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r=Object.keys(e),i=0;i>>3){case 1:c.tensorName=e.string();break;case 2:c.quantParameterTensorNames&&c.quantParameterTensorNames.length||(c.quantParameterTensorNames=[]),c.quantParameterTensorNames.push(o.onnx.StringStringEntryProto.decode(e,e.uint32()));break;default:e.skipType(7&g)}}return c},t.decodeDelimited=function(e){return e instanceof h||(e=new h(e)),this.decode(e,e.uint32())},t.verify=function(e){if(typeof e!="object"||e===null)return"object expected";if(e.tensorName!=null&&e.hasOwnProperty("tensorName")&&!a.isString(e.tensorName))return"tensorName: string expected";if(e.quantParameterTensorNames!=null&&e.hasOwnProperty("quantParameterTensorNames")){if(!Array.isArray(e.quantParameterTensorNames))return"quantParameterTensorNames: array expected";for(var r=0;r>>3){case 1:c.node&&c.node.length||(c.node=[]),c.node.push(o.onnx.NodeProto.decode(e,e.uint32()));break;case 2:c.name=e.string();break;case 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r=0;r>>3){case 1:if(c.dims&&c.dims.length||(c.dims=[]),(7&g)==2)for(var m=e.uint32()+e.pos;e.pos>>0,e.dims[i].high>>>0).toNumber())}if(e.dataType!=null&&(r.dataType=0|e.dataType),e.segment!=null){if(typeof e.segment!="object")throw TypeError(".onnx.TensorProto.segment: object expected");r.segment=o.onnx.TensorProto.Segment.fromObject(e.segment)}if(e.floatData){if(!Array.isArray(e.floatData))throw TypeError(".onnx.TensorProto.floatData: array expected");for(r.floatData=[],i=0;i>>0,e.int64Data[i].high>>>0).toNumber())}if(e.name!=null&&(r.name=String(e.name)),e.docString!=null&&(r.docString=String(e.docString)),e.rawData!=null&&(typeof e.rawData=="string"?a.base64.decode(e.rawData,r.rawData=a.newBuffer(a.base64.length(e.rawData)),0):e.rawData.length&&(r.rawData=e.rawData)),e.externalData){if(!Array.isArray(e.externalData))throw TypeError(".onnx.TensorProto.externalData: array expected");for(r.externalData=[],i=0;i>>0,e.uint64Data[i].high>>>0).toNumber(!0))}return 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c=0;c>>0,e.dims[c].high>>>0).toNumber():e.dims[c]}if(e.dataType!=null&&e.hasOwnProperty("dataType")&&(i.dataType=e.dataType),e.segment!=null&&e.hasOwnProperty("segment")&&(i.segment=o.onnx.TensorProto.Segment.toObject(e.segment,r)),e.floatData&&e.floatData.length)for(i.floatData=[],c=0;c>>0,e.int64Data[c].high>>>0).toNumber():e.int64Data[c];if(e.name!=null&&e.hasOwnProperty("name")&&(i.name=e.name),e.rawData!=null&&e.hasOwnProperty("rawData")&&(i.rawData=r.bytes===String?a.base64.encode(e.rawData,0,e.rawData.length):r.bytes===Array?Array.prototype.slice.call(e.rawData):e.rawData),e.doubleData&&e.doubleData.length)for(i.doubleData=[],c=0;c>>0,e.uint64Data[c].high>>>0).toNumber(!0):e.uint64Data[c];if(e.docString!=null&&e.hasOwnProperty("docString")&&(i.docString=e.docString),e.externalData&&e.externalData.length)for(i.externalData=[],c=0;c>>3){case 1:g.begin=r.int64();break;case 2:g.end=r.int64();break;default:r.skipType(7&m)}}return g},e.decodeDelimited=function(r){return r instanceof 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c={};if(i.dimValue!=null&&i.hasOwnProperty("dimValue")&&(c.value=1,!(a.isInteger(i.dimValue)||i.dimValue&&a.isInteger(i.dimValue.low)&&a.isInteger(i.dimValue.high))))return"dimValue: integer|Long expected";if(i.dimParam!=null&&i.hasOwnProperty("dimParam")){if(c.value===1)return"value: multiple values";if(c.value=1,!a.isString(i.dimParam))return"dimParam: string expected"}return i.denotation!=null&&i.hasOwnProperty("denotation")&&!a.isString(i.denotation)?"denotation: string expected":null},e.fromObject=function(i){if(i instanceof o.onnx.TensorShapeProto.Dimension)return i;var c=new o.onnx.TensorShapeProto.Dimension;return i.dimValue!=null&&(a.Long?(c.dimValue=a.Long.fromValue(i.dimValue)).unsigned=!1:typeof i.dimValue=="string"?c.dimValue=parseInt(i.dimValue,10):typeof i.dimValue=="number"?c.dimValue=i.dimValue:typeof i.dimValue=="object"&&(c.dimValue=new 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1:g.tensorType=o.onnx.TypeProto.Tensor.decode(r,r.uint32());break;case 6:g.denotation=r.string();break;default:r.skipType(7&m)}}return g},t.decodeDelimited=function(r){return r instanceof h||(r=new h(r)),this.decode(r,r.uint32())},t.verify=function(r){if(typeof r!="object"||r===null)return"object expected";if(r.tensorType!=null&&r.hasOwnProperty("tensorType")){var i=o.onnx.TypeProto.Tensor.verify(r.tensorType);if(i)return"tensorType."+i}return r.denotation!=null&&r.hasOwnProperty("denotation")&&!a.isString(r.denotation)?"denotation: string expected":null},t.fromObject=function(r){if(r instanceof o.onnx.TypeProto)return r;var i=new o.onnx.TypeProto;if(r.tensorType!=null){if(typeof r.tensorType!="object")throw TypeError(".onnx.TypeProto.tensorType: object expected");i.tensorType=o.onnx.TypeProto.Tensor.fromObject(r.tensorType)}return r.denotation!=null&&(i.denotation=String(r.denotation)),i},t.toObject=function(r,i){i||(i={});var c={};return 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o.isPacked?this.getPackedOutputSamplingSnippet(o):this.getUnpackedOutputSamplingSnippet(o)}getPackedOutputSamplingSnippet(o){const t=o.unpackedShape,e=[o.width,o.height],r={},i="getOutputCoords";switch(t.length){case 0:r[i]=this.getOutputScalarCoords();break;case 1:r[i]=this.getOutputPacked1DCoords(t,e);break;case 2:r[i]=this.getOutputPacked2DCoords(t,e);break;case 3:r[i]=this.getOutputPacked3DCoords(t,e);break;default:r[i]=this.getOutputPackedNDCoords(t,e)}const c=` void setOutput(vec4 val) { ${(0,p.getGlsl)(this.context.glContext.version).output} = val; } `;return r.floatTextureSetRGBA=new l.GlslLibRoutine(c),r}getUnpackedOutputSamplingSnippet(o){const t=o.unpackedShape,e=[o.width,o.height],r={},i="getOutputCoords";switch(t.length){case 0:r[i]=this.getOutputScalarCoords();break;case 1:r[i]=this.getOutputUnpacked1DCoords(t,e);break;case 2:r[i]=this.getOutputUnpacked2DCoords(t,e);break;case 3:r[i]=this.getOutputUnpacked3DCoords(t,e);break;case 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d2); } `,new l.GlslLibRoutine(e)}getOutputUnpacked5DCoords(o,t){let e="";const r=o.length;let i=null;r<2&&(i=[]),i=new Array(r-1),i[r-2]=o[r-1];for(let m=r-3;m>=0;--m)i[m]=i[m+1]*o[m+1];const c=["r","c","d","d2","d3"],g=i.map((m,b)=>`int ${c[b]} = index / ${m}; ${b===i.length-1?`int ${c[b+1]} = index - ${c[b]} * ${m}`:`index -= ${c[b]} * ${m}`};`).join("");return e=` ivec5 getOutputCoords() { ivec2 resTexRC = ivec2(TexCoords.xy * vec2(${t[0]}, ${t[1]})); int index = resTexRC.y * ${t[0]} + resTexRC.x; ${g} return ivec5(r, c, d, d2, d3); } `,new l.GlslLibRoutine(e)}getOutputUnpacked6DCoords(o,t){let e="";const r=o.length;let i=null;r<2&&(i=[]),i=new Array(r-1),i[r-2]=o[r-1];for(let m=r-3;m>=0;--m)i[m]=i[m+1]*o[m+1];const c=["r","c","d","d2","d3","d4"],g=i.map((m,b)=>`int ${c[b]} = index / ${m}; ${b===i.length-1?`int ${c[b+1]} = index - ${c[b]} * ${m}`:`index -= ${c[b]} * ${m}`};`).join("");return e=` ivec6 getOutputCoords() { ivec2 resTexRC = ivec2(TexCoords.xy * vec2(${t[0]}, ${t[1]})); 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b=c[0].dims,_=c[1].dims,w=[_[0],Math.ceil(b[1]*_[2]*_[3]/4)],v=(0,h.calculateIm2ColDims)(b,_,g),[S,O]=r.calculateTextureWidthAndHeight(w,p.TextureType.packedLastDimension),E=d.ShapeUtil.computeStrides(v),[T,I]=r.calculateTextureWidthAndHeight(v,p.TextureType.packedLastDimension),C=g.length,B=c.length<3?"0.0":"_B(b)",F=Math.ceil(b[1]*_[2]*_[3]/4),{activationFunction:N,applyActivation:H}=(0,s.getActivationSnippet)(m),$=(0,l.getGlsl)(r.session.backend.glContext.version),z=` ${N} float process(int indices[${C}]) { int b[1]; b[0] = indices[1]; int im2col[4]; im2col[0] = indices[0]; im2col[1] = indices[2]; im2col[2] = indices[3]; int im2colOffset = im2col[0] * ${E[0]} + im2col[1] * ${E[1]} + im2col[2] * ${E[2]}; int kernelOffset = indices[1] * ${w[1]}; float value = ${B}; for (int i = 0; i < ${F}; ++i) { vec2 im2colCoords = offsetToCoords(im2colOffset, ${T}, ${I}); vec2 kernelCoords = offsetToCoords(kernelOffset, ${S}, ${O}); value += dot(${$.texture2D}(Im2Col, im2colCoords), 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h={name:"Gather",inputNames:["A","B"],inputTypes:[s.TextureType.unpacked,s.TextureType.unpacked]},f=(o,t,e)=>{const r=Object.assign(Object.assign({},h),{cacheHint:e.cacheKey});return Object.assign(Object.assign({},r),{get:()=>((i,c,g,m)=>{const b=g[0].dims.slice(),_=g[1].dims.slice(),w=new Array(b.length+_.length-1);m=p.ShapeUtil.normalizeAxis(m,b.length);const v=[];for(let O=0;O{if(!o||o.length!==2)throw new Error("Gather requires 2 inputs.");const e=o[0].dims.length;if(e<1)throw new Error("Invalid input shape.");if(t<-e||t>e-1)throw new Error("Invalid axis.");if(l.NUMBER_TYPES.indexOf(o[0].type)===-1)throw new Error("Invaid input type.");if(o[1].type!=="int32"&&o[1].type!=="int16")throw new Error("Invaid input type.")}},4776:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseGemmAttributesV11=n.parseGemmAttributesV7=n.gemm=void 0;const d=u(246),l=u(2517),p=u(2039);n.gemm=(o,t,e)=>(a(t,e),[o.run(h(t,e),t)]);const s=(o,t)=>{const e=o.attributes.getInt("transA",0)!==0,r=o.attributes.getInt("transB",0)!==0,i=o.attributes.getFloat("alpha",1),c=o.attributes.getFloat("beta",1);return(0,d.createAttributeWithCacheKey)({transA:e,transB:r,alpha:i,beta:c,isOptionalC:t})};n.parseGemmAttributesV7=o=>s(o,!1),n.parseGemmAttributesV11=o=>s(o,!0);const h=(o,t)=>{const e={name:"Gemm",inputNames:o.length===3?["A","B","C"]:["A","B"],inputTypes:o.length===3?[p.TextureType.unpacked,p.TextureType.unpacked,p.TextureType.unpacked]:[p.TextureType.unpacked,p.TextureType.unpacked],key:t.cacheKey};return Object.assign(Object.assign({},e),{get:()=>f(e,o,t)})},f=(o,t,e)=>{const r=t[0].dims.slice(),i=t[1].dims.slice(),[c,g]=l.GemmUtil.getShapeOfGemmResult(r,e.transA,i,e.transB,t.length===3?t[2].dims:void 0),m=[c,g];if(!m)throw new Error("Can't use gemm on the given tensors");let b=r[r.length-1],_="";e.transA&&(b=r[0]),e.transA&&e.transB?_="value += _A_T(a) * _B_T(b);":e.transA&&!e.transB?_="value += _A_T(a) * 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Error("Invalid input shape of C");if(o[0].type!=="float32"&&o[0].type!=="float64"||o[1].type!=="float32"&&o[1].type!=="float64"||o.length===3&&o[2].type!=="float32"&&o[2].type!=="float64")throw new Error("Invalid input type.");if(o[0].type!==o[1].type||o.length===3&&o[0].type!