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human
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/*
Human library
homepage: <https://github.com/vladmandic/human>
author: <https://github.com/vladmandic>'
*/
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this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(l,c,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),r&&this.addTapeNode(l,c,t,h,n,u),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-a,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(p=>c[p]!=null?c[p].shape:null),outputShapes:t.map(p=>p.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let r=Ff(e);if(r!=null){let a=r.inputsToSave||[],s=r.outputsToSave||[],i;r.saveAllInputs?(M(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=a.map(l=>t[l]);let 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============================
Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
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pD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.data.get(a.dataId).values,c=n.data.get(s.dataId).values,u=tA(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(a.shape.length===2){let l=n.bufferSync(a),c=n.bufferSync(s),u=qw(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var fD={kernelName:Bh,backendName:"cpu",kernelFunc:pD};function mD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;_.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. 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F=_.toNestedArray(C,c.data.get(s.dataId).values),O=_.makeZerosNestedTypedArray(r.shape,r.dtype);for(let L=0;L<d;++L)for(let V=0;V<A;++V){let j=V*x-g.top;for(let U=0;U<y;++U){let X=U*v-g.left;for(let G=0;G<f;++G){let ee=Number.MIN_SAFE_INTEGER,Y=j<0?0:j,ae=X<0?0:X;for(let te=0;te<b;++te){let ie=j+te*k;if(ie>=0&&ie<p)for(let Q=0;Q<w;++Q){let he=X+Q*N;if(he>=0&&he<m){let oe=u[L][ie][he][G]+h[te][Q][G];oe>ee&&(ee=oe,Y=ie,ae=he)}}}O[L][Y][ae][G]+=F[L][V][U][G]}}}return{dataId:c.write(_.toTypedArray(O,r.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}};function dc(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;_e(a,"sum");let o;a.dtype==="bool"?o=Ga({inputs:{x:a},backend:n,attrs:{dtype:"int32"}}):o=qr({inputs:{x:a},backend:n});let 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Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});function dn(){let e,t,n,r,a,s,i,o,l,c;return J().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",r="in",a="texture",s="outputColor",i="out vec4 outputColor;",o=`
bool isnan_custom(float val) {
return (val > 0.0 || val < 0.0) ? false : val != 0.0;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan_custom(val.x),
isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
}
#define isnan(value) isnan_custom(value)
`,l="",c=`
#define round(value) newRound(value)
int newRound(float value) {
return int(floor(value + 0.5));
}
ivec4 newRound(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`):(e="",t="attribute",n="varying",r="varying",a="texture2D",s="gl_FragColor",i="",o=`
#define isnan(value) isnan_custom(value)
bool isnan_custom(float val) {
return (val > 0. || val < 1. || val == 0.) ? false : true;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
}
`,l=`
uniform float INFINITY;
bool isinf(float val) {
return abs(val) == INFINITY;
}
bvec4 isinf(vec4 val) {
return equal(abs(val), vec4(INFINITY));
}
`,c=`
int round(float value) {
return int(floor(value + 0.5));
}
ivec4 round(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`),{version:e,attribute:t,varyingVs:n,varyingFs:r,texture2D:a,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:c}}function Mi(e,t,n="index"){let r=_.computeStrides(t);return r.map((a,s)=>{let i=`int ${e[s]} = ${n} / ${a}`,o=s===r.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${a}`:`index -= ${e[s]} * ${a}`;return`${i}; ${o};`}).join("")}function bA(e){let t=_.computeStrides(e).map(n=>n.toString());return`
int getFlatIndex(ivec3 coords) {
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
}
`}var h3=`
const float FLOAT_MAX = 1.70141184e38;
const float FLOAT_MIN = 1.17549435e-38;
lowp vec4 encode_float(highp float v) {
if (isnan(v)) {
return vec4(255, 255, 255, 255);
}
highp float av = abs(v);
if(av < FLOAT_MIN) {
return vec4(0.0, 0.0, 0.0, 0.0);
} else if(v > FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
} else if(v < -FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;
}
highp vec4 c = vec4(0,0,0,0);
highp float e = floor(log2(av));
highp float m = exp2(fract(log2(av))) - 1.0;
c[2] = floor(128.0 * m);
m -= c[2] / 128.0;
c[1] = floor(32768.0 * m);
m -= c[1] / 32768.0;
c[0] = floor(8388608.0 * m);
highp float ebias = e + 127.0;
c[3] = floor(ebias / 2.0);
ebias -= c[3] * 2.0;
c[2] += floor(ebias) * 128.0;
c[3] += 128.0 * step(0.0, -v);
return c / 255.0;
}
`,iL=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=yc.DENSE;let t=xc(e),n=dn();this.outputShape=e,this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${Mi(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = 4 * (resTexRC.x * ${t[1]} + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getA(rc.x, rc.y, rc.z);
}
${n.output} = result;
}
`}},oL=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=yc.DENSE;let t=xc(e),n=dn();this.outputShape=e,this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${Mi(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = 4 * (resTexRC.x * ${t[1]} + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
}
${n.output} = result;
}
`}},lL=class{constructor(e){this.variableNames=["A"],this.outTexUsage=ar.DOWNLOAD;let t=dn();this.outputShape=e,this.userCode=`
${h3}
void main() {
float x = getAAtOutCoords();
${t.output} = encode_float(x);
}
`}},uL=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=ar.DOWNLOAD;let t=dn();this.outputShape=e,this.userCode=`
${h3}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t.output} = encode_float(x);
}
`}},cL=class{constructor(e,t,n=!1){this.variableNames=["A"];let r=dn(),[a,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
${bA(e)}
void main() {
ivec3 coords = getOutputCoords();
int flatIndex = getFlatIndex(coords);
int offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / ${s};
int c = imod(flatIndex, ${s});
vec2 uv = (vec2(c, r) + halfCR) / vec2(${s}.0, ${a}.0);
vec4 values = ${r.texture2D}(A, uv);
float result;
if(offset == 0) {
result = values[0];
} else if(offset == 1) {
result = values[1];
} else if(offset == 2) {
result = values[2];
} else {
result = values[3];
}
${r.output} = vec4(${i}, 0., 0., 0.);
}
`}},hL=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let r=dn(),[a,s]=t;this.outputShape=e;let i="",o="result";n&&(o="floor(result * 255. + 0.5)");for(let l=0;l<=1;l++)for(let c=0;c<=1;c++){let u=l*2+c;i+=`
localCoords = coords;
if(localCoords[2] + ${c} < ${e[2]}) {
localCoords[2] += ${c};
if(localCoords[1] + ${l} < ${e[1]}) {
localCoords[1] += ${l};
flatIndex = getFlatIndex(localCoords);
offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
r = flatIndex / ${s};
c = imod(flatIndex, ${s});
uv = (vec2(c, r) + halfCR) / vec2(${s}.0, ${a}.0);
values = ${r.texture2D}(A, uv);
if(offset == 0) {
result[${u}] = values[0];
} else if(offset == 1) {
result[${u}] = values[1];
} else if(offset == 2) {
result[${u}] = values[2];
} else {
result[${u}] = values[3];
}
}
}
`}this.userCode=`
${bA(e)}
void main() {
ivec3 coords = getOutputCoords();
vec4 result = vec4(0.);
int flatIndex, r, c, offset;
ivec3 localCoords;
vec2 uv;
vec4 values;
${i}
${r.output} = ${o};
}
`}},d3={};Me(d3,{bindVertexProgramAttributeStreams:()=>w3,createBufferFromOutputTexture:()=>k3,createFloat16MatrixTexture:()=>y3,createFloat16PackedMatrixTexture:()=>b3,createFloat32MatrixTexture:()=>A3,createIndexBuffer:()=>m3,createPackedMatrixTexture:()=>x3,createUnsignedBytesMatrixTexture:()=>g3,createVertexBuffer:()=>f3,createVertexShader:()=>p3,downloadByteEncodedFloatMatrixFromOutputTexture:()=>S3,downloadFloat32MatrixFromBuffer:()=>I3,downloadMatrixFromPackedOutputTexture:()=>T3,downloadPackedMatrixFromBuffer:()=>N3,getInternalFormatForFloat16MatrixTexture:()=>_A,getInternalFormatForFloat16PackedMatrixTexture:()=>IA,getInternalFormatForFloat32MatrixTexture:()=>wA,getInternalFormatForPackedMatrixTexture:()=>kA,getInternalFormatForUnsignedBytesMatrixTexture:()=>vA,uploadDenseMatrixToTexture:()=>_3,uploadPixelDataToTexture:()=>v3});function p3(e){let t=dn(),n=`${t.version}
precision highp float;
${t.attribute} vec3 clipSpacePos;
${t.attribute} vec2 uv;
${t.varyingVs} vec2 resultUV;
void main() {
gl_Position = vec4(clipSpacePos, 1);
resultUV = uv;
}`;return j_(e,n)}function f3(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return q_(e,t)}function m3(e){let t=new Uint16Array([0,1,2,2,1,3]);return X_(e,t)}function bc(e,t,n,r,a,s){Z_(t,n);let i=K_(e),o=e.TEXTURE_2D;return xe(e,()=>e.bindTexture(o,i)),xe(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),xe(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),xe(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),xe(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),xe(e,()=>e.texImage2D(o,0,r,t,n,0,a,s,null)),xe(e,()=>e.bindTexture(e.TEXTURE_2D,null)),i}function wA(e){return e.internalFormatFloat}function A3(e,t,n,r){let[a,s]=gc(t,n);return bc(e,a,s,wA(r),r.textureFormatFloat,e.FLOAT)}function _A(e){return e.internalFormatHalfFloat}function y3(e,t,n,r){let[a,s]=gc(t,n);return bc(e,a,s,_A(r),r.textureFormatFloat,r.textureTypeHalfFloat)}function vA(e){return e.downloadTextureFormat}function g3(e,t,n,r){let[a,s]=gc(t,n);return bc(e,a,s,vA(r),e.RGBA,e.UNSIGNED_BYTE)}function kA(e){return e.internalFormatPackedFloat}function x3(e,t,n,r){let[a,s]=Dl(t,n);return bc(e,a,s,kA(r),e.RGBA,e.FLOAT)}function IA(e){return e.internalFormatPackedHalfFloat}function b3(e,t,n,r){let[a,s]=Dl(t,n);return bc(e,a,s,IA(r),e.RGBA,r.textureTypeHalfFloat)}function w3(e,t,n){let r=0,a=3*4,s=3*4+2*4;return xe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),fA(e,t,"clipSpacePos",n,3,s,r)&&fA(e,t,"uv",n,2,s,a)}function _3(e,t,n,r,a,s){xe(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;a instanceof Uint8Array?(i=new Uint8Array(n*r*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(n*r*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(a),xe(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,r,0,e.RGBA,o,i)),xe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function v3(e,t,n){xe(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?xe(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):xe(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),xe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function k3(e,t,n,r){let a=e.createBuffer();xe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,a));let s=4*4*t*n;return xe(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),xe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),xe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),a}function I3(e,t,n){let r=e,a=new Float32Array(n);return r.bindBuffer(r.PIXEL_PACK_BUFFER,t),r.getBufferSubData(r.PIXEL_PACK_BUFFER,0,a),r.bindBuffer(r.PIXEL_PACK_BUFFER,null),a}function S3(e,t,n,r){let[a,s]=gc(t,n),i=4,o=new Uint8Array(JP(t*n,i));return xe(e,()=>e.readPixels(0,0,a,s,r.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function N3(e,t,n,r,a,s,i,o){let l=e,c=new Float32Array(QP(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,c),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),c}function T3(e,t,n){let r=new Float32Array(t*n*4);return xe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,r)),r}var bp=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=J().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,Ap(t,e)):this.gl=Xr(t);let n="WEBGL_color_buffer_float",r="EXT_color_buffer_half_float";if(J().getNumber("WEBGL_VERSION")===1){let a="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=fc(this.gl,a),rr(this.gl,s))this.textureHalfFloatExtension=fc(this.gl,s);else if(J().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),rr(this.gl,r))this.colorBufferHalfFloatExtension=fc(this.gl,r);else if(J().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",rr(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(rr(this.gl,r))this.colorBufferHalfFloatExtension=this.gl.getExtension(r);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=f3(this.gl),this.indexBuffer=m3(this.gl),this.framebuffer=Y_(this.gl),this.textureConfig=gA(this.gl,this.textureHalfFloatExtension)}get debug(){return J().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&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.");let e=this.gl;xe(e,()=>e.finish()),xe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),xe(e,()=>e.deleteFramebuffer(this.framebuffer)),xe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),xe(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),xe(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),A3(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),y3(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),g3(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),v3(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,r){this.throwIfDisposed(),_3(this.gl,e,t,n,r,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),b3(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),x3(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(mA(this.gl,this.framebuffer),this.outputTexture=null),xe(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>S3(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,r,a,s){return N3(this.gl,e,t,n,r,a,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return I3(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let r=k3(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(J().getBool("WEBGL_FENCE_API_ENABLED")){let r=e,a=r.fenceSync(r.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let s=r.clientWaitSync(a,0,0);return s===r.ALREADY_SIGNALED||s===r.CONDITION_SATISFIED},t=a}else J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>T3(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=U_(t,e),r=p3(t),a=H_(t);return xe(t,()=>t.attachShader(a,r)),xe(t,()=>t.attachShader(a,n)),G_(t,a),this.debug&&pp(t,a),this.vertexAttrsAreBound||(this.setProgram(a),this.vertexAttrsAreBound=w3(t,this.program,this.vertexBuffer)),a}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&xe(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&pp(this.gl,this.program),xe(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?Q_(this.gl,e,t):e3(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),xe(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(),t3(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[r,a]=Dl(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(){this.program!=null&&pp(this.gl,this.program),mc(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),xe(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),xe(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=fc(this.gl,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,r=this.getQueryTimerExtensionWebGL2(),a=n.createQuery();return n.beginQuery(r.TIME_ELAPSED_EXT,a),a}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await _.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,r=this.getQueryTimerExtensionWebGL2(),a=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),a&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),r=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),r&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=dL(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&_.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),fp(this.gl,e,this.framebuffer),this.debug&&mc(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(fp(this.gl,this.outputTexture,this.framebuffer),this.debug&&mc(this.gl)):mA(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let r=this.gl;fp(r,e,this.framebuffer),this.debug&&mc(r),this.outputTexture=e,xe(r,()=>r.viewport(0,0,t,n)),xe(r,()=>r.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,r){this.throwIfDisposed(),xe(this.gl,()=>this.gl.scissor(e,t,n,r))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function dL(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{getBroadcastDims:E3}=E;function wL(e,t,n,r){let a=[];e.forEach(p=>{let m=_.sizeFromShape(p.shapeInfo.logicalShape);p.shapeInfo.isUniform?a.push(`uniform float ${p.name}${m>1?`[${m}]`:""};`):(a.push(`uniform sampler2D ${p.name};`),a.push(`uniform int offset${p.name};`))});let s=a.join(`
`),i=e.map(p=>pL(p,t,r)).join(`
`),o=t.texShape,l=dn(),c=AL(l),u,h,d=xL(l);return t.isPacked?(u=fL(t.logicalShape,o),h=gL(l)):(u=mL(t.logicalShape,o),h=yL(l)),r&&(d+=bL),[d,c,h,s,u,i,n].join(`
`)}function Ol(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return _L(e);case 1:return vL(e);case 2:return kL(e);case 3:return IL(e);case 4:return SL(e);case 5:return NL(e);case 6:return TL(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function C3(e){switch(e.shapeInfo.logicalShape.length){case 0:return EL(e);case 1:return CL(e);case 2:return RL(e);case 3:return ML(e);default:return FL(e)}}function pL(e,t,n=!1){let r="";n?r+=C3(e):r+=Ol(e);let a=e.shapeInfo.logicalShape,s=t.logicalShape;return a.length<=s.length&&(n?r+=$L(e,t):r+=DL(e,t)),r}function fL(e,t){switch(e.length){case 0:return R3();case 1:return OL(e,t);case 2:return LL(e,t);case 3:return zL(e,t);default:return PL(e,t)}}function mL(e,t){switch(e.length){case 0:return R3();case 1:return WL(e,t);case 2:return HL(e,t);case 3:return BL(e,t);case 4:return VL(e,t);case 5:return jL(e,t);case 6:return UL(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function AL(e){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${e.texture2D}(textureSampler, uv).r;
}
`}function yL(e){return`
void setOutput(float val) {
${e.output} = vec4(val, 0, 0, 0);
}
`}function gL(e){return`
void setOutput(vec4 val) {
${e.output} = val;
}
`}function xL(e){return`${e.version}
precision highp float;
precision highp int;
precision highp sampler2D;
${e.varyingFs} vec2 resultUV;
${e.defineOutput}
const vec2 halfCR = vec2(0.5, 0.5);
struct ivec5
{
int x;
int y;
int z;
int w;
int u;
};
struct ivec6
{
int x;
int y;
int z;
int w;
int u;
int v;
};
uniform float NAN;
${e.defineSpecialNaN}
${e.defineSpecialInf}
${e.defineRound}
int imod(int x, int y) {
return x - y * (x / y);
}
int idiv(int a, int b, float sign) {
int res = a / b;
int mod = imod(a, b);
if (sign < 0. && mod != 0) {
res -= 1;
}
return res;
}
//Based on the work of Dave Hoskins
//https://www.shadertoy.com/view/4djSRW
#define HASHSCALE1 443.8975
float random(float seed){
vec2 p = resultUV * seed;
vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);
p3 += dot(p3, p3.yzx + 19.19);
return fract((p3.x + p3.y) * p3.z);
}
${GL}
${qL}
${XL}
`}var GL=`
vec2 uvFromFlat(int texNumR, int texNumC, int index) {
int texR = index / texNumC;
int texC = index - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
int texelIndex = index / 2;
int texR = texelIndex / texNumC;
int texC = texelIndex - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,qL=`
vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
int texNumC, int row, int col) {
int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
int texR = texelIndex / texNumC;
int texC = texelIndex - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,XL=`
vec2 packedUVfrom3D(int texNumR, int texNumC,
int texelsInBatch, int texelsInLogicalRow, int b,
int row, int col) {
int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
int texR = index / texNumC;
int texC = index - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,bL=`
float getChannel(vec4 frag, vec2 innerDims) {
vec2 modCoord = mod(innerDims, 2.);
return modCoord.x == 0. ?
(modCoord.y == 0. ? frag.r : frag.g) :
(modCoord.y == 0. ? frag.b : frag.a);
}
float getChannel(vec4 frag, int dim) {
float modCoord = mod(float(dim), 2.);
return modCoord == 0. ? frag.r : frag.g;
}
`;function R3(){return`
int getOutputCoords() {
return 0;
}
`}function OL(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?`
int getOutputCoords() {
return 2 * int(resultUV.x * ${n[1]}.0);
}
`:n[1]===1?`
int getOutputCoords() {
return 2 * int(resultUV.y * ${n[0]}.0);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);
}
`}function WL(e,t){return t[0]===1?`
int getOutputCoords() {
return int(resultUV.x * ${t[1]}.0);
}
`:t[1]===1?`
int getOutputCoords() {
return int(resultUV.y * ${t[0]}.0);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
return resTexRC.x * ${t[1]} + resTexRC.y;
}
`}function zL(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
int b = index / ${a};
index -= b * ${a};
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec3(b, r, c);
}
`}function BL(e,t){let n=Mi(["r","c","d"],e);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec3(r, c, d);
}
`}function PL(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),s=a,i="",o="b, r, c";for(let l=2;l<e.length-1;l++)s*=e[e.length-l-1],i=`
int b${l} = index / ${s};
index -= b${l} * ${s};
`+i,o=`b${l}, `+o;return`
ivec${e.length} getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
${i}
int b = index / ${a};
index -= b * ${a};
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec${e.length}(${o});
}
`}function VL(e,t){let n=Mi(["r","c","d","d2"],e);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec4(r, c, d, d2);
}
`}function jL(e,t){let n=Mi(["r","c","d","d2","d3"],e);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function UL(e,t){let n=Mi(["r","c","d","d2","d3","d4"],e);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function LL(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(_.arraysEqual(e,t))return`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
}
`;let r=Math.ceil(e[1]/2);return`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec2(r, c);
}
`}function HL(e,t){return _.arraysEqual(e,t)?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
}
`:e[1]===1?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(index, 0);
}
`:e[0]===1?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(0, index);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
int r = index / ${e[1]};
int c = index - r * ${e[1]};
return ivec2(r, c);
}
`}function Fi(e){return`offset${e}`}function EL(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=dn();return`
vec4 ${n}() {
return ${r.texture2D}(${t}, halfCR);
}
`}function _L(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${t};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`
float ${n}() {
return sampleTexture(${t}, halfCR);
}
`;let[s,i]=e.shapeInfo.texShape,o=Fi(t);return`
float ${n}() {
vec2 uv = uvFromFlat(${s}, ${i}, ${o});
return sampleTexture(${t}, uv);
}
`}function CL(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=e.shapeInfo.texShape,a=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],s=dn();return`
vec4 ${n}(int index) {
vec2 uv = packedUVfrom1D(
${a[0]}, ${a[1]}, index);
return ${s.texture2D}(${t}, uv);
}
`}function vL(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
float ${n}(int index) {
${zl(e)}
}
`;let r=e.shapeInfo.texShape,a=r[0],s=r[1];if(s===1&&a===1)return`
float ${n}(int index) {
return sampleTexture(${t}, halfCR);
}
`;let i=Fi(t);return s===1?`
float ${n}(int index) {
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${a}.0);
return sampleTexture(${t}, uv);
}
`:a===1?`
float ${n}(int index) {
vec2 uv = vec2((float(index + ${i}) + 0.5) / ${s}.0, 0.5);
return sampleTexture(${t}, uv);
}
`:`
float ${n}(int index) {
vec2 uv = uvFromFlat(${a}, ${s}, index + ${i});
return sampleTexture(${t}, uv);
}
`}function RL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape,s=a[0],i=a[1],o=dn();if(a!=null&&_.arraysEqual(t,a))return`
vec4 ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${s}.0);
return ${o.texture2D}(${n}, uv);
}
`;let l=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],c=Math.ceil(t[1]/2);return`
vec4 ${r}(int row, int col) {
vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);
return ${o.texture2D}(${n}, uv);
}
`}function kL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape;if(a!=null&&_.arraysEqual(t,a)){let h=a[0],d=a[1];return`
float ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${d}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`}let{newShape:s,keptDims:i}=_.squeezeShape(t),o=s;if(o.length<t.length){let h=Pl(e,o),d=["row","col"];return`
${Ol(h)}
float ${r}(int row, int col) {
return ${r}(${Ll(d,i)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
${zl(e)}
}
`;let l=a[0],c=a[1],u=Fi(n);return c===1?`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${u}), vec3(${t[1]}, 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
return sampleTexture(${n}, uv);
}
`:l===1?`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${u}), vec3(${t[1]}, 1, 1));
vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);
return sampleTexture(${n}, uv);
}
`:`
float ${r}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${t[1]} + col + ${u};
vec2 uv = uvFromFlat(${l}, ${c}, index);
return sampleTexture(${n}, uv);
}
`}function ML(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape,s=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(t[0]===1){let h=t.slice(1),d=[1,2],p=Pl(e,h),m=["b","row","col"];return`
${C3(p)}
vec4 ${r}(int b, int row, int col) {
return ${r}(${Ll(m,d)});
}
`}let i=s[0],o=s[1],l=Math.ceil(t[2]/2),c=l*Math.ceil(t[1]/2),u=dn();return`
vec4 ${r}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${i}, ${o}, ${c}, ${l}, b, row, col);
return ${u.texture2D}(${n}, uv);
}
`}function IL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[1]*t[2],s=t[2],{newShape:i,keptDims:o}=_.squeezeShape(t),l=i;if(l.length<t.length){let m=Pl(e,l),f=["row","col","depth"];return`
${Ol(m)}
float ${r}(int row, int col, int depth) {
return ${r}(${Ll(f,o)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth) {
int index = round(dot(vec3(row, col, depth),
vec3(${a}, ${s}, 1)));
${zl(e)}
}
`;let c=e.shapeInfo.texShape,u=c[0],h=c[1],d=e.shapeInfo.flatOffset;if(h===a&&d==null)return`
float ${r}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${s}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${u}.0);
return sampleTexture(${n}, uv);
}
`;if(h===s&&d==null)return`
float ${r}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${t[1]}, 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${h}.0, ${u}.0);
return sampleTexture(${n}, uv);
}
`;let p=Fi(n);return`
float ${r}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${a} + col * ${s} + depth + ${p};
vec2 uv = uvFromFlat(${u}, ${h}, index);
return sampleTexture(${n}, uv);
}
`}function FL(e){let t=e.shapeInfo.logicalShape,n=t.length,r=e.name,a="get"+r.charAt(0).toUpperCase()+r.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],o=i[0],l=i[1],c=Math.ceil(t[n-1]/2),u=c*Math.ceil(t[n-2]/2),h="int b, int row, int col",d=`b * ${u} + (row / 2) * ${c} + (col / 2)`;for(let m=2;m<n-1;m++)h=`int b${m}, `+h,u*=t[n-m-1],d=`b${m} * ${u} + `+d;let p=dn();return`
vec4 ${a}(${h}) {
int index = ${d};
int texR = index / ${l};
int texC = index - texR * ${l};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${o});
return ${p.texture2D}(${r}, uv);
}
`}function SL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[3],s=t[2]*a,i=t[1]*s,{newShape:o,keptDims:l}=_.squeezeShape(t);if(o.length<t.length){let m=Pl(e,o),f=["row","col","depth","depth2"];return`
${Ol(m)}
float ${r}(int row, int col, int depth, int depth2) {
return ${r}(${Ll(f,l)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth, int depth2) {
int index = round(dot(vec4(row, col, depth, depth2),
vec4(${i}, ${s}, ${a}, 1)));
${zl(e)}
}
`;let c=e.shapeInfo.flatOffset,u=e.shapeInfo.texShape,h=u[0],d=u[1];if(d===i&&c==null)return`
float ${r}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${s}, ${a}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;if(d===a&&c==null)return`
float ${r}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${t[1]*t[2]}, ${t[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;let p=Fi(n);return`
float ${r}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${i} + col * ${s} +
depth * ${a} + depth2;
vec2 uv = uvFromFlat(${h}, ${d}, index + ${p});
return sampleTexture(${n}, uv);
}
`}function NL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[4],s=t[3]*a,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:c}=_.squeezeShape(t);if(l.length<t.length){let f=Pl(e,l),A=["row","col","depth","depth2","depth3"];return`
${Ol(f)}
float ${r}(int row, int col, int depth, int depth2, int depth3) {
return ${r}(${Ll(A,c)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${o}, ${i}, ${s}, ${a})) +
depth3;
${zl(e)}
}
`;let u=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,d=h[0],p=h[1];if(p===o&&u==null)return`
float ${r}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${i}, ${s}, ${a}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${p}.0, ${d}.0);
return sampleTexture(${n}, uv);
}
`;if(p===a&&u==null)return`
float ${r}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]},
${t[2]*t[3]}, ${t[3]}, 1));
int texC = depth3;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${p}.0, ${d}.0);
return sampleTexture(${n}, uv);
}
`;let m=Fi(n);return`
float ${r}(int row, int col, int depth, int depth2, int depth3) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${o} + col * ${i} + depth * ${s} +
depth2 * ${a} + depth3 + ${m};
vec2 uv = uvFromFlat(${d}, ${p}, index);
return sampleTexture(${n}, uv);
}
`}function TL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:a,keptDims:s}=_.squeezeShape(t);if(a.length<t.length){let A=Pl(e,a),y=["row","col","depth","depth2","depth3","depth4"];return`
${Ol(A)}
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${r}(${Ll(y,s)});
}
`}let i=t[5],o=t[4]*i,l=t[3]*o,c=t[2]*l,u=t[1]*c;if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int index = round(dot(
vec4(row, col, depth, depth2),
vec4(${u}, ${c}, ${l}, ${o})) +
dot(
vec2(depth3, depth4),
vec2(${i}, 1)));
${zl(e)}
}
`;let h=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],m=d[1];if(m===u&&h==null)return`
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${c}, ${l}, ${o}, ${i})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${m}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;if(m===i&&h==null)return`
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
float texR = dot(vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]*t[4]},
${t[2]*t[3]*t[4]},
${t[3]*t[4]},
${t[4]})) + float(depth3);
int texC = depth4;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${m}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;let f=Fi(n);return`
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${u} + col * ${c} + depth * ${l} +
depth2 * ${o} + depth3 * ${i} + depth4 + ${f};
vec2 uv = uvFromFlat(${p}, ${m}, index);
return sampleTexture(${n}, uv);
}
`}function zl(e){let t=e.name,n=_.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
for (int i = 0; i < ${n}; i++) {
if (i == index) {
return ${t}[i];
}
}
`}function $L(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=E3(e.shapeInfo.logicalShape,t.logicalShape),l=lt(i),c=i-s,u,h=["x","y","z","w","u","v"];s===0?u="":i<2&&o.length>=1?u="coords = 0;":u=o.map(A=>`coords.${h[A+c]} = 0;`).join(`
`);let d="";i<2&&s>0?d="coords":d=e.shapeInfo.logicalShape.map((A,y)=>`coords.${h[y+c]}`).join(", ");let p="return outputValue;",m=_.sizeFromShape(e.shapeInfo.logicalShape)===1,f=_.sizeFromShape(t.logicalShape)===1;if(s===1&&!m&&!f)p=`
return vec4(outputValue.xy, outputValue.xy);
`;else if(m&&!f)i===1?p=`
return vec4(outputValue.x, outputValue.x, 0., 0.);
`:p=`
return vec4(outputValue.x);
`;else if(o.length){let A=s-2,y=s-1;o.indexOf(A)>-1&&o.indexOf(y)>-1?p="return vec4(outputValue.x);":o.indexOf(A)>-1?p="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(y)>-1&&(p="return vec4(outputValue.xx, outputValue.zz);")}return`
vec4 ${a}() {
${l} coords = getOutputCoords();
${u}
vec4 outputValue = get${r}(${d});
${p}
}
`}function DL(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&_.arraysEqual(i,s))return`
float ${a}() {
return sampleTexture(${n}, resultUV);
}
`;let c=lt(l),u=E3(e.shapeInfo.logicalShape,t.logicalShape),h=l-o,d,p=["x","y","z","w","u","v"];o===0?d="":l<2&&u.length>=1?d="coords = 0;":d=u.map(f=>`coords.${p[f+h]} = 0;`).join(`
`);let m="";return l<2&&o>0?m="coords":m=e.shapeInfo.logicalShape.map((f,A)=>`coords.${p[A+h]}`).join(", "),`
float ${a}() {
${c} coords = getOutputCoords();
${d}
return get${r}(${m});
}
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Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:t,dtype:n,values:e,usage:ar.UPLOAD,refCount:1}),r}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,r,a){if(J().getBool("DEBUG")&&this.checkNumericalProblems(t),r==="complex64")throw new Error("Cannot write to a complex64 dtype. 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Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:r}=this.texData.get(e),a=_.sizeFromShape(t);if(J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let h=this.decode(e),d=this.texData.get(h.dataId),p=this.gpgpu.downloadMatrixFromPackedTexture(d.texture,...xc(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(h),p}let s=J().getBool("WEBGL_PACK")&&r===!0,i=s?mp(t):t,o=s?new uL(i):new lL(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),c=this.texData.get(l.dataId),u=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(l),u}timerAvailable(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let a=_.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=_.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=_.sum(o),i.getExtraProfileInfo=()=>o.map((l,c)=>({name:s[c],ms:l})).map(l=>`${l.name}: ${l.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 J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:_.now(),endMs:null}}endTimer(e){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=_.now(),e)}async getQueryTime(e){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!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);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:r,usage:a,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(t,r,a,s)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=YW){return J().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&_.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){E.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return qW(e.shape,t)}packedUnaryOp(e,t,n){let r=new Wl(e.shape,t),a=this.compileAndRun(r,[e],n);return ca().makeTensorFromDataId(a.dataId,a.shape,a.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let r=$3(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,r)}if(J().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,W3,e.dtype);let t=new qa(e.shape,W3),n=this.compileAndRun(t,[e]);return ca().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&_.isString(n[0])){let a=n.map(s=>_.encodeString(s));r=this.write(a,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:r}=this.makeTensorInfo(e,t,n);return ca().makeTensorFromDataId(r,e,t,this)}unpackTensor(e){let t=new GW(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new EW(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[Ei(e.shape),...Ci(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},a=[Ei(t),...Ci(t)],s=new O3(a,n),i=!0,o=this.runWebGLProgram(s,[r],e.dtype,null,i);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:r,dtype:a}=t,s=mp(r),i;n?i=new oL(s):i=new iL(s);let o=!0,l=this.runWebGLProgram(i,[{shape:s,dtype:a,dataId:e}],a,null,o);return{dtype:a,shape:r,dataId:l.dataId}}runWebGLProgram(e,t,n,r,a=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===yc.DENSE){let f=xc(e.outputShape);i.texShape=f.map(A=>A*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),_.sizeFromShape(s.shape)===0)return i.values=_.getTypedArrayFromDType(s.dtype,0),s;let o=[],l=t.map(f=>{if(f.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let A=this.texData.get(f.dataId);if(A.texture==null){if(!e.packedInputs&&_.sizeFromShape(f.shape)<=J().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:f.shape,texData:null,isUniform:!0,uniformValues:A.values};e.packedInputs&&(A.isPacked=!0,A.shape=f.shape)}else if(!!A.isPacked!=!!e.packedInputs)f=A.isPacked?this.unpackTensor(f):this.packTensor(f),o.push(f),A=this.texData.get(f.dataId);else if(A.isPacked&&!Ac(A.shape,f.shape)){let y=f,g=f.shape;f.shape=A.shape,f=this.packedReshape(f,g),o.push(f),A=this.texData.get(f.dataId),y.shape=g}return this.uploadToGPU(f.dataId),{shape:f.shape,texData:A,isUniform:!1}});this.uploadToGPU(s.dataId);let c={shape:s.shape,texData:i,isUniform:!1},u=YL(e,l,c),h=this.getAndSaveBinary(u,()=>KL(this.gpgpu,e,l,c)),d=this.activeTimers!=null,p;d&&(p=this.startTimer()),ZL(this.gpgpu,h,l,c,r),o.forEach(f=>this.disposeIntermediateTensorInfo(f)),d&&(p=this.endTimer(p),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(p)}));let m=J().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let f=_.now();f-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=f)}if(!J().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&a===!1){let f=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),f}return s}compileAndRun(e,t,n,r,a=!1){return n=n||t[0].dtype,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(){this.disposed||(J().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=P(()=>{if(!J().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=J().getBool("DEBUG");J().set("DEBUG",!1);let t=this.abs(ve(1e-8)).dataSync()[0];if(J().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?XW:KW}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:r,values:a,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,c;l&&(c=_.now());let u=t.texShape;if(u==null&&(u=r3(n,o),t.texShape=u),a!=null){let h=mp(n),d,p=u[1],m=u[0],f=a instanceof Uint8Array;o?([p,m]=Dl(u[0],u[1]),d=new hL(h,[m,p],f)):d=new cL(h,[m,p],f);let A=this.makeTensorInfo([m,p],r);f?this.texData.get(A.dataId).usage=ar.PIXELS:this.texData.get(A.dataId).usage=ar.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(A.dataId),p,m,a);let y=!0,g=this.runWebGLProgram(d,[A],r,null,y),x=this.texData.get(g.dataId);t.texture=x.texture,t.texShape=x.texShape,t.isPacked=x.isPacked,t.usage=x.usage,this.disposeIntermediateTensorInfo(A),this.texData.delete(g.dataId),t.values=null,l&&(this.uploadWaitMs+=_.now()-c)}else{let h=this.acquireTexture(u,i,r,o);t.texture=h}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:r}=n;return this.releaseGPUData(e),t!=null&&(n.values=eB(t,r)),n.values}acquireTexture(e,t,n,r){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,r)}computeBytes(e,t){return e[0]*e[1]*_.bytesPerElement(t)}};Bl.nextDataId=0;function eB(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let r=0;r<n.length;++r)n[r]=Math.round(e[r]);return n}else throw new Error(`Unknown dtype ${t}`)}var B3="3.5.0";function V3(){J().set("WEBGL_FORCE_F16_TEXTURES",!0)}Hu.isBrowser()&&yl("webgl",()=>new Bl,2);var tB={forceHalfFloat:V3},j3=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,Vl=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.userCode=`
float binaryOperation(float a, float b) {
${e}
}
void main() {
float a = getAAtOutCoords();
float b = getBAtOutCoords();
setOutput(binaryOperation(a, b));
}
`}},_p=`
result.r = isNaN.r > 0. ? NAN : result.r;
result.g = isNaN.g > 0. ? NAN : result.g;
result.b = isNaN.b > 0. ? NAN : result.b;
result.a = isNaN.a > 0. ? NAN : result.a;
`,wc=class{constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=E.assertAndGetBroadcastShape(t,n);let a=this.outputShape.length,s="";if(r)if(a===0||_.sizeFromShape(this.outputShape)===1)s=`
result.y = 0.;
result.z = 0.;
result.w = 0.;
`;else if(s=`
${lt(a)} coords = getOutputCoords();
`,a===1)s+=`
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`;else{let i=pn("coords",a);s+=`
bool nextRowOutOfBounds =
(${i[a-2]} + 1) >= ${this.outputShape[a-2]};
bool nextColOutOfBounds =
(${i[a-1]} + 1) >= ${this.outputShape[a-1]};
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`}this.userCode=`
vec4 binaryOperation(vec4 a, vec4 b) {
${e}
}
void main() {
vec4 a = getAAtOutCoords();
vec4 b = getBAtOutCoords();
vec4 result = binaryOperation(a, b);
${s}
setOutput(result);
}
`}};function Vn(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var nB={kernelName:Cs,backendName:"webgl",kernelFunc:Vn};function Xa(e){let{inputs:t,backend:n}=e,{real:r,imag:a}=t,s=n.makeTensorInfo(r.shape,"complex64"),i=n.texData.get(s.dataId),o=Vn({inputs:{x:r},backend:n}),l=Vn({inputs:{x:a},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var rB={kernelName:zh,backendName:"webgl",kernelFunc:Xa},U3="return (a < 0.) ? b * a : a;",H3=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function aB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r,i=n.makeTensorInfo([],"float32",_.createScalarValue(s,"float32")),o=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new wc(H3,a.shape,i.shape):new Vl(U3,a.shape,i.shape),l=n.runWebGLProgram(o,[a,i],a.dtype);return n.disposeIntermediateTensorInfo(i),l}var sB={kernelName:Rs,backendName:"webgl",kernelFunc:aB},G3="return (a < 0.) ? b * a : a;",q3=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
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`;function iB(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new wc(q3,r.shape,a.shape):new Vl(G3,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)}var oB={kernelName:Us,backendName:"webgl",kernelFunc:iB},X3="if (isnan(x)) return x;",lB=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,uB=`
result.r = isNaN.r > 0. ? NAN : result.r;
result.g = isNaN.g > 0. ? NAN : result.g;
result.b = isNaN.b > 0. ? NAN : result.b;
result.a = isNaN.a > 0. ? NAN : result.a;
`;function Xe({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:a,backend:s})=>{let{x:i}=a,o=s,l=r||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let h=o.texData.get(i.dataId),d=n(h.values,l);return o.makeTensorInfo(i.shape,l,d)}let c=J().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,u;return c?u=new Wl(i.shape,t):u=new qa(i.shape,e),o.runWebGLProgram(u,[i],l)}}function nn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:c}=i,u=o;if(r&&l.dtype==="complex64"){let m=u.texData.get(l.dataId),f=u.texData.get(c.dataId),[A,y]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(x=>{let[v,b]=x,w={dataId:v.dataId,dtype:v.dtype,shape:l.shape},k={dataId:b.dataId,dtype:b.dtype,shape:c.shape},N=new Vl(e,l.shape,c.shape);return u.runWebGLProgram(N,[w,k],dr(v.dtype,b.dtype))}),g=Xa({inputs:{real:A,imag:y},backend:u});return u.disposeIntermediateTensorInfo(A),u.disposeIntermediateTensorInfo(y),g}let h=s||dr(l.dtype,c.dtype);if(u.shouldExecuteOnCPU([l,c])&&a!=null){let m=u.texData.get(l.dataId),f=u.texData.get(c.dataId),[A,y]=a(l.shape,c.shape,m.values,f.values,h),g=u.makeTensorInfo(y,h),x=u.texData.get(g.dataId);return x.values=A,g}let d=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,p;return d?p=new wc(t,l.shape,c.shape,n):p=new Vl(e,l.shape,c.shape),u.runWebGLProgram(p,[l,c],h)}}function vp(e,t=!1){if(e==="linear")return t?BW:OW;if(e==="relu")return t?jW:PW;if(e==="elu")return t?VW:zW;if(e==="relu6")return t?UW:LW;if(e==="prelu")return t?q3:G3;if(e==="leakyrelu")return t?H3:U3;if(e==="sigmoid")return t?HW:WW;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var K3=class{constructor(e,t,n,r=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let c=r?e[1]:e[2],u=Math.ceil(c/2),h=r?"i * 2, rc.y":"rc.y, i * 2",d=a?"rc.z, i * 2":"i * 2, rc.z",p=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",A="";i&&(o?f=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${i}
}`:l?f=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${i}
}`:f=`vec4 activation(vec4 x) {
${i}
}`,A="result = activation(result);");let y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let g="rc.x",x="rc.x";e[0]<t[0]?g=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(x=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
${f}
const float sharedDimension = ${u}.0;
vec4 dot2x2ARowBCol(ivec3 rc) {
vec4 result = vec4(0);
for (int i = 0; i < ${u}; i++) {
int batchA = ${g};
int batchB = ${x};
vec4 a = getMatrixA(batchA, ${h});
vec4 b = getMatrixB(batchB, ${d});
// These swizzled products need to be separately added.
