n(492560),c=h(n(986904)),l=h(n(252518)),u=h(n(468769)),d=n(268778);function p(e){if("function"!=typeof WeakMap)return null;var t=new WeakMap,n=new WeakMap;return(p=function(e){return e?n:t})(e)}function h(e,t){if(!t&&e&&e.__esModule)return e;if(null===e||"object"!=typeof e&&"function"!=typeof e)return{default:e};var n=p(t);if(n&&n.has(e))return n.get(e);var r={__proto__:null},a=Object.defineProperty&&Object.getOwnPropertyDescriptor;for(var o in e)if("default"!==o&&{}.hasOwnProperty.call(e,o)){var s=a?Object.getOwnPropertyDescriptor(e,o):null;s&&(s.get||s.set)?Object.defineProperty(r,o,s):r[o]=e[o]}return r.default=e,n&&n.set(e,r),r}t.InputSpec=class{constructor(e){this.dtype=e.dtype,this.shape=e.shape,null!=e.shape?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}};class f{constructor(e,t,n,r,s,i,c){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=r,this.callArgs=s,this.outputTensorIndex=c,this.id=(0,a.getNextUniqueTensorId)(),null!=i&&(this.originalName=(0,o.getScopedTensorName)(i),this.name=(0,o.getUniqueTensorName)(this.originalName)),this.rank=t.length}}t.SymbolicTensor=f;let m=0;class g{constructor(e,t){this.callArgs=t,this.id=m++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(const t of e.inboundLayers)null!=t&&t.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){const e=[];for(const t of this.inboundLayers)null!=t?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inbou

1ndLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}}t.Node=g;let y=0;class v extends r.serialization.Serializable{constructor(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};super(),this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=y++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){const e=this.getClassName();t=c.toSnakeCase(e)+"_"+(0,a.getUid)(e)}if(this.name=t,this.trainable_=null==e.trainable||e.trainable,null!=e.inputShape||null!=e.batchInputShape){let t;if(null!=e.batchInputShape)t=e.batchInputShape;else if(null!=e.inputShape){let n=null;null!=e.batchSize&&(n=e.batchSize),t=[n].concat(e.inputShape)}this.batchInputShape=t;let n=e.dtype;null==n&&(n=e.inputDType),null==n&&(n="float32"),this.dtype=n}null!=e.weights?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(0===this.inboundNodes.length)throw new s.RuntimeError("The layer has never been called "+"and thus has no defined ".concat(t,"."));if(this.inboundNodes.length<=e)throw new s.ValueError("Asked to get ".concat(t," at node ").concat(e,", ")+"but the layer has only ".concat(this.inboundNodes.length," inbound nodes."));return this.inboundNodes[e]}getInputAt(e){return c.singletonOrArray(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return c.singletonOrArray(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new s.AttributeError("Layer ".concat(this.name)+' has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use `getInputAt(nodeIndex)` instead.');if(0===this.inboundNodes.length)throw new s.AttributeError("Layer ".concat(this.name)+" is not connected, no input to return.");return c.singletonOrArray(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(0===this.inboundNodes.length)throw new s.AttributeError("Layer ".concat(this.name)+" has no inbound nodes.");if(this.inboundNodes.length>1)throw new s.AttributeError("Layer ".concat(this.name)+' has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use `getOutputAt(nodeIndex)` instead.');return c.singletonOrArray(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map((e=>e()))}get updates(){return this._updates}get built(){return this._built}set built(e){this._built=e}get trainable(){return this.trainable_}set trainable(e){this._trainableWeights.forEach((t=>t.trainable=e)),this.trainable_=e}get trainableWeights(){return this.trainable_?this._trainableWeights.filter((e=>e.trainable)):[]}set trainableWeights(e){this._trainableWeights=e}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter((e=>!e.trainable)).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(e){this._nonTrainableWeights=e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(e){if(e=c.toList(e),null==this.inputSpec||0===this.inputSpec.length)return;const t=c.toList(this.inputSpec);if(e.length!==t.length)throw new s.ValueError("Layer ".concat(this.name," expects ").concat(t.length," inputs, ")+"but it received ".concat(e.length," input tensors. ")+"Input received: ".concat(e));for(let n=0;n<e.length;n++){const r=e[n],a=t[n];if(null==a)continue;const o=r.rank;if(null!=a.ndim&&o!==a.ndim)throw new s.ValueError("Input ".concat(n," is incompatible with layer ").concat(this.name,": ")+"expected ndim=".concat(a.ndim,", found ndim=").concat(o));if(null!=a.maxNDim&&o>a.maxNDim)throw new s.ValueError("Input ".concat(n," is incompatible with layer ").concat(this.name)+": expected max_ndim

1=".concat(a.maxNDim,", found ndim=").concat(o));if(null!=a.minNDim&&o<a.minNDim)throw new s.ValueError("Input ".concat(n," is incompatible with layer ").concat(this.name)+": expected min_ndim=".concat(a.minNDim,", found ndim=").concat(o,"."));if(null!=a.dtype&&r.dtype!==a.dtype)throw new s.ValueError("Input ".concat(n," is incompatible with layer ").concat(this.name," ")+": expected dtype=".concat(a.dtype,", found dtype=").concat(r.dtype,"."));if(a.axes){const e=r.shape;for(const t in a.axes){const r=Number(t),o=a.axes[t],i=r>=0?e[r]:e[e.length+r];if(null!=o&&-1===[o,null].indexOf(i))throw new s.ValueError("Input ".concat(n," is incompatible with layer ")+"".concat(this.name,": expected axis ").concat(r," of input shape to ")+"have value ".concat(o," but got shape ").concat(e,"."))}}if(null!=a.shape)for(let e=0;e<a.shape.length;++e){const t=a.shape[e],o=r.shape[e];if(null!=t&&null!=o&&t!==o)throw new s.ValueError("Input ".concat(n," is incompatible with layer ")+"".concat(this.name,": expected shape=").concat(a.shape,", ")+"found shape=".concat(r.shape,"."))}}}call(e,t){return e}invokeCallHook(e,t){null!=this._callHook&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();const n=c.toList(e);let r=!0;for(const e of n)if(!(e instanceof f)){r=!1;break}let a=!0;for(const e of n)if(e instanceof f){a=!1;break}if(r===a)throw new s.ValueError("Arguments to apply() must be all SymbolicTensors or all Tensors");return(0,o.nameScope)(this.name,(()=>{if(!this.built){this.assertInputCompatibility(e);const t=[];for(const n of c.toList(e))t.push(n.shape);this.build(c.singletonOrArray(t)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),null===this._refCount&&a&&(this._refCount=1)}if(this.assertInputCompatibility(e),a){let r=this.call(e,t);const a=c.toList(r),o=[];for(let e of a)-1!==n.indexOf(e)&&(e=e.clone()),o.push(e);if(r=c.singletonOrArray(o),null!=this.activityRegularizer)throw new s.NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return r}{const n=function(e){e=c.toList(e);const t=[];for(const n of e)t.push(n.shape);return c.singletonOrArray(t)}(e),r=this.computeOutputShape(n);let a;const o="float32";if(this.warnOnIncompatibleInputShape(Array.isArray(e)?n[0]:n),a=null!=r&&r.length>0&&Array.isArray(r[0])?r.map(((n,r)=>new f(o,n,this,c.toList(e),t,this.name,r))):new f(o,r,this,c.toList(e),t,this.name),this.addInboundNode(e,a,null,null,n,r,t),this._refCount++,null!=this.activityRegularizer)throw new s.NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return a}}))}warnOnIncompatibleInputShape(e){if(null!=this.batchInputShape)if(e.length!==this.batchInputShape.length)console.warn("The rank of the input tensor provided (shape: "+"".concat(JSON.stringify(e),") does not match that of the ")+"batchInputShape (".concat(JSON.stringify(this.batchInputShape),") ")+"of the layer ".concat(this.name));else{let t=!1;this.batchInputShape.forEach(((n,r)=>{null!=n&&null!=e[r]&&e[r]!==n&&(t=!0)})),t&&console.warn("The shape of the input tensor "+"(".concat(JSON.stringify(e),") does not ")+"match the expectation of layer ".concat(this.name,": ")+"".concat(JSON.stringify(this.batchInputShape)))}}get outputShape(){if(null==this.inboundNodes||0===this.inboundNodes.length)throw new s.AttributeError("The layer ".concat(this.name," has never been called and thus has no ")+"defined output shape.");const e=[];for(const t of this.inboundNodes){const n=JSON.stringify(t.outputShapes);-1===e.indexOf(n)&&e.push(n)}if(1===e.length){const e=this.inboundNodes[0].outputShapes;return Array.isArray(e)&&Array.isArray(e[0])&&1===e.length?e[0]:e}throw new s.AttributeError("The layer ".concat(this.name," has multiple inbound nodes with different ")+'output shapes. Hence the notion of "output shape" is ill-defined for the layer.')}countParams(){if(!this.built)throw new s.RuntimeError("You tried to call countParams() on ".concat(this.name,", ")+"but the layer is not built yet. Build it first by calling build(batchInputShape).");return u.countParamsInWeights(this.weights)}build(e){this.built=!0}getWeights(){let e=arguments.length>0&&void 0!==arguments[0]&&arguments[0];return(0,d.batchGetValue)(e?this.trainableWeights:this.weights)}

1setWeights(e){(0,r.tidy)((()=>{const t=this.weights;if(t.length!==e.length)throw new s.ValueError('You called setWeights(weights) on layer "'.concat(this.name,'" ')+"with a weight list of length ".concat(e.length,", ")+"but the layer was expecting ".concat(t.length," weights. ")+"Provided weights: ".concat(e,"..."));if(0===t.length)return;const n=[],a=(0,d.batchGetValue)(t);for(let o=0;o<a.length;++o){const i=a[o],c=t[o],l=e[o];if(!r.util.arraysEqual(i.shape,l.shape))throw new s.ValueError("Layer weight shape ".concat(i.shape," ")+"not compatible with provided weight shape ".concat(l.shape));n.push([c,l])}(0,d.batchSetValue)(n)}))}addWeight(e,t,n,r,a,o,c,l){if(-1!==this._addedWeightNames.indexOf(e))throw new s.ValueError("Duplicate weight name ".concat(e," for layer ").concat(this.name));this._addedWeightNames.push(e),null==n&&(n="float32"),this.fastWeightInitDuringBuild&&(r=null!=l?l():(0,i.getInitializer)("zeros"));const u=r.apply(t,n),p=new d.LayerVariable(u,n,e,o,c);return u.dispose(),null!=a&&this.addLoss((()=>a.apply(p.read()))),null==o&&(o=!0),o?this._trainableWeights.push(p):this._nonTrainableWeights.push(p),p}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){null==e||Array.isArray(e)&&0===e.length||(e=c.toList(e),void 0!==this._losses&&null!==this._losses&&this.losses.push(...e))}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(null!=t){if(!Array.isArray(t))throw new TypeError("Layer ".concat(this.name," does not support masking, ")+"but was passed an inputMask.");t.forEach((e=>{if(null!=e)throw new TypeError("Layer ".concat(this.name," does not support masking, ")+"but was passed an inputMask.")}))}return null}return t}addInboundNode(e,t,n,r,a,o){let s=arguments.length>6&&void 0!==arguments[6]?arguments[6]:null;const i=c.toList(e);t=c.toList(t),n=c.toList(n),r=c.toList(r),a=l.normalizeShapeList(a),o=l.normalizeShapeList(o);const u=[],d=[],p=[];for(const e of i)u.push(e.sourceLayer),d.push(e.nodeIndex),p.push(e.tensorIndex);new g({outboundLayer:this,inboundLayers:u,nodeIndices:d,tensorIndices:p,inputTensors:i,outputTensors:t,inputMasks:n,outputMasks:r,inputShapes:a,outputShapes:o},s);for(let e=0;e<t.length;e++)t[e].sourceLayer=this,t[e].nodeIndex=this.inboundNodes.length-1,t[e].tensorIndex=e}getConfig(){const e={name:this.name,trainable:this.trainable};return null!=this.batchInputShape&&(e.batchInputShape=this.batchInputShape),null!=this.dtype&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach((e=>e.dispose())),this.weights.length}assertNotDisposed(){if(0===this._refCount)throw new Error("Layer '".concat(this.name,"' is already disposed."))}dispose(){if(!this.built)throw new Error("Cannot dispose Layer ".concat(this.name," because it has not been ")+"built yet.");if(null===this._refCount)throw new Error("Cannot dispose Layer ".concat(this.name," because it has not been used ")+"yet.");this.assertNotDisposed();let e=0;return 0==--this._refCount&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}}t.Layer=v},171320:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.LayersModel=t.Functional=void 0,t.checkArrayLengths=T,t.collectMetrics=E,t.isDataArray=O,t.isDataDict=N,t.isDataTensor=P,t.standardizeInputData=I;var r=C(n(735534)),a=r,o=C(n(984710)),s=n(545188),i=n(608012),c=n(640153),l=C(n(242135)),u=C(n(26267)),d=C(n(736183)),p=n(947525),h=n(986904),f=n(785473),m=n(302774),g=n(290407),y=n(480036),v=n(457270),b=n(179842),x=n(162330),w=n(984039),k=n(117347);function _(e){if("function"!=typeof WeakMap)return null;var t=new WeakMap,n=new WeakMap;return(_=function(e){return e?n:t})(e)}function C(e,t){if(!t&&e&&e.__esModule)return e;if(null===e||"object"!=typeof e&&"function"!=typeof e)return{default:e};var n=_(t);if(n&&n.has(e))return n.get(e);var r={__proto__:null},a=Object.defineProperty&&Object.getOwnPropertyDescriptor;for(var o in e)if("default"!==o&&{}.hasOwnProperty.call(e,o)){var s=a?Object.getOwnPropertyDescriptor(e,o):null;s&&(s.get||s.set)?Object.defineProperty(r,o,s):r[o]=e[o]}return r.default=e,n&&n.set(e,r),r}function P(e){return e instanceof r.Tensor}function O(e){return Array.isArray(e)}function N(e){return!P(e)&&!O(e)}function I(e,t,n){let r,a=!(arguments.length>3&&void 0!==arguments[3])||arguments[3],o=arguments.length>4&&void 0!==arguments[4]?arguments[4]:"";if(null==t||0===t.length){if(null!=e){let t=!1;if(O(e)&&e.length>0)t=!0;else if(N(e)){for(const n in e)if(e.hasOwnProperty(n)){t=!0;break}}else t=!0;if(t)throw new i.ValueError("Error when checking model ".concat(o," expected no data, ")+"but got ".concat(e))}return[]}if(null==e)return t.map((e=>null));if(N(e)){r=[];for(const n of t){if(null==e[n])throw new i.ValueError('No data provided for "'.concat(n,'". Need data for each key in: ')+"".concat(t));

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')+"Only expected the following keys: ".concat(this.outputNames));for(const n of this.outputNames)null==e.loss[n]&&console.warn('Output "'.concat(n,'" is missing from loss dictionary. 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Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};const r=null==n.batchSize?32:n.batchSize;(0,w.checkBatchSize)(r);const a=this.standardizeUserDataXY(e,t,!0,r);try{const e=a[0].concat(a[1]);this.makeTestFunction();const t=this.testFunction,o=this.testLoop(t,e,r,n.verbose,n.steps);return(0,h.singletonOrArray)(o)}finally{(0,w.disposeNewTensors)(a[0],e),(0,w.disposeNewTensors)(a[1],t)}}async evaluateDataset(e,t){return this.makeTestFunction(),(0,x.evaluateDataset)(this,e,t)}checkNumSamples(e,t,n){let r,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:"steps";if(null!=n){if(r=null,null!=t)throw new i.ValueError("If ".concat(a," is set, batchSize must be null or undefined.")+"Got batchSize = ".concat(t))}else{if(null==e)throw new i.ValueError("Either the input data should have a defined shape, or "+"".concat(a," shoud be specified."));r=Array.isArray(e)?e[0].shape[0]:e.shape[0]}return r}execute(e,t){if(Array.isArray(t)&&0===t.length)throw new i.ValueError("`outputs` is an empty Array, which is not allowed.");const n=Array.isArray(t),a=n?t:[t],o=this.retrieveSymbolicTensors(a),s=new b.FeedDict;if(e instanceof r.Tensor&&(e=[e]),Array.isArray(e)){if(e.length!==this.inputs.length)throw new i.ValueError("The number of inputs provided (".concat(e.length,") ")+"does not match the number of inputs of this model "+"(".concat(this.inputs.length,")."));for(let t=0;t<this.inputs.length;++t)s.add(this.inputs[t],e[t])}else for(const t of this.inputs){const n=e[t.name];if(null==n)throw new i.ValueError("No value is provided for the model's input ".concat(t.name));s.add(t,n)}const c=(0,b.execute)(o,s);return n?c:c[0]}retrieveSymbolicTensors(e){const t=(0,h.pyListRepeat)(null,e.length);let n=e.length;for(const r of this.layers){const a=Array.isArray(r.output)?r.output:[r.output],o=a.map((e=>e.name));for(let r=0;r<e.length;++r){const s=o.indexOf(e[r]);if(-1!==s&&(t[r]=a[s],n--),0===n)break}if(0===n)break}if(n>0){const n=[];throw t.forEach(((t,r)=>{null==t&&n.push(e[r])})),new i.ValueError("Cannot find SymbolicTensors for output name(s): "+"".concat(JSON.stringify(n)))}return t}predictLoop(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:32,n=arguments.length>2&&void 0!==arguments[2]&&arguments[2];return a.tidy((()=>{const r=this.checkNumSamples(e);if(n)throw new i.NotImplementedError("Verbose predictLoop() is not implemented yet.");const o=(0,w.makeBatches)(r,t),s=this.outputs.map((e=>[]));for(let t=0;t<o.length;++t){a.tidy((()=>{const n=o[t][0],r=o[t][1],a=(0,w.sliceArrays)(e,n,r),s=[];if(Array.isArray(a))for(let e=0;e<a.length;++e)s.push({key:this.inputs[e],value:a[e]});else s.push({key:this.inputs[0],value:a});const i=new b.FeedDict(s);return(0,b.execute)(this.outputs,i)}