==o[2].type)throw new Error("Input types are mismatched")}},8555:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.createPackedIm2ColProgramInfoLoader=void 0;const d=u(5060),l=u(2039),p=u(2827);n.createPackedIm2ColProgramInfoLoader=(s,h,f,a,o)=>{const t=(e=o.cacheKey,{name:"Im2Col (packed)",inputNames:["A"],inputTypes:[l.TextureType.packed],cacheHint:e});var e;return Object.assign(Object.assign({},t),{get:()=>((r,i,c,g,m,b)=>{const _=c.dims,w=g.dims,v=m.length,S=[w[1]*w[2]*w[3],m[2]*m[3]],O=w[2]*w[3],E=(0,p.unpackFromChannel)(),T=(0,d.getGlsl)(r.session.backend.glContext.version);let I="";for(let B=0;B<=1;B++)for(let F=0;F<=1;F++)I+=` blockIndex = rc.x + ${F}; pos = rc.y + ${B}; if(blockIndex < ${S[1]} && pos < ${S[0]}) { offsetY = int(blockIndex / (${m[v-1]})) * ${b.strides[0]} - ${b.pads[0]}; d0 = offsetY + ${b.dilations[0]} * (imod(pos, ${O}) / ${w[2]}); if(d0 < ${_[2]} && d0 >= 0) { offsetX = imod(blockIndex, ${m[v-1]}) * ${b.strides[1]} - ${b.pads[1]}; d1 = offsetX + ${b.dilations[1]} * imod(imod(pos, ${O}), ${w[2]}); if(d1 < ${_[3]} && d1 >= 0) { ch = int(float(pos)/ ${O}.); innerDims = vec2(d0, d1); result[${2*B+F}] = getChannel( getA(0, ch, int(innerDims.x), int(innerDims.y)), innerDims); } } } `;const C=` ${E} void main() { ivec2 rc = getOutputCoords(); vec4 result = vec4(0.0); int blockIndex, pos, offsetY, d0, offsetX, d1, ch; vec2 innerDims; ${I} ${T.output} = result; } `;return Object.assign(Object.assign({},i),{output:{dims:S,type:c.type,textureType:l.TextureType.packed},shaderSource:C,hasMain:!0})})(s,t,h,f,a,o)})}},3248:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.calculateIm2ColDims=n.createIm2ColProgramInfoLoader=void 0;const d=u(2039);n.createIm2ColProgramInfoLoader=(l,p,s,h,f)=>{const a=(o=f.cacheKey,{name:"Im2Col",inputNames:["X"],inputTypes:[d.TextureType.unpacked],cacheHint:o});var o;return Object.assign(Object.assign({},a),{get:()=>((t,e,r,i,c,g)=>{const m=r.dims,b=i.dims,_=c.length,w=(0,n.calculateIm2ColDims)(m,b,c,4),v=` const int XC = ${m[1]}; const int XH = ${m[2]}; const int XW = ${m[3]}; const int KH = ${g.kernelShape[0]}; const int KW = ${g.kernelShape[1]}; const int dilationH = ${g.dilations[0]}; const int dilationW = ${g.dilations[1]}; const int strideH = ${g.strides[0]}; const int strideW = ${g.strides[1]}; const int padH = ${g.pads[0]}; const int padW = ${g.pads[1]}; const int KHKW = KH*KW; const int XCKHKW = XC * KHKW; const int outputChannels = 4; vec4 process(int indices[${_}]) { int b = indices[0]; // batch size int oh = indices[1] * strideH - padH; //output height int ow = indices[2] * strideW - padW; //output width int p = indices[3] * outputChannels; //patch vec4 value = vec4(0.0); for(int i=0; i < outputChannels; ++i) { if(p < XCKHKW) { int patchC = p / KHKW; int patchH = (p - patchC*KHKW) / KW; int patchW = (p - patchC*KHKW) - patchH * KW; int xh2 = oh + patchH * dilationH; int xw2 = ow + patchW * dilationW; int x[${m.length}]; x[0] = b; x[1] = patchC; x[2] = xh2; x[3] = xw2; if(xh2 >= 0 && xh2 < XH && xw2 >= 0 && xw2 < XW) { value[i] = _X(x); } } ++p; } return value; } `;return Object.assign(Object.assign({},e),{output:{dims:w,type:r.type,textureType:d.TextureType.packedLastDimension},shaderSource:v})})(0,a,p,s,h,f)})},n.calculateIm2ColDims=(l,p,s,h=4)=>[s[0],s[2],s[3],Math.ceil(l[1]*p[2]*p[3]/h)]},6572:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseImageScalerAttributes=n.imageScaler=void 0;const d=u(246),l=u(2039);n.imageScaler=(a,o,t)=>(f(o),[a.run(s(a,o,t),o)]),n.parseImageScalerAttributes=a=>{const o=a.attributes.getFloat("scale"),t=a.attributes.getFloats("bias");return(0,d.createAttributeWithCacheKey)({scale:o,bias:t})};const p={name:"ImageScaler",inputNames:["X"],inputTypes:[l.TextureType.unpacked]},s=(a,o,t)=>{const e=Object.assign(Object.assign({},p),{cacheHint:t.cacheKey});return Object.assign(Object.assign({},e),{get:()=>((r,i,c,g)=>{const m=c[0].dims.slice(),b=m.length,_=` ${h(g.bias.length)} float process(int indices[${b}]) { return _X(indices) * scale + getBias(bias, indices[1]); }`;return Object.assign(Object.assign({},i),{output:{dims:m,type:c[0].type,textureType:l.TextureType.unpacked},variables:[{name:"bias",type:"float",arrayLength:g.bias.length,data:g.bias},{name:"scale",type:"float",data:g.scale}],shaderSource:_})})(0,e,o,t)})},h=a=>{const o=[`float getBias(float bias[${a}], int channel) {`];for(let t=0;t{if(!a||a.length!==1)throw new Error("ImageScaler requires 1 input.");if(a[0].dims.length!==4)throw new Error("Invalid input shape.");if(a[0].type!=="float32"&&a[0].type!=="float64")throw new Error("Invalid input type.")}},3346:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.parseInstanceNormalizationAttributes=n.instanceNormalization=void 0;const d=u(5060),l=u(2039);n.instanceNormalization=(o,t,e)=>{a(t);const r=o.run(s(t[0]),t);return[o.run(f(o,t[0],e,r.dims),[t[0],r,t[1],t[2]])]},n.parseInstanceNormalizationAttributes=o=>o.attributes.getFloat("epsilon",1e-5);const p={name:"InstanceNormalization_MeanAndVariance",inputNames:["X"],inputTypes:[l.TextureType.unpacked]},s=o=>Object.assign(Object.assign({},p),{get:()=>((t,e)=>{const r=e.dims.slice(),i=r[1],c=r[2]*r[3],g=[r[0],i],m=` vec4 process(int[2] indices) { vec4 v = vec4(0.0); int a[4]; a[0] = indices[0]; a[1] = indices[1]; float temp = 0.0; for(int a2=0; a2<${r[2]}; a2++) { a[2] = a2; for(int a3=0; a3<${r[3]}; a3++) { a[3] = a3; float x = _X(a); temp += x; } } float mean = temp / float(${c}); temp = 0.0; for(int a2=0; a2<${r[2]}; a2++) { a[2] = a2; for(int a3=0; a3<${r[3]}; a3++) { a[3] = a3; float x = _X(a); temp += (x - mean) * (x - mean); } } v.r = mean; v.g = temp / float(${c}); return v; }`;return Object.assign(Object.assign({},t),{output:{dims:g,type:e.type,textureType:l.TextureType.packedLastDimension},shaderSource:m})})(p,o)}),h={name:"InstanceNormalization_ComputeOutput",inputNames:["X","MeanAndVariance","Scale","B"],inputTypes:[l.TextureType.unpacked,l.TextureType.packedLastDimension,l.TextureType.unpacked,l.TextureType.unpacked]},f=(o,t,e,r)=>{const i=Object.assign(Object.assign({},h),{cacheHint:`${e}`});return Object.assign(Object.assign({},i),{get:()=>((c,g,m,b,_)=>{const w=(0,d.getGlsl)(c.session.backend.glContext.version),[v,S]=c.calculateTextureWidthAndHeight(_,l.TextureType.packedLastDimension),[O,E]=[v/4,S],T=` vec4 get_MeanAndVariance(int[2] mv) { int offset = indicesToOffset_MeanAndVariance(mv); vec2 coords = offsetToCoords(offset, ${O}, ${E}); return ${w.texture2D}(MeanAndVariance, coords); } float process(int[4] indices) { int mv[2]; mv[0] = indices[0]; mv[1] = indices[1]; vec4 mean_and_variance = get_MeanAndVariance(mv); float mean = mean_and_variance.r; float variance = mean_and_variance.g; int sb[1]; sb[0] = indices[1]; float scale = _Scale(sb); float b = _B(sb); return scale * (_X(indices) - mean) / sqrt(variance + epsilon) + b; }`;return Object.assign(Object.assign({},g),{output:{dims:m.dims,type:m.type,textureType:l.TextureType.unpacked},variables:[{name:"epsilon",type:"float",data:b}],shaderSource:T})})(o,i,t,e,r)})},a=o=>{if(!o||o.length!==3)throw new Error("InstanceNormalization requires 3 inputs.");const t=o[0],e=o[1],r=o[2];if(t.dims.length<3||e.dims.length!==1||r.dims.length!==1)throw new Error("Invalid input shape.");if(e.dims[0]!==t.dims[1]||r.dims[0]!==t.dims[1])throw new Error("Input shapes are mismatched.");if(t.type!=="float32"&&t.type!=="float64"||e.type!=="float32"&&e.type!=="float64"||r.type!=="float32"&&r.type!=="float64")throw new Error("Invalid input type.");if(o[0].dims.length!==4)throw new Error("Only support 4-D input 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(fcoords.w + 0.5) / scaleWHWH.w - 0.5 : 0.0 ); } `;break;case"align_corners":C=` vec4 getSourceFracIndex(ivec4 coords) { vec4 resized = vec4(${v}.0 - 1.0, ${w}.0 - 1.0, ${v}.0 - 1.0, ${w}.0 - 1.0); vec4 original = vec4(${E}.0 - 1.0, ${O}.0 - 1.0, ${E}.0 - 1.0, ${O}.0 - 1.0); vec4 new_scale = original / resized; return vec4(coords) * new_scale; } `;break;default:throw new Error(`resize (packed) does not support coordinateTransformMode: '${c.coordinateTransformMode}'`)}const B=(0,p.getCoordsDataType)(_),F=` const vec2 inputWH = vec2(${O}.0, ${E}.0); const vec4 scaleWHWH = vec4(float(${T}), float(${I}), float(${T}), float(${I})); ${(0,s.unpackFromChannel)()} ${C} float getAValue(int x10, int r, int c, int d) { return getChannel(getA(x10, r, c, d), vec2(c, d)); } void main() { ${B} rc = getOutputCoords(); int batch = rc[0]; int depth = rc[1]; // retrieve the 4 coordinates that is used in the 4 packed output values. ivec4 coords = ivec4(rc.wz, rc.w + 1, rc.z + 1); // calculate the source 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getAValue(batch, depth, x00.x, x00.w), hasNextCol ? getAValue(batch, depth, x01.x, x01.w) : 0.0, hasNextRow ? getAValue(batch, depth, x10.x, x10.w) : 0.0, (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.x, x11.w) : 0.0); // pack x00, x01, x10, x11's bottom-left corner into one vec4 structure vec4 bottomLeft = vec4( getAValue(batch, depth, x00.z, x00.y), hasNextCol ? getAValue(batch, depth, x01.z, x01.y) : 0.0, hasNextRow ? getAValue(batch, depth, x10.z, x10.y) : 0.0, (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.z, x11.y) : 0.0); // pack x00, x01, x10, x11's bottom-right corner into one vec4 structure vec4 bottomRight = vec4( getAValue(batch, depth, x00.z, x00.w), hasNextCol ? getAValue(batch, depth, x01.z, x01.w) : 0.0, hasNextRow ? getAValue(batch, depth, x10.z, x10.w) : 0.0, (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.z, x11.w) : 0.0); // calculate the interpolation fraction on u and v direction vec4 frac = vec4(sourceFrac) - floor(sourceFrac); vec4 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g=Array.from(r.floatData);return(0,h.scalesValidation)(g,i,c),g},e=(r,i,c,g)=>{const m=i.length,b=new Array(m);for(let _=0,w=m;_{Object.defineProperty(n,"__esModule",{value:!0}),n.shape=void 0;const d=u(9162);n.shape=(p,s)=>(l(s),[new d.Tensor([s[0].dims.length],"int32",void 0,void 0,new Int32Array(s[0].dims))]);const l=p=>{if(!p||p.length!==1)throw new Error("Shape requires 1 input.")}},2278:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.sliceV10=n.parseSliceAttributes=n.slice=void 0;const d=u(246),l=u(782),p=u(2517),s=u(2039),h={name:"Slice",inputNames:["A"],inputTypes:[s.TextureType.unpacked]};n.slice=(e,r,i)=>(a(r),[e.run(Object.assign(Object.assign({},h),{cacheHint:i.cacheKey,get:()=>f(e,r[0],i)}),r)]),n.parseSliceAttributes=e=>{const r=e.attributes.getInts("starts"),i=e.attributes.getInts("ends"),c=e.attributes.getInts("axes",[]);return(0,d.createAttributeWithCacheKey)({starts:r,ends:i,axes:c})};const f=(e,r,i)=>{const 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o=(e,r)=>{if(!e.session.isInitializer(r[1].dataId)||!e.session.isInitializer(r[2].dataId)||r.length>=4&&!e.session.isInitializer(r[3].dataId)||r.length>=5&&!e.session.isInitializer(r[4].dataId))throw new Error("dynamic slice attributes are not allowed");if(r.length>=5&&r[4].integerData.some(m=>m!==1))throw new Error("currently non-1 steps is not supported for Slice");const i=Array.from(r[1].integerData),c=Array.from(r[2].integerData),g=r.length>=4?Array.from(r[3].integerData):[];return{starts:i,ends:c,axes:g,cacheKey:`${g};${i};${c}`}},t=e=>{if(!e||e.length<3||e.length>5)throw new Error("Invalid input number.");if(e[1].type!=="int32"||e[1].dims.length!==1)throw new Error("Invalid input type.");if(e[2].type!=="int32"||e[2].dims.length!==1)throw new Error("Invalid input type.");if(e.length>=4&&(e[3].type!=="int32"||e[3].dims.length!==1))throw new Error("Invalid input type.");if(e.length>=5&&(e[4].type!=="int32"||e[4].dims.length!==1))throw new Error("Invalid input 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_=m[0].dims.slice(),w=l.ShapeUtil.normalizeAxis(b.axis,_.length),v=_.length,S=w!==v-1,O=[];let E,T=[],I=[];S&&(T=Array.from({length:v}).map((N,H)=>H),T[w]=v-1,T[v-1]=w,T.map(N=>O.push(_[N])),E=(0,d.createAttributeWithCacheKey)({perm:T}),I=(0,h.transpose)(g,m,E));const C=S?l.ShapeUtil.sizeToDimension(O,v-1):l.ShapeUtil.sizeToDimension(_,v-1),B=S?l.ShapeUtil.sizeFromDimension(O,v-1):l.ShapeUtil.sizeFromDimension(_,v-1),F=t(g,S?I:m,b,C,B);return S?