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this._update(e,this.width,this.height,3)}rgba(){if(4===this.channels)return this;let e=new Uint8ClampedArray(this.width*this.height*4);switch(this.channels){case 1:for(let t=0,r=0;t<this.data.length;++t)e[r++]=this.data[t],e[r++]=this.data[t],e[r++]=this.data[t],e[r++]=255;break;case 3:for(let t=0,r=0;t<this.data.length;t+=3)e[r++]=this.data[t],e[r++]=this.data[t+1],e[r++]=this.data[t+2],e[r++]=255;break;default:throw Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(e,this.width,this.height,4)}async resize(e,t,{resample:r=2}={}){let i=g[r]??r;if(p){let r=this.channels,i=this.toCanvas(),a=n(e,t).getContext("2d");return a.drawImage(i,0,0,e,t),new w(a.getImageData(0,0,e,t).data,e,t,4).convert(r)}{let r=this.toSharp();switch(i){case"box":case"hamming":("box"===i||"hamming"===i)&&(console.warn(`Resampling method ${i} is not yet supported. 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n=e.height,i=e.width,a=t.height,o=t.width,s=Math.min(n,a),l=Math.min(i,o);return s===n&&l===i?e:(n>i?l=Math.floor(i*s/n):i>n&&(s=Math.floor(n*l/i)),await e.resize(l,s,{resample:r}))}async crop_margin(e,t=200){let r=e.clone().grayscale(),n=(0,c.jk)(r.data)[0],i=(0,c.T9)(r.data)[0]-n;if(0===i)return e;let a=t/255,o=r.width,s=r.height,l=0,h=0;for(let e=0;e<r.height;++e){let t=e*r.width;for(let c=0;c<r.width;++c)(r.data[t+c]-n)/i<a&&(o=Math.min(o,c),s=Math.min(s,e),l=Math.max(l,c),h=Math.max(h,e))}return e=await e.crop([o,s,l,h])}pad_image(e,t,r,{mode:n="constant",center:i=!1,constant_values:a=0}={}){let o,s,[h,c,u]=t;if("number"==typeof r?(o=r,s=r):(o=r.width,s=r.height),o!==c||s!==h){let r=new Float32Array(o*s*u);if(Array.isArray(a))for(let e=0;e<r.length;++e)r[e]=a[e%u];else 0!==a&&r.fill(a);let[d,f]=i?[Math.floor((o-c)/2),Math.floor((s-h)/2)]:[0,0];for(let t=0;t<h;++t){let n=(t+f)*o,i=t*c;for(let t=0;t<c;++t){let a=(n+t+d)*u,o=(i+t)*u;for(let t=0;t<u;++t)r[a+t]=e[o+t]}}if("symmetric"===n){if(i)throw Error("`center` padding is not supported when `mode` is set to `symmetric`.");let t=h-1,n=c-1;for(let i=0;i<s;++i){let a=i*o,s=(0,l.nK)(i,t)*c;for(let t=0;t<o;++t){if(i<h&&t<c)continue;let o=(a+t)*u,d=(s+(0,l.nK)(t,n))*u;for(let t=0;t<u;++t)r[o+t]=e[d+t]}}}e=r,t=[s,o,u]}return[e,t]}rescale(e){for(let t=0;t<e.length;++t)e[t]=this.rescale_factor*e[t]}get_resize_output_image_size(e,t){let r,n,[i,a]=e.size;if(this.do_thumbnail){let{height:e,width:n}=t;r=Math.min(e,n)}else Number.isInteger(t)?(r=t,n=this.config.max_size??r):void 0!==t&&(r=t.shortest_edge,n=t.longest_edge);if(void 0!==r||void 0!==n){let e=void 0===r?1:Math.max(r/i,r/a),t=i*e,o=a*e,s=void 0===n?1:Math.min(n/t,n/o),l=Math.floor(Number((t*s).toFixed(2))),h=Math.floor(Number((o*s).toFixed(2)));return void 0!==this.size_divisibility&&([l,h]=P([l,h],this.size_divisibility)),[l,h]}if(void 0!==t&&void 0!==t.width&&void 0!