1)).forEach(((e,t)=>s[t].push(e)))}return(0,h.singletonOrArray)(s.map((e=>a.concat(e,0))))}))}predict(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};const n=(0,w.ensureTensorsRank2OrHigher)(e);S(n,this.inputNames,this.feedInputShapes,!1);try{const e=null==t.batchSize?32:t.batchSize;return(0,w.checkBatchSize)(e),this.predictLoop(n,e)}finally{(0,w.disposeNewTensors)(n,e)}}predictOnBatch(e){S(e,this.inputNames,this.feedInputShapes,!0);const t=(Array.isArray(e)?e[0]:e).shape[0];return this.predictLoop(e,t)}standardizeUserDataXY(e,t){let n=arguments.length>3?arguments[3]:void 0;if(null==this.optimizer_)throw new i.RuntimeError("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");const r=[];for(let e=0;e<this.feedOutputShapes.length;++e){const t=this.feedOutputShapes[e];this.feedLossFns[e]===l.sparseCategoricalCrossentropy?r.push(t.slice(0,t.length-1).concat([1])):r.push(t)}if(T(e=I(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=I(t,this.feedOutputNames,r,!1,"target")),function(e,t,n){const r=[l.meanSquaredError,l.binaryCrossentropy,l.categoricalCrossentropy];for(let a=0;a<e.length;++a){const o=e[a],s=t[a],c=n[a];if(null!=s){if(s===l.categoricalCrossentropy&&1===o.shape[o.shape.length-1])throw new i.ValueError("You are passing a target array of shape ".concat(o.shape," while using ")+"a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].");if(-1!==r.indexOf(s)){const e=o.shape.slice(1),t=c.slice(1);for(let n=0;n<e.length;++n){const r=e[n],a=t[n];if(null!=a&&r!==a)throw new i.ValueError("A target Tensor with shape ".concat(o.shape," was passed for an ")+"output of shape ".concat(c,", while using a loss function that ")+"expects targets to have the same shape as the output.")}}}}}(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&null!=n&&n>0&&e[0].shape[0]%n!=0)throw new i.ValueError("In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size "+"".concat(n,". Found: ").concat(e[0].shape[0]," sample(s)."));return[e,t]}async standardizeUserData(e,t,n,r){let a=!(arguments.length>4&&void 0!==arguments[4])||arguments[4],o=arguments.length>5?arguments[5]:void 0;const[s,i]=this.standardizeUserDataXY(e,t,a,o);if(null!=n)throw new Error("sample weight is not supported yet.");let c=null;if(null!=r){const e=(0,k.standardizeClassWeights)(r,this.outputNames);c=[];for(let t=0;t<e.length;++t)c.push(await(0,k.standardizeWeights)(i[t],null,e[t]))}return[s,i,c]}testLoop(e,t,n){let s=arguments.length>3&&void 0!==arguments[3]?arguments[3]:0,c=arguments.length>4?arguments[4]:void 0;return a.tidy((()=>{const l=this.checkNumSamples(t,n,c,"steps"),u=[];if(s>0)throw new i.NotImplementedError("Verbose mode is not implemented yet.");if(null!=c)throw new i.NotImplementedError("steps mode in testLoop() is not implemented yet");{const s=(0,w.makeBatches)(l,n),i=(0,r.tensor1d)((0,m.range)(0,l));for(let n=0;n<s.length;++n){const c=s[n][0],l=s[n][1],d=o.sliceAlongFirstAxis(i,c,l-c),p=(0,w.sliceArraysByIndices)(t,d),h=e(p);if(0===n)for(let e=0;e<h.length;++e)u.push((0,r.scalar)(0));for(let e=0;e<h.length;++e){const t=h[e];u[e]=a.add(u[e],a.mul(l-c,t))}}for(let e=0;e<u.length;++e)u[e]=a.div(u[e],l)}return u}))}getDedupedMetricsNames(){const e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){const r=e[n];let a=r;if((0,h.count)(e,r)>1){const t=(0,h.count)(e.slice(0,n),r);a+="_".concat(t)}t.push(a)}return t}makeTrainFunction(){return e=>{const t=[],n=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),o=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+2*this.outputs.length),s=[],i=this.collectedTrainableWeights.map((e=>e.read()));return[this.optimizer_.minimize((()=>{const e=[];for(let t=0;t<this.inputs.length;++t)e.push({key:this.inputs[t],value:n[t]});const i=new b.FeedDict(e),c=(0,b.execute)(this.outputs,i,{training:!0});let l;for(let e=0;e<this.lossFunctions.length;++e){let n=(0,this.lossFunctions[e])(r[e],c[e]);null!=o[e]&&(n=(0,k.computeWeightedLoss)(n,o[e]));const s=a.mean(n);t.push(s),l=0===e?n:a.add(l,n)}for(let e=0;e<this.metricsTensors.length;++e){let n;if(this.outputs.length>1&&e<this.outputs.length)n=t[e];else{const t=this.metricsTensors[e][0],o=this.metricsTensors[e][1];n=a.mean(t(r[o],c[o]))}a.keep(n),s.push(n)}

1return l=a.mean(l),this.calculateLosses().forEach((e=>{l=a.add(l,e)})),l}),!0,i)].concat(s)}}makeTestFunction(){this.testFunction=e=>a.tidy((()=>{const t=[];let n;const r=e.slice(0,this.inputs.length),o=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=[];for(let e=0;e<this.inputs.length;++e)s.push({key:this.inputs[e],value:r[e]});const i=new b.FeedDict(s),c=(0,b.execute)(this.outputs,i);for(let e=0;e<this.lossFunctions.length;++e){const r=this.lossFunctions[e],s=a.mean(r(o[e],c[e]));n=0===e?s:a.add(n,s),t.push(n)}for(let e=0;e<this.metricsTensors.length;++e){const n=this.metricsTensors[e][0],r=this.metricsTensors[e][1],s=a.mean(n(o[r],c[r]));t.push(s)}return t}))}async fit(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};return(0,w.fitTensors)(this,e,t,n)}async fitDataset(e,t){return(0,x.fitDataset)(this,e,t)}async trainOnBatch(e,t){const n=await this.standardizeUserData(e,t),r=n[0],o=n[1],s=this.makeTrainFunction()(r.concat(o)),i=[];for(const e of s){const t=await e.data();i.push(t[0])}return a.dispose(s),(0,w.disposeNewTensors)(n[0],e),(0,w.disposeNewTensors)(n[1],t),(0,h.singletonOrArray)(i)}getNamedWeights(e){const t=[],n=null!=e&&e.trainableOnly,r=n?this.trainableWeights:this.weights,a=this.getWeights(n);for(let e=0;e<r.length;++e)n&&!r[e].trainable||t.push({name:r[e].originalName,tensor:a[e]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){const e=super.dispose();if(0===e.refCountAfterDispose&&null!=this.optimizer&&this.isOptimizerOwned){const t=a.memory().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-a.memory().numTensors}return e}getLossIdentifiers(){let e;if("string"==typeof this.loss)e=(0,h.toSnakeCase)(this.loss);else if(Array.isArray(this.loss)){for(const e of this.loss)if("string"!=typeof e)throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map((e=>(0,h.toSnakeCase)(e)))}else{const t=Object.keys(this.loss);e={};const n=this.loss;for(const r of t){if("string"!=typeof n[r])throw new Error("Serialization of non-string loss is not supported.");e[r]=(0,h.toSnakeCase)(n[r])}}return e}getMetricIdentifiers(){if("string"==typeof this.metrics||"function"==typeof this.metrics)return[(0,h.toSnakeCase)(u.getLossOrMetricName(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map((e=>(0,h.toSnakeCase)(u.getLossOrMetricName(e))));{const e={};for(const t in this.metrics)e[t]=(0,h.toSnakeCase)(u.getLossOrMetricName(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(null!=e.weighted_metrics)throw new Error("Loading weight_metrics is not supported yet.");if(null!=e.loss_weights)throw new Error("Loading loss_weights is not supported yet.");if(null!=e.sample_weight_mode)throw new Error("Loading sample_weight_mode is not supported yet.");const t=(0,g.convertPythonicToTs)(e.optimizer_config),n=(0,c.deserialize)(t);let r,a;if("string"==typeof e.loss)r=(0,h.toCamelCase)(e.loss);else if(Array.isArray(e.loss))r=e.loss.map((e=>(0,h.toCamelCase)(e)));else if(null!=e.loss){r={};for(const t in e.loss)r[t]=(0,h.toCamelCase)(e.loss[t])}if(Array.isArray(e.metrics))a=e.metrics.map((e=>(0,h.toCamelCase)(e)));else if(null!=e.metrics){a={};for(const t in e.metrics)a[t]=(0,h.toCamelCase)(e.metrics[t])}this.compile({loss:r,metrics:a,optimizer:n})}async save(e,t){if("string"==typeof e){const t=r.io.getSaveHandlers(e);if(0===t.length)throw new i.ValueError("Cannot find any save handlers for URL '".concat(e,"'"));if(t.length>1)throw new i.ValueError("Found more than one (".concat(t.length,") save handlers for ")+"URL '".concat(e,"'"));e=t[0]}if(null==e.save)throw new i.ValueError("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");const n=await r.io.encodeWeights(this.getNamedWeights(t)),a={modelTopology:this.toJSON(null,!1),format:"layers-model",generatedBy:"TensorFlow.js tfjs-layers v".concat(y.version),convertedBy:null};if(null!=t&&t.includeOptimizer&&null!=this.optimizer){a.trainingConfig=this.getTrainingConfig();const e="optimizer",{data:t,specs:o}=await r.io.encodeWeights(await this.optimizer.getWeights(),e);n.specs.push(...o),n.data=r.io.concatenateArrayBuffers([n.data,t])}if(null!=this.userDefinedMetadata){const e=!0;

1(0,p.checkUserDefinedMetadata)(this.userDefinedMetadata,this.name,e),a.userDefinedMetadata=this.userDefinedMetadata}return a.weightData=n.data,a.weightSpecs=n.specs,e.save(a)}setUserDefinedMetadata(e){(0,p.checkUserDefinedMetadata)(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}}t.LayersModel=M,M.className="Model",r.serialization.registerClass(M);class A extends M{}t.Functional=A,A.className="Functional",r.serialization.registerClass(A)},162330:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.evaluateDataset=async function(e,t,n){const o=null!=(n=n||{}).batches,i=e.testFunction;let l=[];if(n.verbose>0)throw new s.NotImplementedError("Verbose mode is not implemented yet.");a.util.assert(!o||n.batches>0&&Number.isInteger(n.batches),(()=>"Test loop expects `batches` to be a positive integer, but "+"received ".concat(JSON.stringify(n.batches))));const u=(d=t,"function"==typeof d.next?t:await t.iterator());var d;let h=0,f=0;for(;!o||f<n.batches;){const t=await u.next();if(l=a.tidy((()=>{if(t.value){const{xs:n,ys:o}=p(e,t.value),s=n.concat(o),c=a.tidy((()=>i(s)));if(a.dispose(s),0===f)for(let e=0;e<c.length;++e)l.push((0,r.scalar)(0));const u=s[0].shape[0];for(let e=0;e<c.length;++e){const t=c[e],n=l[e];l[e]=a.tidy((()=>a.add(l[e],a.mul(u,t)))),f>0&&a.dispose(n)}a.dispose(c),h+=u,++f}return l})),t.done){o&&console.warn("Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least `batches` "+"batches (in this case, ".concat(n.batches," batches). ")+"You may need to use the repeat() function when building your dataset.");break}}for(let e=0;e<l.length;++e){const t=l[e];l[e]=a.div(l[e],h),a.dispose(t)}return(0,c.singletonOrArray)(l)},t.fitDataset=async function(e,t,n){const r=null!=n.batchesPerEpoch;if(a.util.assert(null!=e.optimizer,(()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig).")),a.util.assert(null!=n,(()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call.")),a.util.assert(null!=n.epochs&&n.epochs>0&&Number.isInteger(n.epochs),(()=>"For fitDataset(), config.epochs is expected to be a positive "+"integer, but got ".concat(n.epochs))),a.util.assert(!r||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),(()=>"For fitDataset(), config.batchesPerEpoch is expected to be a "+"positive integer if specified, but got ".concat(n.batchesPerEpoch))),a.util.assert(null==n.validationSplit,(()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead.")),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{const u=null!=n.validationData;let h,m;if(u)if(f(n.validationData))a.util.assert(null==n.validationBatches||n.validationBatches>0&&Number.isInteger(n.validationBatches),(()=>"For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, "+"but got ".concat(n.validationBatches)));else{const e=function(e){if(3===e.length)throw new s.NotImplementedError("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}(n.validationData);h=e.xs,m=e.ys}const g=e.makeTrainFunction(),y=e.getDedupedMetricsNames();let v;v=u?y.slice().concat(y.map((e=>"val_"+e))):y.slice();const b=(0,o.standardizeCallbacks)(n.callbacks,n.yieldEvery),x=null==n.verbose?1:n.verbose,{callbackList:w,history:k}=(0,o.configureCallbacks)(b,x,n.epochs,null,null,function(e,t){let n=null;null!=t.batchesPerEpoch?n=t.batchesPerEpoch:Number.isFinite(e.size)&&(n=e.size);return n}(t,n),null,u,v);w.setModel(e),e.history=k,await w.onTrainBegin(),e.stopTraining_=!1;let _=null==n.initialEpoch?0:n.initialEpoch,C=await t.iterator();for(;_<n.epochs;){const o={};await w.onEpochBegin(_);let s=0,v=0;for(r||(C=await t.iterator());!r||s<n.batchesPerEpoch;){const t=await C.next();if(r&&t.done){console.warn("You provided `batchesPerEpoch` as "+"".concat(n.batchesPerEpoch,", ")+"but your dataset iterator ran out of data after "+"".concat(s," batches; ")+"interrupting training. Make sure that your dataset can generate at least `batchesPerEpoch * epochs` batches (in this case, "+"".concat(n.batchesPerEpoch*n.epochs," batches). ")+"You may need to use the repeat() function when building your dataset.");break}if(null!=t.value){const{xs:r,ys:o}=p(e,t.value),c={};c.batch=v,c.size=r[0].shape[0],await w.onBatchBegin(v,c);const u=[];

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1return(0,r.mul)(n,(0,r.cast)((0,r.greater)(n,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){const e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}}t.ThresholdedReLU=m,m.className="ThresholdedReLU",r.serialization.registerClass(m);class g extends s.Layer{constructor(e){super(null==e?{}:e),this.DEFAULT_AXIS=1,null==e&&(e={}),this.softmax=(new a.Softmax).apply,this.axis=null==e.axis?this.DEFAULT_AXIS:e.axis}call(e,t){const n=(0,u.getExactlyOneTensor)(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){const e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}}t.Softmax=g,g.className="Softmax",r.serialization.registerClass(g)},180743:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.UpSampling2D=t.SeparableConv2D=t.SeparableConv=t.Cropping2D=t.Conv3DTranspose=t.Conv3D=t.Conv2DTranspose=t.Conv2D=t.Conv1D=t.Conv=t.BaseConv=void 0,t.conv1d=function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:1,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:"valid",o=arguments.length>4?arguments[4]:void 0,s=arguments.length>5&&void 0!==arguments[5]?arguments[5]:1;return(0,r.tidy)((()=>((0,c.checkDataFormat)(o),w(e,t,null,n,a,o,s))))},t.conv1dWithBias=w,t.conv2d=function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:[1,1],a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:"valid",o=arguments.length>4?arguments[4]:void 0,s=arguments.length>5?arguments[5]:void 0;return(0,r.tidy)((()=>((0,c.checkDataFormat)(o),k(e,t,null,n,a,o,s))))},t.conv2dWithBiasActivation=k,t.conv3d=function(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:[1,1,1],a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:"valid",o=arguments.length>4?arguments[4]:void 0,s=arguments.length>5?arguments[5]:void 0;return(0,r.tidy)((()=>((0,c.checkDataFormat)(o),_(e,t,null,n,a,o,s))))},t.conv3dWithBias=_,t.preprocessConv2DInput=b,t.preprocessConv3DInput=x;var r=v(n(735534)),a=r,o=n(457025),s=n(485246),i=v(n(984710)),c=n(545188),l=n(87475),u=n(840889),d=n(608012),p=n(492560),h=n(141115),f=n(308079),m=v(n(986904)),g=n(252518);function y(e){if("function"!=typeof WeakMap)return null;var t=new WeakMap,n=new WeakMap;return(y=function(e){return e?n:t})(e)}function v(e,t){if(!t&&e&&e.__esModule)return e;if(null===e||"object"!=typeof e&&"function"!=typeof e)return{default:e};var n=y(t);if(n&&n.has(e))return n.get(e);var r={__proto__:null},a=Object.defineProperty&&Object.getOwnPropertyDescriptor;for(var o in e)if("default"!==o&&{}.hasOwnProperty.call(e,o)){var s=a?Object.getOwnPropertyDescriptor(e,o):null;s&&(s.get||s.set)?Object.defineProperty(r,o,s):r[o]=e[o]}return r.default=e,n&&n.set(e,r),r}function b(e,t){return(0,r.tidy)((()=>((0,c.checkDataFormat)(t),"channelsFirst"===t?a.transpose(e,[0,2,3,1]):e)))}function x(e,t){return(0,r.tidy)((()=>((0,c.checkDataFormat)(t),"channelsFirst"===t?a.transpose(e,[0,2,3,4,1]):e)))}function w(e,t,n){let o=arguments.length>3&&void 0!==arguments[3]?arguments[3]:1,l=arguments.length>4&&void 0!==arguments[4]?arguments[4]:"valid",u=arguments.length>5?arguments[5]:void 0,p=arguments.length>6&&void 0!==arguments[6]?arguments[6]:1;return(0,r.tidy)((()=>{if(null==u&&(u=(0,s.imageDataFormat)()),(0,c.checkDataFormat)(u),3!==e.shape.length)throw new d.ValueError("The input of a conv1dWithBias operation should be 3, but is "+"".concat(e.shape.length," instead."));if(3!==t.shape.length)throw new d.ValueError("The kernel for a conv1dWithBias operation should be 3, but is "+"".concat(t.shape.length," instead"));if(null!=n&&1!==n.shape.length)throw new d.ValueError("The bias for a conv1dWithBias operation should be 1, but is "+"".concat(t.shape.length," instead"));if("channelsFirst"===u&&(e=a.transpose(e,[0,2,1])),"causal"===l)throw new d.NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let r=a.conv1d(e,t,o,"same"===l?"same":"valid","NWC",p);return null!=n&&(r=i.biasAdd(r,n)),r}))}function k(e,t,n){let o=arguments.length>3&&void 0!==arguments[3]?arguments[3]:[1,1],i=arguments.length>4&&void 0!==arguments[4]?arguments[4]:"valid",l=arguments.length>5?arguments[5]:void 0,u=arguments.length>6?arguments[6]:void 0,p=arguments.length>7&&void 0!==arguments[7]?arguments[7]:null;return(0,r.tidy)((()=>