(0,h.transpose)(g,F,E):F};const t=(g,m,b,_,w)=>{const v=e(g,m[0],_,w,[_]),S=g.run(Object.assign(Object.assign({},f),{cacheHint:b.cacheKey,get:()=>v}),m),O=r(g,m[0],_,w,v.output.dims,[_]),E=g.run(Object.assign(Object.assign({},a),{cacheHint:b.cacheKey,get:()=>O}),[m[0],S]),T=i(g,m[0],_,w,v.output.dims,O.output.dims);return[g.run(Object.assign(Object.assign({},o),{cacheHint:b.cacheKey,get:()=>T}),[m[0],S,E])]},e=(g,m,b,_,w)=>{const[v,S]=g.calculateTextureWidthAndHeight(m.dims,s.TextureType.unpacked),O=w.length;if(b<1||_<1)throw new Error("Logical 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(x01 - x00) / float(scales[2]); float y1 = x10 + float(y_offset) * (x11 - x10) / float(scales[2]); return y0 + float(x_offset) * (y1 - y0) / float(scales[3]); }`:` ${v} float process(int indices[2]) { int input_index = 0; int output_index = coordsToOffset(TexCoords, ${c}, ${g}); ${w} int m; int index_of_dim0, index_of_dim1; index_of_dim0 = output_index / output_pitches[0]; m = output_index - index_of_dim0 * output_pitches[0]; index_of_dim1 = m; int index_of_input_dim0, index_of_input_dim1, x_offset, y_offset; index_of_input_dim0 = index_of_dim0 / scales[0]; y_offset = index_of_dim0 - index_of_input_dim0 * scales[0]; index_of_input_dim1 = index_of_dim1 / scales[1]; x_offset = index_of_dim1 - index_of_input_dim1 * scales[1]; input_index = index_of_input_dim0 * input_pitches[0] + index_of_input_dim1; float x00 = getInputFloat(input_index); float x10, x01, x11; bool end_of_dim0 = false; if (index_of_input_dim0 == (${a[0].dims[0]} - 1)) { // It's the end in dimension 0 x01 = x00; end_of_dim0 = true; } else { x01 = getInputFloat(input_index + input_pitches[0]); } if (index_of_input_dim1 == (input_pitches[0] - 1)) { // It's the end in dimension 1 x10 = x00; x11 = x01; } else { x10 = getInputFloat(input_index + 1); x11 = end_of_dim0 ? x10 : getInputFloat(input_index + input_pitches[0] + 1); } float y0 = x00 + float(y_offset) * (x01 - x00) / float(scales[0]); float y1 = x10 + float(y_offset) * (x11 - x10) / float(scales[0]); return y0 + float(x_offset) * (y1 - y0) / float(scales[1]); }`;return Object.assign(Object.assign({},s),{output:{dims:i,type:a[0].type,textureType:p.TextureType.unpacked},shaderSource:S,variables:[{name:"scales",type:"int",arrayLength:o.scales.length,data:o.scales.map(O=>Math.ceil(O))}]})};n.validateInputs=(f,a)=>{if(!f||a.opset<9&&f.length!==1||a.opset>=9&&a.opset<11&&f.length!==2||a.opset>=11&&f.length<2)throw new Error("invalid inputs.");if(a.scales.length>0&&f[0].dims.length!==a.scales.length)throw new Error("Invalid input 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r=this.maxTextureSize;if(t&&t.breakAxis!==void 0){const g=t.breakAxis>=o.length?1:o.slice(t.breakAxis).reduce((b,_)=>b*_),m=t.breakAxis<=0?1:o.slice(0,t.breakAxis).reduce((b,_)=>b*_);if(!(g>r||m>r))return[g,m];d.Logger.verbose("TextureLayout",`Given width/height preferences were unattainable: shape:${o}, breakAxis:${t.breakAxis}`)}let i=o.slice(0);e&&(r*=2,i=i.map((g,m)=>m>=i.length-2?i[m]%2==0?i[m]:i[m]+1:i[m]),i.length===1&&(i=[2,i[0]])),i.length!==2&&(i=p(i).newShape);const c=f(i);return i.length<=1&&c<=r?[1,c]:i.length===2&&i[0]<=r&&i[1]<=r?i:i.length===3&&i[0]*i[1]<=r&&i[2]<=r?[i[0]*i[1],i[2]]:i.length===3&&i[0]<=r&&i[1]*i[2]<=r?[i[0],i[1]*i[2]]:i.length===4&&i[0]*i[1]*i[2]<=r&&i[3]<=r?[i[0]*i[1]*i[2],i[3]]:i.length===4&&i[0]<=r&&i[1]*i[2]*i[3]<=r?[i[0],i[1]*i[2]*i[3]]:e?a(c/4).map(g=>2*g):a(c)}},n.squeezeShape=p,n.parseAxisParam=s,n.isInt=h,n.sizeFromShape=f,n.getRowsCols=function(o){if(o.length===0)throw Error("Cannot get rows and columns of an empty shape 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startInputsVector(t,e){t.startVector(4,e,4)}static addOutputs(t,e){t.addFieldOffset(6,e,0)}static createOutputsVector(t,e){t.startVector(4,e.length,4);for(let r=e.length-1;r>=0;r--)t.addOffset(e[r]);return t.endVector()}static startOutputsVector(t,e){t.startVector(4,e,4)}static addSparseInitializers(t,e){t.addFieldOffset(7,e,0)}static createSparseInitializersVector(t,e){t.startVector(4,e.length,4);for(let r=e.length-1;r>=0;r--)t.addOffset(e[r]);return t.endVector()}static startSparseInitializersVector(t,e){t.startVector(4,e,4)}static endGraph(t){return t.endObject()}static createGraph(t,e,r,i,c,g,m,b,_){return a.startGraph(t),a.addInitializers(t,e),a.addNodeArgs(t,r),a.addNodes(t,i),a.addMaxNodeIndex(t,c),a.addNodeEdges(t,g),a.addInputs(t,m),a.addOutputs(t,b),a.addSparseInitializers(t,_),a.endGraph(t)}}f.Graph=a})(h.fbs||(h.fbs={}))})(s.experimental||(s.experimental={}))}(n.onnxruntime||(n.onnxruntime={})),function(s){(function(h){(function(f){class 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e=this.bb.__offset(this.bb_pos,12);return e?this.bb.__string(this.bb_pos+e,t):null}modelVersion(){let t=this.bb.__offset(this.bb_pos,14);return t?this.bb.readInt64(this.bb_pos+t):this.bb.createLong(0,0)}docString(t){let e=this.bb.__offset(this.bb_pos,16);return e?this.bb.__string(this.bb_pos+e,t):null}graph(t){let e=this.bb.__offset(this.bb_pos,18);return e?(t||new s.experimental.fbs.Graph).__init(this.bb.__indirect(this.bb_pos+e),this.bb):null}graphDocString(t){let e=this.bb.__offset(this.bb_pos,20);return e?this.bb.__string(this.bb_pos+e,t):null}static startModel(t){t.startObject(9)}static addIrVersion(t,e){t.addFieldInt64(0,e,t.createLong(0,0))}static addOpsetImport(t,e){t.addFieldOffset(1,e,0)}static createOpsetImportVector(t,e){t.startVector(4,e.length,4);for(let r=e.length-1;r>=0;r--)t.addOffset(e[r]);return t.endVector()}static startOpsetImportVector(t,e){t.startVector(4,e,4)}static addProducerName(t,e){t.addFieldOffset(2,e,0)}static addProducerVersion(t,e){t.addFieldOffset(3,e,0)}static addDomain(t,e){t.addFieldOffset(4,e,0)}static addModelVersion(t,e){t.addFieldInt64(5,e,t.createLong(0,0))}static addDocString(t,e){t.addFieldOffset(6,e,0)}static addGraph(t,e){t.addFieldOffset(7,e,0)}static addGraphDocString(t,e){t.addFieldOffset(8,e,0)}static endModel(t){return t.endObject()}static createModel(t,e,r,i,c,g,m,b,_,w){return a.startModel(t),a.addIrVersion(t,e),a.addOpsetImport(t,r),a.addProducerName(t,i),a.addProducerVersion(t,c),a.addDomain(t,g),a.addModelVersion(t,m),a.addDocString(t,b),a.addGraph(t,_),a.addGraphDocString(t,w),a.endModel(t)}}f.Model=a})(h.fbs||(h.fbs={}))})(s.experimental||(s.experimental={}))}(n.onnxruntime||(n.onnxruntime={})),function(s){(function(h){(function(f){class a{constructor(){this.bb=null,this.bb_pos=0}__init(t,e){return this.bb_pos=t,this.bb=e,this}static getRootAsKernelCreateInfos(t,e){return(e||new a).__init(t.readInt32(t.position())+t.position(),t)}static getSizePrefixedRootAsKernelCreateInfos(t,e){return t.setPosition(t.position()+d.flatbuffers.SIZE_PREFIX_LENGTH),(e||new a).__init(t.readInt32(t.position())+t.position(),t)}nodeIndices(t){let e=this.bb.__offset(this.bb_pos,4);return e?this.bb.readUint32(this.bb.__vector(this.bb_pos+e)+4*t):0}nodeIndicesLength(){let t=this.bb.__offset(this.bb_pos,4);return t?this.bb.__vector_len(this.bb_pos+t):0}nodeIndicesArray(){let t=this.bb.__offset(this.bb_pos,4);return t?new Uint32Array(this.bb.bytes().buffer,this.bb.bytes().byteOffset+this.bb.__vector(this.bb_pos+t),this.bb.__vector_len(this.bb_pos+t)):null}kernelDefHashes(t){let e=this.bb.__offset(this.bb_pos,6);return e?this.bb.readUint64(this.bb.__vector(this.bb_pos+e)+8*t):this.bb.createLong(0,0)}kernelDefHashesLength(){let t=this.bb.__offset(this.bb_pos,6);return t?this.bb.__vector_len(this.bb_pos+t):0}static startKernelCreateInfos(t){t.startObject(2)}static addNodeIndices(t,e){t.addFieldOffset(0,e,0)}static createNodeIndicesVector(t,e){t.startVector(4,e.length,4);for(let r=e.length-1;r>=0;r--)t.addInt32(e[r]);return t.endVector()}static startNodeIndicesVector(t,e){t.startVector(4,e,4)}static addKernelDefHashes(t,e){t.addFieldOffset(1,e,0)}static createKernelDefHashesVector(t,e){t.startVector(8,e.length,8);for(let r=e.length-1;r>=0;r--)t.addInt64(e[r]);return t.endVector()}static startKernelDefHashesVector(t,e){t.startVector(8,e,8)}static endKernelCreateInfos(t){return t.endObject()}static createKernelCreateInfos(t,e,r){return a.startKernelCreateInfos(t),a.addNodeIndices(t,e),a.addKernelDefHashes(t,r),a.endKernelCreateInfos(t)}}f.KernelCreateInfos=a})(h.fbs||(h.fbs={}))})(s.experimental||(s.experimental={}))}(n.onnxruntime||(n.onnxruntime={})),function(s){(function(h){(function(f){class a{constructor(){this.bb=null,this.bb_pos=0}__init(t,e){return this.bb_pos=t,this.bb=e,this}static getRootAsSubGraphSessionState(t,e){return(e||new a).__init(t.readInt32(t.position())+t.position(),t)}static getSizePrefixedRootAsSubGraphSessionState(t,e){return t.setPosition(t.position()+d.flatbuffers.SIZE_PREFIX_LENGTH),(e||new a).__init(t.readInt32(t.position())+t.position(),t)}graphId(t){let e=this.bb.__offset(this.bb_pos,4);return e?this.bb.__string(this.bb_pos+e,t):null}sessionState(t){let e=this.bb.__offset(this.bb_pos,6);return e?(t||new s.experimental.fbs.SessionState).__init(this.bb.__indirect(this.bb_pos+e),this.bb):null}static startSubGraphSessionState(t){t.startObject(2)}static addGraphId(t,e){t.addFieldOffset(0,e,0)}static addSessionState(t,e){t.addFieldOffset(1,e,0)}static endSubGraphSessionState(t){let e=t.endObject();return t.requiredField(e,4),e}static createSubGraphSessionState(t,e,r){return a.startSubGraphSessionState(t),a.addGraphId(t,e),a.addSessionState(t,r),a.endSubGraphSessionState(t)}}f.SubGraphSessionState=a})(h.fbs||(h.fbs={}))})(s.experimental||(s.experimental={}))}(n.onnxruntime||(n.onnxruntime={})),function(s){(function(h){(function(f){class a{constructor(){this.bb=null,this.bb_pos=0}__init(t,e){return this.bb_pos=t,this.bb=e,this}static getRootAsSessionState(t,e){return(e||new a).__init(t.readInt32(t.position())+t.position(),t)}static getSizePrefixedRootAsSessionState(t,e){return t.setPosition(t.position()+d.flatbuffers.SIZE_PREFIX_LENGTH),(e||new a).__init(t.readInt32(t.position())+t.position(),t)}kernels(t){let e=this.bb.__offset(this.bb_pos,4);return e?(t||new s.experimental.fbs.KernelCreateInfos).__init(this.bb.__indirect(this.bb_pos+e),this.bb):null}subGraphSessionStates(t,e){let r=this.bb.__offset(this.bb_pos,6);return r?(e||new s.experimental.fbs.SubGraphSessionState).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos+r)+4*t),this.bb):null}subGraphSessionStatesLength(){let t=this.bb.__offset(this.bb_pos,6);return t?this.bb.__vector_len(this.bb_pos+t):0}static startSessionState(t){t.startObject(2)}static addKernels(t,e){t.addFieldOffset(0,e,0)}static addSubGraphSessionStates(t,e){t.addFieldOffset(1,e,0)}static createSubGraphSessionStatesVector(t,e){t.startVector(4,e.length,4);for(let r=e.length-1;r>=0;r--)t.addOffset(e[r]);return t.endVector()}static startSubGraphSessionStatesVector(t,e){t.startVector(4,e,4)}static endSessionState(t){return t.endObject()}static createSessionState(t,e,r){return a.startSessionState(t),a.addKernels(t,e),a.addSubGraphSessionStates(t,r),a.endSessionState(t)}}f.SessionState=a})(h.fbs||(h.fbs={}))})(s.experimental||(s.experimental={}))}(n.onnxruntime||(n.onnxruntime={})),function(s){(function(h){(function(f){class a{constructor(){this.bb=null,this.bb_pos=0}__init(t,e){return this.bb_pos=t,this.bb=e,this}static getRootAsInferenceSession(t,e){return(e||new a).__init(t.readInt32(t.position())+t.position(),t)}static getSizePrefixedRootAsInferenceSession(t,e){return t.setPosition(t.position()+d.flatbuffers.SIZE_PREFIX_LENGTH),(e||new a).__init(t.readInt32(t.position())+t.position(),t)}static bufferHasIdentifier(t){return t.__has_identifier("ORTM")}ortVersion(t){let e=this.bb.__offset(this.bb_pos,4);return e?this.bb.__string(this.bb_pos+e,t):null}model(t){let e=this.bb.__offset(this.bb_pos,6);return e?(t||new s.experimental.fbs.Model).__init(this.bb.__indirect(this.bb_pos+e),this.bb):null}sessionState(t){let e=this.bb.__offset(this.bb_pos,8);return e?(t||new s.experimental.fbs.SessionState).__init(this.bb.