transformers-fork/dist/transformers.min.js

State-of-the-art Machine Learning for the web. Run 🤗 Transformers directly in your browser, with no need for a server!

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Error("Tensor format doesn't match input tensor dims");const p=4;let h=0,m=1,f=2,_=3,g=0,w=c,b=2*c,y=-1;"RGBA"===o?(g=0,w=c,b=2*c,y=3*c):"RGB"===o?(g=0,w=c,b=2*c):"RBG"===o&&(g=0,b=c,w=2*c),r=n.createImageData(s,i);for(let t=0;t<i*s;h+=p,m+=p,f+=p,_+=p,t++)r.data[h]=(e.data[g++]-d[0])*u[0],r.data[m]=(e.data[w++]-d[1])*u[1],r.data[f]=(e.data[b++]-d[2])*u[2],r.data[_]=-1===y?255:(e.data[y++]-d[3])*u[3]}return r}},"./node_modules/onnxruntime-common/dist/esm/tensor-conversion.js":(e,t,n)=>{n.r(t)},"./node_modules/onnxruntime-common/dist/esm/tensor-factory-impl.js":(e,t,n)=>{n.r(t),n.d(t,{bufferToTensor:()=>s,tensorFromGpuBuffer:()=>o,tensorFromImage:()=>i,tensorFromMLTensor:()=>l,tensorFromPinnedBuffer:()=>u,tensorFromTexture:()=>a});var r=n("./node_modules/onnxruntime-common/dist/esm/tensor-impl.js");const s=(e,t)=>{if(void 0===e)throw new Error("Image buffer must be defined");if(void 0===t.height||void 0===t.width)throw new Error("Image height and width must be defined");if("NHWC"===t.tensorLayout)throw new Error("NHWC Tensor layout is not supported yet");const{height:n,width:s}=t,i=t.norm??{mean:255,bias:0};let a,o;a="number"==typeof i.mean?[i.mean,i.mean,i.mean,i.mean]:[i.mean[0],i.mean[1],i.mean[2],i.mean[3]??255],o="number"==typeof i.bias?[i.bias,i.bias,i.bias,i.bias]:[i.bias[0],i.bias[1],i.bias[2],i.bias[3]??0];const l=void 0!==t.format?t.format:"RGBA",u=void 0!==t.tensorFormat&&void 0!==t.tensorFormat?t.tensorFormat:"RGB",d=n*s,c="RGBA"===u?new Float32Array(4*d):new Float32Array(3*d);let p=4,h=0,m=1,f=2,_=3,g=0,w=d,b=2*d,y=-1;"RGB"===l&&(p=3,h=0,m=1,f=2,_=-1),"RGBA"===u?y=3*d:"RBG"===u?(g=0,b=d,w=2*d):"BGR"===u&&(b=0,w=d,g=2*d);for(let t=0;t<d;t++,h+=p,f+=p,m+=p,_+=p)c[g++]=(e[h]+o[0])/a[0],c[w++]=(e[m]+o[1])/a[1],c[b++]=(e[f]+o[2])/a[2],-1!==y&&-1!==_&&(c[y++]=(e[_]+o[3])/a[3]);return"RGBA"===u?new r.Tensor("float32",c,[1,4,n,s]):new r.Tensor("float32",c,[1,3,n,s])},i=async(e,t)=>{const n="undefined"!=typeof HTMLImageElement&&e instanceof HTMLImageElement,r="undefined"!=typeof ImageData&&e instanceof ImageData,i="undefined"!=typeof ImageBitmap&&e instanceof ImageBitmap,a="string"==typeof e;let o,l=t??{};const u=()=>{if("undefined"!=typeof document)return document.createElement("canvas");if("undefined"!=typeof OffscreenCanvas)return new OffscreenCanvas(1,1);throw new Error("Canvas is not supported")},d=e=>"undefined"!=typeof HTMLCanvasElement&&e instanceof HTMLCanvasElement||e instanceof OffscreenCanvas?e.getContext("2d"):null;if(n){const n=u();n.width=e.width,n.height=e.height;const r=d(n);if(null==r)throw new Error("Can not access image data");{let n=e.height,s=e.width;if(void 0!==t&&void 0!==t.resizedHeight&&void 0!==t.resizedWidth&&(n=t.resizedHeight,s=t.resizedWidth),void 0!==t){if(l=t,void 0!==t.tensorFormat)throw new Error("Image input config format must be RGBA for HTMLImageElement");l.tensorFormat="RGBA",l.height=n,l.width=s}else l.tensorFormat="RGBA",l.height=n,l.width=s;r.drawImage(e,0,0),o=r.getImageData(0,0,s,n).data}}else{if(!r){if(i){if(void 0===t)throw new Error("Please provide image config with format for Imagebitmap");const n=u();n.width=e.width,n.height=e.height;const r=d(n);if(null!