1{if(null==l&&(l=(0,s.imageDataFormat)()),(0,c.checkDataFormat)(l),3!==e.rank&&4!==e.rank)throw new d.ValueError("conv2dWithBiasActivation expects input to be of rank 3 or 4, "+"but received ".concat(e.rank,"."));if(3!==t.rank&&4!==t.rank)throw new d.ValueError("conv2dWithBiasActivation expects kernel to be of rank 3 or 4, "+"but received ".concat(e.rank,"."));let r=b(e,l);if("causal"===i)throw new d.NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return r=a.fused.conv2d({x:r,filter:t,strides:o,pad:"same"===i?"same":"valid",dilations:u,dataFormat:"NHWC",bias:n,activation:p}),"channelsFirst"===l&&(r=a.transpose(r,[0,3,1,2])),r}))}function _(e,t,n){let o=arguments.length>3&&void 0!==arguments[3]?arguments[3]:[1,1,1],l=arguments.length>4&&void 0!==arguments[4]?arguments[4]:"valid",u=arguments.length>5?arguments[5]:void 0,p=arguments.length>6?arguments[6]:void 0;return(0,r.tidy)((()=>{if(null==u&&(u=(0,s.imageDataFormat)()),(0,c.checkDataFormat)(u),4!==e.rank&&5!==e.rank)throw new d.ValueError("conv3dWithBias expects input to be of rank 4 or 5, but received "+"".concat(e.rank,"."));if(4!==t.rank&&5!==t.rank)throw new d.ValueError("conv3dWithBias expects kernel to be of rank 4 or 5, but received "+"".concat(e.rank,"."));let r=x(e,u);if("causal"===l)throw new d.NotImplementedError("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return r=a.conv3d(r,t,o,"same"===l?"same":"valid","NDHWC",p),null!=n&&(r=i.biasAdd(r,n)),"channelsFirst"===u&&(r=a.transpose(r,[0,4,1,2,3])),r}))}class C extends u.Layer{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",C.verifyArgs(t),this.rank=e,m.assertPositiveInteger(this.rank,"rank"),1!==this.rank&&2!==this.rank&&3!==this.rank)throw new d.NotImplementedError("Convolution layer for rank other than 1, 2, or 3 (".concat(this.rank,") is ")+"not implemented yet.");if(this.kernelSize=(0,f.normalizeArray)(t.kernelSize,e,"kernelSize"),this.strides=(0,f.normalizeArray)(null==t.strides?1:t.strides,e,"strides"),this.padding=null==t.padding?"valid":t.padding,(0,c.checkPaddingMode)(this.padding),this.dataFormat=null==t.dataFormat?"channelsLast":t.dataFormat,(0,c.checkDataFormat)(this.dataFormat),this.activation=(0,o.getActivation)(t.activation),this.useBias=null==t.useBias||t.useBias,this.biasInitializer=(0,p.getInitializer)(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=(0,l.getConstraint)(t.biasConstraint),this.biasRegularizer=(0,h.getRegularizer)(t.biasRegularizer),this.activityRegularizer=(0,h.getRegularizer)(t.activityRegularizer),this.dilationRate=(0,f.normalizeArray)(null==t.dilationRate?1:t.dilationRate,e,"dilationRate"),1===this.rank&&Array.isArray(this.dilationRate)&&1!==this.dilationRate.length)throw new d.ValueError("dilationRate must be a number or an array of a single number for 1D convolution, but received "+"".concat(JSON.stringify(this.dilationRate)));if(2===this.rank){if("number"==typeof this.dilationRate)this.dilationRate=[this.dilationRate,this.dilationRate];else if(2!==this.dilationRate.length)throw new d.ValueError("dilationRate must be a number or array of two numbers for 2D "+"convolution, but received ".concat(JSON.stringify(this.dilationRate)))}else if(3===this.rank)if("number"==typeof this.dilationRate)this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(3!==this.dilationRate.length)throw new d.ValueError("dilationRate must be a number or array of three numbers for 3D "+"convolution, but received ".concat(JSON.stringify(this.dilationRate)))}static verifyArgs(e){if(m.assert("kernelSize"in e,"required key 'kernelSize' not in config"),"number"!=typeof e.kernelSize&&!m.checkArrayTypeAndLength(e.kernelSize,"number",1,3))throw new d.ValueError("BaseConv expects config.kernelSize to be number or number[] with "+"length 1, 2, or 3, but received ".concat(JSON.stringify(e.kernelSize),"."))}getConfig(){const e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:(0,o.seri

1alizeActivation)(this.activation),useBias:this.useBias,biasInitializer:(0,p.serializeInitializer)(this.biasInitializer),biasRegularizer:(0,h.serializeRegularizer)(this.biasRegularizer),activityRegularizer:(0,h.serializeRegularizer)(this.activityRegularizer),biasConstraint:(0,l.serializeConstraint)(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}t.BaseConv=C;class P extends C{constructor(e,t){super(e,t),this.kernel=null,P.verifyArgs(t),this.filters=t.filters,m.assertPositiveInteger(this.filters,"filters"),this.kernelInitializer=(0,p.getInitializer)(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=(0,l.getConstraint)(t.kernelConstraint),this.kernelRegularizer=(0,h.getRegularizer)(t.kernelRegularizer)}build(e){e=(0,g.getExactlyOneShape)(e);const t="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[t])throw new d.ValueError("The channel dimension of the input should be defined. "+"Found ".concat(e[t]));const n=e[t],r=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",r,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return(0,r.tidy)((()=>{let t;e=(0,g.getExactlyOneTensor)(e);const n=null==this.bias?null:this.bias.read(),r=m.mapActivationToFusedKernel(this.activation.getClassName());if(null!=r&&2===this.rank)t=k(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(1===this.rank)t=w(e,this.kernel.read(),n,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(2===this.rank)t=k(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else{if(3!==this.rank)throw new d.NotImplementedError("convolutions greater than 3D are not implemented yet.");t=_(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate)}null!=this.activation&&(t=this.activation.apply(t))}return t}))}computeOutputShape(e){e=(0,g.getExactlyOneShape)(e);const t=[],n="channelsLast"===this.dataFormat?e.slice(1,e.length-1):e.slice(2);for(let e=0;e<n.length;++e){const r=(0,f.convOutputLength)(n[e],this.kernelSize[e],this.padding,this.strides[e],"number"==typeof this.dilationRate?this.dilationRate:this.dilationRate[e]);t.push(r)}let r=[e[0]];return"channelsLast"===this.dataFormat?(r=r.concat(t),r.push(this.filters)):(r.push(this.filters),r=r.concat(t)),r}getConfig(){const e={filters:this.filters,kernelInitializer:(0,p.serializeInitializer)(this.kernelInitializer),kernelRegularizer:(0,h.serializeRegularizer)(this.kernelRegularizer),kernelConstraint:(0,l.serializeConstraint)(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||"number"!=typeof e.filters||e.filters<1)throw new d.ValueError("Convolution layer expected config.filters to be a 'number' > 0 "+"but got ".concat(JSON.stringify(e.filters)))}}t.Conv=P;class O extends P{constructor(e){super(2,e),O.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if("number"!=typeof e.kernelSize&&!m.checkArrayTypeAndLength(e.kernelSize,"number",1,2))throw new d.ValueError("Conv2D expects config.kernelSize to be number or number[] with "+"length 1 or 2, but received ".concat(JSON.stringify(e.kernelSize),"."))}}t.Conv2D=O,O.className="Conv2D",r.serialization.registerClass(O);class N extends P{constructor(e){super(3,e),N.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if("number"!=typeof e.kernelSize&&(!Array.isArray(e.kernelSize)||1!==e.kernelSize.length&&3!==e.kernelSize.length))throw new d.ValueError("Conv3D expects config.kernelSize to be number or"+" [number, number, number], but received ".concat(JSON.stringify(e.kernelSize),"."))}}t.Conv3D=N,N.className="Conv3D",r.serialization.registerClass(N);class I extends O{constructor(e){if(super(e),this.inputSpec=[new u.InputSpec({ndim:4})],"same"!==this.padding&&"valid"!==this.padding)throw new d.ValueError("Conv2DTranspose currently supports only padding modes '

1same' "+"and 'valid', but received padding mode ".concat(this.padding))}build(e){if(4!==(e=(0,g.getExactlyOneShape)(e)).length)throw new d.ValueError("Input should have rank 4; Received input shape: "+JSON.stringify(e));const t="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[t])throw new d.ValueError("The channel dimension of the inputs should be defined. Found `None`.");const n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new u.InputSpec({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return a.tidy((()=>{let t=(0,g.getExactlyOneTensor)(e);if(4!==t.shape.length)throw new d.ValueError("Conv2DTranspose.call() expects input tensor to be rank-4, but "+"received a tensor of rank-".concat(t.shape.length));const n=t.shape,r=n[0];let o,s;"channelsFirst"===this.dataFormat?(o=2,s=3):(o=1,s=2);const c=n[o],l=n[s],u=this.kernelSize[0],p=this.kernelSize[1],h=this.strides[0],m=this.strides[1],y=[r,(0,f.deconvLength)(c,h,u,this.padding),(0,f.deconvLength)(l,m,p,this.padding),this.filters];"channelsLast"!==this.dataFormat&&(t=a.transpose(t,[0,2,3,1]));let v=a.conv2dTranspose(t,this.kernel.read(),y,this.strides,this.padding);return"channelsLast"!==this.dataFormat&&(v=a.transpose(v,[0,3,1,2])),null!=this.bias&&(v=i.biasAdd(v,this.bias.read(),this.dataFormat)),null!=this.activation&&(v=this.activation.apply(v)),v}))}computeOutputShape(e){const t=(e=(0,g.getExactlyOneShape)(e)).slice();let n,r,a;"channelsFirst"===this.dataFormat?(n=1,r=2,a=3):(n=3,r=1,a=2);const o=this.kernelSize[0],s=this.kernelSize[1],i=this.strides[0],c=this.strides[1];return t[n]=this.filters,t[r]=(0,f.deconvLength)(t[r],i,o,this.padding),t[a]=(0,f.deconvLength)(t[a],c,s,this.padding),t}getConfig(){const e=super.getConfig();return delete e.dilationRate,e}}t.Conv2DTranspose=I,I.className="Conv2DTranspose",r.serialization.registerClass(I);class T extends N{constructor(e){if(super(e),this.inputSpec=[new u.InputSpec({ndim:5})],"same"!==this.padding&&"valid"!==this.padding)throw new d.ValueError("Conv3DTranspose currently supports only padding modes 'same' "+"and 'valid', but received padding mode ".concat(this.padding))}build(e){if(5!==(e=(0,g.getExactlyOneShape)(e)).length)throw new d.ValueError("Input should have rank 5; Received input shape: "+JSON.stringify(e));const t="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[t])throw new d.ValueError("The channel dimension of the inputs should be defined. Found `None`.");const n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new u.InputSpec({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return a.tidy((()=>{let t=(0,g.getExactlyOneTensor)(e);if(5!==t.shape.length)throw new d.ValueError("Conv3DTranspose.call() expects input tensor to be rank-4, but "+"received a tensor of rank-".concat(t.shape.length));const n=t.shape,r=n[0];let o,s,c;"channelsFirst"===this.dataFormat?(c=2,o=3,s=4):(c=1,o=2,s=3);const l=n[c],u=n[o],p=n[s],h=this.kernelSize[0],m=this.kernelSize[1],y=this.kernelSize[2],v=this.strides[0],b=this.strides[1],x=this.strides[2],w=[r,(0,f.deconvLength)(l,v,h,this.padding),(0,f.deconvLength)(u,b,m,this.padding),(0,f.deconvLength)(p,x,y,this.padding),this.filters];"channelsLast"!==this.dataFormat&&(t=a.transpose(t,[0,2,3,4,1]));let k=a.conv3dTranspose(t,this.kernel.read(),w,this.strides,this.padding);return"channelsLast"!==this.dataFormat&&(k=a.transpose(k,[0,4,1,2,3])),null!==this.bias&&(k=i.biasAdd(k,this.bias.read(),this.dataFormat)),null!==this.activation&&(k=this.activation.apply(k)),k}))}computeOutputShape(e){const t=(e=(0,g.getExactlyOneShape)(e)).slice();let n,r,a,o;"channelsFirst"===this.dataFormat?(n=1,r=2,a=3,o=4):(n=4,r=1,a=2,o=3);const s=this.kernelSize[0],i=this.kernelSize[1],c=this.kernelSize[2],l=this.strides[0],u=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[r]=(0,f.deconvLength)(t[r],l,s,this.padding),t[a]=(0,f.deconvLength)(t[a],u,i,this.padding),t[o]=(0,f.deconvLength)(t[o],d,c,this.padding),t}getConfig(){const e=super.getConfig();return delete e.dilationRate,e}}t.Conv3DTranspose=T,T.className="Conv3DTranspose",r.serialization.registerClass(T);class S extends P{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,null==t.filters)throw new d.ValueError("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(null!=t.kernelInitializer||null!=t.kernelRegularizer||null!=t.kernelConstraint)throw new d.ValueError("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(null!=t.padding&&"same"!==t.padding&&"valid"!==t.padding)throw new d.ValueError("SeparableConv".concat(this.rank,"D supports only padding modes: ")+"'same' and 'valid', but received ".concat(JSON.stringify(t.padding)));this.depthMultiplier=null==t.depthMultiplier?1:t.depthMultiplier,this.depthwiseInitializer=(0,p.getInitializer)(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=(0,h.getRegularizer)(t.depthwiseRegularizer),this.depthwiseConstraint=(0,l.getConstraint)(t.depthwiseConstraint),this.pointwiseInitializer=(0,p.getInitializer)(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=(0,h.getRegularizer)(t.pointwiseRegularizer),this.pointwiseConstraint=(0,l.getConstraint)(t.pointwiseConstraint)}build(e){if((e=(0,g.getExactlyOneShape)(e)).length<this.rank+2)throw new d.ValueError("Inputs to SeparableConv".concat(this.rank,"D should have rank ")+"".concat(this.rank+2,", but received input shape: ")+"".concat(JSON.stringify(e)));const t="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[t]||e[t]<0)throw new d.ValueError("The channel dimension of the inputs should be defined, "+"but found ".concat(JSON.stringify(e[t])));const n=e[t],r=this.kernelSize.concat([n,this.depthMultiplier]),a=[];for(let e=0;e<this.rank;++e)a.push(1);a.push(n*this.depthMultiplier,this.filters);const o=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",r,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,o,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",a,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,o,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,o,this.biasConstraint):this.bias=null,this.inputSpec=[new u.InputSpec({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return(0,r.tidy)((()=>{let t;if(e=(0,g.getExactlyOneTensor)(e),1===this.rank)throw new d.NotImplementedError("1D separable convolution is not implemented yet.");return 2===this.rank&&("channelsFirst"===this.dataFormat&&(e=a.transpose(e,[0,2,3,1])),t=a.separableConv2d(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(t=i.biasAdd(t,this.bias.read(),this.dataFormat)),null!=this.activation&&(t=this.activation.apply(t)),"channelsFirst"===this.dataFormat&&(t=a.transpose(t,[0,3,1,2])),t}))}getConfig(){const e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=(0,p.serializeInitializer)(this.depthwiseInitializer),e.pointwiseInitializer=(0,p.serializeInitializer)(this.pointwiseInitializer),e.depthwiseRegularizer=(0,h.serializeRegularizer)(this.depthwiseRegularizer),e.pointwiseRegularizer=(0,h.serializeRegularizer)(this.pointwiseRegularizer),e.depthwiseConstraint=(0,l.serializeConstraint)(this.depthwiseConstraint),e.pointwiseConstraint=(0,l.serializeConstraint)(this.pointwiseConstraint),e}}t.SeparableConv=S,S.className="SeparableConv";class E extends S{constructor(e){super(2,e)}}t.SeparableConv2D=E,E.className="SeparableConv2D",r.serialization.registerClass(E);class M extends P{constructor(e){super(1,e),M.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){const e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if("number"!=typeof e.kernelSize&&!m.checkArrayTypeAndLength(e.kernelSize,"number",1,1))throw new d.ValueError("Conv1D expects config.kernelSize to be number or number[] with "+"length 1, but received ".concat(JSON.stringify(e.kernelSize),"."))}}t.Conv1D=M,M.className="Conv1D",r.serialization.registerClass(M);class A extends u.Layer{constructor(e){super(e),"number"==typeof e.cropping?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:"number"==typeof e.cropping[0]?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=void 0===e.dataFormat?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return"channelsFirst"===this.dataFormat?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return(0,r.tidy)((()=>