__indirect(this.bb_pos+e),this.bb):null}static startInferenceSession(t){t.startObject(3)}static addOrtVersion(t,e){t.addFieldOffset(0,e,0)}static addModel(t,e){t.addFieldOffset(1,e,0)}static addSessionState(t,e){t.addFieldOffset(2,e,0)}static endInferenceSession(t){return t.endObject()}static finishInferenceSessionBuffer(t,e){t.finish(e,"ORTM")}static finishSizePrefixedInferenceSessionBuffer(t,e){t.finish(e,"ORTM",!0)}static createInferenceSession(t,e,r,i){return a.startInferenceSession(t),a.addOrtVersion(t,e),a.addModel(t,r),a.addSessionState(t,i),a.endInferenceSession(t)}}f.InferenceSession=a})(h.fbs||(h.fbs={}))})(s.experimental||(s.experimental={}))}(n.onnxruntime||(n.onnxruntime={}))},7448:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.OnnxjsSessionHandler=void 0;const d=u(1670),l=u(9162);n.OnnxjsSessionHandler=class{constructor(p){this.session=p,this.inputNames=this.session.inputNames,this.outputNames=this.session.outputNames}async dispose(){}async run(p,s,h){const f=new Map;for(const t in p)if(Object.hasOwnProperty.call(p,t)){const e=p[t];f.set(t,new l.Tensor(e.dims,e.type,void 0,void 0,e.data))}const a=await this.session.run(f),o={};return a.forEach((t,e)=>{o[e]=new d.Tensor(t.type,t.data,t.dims)}),o}startProfiling(){this.session.startProfiling()}endProfiling(){this.session.endProfiling()}}},6919:(y,n,u)=>{Object.defineProperty(n,"__esModule",{value:!0}),n.Session=void 0;const d=u(7067),l=u(1296),p=u(7091),s=u(1036),h=u(6231),f=u(2644);n.Session=class{constructor(a={}){this._initialized=!1,this.backendHint=a.backendHint,this.profiler=h.Profiler.create(a.profiler),this.context={profiler:this.profiler,graphInputTypes:[],graphInputDims:[]}}get inputNames(){return this._model.graph.getInputNames()}get outputNames(){return this._model.graph.getOutputNames()}startProfiling(){this.profiler.start()}endProfiling(){this.profiler.stop()}async loadModel(a,o,t){await this.profiler.event("session","Session.loadModel",async()=>{const e=await(0,p.resolveBackend)(this.backendHint);if(this.sessionHandler=e.createSessionHandler(this.context),this._model=new f.Model,typeof a=="string"){const r=a.endsWith(".ort");if(typeof fetch>"u"){const i=await(0,l.promisify)(d.readFile)(a);this.initialize(i,r)}else{const i=await fetch(a),c=await i.arrayBuffer();this.initialize(new Uint8Array(c),r)}}else if(ArrayBuffer.isView(a))this.initialize(a);else{const r=new Uint8Array(a,o||0,t||a.byteLength);this.initialize(r)}})}initialize(a,o){if(this._initialized)throw new Error("already initialized");this.profiler.event("session","Session.initialize",()=>{const t=this.sessionHandler.transformGraph?this.sessionHandler:void 0;this._model.load(a,t,o),this.sessionHandler.onGraphInitialized&&this.sessionHandler.onGraphInitialized(this._model.graph),this.initializeOps(this._model.graph),this._executionPlan=new s.ExecutionPlan(this._model.graph,this._ops,this.profiler)}),this._initialized=!0}async run(a){if(!this._initialized)throw new Error("session not initialized yet");return this.profiler.event("session","Session.run",async()=>{const o=this.normalizeAndValidateInputs(a),t=await this._executionPlan.execute(this.sessionHandler,o);return this.createOutput(t)})}normalizeAndValidateInputs(a){const o=this._model.graph.getInputNames();if(Array.isArray(a)){if(a.length!==o.length)throw new Error(`incorrect input array length: expected ${o.length} but got ${a.length}`)}else{if(a.size!==o.length)throw new Error(`incorrect input map size: expected ${o.length} but got ${a.size}`);const t=new Array(a.size);let e=0;for(let r=0;rtypeof E=="string")))throw new TypeError("cache should be a string array");O&&(this.cache=new Array(S))}else{if(w!==void 0){const E=e(m);if(!(w instanceof E))throw new TypeError(`cache should be type ${E.name}`)}if(O){const E=new ArrayBuffer(S*function(T){switch(T){case"bool":case"int8":case"uint8":return 1;case"int16":case"uint16":return 2;case"int32":case"uint32":case"float32":return 4;case"float64":return 8;default:throw new Error(`cannot calculate sizeof() on type ${T}`)}}(m));this.cache=function(T,I){return new(e(I))(T)}(E,m)}}}static fromProto(g){if(!g)throw new Error("cannot construct Value from an empty tensor");const m=f.ProtoUtil.tensorDataTypeFromProto(g.dataType),b=f.ProtoUtil.tensorDimsFromProto(g.dims),_=new o(b,m);if(m==="string")g.stringData.forEach((w,v)=>{_.data[v]=(0,f.decodeUtf8String)(w)});else if(g.rawData&&typeof g.rawData.byteLength=="number"&&g.rawData.byteLength>0){const w=_.data,v=new DataView(g.rawData.buffer,g.rawData.byteOffset,g.rawData.byteLength),S=t(g.dataType),O=g.rawData.byteLength/S;if(g.rawData.byteLength%S!=0)throw new Error("invalid buffer length");if(w.length!==O)throw new Error("buffer length mismatch");for(let E=0;E0){const w=_.data,v=new DataView(g.rawDataArray().buffer,g.rawDataArray().byteOffset,g.rawDataLength()),S=t(g.dataType()),O=g.rawDataLength()/S;if(g.rawDataLength()%S!=0)throw new Error("invalid buffer length");if(w.length!==O)throw new Error("buffer length mismatch");for(let E=0;E1&&I>1)return;O[S-E]=Math.max(T,I)}return O}static index(m,b){const _=new Array(b.length);return a.fillIndex(m,b,_),_}static fillIndex(m,b,_){const w=m.length-b.length;for(let v=0;v=0;J--)T[J]=B%S[J],B=Math.floor(B/S[J]);H||(a.fillIndex(T,m.dims,I),F=m.get(I)),$||(a.fillIndex(T,b.dims,C),N=b.get(C)),E.set(T,_(F,N))}}return E}}static isValidBroadcast(m,b){const _=m.length,w=b.length;if(_>w)return!1;for(let v=1;v<=_;v++)if(m[_-v]!==1&&m[_-v]!==b[w-v])return!1;return!0}static getBroadcastDims(m,b){const _=m.length,w=[];for(let v=0;v<_;v++){const S=_-1-v,O=m[S]||1;(b[b.length-1-v]||1)>1&&O===1&&w.unshift(S)}return w}}n.BroadcastUtil=a,n.arrayCopyHelper=function(g,m,b,_,w){if(_<0||_>=m.length)throw new Error("sourceIndex out of bounds");if(b<0||b>=g.length)throw new Error("targetIndex out of bounds");if(_+w>m.length)throw new Error("source indices to be copied are outside bounds");if(b+w>g.length)throw new Error("target array is too small to hold result");for(let v=0;vp.default.isLong(b)?b.toNumber():b)}static tensorValueTypeFromProto(m){return{tensorType:o.tensorDataTypeFromProto(m.elemType),shape:{dims:o.tensorDimsFromProto(m.shape.dim.map(b=>b.dimValue))}}}static tensorDimsFromORTFormat(m){const b=[];for(let _=0;_m.length)throw new Error(`invalid dimension of ${b} for sizeFromDimension as Tensor has ${m.length} dimensions.`);return e.getSizeFromDimensionRange(m,b,m.length)}static sizeToDimension(m,b){if(b<0||b>m.length)throw new Error(`invalid dimension of ${b} for sizeToDimension as Tensor has ${m.length} dimensions.`);return e.getSizeFromDimensionRange(m,0,b)}static getSizeFromDimensionRange(m,b,_){let w=1;for(let v=b;v<_;v++){if(m[v]<=0)throw new Error("cannot get valid size from specified dimension range. Most likely the range contains 0 or negative values in them.");w*=m[v]}return w}static computeStrides(m){const b=m.length;if(b===0)return[];if(b===1)return[1];const _=new Array(b);_[b-1]=1,_[b-2]=m[b-1];for(let w=b-3;w>=0;--w)_[w]=_[w+1]*m[w+1];return _}static transpose(m){return m.slice().reverse()}static indicesToOffset(m,b,_){_===void 0&&(_=m.length);let w=0;for(let v=0;v<_;++v)w+=b[v]*m[v];return w}static offsetToIndices(m,b){const _=b.length;if(_===0)return[];if(_===1)return[m*b[0]];const w=new Array(b.length);for(let v=0;v=b)throw new Error("unsupported axis for this operation.");return m<0?m+b:m}static normalizeAxes(m,b){return m.map(_=>this.normalizeAxis(_,b))}static incrementIndex(m,b,_){if(b.length===0||m.length===0)throw new Error("Index incrementing unsupported for scalar Tensor");if(_===void 0)_=b.length;else if(_<=0||_>b.length)throw new Error("Incorrect axis to increment on");for(let w=_-1;w>=0&&(m[w]++,!(m[w]=m.length)throw new Error("the dimension with value zero exceeds the dimension size of the input tensor");w[E]=m[E]}else w[E]=b[E];S*=w[E]}}const O=e.size(m);if(v!==-1){if(O%S!=0)throw new Error(`the input tensor cannot be reshaped to the requested shape. Input shape: [${m}] Output shape: [${b}]`);w[v]=O/S}else if(S!==O)throw new Error("reshapedDims and originalDims don't have matching sizes");return w}static sortBasedOnPerm(m,b){return b?b.map(_=>m[_]):m.slice().reverse()}static padShape(m,b){const _=m.length;return m.map((w,v)=>w+b[v]+b[v+_])}static areEqual(m,b){return m.length===b.length&&m.every((_,w)=>_===b[w])}static validateDimsAndCalcSize(m){if(m.length>6)throw new TypeError("Only rank 0 to 6 is supported for tensor shape.");let b=1;for(const _ of m){if(!Number.isInteger(_))throw new TypeError(`Invalid shape: ${_} is not an integer`);if(_<0||_>2147483647)throw new TypeError(`Invalid shape: length ${_} is not allowed`);b*=_}return b}static flattenShape(m,b){b<0&&(b+=m.length);const _=m.reduce((v,S)=>v*S,1),w=m.slice(b).reduce((v,S)=>v*S,1);return[_/w,w]}static squeezeShape(m,b){const _=new Array;b=e.normalizeAxes(b,m.length);for(let w=0;w=0;if(v&&m[w]!==1)throw new Error("squeeze an axis of size different than 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Will expand buffer when needed.");let d=parseInt(u??"0"),l=new Uint8Array(d),p=0;const s=y.body.getReader();async function h(){const{done:f,value:a}=await s.read();if(f)return;let o=p+a.length;if(o>d){d=o;let e=new Uint8Array(d);e.set(l),l=e}l.set(a,p),p=o;const t=p/d*100;return n({progress:t,loaded:p,total:d}),h()}return await h(),l}function pathJoin(...y){return y=y.map((n,u)=>(u&&(n=n.replace(new RegExp("^/"),"")),u!==y.length-1&&(n=n.replace(new RegExp("/$"),"")),n)),y.join("/")}function transpose_data(y,n,u){const d=new Array(u.length),l=new Array(u.length);for(let h=u.length-1,f=1;h>=0;--h)l[h]=f,d[h]=n[u[h]],f*=d[h];const p=u.map((h,f)=>l[u.indexOf(f)]),s=new y.constructor(y.length);for(let h=0;h=0;--a)f+=o%n[a]*p[a],o=Math.floor(o/n[a]);s[f]=y[h]}return[s,d]}function softmax(y){const n=max(y)[0],u=y.map(p=>Math.exp(p-n)),d=u.reduce((p,s)=>p+s,0);return u.map(p=>p/d)}function log_softmax(y){return softmax(y).map(d=>Math.log(d))}function getTopItems(y,n=0){return y=Array.from(y).map((u,d)=>[d,u]).sort((u,d)=>d[1]-u[1]),n>0&&(y=y.slice(0,n)),y}function min(y){if(y.length===0)throw Error("Array must not be empty");let n=y[0],u=0;for(let d=1;dn&&(n=y[d],u=d);return[n,u]}function medianFilter(y,n){if(n%2===0||n<=0)throw new Error("Window size must be a positive odd number");const u=new y.constructor(y.length),d=new y.constructor(n),l=Math.floor(n/2);for(let p=0;p=y.length&&(f=2*(y.length-1)-f),d[s++]=y[f]}d.sort(),u[p]=d[l]}return u}function round(y,n){const u=Math.pow(10,n);return Math.round(y*u)/u}const ONNXTensor$1=ONNX.Tensor;class Tensor extends ONNXTensor$1{constructor(...n){return n[0]instanceof ONNX.Tensor?super(n[0].type,n[0].data,n[0].dims):super(...n),new Proxy(this,{get:(u,d)=>{if(typeof d=="string"){let l=Number(d);if(Number.isInteger(l))return u._getitem(l)}return u[d]},set:(u,d,l)=>u[d]=l})}*[Symbol.iterator](){const[n,...u]=this.dims;if(u.length>0){const d=u.reduce((l,p)=>l*p);for(let l=0;l0){const l=d.reduce((p,s)=>p*s);return this._subarray(n,l,d)}else return new Tensor(this.type,[this.data[n]],d)}indexOf(n){for(let u=0;ua[1])throw new Error(`Invalid slice: ${a}`);let o=[Math.max(a[0],0),Math.min(a[1],this.dims[f])];d.push(o),u.push(o[1]-o[0])}else throw new Error(`Invalid slice: ${a}`)}let l=d.map(([f,a])=>a-f),p=l.reduce((f,a)=>f*a),s=new this.data.constructor(p);const h=this.stride();for(let f=0;f=0;--o){const e=l[o];a+=(t%e+d[o][0])*h[o],t=Math.floor(t/e)}s[f]=this.data[a]}return new Tensor(this.type,s,u)}transpose(...n){return transpose(this,n)}sum(n=null,u=!1){return this.norm(1,n,u)}norm(n="fro",u=null,d=!1){if(n==="fro")n=2;else if(typeof n=="string")throw Error(`Unsupported norm: ${n}`);if(u===null){let s=this.data.reduce((h,f)=>h+f**n,0)**(1/n);return new Tensor(this.type,[s],[])}u=safeIndex(u,this.dims.length);const l=this.dims.slice();l[u]=1;const p=new this.data.constructor(this.data.length/this.dims[u]);for(let s=0;s=0;--f){const t=this.dims[f];if(f!