==t.height){let e=t.width,r=t.height;if(this.config.keep_aspect_ratio&&this.config.ensure_multiple_of){let t=r/a,n=e/i;Math.abs(1-n)<Math.abs(1-t)?t=n:n=t,r=z(t*a,this.config.ensure_multiple_of),e=z(n*i,this.config.ensure_multiple_of)}return[e,r]}if(void 0!==this.size_divisibility)return P([i,a],this.size_divisibility);throw Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(t)}`)}async resize(e){let[t,r]=this.get_resize_output_image_size(e,this.size);return await e.resize(t,r,{resample:this.resample})}async preprocess(e,{do_normalize:t=null,do_pad:r=null,do_convert_rgb:n=null,do_convert_grayscale:i=null,do_flip_channel_order:a=null}={}){this.do_crop_margin&&(e=await this.crop_margin(e));let[o,s]=e.size;if(n??this.do_convert_rgb?e=e.rgb():i&&(e=e.grayscale()),this.do_resize&&(e=await this.resize(e)),this.do_thumbnail&&(e=await this.thumbnail(e,this.size,this.resample)),this.do_center_crop){let t,r;Number.isInteger(this.crop_size)?(t=this.crop_size,r=this.crop_size):(t=this.crop_size.width,r=this.crop_size.height),e=await e.center_crop(t,r)}let l=[e.height,e.width],h=Float32Array.from(e.data),c=[e.height,e.width,e.channels];if(this.do_rescale&&this.rescale(h),t??this.do_normalize){let t=this.image_mean;Array.isArray(this.image_mean)||(t=Array(e.channels).fill(t));let r=this.image_std;if(Array.isArray(this.image_std)||(r=Array(e.channels).fill(t)),t.length!==e.channels||r.length!==e.channels)throw Error(`When set to arrays, the length of \`image_mean\` (${t.length}) and \`image_std\` (${r.length}) must match the number of channels in the image (${e.channels}).`);for(let n=0;n<h.length;n+=e.channels)for(let i=0;i<e.channels;++i)h[n+i]=(h[n+i]-t[i])/r[i]}if(r??this.do_pad){if(this.pad_size){let t=this.pad_image(h,[e.height,e.width,e.channels],this.pad_size);[h,c]=t}else if(this.size_divisibility){let[e,t]=P([c[1],c[0]],this.size_divisibility);[h,c]=this.pad_image(h,c,{width:e,height:t})}}if(a??this.do_flip_channel_order){if(3!==c[2])throw Error("Flipping channel order is only supported for RGB images.");for(let e=0;e<h.length;e+=3){let t=h[e];h[e]=h[e+2],h[e+2]=t}}return{original_size:[s,o],reshaped_input_size:l,pixel_values:new u.qY("float32",h,c).permute(2,0,1)}}async _call(e){Array.isArray(e)||(e=[e]);let t=await Promise.all(e.map(e=>this.preprocess(e)));return{pixel_values:(0,u.t$)(t.map(e=>e.pixel_values),0),original_sizes:t.map(e=>e.original_size),reshaped_input_sizes:t.map(e=>e.reshaped_input_size)}}}class O extends B{post_process_semantic_segmentation(e,t=null){let r=e.logits,n=r.dims[0];if(null!==t&&t.length!==n)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let i=[];for(let e=0;e<n;++e){let n=null!==t?t[e]:null,a=r[e];null!==n&&(a=(0,u.GW)(a,n,"bilinear",!1));let[o,s]=n??a.dims.slice(-2),l=new u.qY("int32",new Int32Array(o*s),[o,s]),h=a[0].data;for(let e=1;e<a.dims[0];++e){let t=a[e].data;for(let r=0;r<t.length;++r)t[r]>h[r]&&(h[r]=t[r],l.data[r]=e)}let c=Array(a.dims[0]),d=l.data;for(let e=0;e<d.length;++e){let t=d[e];c[t]=t}let f=c.filter(e=>void 0!