// See: https://github.com/tensorflow/tfjs/issues/1735
result += (${p[0]} * ${m[0]});
result += (${p[1]} * ${m[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${y}
${A}
setOutput(result);
}
`}},Z3={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},Y3=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}},J3="return a * b;";function TA(e){let{inputs:t,backend:n}=e,{a:r,b:a}=t,s=E.upcastType(r.dtype,a.dtype);if(r.dtype==="complex64"){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),c=new Y3(Z3.REAL,r.shape,a.shape),u=new Y3(Z3.IMAG,r.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:r.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],d=n.runWebGLProgram(c,h,"float32"),p=n.runWebGLProgram(u,h,"float32"),m=Xa({inputs:{real:d,imag:p},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),m}if(n.shouldExecuteOnCPU([r,a])){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),[c,u]=pW(r.shape,a.shape,o.values,l.values,s),h=n.makeTensorInfo(u,s),d=n.texData.get(h.dataId);return d.values=c,h}let i;return J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new wc(J3,r.shape,a.shape):i=new Vl(J3,r.shape,a.shape),n.runWebGLProgram(i,[r,a],s)}var cB={kernelName:Ws,backendName:"webgl",kernelFunc:TA};function hB(e,t,n){let r=[Ei(e.shape),...Ci(e.shape)],a={dtype:e.dtype,shape:r,dataId:e.dataId},s=[Ei(t),...Ci(t)],i=new O3(s,r),o=!0,l=n.runWebGLProgram(i,[a],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function fe(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=n,o=_.sizeFromShape(a.shape),l=_.inferFromImplicitShape(s,o),c=_.sizeFromShape(l);_.assert(o===c,()=>`The new shape (${l}) has ${c} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let u=i.texData.get(a.dataId);return u.isPacked&&!Ac(a.shape,l)&&!(u.texture!==null&&Ac(u.shape,l))?hB(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var dB={kernelName:Ko,backendName:"webgl",kernelFunc:fe},Q3=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i=Math.floor(n/4)*4,o=n%4,l="sumValue += dot(values, ones);";if(t!=null){let u=1/t;l=`sumValue += dot(values * ${_.isInt(u)?u.toPrecision(2):u}, ones);`}let c="";a%n>0&&(c=`
if (inIdx < 0 || inIdx >= ${a}) {
return 0.0;
}
`),this.userCode=`
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${c}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
float sumValue = 0.0;
for (int i = 0; i < ${i}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${l}
}
int inIdx = inOffset + ${i};
if (${o===1}) {
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
${l}
} else if (${o===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1), 0.0, 0.0);
${l}
} else if (${o===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2), 0.0);
${l}
}
setOutput(sumValue);
}
`}},pB=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let c=Math.floor(n/4)*4,u=n%4,h=`
if (${t==="sum"}) {
sumValue += dot(values, ones);
} else if (${t==="prod"}) {
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
prodValue *= tmp[0] * tmp[1];
} else {
minMaxValue = ${o}(values, minMaxValue);
}
`,d="vec4";t==="all"?(i="1.0",h=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,d="bvec4"):t==="any"&&(i="0.0",h=`
bool reducedAnyValue = any(values);
float floatedReducedAnyValue = float(reducedAnyValue);
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
`,d="bvec4");let p="";a%n>0&&(p=`
if (inIdx < 0 || inIdx >= ${a}) {
return initializationValue;
}
`),this.userCode=`
const float initializationValue = ${i};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${p}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
vec4 minMaxValue = vec4(${i});
float prodValue = 1.0;
float sumValue = 0.0;
float allValue = 1.0;
float anyValue = 0.0;
for (int i = 0; i < ${c}; i += 4) {
int inIdx = inOffset + i;
${d} values = ${d}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${h}
}
int inIdx = inOffset + ${c};
if (${u===1}) {
${d} values = ${d}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${h}
} else if (${u===2}) {
${d} values = ${d}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${h}
} else if (${u===3}) {
${d} values = ${d}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${h}
}
setOutput(${l});
}
`}};function fB(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],r=E.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}function $i(e,t,n,r){let a=fB(e.shape),s=e;for(let i=0;i<a.length;i++){let{inSize:o,windowSize:l,outSize:c}=a[i],u,h;n==="mean"?u=i===0?new Q3({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},o):new Q3({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c}):u=new pB({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},n),h=s,s=r.runWebGLProgram(u,[s],t),h.dataId!==e.dataId&&r.disposeIntermediateTensorInfo(h)}return s}var AB=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.rank=n.length;let r=lt(this.rank),a=mB(t);this.userCode=`
void main() {
${r} resRC = getOutputCoords();
setOutput(getA(${a}));
}
`}};function mB(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let a=0;a<e.length;a++)r[e[a]]=n[a];return r.join()}var yB=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let c=0;c<n.length;c++)n[c]=e[t[c]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let r=lt(this.rank),a=D3("rc",this.rank),s=new Array(this.rank);for(let c=0;c<t.length;c++)s[t[c]]=a[c];let i=`vec2(${s.slice(-2).join()})`,o=`++${a[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
void main() {
${r} rc = getOutputCoords();
vec4 result = vec4(0.);
result[0] = ${l};
if(${o}) {
result[1] = ${l};
}
--${a[this.rank-1]};
if(++${a[this.rank-2]} < ${n[this.rank-2]}) {
result[2] = ${l};
if(${o}) {
result[3] = ${l};
}
}
setOutput(result);
}
`}};function kp(e,t,n){let r=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new yB(e.shape,t):new AB(e.shape,t);return n.runWebGLProgram(r,[e],e.dtype)}function gB(e,t,n,r){let a=t,s=e.shape.length,i=_.parseAxisParam(a,e.shape),o=i,l=E.getAxesPermutation(o,s),c=l!=null,u=e;c&&(u=kp(e,l,r),o=E.getInnerMostAxes(o.length,s)),E.assertAxesAreInnerMostDims("sum",o,s);let[h,d]=E.computeOutAndReduceShapes(u.shape,o),p=h;n&&(p=E.expandShapeToKeepDim(h,i));let m=_.sizeFromShape(d),f=_.sizeFromShape(e.shape)/m,A=fe({inputs:{x:u},attrs:{shape:[f,m]},backend:r}),y=Ad(e.dtype),g=$i(A,y,"sum",r),x=fe({inputs:{x:g},attrs:{shape:p},backend:r});return r.disposeIntermediateTensorInfo(A),r.disposeIntermediateTensorInfo(g),c&&r.disposeIntermediateTensorInfo(u),x}function Ip(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;return gB(a,s,i,n)}var xB={kernelName:ei,backendName:"webgl",kernelFunc:Ip};function fn(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{perm:s}=r,i=n,o=a.shape.length,l=new Array(o);for(let u=0;u<l.length;u++)l[u]=a.shape[s[u]];let c;if(i.shouldExecuteOnCPU([a])){let u=i.texData.get(a.dataId).values,h=SA(u,a.shape,a.dtype,s,l);c=i.makeTensorInfo(l,a.dtype);let d=i.texData.get(c.dataId);d.values=h}else c=kp(a,s,i);return c}var bB={kernelName:ii,backendName:"webgl",kernelFunc:fn},e7=1e3;function Sp({a:e,b:t,transposeA:n,transposeB:r,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,h=n?e.shape[c-2]:e.shape[c-1],d=r?t.shape[u-1]:t.shape[u-2],p=n?e.shape[c-1]:e.shape[c-2],m=r?t.shape[u-2]:t.shape[u-1],f=e.shape.slice(0,-2),A=t.shape.slice(0,-2),y=_.sizeFromShape(f),g=_.sizeFromShape(A),x=y===g||y===1||g===1;_.assert(c>=2&&u>=2&&x,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${A}).`);let v=(y>g?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([p,m]);_.assert(h===d,()=>`Error in matMul: inner shapes (${h}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);let b=n?[y,h,p]:[y,p,h],w=r?[g,m,d]:[g,d,m],k=fe({inputs:{x:e},backend:a,attrs:{shape:b}}),N=fe({inputs:{x:t},backend:a,attrs:{shape:w}}),C=[k,N],F=Math.max(y,g),O=n?k.shape[1]:k.shape[2],L=s!=null,V=i!=null,j=l==="leakyrelu",U=l!=null?vp(l,!0):null,X=L||V||j||U!=null,G;if((p===1||m===1)&&O>e7&&X===!1){let Y=k,ae=N;n&&(Y=fn({inputs:{x:k},backend:a,attrs:{perm:[0,2,1]}}),C.push(Y)),r&&(ae=fn({inputs:{x:N},backend:a,attrs:{perm:[0,2,1]}}),C.push(ae));let te=m!==1,ie=m===1,Q=Y;te&&(Q=fe({inputs:{x:Y},backend:a,attrs:{shape:[F,O,1]}}),C.push(Q));let he=m===1?2:1,oe=ae;ie&&(oe=fe({inputs:{x:ae},backend:a,attrs:{shape:[F,1,O]}}),C.push(oe));let me=TA({inputs:{a:Q,b:oe},backend:a});G=Ip({inputs:{x:me},backend:a,attrs:{axis:he,keepDims:!0}}),C.push(me)}else{let Y=dr(e.dtype,t.dtype),ae=new K3(b,w,[F,p,m],n,r,L,U,V,j),te=[k,N];if(s!=null&&te.push(s),V&&te.push(i),j){let ie=a.makeTensorInfo([],"float32",_.createScalarValue(o,"float32"));te.push(ie),C.push(ie)}G=a.runWebGLProgram(ae,te,Y)}let ee=fe({inputs:{x:G},backend:a,attrs:{shape:v}});C.push(G);for(let Y of C)a.disposeIntermediateTensorInfo(Y);return ee}function wB(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:h}=r;return Sp({a,b:s,transposeA:l,transposeB:c,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:u})}var _B={kernelName:oi,backendName:"webgl",kernelFunc:wB},t7="return abs(x);";function vB(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&r.dtype!=="complex64"){let s=n.texData.get(r.dataId),i=$3(s.values);return n.makeTensorInfo(r.shape,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new Wl(r.shape,t7):a=new qa(r.shape,t7),n.runWebGLProgram(a,[r],r.dtype)}var kB={kernelName:lo,backendName:"webgl",kernelFunc:vB},IB=Nr+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,SB=Xe({opSnippet:IB}),NB={kernelName:uo,backendName:"webgl",kernelFunc:SB},TB=Nr+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,EB=Xe({opSnippet:TB}),CB={kernelName:co,backendName:"webgl",kernelFunc:EB},n7="return a + b;",RB=nn({opSnippet:n7,packedOpSnippet:n7,supportsComplex:!0,cpuKernelImpl:JL}),MB={kernelName:Ra,backendName:"webgl",kernelFunc:RB},FB=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`float v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
float result = ${r};
setOutput(result);
}
`}},$B=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`vec4 v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
vec4 result = ${r};
setOutput(result);
}
`}};function Np(e){let{inputs:t,backend:n}=e,r=t;if(r.length===1)return Vn({inputs:{x:r[0]},backend:n});if(r.length>J().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(r.length/2),l=Np({inputs:r.slice(0,o),backend:n}),c=Np({inputs:r.slice(o),backend:n});return Np({inputs:[l,c],backend:n})}let a=r.map(o=>o.dtype).reduce((o,l)=>dr(o,l)),s=r.map(o=>o.shape),i=J().getBool("WEBGL_PACK")?new $B(r[0].shape,s):new FB(r[0].shape,s);return n.runWebGLProgram(i,r,a)}var DB={kernelName:ps,backendName:"webgl",kernelFunc:Np};function OB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=_.parseAxisParam(s,a.shape),c=l,u=E.getAxesPermutation(c,o),h=a;u!=null&&(h=fn({inputs:{x:a},backend:n,attrs:{perm:u}}),c=E.getInnerMostAxes(c.length,o)),E.assertAxesAreInnerMostDims("all",c,o);let[d,p]=E.computeOutAndReduceShapes(h.shape,c),m=_.sizeFromShape(p),f=fe({inputs:{x:h},backend:n,attrs:{shape:[-1,m]}}),A=$i(f,f.dtype,"all",n),y;if(i){let g=E.expandShapeToKeepDim(d,l);y=fe({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=fe({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(h),y}var zB={kernelName:ho,backendName:"webgl",kernelFunc:OB};function PB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=_.parseAxisParam(s,a.shape),c=l,u=E.getAxesPermutation(c,o),h=a;u!=null&&(h=fn({inputs:{x:a},backend:n,attrs:{perm:u}}),c=E.getInnerMostAxes(c.length,o)),E.assertAxesAreInnerMostDims("any",c,o);let[d,p]=E.computeOutAndReduceShapes(h.shape,c),m=_.sizeFromShape(p),f=fe({inputs:{x:h},backend:n,attrs:{shape:[-1,m]}}),A=$i(f,f.dtype,"any",n),y;if(i){let g=E.expandShapeToKeepDim(d,l);y=fe({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=fe({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(h),y}var LB={kernelName:po,backendName:"webgl",kernelFunc:PB},WB=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:r,batchSize:a,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[a,s];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${r};
int bestIndex = inOffset;
float bestValue = getA(batch, bestIndex);
for (int i = 0; i < ${r}; i++) {
int inIdx = ${o};
float candidate = getA(batch, inIdx);
if (candidate ${i} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}},BB=class{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,_.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let a=e[e.length-1],s=Math.ceil(a/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),r||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=lt(o),c=pn("coords",o),u,h;if(s===1){h=o+1;let k=lt(h);u=`
${k} sourceLocR = ${k}(${c.join()}, 0);
++${c[o-1]};
${k} sourceLocG = ${k}(${c.join()}, 0);
++${c[o-2]};
${k} sourceLocA = ${k}(${c.join()}, 0);
--${c[o-1]};
${k} sourceLocB = ${k}(${c.join()}, 0);
--${c[o-2]};`}else h=o,u=`
${l} sourceLocR = coords;
++${c[o-1]};
${l} sourceLocG = coords;
++${c[o-2]};
${l} sourceLocA = coords;
--${c[o-1]};
${l} sourceLocB = coords;
--${c[o-2]};`;let d=["x","y","z","w","u","v"].slice(0,h),p="."+d[h-1],m=d.map(k=>"int "+k),f=pn("sourceLocR",h-1).concat("inIdx.r"),A=pn("sourceLocG",h-1).concat("inIdx.g"),y=pn("sourceLocB",h-1).concat("inIdx.b"),g=pn("sourceLocA",h-1).concat("inIdx.a"),x=n==="max"?"greaterThan":"lessThan",v=r?"":`
inIdx = round(vec4(getBestIndicesAChannel(${f.join()}),
getBestIndicesAChannel(${A.join()}),
getBestIndicesAChannel(${y.join()}),
getBestIndicesAChannel(${g.join()})));`,b=`vec4(
getAChannel(${f.join()}),
hasNextCol ? getAChannel(${A.join()}) : 0.,
hasNextRow ? getAChannel(${y.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${g.join()}) : 0.)`,w=r?"":`
float getBestIndicesAChannel(${m.join()}) {
return getChannel(getBestIndicesA(${d.join()}),
vec2(${d.slice(-2).join()}));
}`;this.userCode=`
float getAChannel(${m.join()}) {
return getChannel(getA(${d.join()}),
vec2(${d.slice(-2).join()}));
}
${w}
void main() {
${l} coords = getOutputCoords();
bool hasNextCol = ${c[o-1]} < ${i[o-1]-1};
bool hasNextRow = ${c[o-2]} < ${i[o-2]-1};
${u}
ivec4 srcIdx = ivec4(sourceLocR${p}, sourceLocG${p},
sourceLocB${p}, sourceLocA${p}) * ${t};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${b};
for (int i = 0; i < ${t}; i++) {
inIdx = srcIdx;
${v}
vec4 candidate = ${b};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${x}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
bestValue = vec4(replace.x ? candidate.x : bestValue.x,
replace.y ? candidate.y : bestValue.y,
replace.z ? candidate.z : bestValue.z,
replace.w ? candidate.w : bestValue.w);
bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
srcIdx++;
}
setOutput(bestIndex);
}
`}};function r7(e,t,n,r=null){let a=t.shape[0],s=t.shape[1];r!=null&&(a=r.shape[0],s=r.shape[1]);let i=E.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new WB(o,n,r==null),c=[t];r!=null&&c.push(r);let u=e.runWebGLProgram(l,c,"int32");if(u.shape[1]===1)return u;let h=r7(e,t,n,u);return e.disposeIntermediateTensorInfo(u),h}function a7(e,t,n,r=null){let a=r!=null?r.shape:t.shape,s=a[a.length-1],i=E.computeOptimalWindowSize(s),o=new BB(a,i,n,r==null),l=r==null?[t]:[t,r],c=e.runWebGLProgram(o,l,"int32");if(c.shape.length===t.shape.length){let u=a7(e,t,n,c);return e.disposeIntermediateTensorInfo(c),u}return c}function s7(e,t,n,r){let a=[n];if(E.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),a,t.shape.length),!J().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=E.computeOutAndReduceShapes(t.shape,a),l=_.sizeFromShape(o),c=fe({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(c);let u=r7(e,c,r);s.push(u);let h=fe({inputs:{x:u},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),h}return a7(e,t,r)}function VB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=_.parseAxisParam(s,a.shape),o=E.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=fn({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=E.getInnerMostAxes(i.length,l.shape.length)),E.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let u=s7(n,l,i[0],"max");return c.forEach(h=>n.disposeIntermediateTensorInfo(h)),u}var jB={kernelName:fs,backendName:"webgl",kernelFunc:VB};function UB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=_.parseAxisParam(s,a.shape),o=E.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=fn({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=E.getInnerMostAxes(i.length,l.shape.length)),E.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let u=s7(n,l,i[0],"min");return c.forEach(h=>n.disposeIntermediateTensorInfo(h)),u}var HB={kernelName:wu,backendName:"webgl",kernelFunc:UB},GB=Nr+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,qB=Xe({opSnippet:GB}),XB={kernelName:fo,backendName:"webgl",kernelFunc:qB},KB=Nr+"return log(x + sqrt(x * x + 1.0));",ZB=Xe({opSnippet:KB}),YB={kernelName:mo,backendName:"webgl",kernelFunc:ZB},JB=Nr+`
return atan(x);
`,QB=Xe({opSnippet:JB}),eV={kernelName:Ao,backendName:"webgl",kernelFunc:QB},tV=lB+`
return atan(a, b);
`,nV=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+uB+`
return result;
`,rV=nn({opSnippet:tV,packedOpSnippet:nV}),aV={kernelName:go,backendName:"webgl",kernelFunc:rV},sV=Nr+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,iV=Xe({opSnippet:sV}),oV={kernelName:yo,backendName:"webgl",kernelFunc:iV},_c=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterHeight,h=e.effectiveFilterWidth,d=e.padInfo.top,p=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,A=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),n){let k=">=";this.userCode=`
const ivec2 strides = ivec2(${i}, ${o});
const ivec2 pads = ivec2(${d}, ${p});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
float avgValue = 0.0;
for (int wR = 0; wR < ${u};
wR += ${l}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${h};
wC += ${c}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float value = getX(batch, xR, xC, d);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${k} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${r?a?f:A:`wR * ${h} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let g="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let v=Math.floor(s/4)*4,b=s%4,w=`
if (${m}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${g}(values, minMaxValue);
}
`;this.userCode=`
const ivec2 strides = ivec2(${i}, ${o});
const ivec2 pads = ivec2(${d}, ${p});
const float initializationValue = ${y};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xR, int xC, int d) {
if (xC < 0 || xC >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xR, xC, d);
}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
vec4 minMaxValue = vec4(${y});
float avgValue = 0.0;
count = 0.0;
for (int wR = 0; wR < ${u};
wR += ${l}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${v}; wC += 4) {
int xC = xCCorner + wC * ${c};
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
getValue(batch, xR, xC + 2 * ${c}, d),
getValue(batch, xR, xC + 3 * ${c}, d)
);
${w}
}
int xC = xCCorner + ${v};
if (${b===1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${w}
} else if (${b===2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
initializationValue,
initializationValue
);
${w}
} else if (${b===3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
getValue(batch, xR, xC + 2 * ${c}, d),
initializationValue
);
${w}
}
}
setOutput(${x});
}
`}},EA=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,c=e.dilationDepth,u=e.dilationHeight,h=e.dilationWidth,d=e.effectiveFilterDepth,p=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,A=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let g=t==="avg",x="0.0";if(g||(x="-1.0 / 1e-20"),n){let C=">=";this.userCode=`
const ivec3 strides =
ivec3(${i}, ${o}, ${l});
const ivec3 pads = ivec3(${f}, ${A}, ${y});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
for (int wD = 0; wD < ${d};
wD += ${c}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${p};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${m};
wC += ${h}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float value = getX(batch, xD, xR, xC, ch);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${C} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${r?a?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${p} * ${m} +
wR * ${m} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let v="max",b=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(b="avgValue / count");let w=Math.floor(s/4)*4,k=s%4,N=`
if (${g}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${v}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${i}, ${o}, ${l});
const ivec3 pads = ivec3(${f}, ${A}, ${y});
const float initializationValue = ${x};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xD, int xR, int xC, int ch) {
if (xC < 0 || xC >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xD, xR, xC, ch);
}
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
// ? = to be determined
vec4 minMaxValue = vec4(${x});
float avgValue = 0.0;
count = 0.0;
for (int wD = 0; wD < ${d};
wD += ${c}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${p};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${w}; wC += 4) {
int xC = xCCorner + wC * ${h};
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${h}, ch),
getValue(batch, xD, xR, xC + 2 * ${h}, ch),
getValue(batch, xD, xR, xC + 3 * ${h}, ch)
);
${N}
}
int xC = xCCorner + ${w};
if (${k===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${N}
} else if (${k===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${h}, ch),
initializationValue,
initializationValue
);
${N}
} else if (${k===3}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${h}, ch),
getValue(batch, xD, xR, xC + 2 * ${h}, ch),
initializationValue
);
${N}
}
}
setOutput(${b});
}
}
`}};function lV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;$l(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;_.assert(E.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=E.computePool2DInfo(a.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&_.arraysEqual(u.inShape,u.outShape))return Vn({inputs:{x:a},backend:n});let h=new _c(u,"avg",!1);return n.runWebGLProgram(h,[a],"float32")}var uV={kernelName:ms,backendName:"webgl",kernelFunc:lV};function cV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=r,u=[1,1,1],h=E.computePool3DInfo(a.shape,s,i,u,o,l,c),d=new EA(h,"avg",!1);return n.runWebGLProgram(d,[a],"float32")}var hV={kernelName:_u,backendName:"webgl",kernelFunc:cV},dV=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=o-1-e.padInfo.top,u=l-1-e.padInfo.left,h=1/(t*n);this.userCode=`
const ivec2 pads = ivec2(${c}, ${u});
const float avgMultiplier = float(${h});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${o};
wR += ${s}) {
float dyR = float(dyRCorner + wR) / ${r}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${l};
wC+= ${i}) {
float dyC = float(dyCCorner + wC) / ${a}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}},pV=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,h=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=u-1-e.padInfo.front,m=h-1-e.padInfo.top,f=d-1-e.padInfo.left,A=1/(t*n*r);this.userCode=`
const ivec3 pads = ivec3(${p}, ${m}, ${f});
const float avgMultiplier = float(${A});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${u};
wD += ${o}) {
float dyD = float(dyDCorner + wD) / ${a}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${h};
wR += ${l}) {
float dyR = float(dyRCorner + wR) / ${s}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${d};
wC += ${c}) {
float dyC = float(dyCCorner + wC) / ${i}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
dotProd += dyValue * avgMultiplier;
}
}
}
setOutput(dotProd);
}
`}};function fV(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=r,h=[1,1,1],d=E.computePool3DInfo(i.shape,o,l,h,c,u),p=new pV(d);return n.runWebGLProgram(p,[a],i.dtype)}var mV={kernelName:Dh,backendName:"webgl",kernelFunc:fV};function AV(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;$l([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=r,u=E.computePool2DInfo(i.shape,o,l,1,c),h=new dV(u);return n.runWebGLProgram(h,[a],i.dtype)}var yV={kernelName:$h,backendName:"webgl",kernelFunc:AV};function gV(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;return Sp({a,b:s,transposeA:i,transposeB:o,backend:n})}var xV={kernelName:As,backendName:"webgl",kernelFunc:gV},bV=class{constructor(e,t,n,r,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],E.assertAndGetBroadcastShape(e,t),E.assertAndGetBroadcastShape(e,n);let i="0.0";r!=null&&(E.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(E.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
float x = getXAtOutCoords();
float mean = getMeanAtOutCoords();
float variance = getVarianceAtOutCoords();
float offset = ${i};
float scale = ${o};
float inv = scale * inversesqrt(variance + float(${s}));
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
}
`}},wV=class{constructor(e,t,n,r,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],E.assertAndGetBroadcastShape(e,t),E.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";r!=null&&(E.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(E.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
vec4 offset = ${i};
vec4 scale = ${o};
vec4 x = getXAtOutCoords();
vec4 mean = getMeanAtOutCoords();
vec4 variance = getVarianceAtOutCoords();
vec4 inv = scale * inversesqrt(variance + vec4(${s}));
setOutput((x - mean) * inv + offset);
}
`}},_V=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:a,variance:s,offset:i,scale:o}=e;_.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),_.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),_.assert(o==null||a.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let c=[r,a,s],u=null;i!=null&&(u=i.shape,c.push(i));let h=null;o!=null&&(h=o.shape,c.push(o));let d=J().getBool("WEBGL_PACK_NORMALIZATION")?new wV(r.shape,a.shape,s.shape,u,h,l):new bV(r.shape,a.shape,s.shape,u,h,l);return t.runWebGLProgram(d,c,c[0].dtype)},vV={kernelName:Ts,backendName:"webgl",kernelFunc:_V},IV=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=lt(this.rank),n=`uniform int start[${this.rank}];`,r=kV(this.rank),a,s=e.map((i,o)=>`sourceLoc.${CA[o]} = start[${o}] + coords.${CA[o]};`);a=`
${t} sourceLoc;
${t} coords = getOutputCoords();
${s.join(`
`)}
`,this.userCode=`
${n}
void main() {
${a}
setOutput(getSource(${r}));
}
`}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},CA=["x","y","z","w","u","v"];function kV(e){if(e===1)return"sourceLoc";if(e<=6)return CA.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var SV=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=lt(this.rank),n=pn("coords",this.rank),r=pn("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${r.slice(-2).join()})`,s=`getChannel(getSource(${r.join()}), ${a})`,i=`
result.x = ${s};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${r[this.rank-1]};
result.y = ${s};
--${r[this.rank-1]};
}
`,o=this.rank===1?"":`
--${n[this.rank-1]};
if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
++${r[this.rank-2]};
result.z = ${s};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${r[this.rank-1]};
result.w = ${s};
}
}
`,l=this.rank<=4?`sourceLoc = coords +
${t}(${e.map((c,u)=>`start[${u}]`).join()});`:e.map((c,u)=>`${r[u]} = ${n[u]} + start[${u}];`).join(`
`);this.userCode=`
uniform int start[${this.rank}];
void main() {
${t} coords = getOutputCoords();
${t} sourceLoc;
${l}
vec4 result = vec4(0.);
${i}
${o}
setOutput(result);
}
`}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function NV(e,t,n,r){let a=r.texData.get(e.dataId),s=r.makeTensorInfo(n,e.dtype),i=r.texData.get(s.dataId);Object.assign(i,a),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=un.computeFlatOffset(t,_.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=r.dataRefCount.get(i.slice.origDataId)||1;return r.dataRefCount.set(i.slice.origDataId,l+1),s}function vc(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:i}=r,[o,l]=un.parseSliceParams(a,s,i);if(un.assertParamsValid(a,o,l),_.sizeFromShape(l)===0)return n.makeTensorInfo(l,a.dtype,[]);if(n.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=n.texData.get(a.dataId),d=gW(h.values,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,d)}let{isPacked:c}=n.texData.get(a.dataId),u=un.isSliceContinous(a.shape,o,l);if(c||!u){let h=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new SV(l):new IV(l),d=h.getCustomSetupFunc(o);return n.runWebGLProgram(h,[a],a.dtype,d)}return n.uploadToGPU(a.dataId),NV(a,o,l,n)}var TV={kernelName:Qo,backendName:"webgl",kernelFunc:vc},EV=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;_.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((g,x)=>g*x),l=E.getReshaped(a.shape,s,o),c=E.getPermuted(l.length,s.length),u=E.getReshapedPermuted(a.shape,s,o),h=E.getSliceBeginCoords(i,s.length),d=E.getSliceSize(u,i,s.length),p=[],m=fe({inputs:{x:a},backend:n,attrs:{shape:l}}),f=fn({inputs:{x:m},backend:n,attrs:{perm:c}}),A=fe({inputs:{x:f},backend:n,attrs:{shape:u}}),y=vc({inputs:{x:A},backend:n,attrs:{begin:h,size:d}});return p.push(m),p.push(f),p.push(A),p.forEach(g=>n.disposeIntermediateTensorInfo(g)),y},CV={kernelName:vu,backendName:"webgl",kernelFunc:EV};function RV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.readSync(a.dataId),l=n.readSync(s.dataId),c=F3(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var MV={kernelName:Oh,backendName:"webgl",kernelFunc:RV},FV="return float(a != b);",i7=nn({opSnippet:FV,dtype:"bool"}),$V={kernelName:Bo,backendName:"webgl",kernelFunc:i7};function kc(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Vn({inputs:{x:a.complexTensorInfos.real},backend:n})}var DV={kernelName:ad,backendName:"webgl",kernelFunc:kc},OV="return float(int(x));";function zV(e,t){let n=new qa(e.shape,OV),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function RA(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:s}=r;if(s==="complex64"){if(a.dtype==="complex64")return Vn({inputs:{x:a},backend:n});let i=Rt(a.shape),o=RA({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),l=Xa({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=kc({inputs:{input:a},backend:n}),o=RA({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!_.hasEncodingLoss(a.dtype,s)){let i=Vn({inputs:{x:a},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return zV(a,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",_.getTypedArrayFromDType("bool",1)),o=i7({inputs:{a,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var PV={kernelName:ys,backendName:"webgl",kernelFunc:RA},o7="return ceil(x);",LV=Xe({opSnippet:o7,packedOpSnippet:o7,cpuKernelImpl:eW}),WV={kernelName:gs,backendName:"webgl",kernelFunc:LV},BV=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
uniform float minVal;
uniform float maxVal;
void main() {
float value = getAAtOutCoords();
if (isnan(value)) {
setOutput(value);
return;
}
setOutput(clamp(value, minVal, maxVal));
}
`}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},VV=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
uniform float minVal;
uniform float maxVal;
void main() {
vec4 value = getAAtOutCoords();
if (any(isnan(value))) {
setOutput(value);
return;
}
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
}
`}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function jV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=r,o;J().getBool("WEBGL_PACK_CLIP")?o=new VV(a.shape):o=new BV(a.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[a],a.dtype,l)}var UV={kernelName:Ma,backendName:"webgl",kernelFunc:jV},HV=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
void main() {
float re = abs(getRealAtOutCoords());
float im = abs(getImagAtOutCoords());
float mx = max(re, im);
// sadly the length function in glsl is not underflow-safe
// (at least not on Intel GPUs). So the safe solution is
// to ensure underflow-safety in all cases.
setOutput(
mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
);
}
`}};function l7(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function GV(e){let{inputs:t,backend:n}=e,{x:r}=t,a=n.texData.get(r.dataId),s=new HV(r.shape),i=[l7(r,a.complexTensorInfos.real),l7(r,a.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var qV={kernelName:ku,backendName:"webgl",kernelFunc:GV},XV=class{constructor(e){this.outputShape=[],this.outputShape=E.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];n.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let r=t.length,a=t[t.length-1];n.push(`else setOutput(getT${r}(yR, yC-${a}));`),this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${n.join(`
`)}
}
`}},KV=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=E.computeOutShape(e,t);let n=this.outputShape,r=n.length,a=lt(r),s=pn("coords",r),i=["x","y","z","w","u","v"].slice(0,r);this.variableNames=e.map((m,f)=>`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m<o.length;m++)o[m]=o[m-1]+e[m][t];let l=i[t],c=i.slice(-2),u=i.join(),h=`if (${l} < ${o[0]}) {
return getChannel(
getT0(${u}), vec2(${c.join()}));
}`;for(let m=1;m<o.length;m++){let f=o[m-1];h+=`
if (${l} < ${o[m]} && ${l} >= ${o[m-1]}) {
return getChannel(
getT${m}(${Tp(i,l,f)}),
vec2(${Tp(c,l,f)}));
}`}let d=o.length,p=o[o.length-1];h+=`
return getChannel(
getT${d}(${Tp(i,l,p)}),
vec2(${Tp(c,l,p)}));`,this.userCode=`
float getValue(${i.map(m=>"int "+m)}) {
${h}
}
void main() {
${a} coords = getOutputCoords();
vec4 result = vec4(getValue(${s}), 0., 0., 0.);
${s[r-1]} = ${s[r-1]} + 1;
if (${s[r-1]} < ${n[r-1]}) {
result.g = getValue(${s});
}
${s[r-2]} = ${s[r-2]} + 1;
if (${s[r-2]} < ${n[r-2]}) {
result.a = getValue(${s});
}
${s[r-1]} = ${s[r-1]} - 1;
if (${s[r-2]} < ${n[r-2]} &&
${s[r-1]} < ${n[r-1]}) {
result.b = getValue(${s});
}
setOutput(result);
}
`}};function Tp(e,t,n){let r=e.indexOf(t);return e.map((a,s)=>s===r?`${a} - ${n}`:a).join()}function Ep(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Vn({inputs:{x:a.complexTensorInfos.imag},backend:n})}var ZV={kernelName:Yh,backendName:"webgl",kernelFunc:Ep};function jl(e,t,n){let r=e[0].dtype;if(r==="complex64"){let u=e.map(f=>kc({inputs:{input:f},backend:n})),h=e.map(f=>Ep({inputs:{input:f},backend:n})),d=jl(u,t,n),p=jl(h,t,n),m=Xa({inputs:{real:d,imag:p},backend:n});return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),h.forEach(f=>n.disposeIntermediateTensorInfo(f)),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),m}let a=n.shouldExecuteOnCPU(e);if(r==="string"&&(a=!0),a){let u=e.map(y=>{let g=_.sizeFromShape(y.shape.slice(t));return fe({inputs:{x:y},backend:n,attrs:{shape:[-1,g]}})}),h=u.map(y=>({vals:n.readSync(y.dataId),shape:y.shape})),d=E.computeOutShape(u.map(y=>y.shape),1),p=u[0].shape[0]===1,m=tW(h,d,r,p),f=E.computeOutShape(e.map(y=>y.shape),t),A=n.makeTensorInfo(f,r,m);return u.forEach(y=>n.disposeIntermediateTensorInfo(y)),A}if(e.length>J().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(e.length/2),h=jl(e.slice(0,u),t,n),d=jl(e.slice(u),t,n),p=jl([h,d],t,n);return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),p}if(J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let u=new KV(e.map(h=>h.shape),t);return n.runWebGLProgram(u,e,r)}let{tensors2D:s,outShape:i}=YV(e,t,n),o=new XV(s.map(u=>u.shape)),l=n.runWebGLProgram(o,s,r);s.forEach(u=>n.disposeIntermediateTensorInfo(u));let c=fe({inputs:{x:l},attrs:{shape:i},backend:n});return n.disposeIntermediateTensorInfo(l),c}function YV(e,t,n){let r=E.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>fe({inputs:{x:a},attrs:{shape:[-1,_.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:r}}function u7(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=_.parseAxisParam(a,t[0].shape)[0],i=E.computeOutShape(t.map(c=>c.shape),s);if(_.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(c=>_.sizeFromShape(c.shape)>0);if(o.length===1)return Vn({inputs:{x:o[0]},backend:n});let l=o.map(c=>c.shape);return E.assertParamsConsistent(l,s),jl(o,s,n)}var JV={kernelName:xo,backendName:"webgl",kernelFunc:u7},c7=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,c=e.dilationHeight,u=e.dilationWidth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",A=f?1:2,y=f?2:3,g=f?3:1,x="",v="";n&&(r?x=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:a?x=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:x=`
float activation(float x) {
${n}
}
`,v="result = activation(result);");let b=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${x}
const ivec2 strides = ivec2(${o}, ${l});
const ivec2 pads = ivec2(${s}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${g}];
ivec2 xRCCorner =
ivec2(coords[${A}], coords[${y}]) * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${h}; wR++) {
int xR = xRCorner + wR * ${c};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${d}; wC++) {
int xC = xCCorner + wC * ${u};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${p}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${f}) {
vec4 xValues = vec4(
getX(batch, xR, xC, d1),
getX(batch, xR, xC, d1 + 1),
getX(batch, xR, xC, d1 + 2),
getX(batch, xR, xC, d1 + 3)
);
dotProd += dot(xValues, wValues);
} else {
vec4 xValues = vec4(
getX(batch, d1, xR, xC),
getX(batch, d1 + 1, xR, xC),
getX(batch, d1 + 2, xR, xC),
getX(batch, d1 + 3, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
if (${m===1}) {
if (${f}) {
dotProd +=
getX(batch, xR, xC, ${p}) *
getW(wR, wC, ${p}, d2);
} else {
dotProd +=
getX(batch, ${p}, xR, xC) *
getW(wR, wC, ${p}, d2);
}
} else if (${m===2}) {
vec2 wValues = vec2(
getW(wR, wC, ${p}, d2),
getW(wR, wC, ${p} + 1, d2)
);
if (${f}) {
vec2 xValues = vec2(
getX(batch, xR, xC, ${p}),
getX(batch, xR, xC, ${p} + 1)
);
dotProd += dot(xValues, wValues);
} else {
vec2 xValues = vec2(
getX(batch, ${p}, xR, xC),
getX(batch, ${p} + 1, xR, xC)
);
dotProd += dot(xValues, wValues);
}
} else if (${m===3}) {
vec3 wValues = vec3(
getW(wR, wC, ${p}, d2),
getW(wR, wC, ${p} + 1, d2),
getW(wR, wC, ${p} + 2, d2)
);
if (${f}) {
vec3 xValues = vec3(
getX(batch, xR, xC, ${p}),
getX(batch, xR, xC, ${p} + 1),
getX(batch, xR, xC, ${p} + 2)
);
dotProd += dot(xValues, wValues);
} else {
vec3 xValues = vec3(
getX(batch, ${p}, xR, xC),
getX(batch, ${p} + 1, xR, xC),
getX(batch, ${p} + 2, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
}
}
float result = dotProd;
${b}
${v}
setOutput(result);
}
`}},QV=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${a}, ${s}, ${i});
const ivec3 pads = ivec3(${t}, ${n}, ${r});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d2 = coords.u;
ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xFCorner = xFRCCorner.x;
int xRCorner = xFRCCorner.y;
int xCCorner = xFRCCorner.z;
// Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
// y(yF, yR, yC, d2). ? = to be determined. : = across all
// values in that axis.