==u){const e=a%t;h+=e*o,o*=l[f]}a=Math.floor(a/t)}p[h]+=this.data[s]**n}if(n!==1)for(let s=0;s=0;--s){const a=this.dims[s];if(s!==u){const o=h%a;p+=o*f,f*=this.dims[s]}h=Math.floor(h/a)}this.data[l]/=d.data[p]}return this}normalize(n=2,u=1){return this.clone().normalize_(n,u)}stride(){return dimsToStride(this.dims)}squeeze(n=null){return new Tensor(this.type,this.data,calc_squeeze_dims(this.dims,n))}squeeze_(n=null){return this.dims=calc_squeeze_dims(this.dims,n),this}unsqueeze(n=null){return new Tensor(this.type,this.data,calc_unsqueeze_dims(this.dims,n))}unsqueeze_(n=null){return this.dims=calc_unsqueeze_dims(this.dims,n),this}flatten_(n=0,u=-1){u=(u+this.dims.length)%this.dims.length;let d=this.dims.slice(0,n),l=this.dims.slice(n,u+1),p=this.dims.slice(u+1);return this.dims=[...d,l.reduce((s,h)=>s*h,1),...p],this}flatten(n=0,u=-1){return this.clone().flatten_(n,u)}view(...n){let u=-1;for(let d=0;ds!==u?l*p:l,1);n[u]=this.data.length/d}return new Tensor(this.type,this.data,n)}neg_(){for(let n=0;np*s);if(u!==d)throw Error(`cannot reshape array of size ${u} into shape (${n})`);let l=y;for(let p=n.length-1;p>=0;p--)l=l.reduce((s,h)=>{let f=s[s.length-1];return f.lengthu!==1):typeof n=="number"?y[n]===1&&y.splice(n,1):Array.isArray(n)&&(y=y.filter((u,d)=>u!==1||!n.includes(d))),y}function calc_unsqueeze_dims(y,n){return n=safeIndex(n,y.length+1),y=y.slice(),y.splice(n,0,1),y}function safeIndex(y,n,u=null){if(y<-n||y>=n)throw new Error(`IndexError: index ${y} is out of bounds for dimension${u===null?"":" "+u} with size ${n}`);return y<0&&(y=(y%n+n)%n),y}function cat(y,n=0){n=safeIndex(n,y[0].dims.length);const u=y[0].dims.slice();u[n]=y.reduce((s,h)=>s+h.dims[n],0);const d=u.reduce((s,h)=>s*h,1),l=new y[0].data.constructor(d),p=y[0].type;if(n===0){let s=0;for(let h of y)l.set(h.data,s),s+=h.data.length}else{let s=0;for(let h=0;h=0;--t){const i=f.dims[t];let c=e%i;t===n&&(c+=s),o+=c*r,r*=u[t],e=Math.floor(e/i)}l[o]=f.data[a]}s+=f.dims[n]}}return new Tensor(p,l,u)}function stack(y,n=0){return cat(y.map(u=>u.unsqueeze(n)),n)}function std_mean(y,n=null,u=1,d=!1){if(n===null){const a=y.data.reduce((r,i)=>r+i,0)/y.data.length,o=Math.sqrt(y.data.reduce((r,i)=>r+(i-a)**2,0)/(y.data.length-u)),t=new Tensor(y.type,[a],[]);return[new Tensor(y.type,[o],[]),t]}n=safeIndex(n,y.dims.length);const l=mean(y,n,d),p=y.dims.slice();p[n]=1;const s=new y.data.constructor(y.data.length/y.dims[n]);for(let f=0;f=0;--o){const r=y.dims[o];if(o!==n){const i=t%r;a+=i*e,e*=p[o]}t=Math.floor(t/r)}s[a]+=(y.data[f]-l.data[a])**2}for(let f=0;fs+h,0);return new Tensor(y.type,[p/y.data.length],[])}n=safeIndex(n,y.dims.length);const d=y.dims.slice();d[n]=1;const l=new y.data.constructor(y.data.length/y.dims[n]);for(let p=0;p=0;--h){const o=y.dims[h];if(h!==n){const t=f%o;s+=t*a,a*=d[h]}f=Math.floor(f/o)}l[s]+=y.data[p]}if(y.dims[n]!==1)for(let p=0;p0||h>0;)switch(f.push(s-1),a.push(h-1),p[s][h].item()){case 0:--s,--h;break;case 1:--s;break;case 2:--h;break;default:throw new Error(`Internal error in dynamic time warping. Unexpected trace[${s}, ${h}]. Please file a bug report.`)}return f.reverse(),a.reverse(),[f,a]}function dimsToStride(y){const n=new Array(y.length);for(let u=y.length-1,d=1;u>=0;--u)n[u]=d,d*=y[u];return n}async function loadTokenizer(y,n){return await Promise.all([getModelJSON(y,"tokenizer.json",!0,n),getModelJSON(y,"tokenizer_config.json",!0,n)])}function createPattern(y,n=!0){return y.Regex?new RegExp(n?y.Regex:`(${y.Regex})`,"gu"):y.String?y.String:(console.warn("Unknown pattern type:",y),null)}function clean_up_tokenization(y){return y.replace(/ \./g,".").replace(/ \?/g,"?").replace(/ \!/g,"!").replace(/ ,/g,",").replace(/ \' /g,"'").replace(/ n\'t/g,"n't").replace(/ \'m/g,"'m").replace(/ \'s/g,"'s").replace(/ \'ve/g,"'ve").replace(/ \'re/g,"'re")}function fuse(y,n){let u=[],d=0;for(;dthis.tokens_to_ids.get(d)??this.unk_token_id);return this.fuse_unk&&(u=fuse(u,this.unk_token_id)),u}convert_ids_to_tokens(n){return n.map(u=>this.vocab[u]??this.unk_token)}}class WordPieceTokenizer extends TokenizerModel{constructor(n){super(n),this.tokens_to_ids=n.vocab,this.unk_token_id=this.tokens_to_ids.get(n.unk_token),this.unk_token=n.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[u,d]of this.tokens_to_ids)this.vocab[d]=u}encode(n){let u=[];for(let d of n){let l=[...d],p=!1,s=0,h=[];for(;s0&&(o=this.config.continuing_subword_prefix+o),this.tokens_to_ids.has(o)){a=o;break}--f}if(a===null){p=!0;break}h.push(a),s=f}p?u.push(this.unk_token):u.push(...h)}return u}}class Unigram extends TokenizerModel{constructor(n,u){super(n),this.vocab=new Array(n.vocab.size),this.scores=new Array(n.vocab.size);let d=0;n.vocab.forEach((l,p)=>{this.vocab[d]=p,this.scores[d]=l,++d}),this.unk_token_id=n.unk_id,this.unk_token=this.vocab[n.unk_id],this.tokens_to_ids=new Map(this.vocab.map((l,p)=>[l,p])),this.bosToken=" ",this.bosTokenId=this.tokens_to_ids.get(this.bosToken),this.eosToken=u.eos_token,this.eosTokenId=this.tokens_to_ids.get(this.eosToken),this.unkToken=this.vocab[this.unk_token_id],this.minScore=min(this.scores)[0],this.unkScore=this.minScore-10,this.scores[this.unk_token_id]=this.unkScore,this.trie=new CharTrie,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(n){const u=n.sentence,d=u.length;let l=0;for(;l{const y=[...Array.from({length:"~".charCodeAt(0)-"!".charCodeAt(0)+1},(l,p)=>p+"!".charCodeAt(0)),...Array.from({length:"¬".charCodeAt(0)-"¡".charCodeAt(0)+1},(l,p)=>p+"¡".charCodeAt(0)),...Array.from({length:"ÿ".charCodeAt(0)-"®".charCodeAt(0)+1},(l,p)=>p+"®".charCodeAt(0))];let n=y.slice(),u=0;for(let l=0;l<256;++l)y.includes(l)||(y.push(l),n.push(256+u),u+=1);let d=n.map(l=>String.fromCharCode(l));return Object.fromEntries(y.map((l,p)=>[l,d[p]]))})(),UNICODE_TO_BYTES=reverseDictionary(BYTES_TO_UNICODE);class BPE extends TokenizerModel{constructor(n){super(n),this.BPE_SPLIT_TOKEN=" ",this.tokens_to_ids=n.vocab,this.unk_token_id=this.tokens_to_ids.get(n.unk_token),this.unk_token=n.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[u,d]of this.tokens_to_ids)this.vocab[d]=u;this.bpe_ranks=Object.fromEntries(n.merges.map((u,d)=>[u,d])),this.merges=n.merges.map(u=>u.split(this.BPE_SPLIT_TOKEN)),this.end_of_word_suffix=n.end_of_word_suffix,this.byte_fallback=this.config.byte_fallback??!1,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.cache=Object.create(null),this.fuse_unk??(this.fuse_unk=this.config.fuse_unk)}get_pairs(n){let u=new Set,d=n[0];for(let l=1;l{let r=this.bpe_ranks[t]??1/0,i=this.bpe_ranks[e]??1/0;return r<=i?t:e});if(!(p in this.bpe_ranks))break;let[s,h]=p.split(this.BPE_SPLIT_TOKEN),f=[],a=0,o=-1;for(;a`<0x${s.toString(16).toUpperCase().padStart(2,"0")}>`)):u.push(this.unk_token)}return u}}class Normalizer extends Callable{constructor(n){super(),this.config=n}static fromConfig(n){if(n===null)return null;switch(n.type){case"BertNormalizer":return new BertNormalizer(n);case"Precompiled":return new Precompiled(n);case"Sequence":return new NormalizerSequence(n);case"Replace":return new Replace(n);case"NFC":return new NFC(n);case"NFKD":return new NFKD(n);case"StripAccents":return new StripAccents(n);case"Lowercase":return new Lowercase(n);case"Prepend":return new Prepend(n);default:throw new Error(`Unknown Normalizer type: ${n.type}`)}}normalize(n){throw Error("normalize should be implemented in subclass.")}_call(n){return this.normalize(n)}}class Replace extends Normalizer{normalize(n){let u=createPattern(this.config.pattern);return u===null||(n=n.replaceAll(u,this.config.content)),n}}class NFC extends Normalizer{normalize(n){return n=n.normalize("NFC"),n}}class NFKD extends Normalizer{normalize(n){return n=n.normalize("NFKD"),n}}class StripAccents extends Normalizer{normalize(n){return n=n.replace(/[\u0300-\u036f]/g,""),n}}class Lowercase extends Normalizer{normalize(n){return n=n.toLowerCase(),n}}class Prepend extends Normalizer{normalize(n){return n=this.config.prepend+n,n}}class NormalizerSequence extends Normalizer{constructor(n){super(n),this.normalizers=n.normalizers.map(u=>Normalizer.fromConfig(u))}normalize(n){return this.normalizers.reduce((u,d)=>d.normalize(u),n)}}class BertNormalizer extends Normalizer{_tokenize_chinese_chars(n){let u=[];for(let d=0;d=19968&&n<=40959||n>=13312&&n<=19903||n>=131072&&n<=173791||n>=173824&&n<=177983||n>=177984&&n<=178207||n>=178208&&n<=183983||n>=63744&&n<=64255||n>=194560&&n<=195103}stripAccents(n){return n.normalize("NFD").replace(/[\u0300-\u036f]/g,"")}normalize(n){return this.config.handle_chinese_chars&&(n=this._tokenize_chinese_chars(n)),this.config.lowercase?(n=n.toLowerCase(),this.config.strip_accents!==!1&&(n=this.stripAccents(n))):this.config.strip_accents&&(n=this.stripAccents(n)),n}}class PreTokenizer extends Callable{static fromConfig(n){if(n===null)return null;switch(n.type){case"BertPreTokenizer":return new BertPreTokenizer(n);case"Sequence":return new PreTokenizerSequence(n);case"WhitespaceSplit":return new WhitespaceSplit(n);case"Metaspace":return new MetaspacePreTokenizer(n);case"ByteLevel":return new ByteLevelPreTokenizer(n);case"Split":return new SplitPreTokenizer(n);case"Punctuation":return new PunctuationPreTokenizer(n);case"Digits":return new DigitsPreTokenizer(n);default:throw new Error(`Unknown PreTokenizer type: ${n.type}`)}}pre_tokenize_text(n){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize(n){let u=[];return Array.isArray(n)?u=n.map(d=>this.pre_tokenize_text(d)):u=this.pre_tokenize_text(n),u.flat()}_call(n){return this.pre_tokenize(n)}}class BertPreTokenizer extends PreTokenizer{constructor(n){super(),this.pattern=new RegExp(`[^\\s${PUNCTUATION_REGEX}]+|[${PUNCTUATION_REGEX}]`,"gu")}pre_tokenize_text(n){return n.trim().match(this.pattern)||[]}}class ByteLevelPreTokenizer extends PreTokenizer{constructor(n){super(),this.config=n,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=/'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+/gu,this.byte_encoder=BYTES_TO_UNICODE,this.text_encoder=new TextEncoder}pre_tokenize_text(n){return(this.use_regex?n.match(this.pattern)||[]:[n]).map(d=>(this.add_prefix_space&&!d.startsWith(" ")&&(d=" "+d),d=Array.from(this.text_encoder.encode(d),l=>this.byte_encoder[l]).join(""),d))}}class SplitPreTokenizer extends PreTokenizer{constructor(n){super(),this.config=n,this.pattern=createPattern(this.config.pattern,this.config.invert)}pre_tokenize_text(n){return this.pattern===null?[]:this.config.invert?n.match(this.pattern)||[]:n.split(this.pattern).filter(u=>u)}}class PunctuationPreTokenizer extends PreTokenizer{constructor(n){super(),this.config=n,this.pattern=new RegExp(`[^${PUNCTUATION_REGEX}]+|[${PUNCTUATION_REGEX}]+`,"gu")}pre_tokenize_text(n){return n.match(this.pattern)||[]}}class DigitsPreTokenizer extends PreTokenizer{constructor(n){super(),this.config=n;const u=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(u,"gu")}pre_tokenize_text(n){return n.match(this.pattern)||[]}}class PostProcessor extends Callable{constructor(n){super(),this.config=n}static fromConfig(n){if(n===null)return null;switch(n.type){case"TemplateProcessing":return new TemplateProcessing(n);case"ByteLevel":return new ByteLevelPostProcessor(n);case"RobertaProcessing":return new RobertaProcessing(n);default:throw new Error(`Unknown PostProcessor type: ${n.type}`)}}post_process(n,...u){throw Error("post_process should be implemented in subclass.")