==e);i.push({segmentation:l,labels:f})}return i}}class L extends B{}class $ extends L{}class j extends B{}class U extends B{}class N extends B{}class R extends B{}class V extends B{}class q extends B{constructor(e){super(e),this.crop_pct=this.config.crop_pct??.875}async resize(e){let t=this.size?.shortest_edge;if(void 0===t)throw Error("Size dictionary must contain 'shortest_edge' key.");if(t<384){let r=Math.floor(t/this.crop_pct),[n,i]=this.get_resize_output_image_size(e,{shortest_edge:r});e=await e.resize(n,i,{resample:this.resample}),e=await e.center_crop(t,t)}else e=await e.resize(t,t,{resample:this.resample});return e}}class D extends q{}class G extends B{}class W extends B{}class X extends B{constructor(e){super(e),this.include_top=this.config.include_top??!0,this.include_top&&(this.image_std=this.image_std.map(e=>e*e))}}class H extends B{}class Y extends H{}class Q extends B{post_process_object_detection(...e){return F(...e)}}class K extends Q{}class J extends B{}class Z extends B{}class ee extends B{pad_image(e,t,r,n={}){let[i,a,o]=t,s=this.image_mean;Array.isArray(this.image_mean)||(s=Array(o).fill(s));let l=this.image_std;Array.isArray(l)||(l=Array(o).fill(s));let h=s.map((e,t)=>-e/l[t]);return super.pad_image(e,t,r,{center:!0,constant_values:h,...n})}}class et extends ee{}class er extends B{async _call(e){let t=await super._call(e),r=[t.pixel_values.dims[0],64,64],n=new u.qY("int64",new BigInt64Array(r.reduce((e,t)=>
1e*t)).fill(1n),r);return{...t,pixel_mask:n}}post_process_object_detection(...e){return F(...e)}remove_low_and_no_objects(e,t,r,n){let i=[],a=[],o=[];for(let s=0;s<e.dims[0];++s){let l=e[s],h=t[s],u=(0,c.T9)(l.data)[1];if(u===n)continue;let d=(0,c.Vs)(l.data)[u];d>r&&(i.push(h),a.push(d),o.push(u))}return[i,a,o]}check_segment_validity(e,t,r,n=.5,i=.8){let a=[],o=0,s=0;for(let i=0;i<e.length;++i)e[i]===r&&(a.push(i),++o),t[r].data[i]>=n&&++s;let l=o>0&&s>0;return l&&(l=o/s>i),[l,a]}compute_segments(e,t,r,n,i,a=null,o=null){let[s,l]=o??e[0].dims,h=new u.qY("int32",new Int32Array(s*l),[s,l]),c=[];if(null!==o)for(let t=0;t<e.length;++t)e[t]=(0,u.GW)(e[t],o,"bilinear",!1);let d=new Int32Array(e[0].data.length),f=new Float32Array(e[0].data.length);for(let r=0;r<e.length;++r){let n=t[r];for(let t=0;t<e[r].data.length;++t)e[r].data[t]*=n,e[r].data[t]>f[t]&&(d[t]=r,f[t]=e[r].data[t])}let p=0;for(let a=0;a<r.length;++a){let o=r[a],[s,l]=this.check_segment_validity(d,e,a,n,i);if(s){for(let e of(++p,l))h.data[e]=p;c.push({id:p,label_id:o,score:t[a]})}}return[h,c]}post_process_panoptic_segmentation(e,t=.5,r=.5,n=.8,i=null,a=null){null===i&&(console.warn("`label_ids_to_fuse` unset. No instance will be fused."),i=new Set);let o=e.logits,s=e.pred_masks.sigmoid(),[l,h,c]=o.dims;if(c-=1,null!==a&&a.length!==l)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let d=[];for(let e=0;e<l;++e){let l=null!