float dotProd = 0.0;
for (int wF = 0; wF < ${u}; wF++) {
int xF = xFCorner + wF * ${o};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${h}; wR++) {
int xR = xRCorner + wR * ${l};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${d}; wC++) {
int xC = xCCorner + wC * ${c};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${p}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${m===1}) {
dotProd +=
getX(batch, xF, xR, xC, ${p}) *
getW(wF, wR, wC, ${p}, d2);
} else if (${m===2}) {
vec2 xValues = vec2(
getX(batch, xF, xR, xC, ${p}),
getX(batch, xF, xR, xC, ${p} + 1)
);
vec2 wValues = vec2(
getW(wF, wR, wC, ${p}, d2),
getW(wF, wR, wC, ${p} + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (${m===3}) {
vec3 xValues = vec3(
getX(batch, xF, xR, xC, ${p}),
getX(batch, xF, xR, xC, ${p} + 1),
getX(batch, xF, xR, xC, ${p} + 2)
);
vec3 wValues = vec3(
getW(wF, wR, wC, ${p}, d2),
getW(wF, wR, wC, ${p} + 1, d2),
getW(wF, wR, wC, ${p} + 2, d2)
);
dotProd += dot(xValues, wValues);
}
}
}
}
setOutput(dotProd);
}
`}},ej=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:r,inChannels:a,strideWidth:s,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:c,dilationHeight:u,dataFormat:h}=n,{left:d,top:p}=o,m=a*r,f=dn(),A=h==="channelsLast",y=A?0:1,g=A?1:2,x="";for(let v=0;v<=1;v++)for(let b=0;b<=1;b++)x+=`
blockIndex = rc.y + ${b};
pos = rc.x + ${v};
if(blockIndex < ${e[1]} && pos < ${e[0]}) {
offsetY = int(blockIndex / (${l})) * ${i} - ${p};
d0 = offsetY + ${u} * (pos / ${m});
if(d0 < ${t[y]} && d0 >= 0) {
offsetX = int(mod(float(blockIndex), ${l}.) * ${s}. - ${d}.);
d1 = offsetX + ${c} * (int(mod(float(pos), ${m}.) / ${a}.));
if(d1 < ${t[g]} && d1 >= 0) {
ch = int(mod(float(pos), ${a}.));
if (${A}) {
innerDims = vec2(d1, ch);
result[${v*2+b}] = getChannel(
getA(d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${v*2+b}] = getChannel(
getA(ch, int(innerDims.x),
int(innerDims.y)), innerDims);
}
}
}
}
`;this.userCode=`
void main() {
ivec2 rc = getOutputCoords();
vec4 result = vec4(0);
int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
vec2 innerDims;
${x}
${f.output} = result;
}
`}};function h7({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,c=r.texData.get(e.dataId),u=n.inChannels,h=l[0]*l[1]*l[2],d=n.outChannels,p=n.dataFormat==="channelsLast",m=!1,f=!1,A,y=[],g=(h===1||d===1)&&u>e7,x=l[2]%2!=0&&!!c.isPacked;if(g||!J().getBool("WEBGL_LAZILY_UNPACK")||!J().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!x){let v=p?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],b=fe({inputs:{x:e},backend:r,attrs:{shape:[1,v,n.inChannels]}}),w=fe({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),k=Sp({a:b,b:w,transposeA:m,transposeB:f,backend:r,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});A=fe({inputs:{x:k},backend:r,attrs:{shape:n.outShape}}),y.push(b),y.push(w),y.push(k)}else{let v=p?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),b={dataId:e.dataId,shape:[1,v,n.inChannels],dtype:e.dtype},w=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,_.assert(Ac(c.shape,b.shape),()=>`packed reshape ${c.shape} to ${b.shape} isn't free`);let k=fe({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(k);let N=Sp({a:b,b:k,backend:r,transposeA:m,transposeB:f,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),C=r.texData.get(N.dataId);_.assert(C.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=w,C.shape=n.outShape,A=Vn({inputs:{x:N},backend:r}),A.shape=n.outShape,y.push(N)}for(let v of y)r.disposeIntermediateTensorInfo(v);return A}function d7({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:c,inChannels:u,outWidth:h,outHeight:d,dataFormat:p}=n,m=p==="channelsLast",f=l*c*u,A=d*h,y=[f,A],g=!0,x=!1,v=[],b=fe({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),w=fe({inputs:{x:t},backend:r,attrs:{shape:[1,f,_.sizeFromShape(t.shape)/f]}});v.push(b),v.push(w);let k=new ej(y,b.shape,n),N=r.runWebGLProgram(k,[b],"float32"),C=fe({inputs:{x:N},backend:r,attrs:{shape:[1,y[0],y[1]]}});v.push(N),v.push(C);let F=a!=null,O=s!=null,L=o==="leakyrelu",V=o?vp(o,!0):null,j=new K3(C.shape,w.shape,[1,A,n.outChannels],g,x,F,V,O,L),U=[C,w];if(a&&U.push(a),O&&U.push(s),L){let Y=r.makeTensorInfo([],"float32",_.createScalarValue(i,"float32"));U.push(Y),v.push(Y)}let X=r.runWebGLProgram(j,U,"float32"),G=m?[1,d,h,n.outChannels]:[1,n.outChannels,d,h],ee=fe({inputs:{x:X},backend:r,attrs:{shape:G}});v.push(X);for(let Y of v)r.disposeIntermediateTensorInfo(Y);return ee}function tj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:c,dimRoundingMode:u}=r,h=E.convertConv2DDataFormat(l),d=E.computeConv2DInfo(a.shape,s.shape,i,c,o,u,!1,h),p;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))p=h7({x:a,filter:s,convInfo:d,backend:n});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)p=d7({x:a,filter:s,convInfo:d,backend:n});else{let f=new c7(d);p=n.runWebGLProgram(f,[a,s],"float32")}let m=fe({inputs:{x:p},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(p),m}var nj={kernelName:xs,backendName:"webgl",kernelFunc:tj},rj=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int d2 = coords.w;
// Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${r};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${a};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
if (${s}) {
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
} else {
float dyValue = getDy(b, d2, yR, yC);
float xValue = getX(b, d1, xR, xC);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},aj=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,c=s?2:3,u=s?3:1;this.userCode=`
const ivec2 pads = ivec2(${i}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[${u}];
ivec2 dyCorner = ivec2(coords[${l}], coords[${c}]) - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${r}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${a}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
if (${s}) {
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
} else {
float xValue = getDy(batch, d2, idyR, idyC);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}},sj=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=`
void main() {
ivec5 coords = getOutputCoords();
int wF = coords.x;
int wR = coords.y;
int wC = coords.z;
int d1 = coords.w;
int d2 = coords.u;
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yF = 0; yF < ${e.outDepth}; yF++) {
int xF = wF + yF * ${t} - ${a};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${n} - ${s};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${r} - ${i};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},ij=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=r-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${o}, ${l}, ${c});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyFCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
float dotProd = 0.0;
for (int wF = 0; wF < ${t}; wF++) {
float dyF = float(dyFCorner + wF) / ${a}.0;
if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {
continue;
}
int idyF = int(dyF);
int wFPerm = ${t} - 1 - wF;
for (int wR = 0; wR < ${n}; wR++) {
float dyR = float(dyRCorner + wR) / ${s}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${n} - 1 - wR;
for (int wC = 0; wC < ${r}; wC++) {
float dyC = float(dyCCorner + wC) / ${i}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${r} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
float xValue = getDy(batch, idyF, idyR, idyC, d2);
float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}};function oj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,filterShape:u}=r,h=E.convertConv2DDataFormat(l),d=E.computeConv2DInfo(a.shape,u,i,1,o,c,!1,h),p=new rj(d);return n.runWebGLProgram(p,[a,s],"float32")}var lj={kernelName:Ph,backendName:"webgl",kernelFunc:oj};function uj(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:c,dimRoundingMode:u}=r,h=E.convertConv2DDataFormat(c),d=E.computeConv2DInfo(i,s.shape,o,1,l,u,!1,h),p=new aj(d);return n.runWebGLProgram(p,[a,s],"float32")}var cj={kernelName:bs,backendName:"webgl",kernelFunc:uj};function hj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,c=E.computeConv3DInfo(a.shape,s.shape,i,l,o),u=new QV(c);return n.runWebGLProgram(u,[a,s],"float32")}var dj={kernelName:Iu,backendName:"webgl",kernelFunc:hj};function pj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r,c=E.computeConv3DInfo(a.shape,l,i,1,o),u=new sj(c);return n.runWebGLProgram(u,[a,s],"float32")}var fj={kernelName:Lh,backendName:"webgl",kernelFunc:pj};function mj(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r,c=E.computeConv3DInfo(l,s.shape,o,1,i),u=new ij(c);return n.runWebGLProgram(u,[a,s],"float32")}var Aj={kernelName:Wh,backendName:"webgl",kernelFunc:mj},yj=X3+`
return cos(x);
`,gj=Xe({opSnippet:yj}),xj={kernelName:ws,backendName:"webgl",kernelFunc:gj},bj=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,wj=Xe({opSnippet:bj}),_j={kernelName:bo,backendName:"webgl",kernelFunc:wj},vj=class{constructor(e,t,n,r,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[c]=t,[u,h]=n;this.outputShape=[c,u,h,l];let d=r==="bilinear"?1:0,[p,m]=[`${i-1}.0`,`${o-1}.0`],[f,A,y]=u>1?[`${(i-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${p} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${p}`],[g,x,v]=h>1?[`${(o-1)/(h-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=`
const float height_ratio = float(${f});
const float width_ratio = float(${g});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int y = coords[1];
int x = coords[2];
int d = coords[3];
// get box vals
float y1 = getBoxes(b,0);
float x1 = getBoxes(b,1);
float y2 = getBoxes(b,2);
float x2 = getBoxes(b,3);
// get image in batch index
int bInd = round(getBoxInd(b));
if(bInd < 0 || bInd >= ${s}) {
return;
}
float height_scale = ${A};
float width_scale = ${x};
float in_y = ${y};
if( in_y < 0.0 || in_y > ${p} ) {
setOutput(float(${a}));
return;
}
float in_x = ${v};
if( in_x < 0.0 || in_x > ${m} ) {
setOutput(float(${a}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${d} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}},kj=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=r,u=new vj(a.shape,s.shape,o,l,c);return n.runWebGLProgram(u,[a,s,i],"float32")},Ij={kernelName:wo,backendName:"webgl",kernelFunc:kj},m7=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let r=e.length,a=t?"0.0":`getX(${p7(r,"coords")})`,s=e[e.length-1],i="",o="";t?(i=n?`end != ${s-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
uniform float index;
void main() {
${lt(r)} coords = getOutputCoords();
int end = ${f7(r,"coords")};
float val = ${a};
int pow2 = int(pow(2.0, index));
if (${i}) {
int idx = ${o};
${f7(r,"coords")} = idx;
val += getX(${p7(r,"coords")});
}
setOutput(val);
}
`}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function p7(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function f7(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function Sj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length,c=E.getAxesPermutation([s],l),u=a;c!=null&&(u=fn({inputs:{x:a},backend:n,attrs:{perm:c}}));let h=E.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let d=u.shape[h],p=Vn({inputs:{x:u},backend:n});for(let m=0;m<=Math.ceil(Math.log2(d))-1;m++){let f=new m7(u.shape,!1,o),A=f.getCustomSetupFunc(m),y=p;p=n.runWebGLProgram(f,[p],p.dtype,A),n.disposeIntermediateTensorInfo(y)}if(i){let m=new m7(u.shape,i,o),f=p;p=n.runWebGLProgram(m,[p],p.dtype),n.disposeIntermediateTensorInfo(f)}if(c!=null){let m=E.getUndoAxesPermutation(c),f=fn({inputs:{x:p},backend:n,attrs:{perm:m}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(u),f}return p}var Nj={kernelName:_s,backendName:"webgl",kernelFunc:Sj};function Tj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.readSync(a.dataId),c=n.readSync(s.dataId),u=F3(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(a.shape.length===2){let l=n.bufferSync(a),c=n.bufferSync(s),u=QL(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var Ej={kernelName:Bh,backendName:"webgl",kernelFunc:Tj},Cj=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int h = ${this.getHeightCoordString()};
int w = ${this.getWidthCoordString()};
int d = ${this.getDepthCoordString()};
int in_h = h / ${t};
int offset_h = imod(h, ${t});
int in_w = w / ${t};
int offset_w = imod(w, ${t});
int offset_d = (offset_h * ${t} + offset_w) *
${this.getOutputDepthSize()};
int in_d = d + offset_d;
float result = ${this.getInputSamplingString()};
setOutput(result);
}
`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Rj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;_.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],c=i==="NHWC"?a.shape[2]:a.shape[3],u=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,d=c*s,p=u/(s*s),m=i==="NHWC"?[o,h,d,p]:[o,p,h,d],f=new Cj(m,s,i);return n.runWebGLProgram(f,[a],a.dtype)}var Mj={kernelName:_o,backendName:"webgl",kernelFunc:Rj},A7=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,c=e.strideHeight,u=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,m=e.filterWidth,f=e.outChannels/e.inChannels,A="",y="";n&&(r?A=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:a?A=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:A=`
float activation(float x) {
${n}
}
`,y="result = activation(result);");let g=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${A}
const ivec2 strides = ivec2(${c}, ${u});
const ivec2 pads = ivec2(${o}, ${l});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${f};
int q = d2 - d1 * ${f};
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
// TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.
for (int wR = 0; wR < ${p}; wR++) {
int xR = xRCorner + wR * ${h};
if (xR < 0 || xR >= ${s}) {
continue;
}
for (int wC = 0; wC < ${m}; wC++) {
int xC = xCCorner + wC * ${d};
if (xC < 0 || xC >= ${i}) {
continue;
}
float xVal = getX(batch, xR, xC, d1);
float wVal = getW(wR, wC, d1, q);
dotProd += xVal * wVal;
}
}
float result = dotProd;
${g}
${y}
setOutput(result);
}
`}},y7=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.outChannels/e.inChannels,i=e.inHeight,o=e.inWidth,l=e.padInfo.top,c=e.padInfo.left,u=e.strideHeight,h=e.strideWidth,d=e.dilationHeight,p=e.dilationWidth,m=e.filterHeight,f=e.filterWidth,A=f,y=`
int xR; int xC; int xCOffset;
vec4 wTexel; vec4 previous; vec4 final;`;for(let b=0;b<f;b++)y+=`
vec4 xTexelC${b*2};
vec4 xC${b};`;for(let b=0;b<m;b++){for(let w=0;w<f;w++)y+=`
xTexelC${w*2} = vec4(0.0);
xC${w} = vec4(0.0);`;y+=`
xR = xRCorner + ${b*d};
if (xR >=0 && xR < ${i}) {
`;for(let w=0;w<A/2+1;w++){let k=w*2;if(y+=`
xC = xCCorner + ${k*p};
`,h===1){if(k<f&&(c%2==1?(y+=`
xCOffset = xC + 1;
if (xCOffset >= 0 && xCOffset < ${o}) {
xTexelC${k} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= ${o}) {
xTexelC${k}.zw = vec2(0.0);
}
}
`,p===1&&k>0?y+=`
xC${k} = vec4(xTexelC${k-2}.zw, xTexelC${k}.xy);
`:y+=`
xCOffset = xC + 1 - 2;
if (xCOffset >= 0 && xCOffset < ${o}) {
previous = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= ${o}) {
previous.zw = vec2(0.0);
}
xC${k} = vec4(previous.zw, xTexelC${k}.xy);
} else {
xC${k} = vec4(0.0, 0.0, xTexelC${k}.xy);
}
`):y+=`
if (xC >= 0 && xC < ${o}) {
xTexelC${k} = getX(batch, xR, xC, d1);
if (xC + 1 >= ${o}) {
xTexelC${k}.zw = vec2(0.0);
}
}
xC${k} = xTexelC${k};
`,k+1<f)){let N=c%2==0?_.nearestLargerEven(p):p;p%2==0&&c%2==1||p%2!=0&&c%2!=1?(y+=`
xCOffset = xC + ${c%2} + ${N};
if (xCOffset >= 0 && xCOffset < ${o}) {
xTexelC${k+2} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= ${o}) {
xTexelC${k+2}.zw = vec2(0.0);
}
}
`,p>1&&(y+=`
xCOffset -= 2;
if (xCOffset >= 0 && xCOffset < ${o}) {
xTexelC${k} = getX(batch, xR, xCOffset, d1);
}
`),y+=`
xC${k+1} = vec4(xTexelC${k}.zw, xTexelC${k+2}.xy);
`):N===1?y+=`
xC${k+1} = xTexelC${k};
`:y+=`
xCOffset = xC + ${N};
if (xCOffset >= 0 && xCOffset < ${o}) {
xTexelC${k+2} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= ${o}) {
xTexelC${k+2}.zw = vec2(0.0);
}
}
xC${k+1} = xTexelC${k+2};
`}}else k<f&&(c%2==1?(y+=`
xCOffset = xC + 1 - ${h};
if(xCOffset >= 0 && xCOffset < ${o}) {
xTexelC${k} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= ${o}) {
xTexelC${k}.zw = vec2(0.0);
}
}
if(xC + 1 >= 0 && xC + 1 < ${o}) {
xTexelC${k+2} = getX(batch, xR, xC + 1, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xC + 2 >= ${o}) {
xTexelC${k+2}.zw = vec2(0.0);
}
}
xC${k} = vec4(xTexelC${k}.zw, xTexelC${k+2}.zw);
`,k+1<f&&(y+=`
final = vec4(0.0);
xCOffset = xC + 1 + ${h};
if(xCOffset >= 0 && xCOffset < ${o}) {
final = getX(batch, xR, xCOffset, d1);
}
xC${k+1} = vec4(xTexelC${k+2}.xy, final.xy);
`)):(y+=`
if(xC >= 0 && xC < ${o}) {
xTexelC${k} = getX(batch, xR, xC, d1);
if (xC + 1 >= ${o}) {
xTexelC${k}.zw = vec2(0.0);
}
}
xCOffset = xC + ${h};
if(xCOffset >= 0 && xCOffset < ${o}) {
xTexelC${k+2} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= ${o}) {
xTexelC${k+2}.zw = vec2(0.);
}
}
xC${k} = vec4(
xTexelC${k}.xy, xTexelC${k+2}.xy);
`,k+1<f&&(y+=`
xC${k+1} = vec4(xTexelC${k}.zw, xTexelC${k+2}.zw);
`)));k<f&&(y+=`
wTexel = getW(${b}, ${k}, d1, q);
dotProd += xC${k} * vec4(wTexel.xz, wTexel.xz);
`,k+1<f&&(y+=`
wTexel = getW(${b}, ${k+1}, d1, q);
dotProd += xC${k+1} * vec4(wTexel.xz, wTexel.xz);
`))}y+=`
}
`}let g="",x="";n&&(r?g=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${n}
}`:a?g=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${n}
}`:g=`vec4 activation(vec4 x) {
${n}
}`,x="result = activation(result);");let v=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${g}
const ivec2 strides = ivec2(${u}, ${h});
const ivec2 pads = ivec2(${l}, ${c});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${s};
int q = d2 - d1 * ${s};
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
//intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
vec4 dotProd = vec4(0.000000000000001);
${y}
vec4 result = dotProd - vec4(0.000000000000001);
${v}
${x}
setOutput(result);
}
`}};function Fj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:c}=r,u=l;u==null&&(u=[1,1]),_.assert(E.eitherStridesOrDilationsAreOne(i,u),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let h=E.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!0),d;return J().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels==1?d=new y7(h):d=new A7(h),n.runWebGLProgram(d,[a,s],"float32")}var $j={kernelName:vs,backendName:"webgl",kernelFunc:Fj},Dj=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int dm = coords.w;
int d2 = d1 * ${s} + dm;
float dotProd = 0.0;
// TO DO: Vec4 over the batch size
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${r};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${a};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}},Oj=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${s}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[3];
ivec2 dyCorner = coords.yz - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${r}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${a}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
// TO DO: Vec4 over the channelMul
for (int dm = 0; dm < ${o}; dm++) {
int d2 = d1 * ${o} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}};function zj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=r,h=E.computeConv2DInfo(a.shape,u,i,o,l,c,!0),d=new Dj(h);return n.runWebGLProgram(d,[a,s],"float32")}var Pj={kernelName:Vh,backendName:"webgl",kernelFunc:zj};function Lj(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=r,h=E.computeConv2DInfo(u,s.shape,i,o,l,c,!0),d=new Oj(h);return n.runWebGLProgram(d,[a,s],"float32")}var Wj={kernelName:jh,backendName:"webgl",kernelFunc:Lj},Bj=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`}};function Vj(e){let{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],s=_.sizeFromShape(r.shape),i=fe({inputs:{x:r},backend:n,attrs:{shape:[s]}}),o=new Bj(s),l=n.runWebGLProgram(o,[i],i.dtype),c=fe({inputs:{x:l},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),c}var jj={kernelName:Uh,backendName:"webgl",kernelFunc:Vj},Uj=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:c}=e,{top:u,left:h}=r;this.userCode=`
const ivec2 strides = ivec2(${a}, ${s});
const ivec2 pads = ivec2(${u}, ${h});
const float neg_infinity = -3.4e38;
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.w;
ivec2 outTopLeftCorner =
coords.yz * strides - pads;
int hBeg = outTopLeftCorner.x;
int wBeg = outTopLeftCorner.y;
float curVal = neg_infinity;
for (int h = 0; h < ${i}; h++) {
int hIn = hBeg + h * ${l};
if (hIn >= 0 && hIn < ${t}) {
for (int w = 0; w < ${o}; w++) {
int wIn = wBeg + w * ${c};
if (wIn >= 0 && wIn < ${n}) {
float xVal = getX(batch, hIn, wIn, d1);
float wVal = getW(h, w, d1);
float val = xVal + wVal;
if (val > curVal) {
curVal = val;
}
}
}
}
}
float result = curVal;
setOutput(result);
}
`}};function Hj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,c=E.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),u,h=new Uj(c);u=n.runWebGLProgram(h,[a,s],"float32");let d=fe({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),d}var Gj={kernelName:Su,backendName:"webgl",kernelFunc:Hj};function qj(e){let{inputs:t,backend:n,attrs:r}=e,{equation:a}=r,s=t,{allDims:i,summedDims:o,idDims:l}=E.decodeEinsumEquation(a,s.length);E.checkEinsumDimSizes(i.length,l,s);let{path:c,steps:u}=E.getEinsumComputePath(o,l),h=u.length,d=null,p=i.length,m=[];for(let f=0;f<h;++f){for(let A of u[f]){let{permutationIndices:y,expandDims:g}=E.getEinsumPermutation(p,l[A]),x;E.isIdentityPermutation(y)?x=s[A]:(x=fn({inputs:{x:s[A]},backend:n,attrs:{perm:y}}),m.push(x));let v=x.shape.slice();for(let b=0;b<g.length;++b)v.splice(g[b],0,1);_.arraysEqual(x.shape,v)||(x=fe({inputs:{x},backend:n,attrs:{shape:v}}),m.push(x)),d===null?d=x:(d=TA({inputs:{a:x,b:d},backend:n}),m.push(d))}f<h-1&&(c[f]>=0&&(d=Ip({inputs:{x:d},backend:n,attrs:{axis:c[f]-(i.length-p),keepDims:!1}}),m.push(d)),p--)}for(let f of m)f!==d&&n.disposeIntermediateTensorInfo(f);return d}var Xj={kernelName:qh,backendName:"webgl",kernelFunc:qj},Kj="return (x >= 0.0) ? x : (exp(x) - 1.0);",Zj=`
vec4 result;
result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
return result;
`,Yj=Xe({opSnippet:Kj,packedOpSnippet:Zj}),Jj={kernelName:vo,backendName:"webgl",kernelFunc:Yj},Qj="return (b >= 1.0) ? a : a * (b + 1.0);",eU=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,tU=e=>{let{inputs:t,backend:n}=e,{dy:r,y:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new wc(eU,r.shape,a.shape):new Vl(Qj,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)},nU={kernelName:Xh,backendName:"webgl",kernelFunc:tU},rU=`
return vec4(equal(a, b));
`,aU="return float(a == b);",sU=nn({opSnippet:aU,packedOpSnippet:rU,dtype:"bool"}),iU={kernelName:Io,backendName:"webgl",kernelFunc:sU},oU=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${E.ERF_P};
float a1 = ${E.ERF_A1};
float a2 = ${E.ERF_A2};
float a3 = ${E.ERF_A3};
float a4 = ${E.ERF_A4};
float a5 = ${E.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,lU=Xe({opSnippet:oU}),uU={kernelName:ko,backendName:"webgl",kernelFunc:lU},g7="return exp(x);",x7=Xe({opSnippet:g7,packedOpSnippet:g7,cpuKernelImpl:nW}),cU={kernelName:Is,backendName:"webgl",kernelFunc:x7};function MA(e){let{inputs:t,attrs:n,backend:r}=e,{dim:a}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(_.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),fe({inputs:{x:s},backend:r,attrs:{shape:o}})}var hU={kernelName:So,backendName:"webgl",kernelFunc:MA},b7="return exp(x) - 1.0;",dU=Xe({opSnippet:b7,packedOpSnippet:b7,cpuKernelImpl:rW}),pU={kernelName:No,backendName:"webgl",kernelFunc:dU},w7=class{constructor(e,t,n){this.variableNames=["real","imag"];let r=t[1];this.outputShape=t;let a=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${r}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
const float exponentMultiplier = ${a};
float unaryOpComplex(float real, float expR, float imag, float expI) {
${i}
}
float mulMatDFT(int batch, int index) {
float indexRatio = float(index) / float(${r});
float exponentMultiplierTimesIndexRatio =
exponentMultiplier * indexRatio;
float result = 0.0;
for (int i = 0; i < ${r}; i++) {
// x = (-2|2 * PI / N) * index * i;
float x = exponentMultiplierTimesIndexRatio * float(i);
float expR = cos(x);
float expI = sin(x);
float real = getReal(batch, i);
float imag = getImag(batch, i);
result +=
unaryOpComplex(real, expR, imag, expI) / ${s};
}
return result;
}
void main() {
ivec2 coords = getOutputCoords();
setOutput(mulMatDFT(coords[0], coords[1]));
}
`}};function _7(e,t,n){let r=n.texData.get(e.dataId),a=_.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=fe({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,c=new w7("real",l,t),u=new w7("imag",l,t),h=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:l},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:l}],d=n.runWebGLProgram(c,h,"float32"),p=n.runWebGLProgram(u,h,"float32"),m=Xa({inputs:{real:d,imag:p},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p);let f=fe({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function fU(e){let{inputs:t,backend:n}=e,{input:r}=t;return _7(r,!1,n)}var mU={kernelName:Kh,backendName:"webgl",kernelFunc:fU},AU=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=`
uniform float value;
void main() {
// Input can be obtained from uniform value.
setOutput(value);
}
`}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function FA(e){let{backend:t,attrs:n}=e,{shape:r,value:a}=n,{dtype:s}=n;if(s=s||_.inferDtype(a),s==="string"){let i=_.getArrayFromDType(s,_.sizeFromShape(r));return i.fill(a),t.makeTensorInfo(r,s,i)}else{let i=new AU(r,a),o=i.getCustomSetupFunc(a);return t.runWebGLProgram(i,[],s,o)}}var yU={kernelName:Nu,backendName:"webgl",kernelFunc:FA},gU=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int coordX = ${t} - x;
float outputValue;
if(coordX >= 0 && coordX < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}},xU={kernelName:To,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,a=new gU(n.shape);return r.runWebGLProgram(a,[n],n.dtype)}},v7="return floor(x);",bU=Xe({opSnippet:v7,packedOpSnippet:v7,cpuKernelImpl:aW}),wU={kernelName:Ss,backendName:"webgl",kernelFunc:bU},_U=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,vU=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,kU=nn({opSnippet:_U,packedOpSnippet:vU,dtype:"int32"}),IU={kernelName:Ns,backendName:"webgl",kernelFunc:kU},SU=class{constructor(e){this.variableNames=["A"];let t=dn(),[n,r]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}},NU=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=dn(),[n,r]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${r}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
result[row * 2 + col] = floor(value * 255.0 + 0.5);
}
}
${t.output} = result;
}
`}},EU={kernelName:hd,backendName:"webgl",kernelFunc:TU},Ul;function TU(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:a}=t,{numChannels:s}=r,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,[l,c]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],u=[c,l],h=[c,l,s];(o||i)&&(Ul==null&&(Ul=document.createElement("canvas").getContext("2d")),Ul.canvas.width=l,Ul.canvas.height=c,Ul.drawImage(a,0,0,l,c),a=Ul.canvas);let d=n.makeTensorInfo(u,"int32");n.texData.get(d.dataId).usage=ar.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),a);let p=J().getBool("WEBGL_PACK")?new NU(h):new SU(h),m=n.runWebGLProgram(p,[d],"int32");return n.disposeData(d.dataId),m}function CU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:m}=r,f=E.convertConv2DDataFormat(u),A=E.computeConv2DInfo(a.shape,s.shape,l,h,c,d,!1,f),y,g=[];if(A.filterHeight===1&&A.filterWidth===1&&A.dilationHeight===1&&A.dilationWidth===1&&A.strideHeight===1&&A.strideWidth===1&&(A.padInfo.type==="SAME"||A.padInfo.type==="VALID"))y=h7({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:m});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)y=d7({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:m});else{let v=i!=null,b=o!=null,w=p==="leakyrelu",k=p?vp(p,!1):null,N=new c7(A,v,k,b,w),C=[a,s];if(i&&C.push(i),o&&C.push(o),w){let F=n.makeTensorInfo([],"float32",_.createScalarValue(m,"float32"));C.push(F),g.push(F)}y=n.runWebGLProgram(N,C,"float32")}let x=fe({inputs:{x:y},backend:n,attrs:{shape:A.outShape}});return g.push(y),g.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var RU={kernelName:li,backendName:"webgl",kernelFunc:CU};function MU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dimRoundingMode:h,activation:d,leakyreluAlpha:p}=r,m=[],f=u;f==null&&(f=[1,1]),_.assert(E.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let A=E.computeConv2DInfo(a.shape,s.shape,l,f,c,h,!0),y=J().getBool("WEBGL_PACK_DEPTHWISECONV")&&A.strideWidth<=2&&A.outChannels/A.inChannels==1,g=d?vp(d,y):null,x=[a,s],v=i!=null,b=o!=null,w=d==="leakyrelu";if(v&&x.push(i),b&&x.push(o),w){let C=n.makeTensorInfo([],"float32",_.createScalarValue(p,"float32"));x.push(C),m.push(C)}let k;y?k=new y7(A,v,g,b,w):k=new A7(A,v,g,b,w);let N=n.runWebGLProgram(k,x,"float32");return m.forEach(C=>n.disposeIntermediateTensorInfo(C)),N}var FU={kernelName:ui,backendName:"webgl",kernelFunc:MU},$U=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let r=lt(t.length),a=lt(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
${r} strides = ${r}(${this.strides});
void main() {
${a} coords = getOutputCoords();
int flattenIndex = 0;
for (int j = 0; j < ${this.sliceDim}; j++) {
int index = round(getIndices(coords[0], j));
flattenIndex += index * ${s};
}
setOutput(getX(flattenIndex, coords[1]));
}
`}};function DU(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=a.shape,i=s[s.length-1],[o,l,c,u]=E.prepareAndValidate(r,a),h=fe({inputs:{x:a},backend:n,attrs:{shape:[l,i]}}),d=fe({inputs:{x:r},backend:n,attrs:{shape:[_.sizeFromShape(r.shape)/c,c]}}),p=new $U(i,u,[l,c]),m=n.runWebGLProgram(p,[d,h],d.dtype),f=fe({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(m),f}var OU={kernelName:Co,backendName:"webgl",kernelFunc:DU},PU=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=lt(this.rank),r=zU(e,2);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function zU(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let a=0;a<e.length;a++)a===2?r.push("int(getIndices(resRC.x, resRC.z))"):r.push(`${n[a]}`);return r.join()}function LU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=r,l=_.parseAxisParam(i,a.shape)[0],c=E.segment_util.collectGatherOpShapeInfo(a,s,l,o),u=_.sizeFromShape(s.shape),h=[],d=fe({inputs:{x:a},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),p=fe({inputs:{x:s},backend:n,attrs:{shape:[c.batchSize,u/c.batchSize]}});h.push(d),h.push(p);let m=[c.batchSize,c.outerSize,u/c.batchSize,c.sliceSize];if(n.shouldExecuteOnCPU([a,s])||a.dtype==="string"){let g=n.bufferSync(p),x=n.bufferSync(d),v=sW(x,g,m);return h.forEach(b=>n.disposeIntermediateTensorInfo(b)),n.makeTensorInfo(c.outputShape,v.dtype,v.values)}let f=new PU(d.shape,m),A=n.runWebGLProgram(f,[d,p],d.dtype);h.push(A);let y=fe({inputs:{x:A},backend:n,attrs:{shape:c.outputShape}});return h.forEach(g=>n.disposeIntermediateTensorInfo(g)),y}var WU={kernelName:Eo,backendName:"webgl",kernelFunc:LU},BU="return float(a > b);",VU=`
return vec4(greaterThan(a, b));
`,jU=nn({opSnippet:BU,packedOpSnippet:VU,cpuKernelImpl:iW,dtype:"bool"}),UU={kernelName:Ro,backendName:"webgl",kernelFunc:jU},HU="return float(a >= b);",GU=`
return vec4(greaterThanEqual(a, b));
`,qU=nn({opSnippet:HU,packedOpSnippet:GU,dtype:"bool"}),XU={kernelName:Es,backendName:"webgl",kernelFunc:qU};function KU(e){let{inputs:t,backend:n}=e,{input:r}=t;return _7(r,!0,n)}var ZU={kernelName:Zh,backendName:"webgl",kernelFunc:KU},YU="return float(!isnan(x) && !isinf(x));",JU=Xe({opSnippet:YU,dtype:"bool"}),QU={kernelName:Mo,backendName:"webgl",kernelFunc:JU},eH="return float(isinf(x));",tH=Xe({opSnippet:eH,dtype:"bool"}),nH={kernelName:Fo,backendName:"webgl",kernelFunc:tH},rH="return float(isnan(x));",aH=Xe({opSnippet:rH,dtype:"bool"}),sH={kernelName:$o,backendName:"webgl",kernelFunc:aH},iH="return float(a < b);",oH=`
return vec4(lessThan(a, b));
`,lH=nn({opSnippet:iH,packedOpSnippet:oH,cpuKernelImpl:oW,dtype:"bool"}),uH={kernelName:Do,backendName:"webgl",kernelFunc:lH},cH="return float(a <= b);",hH=`
return vec4(lessThanEqual(a, b));
`,dH=nn({opSnippet:cH,packedOpSnippet:hH,dtype:"bool"}),pH={kernelName:Oo,backendName:"webgl",kernelFunc:dH};function fH(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=lW(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var mH={kernelName:Jh,backendName:"webgl",kernelFunc:fH},AH=`if (x < 0.0) return NAN;
return log(x);`,yH=`
vec4 result = log(x);
vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
result.r = isNaN.r == 1.0 ? NAN : result.r;
result.g = isNaN.g == 1.0 ? NAN : result.g;
result.b = isNaN.b == 1.0 ? NAN : result.b;
result.a = isNaN.a == 1.0 ? NAN : result.a;
return result;
`,gH=Xe({opSnippet:AH,packedOpSnippet:yH,cpuKernelImpl:uW}),xH={kernelName:Ms,backendName:"webgl",kernelFunc:gH},bH="return log(1.0 + x);",wH=Xe({opSnippet:bH}),_H={kernelName:zo,backendName:"webgl",kernelFunc:wH},vH="return float(a >= 1.0 && b >= 1.0);",kH=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,IH=nn({opSnippet:vH,packedOpSnippet:kH,dtype:"bool"}),SH={kernelName:Po,backendName:"webgl",kernelFunc:IH},NH="return float(!(x >= 1.0));",TH=Xe({opSnippet:NH}),EH={kernelName:Tu,backendName:"webgl",kernelFunc:TH},CH="return float(a >= 1.0 || b >= 1.0);",RH=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,MH=nn({opSnippet:CH,packedOpSnippet:RH,dtype:"bool"}),FH={kernelName:Eu,backendName:"webgl",kernelFunc:MH},$H=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
int d = coords[3];
float x = getX(b, r, c, d);
float sum = 0.0;
for (int j = -${s}; j <= ${s}; j++) {
int idx = d + j;
if (idx >= 0 && idx <= ${i}) {
float z = getX(b, r, c, idx);
sum += z * z;
}
}
float val = x * ${o};
setOutput(val);
}
`}},DH=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords.x;
int r = coords.y;
int c = coords.z;
int d = coords.w;
bool hasNextCol = d < ${this.outputShape[3]};
bool hasNextRow = c < ${this.outputShape[2]};
vec4 sum = vec4(0.);
vec4 xFragAtOutputCoords = getX(b, r, c, d);
vec4 xAtOutputCoords = vec4(
getChannel(xFragAtOutputCoords, vec2(c, d)),
hasNextCol ?
getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
hasNextRow ?
getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
(hasNextRow && hasNextCol) ?
getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
);
int firstChannel = d - ${s};
vec2 cache = vec2(0.);
if(firstChannel >= 0){
vec4 firstChannelFrag = getX(b, r, c, firstChannel);
cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
if(hasNextRow){
cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
}
}
ivec2 depth = ivec2(d, d + 1);
for (int j = - ${s}; j <= ${s}; j++) {
ivec2 idx = depth + j;
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));
bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;
if(depthInRange || depthPlusOneInRange){
vec4 z = vec4(0.);
vec4 xFragAtCurrentDepth;
z.xz = cache.xy;
if(depthPlusOneInRange && hasNextCol){
xFragAtCurrentDepth = idx.y != d ?
getX(b, r, c, idx.y) : xFragAtOutputCoords;
z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
if(hasNextRow){
z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
}
}
cache.xy = z.yw;
sum += z * z;
}
}
vec4 result = xAtOutputCoords * ${o};
setOutput(result);
}
`}},OH=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r,c=J().getBool("WEBGL_PACK_NORMALIZATION")?new DH(a.shape,s,i,o,l):new $H(a.shape,s,i,o,l);return n.runWebGLProgram(c,[a],a.dtype)},zH={kernelName:Cu,backendName:"webgl",kernelFunc:OH},PH=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=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
float result = 0.0;
for (int d = 0; d < ${this.depth}; ++d) {
int depthBegin = int(max(0.0, float(d - ${t})));
int depthEnd = int(min(float(${this.depth}),
float(d + ${t} + 1)));
const int MIN_DEPTH_BEGIN = 0;
const int MAX_DEPTH_END = ${this.depth};
float norm = 0.0;
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd) {
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
}
else {
break;
}
}
norm = float(${r}) * norm + float(${n});
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd){
float dyi = -2.0 * float(${r})
* float(${a})
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
/ norm;
if (k == d) {
dyi += pow(norm, -1.0 * ${a});
}
if (k == coords[3]) {
dyi *= getDy(b, r, c, d);
result += dyi;
}
}
else {
break;
}
}
}
setOutput(result);
}
`}},LH=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:c,beta:u}=r,h=new PH(a.shape,o,l,c,u);return n.runWebGLProgram(h,[a,s,i],a.dtype)},WH={kernelName:Qh,backendName:"webgl",kernelFunc:LH};function BH(e,t,n,r){let a=_.sizeFromShape(t),s=_.sizeFromShape(e.shape)/a,i=fe({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=$i(i,e.dtype,"max",r),l=fe({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}function k7(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=a.shape.length,l=_.parseAxisParam(s,a.shape),c=l,u=E.getAxesPermutation(c,o),h=u!=null,d=n.shouldExecuteOnCPU([a]),p=a;if(h){if(d){let g=n.texData.get(p.dataId).values,x=new Array(o);for(let w=0;w<x.length;w++)x[w]=a.shape[u[w]];let v=SA(g,a.shape,a.dtype,u,x);p=n.makeTensorInfo(x,a.dtype);let b=n.texData.get(p.dataId);b.values=v}else p=kp(a,u,n);c=E.getInnerMostAxes(c.length,o)}E.assertAxesAreInnerMostDims("max",c,o);let[m,f]=E.computeOutAndReduceShapes(p.shape,c),A=m;i&&(A=E.expandShapeToKeepDim(m,l));let y;if(d){let g=n.texData.get(p.dataId).values,x=cW(g,_.sizeFromShape(f),A,a.dtype);y=n.makeTensorInfo(A,a.dtype);let v=n.texData.get(y.dataId);v.values=x}else y=BH(p,f,A,n);return h&&n.disposeIntermediateTensorInfo(p),y}var VH={kernelName:Fs,backendName:"webgl",kernelFunc:k7},jH=j3+`
return max(a, b);
`,UH=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+_p+`
return result;
`,HH=nn({opSnippet:jH,packedOpSnippet:UH,cpuKernelImpl:hW}),GH={kernelName:$s,backendName:"webgl",kernelFunc:HH};function qH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;$l(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;_.assert(E.eitherStridesOrDilationsAreOne(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=E.computePool2DInfo(a.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&_.arraysEqual(u.inShape,u.outShape))return Vn({inputs:{x:a},backend:n});let h=new _c(u,"max",!1);return n.runWebGLProgram(h,[a],a.dtype)}var XH={kernelName:Ds,backendName:"webgl",kernelFunc:qH};function KH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:c}=r,u=[1,1,1],h=E.computePool3DInfo(a.shape,s,i,u,o,c,l),d=new EA(h,"max",!1);return n.runWebGLProgram(d,[a],a.dtype)}var ZH={kernelName:Ru,backendName:"webgl",kernelFunc:KH},YH=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*s-1;this.userCode=`
const ivec2 pads = ivec2(${i}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${a};
wR += ${r}) {
float dyR = float(dyRCorner + wR) / ${t}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${s}; wC++) {
float dyC = float(dyCCorner + wC) / ${n}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue = wR * ${s} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}},JH=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=o-1-e.padInfo.front,h=l-1-e.padInfo.top,d=c-1-e.padInfo.left,p=o*l*c-1;this.userCode=`
const ivec3 pads = ivec3(${u}, ${h}, ${d});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${o};
wD += ${a}) {
float dyD = float(dyDCorner + wD) / ${t}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${l};
wR += ${s}) {
float dyR = float(dyRCorner + wR) / ${n}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${c};
wC += ${i}) {
float dyC = float(dyCCorner + wC) / ${r}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
int maxPosValue = ${p} -
int(getMaxPos(batch, idyD, idyR, idyC, ch));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue =
wD * ${l} * ${c} +
wR * ${c} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
}
setOutput(dotProd);
}
`}};function QH(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=r,h=[1,1,1],d=E.computePool3DInfo(i.shape,o,l,h,c,u),p=new EA(d,"max",!0),m=n.runWebGLProgram(p,[i],i.dtype),f=new JH(d),A=n.runWebGLProgram(f,[a,m],i.dtype);return n.disposeIntermediateTensorInfo(m),A}var eG={kernelName:td,backendName:"webgl",kernelFunc:QH};function tG(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;$l([s,i],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:h}=r,d=E.computePool2DInfo(o.shape,l,c,1,u,h),p=!0,m=new _c(d,"max",p),f=n.runWebGLProgram(m,[o],o.dtype),A=new YH(d),y=n.runWebGLProgram(A,[a,f],o.dtype);return n.disposeIntermediateTensorInfo(f),y}var nG={kernelName:ed,backendName:"webgl",kernelFunc:tG};function rG(e,t,n,r){let a=new _c(n,"max",!1),s=r.runWebGLProgram(a,[e],"float32");a=new _c(n,"max",!0,!0,t);let i=r.runWebGLProgram(a,[e],"float32");return[s,i]}var aG={kernelName:nd,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;_.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let c=[1,1];_.assert(E.eitherStridesOrDilationsAreOne(s,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${c}'`);let u=E.computePool2DInfo(r.shape,a,s,c,i),[h,d]=rG(r,o,u,l);return[h,d]}};function sG(e,t,n,r){let a=_.sizeFromShape(t),s=_.sizeFromShape(e.shape)/a,i=fe({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=$i(i,"float32","mean",r),l=fe({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}var iG={kernelName:Os,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{keepDims:a,axis:s}=t,i=n,o=r.shape.length,l=_.parseAxisParam(s,r.shape),c=l,u=E.getAxesPermutation(c,o),h=u!=null,d=i.shouldExecuteOnCPU([r]),p=[],m=r;if(h){if(d){let x=i.texData.get(m.dataId).values,v=new Array(o);for(let k=0;k<v.length;k++)v[k]=r.shape[u[k]];let b=SA(x,r.shape,r.dtype,u,v);m=i.makeTensorInfo(v,r.dtype);let w=i.texData.get(m.dataId);w.values=b}else m=kp(r,u,i);p.push(m),c=E.getInnerMostAxes(c.length,o)}E.assertAxesAreInnerMostDims("sum",c,o);let[f,A]=E.computeOutAndReduceShapes(m.shape,c),y=f;a&&(y=E.expandShapeToKeepDim(f,l));let g=sG(m,A,y,i);for(let x of p)i.disposeIntermediateTensorInfo(x);return g}};function oG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=_.parseAxisParam(s,a.shape),c=l,u=E.getAxesPermutation(c,o),h=a;u!=null&&(h=fn({inputs:{x:a},backend:n,attrs:{perm:u}}),c=E.getInnerMostAxes(c.length,a.shape.length)),E.assertAxesAreInnerMostDims("min",c,o);let[d,p]=E.computeOutAndReduceShapes(h.shape,c),m=_.sizeFromShape(p),f=fe({inputs:{x:h},backend:n,attrs:{shape:[-1,m]}}),A=$i(f,f.dtype,"min",n),y;if(i){let g=E.expandShapeToKeepDim(d,l);y=fe({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=fe({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(h),y}var lG={kernelName:zs,backendName:"webgl",kernelFunc:oG},uG=j3+`
return min(a, b);
`,cG=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+_p+`
return result;
`,hG=nn({opSnippet:uG,packedOpSnippet:cG,cpuKernelImpl:dW}),dG={kernelName:Ps,backendName:"webgl",kernelFunc:hG},pG=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((c,u)=>c[0]+e[u]+c[1]);let r=e.length,a=lt(r),s=t.map(c=>c[0]).join(","),i=t.map((c,u)=>c[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),l=n==="reflect"?0:1;if(r===1){this.userCode=`
int start = ${s};
int end = ${i};
void main() {
int outC = getOutputCoords();
if (outC < start) {
outC = start * 2 - outC - ${l};
} else if(outC >= end) {
outC = (end - 1) * 2 - outC + ${l};
}
setOutput(getX(outC - start));
}
`;return}this.userCode=`
${a} start = ${a}(${s});
${a} end = ${a}(${i});
void main() {
${a} outC = getOutputCoords();
for (int i = 0; i < ${r}; i++) {
if (outC[i] < start[i]) {
outC[i] = start[i] * 2 - outC[i] - ${l};
} else if(outC[i] >= end[i]) {
outC[i] = (end[i] - 1) * 2 - outC[i] + ${l};
}
}
${a} coords = outC - start;
setOutput(getX(${o}));
}
`}},fG=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((p,m)=>p[0]+e[m]+p[1]);let r=e.length,a=lt(r),s=t.map(p=>p[0]).join(","),i=t.map((p,m)=>p[0]+e[m]).join(","),o=pn("rc",r),l=pn("source",r),c=`${o[r-1]} < ${this.outputShape[r-1]}`,u=r===1?"source":`vec2(${l.slice(-2).join()})`,h=n==="reflect"?0:1,d="";if(r===1){let p=`
${a} source = rc;
if (source < start) {
source = start * 2 - source - ${h};
} else if (source >= end) {
source = (end - 1) * 2 - source + ${h};
}
source -= start;
`;d=`
${a} rc = outputLoc;
${p}
result[0] = getChannel(getX(${l.join()}), ${u});
${o[r-1]} += 1;
if(${c}) {
${p}
result[1] = getChannel(getX(${l.join()}), ${u});
}
`}else{let p=`
${a} source = rc;
${a} lt = ${a}(lessThan(source, start));
${a} gte = ${a}(greaterThanEqual(source, end));
${a} orig = 1 - (lt + gte);
source = orig * source +
lt * (start * 2 - source - ${h}) +
gte * ((end - 1) * 2 - source + ${h});
source -= start;
`;d=`
${a} rc = outputLoc;
${p}
result[0] = getChannel(getX(${l.join()}), ${u});
${o[r-1]} += 1;
if(${c}) {
${p}
result[1] = getChannel(getX(${l.join()}), ${u});
}
rc = outputLoc;
${o[r-2]} += 1;
if(${o[r-2]} < ${this.outputShape[r-2]}) {
${p}
result[2] = getChannel(getX(${l.join()}), ${u});
${o[r-1]} += 1;
if(${c}) {
${p}
result[3] = getChannel(getX(${l.join()}), ${u});
}
}
`}this.userCode=`
const ${a} start = ${a}(${s});
const ${a} end = ${a}(${i});
void main() {
${a} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${d}
setOutput(result);
}
`}},mG=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:a,mode:s}=n,i=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new fG(r.shape,a,s):new pG(r.shape,a,s);return t.runWebGLProgram(i,[r],r.dtype)},AG={kernelName:Ls,backendName:"webgl",kernelFunc:mG},yG=`if (b == 0.0) return NAN;
return mod(a, b);`,gG=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+_p+`
return result;
`,xG=nn({opSnippet:yG,packedOpSnippet:gG}),bG={kernelName:Lo,backendName:"webgl",kernelFunc:xG},wG=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=`
uniform float seed;
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
float r = random(seed);
float cdf = 0.0;
for (int i = 0; i < ${t-1}; i++) {
cdf += getProbs(batch, i);
if (r < cdf) {
setOutput(float(i));
return;
}
}
// If no other event happened, last event happened.
setOutput(float(${t-1}));
}
`}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}},_G=`
if (a == b) {
return 1.0;
};
return a / b;`,vG=`
// vec4 one = vec4(equal(a, b));
// return one + (vec4(1.0) - one) * a / b;
vec4 result = a / b;
if(a.x == b.x) {
result.x = 1.;
}
if(a.y == b.y) {
result.y = 1.;
}
if(a.z == b.z) {
result.z = 1.;
}
if(a.w == b.w) {
result.w = 1.;
}
return result;
`,I7=nn({opSnippet:_G,packedOpSnippet:vG,checkOutOfBounds:!0}),kG={kernelName:ks,backendName:"webgl",kernelFunc:I7},S7="return a - b;",N7=nn({opSnippet:S7,packedOpSnippet:S7,supportsComplex:!0,cpuKernelImpl:wW}),IG={kernelName:ri,backendName:"webgl",kernelFunc:N7};function T7(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=_.parseAxisParam([s],a.shape),o=k7({inputs:{x:a},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=E.expandShapeToKeepDim(o.shape,i),c=fe({inputs:{x:o},backend:n,attrs:{shape:l}}),u=N7({inputs:{a,b:c},backend:n}),h=x7({inputs:{x:u},backend:n}),d=Ip({inputs:{x:h},backend:n,attrs:{axis:i,keepDims:!1}}),p=fe({inputs:{x:d},backend:n,attrs:{shape:l}}),m=I7({inputs:{a:h,b:p},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),m}var SG={kernelName:ti,backendName:"webgl",kernelFunc:T7};function NG(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r,l=o?a:T7({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),c=l.shape[0],u=l.shape[1],h=new wG(c,u,s),d=h.getCustomSetupFunc(i),p=n.runWebGLProgram(h,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),p}var TG={kernelName:rd,backendName:"webgl",kernelFunc:NG},E7="return -x;";function EG(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let s=n.texData.get(r.dataId),[i,o]=fW(s.values,r.shape,r.dtype);return n.makeTensorInfo(o,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new Wl(r.shape,E7):a=new qa(r.shape,E7),n.runWebGLProgram(a,[r],r.dtype)}var CG={kernelName:Wo,backendName:"webgl",kernelFunc:EG},RG=Gr.nonMaxSuppressionV3Impl;function MG(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=r,c=n.readSync(a.dataId),u=n.readSync(s.dataId),{selectedIndices:h}=RG(c,u,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var FG={kernelName:Vo,backendName:"webgl",kernelFunc:MG},$G=Gr.nonMaxSuppressionV4Impl;function DG(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:c}=r,u=n.readSync(a.dataId),h=n.readSync(s.dataId),{selectedIndices:d,validOutputs:p}=$G(u,h,i,o,l,c);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var OG={kernelName:jo,backendName:"webgl",kernelFunc:DG},zG=Gr.nonMaxSuppressionV5Impl;function PG(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:c}=r,u=n.readSync(a.dataId),h=n.readSync(s.dataId),d=i,p=o,m=l,f=c,{selectedIndices:A,selectedScores:y}=zG(u,h,d,p,m,f);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var LG={kernelName:Uo,backendName:"webgl",kernelFunc:PG},WG=class{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int index = round(getIndices(coords.x));
setOutput(mix(float(${r}), float(${n}),
float(index == coords.y)));
}
`}},BG=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=_.sizeFromShape(a.shape),c=new WG(l,s,i,o),u=fe({inputs:{x:a},backend:n,attrs:{shape:[l]}}),h=n.runWebGLProgram(c,[u],a.dtype);n.disposeIntermediateTensorInfo(u);let d=[...a.shape,s],p=fe({inputs:{x:h},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(h),p},VG={kernelName:Bs,backendName:"webgl",kernelFunc:BG};function Cp(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let a=kc({inputs:{input:r},backend:n}),s=Cp({inputs:{x:a},backend:n}),i=Ep({inputs:{input:r},backend:n}),o=Cp({inputs:{x:i},backend:n}),l=Xa({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return FA({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var jG={kernelName:ol,backendName:"webgl",kernelFunc:Cp};function C7(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(r.dtype==="complex64"){let a=kc({inputs:{input:r},backend:n}),s=C7({inputs:{x:a},backend:n}),i=Ep({inputs:{input:r},backend:n}),o=Cp({inputs:{x:i},backend:n}),l=Xa({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return FA({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var UG={kernelName:Ho,backendName:"webgl",kernelFunc:C7};function HG(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return MA({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{_.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),_.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let h=MA({inputs:{input:u},backend:n,attrs:{dim:a}});return o.push(h),h}),c=u7({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var GG={kernelName:Go,backendName:"webgl",kernelFunc:HG},qG=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let r=e.length,a=lt(r),s=t.map(l=>l[0]).join(","),i=t.map((l,c)=>l[0]+e[c]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);if(r===1){this.userCode=`
int start = ${s};
int end = ${i};
uniform float value;
void main() {
int outC = getOutputCoords();
if (outC < start || outC >= end) {
setOutput(value);
} else {
setOutput(getX(outC - start));
}
}
`;return}this.userCode=`
${a} start = ${a}(${s});
${a} end = ${a}(${i});
uniform float value;
void main() {
${a} outC = getOutputCoords();
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
setOutput(value);
} else {
${a} coords = outC - start;
setOutput(getX(${o}));
}
}
`}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},XG=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let r=e.length,a=lt(r),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=pn("rc",r),l=pn("source",r),c=`${o[r-1]} < ${this.outputShape[r-1]}`,u=r===1?"source":`vec2(${l.slice(-2).join()})`,h=[`${a} rc = outputLoc;`,`${o[r-1]} += 1;
if(${c}) {
`,r===1?"":`}
rc = outputLoc;
${o[r-2]} += 1;
if(${o[r-2]} < ${this.outputShape[r-2]}) {`,r===1?"":` ${o[r-1]} += 1;
if(${c}) {`],d=r===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",p="";for(let m=0,f=r===1?2:4;m<f;m++)p+=`
${h[m]}
if (${d}) {
result[${m}] = float(value);
} else {
${a} source = rc - start;
result[${m}] = getChannel(getX(${l.join()}), ${u});
}
`;p+=r===1?"} ":"}}",this.userCode=`
const ${a} start = ${a}(${s});
const ${a} end = ${a}(${i});
uniform float value;
void main() {
${a} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${p}
setOutput(result);
}
`}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},R7=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,constantValue:i}=r,o=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new XG(a.shape,s,i):new qG(a.shape,s,i),l=o.getCustomSetupFunc(i);return n.runWebGLProgram(o,[a],a.dtype,l)},KG={kernelName:Vs,backendName:"webgl",kernelFunc:R7},ZG=`
if(a < 0.0 && floor(b) < b){
return NAN;
}
if (b == 0.0) {
return 1.0;
}
return (round(mod(b, 2.0)) != 1) ?
pow(abs(a), b) : sign(a) * pow(abs(a), b);
`,YG=`
// isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
vec4 result = multiplier * pow(abs(a), b);
// Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
bvec4 isExpZero = equal(b, vec4(0.0));
result.r = isExpZero.r ? 1.0 : result.r;
result.g = isExpZero.g ? 1.0 : result.g;
result.b = isExpZero.b ? 1.0 : result.b;
result.a = isExpZero.a ? 1.0 : result.a;
vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
`+_p+`
return result;
`,JG=nn({opSnippet:ZG,packedOpSnippet:YG}),QG={kernelName:js,backendName:"webgl",kernelFunc:JG};function eq(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=[],c=_.parseAxisParam(s,a.shape),u=c,h=E.getAxesPermutation(u,o),d=a;h!=null&&(d=fn({inputs:{x:a},backend:n,attrs:{perm:h}}),u=E.getInnerMostAxes(u.length,o),l.push(d)),E.assertAxesAreInnerMostDims("prod",u,o);let p;if(n.shouldExecuteOnCPU([d])){let m=n.texData.get(d.dataId).values,{outVals:f,outShape:A,outDtype:y}=mW(d.shape,d.dtype,m,u);p=n.makeTensorInfo(A,y,f)}else{let[m,f]=E.computeOutAndReduceShapes(d.shape,u),A=_.sizeFromShape(f),y=fe({inputs:{x:d},backend:n,attrs:{shape:[-1,A]}}),g=Ad(a.dtype),x=$i(y,g,"prod",n);p=fe({inputs:{x},backend:n,attrs:{shape:m}}),l.push(y),l.push(x)}if(i){l.push(p);let m=E.expandShapeToKeepDim(p.shape,c);p=fe({inputs:{x:p},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),p}var tq={kernelName:qo,backendName:"webgl",kernelFunc:eq},M7=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=AW(r,a,s,i);return t.makeTensorInfo([o.length],i,o)},nq={kernelName:Mu,backendName:"webgl",kernelFunc:M7},rq="return 1.0 / x;",aq=Xe({opSnippet:rq}),sq={kernelName:Xo,backendName:"webgl",kernelFunc:aq},iq=Nr+`
return (x < 0.0) ? 0.0 : x;
`,oq=`
vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,lq=Xe({opSnippet:iq,packedOpSnippet:oq}),uq={kernelName:Hs,backendName:"webgl",kernelFunc:lq},cq=Nr+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,hq=`
vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,dq=Xe({opSnippet:cq,packedOpSnippet:hq}),pq={kernelName:qs,backendName:"webgl",kernelFunc:dq},fq=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":h="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${c[0]/u[0]},
${c[1]/u[1]});
const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${h};
// Compute the four integer indices.
ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));
ivec2 sourceCeilRC = ivec2(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);
vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);
float top = topLeft + (topRight - topLeft) * fracRC.y;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
float newValue = top + (bottom - top) * fracRC.x;
setOutput(newValue);
}
`}},mq=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":h="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${c[0]/u[0]},
${c[1]/u[1]},
${c[1]/u[1]});
const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
${o}.0);
float getAValue(int b, int r, int c, int d) {
return getChannel(getA(b, r, c, d), vec2(c, d));
}
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
// Calculate values for next column in yRC.z.
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
// Fractional source index.
vec3 sourceFracIndexRC = ${h};
// Compute the four integer indices.
ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
ivec3 sourceCeilRC = ivec3(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${l-1};
bool hasNextRow = coords.z < ${n-1};
// In parallel, construct four corners for all four components in
// packed 2x2 cell.
vec4 topLeft = vec4(
getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);
vec4 bottomLeft = vec4(
getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);
vec4 topRight = vec4(
getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);
vec4 bottomRight = vec4(
getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);
vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);
vec4 top = mix(topLeft, topRight, fracRC.yyzz);
vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
vec4 newValue = mix(top, bottom, fracRC.x);
setOutput(newValue);
}
`}};function Aq(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,u=J().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new mq(a.shape,l,c,s,i):new fq(a.shape,l,c,s,i);return n.runWebGLProgram(u,[a],"float32")}var yq={kernelName:Gs,backendName:"webgl",kernelFunc:Aq},gq=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],h=1/c,d=1/u,p=Math.ceil(h)*2+2,m=Math.ceil(d)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${c});
const float widthScale = float(${u});
const float invHeightScale = float(${h});
const float invWidthScale = float(${d});
const int winHeight = int(${p});
const int winWidth = int(${m});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(startRLerp - float(winHeight / 2));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(startCLerp - float(winWidth / 2));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${s}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${i}) {
continue;
}
float dxR = float(dyR) * heightScale;
int topDxRIndex = int(floor(dxR));
int bottomDxRIndex = int(min(ceil(dxR), ${r-1}.0));
float dxRLerp = dxR - float(topDxRIndex);
float inverseDxRLerp = 1.0 - dxRLerp;
float dxC = float(dyC) * widthScale;
int leftDxCIndex = int(floor(dxC));
int rightDxCIndex = int(min(ceil(dxC), ${a-1}.0));
float dxCLerp = dxC - float(leftDxCIndex);
float inverseDxCLerp = 1.0 - dxCLerp;
if (r == topDxRIndex && c == leftDxCIndex) {
// topLeft
accumulator +=
getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
}
if (r == topDxRIndex && c == rightDxCIndex) {
// topRight
accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
}
if (r == bottomDxRIndex && c == leftDxCIndex) {
// bottomLeft
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
}
if (r == bottomDxRIndex && c == rightDxCIndex) {
// bottomRight
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function xq(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new gq(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var bq={kernelName:id,backendName:"webgl",kernelFunc:xq},wq=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=r?"0.5":"0.0",d;a?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${c[0]/u[0]},
${c[1]/u[1]});
const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${d};
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestRC = ivec2(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${h})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`}};function _q(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,u=new wq(a.shape,l,c,s,i);return n.runWebGLProgram(u,[a],a.dtype)}var vq={kernelName:Fu,backendName:"webgl",kernelFunc:_q},kq=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],h=1/c,d=1/u,p=Math.ceil(h)*2+2,m=Math.ceil(d)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${c});
const float widthScale = float(${u});
const float invHeightScale = float(${h});
const float invWidthScale = float(${d});
const int winHeight = int(${p});
const int winWidth = int(${m});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(floor(startRLerp - float(winHeight / 2)));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(floor(startCLerp - float(winWidth / 2)));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${s}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${i}) {
continue;
}
float sourceFracRow =
float(${o[0]}) *
(float(dyR) / float(${l[0]}));
float sourceFracCol =
float(${o[1]}) *
(float(dyC) / float(${l[1]}));
int sourceNearestRow = int(min(
float(int(${r}) - 1),
${n} ? float(round(sourceFracRow)) :
float(floor(sourceFracRow))));
int sourceNearestCol = int(min(
float(int(${a}) - 1),
${n} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function Iq(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new kq(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Sq={kernelName:sd,backendName:"webgl",kernelFunc:Iq},Nq=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
void main() {
int coord = getOutputCoords();
setOutput(getX(${e[0]} - coord - 1));
}
`;return}let r=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,a=e.map((i,o)=>r(o)).join(","),s=lt(n);this.userCode=`
void main() {
${s} coords = getOutputCoords();
setOutput(getX(${a}));
}
`}},Tq=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let r=pn("rc",n),a=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,i=lt(n);n===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${e[0]} - rc - 1),
${e[0]} - rc - 1);
if(${a}){
result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),
${e[0]} - (rc + 1) - 1);
}
setOutput(result);
}
`:this.userCode=`
void main() {
${i} rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = ${o(r.slice())};
if(${a}){
result.g = ${l(r.slice())};
}
if(${s}) {
result.b = ${c(r.slice())};
if(${a}) {
result.a = ${u(r.slice())};
}
}
setOutput(result);
}
`;function o(p){return h(p)}function l(p){return p[n-1]="("+p[n-1]+" + 1)",h(p)}function c(p){return p[n-2]="("+p[n-2]+" + 1)",h(p)}function u(p){return p[n-1]="("+p[n-1]+" + 1)",p[n-2]="("+p[n-2]+" + 1)",h(p)}function h(p){let m=e.map((y,g)=>d(g,p)),f=m.join(","),A=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${A}))`}function d(p,m){return t.indexOf(p)!==-1&&e[p]!==1?`${e[p]} - ${m[p]} - 1`:`${m[p]}`}}};function Eq(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=a.shape.length,o=_.parseAxisParam(s,a.shape);if(i===0)return Vn({inputs:{x:a},backend:n});let l=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Tq(a.shape,o):new Nq(a.shape,o);return n.runWebGLProgram(l,[a],a.dtype)}var Cq={kernelName:Xs,backendName:"webgl",kernelFunc:Eq},Rq=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[];let n=e[1],r=e[2];this.outputShape=e;let a="";typeof t=="number"?a=`float outputValue = ${t.toFixed(2)};`:a=`
vec3 fill = vec3(${t.join(",")});
float outputValue = fill[coords[3]];`,this.userCode=`
uniform vec4 params;
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int y = coords[1];
float coordXFloat = (float(x) - params[0]) * params[3] -
(float(y) - params[1]) * params[2];
float coordYFloat = (float(x) - params[0]) * params[2] +
(float(y) - params[1]) * params[3];
int coordX = int(round(coordXFloat + params[0]));
int coordY = int(round(coordYFloat + params[1]));
${a}
if(coordX >= 0 && coordX < ${r} && coordY >= 0 && coordY < ${n}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`}getCustomSetupFunc(e,t,n,r){return(a,s)=>{this.paramsLoc==null&&(this.paramsLoc=a.getUniformLocationNoThrow(s,"params")),a.gl.uniform4f(this.paramsLoc,e,t,n,r)}}},Mq={kernelName:ll,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:a,fillValue:s,center:i}=t,o=n,l=new Rq(r.shape,s),[c,u]=E.getImageCenter(i,r.shape[1],r.shape[2]),h=l.getCustomSetupFunc(c,u,Math.sin(a),Math.cos(a));return o.runWebGLProgram(l,[r],r.dtype,h)}},Fq=`
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`,$q=Xe({opSnippet:Fq}),Dq={kernelName:Ks,backendName:"webgl",kernelFunc:$q},Oq="return inversesqrt(x);",zq=Xe({opSnippet:Oq,cpuKernelImpl:yW}),Pq={kernelName:Zs,backendName:"webgl",kernelFunc:zq},F7=class{constructor(e,t,n,r,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=lt(a.length),l=lt(s.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${c})`,h="";r===1?h="i":r===2&&(h="i, coords[1]");let d=`getUpdates(${h})`,p=t>1?"strides[j]":"strides";this.userCode=`
${o} strides = ${o}(${a});
void main() {
${l} coords = getOutputCoords();
float sum = 0.0;
bool found = false;
for (int i = 0; i < ${e}; i++) {
int flattenedIndex = 0;
for (int j = 0; j < ${t}; j++) {
int index = round(${u});
flattenedIndex += index * ${p};
}
if (flattenedIndex == coords[0]) {
sum += ${d};
found = true;
}
}
setOutput(mix(getDefaultValue(), sum, float(found)));
}
`}};function Lq(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a,updates:s}=t,{shape:i}=r,{sliceRank:o,numUpdates:l,sliceSize:c,strides:u,outputSize:h}=E.calculateShapes(s,a,i),d=[h/c,c];if(h===0)return n.makeTensorInfo(i,a.dtype);let p=fe({inputs:{x:a},backend:n,attrs:{shape:[l,o]}}),m=fe({inputs:{x:s},backend:n,attrs:{shape:[l,c]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),A=new F7(l,o,p.shape.length,m.shape.length,u,d),y=n.runWebGLProgram(A,[m,p,f],m.dtype),g=fe({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(f),g}var Wq={kernelName:Zo,backendName:"webgl",kernelFunc:Lq},Bq=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let r,a;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)a="resRC",r="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let c=0;c<t.length;c++)l.push(`${i[c]}`),c<e&&o.push(`${i[c]}`);r=o.join(),a=l.join()}let s=lt(n);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
float cVal = getC(${r});
if (cVal >= 1.0) {
setOutput(getA(${a}));
} else {
setOutput(getB(${a}));
}
}
`}};function Vq(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t,i=new Bq(r.shape.length,a.shape,a.shape.length);return n.runWebGLProgram(i,[r,a,s],dr(a.dtype,s.dtype))}var jq={kernelName:Yo,backendName:"webgl",kernelFunc:Vq},Uq=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${E.SELU_SCALEALPHA};
float scale = ${E.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,Hq=Xe({opSnippet:Uq}),Gq={kernelName:Jo,backendName:"webgl",kernelFunc:Hq},qq="return 1.0 / (1.0 + exp(-1.0 * x));",Xq=Xe({opSnippet:qq}),Kq={kernelName:Js,backendName:"webgl",kernelFunc:Xq},Zq=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,Yq=Xe({opSnippet:Zq}),Jq={kernelName:tl,backendName:"webgl",kernelFunc:Yq},Qq=X3+`
return sin(x);
`,eX=Xe({opSnippet:Qq}),tX={kernelName:Ys,backendName:"webgl",kernelFunc:eX},nX=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,rX=Xe({opSnippet:nX}),aX={kernelName:el,backendName:"webgl",kernelFunc:rX},sX=`
float epsilon = 1.1920928955078125e-7;
float threshold = log(epsilon) + 2.0;
bool too_large = x > -threshold;
bool too_small = x < threshold;
float result;
float exp_x = exp(x);
if (too_large){
result = x;
}
else if (too_small){
result = exp_x;
}
else{
result = log(exp_x + 1.0);
}
return result;
`,iX=Xe({opSnippet:sX}),oX={kernelName:nl,backendName:"webgl",kernelFunc:iX},lX=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,paddings:i}=r;_.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,g)=>y*g),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let c=[],u=R7({inputs:{x:a},backend:n,attrs:{paddings:l,constantValue:0}}),h=E.getReshaped(u.shape,s,o,!1),d=E.getPermuted(h.length,s.length,!1),p=E.getReshapedPermuted(u.shape,s,o,!1),m=fe({inputs:{x:u},backend:n,attrs:{shape:h}}),f=fn({inputs:{x:m},backend:n,attrs:{perm:d}}),A=fe({inputs:{x:f},backend:n,attrs:{shape:p}});return c.push(u),c.push(m),c.push(f),c.forEach(y=>n.disposeIntermediateTensorInfo(y)),A},uX={kernelName:$u,backendName:"webgl",kernelFunc:lX};function cX(e){let{inputs:t,backend:n}=e,{inputIndices:r,inputShape:a,newShape:s}=t;if(r.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${r.shape}`);if(a.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${a.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(n.readSync(a.dataId)),o=n.readSync(r.dataId),l=Array.from(n.readSync(s.dataId)),[c,u,h]=xW(o,r.shape,r.dtype,i,l);return[n.makeTensorInfo(u,r.dtype,c),n.makeTensorInfo([h.length],s.dtype,new Int32Array(h))]}var hX={kernelName:od,backendName:"webgl",kernelFunc:cX};function dX(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=r,{sliceRank:l,numUpdates:c,strides:u,outputSize:h}=E.calculateShapes(s,a,o),d=!1,p=new F7(c,l,a.shape.length,s.shape.length,u,[h,1],d),m=n.runWebGLProgram(p,[s,a,i],s.dtype),f=fe({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(m),f}var pX={kernelName:ld,backendName:"webgl",kernelFunc:dX};function fX(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=_.parseAxisParam(i,a.shape)[0],l=E.prepareSplitSize(a,s,o),c=a.shape.length,u=new Array(c).fill(0),h=a.shape.slice();return l.map(d=>{let p=[...h];p[o]=d;let m=vc({inputs:{x:a},backend:n,attrs:{begin:u,size:p}});return u[o]+=d,m})}var mX={kernelName:rl,backendName:"webgl",kernelFunc:fX},AX="return sqrt(x);",yX=Xe({opSnippet:AX}),gX={kernelName:Qs,backendName:"webgl",kernelFunc:yX},xX="return x * x;",bX=Xe({opSnippet:xX}),wX={kernelName:Du,backendName:"webgl",kernelFunc:bX},$7="return (a - b) * (a - b);",_X=nn({opSnippet:$7,packedOpSnippet:$7}),vX={kernelName:ni,backendName:"webgl",kernelFunc:_X};function kX({inputs:e,attrs:t,backend:n}){let{x:r}=e,a=Nr+`
return x > 0.0 ? 1.0 : float(${t.alpha});
`,s=new qa(r.shape,a);return n.runWebGLProgram(s,[r],r.dtype)}var IX={kernelName:$a,backendName:"webgl",kernelFunc:kX},SX=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let r=n.length,a=lt(n.length),s=lt(n.length),i="";if(r===1)i="coords * strides + begin";else{let o=0;i=n.map((l,c)=>(o++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${o-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
${a} begin = ${a}(${e});
${a} strides = ${a}(${t});
void main() {
${s} coords = getOutputCoords();
setOutput(getX(${i}));
}
`}};function NX(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:h,shrinkAxisMask:d}=r,{nonStrided:p,$begin:m,$strides:f,size:A,newShape:y,outShape:g}=un.sliceInfo(a.shape,s,i,o,l,c,u,h,d),x=fe({inputs:{x:a},backend:n,attrs:{shape:y}}),v;if(p){let w=vc({inputs:{x},backend:n,attrs:{begin:m,size:A}});v=fe({inputs:{x:w},backend:n,attrs:{shape:g}}),n.disposeIntermediateTensorInfo(w)}else if(g.some(w=>w===0))v=n.makeTensorInfo(g,a.dtype,[]);else if(n.shouldExecuteOnCPU([x])){let w=n.texData.get(x.dataId).values,k=Be(x.shape,x.dtype,w),N=bW(g,k,f,m);v=n.makeTensorInfo(g,x.dtype,N.values)}else{let w=new SX(m,f,g);v=n.runWebGLProgram(w,[x],x.dtype)}let b=fe({inputs:{x:v},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(v),b}var TX={kernelName:al,backendName:"webgl",kernelFunc:NX},EX="return tan(x);",CX=Xe({opSnippet:EX}),RX={kernelName:ai,backendName:"webgl",kernelFunc:CX},MX=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,FX=Xe({opSnippet:MX}),$X={kernelName:si,backendName:"webgl",kernelFunc:FX},OX=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.rank=n.length;let r=lt(this.rank),a=DX(e);this.userCode=`
void main() {
${r} resRC = getOutputCoords();
setOutput(getA(${a}));
}
`}};function DX(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[];for(let a=0;a<e.length;a++)r.push(`imod(${n[a]}, ${e[a]})`);return r.join()}function D7(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reps:s}=r;if(a.dtype==="string"||a.shape.length>5){let o=n.readSync(a.dataId).map(u=>_.decodeString(u)),l=Be(a.shape,a.dtype,o),c=_W(l,s);return n.makeTensorInfo(c.shape,c.dtype,c.values)}let i=new OX(a.shape,s);return n.runWebGLProgram(i,[a],a.dtype)}var zX={kernelName:Fa,backendName:"webgl",kernelFunc:D7};function PX(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{k:s,sorted:i}=r,o=n.readSync(a.dataId),[l,c]=vW(o,a.shape,a.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(c.shape,c.dtype,c.values)]}var LX={kernelName:sl,backendName:"webgl",kernelFunc:PX},WX=class{constructor(e,t,n,r,a,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=n==="nearest"?1:2,o;switch(r){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
float mapCoord(float outCoord, float len) {
float inCoord = outCoord;
if(${o} == 2) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
if (inCoord < sz2) {
inCoord = sz2 * float(int(float(-inCoord / sz2))) +
inCoord;
}
inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
inCoord -= sz2 * float(int(float(inCoord / sz2)));
if (inCoord >= len) {
inCoord = sz2 - inCoord - 1.0;
}
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${o} == 3) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord -= len * float(int(float(inCoord / sz)));
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${o} == 4) {
return clamp(outCoord, 0.0, len - 1.0);
} else {
return outCoord;
}
}
float readWithFillValue(int batch, int coordY, int coordX,
int channel) {
float outputValue;
if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {
outputValue = getImage(batch, coordY, coordX, channel);
} else {
outputValue = float(${a});
}
return outputValue;
}
void main() {
ivec4 coords = getOutputCoords();
float outputValue;
int batch = coords[0];
int x = coords[2];
int y = coords[1];
int channel = coords[3];
float xf = float(x);
float yf = float(y);
float a1 = getTransforms(batch, 0);
float a2 = getTransforms(batch, 1);
float a3 = getTransforms(batch, 2);
float b1 = getTransforms(batch, 3);
float b2 = getTransforms(batch, 4);
float b3 = getTransforms(batch, 5);
float c1 = getTransforms(batch, 6);
float c2 = getTransforms(batch, 7);
float projection = c1 * xf + c2 * yf + 1.0;
if (projection == 0.0) {
outputValue = float(${a});
} else {
float inX = (a1 * xf + a2 * yf + a3) / projection;
float inY = (b1 * xf + b2 * yf + b3) / projection;
float mapX = mapCoord(inX, float(${t}));
float mapY = mapCoord(inY, float(${e}));