}_call(n,...u){return this.post_process(n,...u)}}class RobertaProcessing extends PostProcessor{constructor(n){super(n),this.cls=n.cls[0],this.sep=n.sep[0]}post_process(n,u=null){return n=mergeArrays([this.cls],n,[this.sep]),u!==null&&(n=mergeArrays(n,[this.sep],u,[this.sep])),n}}class TemplateProcessing extends PostProcessor{constructor(n){super(n),this.single=n.single,this.pair=n.pair}post_process(n,u=null){let d=u===null?this.single:this.pair,l=[];for(let p of d)"SpecialToken"in p?l.push(p.SpecialToken.id):"Sequence"in p&&(p.Sequence.id==="A"?l=mergeArrays(l,n):p.Sequence.id==="B"&&(l=mergeArrays(l,u)));return l}}class ByteLevelPostProcessor extends PostProcessor{post_process(n){return n}}class Decoder extends Callable{constructor(n){super(),this.config=n,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=n.trim_offsets}static fromConfig(n){switch(n.type){case"WordPiece":return new WordPieceDecoder(n);case"Metaspace":return new MetaspaceDecoder(n);case"ByteLevel":return new ByteLevelDecoder(n);case"Replace":return new ReplaceDecoder(n);case"ByteFallback":return new ByteFallback(n);case"Fuse":return new FuseDecoder(n);case"Strip":return new StripDecoder(n);case"Sequence":return new DecoderSequence(n);default:throw new Error(`Unknown Decoder type: ${n.type}`)}}_call(n){return this.decode(n)}decode(n){return this.decode_chain(n).join("")}decode_chain(n){throw Error("`decode_chain` should be implemented in subclass.")}}class ReplaceDecoder extends Decoder{constructor(n){super(n)}decode_chain(n){let u=createPattern(this.config.pattern);return u===null?n:n.map(d=>d.replaceAll(u,this.config.content))}}class ByteFallback extends Decoder{constructor(n){super(n),this.text_decoder=new TextDecoder}decode_chain(n){let u=[],d=[];for(let l of n){let p=null;if(l.length===6&&l.startsWith("<0x")&&l.endsWith(">")){let s=parseInt(l.slice(3,5),16);isNaN(s)||(p=s)}if(p!==null)d.push(p);else{if(d.length>0){let s=this.text_decoder.decode(Uint8Array.from(d));u.push(s),d=[]}u.push(l)}}if(d.length>0){let l=this.text_decoder.decode(Uint8Array.from(d));u.push(l),d=[]}return u}}class FuseDecoder extends Decoder{constructor(n){super(n)}decode_chain(n){return[n.join("")]}}class StripDecoder extends Decoder{constructor(n){super(n),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(n){return n.map(u=>{let d=0;for(let p=0;p(d!==0&&(u.startsWith(this.config.prefix)?u=u.replace(this.config.prefix,""):u=" "+u),this.cleanup&&(u=clean_up_tokenization(u)),u))}}class ByteLevelDecoder extends Decoder{constructor(n){super(n),this.byte_decoder=UNICODE_TO_BYTES,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(n){let u=n.join(""),d=new Uint8Array([...u].map(p=>this.byte_decoder[p]));return this.text_decoder.decode(d)}decode_chain(n){let u=[],d=[];for(let l of n)this.added_tokens.includes(l)?(d.length>0&&(u.push(this.convert_tokens_to_string(d)),d=[]),u.push(l)):d.push(l);return d.length>0&&u.push(this.convert_tokens_to_string(d)),u}}class DecoderSequence extends Decoder{constructor(n){super(n),this.decoders=n.decoders.map(u=>Decoder.fromConfig(u))}decode_chain(n){return this.decoders.reduce((u,d)=>d.decode_chain(u),n)}}class MetaspacePreTokenizer extends PreTokenizer{constructor(n){super(),this.addPrefixSpace=n.add_prefix_space,this.replacement=n.replacement,this.strRep=n.str_rep||this.replacement}pre_tokenize(n){typeof n=="string"&&(n=n.trimStart().split(/\s+/));const u=[];for(let d of n){let l=d.replaceAll(" ",this.strRep);this.addPrefixSpace&&!l.startsWith(this.replacement)&&(l=this.strRep+l),u.push(l)}return u}}class MetaspaceDecoder extends Decoder{constructor(n){super(n),this.addPrefixSpace=n.add_prefix_space,this.replacement=n.replacement}decode_chain(n){let u=[];for(let d=0;dPreTokenizer.fromConfig(u))}pre_tokenize_text(n){return typeof n=="string"&&(n=[n]),this.tokenizers.reduce((u,d)=>d.pre_tokenize(u),n)}}class WhitespaceSplit extends PreTokenizer{constructor(n){super()}pre_tokenize_text(n){return whitespace_split(n)}}class PreTrainedTokenizer extends Callable{constructor(n,u){super(),this.normalizer=Normalizer.fromConfig(n.normalizer),this.pre_tokenizer=PreTokenizer.fromConfig(n.pre_tokenizer),n.model.vocab&&(Array.isArray(n.model.vocab)||(n.model.vocab=Object.entries(n.model.vocab)),n.model.vocab=new Map(n.model.vocab)),this.model=TokenizerModel.fromConfig(n.model,u),this.post_processor=PostProcessor.fromConfig(n.post_processor),this.decoder=Decoder.fromConfig(n.decoder),this.decoder.end_of_word_suffix=this.model.end_of_word_suffix,this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(let d of n.added_tokens){let l=d.id,p=d.content;this.added_tokens.push(p),this.model.tokens_to_ids.set(p,l),this.model.vocab[l]=p,d.special&&(this.special_tokens.push(p),this.all_special_ids.push(l))}this.decoder.added_tokens=this.added_tokens,this.added_tokens_regex=new RegExp("("+this.added_tokens.map(escapeRegExp).join("|")+")"),this.mask_token=this.getToken(u,"mask_token"),this.mask_token_id=this.model.tokens_to_ids.get(this.mask_token),this.pad_token=this.getToken(u,"pad_token","eos_token"),this.pad_token_id=this.model.tokens_to_ids.get(this.pad_token),this.sep_token=this.getToken(u,"sep_token"),this.sep_token_id=this.model.tokens_to_ids.get(this.sep_token),this.model_max_length=u.model_max_length,this.remove_space=u.remove_space,this.clean_up_tokenization_spaces=u.clean_up_tokenization_spaces??!0,this.padding_side="right"}getToken(n,...u){for(let d of u){let l=n[d];if(l)if(typeof l=="object"){if(l.__type==="AddedToken")return l.content;throw Error(`Unknown token: ${l}`)}else return l}return null}static async from_pretrained(n,{progress_callback:u=null,config:d=null,cache_dir:l=null,local_files_only:p=!1,revision:s="main"}={}){let h=await loadTokenizer(n,{progress_callback:u,config:d,cache_dir:l,local_files_only:p,revision:s});return new this(...h)}prepare_model_inputs(n){return n}_call(n,{text_pair:u=null,padding:d=!1,truncation:l=null,max_length:p=null,return_tensor:s=!0}={}){let h;if(Array.isArray(n)){if(n.length===0)throw Error("text array must be non-empty");if(u!==null){if(Array.isArray(u)){if(n.length!==u.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");h=n.map((t,e)=>this.encode(t,u[e]))}else h=n.map(t=>this.encode(t))}else{if(n===null)throw Error("text may not be null");if(Array.isArray(u))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");h=[this.encode(n,u)]}let f=max(h.map(t=>t.length))[0];p===null&&(p=f),p=Math.min(p,this.model_max_length);let a=[];if(d||l)for(let t=0;tp)l&&(h[t]=h[t].slice(0,p)),a.push(new Array(h[t].length).fill(1));else if(d){let e=p-h[t].length;this.padding_side==="right"?(a.push(new Array(h[t].length).fill(1).concat(new Array(e).fill(0))),h[t].push(...new Array(e).fill(this.pad_token_id))):(a.push(new Array(e).fill(0).concat(new Array(h[t].length).fill(1))),h[t].unshift(...new Array(e).fill(this.pad_token_id)))}else a.push(new Array(h[t].length).fill(1));else a=h.map(t=>new Array(t.length).fill(1));if(s){if(!(d&&l)&&h.some(e=>e.length!==h[0].length))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=true' and 'truncation=true' to have batched tensors with the same length.");let t=[h.length,h[0].length];h=new Tensor("int64",BigInt64Array.from(h.flat().map(BigInt)),t),a=new Tensor("int64",BigInt64Array.from(a.flat().map(BigInt)),t)}else Array.isArray(n)||(h=h[0],a=a[0]);let o={input_ids:h,attention_mask:a};return o=this.prepare_model_inputs(o),o}_encode_text(n){return n===null?null:n.split(this.added_tokens_regex).filter(l=>l).map(l=>{if(this.added_tokens.includes(l))return l;{this.remove_space===!0&&(l=l.trim().split(/\s+/).join(" ")),this.normalizer!==null&&(l=this.normalizer(l));let p=this.pre_tokenizer!==null?this.pre_tokenizer(l):[l];return this.model(p)}}).flat()}encode(n,u=null){let d=this._encode_text(n),l=this._encode_text(u),p=this.post_processor!==null?this.post_processor(d,l):mergeArrays(d??[],l??[]);return this.model.convert_tokens_to_ids(p)}batch_decode(n,u={}){return n.map(d=>this.decode(d,u))}decode(n,u={}){if(!Array.isArray(n)||n.length===0||!isIntegralNumber(n[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(n,u)}decode_single(n,{skip_special_tokens:u=!1,clean_up_tokenization_spaces:d=null}){let l=this.model.convert_ids_to_tokens(n);u&&(l=l.filter(s=>!this.special_tokens.includes(s)));let p=this.decoder(l);return this.decoder.end_of_word_suffix&&(p=p.replaceAll(this.decoder.end_of_word_suffix," "),u&&(p=p.trim())),(d??this.clean_up_tokenization_spaces)&&(p=clean_up_tokenization(p)),p}}function add_token_types(y){if(y.input_ids instanceof Tensor)y.token_type_ids=new Tensor("int64",new BigInt64Array(y.input_ids.data.length),y.input_ids.dims);else if(Array.isArray(y.input_ids))Array.isArray(y.input_ids[0])?y.token_type_ids=y.input_ids.map(n=>new Array(n.length).fill(0)):y.token_type_ids=new Array(y.input_ids.length).fill(0);else throw new Error("Input ids must be a Tensor or an Array");return y}class BertTokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class AlbertTokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class MobileBertTokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class SqueezeBertTokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class DistilBertTokenizer extends PreTrainedTokenizer{}class T5Tokenizer extends PreTrainedTokenizer{}class GPT2Tokenizer extends PreTrainedTokenizer{}class BartTokenizer extends PreTrainedTokenizer{}class RobertaTokenizer extends PreTrainedTokenizer{}class BloomTokenizer extends PreTrainedTokenizer{}class LlamaTokenizer extends PreTrainedTokenizer{}class XLMRobertaTokenizer extends PreTrainedTokenizer{}class MPNetTokenizer extends PreTrainedTokenizer{}class FalconTokenizer extends PreTrainedTokenizer{prepare_model_inputs(n){return add_token_types(n)}}class GPTNeoXTokenizer extends PreTrainedTokenizer{}class NllbTokenizer extends PreTrainedTokenizer{constructor(n,u){super(n,u),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(d=>this.languageRegex.test(d))}_build_translation_inputs(n,u,d){if(!this.language_codes.includes(d.tgt_lang))throw new Error(`Target language code "${d.tgt_lang}" is not valid. Must be one of: {${this.language_codes.join(", ")}}`);if(d.src_lang!