==a?a[e]:null,h=o[e],f=s[e],[p,m,g]=this.remove_low_and_no_objects(h,f,t,c);if(0===g.length){let[e,t]=l??f.dims.slice(-2),r=new u.qY("int32",new Int32Array(e*t).fill(-1),[e,t]);d.push({segmentation:r,segments_info:[]});continue}let[_,w]=this.compute_segments(p,m,g,r,n,i,l);d.push({segmentation:_,segments_info:w})}return d}post_process_instance_segmentation(){throw Error("Not implemented yet")}}class en extends B{post_process_object_detection(...e){return F(...e)}}class ei extends B{reshape_input_points(e,t,r){e=structuredClone(e);let n=(0,l.ky)(e);if(3===n.length)n=[1,...n],e=[e];else if(4!==n.length)throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.");for(let n=0;n<e.length;++n){let i=t[n],a=r[n],o=[a[0]/i[0],a[1]/i[1]];for(let t=0;t<e[n].length;++t)for(let r=0;r<e[n][t].length;++r)for(let i=0;i<e[n][t][r].length;++i)e[n][t][r][i]*=o[i]}return new u.qY("float32",Float32Array.from(e.flat(1/0)),n)}add_input_labels(e,t){let r=(0,l.ky)(e);if(2===r.length)r=[1,...r],e=[e];else if(3!==r.length)throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.");if(r.some((e,r)=>e!==t.dims[r]))throw Error(`The first ${r.length} dimensions of 'input_points' and 'input_labels' must be the same.`);return new u.qY("int64",e.flat(1/0).map(BigInt),r)}async _call(e,t=null,r=null){let n=await super._call(e);if(t&&(n.input_points=this.reshape_input_points(t,n.original_sizes,n.reshaped_input_sizes)),r){if(!n.input_points)throw Error("`input_points` must be provided if `input_labels` are provided.");n.input_labels=this.add_input_labels(r,n.input_points)}return n}post_process_masks(e,t,r,{mask_threshold:n=0,binarize:i=!0,pad_size:a=null}={}){let o=[],s=[(a=a??this.pad_size).height,a.width];for(let a=0;a<t.length;++a){let l=t[a],h=r[a],c=e[a],d=[];for(let e=0;e<c.dims[0];++e){let t=c[e],r=(0,u.GW)(t,s,"bilinear",!1);if(r=r.slice(null,[0,h[0]],[0,h[1]]),r=(0,u.GW)(r,l,"bilinear",!1),i){let e=new Uint8Array(r.data.length);for(let t=0;t<r.data.length;++t)r.data[t]>n&&(e[t]=1);r=new u.qY("bool",e,r.dims)}d.push(r)}o.push((0,u.t$)(d))}return o}}class ea extends B{pad_image(e,t,r,n={}){let[i,a,o]=t;return super.pad_image(e,t,{width:a+(r-a%r)%r,height:i+(r-i%r)%r},{mode:"symmetric",center:!1,constant_values:-1,...n})}}class eo extends B{async _call(e,t){Array.isArray(e)||(e=[e]),Array.isArray(t)||(t=[t]);let r=await Promise.all(e.map(e=>this.preprocess(e))),n=await Promise.all(t.map(e=>this.preprocess(e,{do_normalize:!1,do_convert_rgb:!1,do_convert_grayscale:!0})));return{pixel_values:(0,u.t$)(r.map((e,t)=>(0,u.$q)([e.pixel_values,n[t].pixel_values],0)),0),original_sizes:r.map(e=>e.original_size),reshaped_input_sizes:r.map(e=>e.reshaped_input_size)}}}class es extends I{constructor(e){super(e),this.config.mel_filters??=E(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,8e3,this.config.sampling_rate,"slaney","slaney"),this.window=A(this.config.n_fft,"hann")}_extract_fbank_features(e){let{data:t,dims:r}=C(e,this.window,this.config.n_fft,this.config.hop_length,{power:2,mel_filters:this.config.mel_filters,log_mel:"log10",max_num_frames:this.config.nb_max_frames}),n=(0,c.T9)(t)[0];for(let e=0;e<t.length;++e)t[e]=(Math.max(t[e],n-8)+4)/4;return{data:t,dims:r}}async _call(e){let t;S(e,"WhisperFeatureExtractor"),e.length>this.config.n_samples?