1{if(e=(0,g.getExactlyOneTensor)(e),"channelsLast"===this.dataFormat){const t=i.sliceAlongAxis(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return i.sliceAlongAxis(t,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}{const t=i.sliceAlongAxis(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return i.sliceAlongAxis(t,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}}))}getConfig(){const e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}t.Cropping2D=A,A.className="Cropping2D",r.serialization.registerClass(A);class D extends u.Layer{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=null==e.size?this.DEFAULT_SIZE:e.size,this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,(0,c.checkDataFormat)(this.dataFormat),this.interpolation=null==e.interpolation?"nearest":e.interpolation,(0,c.checkInterpolationFormat)(this.interpolation)}computeOutputShape(e){if("channelsFirst"===this.dataFormat){const t=null==e[2]?null:this.size[0]*e[2],n=null==e[3]?null:this.size[1]*e[3];return[e[0],e[1],t,n]}{const t=null==e[1]?null:this.size[0]*e[1],n=null==e[2]?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return a.tidy((()=>{let t=(0,g.getExactlyOneTensor)(e);const n=t.shape;if("channelsFirst"===this.dataFormat){t=a.transpose(t,[0,2,3,1]);const e=this.size[0]*n[2],r=this.size[1]*n[3],o="nearest"===this.interpolation?a.image.resizeNearestNeighbor(t,[e,r]):a.image.resizeBilinear(t,[e,r]);return a.transpose(o,[0,3,1,2])}{const e=this.size[0]*n[1],r=this.size[1]*n[2];return"nearest"===this.interpolation?a.image.resizeNearestNeighbor(t,[e,r]):a.image.resizeBilinear(t,[e,r])}}))}getConfig(){const e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}t.UpSampling2D=D,D.className="UpSampling2D",r.serialization.registerClass(D)},971420:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.DepthwiseConv2D=void 0,t.depthwiseConv2d=y;var r=g(n(735534)),a=r,o=n(485246),s=g(n(984710)),i=n(545188),c=n(87475),l=n(608012),u=n(492560),d=n(141115),p=n(308079),h=n(252518),f=n(180743);function m(e){if("function"!=typeof WeakMap)return null;var t=new WeakMap,n=new WeakMap;return(m=function(e){return e?n:t})(e)}function g(e,t){if(!t&&e&&e.__esModule)return e;if(null===e||"object"!=typeof e&&"function"!=typeof e)return{default:e};var n=m(t);if(n&&n.has(e))return n.get(e);var r={__proto__:null},a=Object.defineProperty&&Object.getOwnPropertyDescriptor;for(var o in e)if("default"!==o&&{}.hasOwnProperty.call(e,o)){var s=a?Object.getOwnPropertyDescriptor(e,o):null;s&&(s.get||s.set)?Object.defineProperty(r,o,s):r[o]=e[o]}return r.default=e,n&&n.set(e,r),r}function y(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:[1,1],s=arguments.length>3&&void 0!==arguments[3]?arguments[3]:"valid",c=arguments.length>4?arguments[4]:void 0,u=arguments.length>5?arguments[5]:void 0;return(0,r.tidy)((()=>{null==c&&(c=(0,o.imageDataFormat)()),(0,i.checkDataFormat)(c);let r=(0,f.preprocessConv2DInput)(e,c);if(4!==e.rank)throw new l.ValueError("Input for depthwiseConv2d is required to be 4-D, but is instead "+"".concat(e.rank,"-D"));if(4!==t.rank)throw new l.ValueError("depthwiseKernel is required to be 4-D, but is instead "+"".concat(t.rank,"-D"));return r=a.depthwiseConv2d(r,t,n,"same"===s?"same":"valid","NHWC",u),"channelsFirst"===c&&(r=a.transpose(r,[0,3,1,2])),r}))}class v extends f.BaseConv{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=null==e.depthMultiplier?1:e.depthMultiplier,this.depthwiseInitializer=(0,u.getInitializer)(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=(0,c.getConstraint)(e.depthwiseConstraint),this.depthwiseRegularizer=(0,d.getRegularizer)(e.depthwiseRegularizer)}build(e){if((e=(0,h.getExactlyOneShape)(e)).length<4)throw new l.ValueError("Inputs to DepthwiseConv2D should have rank 4. "+"Received input shape: ".concat(JSON.stringify(e),"."));const t="channelsFirst"===this.dataFormat?1:3;if(null==e[t]||e[t]<0)throw new l.ValueError("The channel dimension of the inputs to DepthwiseConv2D should "+"be defined, but is not (".concat(e[t],")."));const n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return(0,r.tidy)((()=>{let t=y(e=(0,h.getExactlyOneTensor)(e),this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(t=s.biasAdd(t,this.bias.read(),this.dataFormat)),null!=this.activation&&(t=this.activation.apply(t)),t}))}computeOutputShape(e){e=(0,h.getExactlyOneShape)(e);const t="channelsFirst"===this.dataFormat?e[2]:e[1],n="channelsFirst"===this.dataFormat?e[3]:e[2],r="channelsFirst"===this.dataFormat?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=(0,p.convOutputLength)(t,this.kernelSize[0],this.padding,this.strides[0]),o=(0,p.convOutputLength)(n,this.kernelSize[1],this.padding,this.strides[1]);return"channelsFirst"===this.dataFormat?[e[0],r,a,o]:[e[0],a,o,r]}getConfig(){const e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=(0,u.serializeInitializer)(this.depthwiseInitializer),e.depthwiseRegularizer=(0,d.seri

1alizeRegularizer)(this.depthwiseRegularizer),e.depthwiseConstraint=(0,c.serializeConstraint)(this.depthwiseRegularizer),e}}t.DepthwiseConv2D=v,v.className="DepthwiseConv2D",r.serialization.registerClass(v)},674858:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.ConvLSTM2DCell=t.ConvLSTM2D=void 0;var r=m(n(735534)),a=r,o=m(n(984710)),s=n(545188),i=n(840889),c=n(608012),l=n(492560),u=n(308079),d=n(986904),p=n(252518),h=n(302909);function f(e){if("function"!=typeof WeakMap)return null;var t=new WeakMap,n=new WeakMap;return(f=function(e){return e?n:t})(e)}function m(e,t){if(!t&&e&&e.__esModule)return e;if(null===e||"object"!=typeof e&&"function"!=typeof e)return{default:e};var n=f(t);if(n&&n.has(e))return n.get(e);var r={__proto__:null},a=Object.defineProperty&&Object.getOwnPropertyDescriptor;for(var o in e)if("default"!==o&&{}.hasOwnProperty.call(e,o)){var s=a?Object.getOwnPropertyDescriptor(e,o):null;s&&(s.get||s.set)?Object.defineProperty(r,o,s):r[o]=e[o]}return r.default=e,n&&n.set(e,r),r}var g=function(e,t){var n={};for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&t.indexOf(r)<0&&(n[r]=e[r]);if(null!=e&&"function"==typeof Object.getOwnPropertySymbols){var a=0;for(r=Object.getOwnPropertySymbols(e);a<r.length;a++)t.indexOf(r[a])<0&&Object.prototype.propertyIsEnumerable.call(e,r[a])&&(n[r[a]]=e[r[a]])}return n};h.RNNCell;class y extends h.RNN{constructor(e){if(e.unroll)throw new c.NotImplementedError("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new c.NotImplementedError("It is not possible at the moment to stack convolutional cells.");super(e),this.inputSpec=[new i.InputSpec({ndim:5})]}call(e,t){return a.tidy((()=>{if(null!=this.cell.dropoutMask&&(a.dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(a.dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new c.ValueError("ConvRNN2D cell does not support constants");const n=null==t?null:t.mask,r=null==t?null:t.training,o=null==t?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:o})}))}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return a.tidy((()=>{const{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),o=[r[0],...r.slice(2)],s=a.zeros(o);return Array.isArray(t)?Array(t.length).fill(s):[s]}))}resetStates(e){let t=arguments.length>1&&void 0!==arguments[1]&&arguments[1];a.tidy((()=>{if(!this.stateful)throw new c.AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");const n=this.inputSpec[0].shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)];if(null==n[0])throw new c.ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(null==this.getStates())Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map((()=>a.zeros(s))):this.states_=[a.zeros(s)];else if(null==e)a.dispose(this.states_),null!=this.keptStates&&(a.dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map((()=>a.zeros(s))):this.states_[0]=a.zeros(s);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new c.ValueError("Layer ".concat(this.name," expects ").concat(this.states_.length," state(s), ")+"but it received ".concat(e.length," state value(s). Input ")+"received: ".concat(e));t?this.keptStates.push(this.states_.slice()):a.dispose(this.states_);for(let t=0;t<this.states_.length;++t){const n=e[t],a=s;if(!r.util.arraysEqual(n.shape,a))throw new c.ValueError("State ".concat(t," is incompatible with layer ").concat(this.name,": ")+"expected shape=".concat(a,", received shape=").concat(n.shape));this.states_[t]=n}}this.states_=this.states_.map((e=>a.keep(e.clone())))}

1))}computeSingleOutputShape(e){const{dataFormat:t,filters:n,kernelSize:r,padding:a,strides:o,dilationRate:s}=this.cell,i="channelsFirst"===t,c=e[i?3:2],l=e[i?4:3],d=(0,u.convOutputLength)(c,r[0],a,o[0],s[0]),p=(0,u.convOutputLength)(l,r[1],a,o[1],s[1]);return[...e.slice(0,2),...i?[n,d,p]:[d,p,n]]}}y.className="ConvRNN2D";class v extends h.LSTMCell{constructor(e){const{filters:t,kernelSize:n,strides:r,padding:a,dataFormat:o,dilationRate:i}=e;super(Object.assign({},e,{units:t})),this.filters=t,(0,d.assertPositiveInteger)(this.filters,"filters"),this.kernelSize=(0,u.normalizeArray)(n,2,"kernelSize"),this.kernelSize.forEach((e=>(0,d.assertPositiveInteger)(e,"kernelSize"))),this.strides=(0,u.normalizeArray)(r||1,2,"strides"),this.strides.forEach((e=>(0,d.assertPositiveInteger)(e,"strides"))),this.padding=a||"valid",(0,s.checkPaddingMode)(this.padding),this.dataFormat=o||"channelsLast",(0,s.checkDataFormat)(this.dataFormat),this.dilationRate=(0,u.normalizeArray)(i||1,2,"dilationRate"),this.dilationRate.forEach((e=>(0,d.assertPositiveInteger)(e,"dilationRate")))}build(e){var t;e=(0,p.getExactlyOneShape)(e);const n="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[n])throw new c.ValueError("The channel dimension of the input should be defined. "+"Found ".concat(e[n]));const r=e[n],s=this.kernelSize.concat([r,4*this.filters]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);const i=this.kernelSize.concat([this.filters,4*this.filters]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let e;if(this.unitForgetBias){const n=this.biasInitializer,r=this.filters;e=new((t=class extends l.Initializer{apply(e,t){const s=n.apply([r]),i=a.ones([r]),c=n.apply([2*r]);return o.concatenate([s,i,c])}}).className="CustomInit",t)}else e=this.biasInitializer;this.bias=this.addWeight("bias",[4*this.filters],null,e,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return a.tidy((()=>{if(3!==e.length)throw new c.ValueError("ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got "+"".concat(e.length,"."));const n=t.training||!1,r=e[0],o=e[1],s=e[2];0<this.dropout&&this.dropout<1&&null==this.dropoutMask&&(this.dropoutMask=(0,h.generateDropoutMask)({ones:()=>a.onesLike(r),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));const i=this.dropoutMask,l=(e,t,n)=>t&&t[n]?a.mul(t[n],e):e;let u=l(r,i,0),d=l(r,i,1),p=l(r,i,2),f=l(r,i,3);0<this.recurrentDropout&&this.recurrentDropout<1&&null==this.recurrentDropoutMask&&(this.recurrentDropoutMask=(0,h.generateDropoutMask)({ones:()=>a.onesLike(o),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));const m=this.recurrentDropoutMask;let g=l(o,m,0),y=l(o,m,1),v=l(o,m,2),b=l(o,m,3);const[x,w,k,_]=a.split(this.kernel.read(),4,3),[C,P,O,N]=this.useBias?a.split(this.bias.read(),4):[null,null,null,null];u=this.inputConv(u,x,C,this.padding),d=this.inputConv(d,w,P,this.padding),p=this.inputConv(p,k,O,this.padding),f=this.inputConv(f,_,N,this.padding);const[I,T,S,E]=a.split(this.recurrentKernel.read(),4,3);g=this.recurrentConv(g,I),y=this.recurrentConv(y,T),v=this.recurrentConv(v,S),b=this.recurrentConv(b,E);const M=this.recurrentActivation.apply(a.add(u,g)),A=this.recurrentActivation.apply(a.add(d,y)),D=a.add(a.mul(A,s),a.mul(M,this.activation.apply(a.add(p,v)))),R=a.mul(this.recurrentActivation.apply(a.add(f,b)),this.activation.apply(D));return[R,R,D]}))}getConfig(){const e=super.getConfig(),{units:t}=e,n=g(e,["units"]),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,r)}inputConv(e,t,n,r){const s=a.conv2d(e,t,this.strides,r||"valid","channelsFirst"===this.dataFormat?"NCHW":"NHWC",this.dilationRate);return n?o.biasAdd(s,n,this.dataFormat):s}recurrentConv(e,t){return a.conv2d(e,t,1,"same","channelsFirst"===this.dataFormat?"NCHW":"NHWC")}}t.ConvLSTM2DCell=v,v.className="ConvLSTM2DCell",a.serialization.registerClass(v);class b extends y{constructor(e){const t=new v(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}}t.ConvLSTM2D=b,b.className="ConvLSTM2D",a.serialization.registerClass(b)},787742:(e,t,n)=>{"use strict";

1Object.defineProperty(t,"__esModule",{value:!0}),t.SpatialDropout1D=t.Reshape=t.RepeatVector=t.Permute=t.Masking=t.Flatten=t.Dropout=t.Dense=t.Activation=void 0;var r=n(735534),a=n(457025),o=function(e,t){if(!t&&e&&e.__esModule)return e;if(null===e||"object"!=typeof e&&"function"!=typeof e)return{default:e};var n=f(t);if(n&&n.has(e))return n.get(e);var r={__proto__:null},a=Object.defineProperty&&Object.getOwnPropertyDescriptor;for(var o in e)if("default"!==o&&{}.hasOwnProperty.call(e,o)){var s=a?Object.getOwnPropertyDescriptor(e,o):null;s&&(s.get||s.set)?Object.defineProperty(r,o,s):r[o]=e[o]}return r.default=e,n&&n.set(e,r),r}(n(984710)),s=n(87475),i=n(840889),c=n(608012),l=n(492560),u=n(141115),d=n(986904),p=n(302774),h=n(252518);function f(e){if("function"!=typeof WeakMap)return null;var t=new WeakMap,n=new WeakMap;return(f=function(e){return e?n:t})(e)}class m extends i.Layer{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(null==this.noiseShape)return this.noiseShape;const t=e.shape,n=[];for(let e=0;e<this.noiseShape.length;++e)n.push(null==this.noiseShape[e]?t[e]:this.noiseShape[e]);return n}call(e,t){return(0,r.tidy)((()=>{this.invokeCallHook(e,t);const n=(0,h.getExactlyOneTensor)(e);if(0<this.rate&&this.rate<1){const e=null!=t.training&&t.training,r=this.getNoiseShape(n);return o.inTrainPhase((()=>o.dropout(n,this.rate,r,this.seed)),(()=>n),e)}return e}))}getConfig(){const e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}}t.Dropout=m,m.className="Dropout",r.serialization.registerClass(m);class g extends m{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){const t=e.shape;return[t[0],1,t[2]]}}t.SpatialDropout1D=g,g.className="SpatialDropout1D",r.serialization.registerClass(g);class y extends i.Layer{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",null==e.batchInputShape&&null==e.inputShape&&null!=e.inputDim){let t=null;null!=e.batchSize&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,(0,d.assertPositiveInteger)(this.units,"units"),this.activation=(0,a.getActivation)(e.activation),null!=e.useBias&&(this.useBias=e.useBias),this.kernelInitializer=(0,l.getInitializer)(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=(0,l.getInitializer)(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=(0,s.getConstraint)(e.kernelConstraint),this.biasConstraint=(0,s.getConstraint)(e.biasConstraint),this.kernelRegularizer=(0,u.getRegularizer)(e.kernelRegularizer),this.biasRegularizer=(0,u.getRegularizer)(e.biasRegularizer),this.activityRegularizer=(0,u.getRegularizer)(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){const t=(e=(0,h.getExactlyOneShape)(e))[e.length-1];null==this.kernel&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){const t=(e=(0,h.getExactlyOneShape)(e)).slice();return t[t.length-1]=this.units,t}call(e,t){return(0,r.tidy)((()=>{this.invokeCallHook(e,t);const n=(0,h.getExactlyOneTensor)(e),r=(0,d.mapActivationToFusedKernel)(this.activation.getClassName());let a;return null!=r?a=o.dot(n,this.kernel.read(),r,this.bias?this.bias.read():null):(a=o.dot(n,this.kernel.read()),null!=this.bias&&(a=o.biasAdd(a,this.bias.read())),null!=this.activation&&(a=this.activation.apply(a))),a}))}getConfig(){const e={units:this.units,activation:(0,a.serializeActivation)(this.activation),useBias:this.useBias,kernelInitializer:(0,l.serializeInitializer)(this.kernelInitializer),biasInitializer:(0,l.serializeInitializer)(this.biasInitializer),kernelRegularizer:(0,u.serializeRegularizer)(this.kernelRegularizer),biasRegularizer:(0,u.serializeRegularizer)(this.biasRegularizer),activityRegularizer:(0,u.serializeRegularizer)(this.activityRegularizer),kernelConstraint:(0,s.serializeConstraint)(this.kernelConstraint),biasConstraint:(0,s.serializeConstraint)(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}t.Dense=y,y.className="Dense",r.serialization.registerClass(y);class v extends i.Layer{constructor(e){super(e=e||{}),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=(0,h.getExactlyOneShape)(e);for(const t of e.slice(1))if(null==t)throw new c.ValueError('The shape of the input to "Flatten" is not fully defined '+"(got ".concat(e.slice(1),"). Make sure to pass a complete ")+'"input_shape" or "batch_input_shape" argument to the first layer in your model.');return[e[0],(0,p.arrayProd)(e,1)]}call(e,t){return(0,r.tidy)((()=>{this.invokeCallHook(e,t);let n=(0,h.getExactlyOneTensor)(e);if("channelsFirst"===this.dataFormat&&n.rank>1){const e=[0];for(let t=2;t<n.rank;++t)e.push(t);e.push(1),n=(0,r.transpose)(n,e)}return o.batchFlatten(n)}))}getConfig(){const e={};null!=this.dataFormat&&(e.dataFormat=this.dataFormat);const t=super.getConfig();return Object.assign(e,t),e}}t.Flatten=v,v.className="Flatten",r.serialization.registerClass(v);class b extends i.Layer{constructor(e){super(e),this.supportsMasking=!0,this.activation=(0,a.getActivation)(e.activation)}call(e,t){return(0,r.tidy)((()=>{this.invokeCallHook(e,t);const n=(0,h.getExactlyOneTensor)(e);return this.activation.apply(n)}))}