==void 0){if(!this.language_codes.includes(d.src_lang))throw new Error(`Source language code "${d.src_lang}" is not valid. Must be one of: {${this.language_codes.join(", ")}}`);for(let l of this.post_processor.config.single)if("SpecialToken"in l&&this.languageRegex.test(l.SpecialToken.id)){l.SpecialToken.id=d.src_lang;break}}return d.forced_bos_token_id=this.model.convert_tokens_to_ids([d.tgt_lang])[0],this._call(n,u)}}const WHISPER_LANGUAGES=[["en","english"],["zh","chinese"],["de","german"],["es","spanish"],["ru","russian"],["ko","korean"],["fr","french"],["ja","japanese"],["pt","portuguese"],["tr","turkish"],["pl","polish"],["ca","catalan"],["nl","dutch"],["ar","arabic"],["sv","swedish"],["it","italian"],["id","indonesian"],["hi","hindi"],["fi","finnish"],["vi","vietnamese"],["he","hebrew"],["uk","ukrainian"],["el","greek"],["ms","malay"],["cs","czech"],["ro","romanian"],["da","danish"],["hu","hungarian"],["ta","tamil"],["no","norwegian"],["th","thai"],["ur","urdu"],["hr","croatian"],["bg","bulgarian"],["lt","lithuanian"],["la","latin"],["mi","maori"],["ml","malayalam"],["cy","welsh"],["sk","slovak"],["te","telugu"],["fa","persian"],["lv","latvian"],["bn","bengali"],["sr","serbian"],["az","azerbaijani"],["sl","slovenian"],["kn","kannada"],["et","estonian"],["mk","macedonian"],["br","breton"],["eu","basque"],["is","icelandic"],["hy","armenian"],["ne","nepali"],["mn","mongolian"],["bs","bosnian"],["kk","kazakh"],["sq","albanian"],["sw","swahili"],["gl","galician"],["mr","marathi"],["pa","punjabi"],["si","sinhala"],["km","khmer"],["sn","shona"],["yo","yoruba"],["so","somali"],["af","afrikaans"],["oc","occitan"],["ka","georgian"],["be","belarusian"],["tg","tajik"],["sd","sindhi"],["gu","gujarati"],["am","amharic"],["yi","yiddish"],["lo","lao"],["uz","uzbek"],["fo","faroese"],["ht","haitian creole"],["ps","pashto"],["tk","turkmen"],["nn","nynorsk"],["mt","maltese"],["sa","sanskrit"],["lb","luxembourgish"],["my","myanmar"],["bo","tibetan"],["tl","tagalog"],["mg","malagasy"],["as","assamese"],["tt","tatar"],["haw","hawaiian"],["ln","lingala"],["ha","hausa"],["ba","bashkir"],["jw","javanese"],["su","sundanese"]],WHISPER_LANGUAGE_MAPPING=new Map(WHISPER_LANGUAGES),WHISPER_TO_LANGUAGE_CODE_MAPPING=new Map([...WHISPER_LANGUAGES.map(([y,n])=>[n,y]),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);class WhisperTokenizer extends PreTrainedTokenizer{_decode_asr(n,{return_timestamps:u=!1,return_language:d=!1,time_precision:l=null,force_full_sequences:p=!0}={}){if(l===null)throw Error("Must specify time_precision");let s=null;const h=u==="word";function f(){return{language:s,timestamp:[null,null],text:""}}const a=[];let o=f(),t=0;const e=this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1;let r=[],i=[],c=!1,g=null;const m=new Set(this.all_special_ids);for(let w of n){const v=w.tokens,S=h?w.token_timestamps:null;let O=null,E=e;if("stride"in w){const[C,B,F]=w.stride;if(t-=B,g=C-F,B&&(E=B/l+e),F)for(let N=v.length-1;N>=0;--N){const H=v[N];if(H>=e){if(O!==null&&(H-e)*l=e){const F=(B-e)*l+t,N=round(F,2);if(O!==null&&B>=O)c=!0;else if(c||r.length>0&&B0?(r.push(T),h&&i.push(I)):r.every(C=>C.length===0)&&(o=f(),r=[],T=[],i=[],I=[])}if(r.length>0){if(p&&u)throw new Error("Whisper did not predict an ending timestamp, which can happen if audio is cut off in the middle of a word. Also make sure WhisperTimeStampLogitsProcessor was used during generation.");const[w,v]=this.findLongestCommonSequence(r,i),S=this.decode(w);o.text=S,h&&(o.words=this.collateWordTimestamps(w,v,s)),a.push(o)}let b=Object.create(null);const _=a.map(w=>w.text).join("");if(u||d){for(let w=0;w0;let h=s?[]:null,f=s?u[0]:null;for(let a=1;aN===C[H]).length,F=B/w+v;B>1&&F>t&&(t=F,e=[S,O,T,I])}const[i,c,g,m]=e,b=Math.floor((c+i)/2),_=Math.floor((m+g)/2);p.push(...d.slice(0,b)),d=o.slice(_),l=d.length,s&&(h.push(...f.slice(0,b)),f=u[a].slice(_))}return p.push(...d),s?(h.push(...f),[p,h]):[p,[]]}collateWordTimestamps(n,u,d){let[l,p,s]=this.combineTokensIntoWords(n,d),h=[];for(let f=0;f=l){let h=(s-l)*d;h=round(h,2),p.push(`<|${h}|>`),p.push([])}else p[p.length-1].push(s);return p=p.map(s=>typeof s=="string"?s:super.decode(s,u)),p.join("")}splitTokensOnUnicode(n){const u=this.decode(n,{decode_with_timestamps:!0}),d="�";let l=[],p=[],s=[],h=[],f=[],a=0;for(let o=0;o=this.model.tokens_to_ids.get("<|endoftext|>"),i=o.startsWith(" "),c=o.trim(),g=f.test(c);if(r||i||g||p.length===0)p.push(o),s.push(t),h.push(e);else{const m=p.length-1;p[m]+=o,s[m].push(...t),h[m].push(...e)}}return[p,s,h]}mergePunctuations(n,u,d,l,p){let s=structuredClone(n),h=structuredClone(u),f=structuredClone(d),a=s.length-2,o=s.length-1;for(;a>=0;)s[a].startsWith(" ")&&l.includes(s[a].trim())?(s[o]=s[a]+s[o],h[o]=mergeArrays(h[a],h[o]),f[o]=mergeArrays(f[a],f[o]),s[a]="",h[a]=[],f[a]=[]):o=a,--a;for(a=0,o=1;ot),h.filter(t=>t.length>0),f.filter(t=>t.length>0)]}get_decoder_prompt_ids({language:n=null,task:u=null,no_timestamps:d=!0}={}){let l=[];if(n){n=n.toLowerCase();let p=WHISPER_TO_LANGUAGE_CODE_MAPPING.get(n);if(p===void 0)if(WHISPER_LANGUAGE_MAPPING.has(n))p=n;else{const f=n.length===2?WHISPER_LANGUAGE_MAPPING.keys():WHISPER_LANGUAGE_MAPPING.values();throw new Error(`Language "${n}" is not supported. Must be one of: ${JSON.stringify(f)}`)}let s=this.model.tokens_to_ids.get(`<|${p}|>`);if(s===void 0)throw new Error(`Unable to find language "${p}" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.`);l.push(s)}else l.push(null);if(u){if(u=u.toLowerCase(),u!=="transcribe"&&u!=="translate")throw new Error(`Task "${u}" is not supported. Must be one of: ["transcribe", "translate"]`);let p=this.model.tokens_to_ids.get(`<|${u}|>`);if(p===void 0)throw new Error(`Unable to find task "${u}" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.`);l.push(p)}else l.push(null);if(d){let p=this.model.tokens_to_ids.get("<|notimestamps|>");if(p===void 0)throw new Error('Unable to find "<|notimestamps|>" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.');l.push(p)}return l.map((p,s)=>[s+1,p]).filter(p=>p[1]!==null)}}class CodeGenTokenizer extends PreTrainedTokenizer{}class CLIPTokenizer extends PreTrainedTokenizer{}class MarianTokenizer extends PreTrainedTokenizer{constructor(n,u){super(n,u),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter(d=>this.languageRegex.test(d)),console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}_encode_text(n){if(n===null)return null;let[u,...d]=n.trim().split(this.languageRegex);if(d.length===0)return super._encode_text(u);if(d.length===2){let[l,p]=d;return this.supported_language_codes.includes(l)||console.warn(`Unsupported language code "${l}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`),mergeArrays([l],super._encode_text(p))}}}class CharTrie{constructor(){this.root=CharTrieNode.default()}extend(n){for(let u of n)this.push(u)}push(n){let u=this.root;for(let d of n){let l=u.children.get(d);l===void 0&&(l=CharTrieNode.default(),u.children.set(d,l)),u=l}u.isLeaf=!0}*commonPrefixSearch(n){let u=this.root,d="";for(let l=0;lf)&&(a=o.clone(),f=t)}if(a!==null)h.prev=a,h.backtraceScore=f;else return[]}++u}const d=[],p=this.beginNodes[n][0].prev;if(p===null)return[];let s=p.clone();for(;s.prev!==null;)d.push(s.clone()),s=s.clone().prev.clone();return d.reverse(),d}piece(n){return this.sentence.slice(n.pos,n.pos+n.length)}tokens(){return this.viterbi().map(u=>this.piece(u))}tokenIds(){return this.viterbi().map(u=>u.tokenId)}}class TokenLatticeNode{constructor(n,u,d,l,p){this.tokenId=n,this.nodeId=u,this.pos=d,this.length=l,this.score=p,this.prev=null,this.backtraceScore=0}clone(){const n=new TokenLatticeNode(this.tokenId,this.nodeId,this.pos,this.length,this.score);return n.prev=this.prev,n.backtraceScore=this.backtraceScore,n}}class AutoTokenizer{static async from_pretrained(n,{quantized:u=!0,progress_callback:d=null,config:l=null,cache_dir:p=null,local_files_only:s=!1,revision:h="main"}={}){let[f,a]=await loadTokenizer(n,{quantized:u,progress_callback:d,config:l,cache_dir:p,local_files_only:s,revision:h}),o=a.tokenizer_class.replace(/Fast$/,""),t=this.TOKENIZER_CLASS_MAPPING[o];return t||(console.warn(`Unknown tokenizer class "${o}", attempting to construct from base class.`),t=PreTrainedTokenizer),new t(f,a)}}jt(AutoTokenizer,"TOKENIZER_CLASS_MAPPING",{T5Tokenizer,DistilBertTokenizer,BertTokenizer,MobileBertTokenizer,SqueezeBertTokenizer,AlbertTokenizer,GPT2Tokenizer,BartTokenizer,RobertaTokenizer,WhisperTokenizer,CodeGenTokenizer,CLIPTokenizer,MarianTokenizer,BloomTokenizer,NllbTokenizer,LlamaTokenizer,XLMRobertaTokenizer,MPNetTokenizer,FalconTokenizer,GPTNeoXTokenizer,PreTrainedTokenizer});async function loadConfig(y,n){return await getModelJSON(y,"config.json",!0,n)}class PretrainedConfig{constructor(n){this.model_type=null,this.is_encoder_decoder=!1,Object.assign(this,n)}static async from_pretrained(n,{progress_callback:u=null,config:d=null,cache_dir:l=null,local_files_only:p=!1,revision:s="main"}={}){let h=d??await loadConfig(n,{progress_callback:u,config:d,cache_dir:l,local_files_only:p,revision:s});return new this(h)}}class AutoConfig{static async from_pretrained(...n){return PretrainedConfig.from_pretrained(...n)}}class LogitsProcessorList extends Callable{constructor(){super(),this.processors=[]}push(n){this.processors.push(n)}extend(n){this.processors.push(...n)}_call(n,u){for(let d of u)this.processors.forEach(l=>l(n,d))}[Symbol.iterator](){return this.processors.values()}}class LogitsProcessor extends Callable{_call(n,u){throw Error("`_call` should be implemented in a subclass")}}class ForceTokensLogitsProcessor extends LogitsProcessor{constructor(n){super(),this.force_token_map=Object.fromEntries(n??[])}_call(n,u){let d=this.force_token_map[n.length];return exists(d)&&(u.data.fill(-1/0),u.data[d]=0),u}}class ForcedBOSTokenLogitsProcessor extends LogitsProcessor{constructor(n){super(),this.bos_token_id=n}_call(n,u){return n.length===1&&(u.data.fill(-1/0),u.data[this.bos_token_id]=0),u}}class ForcedEOSTokenLogitsProcessor extends LogitsProcessor{constructor(n,u){super(),this.max_length=n,this.forced_eos_token_id=u}_call(n,u){}}class SuppressTokensAtBeginLogitsProcessor extends LogitsProcessor{constructor(n,u){super(),this.begin_suppress_tokens=n,this.begin_index=u}_call(n,u){if(n.length===this.begin_index)for(let d of this.begin_suppress_tokens)u.data[d]=-1/0;return u}}class WhisperTimeStampLogitsProcessor extends LogitsProcessor{constructor(n){super(),this.eos_token_id=n.eos_token_id,this.no_timestamps_token_id=n.no_timestamps_token_id,this.timestamp_begin=this.no_timestamps_token_id+1,this.begin_index=(n.forced_decoder_ids||[]).length+2,n.forced_decoder_ids.slice(-1)[0][1]===this.no_timestamps_token_id&&(this.begin_index-=1),this.max_initial_timestamp_index=n.max_initial_timestamp_index}_call(n,u){if(u.data[this.no_timestamps_token_id]=-1/0,n.length===this.begin_index-1)return u.data.fill(-1/0),u.data[this.timestamp_begin]=0,u;const d=n.slice(this.begin_index),l=d.length>=1&&d[d.length-1]>=this.timestamp_begin,p=d.length<2||d[d.length-2]>=this.timestamp_begin;if(l&&(p?u.data.subarray(this.timestamp_begin).fill(-1/0):u.data.subarray(0,this.eos_token_id).fill(-1/0)),n.length===this.begin_index&&this.max_initial_timestamp_index!==null){const a=this.timestamp_begin+this.max_initial_timestamp_index;u.data.subarray(a+1).fill(-1/0)}const s=log_softmax(u.data),h=Math.log(s.subarray(this.timestamp_begin).map(Math.exp).reduce((a,o)=>a+o)),f=max(s.subarray(0,this.timestamp_begin))[0];return h>f&&u.data.subarray(0,this.timestamp_begin).fill(-1/0),u}}class NoRepeatNGramLogitsProcessor extends LogitsProcessor{constructor(n){super(),this.no_repeat_ngram_size=n}getNgrams(n){const u=n.length,d=[];for(let p=0;p0&&(l=l.map(p=>p/this.generation_config.temperature)),l}randomSelect(n){let u=n.reduce((l,p)=>l+p,0),d=Math.random()*u;for(let l=0;l1)return new BeamSearchSampler(n);if(n.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${n.num_return_sequences}.