(console.warn("Attempting to extract features for audio longer than 30 seconds. If using a pipeline to extract transcript from a long audio clip, remember to specify `chunk_length_s` and/or `stride_length_s`."),t=e.slice(0,this.config.n_samples)):(t=new Float32Array(this.config.n_samples)).set(e);let{data:r,dims:n}=this._extract_fbank_features(t);return{input_features:new u.qY("float32",r,[1,...n])}}}class el extends I{_zero_mean_unit_var_norm(e){let t=e.reduce((e,t)=>
1e+t,0)/e.length,r=e.reduce((e,r)=>e+(r-t)**2,0)/e.length;return e.map(e=>(e-t)/Math.sqrt(r+1e-7))}async _call(e){S(e,"Wav2Vec2FeatureExtractor"),e instanceof Float64Array&&(e=new Float32Array(e));let t=e;this.config.do_normalize&&(t=this._zero_mean_unit_var_norm(t));let r=[1,t.length];return{input_values:new u.qY("float32",t,r),attention_mask:new u.qY("int64",new BigInt64Array(t.length).fill(1n),r)}}}class eh extends I{constructor(e){super(e);let t=this.config.sampling_rate,r=E(256,this.config.num_mel_bins,20,Math.floor(t/2),t,null,"kaldi",!0);for(let e=0;e<r.length;++e)r[e].push(0);this.mel_filters=r,this.window=A(400,"povey",{periodic:!1})}_extract_fbank_features(e,t){return C(e=e.map(e=>32768*e),this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:t,transpose:!0})}async _call(e,{padding:t=!0,pad_to_multiple_of:r=2,do_normalize_per_mel_bins:n=!0,return_attention_mask:i=!0}={}){let a;S(e,"SeamlessM4TFeatureExtractor");let o=this._extract_fbank_features(e,this.config.max_length);if(n){let[e,t]=o.dims;for(let r=0;r<t;++r){let n=0;for(let i=0;i<e;++i)n+=o.data[i*t+r];let i=n/e,a=0;for(let n=0;n<e;++n)a+=(o.data[n*t+r]-i)**2;let s=Math.sqrt((a/=e-1)+1e-7);for(let n=0;n<e;++n){let e=n*t+r;o.data[e]=(o.data[e]-i)/s}}}if(t){let[e,t]=o.dims,n=e%r;if(n>0){let r=new Float32Array(t*(e+n));r.set(o.data),r.fill(this.config.padding_value,o.data.length);let s=e+n;o={data:r,dims:[s,t]},i&&(a=new u.qY("int64",new BigInt64Array(s),[1,s])).data.fill(1n,0,e)}}let[s,l]=o.dims,h=this.config.stride;if(0!=s%h)throw Error(`The number of frames (${s}) must be a multiple of the stride (${h}).`);let c=new u.qY("float32",o.data,o.dims).view(1,Math.floor(s/h),l*h),d={input_features:c};if(i){let e=c.dims[1],t=new u.qY("int64",new BigInt64Array(e),[1,e]);if(a)for(let e=1,r=0;e<s;e+=h,++r)t.data[r]=a.data[e];else t.data.fill(1n);d.attention_mask=t}return d}}class ec extends I{constructor(e){super(e);let t=this.config.sampling_rate,r=E(256,this.config.num_mel_bins,20,Math.floor(t/2),t,null,"kaldi",!0);for(let e=0;e<r.length;++e)r[e].push(0);this.mel_filters=r,this.window=A(400,"hann",{periodic:!1}),this.mean=this.config.mean,this.std=this.config.std}_extract_fbank_features(e,t){return C(e,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:t,transpose:!0})}async _call(e){S(e,"ASTFeatureExtractor");let t=this._extract_fbank_features(e,this.config.max_length);if(this.config.do_normalize){let e=2*this.std;for(let r=0;r<t.data.length;++r)t.data[r]=(t.data[r]-this.mean)/e}return{input_values:new u.qY("float32",t.data,[1,...t.dims])}}}class eu extends I{constructor(e){super(e),this.mel_filters=E(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,null,"htk"),this.mel_filters_slaney=E(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,"slaney","slaney"),this.window=A(this.config.fft_window_size,"hann")}_get_input_mel(e,t,r,n){let i,a=!1,o=e.length-t;if(o>0)if("rand_trunc"===r){a=!0;let r=Math.floor(Math.random()*(o+1));e=e.subarray(r,r+t),(i=this._extract_fbank_features(e,this.mel_filters_slaney,this.config.nb_max_samples)).dims=[1,...i.dims]}else throw Error(`Truncation strategy "${r}" not implemented`);else{if(o<0){let r=new Float64Array(t);if(r.set(e),"repeat"===n)for(let n=e.length;n<t;n+=e.length)r.set(e.subarray(0,Math.min(e.length,t-n)),n);else if("repeatpad"===n)for(let t=e.length;t<-o;t+=e.length)r.set(e,t);e=r}if("fusion"===r)throw Error(`Truncation strategy "${r}" not implemented`);(i=this._extract_fbank_features(e,this.mel_filters_slaney,this.config.nb_max_samples)).dims=[1,...i.dims]}return{...i,longer:a}}_extract_fbank_features(e,t,r=null){return C(e,this.window,this.config.fft_window_size,this.config.hop_length,{power:2,mel_filters:t,log_mel:"dB",max_num_frames:r,do_pad:!1,transpose:!0})}async _call(e,{max_length:t=null}={}){S(e,"ClapFeatureExtractor");let r=this._get_input_mel(e,t??this.config.nb_max_samples,this.config.truncation,this.config.padding);return{input_features:new u.qY("float32",r.data,[1,...r.dims])}}}class ed extends I{}class ef extends l.bV{constructor(e){super(),this.feature_extractor=e}async _call(e,...t){return await this.feature_extractor(e,...t)}}class ep extends ef{async _call(...e){return await this.feature_extractor(...e)}post_process_masks(...e){return this.feature_extractor.post_process_masks(...e)}
1reshape_input_points(...e){return this.feature_extractor.reshape_input_points(...e)}}class em extends ef{async _call(e){return await this.feature_extractor(e)}}class eg extends ef{async _call(e){return await this.feature_extractor(e)}}class e_ extends ef{async _call(e){return await this.feature_extractor(e)}}class ew extends ef{}class ey{static FEATURE_EXTRACTOR_CLASS_MAPPING={ImageFeatureExtractor:B,WhisperFeatureExtractor:es,ViTFeatureExtractor:G,MobileViTFeatureExtractor:H,MobileViTImageProcessor:Y,OwlViTFeatureExtractor:Q,Owlv2ImageProcessor:K,CLIPFeatureExtractor:N,ChineseCLIPFeatureExtractor:R,SiglipImageProcessor:V,ConvNextFeatureExtractor:q,ConvNextImageProcessor:D,SegformerFeatureExtractor:O,BitImageProcessor:j,DPTImageProcessor:$,DPTFeatureExtractor:L,GLPNFeatureExtractor:U,BeitFeatureExtractor:Z,DeiTFeatureExtractor:J,DetrFeatureExtractor:er,YolosFeatureExtractor:en,DonutFeatureExtractor:ee,NougatImageProcessor:et,EfficientNetImageProcessor:X,ViTImageProcessor:W,VitMatteImageProcessor:eo,SamImageProcessor:ei,Swin2SRImageProcessor:ea,Wav2Vec2FeatureExtractor:el,SeamlessM4TFeatureExtractor:eh,SpeechT5FeatureExtractor:ed,ASTFeatureExtractor:ec,ClapFeatureExtractor:eu};static