1getConfig(){const e={activation:(0,a.serializeActivation)(this.activation)},t=super.getConfig();return Object.assign(e,t),e}}t.Activation=b,b.className="Activation",r.serialization.registerClass(b);class x extends i.Layer{constructor(e){super(e),this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return(0,r.tidy)((()=>(e=(0,h.getExactlyOneTensor)(e),o.repeat(e,this.n))))}getConfig(){const e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}}t.RepeatVector=x,x.className="RepeatVector",r.serialization.registerClass(x);class w extends i.Layer{constructor(e){super(e),this.targetShape=e.targetShape;for(let e=0;e<this.targetShape.length;++e)this.isUnknown(this.targetShape[e])&&(this.targetShape[e]=null)}isUnknown(e){return e<0||null==e}fixUnknownDimension(e,t){const n="Total size of new array must be unchanged.",r=t.slice();let a=1,o=null;for(let e=0;e<r.length;++e){const t=r[e];if(this.isUnknown(t)){if(null!==o)throw new c.ValueError("Can only specifiy one unknown dimension.");o=e}else a*=t}const s=(0,p.arrayProd)(e);if(null!==o){if(0===a||s%a!=0)throw new c.ValueError(n);r[o]=s/a}else if(s!==a)throw new c.ValueError(n);return r}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return(0,r.tidy)((()=>{this.invokeCallHook(e,t);const n=(0,h.getExactlyOneTensor)(e),a=n.shape,o=a.slice(0,1).concat(this.fixUnknownDimension(a.slice(1),this.targetShape));return(0,r.reshape)(n,o)}))}getConfig(){const e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}}t.Reshape=w,w.className="Reshape",r.serialization.registerClass(w);class k extends i.Layer{constructor(e){if(super(e),null==e.dims)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error("Permute constructor requires `dims` to be an Array, but received "+"".concat(e.dims," instead."));const t=(0,p.range)(1,e.dims.length+1);if(!r.util.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new i.InputSpec({ndim:this.dims.length+1})]}computeOutputShape(e){const t=(e=(0,h.getExactlyOneShape)(e)).slice();return this.dims.forEach(((n,r)=>{t[r+1]=e[n]})),t}call(e,t){return(0,r.transpose)((0,h.getExactlyOneTensor)(e),this.dimsIncludingBatch)}getConfig(){const e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}}t.Permute=k,k.className="Permute",r.serialization.registerClass(k);class _ extends i.Layer{constructor(e){super(null==e?{}:e),this.supportsMasking=!0,this.maskValue=null!=e?null==e.maskValue?0:e.maskValue:0}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){const n=(0,h.getExactlyOneTensor)(e);return(0,r.any)((0,r.notEqual)(n,this.maskValue),-1)}call(e,t){return(0,r.tidy)((()=>{this.invokeCallHook(e,t);const n=(0,h.getExactlyOneTensor)(e),a=(0,r.any)((0,r.notEqual)(n,this.maskValue),-1,!0);return(0,r.mul)(n,(0,r.cast)(a,n.dtype))}))}}t.Masking=_,_.className="Masking",r.serialization.registerClass(_)},939386:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.Embedding=void 0;var r=n(735534),a=h(n(984710)),o=n(87475),s=n(840889),i=n(608012),c=n(492560),l=n(141115),u=h(n(986904)),d=n(252518);function p(e){if("function"!=typeof WeakMap)return null;var t=new WeakMap,n=new WeakMap;return(p=function(e){return e?n:t})(e)}function h(e,t){if(!t&&e&&e.__esModule)return e;if(null===e||"object"!=typeof e&&"function"!=typeof e)return{default:e};var n=p(t);if(n&&n.has(e))return n.get(e);var r={__proto__:null},a=Object.defineProperty&&Object.getOwnPropertyDescriptor;for(var o in e)if("default"!==o&&{}.hasOwnProperty.call(e,o)){var s=a?Object.getOwnPropertyDescriptor(e,o):null;s&&(s.get||s.set)?Object.defineProperty(r,o,s):r[o]=e[o]}return r.default=e,n&&n.set(e,r),r}class f extends s.Layer{constructor(e){if(super(e),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",null==e.batchInputShape&&null==e.inputShape){let t=null;null!=e.batchSize&&(t=e.batchSize),null==e.inputLength?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(u.toList(e.inputLength))}this.inputDim=e.inputDim,u.assertPositiveInteger(this.inputDim,"inputDim"),this.outputDim=e.outputDim,u.assertPositiveInteger(this.outputDim,"outputDim"),this.embeddingsInitializer=(0,c.getInitializer)(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=(0,l.getRegularizer)(e.embeddingsRegularizer),this.activityRegularizer=(0,l.getRegularizer)(e.activityRegularizer),this.embeddingsConstraint=(0,o.getConstraint)(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return(0,r.tidy)((()=>this.maskZero?(e=(0,d.getExactlyOneTensor)(e),(0,r.notEqual)(e,(0,r.zerosLike)(e))):null))}computeOutputShape(e){if(e=(0,d.getExactlyOneShape)(e),null==this.inputLength)return[...e,this.outputDim];const t=u.toList(this.inputLength);if(t.length!==e.length-1)throw new i.ValueError('"inputLength" is '.concat(this.inputLength,", but received ")+"input shape has shape ".concat(e));{let n=0;for(let r=0;r<t.length;++r){const a=t[r],o=e[r+1];if(null!=a&&null!=o&&a!==o)throw new i.ValueError('"inputLength" is '.concat(this.inputLength,", but received ")+"input shape has shape ".concat(e));null==a&&(t[n]=o),n++}}return[e[0],...t,this.outputDim]}call(e,t){return(0,r.tidy)((()=>{this.invokeCallHook(e,t);let n=(0,d.getExactlyOneTensor)(e);"int32"!==n.dtype&&(n=a.cast(n,"int32"));const o=a.gather(this.embeddings.read(),(0,r.reshape)(n,[n.size]));return(0,r.reshape)(o,(0,d.getExactlyOneShape)(this.computeOutputShape(n.shape)))}))}getConfig(){const e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:(0,c.serializeInitializer)(this.embeddingsInitializer),embeddingsRegularizer:(0,l.serializeRegularizer)(this.embeddingsRegularizer),activityRegularizer:(0,l.serializeRegularizer)(this.activityRegularizer),embeddingsConstraint:(0,o.seri

1alizeConstraint)(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}}t.Embedding=f,f.className="Embedding",r.serialization.registerClass(f)},185333:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.Multiply=t.Minimum=t.Merge=t.Maximum=t.Dot=t.Concatenate=t.Average=t.Add=void 0,t.add=function(e){if(Array.isArray(e)){return new m({}).apply(e)}return new m(e)},t.average=function(e){if(Array.isArray(e)){return new y({}).apply(e)}return new y(e)},t.concatenate=function(e){if(Array.isArray(e)){return new x({}).apply(e)}return new x(e)},t.maximum=function(e){if(Array.isArray(e)){return new v({}).apply(e)}return new v(e)},t.minimum=function(e){if(Array.isArray(e)){return new b({}).apply(e)}return new b(e)},t.multiply=function(e){if(Array.isArray(e)){return new g({}).apply(e)}return new g(e)};var r=h(n(735534)),a=r,o=h(n(984710)),s=n(840889),i=n(608012),c=n(242135),l=h(n(986904)),u=h(n(302774)),d=n(252518);function p(e){if("function"!=typeof WeakMap)return null;var t=new WeakMap,n=new WeakMap;return(p=function(e){return e?n:t})(e)}function h(e,t){if(!t&&e&&e.__esModule)return e;if(null===e||"object"!=typeof e&&"function"!=typeof e)return{default:e};var n=p(t);if(n&&n.has(e))return n.get(e);var r={__proto__:null},a=Object.defineProperty&&Object.getOwnPropertyDescriptor;for(var o in e)if("default"!==o&&{}.hasOwnProperty.call(e,o)){var s=a?Object.getOwnPropertyDescriptor(e,o):null;s&&(s.get||s.set)?Object.defineProperty(r,o,s):r[o]=e[o]}return r.default=e,n&&n.set(e,r),r}class f extends s.Layer{constructor(e){super(e||{}),this.supportsMasking=!0}mergeFunction(e){throw new i.NotImplementedError}computeElementwiseOpOutputShape(e,t){if(null==e||null==t)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(0===t.length)return e;const n=e.slice(0,e.length-t.length);for(let r=0;r<t.length;++r){const a=e[e.length-t.length+r],o=t[r];if(null==a||null==o||a<0||o<0)n.push(null);else if(1===a)n.push(o);else if(1===o)n.push(a);else{if(a!==o)throw new i.ValueError("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(a)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[(0,d.getExactlyOneShape)(e)]),e.length<2)throw new i.ValueError("A merge layer should be called on an Array of at least 2 inputs."+" Got ".concat(e.length," input(s)."));let t=[];for(const n of e)null!=n&&null!==n[0]&&t.push(n[0]);if(t=l.unique(t),t.length>1)throw new i.ValueError("Can not merge tensors with different batch sizes. "+"Got tensors with shapes: ".concat(JSON.stringify(e),"."));let n=null==e[0]?null:e[0].slice(1);for(let t=1;t<e.length;++t){const r=null==e[t]?null:e[t].slice(1);n=this.computeElementwiseOpOutputShape(n,r)}const r=e.map((e=>e.length));-1===e.indexOf(null)&&1===l.unique(r).length?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return(0,r.tidy)((()=>{if(this.reshapeRequired){const t=[],n=e.map((e=>e.rank));if(-1===n.indexOf(null)){const r=u.max(n);for(let n of e){const e=n.rank;for(let t=0;t<r-e;++t)n=o.expandDims(n,1);t.push(n)}return this.mergeFunction(t)}{let n=!1;for(const r of e){const e=r.rank;if(null==e){const e=r.shape,o=e[0],s=e.slice(1).concat([o]);let i=a.reshape(r,[o].concat(u.arrayProd(e.slice(1))));i=a.transpose(i,[1,0]),i=a.reshape(i,s),t.push(i),n=!0}else if(e>1){const o=u.range(1,e).concat([0]);t.push(a.transpose(r,o)),n=!0}else t.push(r)}let r=this.mergeFunction(t);const o=r.rank;if(n)if(null==o){const e=r.shape,t=e[e.length-1],n=[t].concat(e.slice(0,e.length-1));r=a.reshape(a.transpose(a.reshape(r,[-1,t]),[1,0]),n)}else if(o>1){const e=[o-1].concat(u.range(0,o-1));r=a.transpose(r,e)}return r}}return this.mergeFunction(e)}))}computeOutputShape(e){let t;t=null==e[0]?null:e[0].slice(1);for(let n=1;n<e.length;++n){const r=null==e[n]?null:e[n].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(const t of e)null!=t&&null!==t[0]&&n.push(t[0]);return n=l.unique(n),t=1===n.length?n.concat(t):[null].concat(t),t}computeMask(e,t){return a.tidy((()=>

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Object.assign(t,e),t}call(e,t){return(0,r.tidy)((()=>{this.invokeCallHook(e,t);const n=(0,s.getExactlyOneTensor)(e);return a.inTrainPhase((()=>(0,r.add)(a.randomNormal(n.shape,0,this.stddev),n)),(()=>n),t.training||!1)}))}}t.GaussianNoise=c,c.className="GaussianNoise",r.serialization.registerClass(c);class l extends o.Layer{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return(0,r.tidy)((()=>{this.invokeCallHook(e,t);const n=(0,s.getExactlyOneTensor)(e);if(this.rate>0&&this.rate<1){const e=()=>{const e=Math.sqrt(this.rate/(1-this.rate));return(0,r.mul)(n,a.randomNormal(n.shape,1,e))};return a.inTrainPhase(e,(()=>n),t.training||!1)}return n}))}}t.GaussianDropout=l,l.className="GaussianDropout",r.serialization.registerClass(l);class u extends 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1function m(e,t){if(!t&&e&&e.__esModule)return e;if(null===e||"object"!=typeof e&&"function"!=typeof e)return{default:e};var n=f(t);if(n&&n.has(e))return n.get(e);var r={__proto__:null},a=Object.defineProperty&&Object.getOwnPropertyDescriptor;for(var o in e)if("default"!==o&&{}.hasOwnProperty.call(e,o)){var s=a?Object.getOwnPropertyDescriptor(e,o):null;s&&(s.get||s.set)?Object.defineProperty(r,o,s):r[o]=e[o]}return r.default=e,n&&n.set(e,r),r}function g(e,t,n,s,c,l){return(0,r.tidy)((()=>{let r;(0,i.checkDataFormat)(c),(0,i.checkPoolMode)(l),(0,i.checkPaddingMode)(s),null==n&&(n=[1,1]),null==s&&(s="valid"),null==c&&(c=(0,o.imageDataFormat)()),null==l&&(l="max"),e=(0,h.preprocessConv2DInput)(e,c);const u="same"===s?"same":"valid";return r="max"===l?a.maxPool(e,t,n,u):a.avgPool(e,t,n,u),"channelsFirst"===c&&(r=a.transpose(r,[0,3,1,2])),r}))}function y(e,t,n,s,c,l){return(0,r.tidy)((()=>{let r;(0,i.checkDataFormat)(c),(0,i.checkPoolMode)(l),(0,i.checkPaddingMode)(s),null==n&&(n=[1,1,1]),null==s&&(s="valid"),null==c&&(c=(0,o.imageDataFormat)()),null==l&&(l="max"),e=(0,h.preprocessConv3DInput)(e,c);const u="same"===s?"same":"valid";return r="max"===l?a.maxPool3d(e,t,n,u):a.avgPool3d(e,t,n,u),"channelsFirst"===c&&(r=a.transpose(r,[0,4,1,2,3])),r}))}class v extends c.Layer{constructor(e){if(null==e.poolSize&&(e.poolSize=2),super(e),"number"==typeof e.poolSize)this.poolSize=[e.poolSize];else{if(!Array.isArray(e.poolSize)||1!==e.poolSize.length||"number"!=typeof e.poolSize[0])throw new l.ValueError("poolSize for 1D convolutional layer must be a number or an Array of a single number, but received "+"".concat(JSON.stringify(e.poolSize)));this.poolSize=e.poolSize}if((0,d.assertPositiveInteger)(this.poolSize,"poolSize"),null==e.strides)this.strides=this.poolSize;else if("number"==typeof e.strides)this.strides=[e.strides];else{if(!Array.isArray(e.strides)||1!==e.strides.length||"number"!=typeof e.strides[0])throw new l.ValueError("strides for 1D convolutional layer must be a number or an Array of a single number, but received "+"".concat(JSON.stringify(e.strides)));this.strides=e.strides}(0,d.assertPositiveInteger)(this.strides,"strides"),this.padding=null==e.padding?"valid":e.padding,(0,i.checkPaddingMode)(this.padding),this.inputSpec=[new c.InputSpec({ndim:3})]}computeOutputShape(e){e=(0,p.getExactlyOneShape)(e);const t=(0,u.convOutputLength)(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return(0,r.tidy)((()=>{this.invokeCallHook(e,t),e=s.expandDims((0,p.getExactlyOneTensor)(e),2);const n=this.poolingFunction((0,p.getExactlyOneTensor)(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return a.squeeze(n,[2])}))}getConfig(){const 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e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}t.Pooling2D=w;class k extends w{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return(0,i.checkDataFormat)(a),(0,i.checkPaddingMode)(r),g(e,t,n,r,a,"max")}}t.MaxPooling2D=k,k.className="MaxPooling2D",r.serialization.registerClass(k);class _ extends w{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return(0,i.checkDataFormat)(a),(0,i.checkPaddingMode)(r),g(e,t,n,r,a,"avg")}}t.AveragePooling2D=_,_.className="AveragePooling2D",r.serialization.registerClass(_);class C extends c.Layer{constructor(e){if(null==e.poolSize&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],null==e.strides)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(3!==e.strides.length)throw new l.ValueError("If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length "+"".concat(e.strides.length,"."));this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];(0,d.assertPositiveInteger)(this.poolSize,"poolSize"),(0,d.assertPositiveInteger)(this.strides,"strides"),this.padding=null==e.padding?"valid":e.padding,this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,(0,i.checkDataFormat)(this.dataFormat),(0,i.checkPaddingMode)(this.padding),this.inputSpec=[new c.InputSpec({ndim:5})]}computeOutputShape(e){e=(0,p.getExactlyOneShape)(e);let t="channelsFirst"===this.dataFormat?e[2]:e[1],n="channelsFirst"===this.dataFormat?e[3]:e[2],r="channelsFirst"===this.dataFormat?e[4]:e[3];return t=(0,u.convOutputLength)(t,this.poolSize[0],this.padding,this.strides[0]),n=(0,u.convOutputLength)(n,this.poolSize[1],this.padding,this.strides[1]),r