=r){const t=e.height,n=e.width;return r.drawImage(e,0,0,n,t),o=r.getImageData(0,0,n,t).data,l.height=t,l.width=n,s(o,l)}throw new Error("Can not access image data")}if(a)return new Promise(((t,n)=>{const r=u(),i=d(r);if(!e||!i)return n();const a=new Image;a.crossOrigin="Anonymous",a.src=e,a.onload=()=>{r.width=a.width,r.height=a.height,i.drawImage(a,0,0,r.width,r.height);const e=i.getImageData(0,0,r.width,r.height);l.height=r.height,l.width=r.width,t(s(e.data,l))}}));throw new Error("Input data provided is not supported - aborted tensor creation")}{let n,r;if(void 0!==t&&void 0!==t.resizedWidth&&void 0!==t.resizedHeight?(n=t.resizedHeight,r=t.resizedWidth):(n=e.height,r=e.width),void 0!==t&&(l=t),l.format="RGBA",l.height=n,l.width=r,void 0!==t){const t=u();t.width=r,t.height=n;const s=d(t);if(null==s)throw new Error("Can not access image data");s.putImageData(e,0,0),o=s.getImageData(0,0,r,n).data}else o=e.data}}if(void 0!==o)return s(o,l);throw new Error("Input data provided is not supported - aborted tensor creation")},a=(e,t)=>{const{width:n,height:s,download:i,dispose:a}=t,o=[1,s,n,4];return new r.Tensor({location:"texture",type:"float32",texture:e,dims:o,download:i,dispose:a})},o=(e,t)=>{const{dataType:n,dims:s,download:i,dispose:a}=t;return new r.Tensor({location:"gpu-buffer",type:n??"float32",gpuBuffer:e,dims:s,download:i,dispose:a})},l=(e,t)=>{const{dataType:n,dims:s,download:i,dispose:a}=t;return new r.Tensor({location:"ml-tensor",type:n??"float32",mlTensor:e,dims:s,download:i,dispose:a})},u=(e,t,n)=>new r.Tensor({location:"cpu-pinned",type:e,data:t,dims:n??[t.length]})},"./node_modules/onnxruntime-common/dist/esm/tensor-factory.js":(e,t,n)=>{n.r(t)},"./node_modules/onnxruntime-common/dist/esm/tensor-impl-type-mapping.js":(e,t,n)=>{n.r(t),n.d(t,{NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP:()=>s,NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP:()=>r,checkTypedArray:()=>a});const r=new Map([["float32",Float32Array],["uint8",Uint8Array],["int8",Int8Array],["uint16",Uint16Array],["int16",Int16Array],["int32",Int32Array],["bool",Uint8Array],["float64",Float64Array],["uint32",Uint32Array],["int4",Uint8Array],["uint4",Uint8Array]]),s=new Map([[Float32Array,"float32"],[Uint8Array,"uint8"],[Int8Array,"int8"],[Uint16Array,"uint16"],[Int16Array,"int16"],[Int32Array,"int32"],[Float64Array,"float64"],[Uint32Array,"uint32"]]);let i=!1;const a=()=>{if(!i){i=!0;const e="undefined"!=typeof BigInt64Array&&BigInt64Array.from,t="undefined"!=typeof BigUint64Array&&BigUint64Array.from,n="undefined"!=typeof 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instanceof t))throw new TypeError(`buffer should be of type ${t.name}`);this.cpuData=e.data;break}case"texture":if("float32"!==r)throw new TypeError(`unsupported type "${r}" to create tensor from texture`);this.gpuTextureData=e.texture,this.downloader=e.download,this.disposer=e.dispose;break;case"gpu-buffer":if("float32"!==r&&"float16"!==r&&"int32"!==r&&"int64"!==r&&"uint32"!==r&&"uint8"!==r&&"bool"!==r&&"uint4"!==r&&"int4"!==r)throw new TypeError(`unsupported type "${r}" to create tensor from gpu buffer`);this.gpuBufferData=e.gpuBuffer,this.downloader=e.download,this.disposer=e.dispose;break;case"ml-tensor":if("float32"!==r&&"float16"!==r&&"int32"!==r&&"int64"!==r&&"uint32"!==r&&"uint64"!