if (${i} == 1) {
int coordY = int(round(mapY));
int coordX = int(round(mapX));
outputValue = readWithFillValue(batch, coordY, coordX,
channel);
} else {
float yFloor = floor(mapY);
float xFloor = floor(mapX);
float yCeil = yFloor + 1.0;
float xCeil = xFloor + 1.0;
float valueYFloor = (xCeil - mapX) *
readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yFloor), int(xCeil), channel);
float valueYCeil = (xCeil - mapX) *
readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yCeil), int(xCeil), channel);
outputValue = (yCeil - mapY) * valueYFloor +
(mapY - yFloor) * valueYCeil;
}
}
setOutput(outputValue);
}
`}};function BX(e){let{inputs:t,backend:n,attrs:r}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:c}=r,[u,h,d,p]=a.shape,[m,f]=c!=null?c:[h,d],A=[u,m,f,p],y=new WX(h,d,i,o,l,A);return n.runWebGLProgram(y,[a,s],"float32")}var VX={kernelName:ud,backendName:"webgl",kernelFunc:BX};function jX(e){let{inputs:t,attrs:n,backend:r}=e,{axis:a}=n,{x:s}=t;$l(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=r.readSync(s.dataId),{outputValues:o,outputShape:l,indices:c}=kW(i,a,s.shape,s.dtype);return[r.makeTensorInfo(l,s.dtype,o),r.makeTensorInfo([c.length],"int32",c)]}var UX={kernelName:cd,backendName:"webgl",kernelFunc:jX};function HX(e){let{inputs:t,backend:n,attrs:r}=e,{value:a}=t,{axis:s}=r;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],c=new Array(o-1),u=0;for(let f=0;f<o;f++)f!==s&&(c[u++]=i.shape[f]);let h=[],d=new Array(o).fill(0),p=i.shape.slice();p[s]=1;let m=new Array(l);for(let f=0;f<m.length;f++){d[s]=f;let A=vc({inputs:{x:i},backend:n,attrs:{begin:d,size:p}}),y=fe({inputs:{x:A},backend:n,attrs:{shape:c}});m[f]=y,h.push(A)}return h.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var GX={kernelName:il,backendName:"webgl",kernelFunc:HX},qX=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,r=e.batchSize,a=e.inSize,s=e.numSegments,i=s*Math.ceil(a/n);this.outputShape=[r,i];let o="0.0",l="sumValue",c=Math.floor(n/4)*4,u=n%4,h=`
sumValue += dot(values, segFilter);
`,d="";a%n>0&&(d=`
if (inIdx < 0 || inIdx >= ${a}) {
return initializationValue;
}
`);let p="";a%n>0&&(p=`
if (inIdx < 0 || inIdx >= ${a}) {
return -1.0;
}
`),this.userCode=`
const float initializationValue = ${o};
float getValue(int batch, int inIdx) {
${d}
return getX(batch, inIdx);
}
float getSegmentIdAtIndex(int inIdx) {
${p}
return getSegmentIds(inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = int(floor(float(outIdx) / float(
${s})) * float(${n}));
int currentSeg = int(mod(float(outIdx), float(${s})));
float sumValue = 0.0;
for (int i = 0; i < ${c}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
);
${h}
}
int inIdx = inOffset + ${c};
if (${u===1}) {
vec4 values = vec4(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
int inIdxSeg = int(getSegmentIdAtIndex(inIdx));
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
0,
0,
0
);
${h}
} else if (${u===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
0,
0
);
${h}
} else if (${u===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
0
);
${h}
}
setOutput(${l});
}
`}};function XX(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,segmentIds:s}=t,{numSegments:i}=r,o=a.shape.length,l=[],c=0,u=E.getAxesPermutation([c],o),h=a;u!=null&&(h=fn({inputs:{x:a},backend:n,attrs:{perm:u}}),l.push(h),c=E.getInnerMostAxes(1,o)[0]);let d=E.segment_util.computeOutShape(h.shape,c,i),p=_.sizeFromShape([h.shape[c]]),m=fe({inputs:{x:h},backend:n,attrs:{shape:[-1,p]}});l.push(m);let f=Ad(a.dtype),A=(v,b,w,k,N)=>{let C=v.shape[0],F=v.shape[1],O=E.segment_util.segOpComputeOptimalWindowSize(F,N),L={windowSize:O,inSize:F,batchSize:C,numSegments:N},V=new qX(L,b),j=n.compileAndRun(V,[v,w],k);if(l.push(j),j.shape[1]===N)return j;let U=M7({backend:n,attrs:{start:0,stop:N,step:1,dtype:"float32"}}),X=D7({inputs:{x:U},backend:n,attrs:{reps:[F/O]}});return l.push(U),l.push(X),A(j,b,X,k,N)},y=A(m,"unsortedSegmentSum",s,f,i),g=fe({inputs:{x:y},backend:n,attrs:{shape:d}}),x=g;if(u!=null){l.push(g);let v=E.getUndoAxesPermutation(u);x=fn({inputs:{x},backend:n,attrs:{perm:v}})}return l.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var KX={kernelName:Ou,backendName:"webgl",kernelFunc:XX},ZX=[zH,WH,_B,kB,NB,CB,MB,DB,zB,LB,jB,HB,XB,YB,aV,eV,oV,hV,uV,mV,yV,xV,vV,CV,MV,PV,WV,UV,qV,rB,JV,lj,cj,nj,fj,Aj,dj,xj,_j,Ij,Nj,Ej,Mj,Pj,Wj,$j,jj,Gj,Xj,Jj,nU,iU,uU,cU,hU,pU,mU,yU,xU,wU,IU,EU,RU,FU,OU,WU,UU,XU,nB,ZU,ZV,QU,nH,sH,sB,uH,pH,mH,_H,xH,SH,EH,FH,VH,ZH,XH,eG,nG,aG,GH,iG,lG,dG,AG,bG,TG,cB,CG,FG,OG,LG,$V,VG,UG,GG,KG,QG,oB,tq,nq,DV,kG,sq,pq,uq,dB,yq,bq,vq,Sq,Cq,Mq,Dq,Pq,Wq,jq,Gq,Kq,Jq,tX,aX,TV,SG,oX,uX,hX,pX,mX,gX,wX,vX,IX,TX,IG,xB,RX,$X,zX,LX,VX,bB,UX,GX,KX,jG];for(let e of ZX)ci(e);var Mn;(function(e){e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64"})(Mn||(Mn={}));var Ic;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid"})(Ic||(Ic={}));var O7;function YX(e){O7=e.wasm.cwrap(oi,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function JX(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t;if(a.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:h}=r,d=n.dataIdMap.get(a.dataId).id,p=n.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let N=n.dataIdMap.get(i.dataId);if(N.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${N.shape.length}.`);m=N.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,A=Ic[u];if(A==null)throw new Error(`${u} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?a.shape[2]:a.shape[1],g=c?s.shape[1]:s.shape[2],x=a.shape[0],v=n.makeOutput([x,y,g],a.dtype),b=n.dataIdMap.get(v.dataId).id,w=new Uint8Array(new Int32Array(a.shape).buffer),k=new Uint8Array(new Int32Array(s.shape).buffer);return O7(d,w,a.shape.length,p,k,s.shape.length,l,c,A,m,f,h||0,b),v}var QX={kernelName:oi,backendName:"wasm",setupFunc:YX,kernelFunc:JX};function mn(e){let t;function n(a){t=a.wasm.cwrap(e,null,["number","number"])}function r(a){let{backend:s,inputs:{x:i}}=a,o=s.dataIdMap.get(i.dataId).id,l=s.makeOutput(i.shape,i.dtype),c=s.dataIdMap.get(l.dataId).id;return _.sizeFromShape(l.shape)===0||t(o,c),l}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:r}}var eK=mn(lo);function An(e,t,n){let r;function a(i){r=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:l}=i,{a:c,b:u}=l,h=o.dataIdMap.get(c.dataId).id,d=o.dataIdMap.get(u.dataId).id,p=n!=null?n:c.dtype,m=E.assertAndGetBroadcastShape(c.shape,u.shape),f=o.makeOutput(m,p);if(_.sizeFromShape(m)===0)return f;let A=new Uint8Array(new Int32Array(c.shape).buffer),y=new Uint8Array(new Int32Array(u.shape).buffer),g=o.dataIdMap.get(f.dataId).id,x=()=>r(h,A,c.shape.length,d,y,u.shape.length,Mn[c.dtype],g);if(t&&c.dtype==="float32")return x(),f;let v=E.getBroadcastDims(c.shape,m),b=E.getBroadcastDims(u.shape,m),w=v.every((N,C)=>N===C),k=b.every((N,C)=>N===C);if(w&&k)return x(),f;throw new Error(`Broadcasting along outer dims is not yet supported for ${c.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:a,kernelFunc:s}}var tK=!0,nK=An(Ra,tK),z7;function rK(e){z7=e.wasm.cwrap(ps,null,["array","number","number","number"])}function aK(e){let{inputs:t,backend:n}=e,r=n.makeOutput(t[0].shape,t[0].dtype);if(_.sizeFromShape(r.shape)===0)return r;let a=t.map(o=>n.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(a).buffer),i=n.dataIdMap.get(r.dataId).id;return z7(s,a.length,Mn[r.dtype],i),r}var sK={kernelName:ps,backendName:"wasm",setupFunc:rK,kernelFunc:aK};function Rp(e){let{inputs:{x:t},backend:n}=e,r=n.makeOutput(t.shape,t.dtype),a=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(r).set(a),r}var iK={kernelName:Cs,backendName:"wasm",kernelFunc:Rp},P7;function oK(e){P7=e.wasm.cwrap(ii,null,["number","array","number","number","number","array","number"])}function Mp(e){let{inputs:t,backend:n,attrs:r}=e,[a,s]=uK(t.x.shape,r.perm),i=!0;for(let m=0;m<s.length;m++)s[m]!==m&&(i=!1);let o=lK(t.x.shape,r.perm),l={dataId:t.x.dataId,shape:a,dtype:t.x.dtype};if(i){let m=Rp({inputs:t,backend:n});return m.shape=o,m}let c=n.makeOutput(o,l.dtype),u=n.dataIdMap.get(l.dataId).id,h=n.dataIdMap.get(c.dataId).id,d=new Uint8Array(new Int32Array(s).buffer),p=new Uint8Array(new Int32Array(l.shape).buffer);return P7(u,p,l.shape.length,Mn[l.dtype],h,d,s.length),c}function lK(e,t){let n=new Array(e.length);for(let r=0;r<n.length;r++)n[r]=e[t[r]];return n}function uK(e,t){let n=[],r=[];for(let a=0;a<e.length;++a)e[a]!==1&&n.push(e[a]),e[t[a]]!==1&&r.push(t[a]);for(let a=0;a<r.length;++a){let s=-1;for(let i=0;i<r.length;++i)r[i]>=a&&(s===-1||r[s]>r[i])&&(s=i);r[s]=a}return[n,r]}var cK={kernelName:ii,backendName:"wasm",kernelFunc:Mp,setupFunc:oK};function Ka(e,t,n){let r=e.shape,a=e.shape.length,s=_.parseAxisParam(t,r),i=s,o=E.getAxesPermutation(i,a),l=null,c=!1;if(o!=null){let u=new Array(a);for(let d=0;d<u.length;d++)u[d]=r[o[d]];i=E.getInnerMostAxes(i.length,a),l=Mp({inputs:{x:e},attrs:{perm:o},backend:n});let h=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(l.dataId).id!==h&&(c=!0)}return{transposed:l,originalAxes:s,axes:i,inputWasTransposed:c}}var L7;function hK(e){L7=e.wasm.cwrap(ho,null,["number, number, number"])}function dK(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:u,originalAxes:h,inputWasTransposed:d}=Ka(i,a,t);if(d){let g=t.dataIdMap.get(c.dataId).id;l=c,o=g}let p=l.shape.length;E.assertAxesAreInnerMostDims("all",u,p);let[m,f]=E.computeOutAndReduceShapes(l.shape,u),A=_.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(_.sizeFromShape(l.shape)!==0){let g=t.dataIdMap.get(y.dataId).id;L7(o,A,g)}if(d&&t.disposeData(c.dataId),s){let g=E.expandShapeToKeepDim(y.shape,h);y.shape=g}return y}var pK={kernelName:ho,backendName:"wasm",setupFunc:hK,kernelFunc:dK},W7;function fK(e){W7=e.wasm.cwrap(po,null,["number, number, number"])}function mK(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:u,originalAxes:h,inputWasTransposed:d}=Ka(i,a,t);if(d){let g=t.dataIdMap.get(c.dataId).id;l=c,o=g}let p=l.shape.length;E.assertAxesAreInnerMostDims("any",u,p);let[m,f]=E.computeOutAndReduceShapes(l.shape,u),A=_.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(_.sizeFromShape(l.shape)!==0){let g=t.dataIdMap.get(y.dataId).id;W7(o,A,g)}if(d&&t.disposeData(c.dataId),s){let g=E.expandShapeToKeepDim(y.shape,h);y.shape=g}return y}var AK={kernelName:po,backendName:"wasm",setupFunc:fK,kernelFunc:mK},B7;function yK(e){B7=e.wasm.cwrap(fs,null,["number","number","number","number","number"])}function gK(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a}=r,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=i,l=s,{transposed:c,axes:u,inputWasTransposed:h}=Ka(s,a,t);if(h){let y=t.dataIdMap.get(c.dataId).id;y!==i&&(l=c,o=y)}let d=l.shape.slice(0,-1),p=t.makeOutput(d,"int32"),m=t.dataIdMap.get(p.dataId).id,f=_.sizeFromShape(p.shape),A=l.shape[u[0]];return B7(o,Mn[l.dtype],f,A,m),h&&t.disposeData(c.dataId),p}var xK={kernelName:fs,backendName:"wasm",kernelFunc:gK,setupFunc:yK},V7;function bK(e){V7=e.wasm.cwrap(ms,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function wK(e){let{inputs:t,attrs:n,backend:r}=e,a=t.x,s=r.dataIdMap.get(a.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=n,u=E.computePool2DInfo(a.shape,i,o,1,l,c),h=u.filterHeight,d=u.filterWidth,p=u.padInfo.top,m=u.padInfo.right,f=u.padInfo.bottom,A=u.padInfo.left,y=u.strideHeight,g=u.strideWidth,x=u.inChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);if(u.dilationWidth!==1||u.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${u.dilationHeight}, ${u.dilationWidth}].`);let v=r.makeOutput(u.outShape,"float32"),b=r.dataIdMap.get(v.dataId).id;return V7(s,a.shape[0],a.shape[1],a.shape[2],h,d,p,m,f,A,y,g,x,b),v}var _K={kernelName:ms,backendName:"wasm",setupFunc:bK,kernelFunc:wK};function Tr(e){let{inputs:t,attrs:n}=e,{x:r}=t,{shape:a}=n,s=_.sizeFromShape(r.shape),i=_.inferFromImplicitShape(a,s);return _.assert(s===_.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${r.shape}. 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All inputs should only appear once. Found: ${this.inputs.map(y=>y.name)}`);Za(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(y=>y.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let y of this.outputs){let g=y.sourceLayer,x=y.nodeIndex,v=y.tensorIndex;this.outputLayers.push(g),this.outputLayersNodeIndices.push(x),this.outputLayersTensorIndices.push(v)}for(let y of this.inputs){let g=y.sourceLayer,x=y.nodeIndex,v=y.tensorIndex;Kr(x===0,"input layer has >1 nodes"),Kr(v===0,"input layer has >1 tensors"),this.inputLayers.push(g),this.inputLayersNodeIndices.push(x),this.inputLayersTensorIndices.push(v)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let y=0;y<this.inputLayers.length;y++){let g=this.inputLayers[y];if(!(g instanceof ql))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${y} (0-based) originates from layer type ${g.getClassName()}.`);this.inputNames.push(g.name),this.feedInputShapes.push(g.batchInputShape),this.feedInputNames.push(g.name)}for(let y of this.outputLayers)this.outputNames.push(y.name);this.internalInputShapes=this.inputs.map(y=>y.shape),this.internalOutputShapes=this.outputs.map(y=>y.shape);let t={},n={},r={},a={},s={},i=[],o=(y,g,x,v,b,w)=>{(v==null||b==null||w==null)&&(v=y.sourceLayer,b=y.nodeIndex,w=y.tensorIndex);let k=v.inboundNodes[b];if(x.indexOf(k)!==-1)throw new Cr(`The tensor ${y.name} at layer "${v.name}" is part of a cycle.`);if(g.indexOf(k)!==-1)return;this.containerNodes.add(Jr.nodeKey(v,b)),v.id in s||(s[v.id]=Object.keys(s).length),x.indexOf(k)===-1&&x.push(k);let N=k.inboundLayers.length;for(let C=0;C<N;C++){let F=k.inputTensors[C],O=k.inboundLayers[C],L=k.nodeIndices[C],V=k.tensorIndices[C];o(F,g,x,O,L,V)}for(g.push(k);x.indexOf(k)>=0;)x.splice(x.indexOf(k),1);i.push(k)},l=[],c=[];for(let y of this.outputs)o(y,l,c);let u=i.slice().reverse();for(let y of u){n[y.id]=y,y.id in t||(t[y.id]=0);let g=t[y.id],x=r[y.outboundLayer.id]==null?0:r[y.outboundLayer.id];g=Math.max(g,x),r[y.outboundLayer.id]=g,a[y.outboundLayer.id]=y.outboundLayer,t[y.id]=g;for(let v=0;v<y.inboundLayers.length;v++){let b=y.inboundLayers[v],w=y.nodeIndices[v],k=b.inboundNodes[w],N=t[k.id]==null?0:t[k.id];t[k.id]=Math.max(g+1,N),n[k.id]=k}}let h={};for(let y in t){let g=t[y];g in h||(h[g]=[]),h[g].push(n[y])}let d={};for(let y in r){let g=r[y];g in d||(d[g]=[]),d[g].push(a[y])}let p=Object.keys(d).map(y=>parseInt(y,10)).sort(Op);this.layers=[];for(let y of p){let g=d[y];g.sort((x,v)=>{let b=s[x.id],w=s[v.id];return b<w?-1:b>w?1:0});for(let x of g)x instanceof Jr&&this.internalContainerRefs.push(x),this.layers.push(x)}this.layersByDepth=d,p=Object.keys(h).map(y=>parseInt(y,10)).sort(Op);let m=this.inputs.slice(),f=[];for(let y of p)for(let g of h[y]){let x=g.outboundLayer;if(x!=null){for(let v of g.inputTensors)if(m.indexOf(v)===-1)throw new Cr(`Graph disconnected: cannot obtain value for tensor ${v} at layer "${x.name}". The following previous layers were accessed without issue: ${f}`);for(let v of g.outputTensors)m.push(v);f.push(x.name)}}this.nodesByDepth=h;let A=this.layers.map(y=>y.name);for(let y of A){let g=A.filter(x=>x===y).length;if(g!==1)throw new Cr(`The name "${y}" is used ${g} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(A))}this.outboundNodes=[],this.inboundNodes=[],new Zp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let 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(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new B("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(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},r=0;for(let s of this.layers)for(let i of s.weights){if(n[i.originalName]!=null)throw new B(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,r++}let a=[];for(let s in e){let i=s;if(n[s]==null){let o=s.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(n[i]!=null)a.push([n[i],e[s]]);else if(t)throw new B(`Provided weight data has no target variable: ${s}`);delete n[i]}if(t){let s=[];for(let i in n)s.push(i);if(s.length>0)throw new B(`${s.length} of ${r} weights are not set: ${s}`)}hy(a)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${xy}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=gy(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return P(()=>{e=At(e);let n=new Bi;for(let r=0;r<this.inputs.length;++r)n.add(this.inputs[r],e[r]);return Lc(this.outputs,n,t)})}computeMask(e,t){return P(()=>{e=At(e);let n;return t==null?n=Di(null,e.length):n=At(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Xp(e);if(t.length!==this.inputLayers.length)throw new B(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],c=o.name+"_0_0";n[c]=l}let r=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Op);if(r.length>1)for(let i of r){let o=this.nodesByDepth[i];for(let l of o){let c=l.outboundLayer;if(this.inputLayers.map(m=>m.id).indexOf(c.id)!==-1)continue;let u=[];for(let m=0;m<l.inboundLayers.length;m++){let f=l.inboundLayers[m],A=l.nodeIndices[m],y=l.tensorIndices[m],g=`${f.name}_${A}_${y}`,x=n[g];u.push(x)}let h=c.computeOutputShape(Fn(u)),d=Xp(h),p=c.inboundNodes.indexOf(l);for(let m=0;m<d.length;m++){let f=`${c.name}_${p}_${m}`;n[f]=d[m]}}}let a=[],s=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],l=this.outputLayersNodeIndices[i],c=this.outputLayersTensorIndices[i],u=`${o.name}_${l}_${c}`;s.push(u)}for(let i=0;i<s.length;i++){let o=s[i];Kr(o in n),a.push(n[o])}return Fn(a)}runInternalGraph(e,t){t==null&&(t=Di(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let l=this.inputs[o],c=e[o],u=t[o];n[l.id]=[c,u]}let r=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Op);for(let o of r){let l=this.nodesByDepth[o];for(let c of l){let u=c.outboundLayer,h=c.inputTensors,d=c.outputTensors,p=new Array;for(let m of h)m.id in n&&p.push(n[m.id]);if(p.length===h.length){let m={},f,A,y,g;if(c.callArgs!=null&&(m=c.callArgs),p.length===1){let[x,v]=p[0];m.mask==null&&(m.mask=v),y=At(u.call(x,m)),g=At(u.computeMask(x,v)),f=[x],A=[v]}else f=p.map(x=>x[0]),A=p.map(x=>x[1]),m.mask==null&&(m.mask=A),y=At(u.call(f,m)),g=At(u.computeMask(f,A));if(u.activityRegularizer)throw new De("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let x=0;x<d.length;++x){let v=d[x],b=y[x],w=g[x];n[v.id]=[b,w]}}}}let a=[],s=[],i=[];for(let o of this.outputs){Kr(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[l,c]=n[o.id];i.push(l.shape),a.push(l),s.push(c)}return[a,s,i]}buildNodeConversionMap(e){let t={},n;for(let r of this.layers){n=r instanceof Jr?1:0;for(let a=0;a<r.inboundNodes.length;a++){let s=Jr.nodeKey(r,a);this.containerNodes.has(s)&&(t[s]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new B(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new B("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new B(`No such layer: ${e}`)}calculateLosses(){return P(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let r=Jr.nodeKey(t,n);this.containerNodes.has(r)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let s of this.layers){let i=s.getClassName(),o=s.getConfig(),l=[];for(let u=0;u<s.inboundNodes.length;u++){let h=s.inboundNodes[u],d=Jr.nodeKey(s,u),p={};if(this.containerNodes.has(d)){if(h.callArgs)try{JSON.stringify(h.callArgs),p=h.callArgs}catch(m){console.warn(`Layer ${s.name} was passed non-serializable keyword arguments: ${h.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),p={}}if(h.inboundLayers.length>0){let m=[];for(let f=0;f<h.inboundLayers.length;f++){let A=h.inboundLayers[f],y=h.nodeIndices[f],g=h.tensorIndices[f],x=Jr.nodeKey(A,y),v=t[x];v==null&&(v=0),m.push([A.name,v,g,p])}l.push(m)}}}let c={};c.name=s.name,c.className=i,c.config=o,c.inboundNodes=l,n.push(c)}e.layers=n;let r=[];for(let s=0;s<this.inputLayers.length;s++){let i=this.inputLayers[s],o=this.inputLayersNodeIndices[s],l=Jr.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.inputLayersTensorIndices[s];r.push([i.name,c,u])}e.inputLayers=r;let a=[];for(let s=0;s<this.outputLayers.length;s++){let i=this.outputLayers[s],o=this.outputLayersNodeIndices[s],l=Jr.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.outputLayersTensorIndices[s];a.push([i.name,c,u])}return e.outputLayers=a,e}static fromConfig(e,t,n={},r=!1){let a={},s={};function i(f,A){f.name in s?s[f.name].push(A):s[f.name]=[A]}function o(f,A){let y=[],g;for(let x of A){let v=x[0],b=x[1],w=x[2];if(g=x[3]==null?{}:x[3],!(v in a)){i(f,A);return}let k=a[v];if(k.inboundNodes.length<=b){i(f,A);return}let N=k.inboundNodes[b];y.push(N.outputTensors[w])}y.length>0&&f.apply(Fn(y),g)}function l(f){let A=f.name,y=$r(f,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(r),a[A]=y,f.inboundNodes.forEach(g=>{if(!(g instanceof Array))throw new B(`Corrupted configuration, expected array for nodeData: ${g}`);i(y,g)})}let c=t.name,u=t.layers;for(let f of u)l(f);for(;!ote(s);)for(let f of u){let A=a[f.name];if(A.name in s){let y=s[A.name];delete s[A.name];for(let g of y)o(A,g)}}let h=[],d=[],p=t.inputLayers;for(let f of p){let A=f[0],y=f[1],g=f[2];Kr(A in a);let x=a[A].inboundNodes[y].outputTensors;h.push(x[g])}let m=t.outputLayers;for(let f of m){let A=f[0],y=f[1],g=f[2];Kr(A in a);let x=a[A].inboundNodes[y].outputTensors;d.push(x[g])}return new e({inputs:h,outputs:d,name:c})}get stateful(){if(this._stateful)throw new B("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(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){P(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function zre(e,t,n){let r=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(a=>null);if(r===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==r)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${r} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let a=[];return t.forEach(s=>{s in e?a.push(e[s]):a.push(null)}),a}else throw new Error(`The model has multiple (${r}) outputs, so ${n} must be either an array with ${r} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function $6(e,t){return zre(e,t,"classWeight")}async function D6(e,t,n,r){if(t!=null||r!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let a=P(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let o=1;return e.argMax(o)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await a.data());we(a);let i=[];return s.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(n[o])}),sn(i,"float32")}else return null}function Pre(e,t){return z(e,t)}var Lre=32;function z6(e,t){let n,r,a=t;n=a.xs,r=a.ys,_.assert(n!=null&&r!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor. The provided Dataset instead generates ${t}`);let s=O6("input",e.inputNames,n),i=O6("output",e.outputNames,r),o=s[0].shape[0];_.assert(s.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${s.length} inputs. (Expected input keys: ${JSON.stringify(e.inputNames)})`),_.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs. (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<s.length;l++)_.assert(s[l].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${s[l].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);for(let l=0;l<i.length;l++)_.assert(i[l].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${i[l].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);return{xs:s,ys:i}}function O6(e,t,n){if(n instanceof Pe)return[n];if(Array.isArray(n))return _.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let r=[];for(let a of t){if(n[a]==null)throw new B(`The feature data generated by the dataset lacks the required ${e} key '${a}'.`);r.push(n[a])}return r}}function Wre(e){if(e.length===3)throw new De("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function Vre(e,t,n){let r=n.batchesPerEpoch!=null;if(_.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),_.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),_.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),_.assert(!r||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),_.assert(n.validationSplit==null,()=>"`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{let a=n.validationData!=null,s,i;if(a)if(P6(n.validationData))_.assert(n.validationBatches==null||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 ${n.validationBatches}`);else{let A=Wre(n.validationData);s=A.xs,i=A.ys}let o=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),c;a?c=l.slice().concat(l.map(A=>"val_"+A)):c=l.slice();let u=_6(n.callbacks,n.yieldEvery),h=n.verbose==null?1:n.verbose,{callbackList:d,history:p}=v6(u,h,n.epochs,null,null,Bre(t,n),null,a,c);d.setModel(e),e.history=p,await d.onTrainBegin(),e.stopTraining_=!1;let m=n.initialEpoch==null?0:n.initialEpoch,f=await t.iterator();for(;m<n.epochs;){let A={};await d.onEpochBegin(m);let y=0,g=0;for(r||(f=await t.iterator());r?y<n.batchesPerEpoch:!0;){let x=await f.next();if(r&&x.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${y} batches; interrupting training. 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t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),s=[],i=()=>{let c=[];for(let p=0;p<this.inputs.length;++p)c.push({key:this.inputs[p],value:n[p]});let u=new Bi(c),h=Lc(this.outputs,u,{training:!0}),d;for(let p=0;p<this.lossFunctions.length;++p){let m=this.lossFunctions[p](r[p],h[p]);a[p]!=null&&(m=Pre(m,a[p]));let f=It(m);t.push(f),p===0?d=m:d=se(d,m)}for(let p=0;p<this.metricsTensors.length;++p){let m;if(this.outputs.length>1&&p<this.outputs.length)m=t[p];else{let f=this.metricsTensors[p][0],A=this.metricsTensors[p][1];m=It(f(r[A],h[A]))}Ht(m),s.push(m)}return d=It(d),this.calculateLosses().forEach(p=>{d=se(d,p)}),d},o=this.collectedTrainableWeights.map(c=>c.read()),l=!0;return[this.optimizer_.minimize(i,l,o)].concat(s)}}makeTestFunction(){this.testFunction=e=>P(()=>{let t=[],n,r=e.slice(0,this.inputs.length),a=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=[];for(let l=0;l<this.inputs.length;++l)s.push({key:this.inputs[l],value:r[l]});let i=new Bi(s),o=Lc(this.outputs,i);for(let l=0;l<this.lossFunctions.length;++l){let c=this.lossFunctions[l],u=It(c(a[l],o[l]));l===0?n=u:n=se(n,u),t.push(n)}for(let l=0;l<this.metricsTensors.length;++l){let c=this.metricsTensors[l][0],u=this.metricsTensors[l][1],h=It(c(a[u],o[u]));t.push(h)}return t})}async fit(e,t,n={}){return Gre(this,e,t,n)}async fitDataset(e,t){return Vre(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),r=n[0],a=n[1],s=this.makeTrainFunction()(r.concat(a)),i=[];for(let o of s){let l=await o.data();i.push(l[0])}return we(s),Fn(i)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,r=n?this.trainableWeights:this.weights,a=this.getWeights(n);for(let s=0;s<r.length;++s)n&&!r[s].trainable||t.push({name:r[s].originalName,tensor:a[s]});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(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=wd().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-wd().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=ga(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>ga(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let r of t)if(typeof n[r]=="string")e[r]=ga(n[r]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[ga(r0(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>ga(r0(e)));{let e={};for(let t in this.metrics)e[t]=ga(r0(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(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let t=Pc(e.optimizer_config),n=$r(t),r;if(typeof e.loss=="string")r=Oi(e.loss);else if(Array.isArray(e.loss))r=e.loss.map(s=>Oi(s));else if(e.loss!=null){r={};for(let s in e.loss)r[s]=Oi(e.loss[s])}let a;if(Array.isArray(e.metrics))a=e.metrics.map(s=>Oi(s));else if(e.metrics!=null){a={};for(let s in e.metrics)a[s]=Oi(e.metrics[s])}this.compile({loss:r,metrics:a,optimizer:n})}async save(e,t){if(typeof e=="string"){let i=Nn.getSaveHandlers(e);if(i.length===0)throw new B(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new B(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new B("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await Nn.encodeWeights(this.getNamedWeights(t)),r=!1,a=null,s={modelTopology:this.toJSON(a,r),format:Yre,generatedBy:`TensorFlow.js tfjs-layers v${xy}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await Nn.encodeWeights(await this.optimizer.getWeights(),i);n.specs.push(...l),n.data=Nn.concatenateArrayBuffers([n.data,o])}if(this.userDefinedMetadata!=null){let i=!0;C6(this.userDefinedMetadata,this.name,i),s.userDefinedMetadata=this.userDefinedMetadata}return s.weightData=n.data,s.weightSpecs=n.specs,e.save(s)}setUserDefinedMetadata(e){C6(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};xa.className="Model";re.registerClass(xa);var j6=class extends xa{};j6.className="Functional";re.registerClass(j6);async function Jre(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let r=Pc(n),a=$r(r,t);if(e.weightsManifest!=null){let s=await Nn.loadWeights(e.weightsManifest,e.pathPrefix,a.weights.map(o=>o.originalName)),i={};for(let o of a.weights)i[o.originalName]=s[o.originalName];a.loadWeights(i),we(s)}return a}async function eae(e,t){if(t==null&&(t={}),typeof e=="string"){let n=Nn.getLoadHandlers(e,t);if(n.length===0)n.push(Nn.browserHTTPRequest(e,t));else if(n.length>1)throw new B(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return Qre(e,void 0,t)}async function Qre(e,t,n){if(n==null&&(n={}),e.load==null)throw new B("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let r=await e.load(),a=r.modelTopology;a.model_config!=null&&(a=a.model_config);let s=n.strict==null?!0:n.strict,i=r.weightData!=null&&r.weightSpecs!=null&&s,o=$r(Pc(a),t,i),l=r.trainingConfig;if(l!=null&&o.loadTrainingConfig(l),r.userDefinedMetadata!=null&&o.setUserDefinedMetadata(r.userDefinedMetadata),r.weightData!=null){if(r.weightSpecs==null)throw new B("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:c,optimizerWeights:u}=tae(r.weightData,r.weightSpecs);o.loadWeights(c,s),o.optimizer!=null&&u.length>0&&await o.optimizer.setWeights(u),we(c),we(u.map(h=>h.tensor))}return o}function tae(e,t){let n=Nn.decodeWeights(e,t),r={},a=[];return t.forEach(s=>{s.group==="optimizer"?a.push({name:s.name,tensor:n[s.name]}):r[s.name]=n[s.name]}),{modelWeights:r,optimizerWeights:a}}var Zl=class extends xa{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:qp("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(t=>t<0))throw new B(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof Zl||e instanceof xa,n;if(t){if(n=e,n.outputs.length!==1)throw new B("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(n.inputs.length!==1)throw new B("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new B("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let r=A6({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(r)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new B(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new B("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=m6(this.outputs[0])}this.inboundNodes=[],new Zp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:Di(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(r=>r.shape),outputShapes:this.outputs[0].shape})}else{let r=e.apply(this.outputs[0]);if(Array.isArray(r))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[r],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(at(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new xa({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new Cr("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new Cr("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new Cr("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new Cr("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},r=!1){let a,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new B("Legacy serialization format not supported yet.");a=t}else _.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),a=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof Zl))throw new De(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of a){let l=$r(o,void 0,r);r&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new B("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new B("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}};Zl.className="Sequential";re.registerClass(Zl);function nae(e){return new xa(e)}function rae(e){return new Zl(e)}function aae(e,t){return t==null&&(t={}),eae(e,t)}function o6(e){return A6(e)}function sae(e,t){gr.registerCallbackConstructor(e,t)}var Dn=class extends re.Serializable{getConfig(){return{}}},U6=class extends Dn{apply(e,t=1){return Ote(e,t)}};U6.className="elu";re.registerClass(U6);var H6=class extends Dn{apply(e){return Wd(e)}};H6.className="selu";re.registerClass(H6);var G6=class extends Dn{apply(e){return Hr(e)}};G6.className="relu";re.registerClass(G6);var q6=class extends Dn{apply(e){return P(()=>Il(6,Hr(e)))}};q6.className="relu6";re.registerClass(q6);var X6=class extends Dn{apply(e){return e}};X6.className="linear";re.registerClass(X6);var K6=class extends Dn{apply(e){return Tn(e)}};K6.className="sigmoid";re.registerClass(K6);var Z6=class extends Dn{apply(e){return Pte(e)}};Z6.className="hardSigmoid";re.registerClass(Z6);var Y6=class extends Dn{apply(e){return _i(e)}};Y6.className="softplus";re.registerClass(Y6);var J6=class extends Dn{apply(e){return zte(e)}};J6.className="softsign";re.registerClass(J6);var Q6=class extends Dn{apply(e){return gi(e)}};Q6.className="tanh";re.registerClass(Q6);var Iy=class extends Dn{apply(e,t=-1){return oc(e,t)}};Iy.className="softmax";re.registerClass(Iy);var e4=class extends Dn{apply(e,t=-1){return Fd(e,t)}};e4.className="logSoftmax";re.registerClass(e4);var t4=class extends Dn{apply(e,t=1){return P(()=>Tn(e.mul(t)).mul(e))}};t4.className="swish";re.registerClass(t4);var n4=class extends Dn{apply(e){return P(()=>z(e,gi(_i(e))))}};n4.className="mish";re.registerClass(n4);function es(e){return e.getClassName()}function Sy(e,t={}){return Ec(e,re.SerializationMap.getMap().classNameMap,t,"activation")}function ts(e){if(e==null){let t={};return t.className="linear",t.config={},Sy(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},Sy(t)}else return e instanceof Dn?e:Sy(e)}function Ny(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var r4=class extends re.Serializable{},Bc=class extends r4{constructor(e){super();Ny(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return P(()=>{let t=Rt([1]);return this.hasL1&&(t=se(t,Te(z(this.l1,zt(e))))),this.hasL2&&(t=se(t,Te(z(this.l2,$c(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Bc.className="L1L2";re.registerClass(Bc);function iae(e){return Ny(e),new Bc({l1:e!=null?e.l1:null,l2:0})}function oae(e){return Ny(e),new Bc({l2:e!=null?e.l2:null,l1:0})}var a4={l1l2:"L1L2"};function ut(e){return BA(e)}function s4(e,t={}){return Ec(e,re.SerializationMap.getMap().classNameMap,t,"regularizer")}function xt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in a4?a4[e]:e,config:{}};return s4(t)}else return e instanceof r4?e:s4(e)}var Ty=class extends qe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=ze(e);let n=Hr(e);return this.maxValue!=null&&(n=En(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};Ty.className="ReLU";re.registerClass(Ty);var Ey=class extends qe{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=ze(e);return ec(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Ey.className="LeakyReLU";re.registerClass(Ey);var Cy=class extends qe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=gt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=xt(e.alphaRegularizer),this.alphaConstraint=Vt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new B(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=at(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let r of this.sharedAxes)t[r-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let r=1;r<e.length;++r)n[r]=e[r];this.inputSpec=[new Ft({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=ze(e),ac(e,this.alpha.read())}getConfig(){let e={alphaInitializer:St(this.alphaInitializer),alphaRegularizer:ut(this.alphaRegularizer),alphaConstraint:Bt(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};Cy.className="PReLU";re.registerClass(Cy);var Ry=class extends qe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new De(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=ze(e);return _l(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Ry.className="ELU";re.registerClass(Ry);var My=class extends qe{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=ze(e);return n.mul(Mc(n.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};My.className="ThresholdedReLU";re.registerClass(My);var Fy=class extends qe{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Iy().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=ze(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Fy.className="Softmax";re.registerClass(Fy);function Yl(e,t,n){if(typeof e=="number")return Di(e,t);if(e.length!==t)throw new B(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let r=0;r<t;++r){let a=e[r];if(!Mte(a))throw new B(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${a}`)}return e}function Dr(e,t,n,r,a=1){if(e==null)return e;let s=t+(t-1)*(a-1),i;return n==="same"?i=e:i=e-s+1,Math.floor((i+r-1)/r)}function Qr(e,t,n,r){if(e==null)return null;if(r==="valid")e=e*t+Ja([n-t,0]);else if(r==="same")e=e*t;else throw new B(`Unsupport padding mode: ${r}.`);return e}function $y(e,t){return P(()=>(Ct(t),t==="channelsFirst"?Je(e,[0,2,3,1]):e))}function i4(e,t){return P(()=>(Ct(t),t==="channelsFirst"?Je(e,[0,2,3,4,1]):e))}function lae(e,t,n,r=1,a="valid",s,i=1){return P(()=>{if(s==null&&(s=Er()),Ct(s),e.shape.length!==3)throw new B(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new B(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new B(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=Je(e,[0,2,1])),a==="causal")throw new De("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Id(e,t,r,a==="same"?"same":"valid","NWC",i);return n!=null&&(o=Mr(o,n)),o})}function o4(e,t,n,r=[1,1],a="valid",s,i,o=null){return P(()=>{if(s==null&&(s=Er()),Ct(s),e.rank!==3&&e.rank!==4)throw new B(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new B(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=$y(e,s);if(a==="causal")throw new De("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Ha.conv2d({x:l,filter:t,strides:r,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=Je(l,[0,3,1,2])),l})}function uae(e,t,n,r=[1,1,1],a="valid",s,i){return P(()=>{if(s==null&&(s=Er()),Ct(s),e.rank!==4&&e.rank!