PROCESSOR_CLASS_MAPPING={WhisperProcessor:em,Wav2Vec2ProcessorWithLM:eg,SamProcessor:ep,SpeechT5Processor:e_,OwlViTProcessor:ew};static async from_pretrained(e,{progress_callback:t=null,config:r=null,cache_dir:n=null,local_files_only:i=!1,revision:a="main"}={}){let o=r??await (0,h.wc)(e,"preprocessor_config.json",!0,{progress_callback:t,config:r,cache_dir:n,local_files_only:i,revision:a}),s=o.feature_extractor_type??o.image_processor_type,l=this.FEATURE_EXTRACTOR_CLASS_MAPPING[s];if(!l)if(void 0!==o.size)console.warn(`Feature extractor type "${s}" not found, assuming ImageFeatureExtractor due to size parameter in config.`),l=B;else throw Error(`Unknown Feature Extractor type: ${s}`);return new(this.PROCESSOR_CLASS_MAPPING[o.processor_class]??ef)(new l(o))}}async function eb(e){return Array.isArray(e)||(e=[e]),await Promise.all(e.map(e=>w.read(e)))}async function ev(e,t){return Array.isArray(e)||(e=[e]),await Promise.all(e.map(e=>"string"==typeof e||e instanceof URL?y(e,t):e instanceof Float64Array?new Float32Array(e):e))}function eM(e,t){t&&(e=e.map(e=>0|e));let[r,n,i,a]=e;return{xmin:r,ymin:n,xmax:i,ymax:a}}class ex extends l.bV{constructor({task:e,model:t,tokenizer:r=null,processor:n=null}){super(),this.task=e,this.model=t,this.tokenizer=r,this.processor=n}async dispose(){await this.model.dispose()}}class ek extends ex{constructor(e){super(e)}async _call(e,{topk:t=1}={}){let r=this.tokenizer(e,{padding:!0,truncation:!0}),n=await this.model(r),i="multi_label_classification"===this.model.config.problem_type?e=>e.sigmoid().data:e=>(0,c.Vs)(e.data),a=this.model.config.id2label,o=[];for(let e of n.logits){let r=i(e),n=(0,c.Em)(r,t).map(e=>({label:a[e[0]],score:e[1]}));1===t?o.push(...n):o.push(n)}return Array.isArray(e)||1===t?o:o[0]}}class eE extends ex{constructor(e){super(e)}async _call(e,{ignore_labels:t=["O"]}={}){let r=Array.isArray(e),n=this.tokenizer(r?e:[e],{padding:!0,truncation:!0}),i=(await this.model(n)).logits,a=this.model.config.id2label,o=[];for(let e=0;
1e<i.dims[0];++e){let r=n.input_ids[e],s=i[e],l=[];for(let e=0;e<s.dims[0];++e){let n=s[e],i=(0,c.T9)(n.data)[1],o=a?a[i]:`LABEL_${i}`;if(t.includes(o))continue;let h=this.tokenizer.decode([r[e].item()],{skip_special_tokens:!0});if(""===h)continue;let u=(0,c.Vs)(n.data);l.push({entity:o,score:u[i],index:e,word:h,start:null,end:null})}o.push(l)}return r?o:o[0]}}class eT extends ex{constructor(e){super(e)}async _call(e,t,{topk:r=1}={}){let n=this.tokenizer(e,{text_pair:t,padding:!0,truncation:!0}),i=await this.model(n),a=[];for(let e=0;e<i.start_logits.dims[0];++e){let