==r&&"int8"!==r&&"uint8"!==r&&"bool"!==r)throw new TypeError(`unsupported type "${r}" to create tensor from MLTensor`);this.mlTensorData=e.mlTensor,this.downloader=e.download,this.disposer=e.dispose;break;default:throw new Error(`Tensor constructor: unsupported location '${this.dataLocation}'`)}else{let a,o;if("string"==typeof e)if(r=e,o=n,"string"===e){if(!Array.isArray(t))throw new TypeError("A string tensor's data must be a string array.");a=t}else{const n=i.NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.get(e);if(void 0===n)throw new TypeError(`Unsupported tensor type: ${e}.`);if(Array.isArray(t)){if("float16"===e&&n===Uint16Array||"uint4"===e||"int4"===e)throw new TypeError(`Creating a ${e} tensor from number array is not supported. Please use ${n.name} as data.`);a="uint64"===e||"int64"===e?n.from(t,BigInt):n.from(t)}else if(t instanceof n)a=t;else{if(!(t instanceof Uint8ClampedArray))throw new TypeError(`A ${r} tensor's data must be type of ${n}`);if("uint8"!==e)throw new TypeError("A Uint8ClampedArray tensor's data must be type of uint8");a=Uint8Array.from(t)}}else if(o=t,Array.isArray(e)){if(0===e.length)throw new TypeError("Tensor type cannot be inferred from an empty array.");const t=typeof e[0];if("string"===t)r="string",a=e;else{if("boolean"!==t)throw new TypeError(`Invalid element type of data array: ${t}.`);r="bool",a=Uint8Array.from(e)}}else if(e instanceof Uint8ClampedArray)r="uint8",a=Uint8Array.from(e);else{const t=i.NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP.get(e.constructor);if(void 0===t)throw new TypeError(`Unsupported type for tensor data: ${e.constructor}.`);r=t,a=e}if(void 0===o)o=[a.length];else if(!Array.isArray(o))throw new TypeError("A tensor's dims must be a number array");s=o,this.cpuData=a,this.dataLocation="cpu"}const o=(0,a.calculateSize)(s);if(this.cpuData&&o!==this.cpuData.length&&("uint4"!==r&&"int4"!==r||Math.ceil(o/2)!==this.cpuData.length))throw new Error(`Tensor's size(${o}) does not match data length(${this.cpuData.length}).`);this.type=r,this.dims=s,this.size=o}static async fromImage(e,t){return(0,s.tensorFromImage)(e,t)}static fromTexture(e,t){return(0,s.tensorFromTexture)(e,t)}static fromGpuBuffer(e,t){return(0,s.tensorFromGpuBuffer)(e,t)}static fromMLTensor(e,t){return(0,s.tensorFromMLTensor)(e,t)}static fromPinnedBuffer(e,t,n){return(0,s.tensorFromPinnedBuffer)(e,t,n)}toDataURL(e){return(0,r.tensorToDataURL)(this,e)}toImageData(e){return(0,r.tensorToImageData)(this,e)}get data(){if(this.ensureValid(),!this.cpuData)throw new Error("The data is not on CPU. Use `getData()` to download GPU data to CPU, or use `texture` or `gpuBuffer` property to access the GPU data directly.");return this.cpuData}get location(){return this.dataLocation}get texture(){if(this.ensureValid(),!this.gpuTextureData)throw new Error("The data is not stored as a WebGL texture.");return this.gpuTextureData}get gpuBuffer(){if(this.ensureValid(),!this.gpuBufferData)throw new Error("The data is not stored as a WebGPU buffer.");return this.gpuBufferData}get mlTensor(){if(this.ensureValid(),!this.mlTensorData)throw new Error("The data is not stored as a WebNN MLTensor.");return this.mlTensorData}async getData(e){switch(this.ensureValid(),this.dataLocation){case"cpu":case"cpu-pinned":return this.data;case"texture":case"gpu-buffer":case"ml-tensor":if(!this.d