==5)throw new B(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new B(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=i4(e,s);if(a==="causal")throw new De("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=wm(o,t,r,a==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Mr(o,n)),s==="channelsFirst"&&(o=Je(o,[0,4,1,2,3])),o})}var Dy=class extends qe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Dy.verifyArgs(t),this.rank=e,qt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new De(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Yl(t.kernelSize,e,"kernelSize"),this.strides=Yl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,sr(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ct(this.dataFormat),this.activation=ts(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=gt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Vt(t.biasConstraint),this.biasRegularizer=xt(t.biasRegularizer),this.activityRegularizer=xt(t.activityRegularizer),this.dilationRate=Yl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new B(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new B(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new B(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Kr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!jA(e.kernelSize,"number",1,3))throw new B(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:es(this.activation),useBias:this.useBias,biasInitializer:St(this.biasInitializer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),biasConstraint:Bt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Vc=class extends Dy{constructor(e,t){super(e,t);this.kernel=null,Vc.verifyArgs(t),this.filters=t.filters,qt(this.filters,"filters"),this.kernelInitializer=gt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Vt(t.kernelConstraint),this.kernelRegularizer=xt(t.kernelRegularizer)}build(e){e=at(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[t]}`);let 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 P(()=>{e=ze(e);let n,r=this.bias==null?null:this.bias.read(),a=Hv(this.activation.getClassName());if(a!=null&&this.rank===2)n=o4(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)n=lae(e,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=o4(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=uae(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new De("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=at(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let a=0;a<n.length;++a){let s=Dr(n[a],this.kernelSize[a],this.padding,this.strides[a],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[a]);t.push(s)}let r=[e[0]];return this.dataFormat==="channelsLast"?(r=r.concat(t),r.push(this.filters)):(r.push(this.filters),r=r.concat(t)),r}getConfig(){let e={filters:this.filters,kernelInitializer:St(this.kernelInitializer),kernelRegularizer:ut(this.kernelRegularizer),kernelConstraint:Bt(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new B(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},jc=class extends Vc{constructor(e){super(2,e);jc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!jA(e.kernelSize,"number",1,2))throw new B(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};jc.className="Conv2D";re.registerClass(jc);var Uc=class extends Vc{constructor(e){super(3,e);Uc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new B(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};Uc.className="Conv3D";re.registerClass(Uc);var Oy=class extends jc{constructor(e){super(e);if(this.inputSpec=[new Ft({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new B(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=at(e),e.length!==4)throw new B("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ft({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return P(()=>{let n=ze(e);if(n.shape.length!==4)throw new B(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,a=r[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=r[s],l=r[i],c=this.kernelSize[0],u=this.kernelSize[1],h=this.strides[0],d=this.strides[1],p=Qr(o,h,c,this.padding),m=Qr(l,d,u,this.padding),f=[a,p,m,this.filters];this.dataFormat!=="channelsLast"&&(n=Je(n,[0,2,3,1]));let A=Sd(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=Je(A,[0,3,1,2])),this.bias!=null&&(A=Mr(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=at(e);let t=e.slice(),n,r,a;this.dataFormat==="channelsFirst"?(n=1,r=2,a=3):(n=3,r=1,a=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[r]=Qr(t[r],o,s,this.padding),t[a]=Qr(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Oy.className="Conv2DTranspose";re.registerClass(Oy);var zy=class extends Uc{constructor(e){super(e);if(this.inputSpec=[new Ft({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new B(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=at(e),e.length!==5)throw new B("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ft({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return P(()=>{let n=ze(e);if(n.shape.length!==5)throw new B(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,a=r[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=r[o],c=r[s],u=r[i],h=this.kernelSize[0],d=this.kernelSize[1],p=this.kernelSize[2],m=this.strides[0],f=this.strides[1],A=this.strides[2],y=Qr(l,m,h,this.padding),g=Qr(c,f,d,this.padding),x=Qr(u,A,p,this.padding),v=[a,y,g,x,this.filters];this.dataFormat!=="channelsLast"&&(n=Je(n,[0,2,3,4,1]));let b=nw(n,this.kernel.read(),v,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(b=Je(b,[0,4,1,2,3])),this.bias!==null&&(b=Mr(b,this.bias.read(),this.dataFormat)),this.activation!==null&&(b=this.activation.apply(b)),b})}computeOutputShape(e){e=at(e);let t=e.slice(),n,r,a,s;this.dataFormat==="channelsFirst"?(n=1,r=2,a=3,s=4):(n=4,r=1,a=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],c=this.strides[0],u=this.strides[1],h=this.strides[2];return t[n]=this.filters,t[r]=Qr(t[r],c,i,this.padding),t[a]=Qr(t[a],u,o,this.padding),t[s]=Qr(t[s],h,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};zy.className="Conv3DTranspose";re.registerClass(zy);var l4=class extends Vc{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new B("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new B("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new B(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=gt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=xt(t.depthwiseRegularizer),this.depthwiseConstraint=Vt(t.depthwiseConstraint),this.pointwiseInitializer=gt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=xt(t.pointwiseRegularizer),this.pointwiseConstraint=Vt(t.pointwiseConstraint)}build(e){if(e=at(e),e.length<this.rank+2)throw new B(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],r=this.kernelSize.concat([n,this.depthMultiplier]),a=[];for(let i=0;i<this.rank;++i)a.push(1);a.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",r,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",a,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new Ft({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return P(()=>{e=ze(e);let n;if(this.rank===1)throw new De("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Je(e,[0,2,3,1])),n=Wm(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Mr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Je(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=St(this.depthwiseInitializer),e.pointwiseInitializer=St(this.pointwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.pointwiseRegularizer=ut(this.pointwiseRegularizer),e.depthwiseConstraint=Bt(this.depthwiseConstraint),e.pointwiseConstraint=Bt(this.pointwiseConstraint),e}};l4.className="SeparableConv";var Py=class extends l4{constructor(e){super(2,e)}};Py.className="SeparableConv2D";re.registerClass(Py);var s0=class extends Vc{constructor(e){super(1,e);s0.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!jA(e.kernelSize,"number",1,1))throw new B(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};s0.className="Conv1D";re.registerClass(s0);var Ly=class extends qe{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[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 P(()=>{if(e=ze(e),this.dataFormat==="channelsLast"){let n=zp(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return zp(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=zp(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return zp(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ly.className="Cropping2D";re.registerClass(Ly);var Wy=class extends qe{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,Ete(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return P(()=>{let n=ze(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=Je(n,[0,2,3,1]);let a=this.size[0]*r[2],s=this.size[1]*r[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s]);return Je(i,[0,3,1,2])}else{let a=this.size[0]*r[1],s=this.size[1]*r[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Wy.className="UpSampling2D";re.registerClass(Wy);function cae(e,t,n=[1,1],r="valid",a,s){return P(()=>{a==null&&(a=Er()),Ct(a);let i=$y(e,a);if(e.rank!==4)throw new B(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new B(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=wl(i,t,n,r==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=Je(i,[0,3,1,2])),i})}var By=class extends Dy{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=gt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Vt(e.depthwiseConstraint),this.depthwiseRegularizer=xt(e.depthwiseRegularizer)}build(e){if(e=at(e),e.length<4)throw new B(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let 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 P(()=>{e=ze(e);let n=cae(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Mr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=at(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=Dr(t,this.kernelSize[0],this.padding,this.strides[0]),s=Dr(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],r,a,s]:[e[0],a,s,r]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=St(this.depthwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.depthwiseConstraint=Bt(this.depthwiseRegularizer),e}};By.className="DepthwiseConv2D";re.registerClass(By);function u4(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new B("When inputs is an array, neither initialState or constants should be provided");r!=null&&(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(s){return s==null||Array.isArray(s)?s:[s]}return t=a(t),n=a(n),{inputs:e,initialState:t,constants:n}}function c4(e,t,n,r=!1,a,s,i=!1,o=!1){return P(()=>{let l=t.shape.length;if(l<3)throw new B(`Input should be at least 3D, but is ${l}D.`);let c=[1,0].concat(Rr(2,l));if(t=Je(t,c),s!=null)throw new De("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."),a!=null&&(a=a.asType("bool").asType("float32"),a.rank===l-1&&(a=Qt(a,-1)),a=Je(a,c)),r&&(t=Wn(t,0),a!=null&&(a=Wn(a,0)));let u=[],h,d=n,p=t.shape[0],m=mr(t),f;a!=null&&(f=mr(a));for(let y=0;y<p;++y){let g=m[y],x=P(()=>e(g,d));if(a==null)h=x[0],d=x[1];else{let v=P(()=>{let b=f[y],w=Ln(b).sub(b),k=x[0].mul(b).add(d[0].mul(w)),N=d.map((C,F)=>x[1][F].mul(b).add(C.mul(w)));return{output:k,newStates:N}});h=v.output,d=v.newStates}o&&u.push(h)}let A;return o&&(A=cn(u,1)),[h,A,d]})}var Yr=class extends qe{constructor(e){super(e);let t;if(e.cell==null)throw new B("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new i0({cells:e.cell}):t=e.cell,t.stateSize==null)throw new B("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Ft({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Rr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){uy(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],r;if(this.returnSequences?r=[e[0],e[1],n]:r=[e[0],n],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[r].concat(a)}else return r}computeMask(e,t){return P(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let r=this.states.map(a=>null);return[n].concat(r)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new De("Constants support is not implemented in RNN yet.");uy(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new Ft({shape:[n,null,...r]});let a=[e[0]].concat(e.slice(2));if(t!=null)throw new De("Constants support is not implemented in RNN yet.");this.cell.build(a);let s;if(Array.isArray(this.cell.stateSize)?s=this.cell.stateSize:s=[this.cell.stateSize],this.stateSpec!=null){if(!_.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),s))throw new B(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new Ft({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){P(()=>{if(!this.stateful)throw new ya("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new B("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(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Rt([n,r])):this.states_=[Rt([n,this.cell.stateSize])];else if(e==null)we(this.states_),this.keptStates!=null&&(we(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Rt([n,r])):this.states_[0]=Rt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):we(this.states_);for(let r=0;r<this.states_.length;++r){let a=e[r],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[r]:this.cell.stateSize,i=[n,s];if(!_.arraysEqual(a.shape,i))throw new B(`State ${r} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${a.shape}`);this.states_[r]=a}}this.states_=this.states_.map(r=>Ht(r.clone()))})}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=u4(e,n,r,this.numConstants);e=a.inputs,n=a.initialState,r=a.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new Ft({shape:o.shape}));i=i.concat(this.stateSpec)}if(r!=null&&(t.constants=r,s=s.concat(r),this.numConstants=r.length),s[0]instanceof Fr){let o=[e].concat(s),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let u=super.apply(o,t);return this.inputSpec=c,u}else return super.apply(e,t)}call(e,t){return P(()=>{let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;e=ze(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new B(`RNN Layer has ${s} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:r},o=c4((d,p)=>{let m=this.cell.call([d].concat(p),i);return[m[0],m.slice(1)]},e,a,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],c=o[1],u=o[2];this.stateful&&this.resetStates(u,r);let h=this.returnSequences?c:l;return this.returnState?[h].concat(u):h})}getInitialState(e){return P(()=>{let t=Rt(e.shape);return t=Te(t,[1,2]),t=Fc(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?KA(t,[1,n]):t):this.cell.stateSize>1?[KA(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===Yr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let r=t.cell,a=$r(r,n);return new e(Object.assign(t,{cell:a}))}};Yr.className="RNN";re.registerClass(Yr);var Oc=class extends qe{},o0=class extends Oc{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,qt(this.units,"units"),this.activation=ts(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=Vt(e.kernelConstraint),this.recurrentConstraint=Vt(e.recurrentConstraint),this.biasConstraint=Vt(e.biasConstraint),this.dropout=Gl([1,Ja([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Gl([1,Ja([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=at(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 P(()=>{if(e=e,e.length!==2)throw new B(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let r=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ns({ones:()=>Ln(e),rate:this.dropout,training:r})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ns({ones:()=>Ln(n),rate:this.recurrentDropout,training:r}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=Zr(z(e,s),this.kernel.read()):a=Zr(e,this.kernel.read()),this.bias!=null&&(a=Mr(a,this.bias.read())),i!=null&&(n=z(n,i));let o=se(a,Zr(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:es(this.activation),useBias:this.useBias,kernelInitializer:St(this.kernelInitializer),recurrentInitializer:St(this.recurrentInitializer),biasInitializer:St(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Bt(this.kernelConstraint),recurrentConstraint:Bt(this.recurrentConstraint),biasConstraint:Bt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};o0.className="SimpleRNNCell";re.registerClass(o0);var Vy=class extends Yr{constructor(e){e.cell=new o0(e),super(e)}call(e,t){return P(()=>{this.cell.dropoutMask!=null&&(we(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(we(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return new e(t)}};Vy.className="SimpleRNN";re.registerClass(Vy);var l0=class extends Oc{constructor(e){super(e);if(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 B("GRUCell does not support reset_after parameter set to true.");this.units=e.units,qt(this.units,"units"),this.activation=ts(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ts(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=Vt(e.kernelConstraint),this.recurrentConstraint=Vt(e.recurrentConstraint),this.biasConstraint=Vt(e.biasConstraint),this.dropout=Gl([1,Ja([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Gl([1,Ja([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=at(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return P(()=>{if(e=e,e.length!==2)throw new B(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,r=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ns({ones:()=>Ln(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ns({ones:()=>Ln(r),rate:this.recurrentDropout,training:n,count:3}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=z(e,a[0]));let c=Zr(e,this.kernel.read());this.useBias&&(c=Mr(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(r=z(r,s[0]));let u=this.recurrentKernel.read(),[h,d]=Lt(u,[2*this.units,this.units],u.rank-1),p=Zr(r,h),[m,f,A]=Lt(c,3,c.rank-1),[y,g]=Lt(p,2,p.rank-1);i=this.recurrentActivation.apply(se(m,y)),o=this.recurrentActivation.apply(se(f,g));let x=Zr(z(o,r),d);l=this.activation.apply(se(A,x));let v=se(z(i,r),z(se(1,kt(i)),l));return[v,v]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:es(this.activation),recurrentActivation:es(this.recurrentActivation),useBias:this.useBias,kernelInitializer:St(this.kernelInitializer),recurrentInitializer:St(this.recurrentInitializer),biasInitializer:St(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Bt(this.kernelConstraint),recurrentConstraint:Bt(this.recurrentConstraint),biasConstraint:Bt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};l0.className="GRUCell";re.registerClass(l0);var jy=class extends Yr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new l0(e),super(e)}call(e,t){return P(()=>{this.cell.dropoutMask!=null&&(we(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(we(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};jy.className="GRU";re.registerClass(jy);var Hc=class extends Oc{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,qt(this.units,"units"),this.activation=ts(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ts(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=Vt(e.kernelConstraint),this.recurrentConstraint=Vt(e.recurrentConstraint),this.biasConstraint=Vt(e.biasConstraint),this.dropout=Gl([1,Ja([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Gl([1,Ja([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=at(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let r;if(this.useBias){if(this.unitForgetBias){let a=this.biasInitializer,s=this.units;r=new(t=class extends yr{apply(i,o){let l=a.apply([s]),c=new Lp().apply([s]),u=a.apply([s*2]);return t6(t6(l,c),u)}},t.className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return P(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new B(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1],a=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ns({ones:()=>Ln(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ns({ones:()=>Ln(r),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,c,u;0<this.dropout&&this.dropout<1&&(e=z(e,s[0]));let h=Zr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(r=z(r,i[0])),h=se(h,Zr(r,this.recurrentKernel.read())),this.useBias&&(h=Mr(h,this.bias.read()));let[d,p,m,f]=Lt(h,4,h.rank-1);o=this.recurrentActivation.apply(d),l=this.recurrentActivation.apply(p),c=se(z(l,a),z(o,this.activation.apply(m))),u=this.recurrentActivation.apply(f);let A=z(u,this.activation.apply(c));return[A,A,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:es(this.activation),recurrentActivation:es(this.recurrentActivation),useBias:this.useBias,kernelInitializer:St(this.kernelInitializer),recurrentInitializer:St(this.recurrentInitializer),biasInitializer:St(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Bt(this.kernelConstraint),recurrentConstraint:Bt(this.recurrentConstraint),biasConstraint:Bt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};Hc.className="LSTMCell";re.registerClass(Hc);var Uy=class extends Yr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Hc(e),super(e)}call(e,t){return P(()=>{this.cell.dropoutMask!=null&&(we(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(we(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Uy.className="LSTM";re.registerClass(Uy);var i0=class extends Oc{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return P(()=>{e=e;let n=e.slice(1),r=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?r.push(n.splice(0,i.stateSize.length)):r.push(n.splice(0,1));r.reverse();let a=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=r[i],i===0?s=[e[0]].concat(n):s=[s[0]].concat(n),s=o.call(s,t),a.push(s.slice(1))}n=[];for(let i of a.slice().reverse())n.push(...i);return[s[0]].concat(n)})}build(e){uy(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,r)=>{Pi(`RNNCell_${r}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let r=[];for(let a of t.cells)r.push($r(a,n));return new e({cells:r})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return cy(e)}setWeights(e){let t=[];for(let n of this.cells){let r=n.weights.length,a=e.splice(r);for(let s=0;s<n.weights.length;++s)t.push([n.weights[s],a[s]])}hy(t)}};i0.className="StackedRNNCells";re.registerClass(i0);function ns(e){let{ones:t,rate:n,training:r=!1,count:a=1}=e,s=()=>r6(t(),n),i=()=>Dc(s,t,r);return!a||a<=1?Ht(i().clone()):Array(a).fill(void 0).map(i).map(o=>Ht(o.clone()))}var hae=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(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var a=0,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},h4=class extends Yr{constructor(e){if(e.unroll)throw new De("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new De("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Ft({ndim:5})]}call(e,t){return P(()=>{if(this.cell.dropoutMask!=null&&(we(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(we(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new B("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}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 P(()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)],s=Rt(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){P(()=>{if(!this.stateful)throw new ya("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)];if(n[0]==null)throw new B("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(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Rt(a)):this.states_=[Rt(a)];else if(e==null)we(this.states_),this.keptStates!=null&&(we(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Rt(a)):this.states_[0]=Rt(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):we(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=a;if(!_.arraysEqual(i.shape,o))throw new B(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>Ht(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:r,padding:a,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],c=e[o?4:3],u=Dr(l,r[0],a,s[0],i[0]),h=Dr(c,r[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[n,u,h]:[u,h,n]]}};h4.className="ConvRNN2D";var u0=class extends Hc{constructor(e){let{filters:t,kernelSize:n,strides:r,padding:a,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,qt(this.filters,"filters"),this.kernelSize=Yl(n,2,"kernelSize"),this.kernelSize.forEach(o=>qt(o,"kernelSize")),this.strides=Yl(r||1,2,"strides"),this.strides.forEach(o=>qt(o,"strides")),this.padding=a||"valid",sr(this.padding),this.dataFormat=s||"channelsLast",Ct(this.dataFormat),this.dilationRate=Yl(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>qt(o,"dilationRate"))}build(e){var t;e=at(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[n]}`);let r=e[n],a=4,s=this.kernelSize.concat([r,this.filters*a]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*a]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;o=new(t=class extends yr{apply(u,h){let d=l.apply([c]),p=Pn([c]),m=l.apply([c*2]);return YA([d,p,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*a],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return P(()=>{if(e.length!==3)throw new B(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,r=e[0],a=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ns({ones:()=>Ln(r),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(Y,ae,te)=>!ae||!ae[te]?Y:z(ae[te],Y),c=l(r,o,0),u=l(r,o,1),h=l(r,o,2),d=l(r,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ns({ones:()=>Ln(a),rate:this.recurrentDropout,training:n,count:i}));let p=this.recurrentDropoutMask,m=l(a,p,0),f=l(a,p,1),A=l(a,p,2),y=l(a,p,3),g=3,[x,v,b,w]=Lt(this.kernel.read(),i,g),[k,N,C,F]=this.useBias?Lt(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,x,k,this.padding),u=this.inputConv(u,v,N,this.padding),h=this.inputConv(h,b,C,this.padding),d=this.inputConv(d,w,F,this.padding);let[O,L,V,j]=Lt(this.recurrentKernel.read(),i,g);m=this.recurrentConv(m,O),f=this.recurrentConv(f,L),A=this.recurrentConv(A,V),y=this.recurrentConv(y,j);let U=this.recurrentActivation.apply(se(c,m)),X=this.recurrentActivation.apply(se(u,f)),G=se(z(X,s),z(U,this.activation.apply(se(h,A)))),ee=z(this.recurrentActivation.apply(se(d,y)),this.activation.apply(G));return[ee,ee,G]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=hae(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){let a=ha(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Mr(a,n,this.dataFormat):a}recurrentConv(e,t){return ha(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};u0.className="ConvLSTM2DCell";re.registerClass(u0);var Hy=class extends h4{constructor(e){let t=new u0(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};Hy.className="ConvLSTM2D";re.registerClass(Hy);var c0=class extends qe{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(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let r=0;r<this.noiseShape.length;++r)n.push(this.noiseShape[r]==null?t[r]:this.noiseShape[r]);return n}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=ze(e);if(0<this.rate&&this.rate<1){let r=t.training==null?!1:t.training,a=this.getNoiseShape(n);return Dc(()=>r6(n,this.rate,a,this.seed),()=>n,r)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};c0.className="Dropout";re.registerClass(c0);var Gy=class extends c0{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Gy.className="SpatialDropout1D";re.registerClass(Gy);var qy=class extends qe{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,qt(this.units,"units"),this.activation=ts(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Vt(e.kernelConstraint),this.biasConstraint=Vt(e.biasConstraint),this.kernelRegularizer=xt(e.kernelRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.activityRegularizer=xt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=at(e);let t=e[e.length-1];this.kernel==null&&(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){e=at(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=ze(e),r=Hv(this.activation.getClassName()),a;return r!=null?a=Zr(n,this.kernel.read(),r,this.bias?this.bias.read():null):(a=Zr(n,this.kernel.read()),this.bias!=null&&(a=Mr(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:es(this.activation),useBias:this.useBias,kernelInitializer:St(this.kernelInitializer),biasInitializer:St(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Bt(this.kernelConstraint),biasConstraint:Bt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};qy.className="Dense";re.registerClass(qy);var Xy=class extends qe{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=at(e);for(let t of e.slice(1))if(t==null)throw new B(`The shape of the input to "Flatten" is not fully defined (got ${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],Ya(e,1)]}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=ze(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let r=[0];for(let a=2;a<n.rank;++a)r.push(a);r.push(1),n=n.transpose(r)}return Dte(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};Xy.className="Flatten";re.registerClass(Xy);var Ky=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.activation=ts(e.activation)}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=ze(e);return this.activation.apply(n)})}getConfig(){let e={activation:es(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};Ky.className="Activation";re.registerClass(Ky);var Zy=class extends qe{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 P(()=>(e=ze(e),Fte(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Zy.className="RepeatVector";re.registerClass(Zy);var Yy=class extends qe{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",r=t.slice(),a=1,s=null;for(let o=0;o<r.length;++o){let l=r[o];if(this.isUnknown(l))if(s===null)s=o;else throw new B("Can only specifiy one unknown dimension.");else a*=l}let i=Ya(e);if(s!==null){if(a===0||i%a!=0)throw new B(n);r[s]=i/a}else if(i!==a)throw new B(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 P(()=>{this.invokeCallHook(e,t);let n=ze(e),r=n.shape,a=r.slice(0,1).concat(this.fixUnknownDimension(r.slice(1),this.targetShape));return n.reshape(a)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Yy.className="Reshape";re.registerClass(Yy);var Jy=class extends qe{constructor(e){super(e);if(e.dims==null)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 ${e.dims} instead.`);let t=Rr(1,e.dims.length+1);if(!_.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 Ft({ndim:this.dims.length+1})]}computeOutputShape(e){e=at(e);let t=e.slice();return this.dims.forEach((n,r)=>{t[r+1]=e[n]}),t}call(e,t){return Je(ze(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};Jy.className="Permute";re.registerClass(Jy);var Qy=class extends qe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=ze(e),r=-1;return Xu(ki(n,this.maskValue),r)}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=ze(e),r=-1,a=!0,s=Xu(ki(n,this.maskValue),r,a);return n.mul(s.asType(n.dtype))})}};Qy.className="Masking";re.registerClass(Qy);var e2=class extends qe{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(At(e.inputLength))}this.inputDim=e.inputDim,qt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,qt(this.outputDim,"outputDim"),this.embeddingsInitializer=gt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=xt(e.embeddingsRegularizer),this.activityRegularizer=xt(e.activityRegularizer),this.embeddingsConstraint=Vt(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 P(()=>this.maskZero?(e=ze(e),ki(e,He(e))):null)}computeOutputShape(e){if(e=at(e),this.inputLength==null)return[...e,this.outputDim];let t=At(this.inputLength);if(t.length!==e.length-1)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let r=0;r<t.length;++r){let a=t[r],s=e[r+1];if(a!=null&&s!=null&&a!==s)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);a==null&&(t[n]=s),n++}}return[e[0],...t,this.outputDim]}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=ze(e);return n.dtype!=="int32"&&(n=Mc(n,"int32")),n6(this.embeddings.read(),n.as1D()).reshape(at(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:St(this.embeddingsInitializer),embeddingsRegularizer:ut(this.embeddingsRegularizer),activityRegularizer:ut(this.activityRegularizer),embeddingsConstraint:Bt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};e2.className="Embedding";re.registerClass(e2);var ji=class extends qe{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new De}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let r=0;r<t.length;++r){let a=e[e.length-t.length+r],s=t[r];if(a==null||s==null||a<0||s<0)n.push(null);else if(a===1)n.push(s);else if(s===1)n.push(a);else{if(a!==s)throw new B("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=[at(e)]),e=e,e.length<2)throw new B(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let a of e)a!=null&&a[0]!==null&&t.push(a[0]);if(t=Za(t),t.length>1)throw new B(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let a=1;a<e.length;++a){let s=e[a]==null?null:e[a].slice(1);n=this.computeElementwiseOpOutputShape(n,s)}let r=e.map(a=>a.length);e.indexOf(null)===-1&&Za(r).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return P(()=>{if(e=e,this.reshapeRequired){let n=[],r=e.map(a=>a.rank);if(r.indexOf(null)===-1){let a=Ja(r);for(let s of e){let i=s.rank;for(let o=0;o<a-i;++o)s=Fc(s,1);n.push(s)}return this.mergeFunction(n)}else{let a=!1;for(let o of e){let l=o.rank;if(l==null){let c=o.shape,u=c[0],h=c.slice(1).concat([u]),d=o.reshape([u].concat(Ya(c.slice(1))));d=Je(d,[1,0]),d=d.reshape(h),n.push(d),a=!0}else if(l>1){let c=Rr(1,l).concat([0]);n.push(Je(o,c)),a=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(a){if(i==null){let o=s.shape,l=o.length,c=o[l-1],u=[c].concat(o.slice(0,o.length-1));s=Je(s.reshape([-1,c]),[1,0]).reshape(u)}else if(i>1){let o=[i-1].concat(Rr(0,i-1));s=Je(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);t=this.computeElementwiseOpOutputShape(t,a)}let n=[];for(let r of e)r!=null&&r[0]!==null&&n.push(r[0]);return n=Za(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return P(()=>{if(t==null)return null;if(!Array.isArray(t))throw new B("`mask` should be an Array");if(!Array.isArray(e))throw new B("`inputs` should be an Array");if(t.length!==e.length)throw new B(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(r=>r==null))return null;t=t.map(r=>r==null?r:Qt(r,0));let n=t[0];for(let r=1;r<t.length-1;++r)n=fr(n,t[r]);return n})}},t2=class extends ji{constructor(e){super(e)}mergeFunction(e){return P(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=se(t,e[n]);return t})}};t2.className="Add";re.registerClass(t2);var n2=class extends ji{constructor(e){super(e)}mergeFunction(e){return P(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=z(t,e[n]);return t})}};n2.className="Multiply";re.registerClass(n2);var r2=class extends ji{constructor(e){super(e)}mergeFunction(e){return P(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=se(t,e[n]);return z(1/e.length,t)})}};r2.className="Average";re.registerClass(r2);var a2=class extends ji{constructor(e){super(e)}mergeFunction(e){return P(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Ur(t,e[n]);return t})}};a2.className="Maximum";re.registerClass(a2);var s2=class extends ji{constructor(e){super(e)}mergeFunction(e){return P(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Il(t,e[n]);return t})}};s2.className="Minimum";re.registerClass(s2);var i2=class extends ji{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new B("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let r of e)if(r!=null){t=!1;break}if(t)return;let n=[];for(let r=0;r<e.length;++r){let a=e[r].slice();a.splice(this.axis,1);let s=!1;for(let i of n)if(_.arraysEqual(i,a)){s=!0;break}s||n.push(a)}if(n.length>1)throw new B("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return P(()=>YA(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new B("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),r=this.axis<0?n.length+this.axis:this.axis;for(let a of t.slice(1)){if(n[r]==null||a[r]==null){n[r]=null;break}n[r]+=a[r]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new B("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new B("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new B(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return P(()=>{let n=!0;if(t.forEach(s=>{if(s!=null){n=!1;return}}),n)return null;let r=[];for(let s=0;s<e.length;++s)t[s]==null?r.push(Ln(e[s]).asType("bool")):t[s].rank<e[s].rank?r.push(Qt(t[s],-1)):r.push(t[s]);let a=ot(r,this.axis);return vd(a,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};i2.className="Concatenate";re.registerClass(i2);function Gc(e,t){for(;e<0;)e+=t;return e}function dae(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new De("batchDot is not implemented for tensors of 4D or higher rank yet");if(_.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),_.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new De("batchDot is not implemented for complex64-type Tensors yet.");let r=e.shape.length,a=t.shape.length;n==null&&(n=[r-1,a-2]);let s=n;return P(()=>{let i;if(r>a){i=r-a;let l=[];for(let c=0;c<i;++c)l.push(1);t=t.reshape(t.shape.concat(l))}else if(a>r){i=a-r;let l=[];for(let c=0;c<i;++c)l.push(1);e=e.reshape(e.shape.concat(l))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=e.mul(t).sum(s[0]):o=e.transpose([1,0]).mul(t).sum(s[1]);else{let l=s[0]!==e.shape.length-1,c=s[1]===t.shape.length-1;o=e.matMul(t,l,c)}if(i>0){let l;r>a?l=r+a-3:l=r-1;let c=[];for(let u=l;u<l+i;++u)c.push(u);o=o.squeeze(c)}return o.shape.length===1&&(o=o.expandDims(1)),o})}var o2=class extends ji{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){_.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new De("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);if(t[r[0]]!==n[r[1]])throw new B(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(e.length!==2)throw new B(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],r;return Array.isArray(this.axes)?r=this.axes.map((a,s)=>Gc(a,e[s].shape.length)):r=[Gc(this.axes,t.shape.length),Gc(this.axes,n.shape.length)],this.normalize&&(t=Yp(t,r[0]),n=Yp(n,r[1])),dae(t,n,r)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Gc(this.axes,e.length),Gc(this.axes,t.length)],n}computeOutputShape(e){_.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new De("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);t.splice(r[0],1),n.splice(r[1],1),n.splice(0,1);let a=t.concat(n);return a.length===1&&a.push(1),a}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};o2.className="Dot";re.registerClass(o2);var l2=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=ze(e);return Dc(()=>Pp(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};l2.className="GaussianNoise";re.registerClass(l2);var u2=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=ze(e);return this.rate>0&&this.rate<1?Dc(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return n.mul(Pp(n.shape,1,r))},()=>n,t.training||!1):n})}};u2.className="GaussianDropout";re.registerClass(u2);var c2=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||ze(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return P(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Dc(()=>{let r=ze(e),a=1.6732632423543772,s=1.0507009873554805,i=-a*s,o=ja(Sl(n),this.rate);o=Mc(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,c=-l*i*this.rate;return r.mul(o).add(o.add(-1).mul(i)).mul(l).add(c)},()=>ze(e),t.training||!1)}return e})}};c2.className="AlphaDropout";re.registerClass(c2);function qc(e,t,n,r,a,s=.001){let i;if(e.rank===2)i=Xb(e,t,n,r,a,s);else if(e.rank===3)i=Kb(e,t,n,r,a,s);else if(e.rank===4)i=Zb(e,t,n,r,a,s);else throw new De(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function pae(e,t,n,r,a=.001){return P(()=>{let s=Dd(e,r),i=s.mean,o=s.variance;return[qc(e,i,o,n,t,a),i,o]})}function fae(e,t,n,r,a=.001){return P(()=>{let s=Dd(e,r),i=s.mean,o=s.variance,l=[];for(let p of Rr(0,e.rank))r.indexOf(p)!