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e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}t.GlobalPooling2D=S;class E extends S{call(e,t){return(0,r.tidy)((()=>{const t=(0,p.getExactlyOneTensor)(e);return"channelsLast"===this.dataFormat?a.mean(t,[1,2]):a.mean(t,[2,3])}))}}t.GlobalAveragePooling2D=E,E.className="GlobalAveragePooling2D",r.serialization.registerClass(E);class M extends S{call(e,t){return(0,r.tidy)((()=>{const t=(0,p.getExactlyOneTensor)(e);return"channelsLast"===this.dataFormat?a.max(t,[1,2]):a.max(t,[2,3])}))}}t.GlobalMaxPooling2D=M,M.className="GlobalMaxPooling2D",r.serialization.registerClass(M)},302909:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.StackedRNNCells=t.SimpleRNNCell=t.SimpleRNN=t.RNNCell=t.RNN=t.LSTMCell=t.LSTM=t.GRUCell=t.GRU=void 0,t.generateDropoutMask=E,t.rnn=w,t.standardizeArgs=x;var r=b(n(735534)),a=r,o=n(457025),s=b(n(984710)),i=n(545188),c=n(87475),l=n(840889),u=n(608012),d=n(492560),p=n(141115),h=n(986904),f=b(n(302774)),m=n(252518),g=n(268778),y=n(640153);function v(e){if("function"!=typeof WeakMap)return null;var t=new WeakMap,n=new WeakMap;return(v=function(e){return e?n:t})(e)}function b(e,t){if(!t&&e&&e.__esModule)return e;if(null===e||"object"!=typeof e&&"function"!=typeof e)return{default:e};var n=v(t);if(n&&n.has(e))return n.get(e);var r={__proto__:null},a=Object.defineProperty&&Object.getOwnPropertyDescriptor;for(var o in e)if("default"!==o&&{}.hasOwnProperty.call(e,o)){var s=a?Object.getOwnPropertyDescriptor(e,o):null;s&&(s.get||s.set)?Object.defineProperty(r,o,s):r[o]=e[o]}return r.default=e,n&&n.set(e,r),r}function x(e,t,n,r){if(Array.isArray(e)){if(null!=t||null!=n)throw new u.ValueError("When inputs is an array, neither initialState or constants should be provided");null!=r&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(e){return null==e||Array.isArray(e)?e:[e]}return{inputs:e,initialState:t=a(t),constants:n=a(n)}}function w(e,t,n){let r=arguments.length>3&&void 0!==arguments[3]&&arguments[3],o=arguments.length>4?arguments[4]:void 0,s=arguments.length>5?arguments[5]:void 0,i=arguments.length>6&&void 0!==arguments[6]&&arguments[6],c=arguments.length>7&&void 0!==arguments[7]&&arguments[7];return a.tidy((()=>{const l=t.shape.length;if(l<3)throw new u.ValueError("Input should be at least 3D, but is ".concat(l,"D."));const d=[1,0].concat(f.range(2,l));if(t=a.transpose(t,d),null!=s)throw new u.NotImplementedError("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),null!=o&&(o=a.cast(a.cast(o,"bool"),"float32"),o.rank===l-1&&(o=a.expandDims(o,-1)),o=a.transpose(o,d)),r&&(t=a.reverse(t,0),null!=o&&(o=a.reverse(o,0)));const p=[];let h,m=n;const g=t.shape[0],y=a.unstack(t);let v,b;null!=o&&(v=a.unstack(o));for(let t=0;t<g;++t){const n=y[t],r=a.tidy((()=>e(n,m)));if(null==o)h=r[0],m=r[1];else{const e=a.tidy((()=>{const e=v[t],n=a.sub(a.onesLike(e),e);

1return{output:a.add(a.mul(r[0],e),a.mul(m[0],n)),newStates:m.map(((t,o)=>a.add(a.mul(r[1][o],e),a.mul(t,n))))}}));h=e.output,m=e.newStates}c&&p.push(h)}if(c){const e=1;b=a.stack(p,e)}return[h,b,m]}))}class k extends l.Layer{constructor(e){let t;if(super(e),null==e.cell)throw new u.ValueError("cell property is missing for the constructor of RNN.");if(t=Array.isArray(e.cell)?new S({cells:e.cell}):e.cell,null==t.stateSize)throw new u.ValueError("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=null!=e.returnSequences&&e.returnSequences,this.returnState=null!=e.returnState&&e.returnState,this.goBackwards=null!=e.goBackwards&&e.goBackwards,this._stateful=null!=e.stateful&&e.stateful,this.unroll=null!=e.unroll&&e.unroll,this.supportsMasking=!0,this.inputSpec=[new l.InputSpec({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(null==this.states_){const e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return f.range(0,e).map((e=>null))}return this.states_}setStates(e){this.states_=e}computeOutputShape(e){(0,m.isArrayOfShapes)(e)&&(e=e[0]);let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);const n=t[0];let r;if(r=this.returnSequences?[e[0],e[1],n]:[e[0],n],this.returnState){const n=[];for(const r of t)n.push([e[0],r]);return[r].concat(n)}return r}computeMask(e,t){return a.tidy((()=>{Array.isArray(t)&&(t=t[0]);const e=this.returnSequences?t:null;if(this.returnState){const t=this.states.map((e=>null));return[e].concat(t)}return e}))}get states(){if(null==this.states_){const e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}return this.states_}set states(e){this.states_=e}build(e){if(null!=this.numConstants)throw new u.NotImplementedError("Constants support is not implemented in RNN yet.");(0,m.isArrayOfShapes)(e)&&(e=e[0]);const t=this.stateful?e[0]:null,n=e.slice(2);this.inputSpec[0]=new l.InputSpec({shape:[t,null,...n]});const a=[e[0]].concat(e.slice(2));let o;if(this.cell.build(a),o=Array.isArray(this.cell.stateSize)?this.cell.stateSize:[this.cell.stateSize],null!=this.stateSpec){if(!r.util.arraysEqual(this.stateSpec.map((e=>e.shape[e.shape.length-1])),o))throw new u.ValueError("An initialState was passed that is not compatible with "+"cell.stateSize. Received stateSpec=".concat(this.stateSpec,"; ")+"However cell.stateSize is ".concat(this.cell.stateSize))}else this.stateSpec=o.map((e=>new l.InputSpec({shape:[null,e]})));this.stateful&&this.resetStates()}resetStates(e){let t=arguments.length>1&&void 0!==arguments[1]&&arguments[1];(0,r.tidy)((()=>{if(!this.stateful)throw new u.AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");const n=this.inputSpec[0].shape[0];if(null==n)throw new u.ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(null==this.states_)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map((e=>a.zeros([n,e]))):this.states_=[a.zeros([n,this.cell.stateSize])];else if(null==e)a.dispose(this.states_),null!=this.keptStates&&(a.dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map((e=>a.zeros([n,e]))):this.states_[0]=a.zeros([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new u.ValueError("Layer ".concat(this.name," expects ").concat(this.states_.length," state(s), ")+"but it received ".concat(e.length," state value(s). Input ")+"received: ".concat(e));!0===t?this.keptStates.push(this.states_.slice()):a.dispose(this.states_);for(let t=0;t<this.states_.length;++t){const a=e[t],o=Array.isArray(this.cell.stateSize)?this.cell.stateSize[t]:this.cell.stateSize,s=[n,o];if(!r.util.arraysEqual(a.shape,s))throw new u.ValueError("State ".concat(t," is incompatible with layer ").concat(this.name,": ")+"expected shape=".concat(s,", received shape=").concat(a.shape));this.states_[t]=a}}this.states_=this.states_.map((e=>a.keep(e.clone())))}

1))}apply(e,t){let n=null==t?null:t.initialState,r=null==t?null:t.constants;null==t&&(t={});const a=x(e,n,r,this.numConstants);e=a.inputs,n=a.initialState,r=a.constants;let o=[],s=[];if(null!=n){t.initialState=n,o=o.concat(n),this.stateSpec=[];for(const e of n)this.stateSpec.push(new l.InputSpec({shape:e.shape}));s=s.concat(this.stateSpec)}null!=r&&(t.constants=r,o=o.concat(r),this.numConstants=r.length);if(o[0]instanceof l.SymbolicTensor){const n=[e].concat(o),r=this.inputSpec.concat(s),a=this.inputSpec;this.inputSpec=r;const i=super.apply(n,t);return this.inputSpec=a,i}return super.apply(e,t)}call(e,t){return(0,r.tidy)((()=>{const n=null==t?null:t.mask,r=null==t?null:t.training;let a=null==t?null:t.initialState;e=(0,m.getExactlyOneTensor)(e),null==a&&(a=this.stateful?this.states_:this.getInitialState(e));const o=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==o)throw new u.ValueError("RNN Layer has ".concat(o," state(s) but was passed ")+"".concat(a.length," initial state(s)."));this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");const s={training:r},i=w(((e,t)=>{const n=this.cell.call([e].concat(t),s);return[n[0],n.slice(1)]}),e,a,this.goBackwards,n,null,this.unroll,this.returnSequences),c=i[0],l=i[1],d=i[2];this.stateful&&this.resetStates(d,r);const p=this.returnSequences?l:c;return this.returnState?[p].concat(d):p}))}getInitialState(e){return(0,r.tidy)((()=>{let t=a.zeros(e.shape);return t=a.sum(t,[1,2]),t=s.expandDims(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map((e=>e>1?s.tile(t,[1,e]):t)):this.cell.stateSize>1?[s.tile(t,[1,this.cell.stateSize])]:[t]}))}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),null!=this.cell&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){const e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};null!=this.numConstants&&(t.numConstants=this.numConstants);const n=this.cell.getConfig();return this.getClassName()===k.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};const r=t.cell,a=(0,y.deserialize)(r,n);return new e(Object.assign(t,{cell:a}))}}t.RNN=k,k.className="RNN",r.serialization.registerClass(k);class _ extends l.Layer{}t.RNNCell=_;class C extends _{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,(0,h.assertPositiveInteger)(this.units,"units"),this.activation=(0,o.getActivation)(null==e.activation?this.DEFAULT_ACTIVATION:e.activation),this.useBias=null==e.useBias||e.useBias,this.kernelInitializer=(0,d.getInitializer)(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=(0,d.getInitializer)(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=(0,d.getInitializer)(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=(0,p.getRegularizer)(e.kernelRegularizer),this.recurrentRegularizer=(0,p.getRegularizer)(e.recurrentRegularizer),this.biasRegularizer=(0,p.getRegularizer)(e.biasRegularizer),this.kernelConstraint=(0,c.getConstraint)(e.kernelConstraint),this.recurrentConstraint=(0,c.getConstraint)(e.recurrentConstraint),this.biasConstraint=(0,c.getConstraint)(e.biasConstraint),this.dropout=f.min([1,f.max([0,null==e.dropout?0:e.dropout])]),this.recurrentDropout=f.min([1,f.max([0,null==e.recurrentDropout?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=(0,m.getExactlyOneShape)(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return(0,r.tidy)((()=>

1{if(2!==e.length)throw new u.ValueError("SimpleRNNCell expects 2 input Tensors, got ".concat(e.length,"."));let n=e[1];e=e[0];const r=null!=t.training&&t.training;let o;0<this.dropout&&this.dropout<1&&null==this.dropoutMask&&(this.dropoutMask=E({ones:()=>a.onesLike(e),rate:this.dropout,training:r,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&null==this.recurrentDropoutMask&&(this.recurrentDropoutMask=E({ones:()=>a.onesLike(n),rate:this.recurrentDropout,training:r,dropoutFunc:this.dropoutFunc}));const i=this.dropoutMask,c=this.recurrentDropoutMask;o=null!=i?s.dot(a.mul(e,i),this.kernel.read()):s.dot(e,this.kernel.read()),null!=this.bias&&(o=s.biasAdd(o,this.bias.read())),null!=c&&(n=a.mul(n,c));let l=a.add(o,s.dot(n,this.recurrentKernel.read()));return null!=this.activation&&(l=this.activation.apply(l)),[l,l]}))}getConfig(){const e=super.getConfig(),t={units:this.units,activation:(0,o.serializeActivation)(this.activation),useBias:this.useBias,kernelInitializer:(0,d.serializeInitializer)(this.kernelInitializer),recurrentInitializer:(0,d.serializeInitializer)(this.recurrentInitializer),biasInitializer:(0,d.serializeInitializer)(this.biasInitializer),kernelRegularizer:(0,p.serializeRegularizer)(this.kernelRegularizer),recurrentRegularizer:(0,p.serializeRegularizer)(this.recurrentRegularizer),biasRegularizer:(0,p.serializeRegularizer)(this.biasRegularizer),activityRegularizer:(0,p.serializeRegularizer)(this.activityRegularizer),kernelConstraint:(0,c.serializeConstraint)(this.kernelConstraint),recurrentConstraint:(0,c.serializeConstraint)(this.recurrentConstraint),biasConstraint:(0,c.serializeConstraint)(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}}t.SimpleRNNCell=C,C.className="SimpleRNNCell",r.serialization.registerClass(C);class P extends k{constructor(e){e.cell=new C(e),super(e)}call(e,t){return(0,r.tidy)((()=>{null!=this.cell.dropoutMask&&(a.dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(a.dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=null==t?null:t.mask,r=null==t?null:t.training,o=null==t?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:o})}))}static fromConfig(e,t){return new e(t)}}t.SimpleRNN=P,P.className="SimpleRNN",r.serialization.registerClass(P);class O extends _{constructor(e){if(super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new u.ValueError("GRUCell does not support reset_after parameter set to true.");this.units=e.units,(0,h.assertPositiveInteger)(this.units,"units"),this.activation=(0,o.getActivation)(void 0===e.activation?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=(0,o.getActivation)(void 0===e.recurrentActivation?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=null==e.useBias||e.useBias,this.kernelInitializer=(0,d.getInitializer)(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=(0,d.getInitializer)(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=(0,d.getInitializer)(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=(0,p.getRegularizer)(e.kernelRegularizer),this.recurrentRegularizer=(0,p.getRegularizer)(e.recurrentRegularizer),this.biasRegularizer=(0,p.getRegularizer)(e.biasRegularizer),this.kernelConstraint=(0,c.getConstraint)(e.kernelConstraint),this.recurrentConstraint=(0,c.getConstraint)(e.recurrentConstraint),this.biasConstraint=(0,c.getConstraint)(e.biasConstraint),this.dropout=f.min([1,f.max([0,null==e.dropout?0:e.dropout])]),this.recurrentDropout=f.min([1,f.max([0,null==e.recurrentDropout?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){const t=(e=(0,m.getExactlyOneShape)(e))[e.length-1];this.kernel=this.addWeight("kernel",[t,3*this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,3*this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[3*this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return(0,r.tidy)((()=>

1{if(2!==e.length)throw new u.ValueError("GRUCell expects 2 input Tensors (inputs, h, c), got "+"".concat(e.length,"."));const n=null!=t.training&&t.training;let r=e[1];e=e[0],0<this.dropout&&this.dropout<1&&null==this.dropoutMask&&(this.dropoutMask=E({ones:()=>a.onesLike(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&null==this.recurrentDropoutMask&&(this.recurrentDropoutMask=E({ones:()=>a.onesLike(r),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));const o=this.dropoutMask,i=this.recurrentDropoutMask;let c,l,d;0<this.dropout&&this.dropout<1&&(e=a.mul(e,o[0]));let p=s.dot(e,this.kernel.read());this.useBias&&(p=s.biasAdd(p,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(r=a.mul(r,i[0]));const h=this.recurrentKernel.read(),[f,m]=a.split(h,[2*this.units,this.units],h.rank-1),g=s.dot(r,f),[y,v,b]=a.split(p,3,p.rank-1),[x,w]=a.split(g,2,g.rank-1);c=this.recurrentActivation.apply(a.add(y,x)),l=this.recurrentActivation.apply(a.add(v,w));const k=s.dot(a.mul(l,r),m);d=this.activation.apply(a.add(b,k));const _=a.add(a.mul(c,r),a.mul(a.add(1,a.neg(c)),d));return[_,_]}))}getConfig(){const e=super.getConfig(),t={units:this.units,activation:(0,o.seri