`);return new GreedySampler(n)}}class GreedySampler extends Sampler{sample(n,u=-1){let d=this.getLogits(n,u);return[[max(d)[1],0]]}}class MultinomialSampler extends Sampler{sample(n,u=-1){let d=n.dims.at(-1);this.generation_config.top_k>0&&(d=Math.min(this.generation_config.top_k,d));const l=this.getLogits(n,u),p=getTopItems(l,d),s=softmax(p.map(h=>h[1]));return Array.from({length:this.generation_config.num_beams},()=>{const h=this.randomSelect(s);return[p[h][0],Math.log(s[h])]})}}class BeamSearchSampler extends Sampler{sample(n,u=-1){let d=n.dims.at(-1);this.generation_config.top_k>0&&(d=Math.min(this.generation_config.top_k,d));const l=this.getLogits(n,u),p=getTopItems(l,d),s=softmax(p.map(h=>h[1]));return Array.from({length:this.generation_config.num_beams},(h,f)=>[p[f][0],Math.log(s[f])])}}const{InferenceSession,Tensor:ONNXTensor}=ONNX;class ModelType{}class EncoderOnlyModelType extends ModelType{}class EncoderDecoderModelType extends ModelType{}class Seq2SeqModelType extends EncoderDecoderModelType{}class DecoderOnlyModelType extends ModelType{}const MODEL_TYPE_MAPPING=new Map([["CLIPTextModelWithProjection",EncoderOnlyModelType],["CLIPVisionModelWithProjection",EncoderOnlyModelType]]);async function forward(y,n){return MODEL_TYPE_MAPPING.get(y.constructor.name)===DecoderOnlyModelType?await decoderForward(y,n):await encoderForward(y,n)}async function constructSession(y,n,u){let d=`onnx/${n}${u.quantized?"_quantized":""}.onnx`,l=await getModelFile(y,d,!0,u);try{return await InferenceSession.create(l,{executionProviders})}catch(p){if(executionProviders.length===1&&executionProviders[0]==="wasm")throw p;return console.warn(p),console.warn("Something went wrong during model construction (most likely a missing operation). Using `wasm` as a fallback. "),await InferenceSession.create(l,{executionProviders:["wasm"]})}}async function validateInputs(y,n){const u={},d=[];for(let s of y.inputNames)n[s]===void 0?d.push(s):u[s]=n[s];if(d.length>0)throw new Error(`An error occurred during model execution: "Missing the following inputs: ${d.join(", ")}.`);const l=Object.keys(n).length,p=y.inputNames.length;if(l>p){let s=Object.keys(n).filter(h=>!y.inputNames.includes(h));console.warn(`WARNING: Too many inputs were provided (${l} > ${p}). The following inputs will be ignored: "${s.join(", ")}".`)}return u}async function sessionRun(y,n){const u=await validateInputs(y,n);try{let d=await y.run(u);return d=replaceTensors(d),d}catch(d){throw console.error(`An error occurred during model execution: "${d}".`),console.error("Inputs given to model:",u),d}}function replaceTensors(y){for(let n in y)y[n]instanceof ONNXTensor?y[n]=new Tensor(y[n]):typeof y[n]=="object"&&replaceTensors(y[n]);return y}function toI64Tensor(y){if(y instanceof Tensor)return y;if(y.length===0)throw Error("items must be non-empty");if(Array.isArray(y[0])){if(y.some(n=>n.length!==y[0].length))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' and/or 'truncation=True' to have batched tensors with the same length.");return new Tensor("int64",BigInt64Array.from(y.flat().map(n=>BigInt(n))),[y.length,y[0].length])}else return new Tensor("int64",BigInt64Array.from(y.map(n=>BigInt(n))),[1,y.length])}function prepareAttentionMask(y,n){let u=y.config.pad_token_id??null,d=y.config.eos_token_id??null;isIntegralNumber(d)&&(d=[d]);let l=n.indexOf(u)!==-1,p=d===null||!d.includes(u);if(l&&p){let s=BigInt64Array.from(n.data.map(h=>h!=u));return new Tensor("int64",s,n.dims)}else return new Tensor("int64",new BigInt64Array(n.data.length).fill(1n),n.dims)}function boolTensor(y){return new Tensor("bool",[y],[1])}async function seq2seqForward(y,n,{add_decoder_pkv:u=!0}={}){let{encoder_outputs:d,past_key_values:l}=n;d||(d=(await encoderForward(y,n)).last_hidden_state);let p={input_ids:n.decoder_input_ids,encoder_hidden_states:d,use_cache_branch:boolTensor(!!l)};y.decoder_merged_session.inputNames.includes("encoder_attention_mask")&&(p.encoder_attention_mask=n.attention_mask),y.addPastKeyValues(p,l,u);const s=await sessionRun(y.decoder_merged_session,p);let h=s.logits;l=y.getPastKeyValues(s,l);const f=y.getAttentions(s);return new Seq2SeqLMOutput({logits:h,past_key_values:l,encoder_outputs:d,...f})}function seq2seqStartBeams(y,n,u,d=!0){let l=[],p=0,s=y.config.decoder_start_token_id;Array.isArray(s)||(s=[s]);for(let h of n){h.dims=[1,...h.dims];let f={inputs:h,encoder_outputs:null,prev_model_outputs:null,output_token_ids:s,done:!1,score:0,id:p++};d&&(f.attention_mask=prepareAttentionMask(y,h)),l.push(f)}return l}async function seq2seqRunBeam(y,n,{input_name:u="input_ids"}={}){var p;let d={[u]:n.inputs,decoder_input_ids:toI64Tensor(n.output_token_ids.slice(-1)),encoder_outputs:n.encoder_outputs,past_key_values:(p=n.prev_model_outputs)==null?void 0:p.past_key_values};n.attention_mask&&(d.attention_mask=n.attention_mask);let l=await y.forward(d);return n.prev_model_outputs=l,n.encoder_outputs=l.encoder_outputs,l}async function encoderForward(y,n){let u={};for(let d of y.session.inputNames)u[d]=n[d];return await sessionRun(y.session,u)}async function decoderForward(y,n){let{input_ids:u,past_key_values:d,attention_mask:l}=n,p={input_ids:u,attention_mask:l??prepareAttentionMask(y,u),use_cache_branch:boolTensor(d!==null)};y.addPastKeyValues(p,d);let s=await sessionRun(y.session,p),h=s.logits;return d=y.getPastKeyValues(s,d),{logits:h,past_key_values:d}}function decoderStartBeams(y,n,u,d){let l=[],p=0;for(let s of n){let h=s.tolist().map(Number);s.dims=[1,...s.dims];let f;d?(f=d[p],f.dims=[1,...f.dims]):f=prepareAttentionMask(y,s);let a={input:s,model_input_ids:s,attention_mask:f,prev_model_outputs:null,output_token_ids:h,num_output_tokens:u,done:!1,score:0,id:p++};l.push(a)}return l}async function decoderRunBeam(y,n){var p;let u=new BigInt64Array(n.output_token_ids.length).fill(1n),d={input_ids:n.model_input_ids,attention_mask:new Tensor("int64",u,[1,u.length]),past_key_values:(p=n.prev_model_outputs)==null?void 0:p.past_key_values},l=await y.forward(d);return n.prev_model_outputs=l,l}function decoderUpdatebeam(y,n){y.output_token_ids=[...y.output_token_ids,n],y.model_input_ids=new Tensor("int64",[BigInt(n)],[1,1])}class PreTrainedModel extends Callable{constructor(n,u){super(),this.config=n,this.session=u}async dispose(){let n=[];for(let u of Object.keys(this)){let d=this[u];d instanceof InferenceSession&&n.push(d.handler.dispose())}return await Promise.all(n)}static async from_pretrained(n,{quantized:u=!0,progress_callback:d=null,config:l=null,cache_dir:p=null,local_files_only:s=!1,revision:h="main",model_file_name:f=null}={}){let a={quantized:u,progress_callback:d,config:l,cache_dir:p,local_files_only:s,revision:h,model_file_name:f},o=MODEL_TYPE_MAPPING.get(this.name),t;if(o===DecoderOnlyModelType)t=await Promise.all([AutoConfig.from_pretrained(n,a),constructSession(n,a.model_file_name??"decoder_model_merged",a)]);else if(o===Seq2SeqModelType)t=await Promise.all([AutoConfig.from_pretrained(n,a),constructSession(n,"encoder_model",a),constructSession(n,"decoder_model_merged",a),getModelJSON(n,"generation_config.json",!1,a)]);else if(o===EncoderDecoderModelType)t=await Promise.all([AutoConfig.from_pretrained(n,a),constructSession(n,"encoder_model",a),constructSession(n,"decoder_model_merged",a)]);else if(o===EncoderOnlyModelType)t=await Promise.all([AutoConfig.from_pretrained(n,a),constructSession(n,a.model_file_name??"model",a)]);else throw console.warn("Malformed class definition.",this),Error(`Unable to load model: ${n}. Please report this bug at https://github.com/xenova/transformers.js/issues/new/choose.`);return new this(...t)}async _call(n){return await this.forward(n)}async forward(n){return await forward(this,n)}_get_logits_processor(n,u,d=null){const l=new LogitsProcessorList;if(n.repetition_penalty!==null&&n.repetition_penalty!==1&&l.push(new RepetitionPenaltyLogitsProcessor(n.repetition_penalty)),n.no_repeat_ngram_size!==null&&n.no_repeat_ngram_size>0&&l.push(new NoRepeatNGramLogitsProcessor(n.no_repeat_ngram_size)),n.forced_bos_token_id!==null&&l.push(new ForcedBOSTokenLogitsProcessor(n.forced_bos_token_id)),n.forced_eos_token_id!==null&&l.push(new ForcedEOSTokenLogitsProcessor(n.max_length,n.forced_eos_token_id)),n.begin_suppress_tokens!==null){let p=u>1||n.forced_bos_token_id===null?u:u+1;n.forced_decoder_ids!==null&&(p+=n.forced_decoder_ids[n.forced_decoder_ids.length-1][0]),l.push(new SuppressTokensAtBeginLogitsProcessor(n.begin_suppress_tokens,p))}return n.forced_decoder_ids!==null&&l.push(new ForceTokensLogitsProcessor(n.forced_decoder_ids)),d!==null&&l.extend(d),l}_get_generation_config(n){let u=new GenerationConfig;return"generation_config"in this&&Object.assign(u,this.generation_config),n!==null&&Object.assign(u,n),u}async generate(n,u=null,d=null,{inputs_attention_mask:l=null}={}){if(!(n instanceof Tensor)&&!isTypedArray(n)&&!Array.isArray(n))throw Error(`\`inputs\` must be a Tensor, TypedArray, or Array, but is "${n.constructor.name}".`);let p;if(this.config.is_encoder_decoder)p=0;else if(p=n instanceof Tensor?n.dims[0]:n.length,p===0)throw Error("Must supply a non-empty array of input token ids.");u=this._get_generation_config(u),d=d??new LogitsProcessorList,d=this._get_logits_processor(u,p,d);let s=1;const h=s+(u.max_new_tokens??1/0),f=Number.isInteger(u.max_length)&&(u.max_new_tokens??null)===null;let a=Sampler.getSampler(u),o=this.getStartBeams(n,s,l);for(;o.some(i=>!i.done)&&s=u.max_length){c.done=!0,i.push(c);continue}let g=await this.runBeam(c);u.output_attentions&&this.addAttentionsToBeam(c,g),u.output_scores;let m=g.logits.slice(null,-1,null);d(c.output_token_ids,m);let b=a(m);for(let[_,w]of b){let v={...c};this.updateBeam(v,_),v.score+=w,_===this.config.eos_token_id&&(v.done=!0),i.push(v)}}++s,i=this.groupBeams(i).map(c=>c.sort((g,m)=>m.score-g.score).slice(0,u.num_beams)),o=i.flat(),u.callback_function&&u.callback_function(o)}const t=this.groupBeams(o),e=i=>t.map(c=>u.num_return_sequences>1?c.slice(0,u.num_return_sequences).map(g=>g[i]):[c[0][i]]).flat(),r=e("output_token_ids");if(u.return_dict_in_generate){const i=e("decoder_attentions"),c=e("cross_attentions");return{sequences:r,decoder_attentions:i,cross_attentions:c}}else return r}addAttentionsToBeam(n,u){if(this.config.is_encoder_decoder){if(!u.cross_attentions||u.cross_attentions.length===0)throw Error("`output_attentions` is true, but the model did not produce cross-attentions. This is most likely because the model was not exported with `output_attentions=True`.");n.cross_attentions||(n.cross_attentions=[]),n.cross_attentions.push(u.cross_attentions)}if(!u.decoder_attentions||u.decoder_attentions.length===0)throw Error("`output_attentions` is true, but the model did not produce decoder-attentions. This is most likely because the model was not exported with `output_attentions=True`.");n.decoder_attentions||(n.decoder_attentions=[]),n.decoder_attentions.push(u.decoder_attentions)}groupBeams(n){const u=Object.create(null);for(const d of n)u[d.id]===void 0?u[d.id]=[d]:u[d.id].push(d);return Object.values(u)}getPastKeyValues(n,u){const d=Object.create(null);for(const l in n)if(l.startsWith("present")){let p=l.replace("present","past_key_values");u&&l.includes("encoder")?d[p]=u[p]:d[p]=n[l]}return d}getAttentions(n){const u=Object.create(null);for(const d of["cross_attentions","decoder_attentions"]){const l=[];for(const p in n)if(p.startsWith(d)){const s=p.split(".").pop();l[s]=n[p]}u[d]=l}return u}addPastKeyValues(n,u,d=!1){if(u)Object.assign(n,u);else if(d){let l=[1,this.num_encoder_heads,0,this.encoder_dim_kv];for(let s=0;s{let a=Array.from({length:this.config.decoder_layers},(c,g)=>cat(f.map(m=>m[g]),2)),o=stack(u.map(([c,g])=>a[c].slice(null,g)));o=o.transpose(1,0,2,3);let[t,e]=std_mean(o,-2,0,!0),r=o.clone();for(let c=0;co[g+1]-o[g]),r=mergeArrays([1],e).map(c=>!!c),i=[];for(let c=0;c{let n=TOKENIZER_MAPPINGS.get(y.data.model_id);n||(n=AutoTokenizer.from_pretrained(y.data.model_id),TOKENIZER_MAPPINGS.set(y.data.model_id,n));const u=await n,d=y.data.text,l=performance.now(),p=u.encode(d),s=performance.now();console.log("[INFO]",`Tokenized ${d.length} characters in ${(s-l).toFixed(2)}ms`);const h=p.map(f=>u.decode([f]));self.postMessage({token_ids:p,decoded:h})})})();