==-1?l.push(1):l.push(e.shape[p]);let c=i.reshape(l),u=o.reshape(l),h=t==null?null:t.reshape(l),d=n==null?null:n.reshape(l);return[qc(e,c,u,d,h,a),i,o]})}function mae(e,t,n,r,a=.001){return _.arraysEqual(r.slice().sort(),Rr(0,e.rank-1))?pae(e,t,n,r,a):fae(e,t,n,r,a)}var h2=class extends qe{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=gt(e.betaInitializer||"zeros"),this.gammaInitializer=gt(e.gammaInitializer||"ones"),this.movingMeanInitializer=gt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=gt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Vt(e.betaConstraint),this.gammaConstraint=Vt(e.gammaConstraint),this.betaRegularizer=xt(e.betaRegularizer),this.gammaRegularizer=xt(e.gammaRegularizer)}build(e){e=at(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new B(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Ft({ndim:e.length,axes:{[t]:n}})];let r=[n];this.scale&&(this.gamma=this.addWeight("gamma",r,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",r,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",r,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",r,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return P(()=>{let n=t.training==null?!1:t.training,r=ze(e),a=r.shape,s=a.length,i=Rr(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=Di(1,s);l[o]=a[o];let c=i.slice();c.sort();let u=!_.arraysEqual(c,Rr(0,s).slice(0,s-1)),h=()=>{if(u){let A=this.movingMean.read().reshape(l),y=this.movingVariance.read().reshape(l),g=this.center?this.beta.read().reshape(l):null,x=this.scale?this.gamma.read().reshape(l):null;return qc(r,A,y,g,x,this.epsilon)}else return qc(r,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return h();let[d,p,m]=mae(r,this.gamma.read(),this.beta.read(),i,this.epsilon),f=(A,y,g)=>{P(()=>{let x=1-g,v=A.read(),b=v.sub(y).mul(x);A.write(v.sub(b))})};return(()=>{f(this.movingMean,p,this.momentum),f(this.movingVariance,m,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:St(this.betaInitializer),gammaInitializer:St(this.gammaInitializer),movingMeanInitializer:St(this.movingMeanInitializer),movingVarianceInitializer:St(this.movingVarianceInitializer),betaRegularizer:ut(this.betaRegularizer),gammaRegularizer:ut(this.gammaRegularizer),betaConstraint:Bt(this.betaConstraint),gammaConstraint:Bt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};h2.className="BatchNormalization";re.registerClass(h2);var d2=class extends qe{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=gt(e.betaInitializer||"zeros"),this.gammaInitializer=gt(e.gammaInitializer||"ones"),this.betaRegularizer=xt(e.betaRegularizer),this.gammaRegularizer=xt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=at(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let a=0;a<this.axis.length;++a)this.axis[a]<0&&(this.axis[a]+=t);for(let a of this.axis)if(a<0||a>=t)throw new Error(`Invalid axis: ${a}`);if(this.axis.length!==Za(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(a=>e[a]),r=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,r):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,r):this.beta=null,this.built=!0}call(e,t){let n=ze(e),r=n.shape,a=r.length;return P(()=>{let s=!0,{mean:i,variance:o}=Dd(n,this.axis,s),l=Di(1,a);for(let m of this.axis)l[m]=r[m];let c=m=>m!=null&&m.shape.length!==a&&this.axis!==[a-1]?m.reshape(l):m,u=c(this.gamma.read()),h=c(this.beta.read()),d=[],p=[];for(let m=0;m<a;++m)this.axis.indexOf(m)!==-1?(d.push(r[m]),p.push(1)):(d.push(1),p.push(r[m]));return i=i.tile(d),o=o.tile(d),u=u.tile(p),h=h.tile(p),qc(n,i,o,h,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:St(this.betaInitializer),gammaInitializer:St(this.gammaInitializer),betaRegularizer:ut(this.betaRegularizer),gammaRegularizer:ut(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};d2.className="LayerNormalization";re.registerClass(d2);function Aae(e,t,n){return P(()=>{if(e.rank!==4)throw new B(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new B("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=Er()),n!=="channelsLast"&&n!=="channelsFirst")throw new B(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let r;return n==="channelsFirst"?r=[[0,0],[0,0],t[0],t[1]]:r=[[0,0],t[0],t[1],[0,0]],da(e,r)})}var p2=class extends qe{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?Er():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new B(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new B(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new B(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Ft({ndim:4})]}computeOutputShape(e){e=at(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return P(()=>Aae(ze(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};p2.className="ZeroPadding2D";re.registerClass(p2);function h0(e,t,n,r,a,s){return P(()=>{Ct(a),Kv(s),sr(r),n==null&&(n=[1,1]),r==null&&(r="valid"),a==null&&(a=Er()),s==null&&(s="max"),e=$y(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=nc(e,t,n,o):i=Zu(e,t,n,o),a==="channelsFirst"&&(i=Je(i,[0,3,1,2])),i})}function d4(e,t,n,r,a,s){return P(()=>{Ct(a),Kv(s),sr(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),a==null&&(a=Er()),s==null&&(s="max"),e=i4(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=Fm(e,t,n,o):i=gm(e,t,n,o),a==="channelsFirst"&&(i=Je(i,[0,4,1,2,3])),i})}var p4=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new B(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(qt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new B(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);qt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,sr(this.padding),this.inputSpec=[new Ft({ndim:3})]}computeOutputShape(e){e=at(e);let t=Dr(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return P(()=>{this.invokeCallHook(e,t),e=Fc(ze(e),2);let n=this.poolingFunction(ze(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Ua(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},f2=class extends p4{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Ct(a),sr(r),h0(e,t,n,r,a,"max")}};f2.className="MaxPooling1D";re.registerClass(f2);var m2=class extends p4{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Ct(a),sr(r),h0(e,t,n,r,a,"avg")}};m2.className="AveragePooling1D";re.registerClass(m2);var f4=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new B(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];qt(this.poolSize,"poolSize"),qt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),sr(this.padding),this.inputSpec=[new Ft({ndim:4})]}computeOutputShape(e){e=at(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Dr(t,this.poolSize[0],this.padding,this.strides[0]),n=Dr(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return P(()=>(this.invokeCallHook(e,t),this.poolingFunction(ze(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},A2=class extends f4{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Ct(a),sr(r),h0(e,t,n,r,a,"max")}};A2.className="MaxPooling2D";re.registerClass(A2);var y2=class extends f4{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Ct(a),sr(r),h0(e,t,n,r,a,"avg")}};y2.className="AveragePooling2D";re.registerClass(y2);var m4=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new B(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];qt(this.poolSize,"poolSize"),qt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),sr(this.padding),this.inputSpec=[new Ft({ndim:5})]}computeOutputShape(e){e=at(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Dr(t,this.poolSize[0],this.padding,this.strides[0]),n=Dr(n,this.poolSize[1],this.padding,this.strides[1]),r=Dr(r,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return P(()=>(this.invokeCallHook(e,t),this.poolingFunction(ze(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},g2=class extends m4{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Ct(a),sr(r),d4(e,t,n,r,a,"max")}};g2.className="MaxPooling3D";re.registerClass(g2);var x2=class extends m4{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Ct(a),sr(r),d4(e,t,n,r,a,"avg")}};x2.className="AveragePooling3D";re.registerClass(x2);var A4=class extends qe{constructor(e){super(e);this.inputSpec=[new Ft({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new De}},b2=class extends A4{constructor(e){super(e||{})}call(e,t){return P(()=>{let n=ze(e);return It(n,1)})}};b2.className="GlobalAveragePooling1D";re.registerClass(b2);var w2=class extends A4{constructor(e){super(e||{})}call(e,t){return P(()=>{let n=ze(e);return Rn(n,1)})}};w2.className="GlobalMaxPooling1D";re.registerClass(w2);var y4=class extends qe{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),this.inputSpec=[new Ft({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new De}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},_2=class extends y4{call(e,t){return P(()=>{let n=ze(e);return this.dataFormat==="channelsLast"?It(n,[1,2]):It(n,[2,3])})}};_2.className="GlobalAveragePooling2D";re.registerClass(_2);var v2=class extends y4{call(e,t){return P(()=>{let n=ze(e);return this.dataFormat==="channelsLast"?Rn(n,[1,2]):Rn(n,[2,3])})}};v2.className="GlobalMaxPooling2D";re.registerClass(v2);var g4=class extends qe{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(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(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let r=t.layer,a=$r(r,n);delete t.layer;let s={layer:a};return Object.assign(s,t),new e(s)}},k2=class extends g4{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=at(e),e.length<3)throw new B(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=at(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),r=e[1];return[n[0],r].concat(n.slice(1))}call(e,t){return P(()=>(e=ze(e),c4((n,r)=>[ze(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};k2.className="TimeDistributed";re.registerClass(k2);function yae(e){zi(Tte,"BidirectionalMergeMode",e)}var gae="concat",I2=class extends g4{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=$r(n),t.goBackwards=t.goBackwards!==!0;let r={};if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=$r(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?gae:e.mergeMode,yae(this.mergeMode),e.weights)throw new De("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,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let 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=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,r,a;return this.returnState&&(a=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,r=[n]):this.mergeMode==null?r=[n,n.slice()]:r=[n],this.returnState?this.mergeMode==null?r.concat(a).concat(a.slice()):[n].concat(a).concat(a.slice()):Fn(r)}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=u4(e,n,r,this.numConstants);if(e=a.inputs,n=a.initialState,r=a.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&r==null)return super.apply(e,t);let s=[],i=[];if(n!=null){let l=n.length;if(l%2>0)throw new B("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,s.push(...n);let c=n.map(u=>new Ft({shape:u.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),i.push(...c)}if(r!=null)throw new De("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof Fr;for(let l of s)if(l instanceof Fr!==o)throw new B("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let l=[e].concat(s),c=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=c;let h=super.apply(l,t);return this.inputSpec=u,h}else return super.apply(e,t)}call(e,t){return P(()=>{let n=t.initialState,r,a;if(n==null)r=this.forwardLayer.call(e,t),a=this.backwardLayer.call(e,t);else{let o=n.slice(0,n.length/2),l=n.slice(n.length/2);r=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),a=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let s;this.returnState&&(Array.isArray(r)&&(s=r.slice(1).concat(a.slice(1))),r=r[0],a=a[0]),this.returnSequences&&(a=Wn(a,1));let 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tensor.shape[0], but sum of lengths is
${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);let a=n===0?0:t.size/n,s=[];P(()=>{t=H(t,[1,n,a]);for(let o=0;o<e.length;++o){let l=o===0?0:r[o-1],c=[0,l,0],u=[1,e[o],a];s[o]=H(Re(t,c,u),this.elementShape)}return s});let i=[];for(let o=0;o<e.length;o++)i[o]=o;this.writeMany(i,s)}},Kc=class{constructor(e,t,n,r=-1){this.tensors=e,this.elementShape=t,this.elementDtype=n,e!=null&&e.forEach(a=>{if(n!==a.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${a.dtype}`);xr(t,a.shape,"TensorList shape mismatch: "),Ht(a)}),this.idTensor=ve(0),this.maxNumElements=r,Ht(this.idTensor)}get id(){return this.idTensor.id}copy(){return new Kc([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.id))&&t.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(e,t,n=-1){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(n!==-1&&this.tensors.length!==n)throw new Error(`Operation expected a list with ${n} elements but got a list with ${this.tensors.length} elements.`);xr(e,this.elementShape,"TensorList shape mismatch: ");let r=Xc(this.elementShape,this.tensors,e);return P(()=>{let a=this.tensors.map(s=>H(s,r));return cn(a,0)})}popBack(e,t){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let n=Xc(this.elementShape,this.tensors,e),r=this.tensors.pop();return xr(r.shape,e,"TensorList shape mismatch: "),H(r,n)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(xr(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");Ht(e),this.tensors.push(e)}resize(e){if(e<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${e}`);if(this.maxNumElements!==-1&&e>this.maxNumElements)throw new Error(`TensorListResize input size ${e} is greater maxNumElement ${this.maxNumElements}.`);this.tensors.length=e}getItem(e,t,n){if(n!==this.elementDtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${this.elementDtype}`);if(e<0||e>this.tensors.length)throw new Error(`Trying to access element ${e} in a list with ${this.tensors.length} elements.`);if(this.tensors[e]==null)throw new Error(`element at index ${e} is null.`);xr(this.tensors[e].shape,t,"TensorList shape mismatch: ");let r=Xc(this.elementShape,this.tensors,t);return H(this.tensors[e],r)}setItem(e,t){if(t.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t.dtype}, but list elements ${this.elementDtype}`);if(e<0||this.maxNumElements!==-1&&e>=this.maxNumElements)throw new Error(`Trying to set element ${e} in a list with max ${this.maxNumElements} elements.`);xr(this.elementShape,t.shape,"TensorList shape mismatch: "),Ht(t),this.tensors[e]=t}gather(e,t,n){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);xr(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size());let r=Xc(this.elementShape,this.tensors,n);return e.length===0?Sr([],[0].concat(r)):P(()=>{let a=e.map(s=>H(this.tensors[s],r));return cn(a,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);xr(this.elementShape,t,"TensorList shape mismatch: ");let n=Xc(this.elementShape,this.tensors,t);return this.size()===0?Sr([],[0].concat(n)):P(()=>{let r=this.tensors.map(a=>H(a,n));return ot(r,0)})}};function cse(e,t,n){let r=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);if(e.dtype!==n)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${n}`);let a=e.shape.slice(1);xr(a,t,"TensorList shape mismatch: ");let s=mr(e);return new Kc(s,t,r)}function hse(e,t,n){return new Kc([],e,t,n)}function dse(e,t,n,r){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);let a=Math.max(...t);if(r!=null&&r!==-1&&a>=r)throw new Error(`Max index must be < array size (${a} vs. ${r})`);let s=new Kc([],n,e.dtype,r),i=mr(e,0);return t.forEach((o,l)=>{s.setItem(o,i[l])}),s}function pse(e,t,n){let r=0,a=t.map(u=>(r+=u,r));if(r!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to
tensor.shape[0], but sum of lengths is
${r}, and tensor's shape is: ${e.shape}`);let s=e.shape.slice(1),i=P2(s,n),o=r===0?0:e.size/r,l=P(()=>{let u=[];e=H(e,[1,r,o]);for(let h=0;h<t.length;++h){let d=h===0?0:a[h-1],p=[0,d,0],m=[1,t[h],o];u[h]=H(Re(e,p,m),i)}return e.dispose(),u}),c=new Kc([],n,e.dtype,t.length);for(let u=0;u<l.length;u++)c.setItem(u,l[u]);return c}var fse=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{let r=I("thenBranch",e,t,n),a=I("elseBranch",e,t,n),s=I("cond",e,t,n),i=I("args",e,t,n);return(await s.data())[0]?n.functionMap[r].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap):n.functionMap[a].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{let r=I("body",e,t,n),a=I("cond",e,t,n),s=I("args",e,t,n),i=await n.functionMap[a].executeFunctionAsync(s,n.tensorArrayMap,n.tensorListMap),o=s.map(u=>u.id),l=await i[0].data();i.forEach(u=>{!u.kept&&o.indexOf(u.id)===-1&&u.dispose()});let c=s;for(;l[0];){let u=c;c=await n.functionMap[r].executeFunctionAsync(c,n.tensorArrayMap,n.tensorListMap);let h=c.map(p=>p.id);u.forEach(p=>{!p.kept&&o.indexOf(p.id)===-1&&h.indexOf(p.id)===-1&&p.dispose()});let d=await n.functionMap[a].executeFunctionAsync(c,n.tensorArrayMap,n.tensorListMap);l=await d[0].data(),d.forEach(p=>{!p.kept&&o.indexOf(p.id)===-1&&h.indexOf(p.id)===-1&&p.dispose()})}return c}case"LoopCond":{let r=I("pred",e,t,n);return[wa(r)]}case"Switch":{let r=I("pred",e,t,n),a=I("data",e,t,n);return a.kept||(a=wa(a)),(await r.data())[0]?[void 0,a]:[a,void 0]}case"Merge":{let r=e.inputNames.find(a=>yn(a,t,n)!==void 0);if(r){let a=yn(r,t,n);return[wa(a)]}return}case"Enter":{let r=I("frameName",e,t,n),a=I("tensor",e,t,n);return n.enterFrame(r),[wa(a)]}case"Exit":{let r=I("tensor",e,t,n);return n.exitFrame(),[wa(r)]}case"NextIteration":{let r=I("tensor",e,t,n);return n.nextIteration(),[wa(r)]}case"TensorArrayV3":{let r=I("size",e,t,n),a=I("dtype",e,t,n),s=I("elementShape",e,t,n),i=I("dynamicSize",e,t,n),o=I("clearAfterRead",e,t,n),l=I("identicalElementShapes",e,t,n),c=I("name",e,t,n),u=new use(c,a,r,s,l,i,o);return n.addTensorArray(u),[u.idTensor,ve(1)]}case"TensorArrayWriteV3":{let r=I("tensorArrayId",e,t,n),a=I("index",e,t,n),s=I("tensor",e,t,n),i=n.getTensorArray(r.id);return i.write(a,s),[i.idTensor]}case"TensorArrayReadV3":{let r=I("tensorArrayId",e,t,n),a=I("index",e,t,n);return[n.getTensorArray(r.id).read(a)]}case"TensorArrayGatherV3":{let r=I("tensorArrayId",e,t,n),a=I("indices",e,t,n),s=I("dtype",e,t,n);return[n.getTensorArray(r.id).gather(a,s)]}case"TensorArrayScatterV3":{let r=I("tensorArrayId",e,t,n),a=I("indices",e,t,n),s=I("tensor",e,t,n),i=n.getTensorArray(r.id);return i.scatter(a,s),[i.idTensor]}case"TensorArrayConcatV3":{let r=I("tensorArrayId",e,t,n),a=n.getTensorArray(r.id),s=I("dtype",e,t,n);return[a.concat(s)]}case"TensorArraySplitV3":{let r=I("tensorArrayId",e,t,n),a=I("tensor",e,t,n),s=I("lengths",e,t,n),i=n.getTensorArray(r.id);return i.split(s,a),[i.idTensor]}case"TensorArraySizeV3":{let r=I("tensorArrayId",e,t,n),a=n.getTensorArray(r.id);return[ve(a.size(),"int32")]}case"TensorArrayCloseV3":{let r=I("tensorArrayId",e,t,n),a=n.getTensorArray(r.id);return a.clearAndClose(),[a.idTensor]}case"TensorListSetItem":{let r=I("tensorListId",e,t,n),a=I("index",e,t,n),s=I("tensor",e,t,n),i=n.getTensorList(r.id);return i.setItem(a,s),[i.idTensor]}case"TensorListGetItem":{let r=I("tensorListId",e,t,n),a=I("index",e,t,n),s=I("elementShape",e,t,n),i=I("elementDType",e,t,n);return[n.getTensorList(r.id).getItem(a,s,i)]}case"TensorListScatterV2":case"TensorListScatter":{let r=I("indices",e,t,n),a=I("tensor",e,t,n),s=I("elementShape",e,t,n),i=I("numElements",e,t,n),o=dse(a,r,s,i);return n.addTensorList(o),[o.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let r=I("elementShape",e,t,n),a=I("elementDType",e,t,n),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=I(s,e,t,n),o=hse(r,a,i);return n.addTensorList(o),[o.idTensor]}case"TensorListGather":{let r=I("tensorListId",e,t,n),a=I("indices",e,t,n),s=I("elementShape",e,t,n),i=I("elementDType",e,t,n);return[n.getTensorList(r.id).gather(a,i,s)]}case"TensorListStack":{let r=I("tensorListId",e,t,n),a=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=I("numElements",e,t,n);return[n.getTensorList(r.id).stack(a,s,i)]}case"TensorListFromTensor":{let r=I("tensor",e,t,n),a=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=cse(r,a,s);return n.addTensorList(i),[i.idTensor]}case"TensorListConcat":{let r=I("tensorListId",e,t,n),a=n.getTensorList(r.id),s=I("dtype",e,t,n),i=I("elementShape",e,t,n);return[a.concat(s,i)]}case"TensorListPushBack":{let r=I("tensorListId",e,t,n),a=I("tensor",e,t,n),s=n.getTensorList(r.id);return s.pushBack(a),[s.idTensor]}case"TensorListPopBack":{let r=I("tensorListId",e,t,n),a=I("elementShape",e,t,n),s=I("elementDType",e,t,n);return[n.getTensorList(r.id).popBack(a,s)]}case"TensorListSplit":{let r=I("tensor",e,t,n),a=I("elementShape",e,t,n),s=I("lengths",e,t,n),i=pse(r,s,a);return n.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function K4(e,t,n){let[r,a]=I("fusedOps",e,t,n),s=r==="biasadd",i=a==="prelu",o=r==="fusedbatchnorm",l=I("numArgs",e,t,n);if(s){if(i&&l!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&l!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(o)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");let c=I("strides",e,t,n),u=f0(e,t,n),h=I("dataFormat",e,t,n).toUpperCase(),d=I("dilations",e,t,n),[p,m]=I("args",e,t,n),f=I("leakyreluAlpha",e,t,n);return{stride:c,pad:u,dataFormat:h,dilations:d,biasArg:p,preluArg:m,activationFunc:a,leakyreluAlpha:f}}var mse=(e,t,n)=>{switch(e.op){case"Conv1D":{let r=I("stride",e,t,n),a=I("pad",e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilation",e,t,n);return[Id(I("x",e,t,n),I("filter",e,t,n),r,a,s,i)]}case"Conv2D":{let r=I("strides",e,t,n),a=f0(e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilations",e,t,n);return[ha(I("x",e,t,n),I("filter",e,t,n),[r[1],r[2]],a,s,[i[1],i[2]])]}case"_FusedConv2D":{let{stride:r,pad:a,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:c,leakyreluAlpha:u}=K4(e,t,n);return[Ha.conv2d({x:I("x",e,t,n),filter:I("filter",e,t,n),strides:[r[1],r[2]],pad:a,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:c,preluActivationWeights:l,leakyreluAlpha:u})]}case"FusedDepthwiseConv2dNative":{let{stride:r,pad:a,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:c,leakyreluAlpha:u}=K4(e,t,n);return[Ha.depthwiseConv2d({x:I("x",e,t,n),filter:I("filter",e,t,n),strides:[r[1],r[2]],pad:a,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:c,preluActivationWeights:l,leakyreluAlpha:u})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let r=I("outputShape",e,t,n),a=I("strides",e,t,n),s=f0(e,t,n);return[Sd(I("x",e,t,n),I("filter",e,t,n),r,[a[1],a[2]],s)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let r=I("strides",e,t,n),a=f0(e,t,n),s=I("dilations",e,t,n),i=I("dataFormat",e,t,n).toUpperCase();return[wl(I("input",e,t,n),I("filter",e,t,n),[r[1],r[2]],a,i,[s[1],s[2]])]}case"Conv3D":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilations",e,t,n);return[wm(I("x",e,t,n),I("filter",e,t,n),[r[1],r[2],r[3]],a,s,[i[1],i[2],i[3]])]}case"AvgPool":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[Zu(I("x",e,t,n),[s[1],s[2]],[r[1],r[2]],a)]}case"MaxPool":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[nc(I("x",e,t,n),[s[1],s[2]],[r[1],r[2]],a)]}case"MaxPoolWithArgmax":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("kernelSize",e,t,n),i=I("includeBatchInIndex",e,t,n),{result:o,indexes:l}=mw(I("x",e,t,n),[s[1],s[2]],[r[1],r[2]],a,i);return[o,l]}case"AvgPool3D":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[gm(I("x",e,t,n),[s[1],s[2],s[3]],[r[1],r[2],r[3]],a)]}case"MaxPool3D":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[Fm(I("x",e,t,n),[s[1],s[2],s[3]],[r[1],r[2],r[3]],a)]}case"Dilation2D":{let r=I("strides",e,t,n),a=I("pad",e,t,n),s=I("dilations",e,t,n),i=r[1],o=r[2],l=s[1],c=s[2];return[vm(I("x",e,t,n),I("filter",e,t,n),[i,o],a,[l,c],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Ase=(e,t,n)=>{switch(e.op){case"Fill":{let r=I("shape",e,t,n),a=I("dtype",e,t,n),s=I("value",e,t,n);return[Qu(r,s,a)]}case"LinSpace":{let r=I("start",e,t,n),a=I("stop",e,t,n),s=I("num",e,t,n);return[lw(r,a,s)]}case"Multinomial":{let r=I("logits",e,t,n),a=I("numSamples",e,t,n),s=I("seed",e,t,n);return[Aw(r,a,s)]}case"OneHot":{let r=I("indices",e,t,n),a=I("depth",e,t,n),s=I("onValue",e,t,n),i=I("offValue",e,t,n);return[ml(r,a,s,i)]}case"Ones":return[Pn(I("shape",e,t,n),I("dtype",e,t,n))];case"OnesLike":return[Ln(I("x",e,t,n))];case"RandomUniform":return[Sl(I("shape",e,t,n),I("minval",e,t,n),I("maxval",e,t,n),I("dtype",e,t,n))];case"Range":{let r=I("start",e,t,n),a=I("stop",e,t,n),s=I("step",e,t,n);return[zd(r,a,s,I("dtype",e,t,n))]}case"TruncatedNormal":{let r=I("shape",e,t,n),a=I("mean",e,t,n),s=I("stdDev",e,t,n),i=I("seed",e,t,n);return[qd(r,a,s,I("dtype",e,t,n),i)]}case"Zeros":return[Rt(I("shape",e,t,n),I("dtype",e,t,n))];case"ZerosLike":return[He(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function L2(e,t,n){let r=I("boxes",e,t,n),a=I("scores",e,t,n),s=I("maxOutputSize",e,t,n),i=I("iouThreshold",e,t,n),o=I("scoreThreshold",e,t,n),l=I("softNmsSigma",e,t,n);return{boxes:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}}var yse=async(e,t,n)=>{switch(e.op){case"NonMaxSuppressionV5":{let{boxes:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}=L2(e,t,n),c=await Le.nonMaxSuppressionWithScoreAsync(r,a,s,i,o,l);return[c.selectedIndices,c.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o}=L2(e,t,n),l=I("padToMaxOutputSize",e,t,n),c=await Le.nonMaxSuppressionPaddedAsync(r,a,s,i,o,l);return[c.selectedIndices,c.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o}=L2(e,t,n);return[await Le.nonMaxSuppressionAsync(r,a,s,i,o)]}case"Where":{let r=ge(I("condition",e,t,n),"bool"),a=[await qm(r)];return 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I("x",e,t,n).map(c=>sn(c.shape));case"Size":return[ve(I("x",e,t,n).size,"int32")];case"Rank":return[ve(I("x",e,t,n).rank,"int32")];case"NoOp":return[ve(1)];case"Print":let s=I("x",e,t,n),i=I("data",e,t,n),o=I("message",e,t,n),l=I("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(o);for(let c=0;c<i.length;c++)console.log(Array.prototype.slice.call(i[c].dataSync()).slice(0,l));return[s];default:throw TypeError(`Node type ${e.op} is not implemented`)}},bse=class{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=ve(0),this.tensorMap=new Map,Ht(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(e=>e.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return ve(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return this.tensorMap.forEach(r=>r.dispose()),this.tensorMap.clear(),P(()=>{let r=mr(t),a=n.length,s=r.length;_.assert(a===s,()=>`The number of elements doesn't match, keys has ${a} elements, the values has ${s} elements.`);for(let i=0;i<a;i++){let o=n[i],l=r[i];Ht(l),this.tensorMap.set(o,l)}return this.handle})}async find(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return P(()=>{let r=[];for(let a=0;a<n.length;a++){let s=n[a],i=this.findWithDefault(s,t);r.push(i)}return cn(r)})}findWithDefault(e,t){let n=this.tensorMap.get(e);return n!=null?n:t}checkKeyAndValueTensor(e,t){if(e.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${e.dtype}`);if(t.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${t.dtype}`)}},wse=async(e,t,n,r)=>{switch(e.op){case"HashTable":case"HashTableV2":{let a=I("keyDType",e,t,n),s=I("valueDType",e,t,n),i=new bse(a,s);return r.addHashTable(e.name,i),[i.handle]}case"LookupTableImport":case"LookupTableImportV2":{let a=I("tableHandle",e,t,n,r),s=I("keys",e,t,n),i=I("values",e,t,n);return[await r.getHashTableById(a.id).import(s,i)]}case"LookupTableFind":case"LookupTableFindV2":{let a=I("tableHandle",e,t,n,r),s=I("keys",e,t,n),i=I("defaultValue",e,t,n);return[await r.getHashTableById(a.id).find(s,i)]}case"LookupTableSize":case"LookupTableSizeV2":{let a=I("tableHandle",e,t,n,r);return[r.getHashTableById(a.id).tensorSize()]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},_se=(e,t,n)=>{switch(e.op){case"ResizeBilinear":{let r=I("images",e,t,n),a=I("size",e,t,n),s=I("alignCorners",e,t,n),i=I("halfPixelCenters",e,t,n);return[Le.resizeBilinear(r,[a[0],a[1]],s,i)]}case"ResizeNearestNeighbor":{let r=I("images",e,t,n),a=I("size",e,t,n),s=I("alignCorners",e,t,n),i=I("halfPixelCenters",e,t,n);return[Le.resizeNearestNeighbor(r,[a[0],a[1]],s,i)]}case"CropAndResize":{let r=I("image",e,t,n),a=I("boxes",e,t,n),s=I("boxInd",e,t,n),i=I("cropSize",e,t,n),o=I("method",e,t,n),l=I("extrapolationValue",e,t,n);return[Le.cropAndResize(r,a,s,i,o,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},vse=(e,t,n)=>{switch(e.op){case"Equal":return[Ba(I("a",e,t,n),I("b",e,t,n))];case"NotEqual":return[ki(I("a",e,t,n),I("b",e,t,n))];case"Greater":return[pr(I("a",e,t,n),I("b",e,t,n))];case"GreaterEqual":return[ja(I("a",e,t,n),I("b",e,t,n))];case"Less":return[Cd(I("a",e,t,n),I("b",e,t,n))];case"LessEqual":return[wi(I("a",e,t,n),I("b",e,t,n))];case"LogicalAnd":return[fr(I("a",e,t,n),I("b",e,t,n))];case"LogicalNot":return[tc(I("a",e,t,n))];case"LogicalOr":return[$d(I("a",e,t,n),I("b",e,t,n))];case"Select":case"SelectV2":return[Cn(I("condition",e,t,n),I("a",e,t,n),I("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},kse=(e,t,n)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[Ve(I("a",e,t,n),I("b",e,t,n),I("transposeA",e,t,n),I("transposeB",e,t,n))];case"Einsum":return[sw(I("equation",e,t,n),...I("tensors",e,t,n))];case"Transpose":return[Je(I("x",e,t,n),I("perm",e,t,n))];case"_FusedMatMul":let[r,a]=I("fusedOps",e,t,n),s=r==="biasadd",i=a==="prelu",o=I("numArgs",e,t,n),l=I("leakyreluAlpha",e,t,n);if(s){if(i&&o!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&o!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[c,u]=I("args",e,t,n);return[Ha.matMul({a:I("a",e,t,n),b:I("b",e,t,n),transposeA:I("transposeA",e,t,n),transposeB:I("transposeB",e,t,n),bias:c,activation:a,preluActivationWeights:u,leakyreluAlpha:l})];default:throw TypeError(`Node type ${e.op} is not 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i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[kl(I("x",e,t,n),i,o)]}case"Sum":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Te(I("x",e,t,n),i,o)]}case"All":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[vd(I("x",e,t,n),i,o)]}case"Any":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Xu(I("x",e,t,n),i,o)]}case"ArgMax":{let i=I("axis",e,t,n);return[Ai(I("x",e,t,n),i)]}case"ArgMin":{let i=I("axis",e,t,n);return[cm(I("x",e,t,n),i)]}case"Prod":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[Od(I("x",e,t,n),i,o)]}case"Cumsum":{let i=I("axis",e,t,n),o=I("exclusive",e,t,n),l=I("reverse",e,t,n);return[Td(I("x",e,t,n),i,o,l)]}case"Bincount":let r=I("x",e,t,n),a=I("weights",e,t,n),s=I("size",e,t,n);return[Yb(r,a,s)];case"DenseBincount":{let i=I("x",e,t,n),o=I("weights",e,t,n),l=I("size",e,t,n),c=I("binaryOutput",e,t,n);return[rw(i,o,l,c)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Nse=(e,t,n)=>{switch(e.op){case"ConcatV2":case"Concat":{let 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e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.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)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let 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))}else throw new Error("Cannot increase frame iteration, the context is empty")}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(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function Q4(e,t,n,r){let a=new Set,s=[],i=null,o=null,l=new Set,c=Object.keys(e).map(d=>jn(d)[0]),u=[];r!=null&&(u=r.map(d=>jn(d.name)[0]));let h=[...t];for(;h.length>0;){let d=h.pop();if((J4(d)||Rse(d)||Mse(d))&&i==null&&(i=d,o=i.children.map(p=>p.name).filter(p=>a.has(p))),a.add(d.name),n[d.name]==null&&c.indexOf(d.name)===-1&&u.indexOf(d.name)===-1){if(d.inputs.length===0){s.push(d.name);continue}d.inputs.forEach(p=>{l.has(p.name)||(l.add(p.name),h.push(p))})}}return{inputs:e,outputs:t,usedNodes:a,missingInputs:s,dynamicNode:i,syncInputs:o}}function Fse(e,t,n){let{usedNodes:r,inputs:a}=n,s=[],i=Object.keys(a).map(u=>jn(u)[0]).map(u=>e.nodes[u]),o=e.initNodes;i.forEach(u=>{r.has(u.name)&&s.push(u)}),e.weights.forEach(u=>{r.has(u.name)&&s.push(u)}),o!=null&&o.forEach(u=>{r.has(u.name)&&s.push(u)});let l=new Set,c=[];for(;s.length>0;){let u=s.pop();l.add(u.name),t[u.name]||c.push(u),u.children.forEach(h=>{!l.has(h.name)&&r.has(h.name)&&h.inputs.every(d=>l.has(d.name))&&s.push(h)})}return c}var $se=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Dse=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],Ose=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function J4(e){return $se.indexOf(e.op)>=0}function Rse(e){return Dse.indexOf(e.op)>=0}function Mse(e){return Ose.indexOf(e.op)>=0}var W2=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new W2(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(r=>r.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(a=>a.name).sort(),r=t.map(a=>a.name).sort();return n.join(this.SEPERATOR)+"--"+r.join(this.SEPERATOR)}compile(e,t){let n=Q4(e,t,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:a,syncInputs:s}=n;if(a!=null)throw new Error(`This execution contains the node '${a.name}', which has the dynamic op '${a.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(r.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${r}]`)}return Fse(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let r=n.map(u=>this.graph.nodes[jn(u)[0]]),a=t.map(u=>jn(u)[0]),s=a.map(u=>this.graph.nodes[u]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(r,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},c={};return P(()=>{let u=new Y4(this.weightMap,l,c,this.functionExecutorMap),h=Object.assign({},this.weightMap);Object.keys(e).forEach(m=>{let[f,A]=jn(m),y=[];y[A]=e[m],h[f]=y});let d=this.getFrozenTensorIds(h),p={};for(let m=0;m<o.length;m++){let f=o[m];if(!h[f.name]){let A=Z4(f,h,u,this._resourceManager);if(_.isPromise(A))throw new Error(`The execution of the op '${f.op}' returned a promise. Please use model.executeAsync() instead.`);h[f.name]=A,this.checkTensorForDisposal(f.name,f,h,u,d,a,p)}}return this.parent==null&&u.dispose(d),t.map(m=>yn(m,h,u))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(r=>r.id)));return new Set(t)}checkTensorForDisposal(e,t,n,r,a,s,i){t.category==="control"||s.indexOf(e)!==-1||(n[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let l=Bae(o.name,n,r);l!=null&&l.forEach(c=>{if(c&&!c.kept&&!a.has(c.id)){let u=i[c.id];u===1?(c.dispose(),delete i[c.id]):u!=null&&i[c.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,r={},a={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));let s=new Y4(this.weightMap,r,a,this.functionExecutorMap),i=await this.executeWithControlFlow(e,s,t,n),o=t.map(h=>yn(h,i,s)),l=o.map(h=>h.id),c=Object.keys(e).map(h=>e[h].id),u=new Set([...l,...c,...this.weightIds]);return Object.keys(i).forEach(h=>{i[h].forEach(d=>{d&&!d.kept&&!d.isDisposed&&!u.has(d.id)&&d.dispose()})}),this.parent==null&&s.dispose(u),o}async executeFunctionAsync(e,t,n){let r=e.reduce((a,s,i)=>(a[this.inputs[i].name]=s,a),{});return this._executeAsync(r,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,r){let a=Object.keys(e),s=a.map(g=>this.graph.nodes[jn(g)[0]]),i=n.map(g=>jn(g)[0]),o=i.map(g=>this.graph.nodes[g]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:c,dynamicNode:u,syncInputs:h}=Q4(e,o,this.weightMap,this._initNodes),d=[...s,...this.graph.weights,...this._initNodes||[]].map(g=>({node:g,contexts:t.currentContext})),p=Object.assign({},this.weightMap);Object.keys(e).forEach(g=>{let[x,v]=jn(g),b=[];b[v]=e[g],p[x]=b});let m={},f=this.getFrozenTensorIds(p),A={};for(;d.length>0;){let g=this.processStack(s,d,t,p,A,f,i,m,l);await Promise.all(g)}u==null&&!r&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=o.filter(g=>!J4(g)&&!yn(g.name,p,t)).map(g=>g.name);if(y.length>0){let g="";throw u!=null&&(g=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${h}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${a}]. Consider providing the following inputs: [${c}]. ${g}`)}return p}processStack(e,t,n,r,a,s,i,o,l){let c=[];for(;t.length>0;){let u=t.pop();n.currentContext=u.contexts;let h="";if(u.node.op==="Enter"&&I("isConstant",u.node,r,n)&&([h]=ba(u.node.name,n)),r[u.node.name]==null){let d=Z4(u.node,r,n,this._resourceManager);h||([h]=ba(u.node.name,n));let p=n.currentContext;_.isPromise(d)?c.push(d.then(m=>(r[h]=m,n.currentContext=p,this.checkTensorForDisposal(h,u.node,r,n,s,i,o),this.processChildNodes(u.node,t,n,r,a,l),m))):(r[h]=d,this.checkTensorForDisposal(h,u.node,r,n,s,i,o),this.processChildNodes(u.node,t,n,r,a,l))}else this.processChildNodes(u.node,t,n,r,a,l)}return c}processChildNodes(e,t,n,r,a,s){e.children.forEach(i=>{let[o]=ba(i.name,n);a[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!yn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!yn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[r]=jn(t),a=this.graph.nodes[r];if(a.attrParams.shape&&a.attrParams.shape.value){let s=a.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);_.assert(i,()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}a.attrParams.dtype&&a.attrParams.dtype.value&&_.assert(n.dtype===a.attrParams.dtype.value,()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let r=this._signature.inputs[n];t[r.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[r]=jn(n);return this.graph.nodes[r]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=jn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},zse=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},Pse="?tfjs-format=file",Lse="model.json",e8=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new zse}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=Nn.browserHTTPRequest(e,this.loadOptions);else{let t=Nn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Nn.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let r=Nn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new W2(H4.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(r),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let a=H4.Instance.transformGraph(e.modelInitializer);this.initializer=new W2(a),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=Nn.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)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 Pe)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,r)=>(t[n]=e[r],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function ct(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${Lse}${Pse}`);let n=new e8(e,t);return await n.load(),n}var Wse="3.5.0",t8={};Me(t8,{CSVDataset:()=>r8,Dataset:()=>Jl,FileDataSource:()=>a8,TextLineDataset:()=>n8,URLDataSource:()=>s8,array:()=>Bse,csv:()=>jse,func:()=>Use,generator:()=>Hse,microphone:()=>qse,version_data:()=>Xse,webcam:()=>Gse,zip:()=>Vse});var Kse=so(l5()),Zse=so(l5());function Yse(e,t){return m0(e,t)}function m0(e,t,n=new Map,r=new Set){if(e==null)return null;if(r.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);let a=t(e);if(a.recurse&&a.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(a.recurse)if(Ql(e)){let s=Array.isArray(e)?[]:{};r.add(e);for(let i in e){let o=e[i],l=m0(o,t,n,r);s[i]=l}return r.delete(e),s}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else 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RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}},B2=class extends u8{constructor(){super(B2.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),n=this.length();for(let r=0;r<n;r++)t[r]=this.get(this.wrap(this.begin+r));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}};B2.INITIAL_CAPACITY=32;function c8(e){return new rie(e)}function V2(e){return new aie(e)}function sie(e,t){return new h8(e,t)}function oie(e,t=rs.FAIL){return new iie(e,t)}var Xt=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],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 fie(this,e)}filter(e){return new die(this,e)}map(e){return new pie(this,e)}mapAsync(e){return new d8(this,e)}serialMapAsync(e){return new d8(this,e).serial()}flatmap(e){return new mie(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new hie(this,e,t)}columnMajorBatch(e,t=!0,n=o8){return this.rowMajorBatch(e,t).map(r=>Jse(r,n))}concatenate(e,t){return new h8(c8([this,e]),t)}take(e){return e<0||e==null?this:new cie(this,e)}skip(e){return e<0||e==null?this:new uie(this,e)}prefetch(e){return new p8(this,e)}shuffle(e,t){return new Aie(this,e,t)}serial(){return new lie(this)}},rie=class extends Xt{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:nie(e),done:!1}}},aie=class extends Xt{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: ${e.message}`,e}}},lie=class extends Xt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},uie=class extends Xt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;we(e.value)}return this.upstream.next()}},cie=class extends Xt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},hie=class extends Xt{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},die=class extends Xt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;we(e.value)}}},pie=class extends Xt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=kr.getTensorsInContainer(e.value),n=this.transform(e.value),r=kr.getTensorsInContainer(n);for(let a of t)kr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},fie=class extends Xt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},d8=class extends Xt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=kr.getTensorsInContainer(e.value),n=await this.transform(e.value),r=kr.getTensorsInContainer(n);for(let a of t)kr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},j2=class extends Xt{constructor(){super();this.outputQueue=new B2,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},mie=class extends j2{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=kr.getTensorsInContainer(e.value),n=this.transform(e.value),r=kr.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let a of t)kr.isTensorInList(a,r)||a.dispose();return!0}},h8=class extends Xt{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"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,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},rs;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(rs||(rs={}));var iie=class extends Xt{constructor(e,t=rs.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function r(s){return s instanceof Xt?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let a=await l8(this.iterators,r);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case rs.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case rs.SHORTEST:return{value:null,done:!0};case rs.LONGEST:default:}return this.count++,{value:a,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},p8=class extends Xt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new u8(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},Aie=class extends p8{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Zse.alea(n||_.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();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},Jl=class{constructor(){this.size=null}batch(e,t=!0){let n=this;_.assert(e>0,()=>`batchSize needs to be positive, but it is