1alizeActivation)(this.activation),recurrentActivation:(0,o.serializeActivation)(this.recurrentActivation),useBias:this.useBias,kernelInitializer:(0,d.serializeInitializer)(this.kernelInitializer),recurrentInitializer:(0,d.serializeInitializer)(this.recurrentInitializer),biasInitializer:(0,d.serializeInitializer)(this.biasInitializer),kernelRegularizer:(0,p.serializeRegularizer)(this.kernelRegularizer),recurrentRegularizer:(0,p.serializeRegularizer)(this.recurrentRegularizer),biasRegularizer:(0,p.serializeRegularizer)(this.biasRegularizer),activityRegularizer:(0,p.serializeRegularizer)(this.activityRegularizer),kernelConstraint:(0,c.serializeConstraint)(this.kernelConstraint),recurrentConstraint:(0,c.serializeConstraint)(this.recurrentConstraint),biasConstraint:(0,c.serializeConstraint)(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}}t.GRUCell=O,O.className="GRUCell",r.serialization.registerClass(O);class N extends k{constructor(e){0===e.implementation&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new O(e),super(e)}call(e,t){return(0,r.tidy)((()=>{null!=this.cell.dropoutMask&&(a.dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(a.dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=null==t?null:t.mask,r=null==t?null:t.training,o=null==t?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:o})}))}static fromConfig(e,t){return 0===t.implmentation&&(t.implementation=1),new e(t)}}t.GRU=N,N.className="GRU",r.serialization.registerClass(N);class I extends _{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,(0,h.assertPositiveInteger)(this.units,"units"),this.activation=(0,o.getActivation)(void 0===e.activation?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=(0,o.getActivation)(void 0===e.recurrentActivation?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=null==e.useBias||e.useBias,this.kernelInitializer=(0,d.getInitializer)(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=(0,d.getInitializer)(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=(0,d.getInitializer)(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=(0,p.getRegularizer)(e.kernelRegularizer),this.recurrentRegularizer=(0,p.getRegularizer)(e.recurrentRegularizer),this.biasRegularizer=(0,p.getRegularizer)(e.biasRegularizer),this.kernelConstraint=(0,c.getConstraint)(e.kernelConstraint),this.recurrentConstraint=(0,c.getConstraint)(e.recurrentConstraint),this.biasConstraint=(0,c.getConstraint)(e.biasConstraint),this.dropout=f.min([1,f.max([0,null==e.dropout?0:e.dropout])]),this.recurrentDropout=f.min([1,f.max([0,null==e.recurrentDropout?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;const n=(e=(0,m.getExactlyOneShape)(e))[e.length-1];let r;if(this.kernel=this.addWeight("kernel",[n,4*this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,4*this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){if(this.unitForgetBias){const e=this.biasInitializer,n=this.units;r=new((t=class extends d.Initializer{apply(t,r){const a=e.apply([n]),o=(new d.Ones).apply([n]),i=e.apply([2*n]);return s.concatAlongFirstAxis(s.concatAlongFirstAxis(a,o),i)}}).className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[4*this.units],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return(0,r.tidy)((()=>{const n=null!=t.training&&t.training;if(3!==e.length)throw new u.ValueError("LSTMCell expects 3 input Tensors (inputs, h, c), got "+"".concat(e.length,"."));let r=e[1];const o=e[2];e=e[0],0<this.dropout&&this.dropout<1&&null==this.dropoutMask&&(this.dropoutMask=E({ones:()=>a.onesLike(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&null==this.recurrentDropoutMask&&(this.recurrentDropoutMask=E({ones:()=>a.onesLike(r),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));const i=this.dropoutMask,c=this.recurrentDropoutMask;let l,d,p,h;0<this.dropout&&this.dropout<1&&(e=a.mul(e,i[0]));let f=s.dot(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(r=a.mul(r,c[0])),f=a.add(f,s.dot(r,this.recurrentKernel.read())),this.useBias&&(f=s.biasAdd(f,this.bias.read()));const[m,g,y,v]=a.split(f,4,f.rank-1);l=this.recurrentActivation.apply(m),d=this.recurrentActivation.apply(g),p=a.add(a.mul(d,o),a.mul(l,this.activation.apply(y))),h=this.recurrentActivation.apply(v);const b=a.mul(h,this.activation.apply(p));return[b,b,p]}))}getConfig(){const e=super.getConfig(),t={units:this.units,activation:(0,o.seri

1alizeActivation)(this.activation),recurrentActivation:(0,o.serializeActivation)(this.recurrentActivation),useBias:this.useBias,kernelInitializer:(0,d.serializeInitializer)(this.kernelInitializer),recurrentInitializer:(0,d.serializeInitializer)(this.recurrentInitializer),biasInitializer:(0,d.serializeInitializer)(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:(0,p.serializeRegularizer)(this.kernelRegularizer),recurrentRegularizer:(0,p.serializeRegularizer)(this.recurrentRegularizer),biasRegularizer:(0,p.serializeRegularizer)(this.biasRegularizer),activityRegularizer:(0,p.serializeRegularizer)(this.activityRegularizer),kernelConstraint:(0,c.serializeConstraint)(this.kernelConstraint),recurrentConstraint:(0,c.serializeConstraint)(this.recurrentConstraint),biasConstraint:(0,c.serializeConstraint)(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}}t.LSTMCell=I,I.className="LSTMCell",r.serialization.registerClass(I);class T extends k{constructor(e){0===e.implementation&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new I(e),super(e)}call(e,t){return(0,r.tidy)((()=>{null!=this.cell.dropoutMask&&(a.dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(a.dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=null==t?null:t.mask,r=null==t?null:t.training,o=null==t?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:o})}))}static fromConfig(e,t){return 0===t.implmentation&&(t.implementation=1),new e(t)}}t.LSTM=T,T.className="LSTM",r.serialization.registerClass(T);class S extends _{constructor(e){super(e),this.cells=e.cells}get stateSize(){const e=[];for(const t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return(0,r.tidy)((()=>{let n=e.slice(1);const r=[];for(const e of this.cells.slice().reverse())Array.isArray(e.stateSize)?r.push(n.splice(0,e.stateSize.length)):r.push(n.splice(0,1));r.reverse();const a=[];let o;for(let s=0;s<this.cells.length;++s){const i=this.cells[s];n=r[s],o=0===s?[e[0]].concat(n):[o[0]].concat(n),o=i.call(o,t),a.push(o.slice(1))}n=[];for(const e of a.slice().reverse())n.push(...e);return[o[0]].concat(n)}))}build(e){let t;(0,m.isArrayOfShapes)(e)&&(e=e[0]),this.cells.forEach(((n,r)=>{(0,i.nameScope)("RNNCell_".concat(r),(()=>{n.build(e),t=Array.isArray(n.stateSize)?n.stateSize[0]:n.stateSize,e=[e[0],t]}))})),this.built=!0}getConfig(){const e=super.getConfig(),t={cells:this.cells.map((e=>({className:e.getClassName(),config:e.getConfig()})))};return Object.assign({},e,t)}static fromConfig(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};const r=[];for(const e of t.cells)r.push((0,y.deserialize)(e,n));return new e({cells:r})}get trainableWeights(){if(!this.trainable)return[];const e=[];for(const t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){const e=[];for(const t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){const t=[];for(const e of this.cells)t.push(...e.trainableWeights);return t.concat(e)}return e}getWeights(){const e=[];for(const t of this.cells)e.push(...t.weights);return(0,g.batchGetValue)(e)}setWeights(e){const t=[];for(const n of this.cells){const r=n.weights.length,a=e.splice(r);for(let e=0;e<n.weights.length;++e)t.push([n.weights[e],a[e]])}(0,g.batchSetValue)(t)}}function E(e){const{ones:t,rate:n,training:r=!1,count:o=1,dropoutFunc:i}=e,c=()=>null!=i?i(t(),n):s.dropout(t(),n),l=()=>s.inTrainPhase(c,t,r);if(!o||o<=1)return a.keep(l().clone());return Array(o).fill(void 0).map(l).map((e=>a.keep(e.clone())))}t.StackedRNNCells=S,S.className="StackedRNNCells",r.serialization.registerClass(S)},640153:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.deserialize=function(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},n=arguments.length>2&&void 0!==arguments[2]&&arguments[2];return(0,a.deserializeKerasObject)(e,r.serialization.SerializationMap.getMap().classNameMap,t,"layer",n)};var r=n(735534),a=n(986904)},447767:(e,t,n)=>{"use strict";

1Object.defineProperty(t,"__esModule",{value:!0}),t.Wrapper=t.TimeDistributed=t.Bidirectional=void 0,t.checkBidirectionalMergeMode=v;var r=m(n(735534)),a=r,o=m(n(984710)),s=n(545188),i=n(840889),c=n(608012),l=n(940826),u=m(n(986904)),d=n(252518),p=n(302909),h=n(640153);function f(e){if("function"!=typeof WeakMap)return null;var t=new WeakMap,n=new WeakMap;return(f=function(e){return e?n:t})(e)}function m(e,t){if(!t&&e&&e.__esModule)return e;if(null===e||"object"!=typeof e&&"function"!=typeof e)return{default:e};var n=f(t);if(n&&n.has(e))return n.get(e);var r={__proto__:null},a=Object.defineProperty&&Object.getOwnPropertyDescriptor;for(var o in e)if("default"!==o&&{}.hasOwnProperty.call(e,o)){var s=a?Object.getOwnPropertyDescriptor(e,o):null;s&&(s.get||s.set)?Object.defineProperty(r,o,s):r[o]=e[o]}return r.default=e,n&&n.set(e,r),r}class g extends i.Layer{constructor(e){super(e),this.layer=e.layer}build(e){this.built=!0}get trainable(){return null!=this.layer&&this.layer.trainable}set trainable(e){null!=this.layer&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){const e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),null!=this.layer&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};const r=t.layer,a=(0,h.deserialize)(r,n);delete t.layer;const o={layer:a};return Object.assign(o,t),new e(o)}}t.Wrapper=g;class y extends g{constructor(e){super(e),this.supportsMasking=!0}build(e){if((e=(0,d.getExactlyOneShape)(e)).length<3)throw new c.ValueError("TimeDistributed layer expects an input shape >= 3D, but received "+"input shape ".concat(JSON.stringify(e)));this.inputSpec=[{shape:e}];const t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){const t=[(e=(0,d.getExactlyOneShape)(e))[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),r=e[1];return[n[0],r].concat(n.slice(1))}call(e,t){return(0,r.tidy)((()=>{e=(0,d.getExactlyOneTensor)(e);return(0,p.rnn)(((e,n)=>[(0,d.getExactlyOneTensor)(this.layer.call(e,t)),[]]),e,[],!1,null,null,!1,!0)[1]}))}}function v(e){u.checkStringTypeUnionValue(l.VALID_BIDIRECTIONAL_MERGE_MODES,"BidirectionalMergeMode",e)}t.TimeDistributed=y,y.className="TimeDistributed",r.serialization.registerClass(y);class b extends g{constructor(e){super(e);const t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=(0,h.deserialize)(n),t.goBackwards=!0!==t.goBackwards;const r={};if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=(0,h.deserialize)(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=void 0===e.mergeMode?"concat":e.mergeMode,v(this.mergeMode),e.weights)throw new c.NotImplementedError("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,null!=this.forwardLayer&&(this.forwardLayer.trainable=e),null!=this.backwardLayer&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){const t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t,n,r,a=this.forwardLayer.computeOutputShape(e);return Array.isArray(a)&&Array.isArray(a[0])||(a=[a]),this.returnState?(r=a.slice(1),t=a[0]):t=a[0],"concat"===this.mergeMode?(t[t.length-1]*=2,n=[t]):n=null==this.mergeMode?[t,t.slice()]:[t],this.returnState?null==this.mergeMode?n.concat(r).concat(r.slice()):[t].concat(r).concat(r.slice()):u.singletonOrArray(n)}apply(e,t){let n=null==t?null:t.initialState,r=null==t?null:t.constants;null==t&&(t={});const a=(0,p.standardizeArgs)(e,n,r,this.numConstants);

1if(e=a.inputs,n=a.initialState,r=a.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(null==n||0===n.length)&&null==r)return super.apply(e,t);const o=[],s=[];if(null!=n){const e=n.length;if(e%2>0)throw new c.ValueError("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,o.push(...n);const r=n.map((e=>new i.InputSpec({shape:e.shape})));this.forwardLayer.stateSpec=r.slice(0,e/2),this.backwardLayer.stateSpec=r.slice(e/2),s.push(...r)}if(null!=r)throw new c.NotImplementedError("Support for constants in Bidirectional layers is not implemented yet.");const l=o[0]instanceof i.SymbolicTensor;for(const e of o)if(e instanceof i.SymbolicTensor!==l)throw new c.ValueError("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(l){const n=[e].concat(o),r=this.inputSpec.concat(s),a=this.inputSpec;this.inputSpec=r;const i=super.apply(n,t);return this.inputSpec=a,i}return super.apply(e,t)}call(e,t){return(0,r.tidy)((()=>{const n=t.initialState;let r,s,i,c;if(null==n)r=this.forwardLayer.call(e,t),s=this.backwardLayer.call(e,t);else{const a=n.slice(0,n.length/2),o=n.slice(n.length/2);r=this.forwardLayer.call(e,Object.assign(t,{initialState:a})),s=this.backwardLayer.call(e,Object.assign(t,{initialState:o}))}return this.returnState&&(Array.isArray(r)&&(i=r.slice(1).concat(s.slice(1))),r=r[0],s=s[0]),this.returnSequences&&(s=a.reverse(s,1)),"concat"===this.mergeMode?c=o.concatenate([r,s]):"sum"===this.mergeMode?c=a.add(r,s):"ave"===this.mergeMode?c=a.mul(.5,a.add(r,s)):"mul"===this.mergeMode?c=a.mul(r,s):null==this.mergeMode&&(c=[r,s]),this.returnState?null==this.mergeMode?c.concat(i):[c].concat(i):c}))}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){(0,s.nameScope)(this.forwardLayer.name,(()=>{this.forwardLayer.build(e)})),(0,s.nameScope)(this.backwardLayer.name,(()=>{this.backwardLayer.build(e)})),this.built=!0}computeMask(e,t){let n;if(Array.isArray(t)&&(t=t[0]),n=this.returnSequences?null==this.mergeMode?[t,t]:t:null==this.mergeMode?[null,null]:null,this.returnState){const e=this.forwardLayer.states.map((e=>null));return Array.isArray(n)?n.concat(e).concat(e):[n].concat(e).concat(e)}return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),null!=this.forwardLayer&&this.forwardLayer.setFastWeightInitDuringBuild(e),null!=this.backwardLayer&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){const e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){const n=(0,h.deserialize)(t.layer);if(delete t.layer,null!=t.numConstants)throw new c.NotImplementedError("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");const r=t;return r.layer=n,new e(r)}}t.Bidirectional=b,b.className="Bidirectional",r.serialization.registerClass(b)},782668:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.disposeTensorsInLogs=function(e){if(null==e)return;for(const t in e){const n=e[t];"number"!=typeof n&&n.dispose()}},t.resolveScalarsInLogs=async function(e){if(null==e)return;const t=[],n=[],a=[];for(const r in e){const o=e[r];if("number"!=typeof o){const e=o;t.push(e.data()),n.push(r),a.push(e)}}if(t.length>0){const o=await Promise.all(t);for(let t=0;t<o.length;++t)e[n[t]]=o[t][0];(0,r.dispose)(a)}};var r=n(735534)},242135:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.MSLE=t.MSE=t.MAPE=t.MAE=t.KLD=void 0,t.binaryCrossentropy=k,t.categoricalCrossentropy=b,t.categoricalHinge=y,t.cosine=void 0,t.cosineProximity=P,t.get=function(e){if("string"==typeof e){if(e in O)return O[e];let t="Unknown loss ".concat(e);throw e.toLowerCase().includes("softmaxcrossentropy")&&(t="Unknown loss ".concat(e,". ")+'Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy'),new i.ValueError(t)}return e},t.hinge=g,t.kld=void 0,t.kullbackLeiblerDivergence=_,t.l2Normalize=u,t.logc

1osh=v,t.mape=t.mae=t.lossesMap=void 0,t.meanAbsoluteError=p,t.meanAbsolutePercentageError=h,t.meanSquaredError=d,t.meanSquaredLogarithmicError=f,t.msle=t.mse=void 0,t.poisson=C,t.sigmoidCrossEntropyWithLogits=w,t.sparseCategoricalCrossentropy=x,t.squaredHinge=m;var r=l(n(735534)),a=r,o=n(485246),s=l(n(984710)),i=n(608012);function c(e){if("function"!=typeof WeakMap)return null;var t=new WeakMap,n=new WeakMap;return(c=function(e){return e?n:t})(e)}function l(e,t){if(!t&&e&&e.__esModule)return e;if(null===e||"object"!=typeof e&&"function"!=typeof e)return{default:e};var n=c(t);if(n&&n.has(e))return n.get(e);var r={__proto__:null},a=Object.defineProperty&&Object.getOwnPropertyDescriptor;for(var o in e)if("default"!==o&&{}.hasOwnProperty.call(e,o)){var s=a?Object.getOwnPropertyDescriptor(e,o):null;s&&(s.get||s.set)?Object.defineProperty(r,o,s):r[o]=e[o]}return r.default=e,n&&n.set(e,r),r}function u(e,t){return(0,r.tidy)((()=>{"float32"!==e.dtype&&(e=a.cast(e,"float32"));const 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a=0;a<this.fullColumnNames.length;a++){const o=this.fullColumnNames[a],s=this.columnConfigs?this.columnConfigs[o]:null;if(!this.configuredColumnsOnly||s){const i=t[a];let c=null;if(""===i)if(s&&void 0!==s.default)c=s.default;else{if(s&&(s.required||s.isLabel))throw new Error("Required column ".concat(o," is empty in this line: ").concat(e));c=void 0}else{const e=Number(i);if(isNaN(e))c=s&&"bool"===s.dtype?this.getBoolean(i):i;else if(s&&s.dtype)switch(s.dtype){case"float32":default:c=e;break;case"int32":c=Math.floor(e);break;case"bool":c=this.getBoolean(i)}else c=e}s&&s.isLabel?r[o]=c:n[o]=c}}return 0===Object.keys(r).length?n:{xs:n,ys:r}}getBoolean(e){return"1"===e||"true"===e.toLowerCase()?1:0}parseRow(e){let t=!(arguments.length>1&&void 0!==arguments[1])||arguments[1];const n=[];let r=0;const a=e.length;let o=i;for(let t=0;t<a;t++)switch(o){case i:switch(e.charAt(t)){case s:r=t+1,o=l;break;case this.delimiter:if(r=t+1," "===this.delimiter&&this.delimWhitespace)break;n.push(""),o=i;break;default:o=c,r=t}break;case c:if(e.charAt(t)===this.delimiter)n.push(e.substring(r,t)),o=i,r=t+1;break;case l:if(e.charAt(t)===s)o=u;break;case u:switch(e.charAt(t)){case this.delimiter:n.push(e.substring(r,t-1)),o=i,r=t+1;break;case s:o=l;break;default:o=d}break;case d:if(e.charAt(t)===s)o=l}if(o===u?n.push(e.substring(r,a-1)):n.push(e.substring(r)),t&&n.length!==this.fullColumnNames.length)throw new Error("Invalid row in csv file. Should have ".concat(this.fullColumnNames.length," elements in a row, but got ").concat(n));return n}}t.CSVDataset=p},13369:(e,t,n)=>{"use strict";