${e}`);let r;return this.size===Infinity||this.size==null?r=this.size:t?r=Math.ceil(this.size/e):r=Math.floor(this.size/e),Un(async()=>(await n.iterator()).columnMajorBatch(e,t,yie),r)}concatenate(e){let t=this,n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Un(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,Un(async()=>(await t.iterator()).filter(r=>P(()=>e(r))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Un(async()=>(await t.iterator()).map(n=>P(()=>e(n))),this.size)}mapAsync(e){let t=this;return Un(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Un(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,Un(async()=>{let r=V2(async()=>({value:await t.iterator(),done:!1}));return sie(r.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,Un(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let r=this,a=Kse.alea(t||_.now().toString());return Un(async()=>{let s=a.int32();return n&&(s+=a.int32()),(await r.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,Un(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Jl.MAX_BUFFER_SIZE=1e4;function Un(e,t=null){return new class extends Jl{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function Bse(e){return Un(async()=>c8(e),e.length)}function Vse(e){if(!Ql(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(let n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return Un(async()=>{let n=await l8(e,r=>{if(r instanceof Jl)return{value:r.iterator(),recurse:!1};if(Ql(r))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return oie(n,rs.SHORTEST)},t)}function yie(e){if(e===null)return null;let t=e[0];return eie(t)?{value:gie(e),recurse:!1}:{value:null,recurse:!0}}function gie(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Pe?cn(e):Sr(e)}var n8=class extends Jl{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},A0='"',Zc=Symbol("out"),f8=Symbol("field"),y0=Symbol("quote"),U2=Symbol("quoteafterquote"),m8=Symbol("quoteinquote"),r8=class extends Jl{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new n8(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(_.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&_.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((r,a)=>(r[a]=r[a]+1||1,r),{}),n=Object.keys(t).filter(r=>t[r]>1);if(_.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let r of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(r)===-1)throw new Error('The key "'+r+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},r={};for(let a=0;a<this.fullColumnNames.length;a++){let s=this.fullColumnNames[a],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[a],l=null;if(o==="")if(i&&i.default!==void 0)l=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${s} is empty in this line: ${e}`);l=void 0}else{let c=Number(o);if(isNaN(c))i&&i.dtype==="bool"?l=this.getBoolean(o):l=o;else if(!i||!i.dtype)l=c;else switch(i.dtype){case"float32":l=c;break;case"int32":l=Math.floor(c);break;case"bool":l=this.getBoolean(o);break;default:l=c}}i&&i.isLabel?r[s]=l:n[s]=l}}return Object.keys(r).length===0?n:{xs:n,ys:r}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],r=0,a=e.length,s=Zc;for(let i=0;i<a;i++)switch(s){case Zc:switch(e.charAt(i)){case A0:r=i+1,s=y0;break;case this.delimiter:if(r=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),s=Zc;break;default:s=f8,r=i;break}break;case f8:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(r,i)),s=Zc,r=i+1;break;default:}break;case y0:switch(e.charAt(i)){case A0:s=U2;break;default:}break;case U2:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(r,i-1)),s=Zc,r=i+1;break;case A0:s=y0;break;default:s=m8;break}break;case m8:switch(e.charAt(i)){case A0:s=y0;break;default:}break;default:}if(s===U2?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 ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},A8=class extends Xt{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let 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 ${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=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!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(e={}){if(J().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new A8(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,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,n=await this.getAudioData();if(this.includeSpectrogram){let r=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(r,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let r=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(r,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(r=>{let a=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&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(),this.stream!=null&&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){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((r,a)=>n.set(r,a*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(_.sizeFromShape(t));return n.set(e,n.length-e.length),Sr(n,t)}},y8=class extends Xt{constructor(e,t){super();if(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=sn([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,r=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,a=(1-n)/2,s=(1-r)/2,i=a+n,o=r+s;this.cropBox=tr([s,a,o,i],[1,4])}else this.cropBox=tr([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(J().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}let n=new y8(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&_.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. 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Die=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],Oie=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],zie=[33,133,362,263,1,78,308],Voe=Die.map(e=>Jc[e]),joe=Oie.map(e=>Jc[e]),Uoe=zie.map(e=>Jc[e]);var hg=ea.leftEyeLower0,dg=ea.rightEyeLower0,nu={leftBounds:[hg[0],hg[hg.length-1]],rightBounds:[dg[0],dg[dg.length-1]]},F0={count:468,mouth:13,symmetryLine:[13,ea.midwayBetweenEyes[0]]},F8={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},ru={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function $0(e,t,n,r){for(let a=0;a<cg.length;a++){let{key:s,indices:i}=cg[a],o=ea[`${n}${s}`];if(!r||r.includes(s))for(let l=0;l<i.length;l++){let c=i[l];e[o[l]]=[t[c][0],t[c][1],(t[c][2]+e[o[l]][2])/2]}}}var pg=class{constructor(t,n,r){var a,s;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=r,this.boxSize=((a=t==null?void 0:t.model)==null?void 0:a.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((s=t==null?void 0:t.model)==null?void 0:s.inputs[0].shape[2]),this.irisSize=(r==null?void 0:r.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,r,a){let s=Yc({startPoint:n.startPoint,endPoint:n.endPoint}),i=t.map(h=>[s[0]/this.meshSize*(h[0]-this.meshSize/2),s[1]/this.meshSize*(h[1]-this.meshSize/2),h[2]]),o=r!==0?M0(r,[0,0]):R0,l=r!==0?i.map(h=>[...M8(h,o),h[2]]):i,c=r!==0?R8(a):R0,u=[...eu({startPoint:n.startPoint,endPoint:n.endPoint}),1];return l.map(h=>[h[0]+as(u,c[0]),h[1]+as(u,c[1]),h[2]])}getLeftToRightEyeDepthDifference(t){let n=t[nu.leftBounds[0]][2],r=t[nu.rightBounds[0]][2];return n-r}getEyeBox(t,n,r,a,s=!1){let i=C0(E0(this.calculateLandmarksBoundingBox([t[r],t[a]]),this.irisEnlarge)),o=Yc(i),l=Le.cropAndResize(n,[[i.startPoint[1]/this.meshSize,i.startPoint[0]/this.meshSize,i.endPoint[1]/this.meshSize,i.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return s&&vr.flags.IS_BROWSER&&(l=Le.flipLeftRight(l)),{box:i,boxSize:o,crop:l}}getEyeCoords(t,n,r,a=!1){let s=[];for(let i=0;i<ru.numCoordinates;i++){let o=t[i*3],l=t[i*3+1],c=t[i*3+2];s.push([(a?1-o/this.irisSize:o/this.irisSize)*r[0]+n.startPoint[0],l/this.irisSize*r[1]+n.startPoint[1],c])}return{rawCoords:s,iris:s.slice(ru.index)}}getAdjustedIrisCoords(t,n,r){let a=t[ea[`${r}EyeUpper0`][ru.upperCenter]][2],s=t[ea[`${r}EyeLower0`][ru.lowerCenter]][2],i=(a+s)/2;return n.map((o,l)=>{let c=i;return l===2?c=a:l===4&&(c=s),[o[0],o[1],c]})}async predict(t,n){let r=!1,a;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.videoOptimized)&&(a=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.videoOptimized&&this.skipped++,!n.videoOptimized||a&&a.boxes&&(!n.face.mesh.enabled||a.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let i of a.boxes)this.storedBoxes.push({startPoint:i.box.startPoint.dataSync(),endPoint:i.box.endPoint.dataSync(),landmarks:i.landmarks,confidence:i.confidence});this.storedBoxes.length>0&&(r=!0)}if(n.face.detector.skipInitial&&this.detectedFaces===0&&(this.skipped=0),r){if(!a||!a.boxes||a.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i<this.storedBoxes.length;i++){let o=T8({startPoint:this.storedBoxes[i].startPoint,endPoint:this.storedBoxes[i].endPoint},a.scaleFactor),l=E0(o),c=C0(l),u=this.storedBoxes[i].landmarks.arraySync(),h=this.storedBoxes[i].confidence;this.storedBoxes[i]={...c,confidence:h,landmarks:u}}}a&&a.boxes&&a.boxes.forEach(i=>{i.box.startPoint.dispose(),i.box.endPoint.dispose(),i.landmarks.dispose()});let s=P(()=>this.storedBoxes.map((i,o)=>{let l=i.confidence,c,u=0,h;if(n.face.detector.rotation&&n.face.mesh.enabled&&vr.flags.IS_BROWSER){let[b,w]=i.landmarks.length>=F0.count?F0.symmetryLine:F8.symmetryLine;u=ug(i.landmarks[b],i.landmarks[w]);let k=eu({startPoint:i.startPoint,endPoint:i.endPoint}),N=[k[0]/t.shape[2],k[1]/t.shape[1]],C=Le.rotateWithOffset(t,u,0,N);h=M0(-u,k),n.face.mesh.enabled?c=tu({startPoint:i.startPoint,endPoint:i.endPoint},C,[this.meshSize,this.meshSize]).div(255):c=tu({startPoint:i.startPoint,endPoint:i.endPoint},C,[this.boxSize,this.boxSize]).div(255)}else{h=R0;let b=t.clone();n.face.mesh.enabled?c=tu({startPoint:i.startPoint,endPoint:i.endPoint},b,[this.meshSize,this.meshSize]).div(255):c=tu({startPoint:i.startPoint,endPoint:i.endPoint},b,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:i,faceConfidence:null,boxConfidence:l,confidence:i.confidence,image:c};let[,d,p]=this.meshDetector.predict(c),m=d.dataSync()[0];if(m<n.face.detector.minConfidence)return null;let A=H(p,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:b,boxSize:w,crop:k}=this.getEyeBox(A,c,nu.leftBounds[0],nu.leftBounds[1],!0),{box:N,boxSize:C,crop:F}=this.getEyeBox(A,c,nu.rightBounds[0],nu.rightBounds[1]),L=this.irisModel.predict(ot([k,F])).dataSync(),V=L.slice(0,ru.numCoordinates*3),{rawCoords:j,iris:U}=this.getEyeCoords(V,b,w,!0),X=L.slice(ru.numCoordinates*3),{rawCoords:G,iris:ee}=this.getEyeCoords(X,N,C),Y=this.getLeftToRightEyeDepthDifference(A);Math.abs(Y)<30?($0(A,j,"left",null),$0(A,G,"right",null)):Y<1?$0(A,j,"left",["EyeUpper0","EyeLower0"]):$0(A,G,"right",["EyeUpper0","EyeLower0"]);let ae=this.getAdjustedIrisCoords(A,U,"left"),te=this.getAdjustedIrisCoords(A,ee,"right");A=A.concat(ae).concat(te)}let y=this.transformRawCoords(A,i,u,h);i=E0(this.calculateLandmarksBoundingBox(y),1.5);let g=tr(y);if(n.face.detector.rotation&&n.face.mesh.enabled&&(n.face.description.enabled||n.face.embedding.enabled)&&vr.flags.IS_BROWSER){let[b,w]=i.landmarks.length>=F0.count?F0.symmetryLine:F8.symmetryLine;u=ug(i.landmarks[b],i.landmarks[w]);let k=eu({startPoint:i.startPoint,endPoint:i.endPoint}),N=[k[0]/t.shape[2],k[1]/t.shape[1]],C=Le.rotateWithOffset(t.toFloat(),u,0,N);h=M0(-u,k),c=tu({startPoint:i.startPoint,endPoint:i.endPoint},C,[this.meshSize,this.meshSize]).div(255)}let x={coords:g,box:i,faceConfidence:m,boxConfidence:l,image:c,rawCoords:A},v=C0(i);return this.storedBoxes[o]={...v,landmarks:y,confidence:i.confidence,faceConfidence:m},x}));return s=s.filter(i=>i!==null),n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(i=>i.faceConfidence>n.face.detector.minConfidence)),this.detectedFaces=s.length,s}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s,landmarks:t}}};var mg=class{constructor(t,n,r,a){this.facePipeline=new pg(t,n,r),this.config=a}async estimateFaces(t,n){let r=await this.facePipeline.predict(t,n),a=[];for(let s of r||[]){if(s.isDisposedInternal)continue;let i=s.coords?s.coords.arraySync():[],o=i.map(h=>[h[0]/t.shape[2],h[1]/t.shape[1],h[2]/this.facePipeline.meshSize]),l={};if(i&&i.length>0)for(let h of Object.keys(ea))l[h]=ea[h].map(d=>i[d]);let c=s.box?[Math.max(0,s.box.startPoint[0]),Math.max(0,s.box.startPoint[1]),Math.min(t.shape[2],s.box.endPoint[0])-Math.max(0,s.box.startPoint[0]),Math.min(t.shape[1],s.box.endPoint[1])-Math.max(0,s.box.startPoint[1])]:0,u=s.box?[s.box.startPoint[0]/t.shape[2],s.box.startPoint[1]/t.shape[1],(s.box.endPoint[0]-s.box.startPoint[0])/t.shape[2],(s.box.endPoint[1]-s.box.startPoint[1])/t.shape[1]]:[];a.push({confidence:Math.round(100*s.faceConfidence||100*s.boxConfidence||0)/100,boxConfidence:Math.round(100*s.boxConfidence)/100,faceConfidence:Math.round(100*s.faceConfidence)/100,box:c,boxRaw:u,mesh:i,meshRaw:o,annotations:l,image:s.image?s.image.clone():null}),s.coords&&s.coords.dispose(),s.image&&s.image.dispose()}return a}},Kt=[null,null,null];async function Ag(e){return!Kt[0]&&e.face.enabled||!Kt[1]&&e.face.mesh.enabled||!Kt[2]&&e.face.iris.enabled?(Kt=await Promise.all([!Kt[0]&&e.face.enabled?N8(e):null,!Kt[1]&&e.face.mesh.enabled?ct(pt(e.modelBasePath,e.face.mesh.modelPath),{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!Kt[2]&&e.face.iris.enabled?ct(pt(e.modelBasePath,e.face.iris.modelPath),{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]),e.face.mesh.enabled&&(!Kt[1]||!Kt[1].modelUrl?le("load model failed:",e.face.mesh.modelPath):e.debug&&le("load model:",Kt[1].modelUrl)),e.face.iris.enabled&&(!Kt[2]||!Kt[1].modelUrl?le("load model failed:",e.face.iris.modelPath):e.debug&&le("load model:",Kt[2].modelUrl))):e.debug&&(le("cached model:",Kt[0].model.modelUrl),le("cached model:",Kt[1].modelUrl),le("cached model:",Kt[2].modelUrl)),new mg(Kt[0],Kt[1],Kt[2],e)}var $8=Ui,D8=Jc;var _g={};Yn(_g,{load:()=>kg,predict:()=>vg});var Qc=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],O8=Qc.length,eh=Qc.reduce((e,t,n)=>(e[t]=n,e),{}),Pie=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Lie=Pie.map(([e,t])=>[eh[e],eh[t]]),z8=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function P8(e){let t=e.reduce(({maxX:n,maxY:r,minX:a,minY:s},{position:{x:i,y:o}})=>({maxX:Math.max(n,i),maxY:Math.max(r,o),minX:Math.min(a,i),minY:Math.min(s,o)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function L8(e,[t,n],[r,a]){let s=(o,l,c)=>({score:o.score,box:[Math.trunc(o.box[0]*c),Math.trunc(o.box[1]*l),Math.trunc(o.box[2]*c),Math.trunc(o.box[3]*l)],keypoints:o.keypoints.map(({score:u,part:h,position:d})=>({score:u,part:h,position:{x:Math.trunc(d.x*c),y:Math.trunc(d.y*l)}}))});return e.map(o=>s(o,t/r,n/a))}var yg=class{constructor(t,n){this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(Math.floor(t/2),t);)this.exchange(t,Math.floor(t/2)),t=Math.floor(t/2)}sink(t){for(;2*t<=this.numberOfElements;){let n=2*t;if(n<this.numberOfElements&&this.less(n,n+1)&&n++,!this.less(t,n))break;this.exchange(t,n),t=n}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,n){return this.getValueAt(t)<this.getValueAt(n)}exchange(t,n){let r=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[n],this.priorityQueue[n]=r}};function gg(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+O8)}}function xg(e,t,n){let{heatmapY:r,heatmapX:a,id:s}=e,{y:i,x:o}=gg(r,a,s,n);return{x:e.heatmapX*t+o,y:e.heatmapY*t+i}}function bg(e,t,n){return e<t?t:e>n?n:e}function W8(e,t,n,r){let a=n-e,s=r-t;return a*a+s*s}function wg(e,t){return{x:e.x+t.x,y:e.y+t.y}}var D0=1,B8=16;function V8(e,t,n,r,a,s,i,o=2){let l=g=>({y:i.get(g.y,g.x,e),x:i.get(g.y,g.x,i.shape[2]/2+e)}),c=(g,x,v)=>({y:bg(Math.round(g.y/s),0,x-1),x:bg(Math.round(g.x/s),0,v-1)}),[u,h]=r.shape,d=c(t.position,u,h),p=l(d),f=wg(t.position,p);for(let g=0;g<o;g++){let x=c(f,u,h),v=gg(x.y,x.x,n,a);f=wg({x:x.x*s,y:x.y*s},{x:v.x,y:v.y})}let A=c(f,u,h),y=r.get(A.y,A.x,n);return{position:f,part:Qc[n],score:y}}function Wie(e,t,n,r,a,s){let i=z8.map(([f,A])=>[eh[f],eh[A]]),o=i.map(([,f])=>f),l=i.map(([f])=>f),c=t.shape[2],u=o.length,h=new Array(c),{part:d,score:p}=e,m=xg(d,r,n);h[d.id]={score:p,part:Qc[d.id],position:m};for(let f=u-1;f>=0;--f){let A=o[f],y=l[f];h[A]&&!h[y]&&(h[y]=V8(f,h[A],y,t,n,r,s))}for(let f=0;f<u;++f){let A=l[f],y=o[f];h[A]&&!h[y]&&(h[y]=V8(f,h[A],y,t,n,r,a))}return h}function Bie(e,t,n,r,a){let[s,i]=a.shape,o=!0,l=Math.max(n-D0,0),c=Math.min(n+D0+1,s);for(let u=l;u<c;++u){let h=Math.max(r-D0,0),d=Math.min(r+D0+1,i);for(let p=h;p<d;++p)if(a.get(u,p,e)>t){o=!1;break}if(!o)break}return o}function Vie(e,t){let[n,r,a]=t.shape,s=new yg(n*r*a,({score:i})=>i);for(let i=0;i<n;++i)for(let o=0;o<r;++o)for(let l=0;l<a;++l){let c=t.get(i,o,l);c<e||Bie(l,c,i,o,t)&&s.enqueue({score:c,part:{heatmapY:i,heatmapX:o,id:l}})}return s}function j8(e,t,{x:n,y:r},a){return e.some(({keypoints:s})=>{let i=s[a].position;return W8(r,n,i.y,i.x)<=t})}function jie(e,t,n){return n.reduce((a,{position:s,score:i},o)=>(j8(e,t,s,o)||(a+=i),a),0)/n.length}function U8(e,t,n,r,a,s,i){let o=[],l=Vie(i,t),c=a**2;for(;o.length<s&&!l.empty();){let u=l.dequeue(),h=xg(u.part,B8,e);if(j8(o,c,h,u.part.id))continue;let p=Wie(u,t,e,B8,n,r).filter(A=>A.score>i),m=jie(o,c,p),f=P8(p);m>i&&o.push({keypoints:p,box:f,score:Math.round(100*m)/100})}return o}var _r,Uie=["MobilenetV1/offset_2/BiasAdd","MobilenetV1/heatmap_2/BiasAdd","MobilenetV1/displacement_fwd_2/BiasAdd","MobilenetV1/displacement_bwd_2/BiasAdd"];async function vg(e,t){let n=P(()=>{let o=e.resizeBilinear([_r.inputs[0].shape[2],_r.inputs[0].shape[1]]).toFloat().div(127.5).sub(1),c=_r.execute(o,Uie).map(u=>u.squeeze([0]));return c[1]=c[1].sigmoid(),c}),r=await Promise.all(n.map(i=>i.buffer()));for(let i of n)i.dispose();let a=await U8(r[0],r[1],r[2],r[3],t.body.nmsRadius,t.body.maxDetections,t.body.scoreThreshold);return L8(a,[e.shape[1],e.shape[2]],[_r.inputs[0].shape[2],_r.inputs[0].shape[1]])}async function kg(e){return _r?e.debug&&le("cached model:",_r.modelUrl):(_r=await ct(pt(e.modelBasePath,e.body.modelPath)),!_r||!_r.modelUrl?le("load model failed:",e.body.modelPath):e.debug&&le("load model:",_r.modelUrl)),_r}var Eg={};Yn(Eg,{HandPose:()=>Rg,load:()=>Mg});function O0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function th(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function H8(e,t,n){let r=t.shape[1],a=t.shape[2],s=[[e.startPoint[1]/r,e.startPoint[0]/a,e.endPoint[1]/r,e.endPoint[0]/a]];return Le.cropAndResize(t,s,[0],n)}function G8(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],r=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],a=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:n,endPoint:r,palmLandmarks:a,confidence:e.confidence}}function z0(e,t=1.5){let n=th(e),r=O0(e),a=[t*r[0]/2,t*r[1]/2],s=[n[0]-a[0],n[1]-a[1]],i=[n[0]+a[0],n[1]+a[1]];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function P0(e){let t=th(e),n=O0(e),a=Math.max(...n)/2,s=[t[0]-a,t[1]-a],i=[t[0]+a,t[1]+a];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}var Ig=class{constructor(t,n,r){this.model=t,this.anchors=r.map(a=>[a.x_center,a.y_center]),this.anchorsTensor=tr(this.anchors),this.inputSize=n,this.inputSizeTensor=sn([n,n]),this.doubleInputSizeTensor=sn([n*2,n*2])}normalizeBoxes(t){return P(()=>{let n=Re(t,[0,0],[-1,2]),r=Re(t,[0,2],[-1,2]),a=se(Ae(n,this.inputSizeTensor),this.anchorsTensor),s=Ae(r,this.doubleInputSizeTensor),i=z(ye(a,s),this.inputSizeTensor),o=z(se(a,s),this.inputSizeTensor);return bl([i,o],1)})}normalizeLandmarks(t,n){return P(()=>{let r=se(Ae(t.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[n]);return z(r,this.inputSizeTensor)})}async getBoxes(t,n){let r=this.model.predict(t),a=r.squeeze();r.dispose();let s=P(()=>Tn(Re(a,[0,0],[-1,1])).squeeze()),i=s.dataSync(),o=Re(a,[0,1],[-1,4]),l=this.normalizeBoxes(o);o.dispose();let c=await Le.nonMaxSuppressionAsync(l,i,n.hand.maxHands,n.hand.iouThreshold,n.hand.scoreThreshold),u=c.arraySync();s.dispose(),c.dispose();let h=[];for(let d of u)if(i[d]>=n.hand.minConfidence){let p=Re(l,[d,0],[1,-1]),m=Re(a,[d,5],[1,14]),f=P(()=>this.normalizeLandmarks(m,d).reshape([-1,2]));m.dispose(),h.push({box:p,palmLandmarks:f,confidence:i[d]})}return a.dispose(),l.dispose(),h}async estimateHandBounds(t,n){let r=t.shape[1],a=t.shape[2],s=P(()=>t.resizeBilinear([this.inputSize,this.inputSize]).div(127.5).sub(1)),i=await this.getBoxes(s,n);s.dispose();let o=[];if(!i||i.length===0)return o;for(let l of i){let c=l.box.dataSync(),u=c.slice(0,2),h=c.slice(2,4),d=l.palmLandmarks.arraySync();l.box.dispose(),l.palmLandmarks.dispose(),o.push(G8({startPoint:u,endPoint:h,palmLandmarks:d,confidence:l.confidence},[a/this.inputSize,r/this.inputSize]))}return o}};function Hie(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function q8(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return Hie(n)}var X8=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function ss(e,t){let n=0;for(let r=0;r<e.length;r++)n+=e[r]*t[r];return n}function Gie(e,t){let n=[];for(let r=0;r<e.length;r++)n.push(e[r][t]);return n}function K8(e,t){let n=[],r=e.length;for(let a=0;a<r;a++){n.push([]);for(let s=0;s<r;s++)n[a].push(ss(e[a],Gie(t,s)))}return n}function Sg(e,t){let n=Math.cos(e),r=Math.sin(e),a=[[n,-r,0],[r,n,0],[0,0,1]],s=X8(t[0],t[1]),i=K8(s,a),o=X8(-t[0],-t[1]);return K8(i,o)}function Z8(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],r=[-ss(t[0],n),-ss(t[1],n)];return[t[0].concat(r[0]),t[1].concat(r[1]),[0,0,1]]}function Ng(e,t){return[ss(e,t[0]),ss(e,t[1])]}var qie=5,Y8=1.65,J8=[0,5,9,13,17,1,2],Xie=0,Kie=2,Tg=class{constructor(t,n,r){this.handDetector=t,this.landmarkDetector=n,this.inputSize=r,this.storedBoxes=[],this.skipped=0,this.detectedHands=0}getBoxForPalmLandmarks(t,n){let r=t.map(s=>Ng([...s,1],n)),a=this.calculateLandmarksBoundingBox(r);return z0(P0(a),qie)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),r=z0(P0(n),Y8);r.palmLandmarks=[];for(let a=0;a<J8.length;a++)r.palmLandmarks.push(t[J8[a]].slice(0,2));return r}transformRawCoords(t,n,r,a){let s=O0(n),i=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],o=t.map(p=>[i[0]*(p[0]-this.inputSize/2),i[1]*(p[1]-this.inputSize/2),i[2]*p[2]]),l=Sg(r,[0,0]),c=o.map(p=>[...Ng(p,l),p[2]]),u=Z8(a),h=[...th(n),1],d=[ss(h,u[0]),ss(h,u[1])];return c.map(p=>[p[0]+d[0],p[1]+d[1],p[2]])}async estimateHands(t,n){let r=!1,a;(this.skipped===0||this.skipped>n.hand.skipFrames||!n.hand.landmarks||!n.videoOptimized)&&(a=await this.handDetector.estimateHandBounds(t,n),this.skipped=0),n.videoOptimized&&this.skipped++,a&&a.length>0&&(a.length!==this.detectedHands&&this.detectedHands!==n.hand.maxHands||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...a],this.storedBoxes.length>0&&(r=!0));let s=[];n.hand.skipInitial&&this.detectedHands===0&&(this.skipped=0);for(let i=0;i<this.storedBoxes.length;i++){let o=this.storedBoxes[i];if(!!o)if(n.hand.landmarks){let l=n.hand.rotation?q8(o.palmLandmarks[Xie],o.palmLandmarks[Kie]):0,c=th(o),u=[c[0]/t.shape[2],c[1]/t.shape[1]],h=n.hand.rotation?Le.rotateWithOffset(t,l,0,u):t.clone(),d=Sg(-l,c),p=r?this.getBoxForPalmLandmarks(o.palmLandmarks,d):o,m=H8(p,h,[this.inputSize,this.inputSize]),f=m.div(255);m.dispose(),h.dispose();let[A,y]=await this.landmarkDetector.predict(f);f.dispose();let g=A.dataSync()[0];if(A.dispose(),g>=n.hand.minConfidence){let x=H(y,[-1,3]),v=x.arraySync();y.dispose(),x.dispose();let b=this.transformRawCoords(v,p,l,d),w=this.getBoxForHandLandmarks(b);this.storedBoxes[i]=w;let k={landmarks:b,confidence:g,box:{topLeft:w.startPoint,bottomRight:w.endPoint}};s.push(k)}else this.storedBoxes[i]=null;y.dispose()}else{let l=z0(P0(o),Y8),c={confidence:o.confidence,box:{topLeft:l.startPoint,bottomRight:l.endPoint}};s.push(c)}}return this.storedBoxes=this.storedBoxes.filter(i=>i!==null),this.detectedHands=s.length,s}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s}}};var 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ek=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],tk=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var xn;async function $g(e){return xn?e.debug&&le("cached model:",xn.modelUrl):(xn=await ct(pt(e.modelBasePath,e.body.modelPath)),xn.width=parseInt(xn.signature.inputs["input_1:0"].tensorShape.dim[2].size),xn.height=parseInt(xn.signature.inputs["input_1:0"].tensorShape.dim[1].size),!xn||!xn.modelUrl?le("load model failed:",e.body.modelPath):e.debug&&le("load model:",xn.modelUrl)),xn}async function Dg(e,t){if(!xn||!t.body.enabled)return null;let n={width:e.shape[2],height:e.shape[1]},r=Le.resizeBilinear(e,[xn.width,xn.height],!1),a=Ae(r,[255]);r.dispose();let s;if(t.profile){let u=await an(()=>xn.predict(a));s=u.result.find(h=>h.size===195||h.size===155).dataSync(),u.result.forEach(h=>h.dispose()),gn("blazepose",u)}else{let u=await xn.predict(a);s=u.find(h=>h.size===195||h.size===155).dataSync(),u.forEach(h=>h.dispose())}a.dispose();let i=[],o=s.length===195?ek:tk,l=5;for(let 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if(t.backend==="webgl"||t.backend==="humangl"){let h=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas");h.width=i,h.height=o;let d=h.getContext("2d");d==null||d.drawImage(bt,0,0),c=fi.fromPixels(h)}else{let h=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas");h.width=i,h.height=o;let d=h.getContext("2d");d==null||d.drawImage(bt,0,0);let p=d==null?void 0:d.getImageData(0,0,i,o);c=fi.fromPixels(p)}let u=c.toFloat();n=u.expandDims(0),c.dispose(),u.dispose()}let r=t.filter.return?bt:null;return{tensor:n,canvas:r}}var Ug={};Yn(Ug,{all:()=>toe,body:()=>uk,canvas:()=>eoe,face:()=>lk,gesture:()=>ok,hand:()=>ck,object:()=>hk,options:()=>Hi});var ht={backend:"webgl",modelBasePath:"../models/",wasmPath:"../assets/",debug:!0,async:!0,profile:!1,deallocate:!1,scoped:!1,videoOptimized:!0,warmup:"face",filter:{enabled:!0,width:0,height:0,flip:!1,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface-back.json",rotation:!1,maxFaces:10,skipFrames:21,skipInitial:!1,minConfidence:.2,iouThreshold:.1,scoreThreshold:.2,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json"},iris:{enabled:!0,modelPath:"iris.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:31},emotion:{enabled:!0,minConfidence:.1,skipFrames:32,modelPath:"emotion.json"},age:{enabled:!1,modelPath:"age.json",skipFrames:33},gender:{enabled:!1,minConfidence:.1,modelPath:"gender.json",skipFrames:34},embedding:{enabled:!1,modelPath:"mobileface.json"}},body:{enabled:!0,modelPath:"posenet.json",maxDetections:1,scoreThreshold:.2,nmsRadius:20},hand:{enabled:!0,rotation:!1,skipFrames:12,skipInitial:!1,minConfidence:.1,iouThreshold:.1,scoreThreshold:.5,maxHands:1,landmarks:!0,detector:{modelPath:"handdetect.json"},skeleton:{modelPath:"handskeleton.json"}},object:{enabled:!1,modelPath:"nanodet.json",minConfidence:.2,iouThreshold:.4,maxResults:10,skipFrames:41}};var Hi={color:"rgba(173, 216, 230, 0.3)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",font:'small-caps 16px "Segoe UI"',lineHeight:24,lineWidth:6,pointSize:2,roundRect:28,drawPoints:!1,drawLabels:!0,drawBoxes:!1,drawPolygons:!0,fillPolygons:!1,useDepth:!0,useCurves:!1,bufferedOutput:!1,useRawBoxes:!1,calculateHandBox:!0};function U0(e,t,n,r=0,a){e.fillStyle=a.useDepth&&r?`rgba(${127.5+2*r}, ${127.5-2*r}, 255, 0.3)`:a.color,e.beginPath(),e.arc(t,n,a.pointSize,0,2*Math.PI),e.fill()}function Gi(e,t,n,r,a,s){if(e.beginPath(),s.useCurves){let i=(t+t+r)/2,o=(n+n+a)/2;e.ellipse(i,o,r/2,a/2,0,0,2*Math.PI)}else e.lineWidth=s.lineWidth,e.moveTo(t+s.roundRect,n),e.lineTo(t+r-s.roundRect,n),e.quadraticCurveTo(t+r,n,t+r,n+s.roundRect),e.lineTo(t+r,n+a-s.roundRect),e.quadraticCurveTo(t+r,n+a,t+r-s.roundRect,n+a),e.lineTo(t+s.roundRect,n+a),e.quadraticCurveTo(t,n+a,t,n+a-s.roundRect),e.lineTo(t,n+s.roundRect),e.quadraticCurveTo(t,n,t+s.roundRect,n),e.closePath();e.stroke()}function Hg(e,t=[],n){if(!(t===void 0||t.length===0)){e.beginPath(),e.moveTo(t[0][0],t[0][1]);for(let r of t)e.strokeStyle=n.useDepth&&r[2]?`rgba(${127.5+2*r[2]}, ${127.5-2*r[2]}, 255, 0.3)`:n.color,e.fillStyle=n.useDepth&&r[2]?`rgba(${127.5+2*r[2]}, ${127.5-2*r[2]}, 255, 0.3)`:n.color,e.lineTo(r[0],parseInt(r[1]));e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function rh(e,t=[],n){if(!(t===void 0||t.length===0)){if(!n.useCurves||t.length<=2){Hg(e,t,n);return}e.moveTo(t[0][0],t[0][1]);for(let r=0;r<t.length-2;r++){let a=(t[r][0]+t[r+1][0])/2,s=(t[r][1]+t[r+1][1])/2;e.quadraticCurveTo(t[r][0],t[r][1],a,s)}e.quadraticCurveTo(t[t.length-2][0],t[t.length-2][1],t[t.length-1][0],t[t.length-1][1]),e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}async function ok(e,t,n){let r=Jn(Hi,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let a=e.getContext("2d");if(!a)return;a.font=r.font,a.fillStyle=r.color;let s=1;for(let i=0;i<t.length;i++){let o=[],l=[];if([o,l]=Object.entries(t[i]),l.length>1&&l[1].length>0){let c=o[1]>0?`#${o[1]}`:"",u=`${o[0]} ${c}: ${l[1]}`;r.shadowColor&&r.shadowColor!==""&&(a.fillStyle=r.shadowColor,a.fillText(u,8,2+s*r.lineHeight)),a.fillStyle=r.labelColor,a.fillText(u,6,0+s*r.lineHeight),s+=1}}}async function lk(e,t,n){let r=Jn(Hi,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let a=e.getContext("2d");if(!!a)for(let s of t){a.font=r.font,a.strokeStyle=r.color,a.fillStyle=r.color,r.drawBoxes&&(r.useRawBoxes?Gi(a,e.width*s.boxRaw[0],e.height*s.boxRaw[1],e.width*s.boxRaw[2],e.height*s.boxRaw[3],r):Gi(a,s.box[0],s.box[1],s.box[2],s.box[3],r));let i=[];if(i.push(`face confidence: ${Math.trunc(100*s.confidence)}%`),s.genderConfidence&&i.push(`${s.gender||""} ${Math.trunc(100*s.genderConfidence)}% confident`),s.age&&i.push(`age: ${s.age||""}`),s.iris&&i.push(`iris distance: ${s.iris}`),s.emotion&&s.emotion.length>0){let o=s.emotion.map(l=>`${Math.trunc(100*l.score)}% ${l.emotion}`);i.push(o.join(" 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${this.tf.version_core}`),this.config.debug&&le("platform:",this.sysinfo.platform),this.config.debug&&le("agent:",this.sysinfo.agent),await ur(this,ih).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&le("configuration:",this.config),this.config.debug&&le("tf flags:",this.tf.ENV.flags))),this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.handpose,this.models.posenet,this.models.blazepose,this.models.efficientpose,this.models.nanodet,this.models.faceres]=await Promise.all([this.models.face||(this.config.face.enabled?Ag(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?G2(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?Y2(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?tg(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?ng(this.config):null),this.models.handpose||(this.config.hand.enabled?Mg(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?kg(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?$g(this.config):null),this.models.efficientpose||(this.config.body.enabled&&this.config.body.modelPath.includes("efficientpose")?Og(this.config):null),this.models.nanodet||(this.config.object.enabled?Wg(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?sg(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await Ag(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await G2(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await Y2(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await tg(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await ng(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await Mg(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await kg(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await $g(this.config)),this.config.body.enabled&&!this.models.efficientpose&&this.config.body.modelPath.includes("efficientpose")&&(this.models.efficientpose=await Og(this.config)),this.config.object.enabled&&!this.models.nanodet&&(this.models.nanodet=await Wg(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await sg(this.config))),ur(this,qi)&&(this.config.debug&&le("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),cs(this,qi,!1));let r=Math.trunc(Ye()-n);r>(this.perf.load||0)&&(this.perf.load=r)}async detect(t,n={}){return new Promise(async r=>{var A,y;this.state="config";let a;this.config=Jn(this.config,n),this.state="check";let s=ur(this,q0).call(this,t);s&&(le(s,t),r({error:s}));let i=Ye();await ur(this,ih).call(this),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:");let o;t&&this.config.videoOptimized&&(typeof HTMLImageElement!="undefined"&&t instanceof HTMLImageElement||typeof Image!="undefined"&&t instanceof Image||typeof ImageData!="undefined"&&t instanceof ImageData||typeof ImageBitmap!="undefined"&&Vg instanceof ImageBitmap)&&(le("disabling video optimization"),o=this.config.videoOptimized,this.config.videoOptimized=!1),a=Ye();let l=jg(t,this.config);if(!l||!l.tensor){le("could not convert input to tensor"),r({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(Ye()-a),this.analyze("Get Image:");let c,u,h,d,p;this.config.async?(h=this.config.face.enabled?lg(this,l.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",a=Ye(),h=this.config.face.enabled?await lg(this,l.tensor):[],p=Math.trunc(Ye()-a),p>0&&(this.perf.face=p)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?c=this.config.body.enabled?vg(l.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?c=this.config.body.enabled?Dg(l.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(c=this.config.body.enabled?zg(l.tensor,this.config):[]),this.perf.body&&delete this.perf.body):(this.state="run:body",a=Ye(),this.config.body.modelPath.includes("posenet")?c=this.config.body.enabled?await vg(l.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?c=this.config.body.enabled?await Dg(l.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(c=this.config.body.enabled?await zg(l.tensor,this.config):[]),p=Math.trunc(Ye()-a),p>0&&(this.perf.body=p)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(u=this.config.hand.enabled?(A=this.models.handpose)==null?void 0:A.estimateHands(l.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",a=Ye(),u=this.config.hand.enabled?await((y=this.models.handpose)==null?void 0:y.estimateHands(l.tensor,this.config)):[],p=Math.trunc(Ye()-a),p>0&&(this.perf.hand=p)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(d=this.config.object.enabled?Bg(l.tensor,this.config):[],this.perf.object&&delete this.perf.object):(this.state="run:object",a=Ye(),d=this.config.object.enabled?await Bg(l.tensor,this.config):[],p=Math.trunc(Ye()-a),p>0&&(this.perf.object=p)),this.analyze("End Object:"),this.config.async&&([h,c,u,d]=await Promise.all([h,c,u,d])),we(l.tensor),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let m=[];this.config.gesture.enabled&&(a=Ye(),m=[...rk(h),...nk(c),...sk(u),...ak(h)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(Ye()-a)),o&&(this.config.videoOptimized=o),this.perf.total=Math.trunc(Ye()-i),this.state="idle";let f={face:h,body:c,hand:u,gesture:m,object:d,performance:this.perf,canvas:l.canvas};r(f)})}async warmup(t={}){let n=Ye();if(t&&(this.config=Jn(this.config,t)),!this.config.warmup||this.config.warmup==="none")return{error:"null"};let r=this.config.videoOptimized;this.config.videoOptimized=!1;let a;typeof createImageBitmap=="function"?a=await ur(this,X0).call(this):typeof Image!="undefined"?a=await ur(this,K0).call(this):a=await ur(this,Z0).call(this),this.config.videoOptimized=r;let s=Ye();return this.config.debug&&le("Warmup",this.config.warmup,Math.round(s-n),"ms",a),a}};au=new WeakMap,ah=new WeakMap,sh=new WeakMap,qi=new WeakMap,q0=new WeakMap,ih=new WeakMap,X0=new WeakMap,K0=new WeakMap,Z0=new WeakMap;return roe;})();
/**
* @license
* Copyright 2017 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the License);
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an AS IS BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/** @license See the LICENSE file. */
//# sourceMappingURL=human.js.map
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