1Object.defineProperty(t,"__esModule",{value:!0}),t.TextLineDataset=void 0;var r=n(931841);class a extends r.Dataset{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split("\n").map((e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e)))}}t.TextLineDataset=a},147478:(e,t)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.DataSource=void 0;t.DataSource=class{}},8776:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),Object.defineProperty(t,"CSVDataset",{enumerable:!0,get:function(){return a.CSVDataset}}),Object.defineProperty(t,"Dataset",{enumerable:!0,get:function(){return r.Dataset}}),Object.defineProperty(t,"FileDataSource",{enumerable:!0,get:function(){return i.FileDataSource}}),Object.defineProperty(t,"TextLineDataset",{enumerable:!0,get:function(){return o.TextLineDataset}}),Object.defineProperty(t,"URLDataSource",{enumerable:!0,get:function(){return c.URLDataSource}}),Object.defineProperty(t,"array",{enumerable:!0,get:function(){return r.array}}),Object.defineProperty(t,"csv",{enumerable:!0,get:function(){return s.csv}}),Object.defineProperty(t,"func",{enumerable:!0,get:function(){return s.func}}),Object.defineProperty(t,"generator",{enumerable:!0,get:function(){return s.generator}}),Object.defineProperty(t,"microphone",{enumerable:!0,get:function(){return s.microphone}}),Object.defineProperty(t,"version_data",{enumerable:!0,get:function(){return l.version}}),Object.defineProperty(t,"webcam",{enumerable:!0,get:function(){return s.webcam}}),Object.defineProperty(t,"zip",{enumerable:!0,get:function(){return r.zip}});var r=n(931841),a=n(176100),o=n(13369),s=n(760124),i=n(289826),c=n(478113),l=n(712209)},540167:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.ByteChunkIterator=void 0;var r=n(735534),a=n(162969),o=n(221402);class s extends a.LazyIterator{decodeUTF8(){return new i(this)}}t.ByteChunkIterator=s;class i extends o.StringIterator{constructor(e){super(),this.upstream=e,this.impl=new c(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class c extends a.OneToManyIterator{constructor(e){if(super(),this.upstream=e,(0,r.env)().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{const{StringDecoder:e}=n(186759);this.decoder=new e("utf8")}}summary(){return"".concat(this.upstream.summary()," -> Utf8")}async pump(){const e=await this.upstream.next();let t,n;return!e.done&&(t=e.value,n=(0,r.env)().get("IS_BROWSER")?this.decoder.decode(t,{stream:!0}):this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0)}}},176290:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.FileChunkIterator=void 0;var r=n(735534),a=n(540167);class o extends a.ByteChunkIterator{constructor(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};super(),this.file=e,this.options=t,r.util.assert(e instanceof Uint8Array||!!(0,r.env)().get("IS_BROWSER")&&(e instanceof File||e instanceof Blob),(()=>"FileChunkIterator only supports File, Blob and Uint8Array right now.")),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1048576}summary(){return"FileChunks ".concat(this.file)}async next(){if(this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size))return{value:null,done:!0};const e=new Promise(((e,t)=>{const n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{const r=new FileReader;r.onload=n=>{let a=r.result;if(a instanceof ArrayBuffer&&(a=new Uint8Array(a)),!(a instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(a)},r.onabort=e=>t(new Error("Aborted")),r.onerror=e=>t(new Error(e.type));const a=this.file.slice(this.offset,n);r.readAsArrayBuffer(a)}this.offset=n}));return{value:await e,done:!1}}}t.FileChunkIterator=o},162969:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.ZipMismatchMode=t.ShuffleIterator=t.PrefetchIterator=t.OneToManyIterator=t.LazyIterator=t.ChainedIterator=void 0,t.iteratorFromConcatenated=f,t.iteratorFromConcatenatedFunction=function(e,t,n){return f(h(e).take(t),n)},t.iteratorFromFunction=h,t.iteratorFromIncrementing=function(e){let t=e;return h((()=>({value:t++,done:!1})))},t.iteratorFromItems=p,t.iteratorFromZipped=function(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:r.FAIL;return new T(e,t)};var r,a=d(n(735534)),o=d(n(236377)),s=n(426830),i=n(881638),c=n(614472),l=n(3013);function u(e){if("function"!=typeof WeakMap)return null;var t=new WeakMap,n=new WeakMap;return(u=function(e){return e?n:t})(e)}

1function d(e,t){if(!t&&e&&e.__esModule)return e;if(null===e||"object"!=typeof e&&"function"!=typeof e)return{default:e};var n=u(t);if(n&&n.has(e))return n.get(e);var r={__proto__:null},a=Object.defineProperty&&Object.getOwnPropertyDescriptor;for(var o in e)if("default"!==o&&{}.hasOwnProperty.call(e,o)){var s=a?Object.getOwnPropertyDescriptor(e,o):null;s&&(s.get||s.set)?Object.defineProperty(r,o,s):r[o]=e[o]}return r.default=e,n&&n.set(e,r),r}function p(e){return new g(e)}function h(e){return new y(e)}function f(e,t){return new I(e,t)}class m{async toArray(){const e=[];let t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){const e=this.prefetch(100),t=[];let n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new C(this,e)}filter(e){return new k(this,e)}map(e){return new _(this,e)}mapAsync(e){return new P(this,e)}serialMapAsync(e){return new P(this,e).serial()}flatmap(e){return new N(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile((e=>!0===e))}rowMajorBatch(e){return new w(this,e,!(arguments.length>1&&void 0!==arguments[1])||arguments[1])}columnMajorBatch(e){let t=!(arguments.length>1&&void 0!==arguments[1])||arguments[1],n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:i.zipToList;return this.rowMajorBatch(e,t).map((e=>(0,i.deepZip)(e,n)))}concatenate(e,t){return new I(p([this,e]),t)}take(e){return e<0||null==e?this:new x(this,e)}skip(e){return e<0||null==e?this:new b(this,e)}prefetch(e){return new S(this,e)}shuffle(e,t){return new E(this,e,t)}serial(){return new v(this)}}t.LazyIterator=m;class g extends m{constructor(e){super(),this.items=e,this.trav=0}summary(){return"Array of ".concat(this.items.length," items")}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};const e=this.items[this.trav];return this.trav++,{value:(0,s.deepClone)(e),done:!1}}}class y extends m{constructor(e){super(),this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message="Error thrown while iterating through a dataset: ".concat(e.message),e}}}class v extends m{constructor(e){super(),this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return"".concat(this.upstream.summary()," -> Serial")}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){return this.upstream.next()}}class b extends m{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return"".concat(this.upstream.summary()," -> Skip")}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){const e=await this.upstream.next();if(e.done)return e;a.dispose(e.value)}return this.upstream.next()}}class x extends m{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0}summary(){return"".concat(this.upstream.summary()," -> Take")}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}}class w extends m{constructor(e,t){let n=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];super(),this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return"".concat(this.upstream.summary()," -> RowMajorBatch")}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){const e=[];for(;e.length<this.batchSize;){const t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}}class k extends m{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return"".concat(this.upstream.summary()," -> Filter")}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){for(;;){const e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;a.dispose(e.value)}}}class _ extends m{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return"".concat(this.upstream.summary()," -> Map")}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=a.tensor_util.getTensorsInContainer(e.value),n=this.transform(e.value),r=a.tensor_util.getTensorsInContainer(n);for(const e of t)a.tensor_util.isTensorInList(e,r)||e.dispose();return{value:n,done:!1}}}class C extends m{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return"".concat(this.upstream.summary()," -> handleErrors")}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}}class P extends m{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return"".concat(this.upstream.summary()," -> AsyncMap")}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=a.tensor_util.getTensorsInContainer(e.value),n=await this.transform(e.value),r=a.tensor_util.getTensorsInContainer(n);for(const e of t)a.tensor_util.isTensorInList(e,r)||e.dispose();return{value:n,done:!1}}}class O extends m{constructor(){super(),this.outputQueue=new c.GrowingRingBuffer,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){for(;0===this.outputQueue.length();)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}}t.OneToManyIterator=O;class N extends O{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return"".concat(this.upstream.summary()," -> Flatmap")}async pump(){const e=await this.upstream.next();if(e.done)return!1;const t=a.tensor_util.getTensorsInContainer(e.value),n=this.transform(e.value),r=a.tensor_util.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(const e of t)a.tensor_util.isTensorInList(e,r)||e.dispose();return!0}}class I extends m{constructor(e,t){super(),this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"".concat("TODO: fill in upstream of chained summaries"," -> Chained")}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,null==this.iterator){const e=await this.moreIterators.next();if(e.done)return{value:null,done:!0};this.iterator=e.value,null!=this.baseErrorHandler&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}const t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}}

1t.ChainedIterator=I,function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"}(r||(t.ZipMismatchMode=r={}));class T extends m{constructor(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:r.FAIL;super(),this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{".concat("TODO: fill in upstream of zip summaries","} -> Zip")}async nextState(e){await e;let t=0,n=0;const a=await(0,i.deepMapAndAwaitAll)(this.iterators,(function(e){if(e instanceof m){return{value:e.next().then((e=>(t++,e.done&&n++,e.value))),recurse:!1}}return{value:null,recurse:!0}}));if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case r.FAIL:throw new Error("Zipped streams should have the same length. "+"Mismatched at element ".concat(this.count,"."));case r.SHORTEST:return{value:null,done:!0};case r.LONGEST:}return this.count++,{value:a,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}}class S extends m{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new l.RingBuffer(t)}summary(){return"".concat(this.upstream.summary()," -> Prefetch")}refill(){for(;!this.buffer.isFull();){const e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}}t.PrefetchIterator=S;class E extends S{constructor(e,t,n){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=o.alea(n||a.util.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){const e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(!t.done)return this.refill(),t;this.upstreamExhausted=!0}return{value:null,done:!0}}}t.ShuffleIterator=E},373389:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.MicrophoneIterator=void 0;var r=n(735534),a=n(162969);class o extends a.LazyIterator{constructor(e){super(),this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;const t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error("Invalid fftSize: it must be a power of 2 between "+"2 to 4 and 2 to 14, but got ".concat(this.fftSize));if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=!1!==e.includeSpectrogram,this.includeWaveform=!0===e.includeWaveform,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};if((0,r.env)().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");const t=new o(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:null==this.audioTrackConstraints||this.audioTrackConstraints,video:!1})}catch(e){throw new Error("Error thrown while initializing video stream: ".concat(e.message))}if(!this.stream)throw new Error("Could not obtain audio from microphone.");const e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,this.sampleRateHz){if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error("Mismatch in sampling rate: "+"Expected: ".concat(this.sampleRateHz,"; ")+"Actual: ".concat(this.audioContext.sampleRate))}else this.sampleRateHz=this.audioContext.sampleRate;const t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=2*this.fftSize,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t;const n=await this.getAudioData();if(this.includeSpectrogram){const t=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(t,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){const e=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(e,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){const e=[],t=[];let n=0;return new Promise((r=>{const a=setInterval((()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&r({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(a),r({freqDataQueue:e,timeDataQueue:t}))}),this.fftSize/this.sampleRateHz*1e3)}))}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),null!=this.stream&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){const t=e[0].length,n=new Float32Array(e.length*t);return e.forEach(((e,r)=>n.set(e,r*t))),n}getTensorFromAudioDataArray(e,t){const n=new Float32Array(r.util.sizeFromShape(t));return n.set(e,n.length-e.length),(0,r.tensor)(n,t)}}t.MicrophoneIterator=o}

1,221402:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.StringIterator=void 0;var r=n(162969);class a extends r.LazyIterator{split(e){return new o(this,e)}}t.StringIterator=a;class o extends a{constructor(e,t){super(),this.upstream=e,this.impl=new s(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class s extends r.OneToManyIterator{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return"".concat(this.upstream.summary()," -> Split('").concat(this.separator,"')")}async pump(){const e=await this.upstream.next();if(e.done)return""!==this.carryover&&(this.outputQueue.push(this.carryover),this.carryover="",!0);const t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(const e of t.slice(0,-1))this.outputQueue.push(e);return this.carryover=t[t.length-1],!0}}},234752:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.urlChunkIterator=async function(e){let t,n,s=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},i=arguments.length>2?arguments[2]:void 0;"string"==typeof e?t=e:(t=e.url,n=o(e));const c=await(i||r.util.fetch)(t,n);if(c.ok){const e=new Uint8Array(await c.arrayBuffer());return new a.FileChunkIterator(e,s)}throw new Error(c.statusText)};var r=n(735534),a=n(176290);const o=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity})},459345:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.WebcamIterator=void 0;var r=n(735534),a=n(162969);class o extends a.LazyIterator{constructor(e,t){if(super(),this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=(0,r.tensor1d)([0],"int32"),this.webcamConfig.centerCrop){const e=1*this.webcamConfig.resizeWidth/this.webcamVideoElement.width,t=1*this.webcamConfig.resizeHeight/this.webcamVideoElement.height,n=(1-e)/2,a=(1-t)/2,o=n+e,s=t+a;this.cropBox=(0,r.tensor2d)([a,n,s,o],[1,4])}else this.cropBox=(0,r.tensor2d)([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};if((0,r.env)().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}const n=new o(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&r.util.assert("user"===this.webcamConfig.facingMode||"environment"===this.webcamConfig.facingMode,(()=>"Invalid webcam facing mode: ".concat(this.webcamConfig.facingMode,". ")+"Please provide 'user' or 'environment'"));try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message="Error thrown while initializing video stream: ".concat(e.message),e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise((e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}}))}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=r.browser.fromPixels(this.webcamVideoElement)}catch(e){throw new Error("Error thrown converting video to pixels: ".concat(JSON.stringify(e)))}if(!this.resize)return{value:e,done:!1};try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(e){throw new Error("Error thrown cropping the video: ".concat(e.message))}finally{e.dispose()}}needToResize(){return!(!this.webcamConfig.resizeWidth||!this.webcamConfig.resizeHeight||this.webcamVideoElement.width===this.webcamConfig.resizeWidth&&this.webcamVideoElement.height===this.webcamConfig.resizeHeight)}cropAndResizeFrame(e){return(0,r.tidy)((()=>{const t=(0,r.expandDims)((0,r.cast)(e,"float32"),0);let n;n=r.image.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");const a=n.shape;return(0,r.reshape)(n,a.slice(1))}))}async capture(){return(await this.next()).value}

1stop(){this.stream.getTracks().forEach((e=>e.stop()));try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}}t.WebcamIterator=o},760124:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.csv=function(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return new a.CSVDataset(new c.URLDataSource(e),t)},t.func=function(e){const t=(0,o.iteratorFromFunction)(e);return(0,r.datasetFromIteratorFn)((async()=>t))},t.generator=function(e){return(0,r.datasetFromIteratorFn)((async()=>{const t=await e();return(0,o.iteratorFromFunction)((()=>t.next()))}))},t.microphone=async function(e){return s.MicrophoneIterator.create(e)},t.webcam=async function(e,t){return i.WebcamIterator.create(e,t)};var r=n(931841),a=n(176100),o=n(162969),s=n(373389),i=n(459345),c=n(478113)},289826:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.FileDataSource=void 0;var r=n(735534),a=n(147478),o=n(176290),s=n(943224);class i extends a.DataSource{constructor(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};super(),this.input=e,this.options=t}async iterator(){if((0,s.isLocalPath)(this.input)&&(0,r.env)().get("IS_NODE")){const e=n(140549);this.input=e.readFileSync(this.input.substr(7))}return new o.FileChunkIterator(this.input,this.options)}}t.FileDataSource=i},478113:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.URLDataSource=void 0;var r=n(147478),a=n(234752),o=n(943224),s=n(289826);class i extends r.DataSource{constructor(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};super(),this.url=e,this.fileOptions=t}async iterator(){return(0,o.isLocalPath)(this.url)?new s.FileDataSource(this.url,this.fileOptions).iterator():(0,a.urlChunkIterator)(this.url,this.fileOptions)}}t.URLDataSource=i},426830:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.deepClone=function(e){return(0,a.deepMap)(e,s)};var r=function(e,t){if(!t&&e&&e.__esModule)return e;if(null===e||"object"!=typeof e&&"function"!=typeof e)return{default:e};var n=o(t);if(n&&n.has(e))return n.get(e);var r={__proto__:null},a=Object.defineProperty&&Object.getOwnPropertyDescriptor;for(var s in e)if("default"!==s&&{}.hasOwnProperty.call(e,s)){var i=a?Object.getOwnPropertyDescriptor(e,s):null;i&&(i.get||i.set)?Object.defineProperty(r,s,i):r[s]=e[s]}return r.default=e,n&&n.set(e,r),r}(n(735534)),a=n(881638);function o(e){if("function"!=typeof WeakMap)return null;var t=new WeakMap,n=new WeakMap;return(o=function(e){return e?n:t})(e)}function s(e){return e instanceof r.Tensor?{value:e.clone(),recurse:!1}:(0,a.isIterable)(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}},881638:(e,t,n)=>{"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.canTensorify=function(e){return null==e||(t=e,null===t||"object"!=typeof t&&"function"!=typeof t)||Array.isArray(e)||"object"==typeof e&&e instanceof r.Tensor||r.util.isTypedArray(e);var t},t.deepMap=function(e,t){return o(e,t)},t.deepMapAndAwaitAll=async function(e,t){const n=new Map;o(e,t,n);for(const e of Array.from(n.keys())){const t=n.get(e);if(r.util.isPromise(t)){const r=await t;n.set(e,r)}}return o(e,t,n)},t.deepZip=function(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:i;return s(e,t)},t.isIterable=c,t.zipToList=i;var r=function(e,t){if(!t&&e&&e.__esModule)return e;if(null===e||"object"!=typeof e&&"function"!=typeof e)return{default:e};var n=a(t);if(n&&n.has(e))return n.get(e);var r={__proto__:null},o=Object.defineProperty&&Object.getOwnPropertyDescriptor;for(var s in e)if("default"!==s&&{}.hasOwnProperty.call(e,s)){var i=o?Object.getOwnPropertyDescriptor(e,s):null;i&&(i.get||i.set)?Object.defineProperty(r,s,i):r[s]=e[s]}return r.default=e,n&&n.set(e,r),r}(n(735534));function a(e){if("function"!=typeof WeakMap)return null;var t=new WeakMap,n=new WeakMap;return(a=function(e){return e?n:t})(e)}function o(e,t){let n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:new Map,r=arguments.length>3&&void 0!==arguments[3]?arguments[3]:new Set;if(null==e)return null;if("function"==typeof Blob&&e instanceof Blob)return e.slice();if(r.has(e))throw new Error("Circular references are not supported.");

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