t=n.input_ids[e],o=t.indexOf(this.tokenizer.sep_token_id),s=Array.from((0,c.Vs)(i.start_logits[e].data)).map((e,t)=>[e,t]).filter(e=>e[1]>o),h=Array.from((0,c.Vs)(i.end_logits[e].data)).map((e,t)=>[e,t]).filter(e=>e[1]>o),u=(0,l.qM)(s,h).filter(e=>e[0][1]<=e[1][1]).map(e=>[e[0][1],e[1][1],e[0][0]*e[1][0]]).sort((e,t)=>t[2]-e[2]);for(let e=0;e<Math.min(u.length,r);++e){let[r,n,i]=u[e],o=[...t].slice(r,n+1),s=this.tokenizer.decode(o,{skip_special_tokens:!0});a.push({answer:s,score:i})}}return 1===r?a[0]:a}}class eC extends ex{constructor(e){super(e)}async _call(e,{topk:t=5}={}){let r=this.tokenizer(e,{padding:!0,truncation:!0}),n=await this.model(r),i=[];for(let e=0;e<r.input_ids.dims[0];++e){let a=r.input_ids[e],o=a.indexOf(this.tokenizer.mask_token_id);if(-1===o)throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`);let s=n.logits[e][o],l=(0,c.Em)((0,c.Vs)(s.data),t);i.push(l.map(e=>{let t=[...a];return t[o]=e[0],{score:e[1],token:e[0],token_str:this.tokenizer.model.vocab[e[0]],sequence:this.tokenizer.decode(t,{skip_special_tokens:!0})}}))}return Array.isArray(e)?i:i[0]}}class eA extends ex{_key="generated_text";constructor(e){super(e)}async _call(e,t={}){let r;Array.isArray(e)||(e=[e]),this.model.config.prefix&&(e=e.map(e=>this.model.config.prefix+e));let n=this.model.config.task_specific_params;n&&n[this.task]&&n[this.task].prefix&&(e=e.map(e=>n[this.task].prefix+e));let i=this.tokenizer,a={padding:!0,truncation:!0};r=this instanceof eS&&"_build_translation_inputs"in i?i._build_translation_inputs(e,a,t).input_ids:i(e,a).input_ids;let o=await this.model.generate(r,t);return i.batch_decode(o,{skip_special_tokens:!0}).map(e=>({[this._key]:e}))}}class eF extends eA{_key="summary_text";constructor(e){super(e)}}class eS extends eA{_key="translation_text";constructor(e){super(e)}}function ez(e){return Array.isArray(e)&&e.every(e=>"role"in e&&"content"in e)}class eP extends ex{constructor(e){super(e)}async _call(e,t={}){let r,n,i=!1,a=!1;if("string"==typeof e)r=e=[e];else if(Array.isArray(e)&&e.every(e=>"string"==typeof e))i=!0,r=e;else{if(ez(e))e=[e];else if(Array.isArray(e)&&e.every(ez))i=!0;else throw Error("Input must be a string, an array of strings, a Chat, or an array of Chats");a=!0,r=e.map(e=>this.tokenizer.apply_chat_template(e,{tokenize:!1,add_generation_prompt:!0}))}let o=t.add_special_tokens??!1,s=!a&&(t.return_full_text??!0);this.tokenizer.padding_side="left";let{input_ids:l,attention_mask:h}=this.tokenizer(r,{add_special_tokens:o,padding:!0,truncation:!0}),c=await this.model.generate(l,t,null,{inputs_attention_mask:h}),u=this.tokenizer.batch_decode(c,{skip_special_tokens:!0});!s&&l.dims.at(-1)>0&&(n=this.tokenizer.batch_decode(l,{skip_special_tokens:!0}).map(e=>e.length));let d=Array.from({length:e.length},e=>[]);for(let t=0;t<u.length;++t){let r=Math.floor(t/c.length*e.length);n&&(u[t]=u[t].slice(n[r])),d[r].push({generated_text:a?[...e[r],{role:"assistant",content:u[t]}]:u[t]})}return i||1!==d.length?d:d[0]}}class eI extends ex{constructor(e){super(e),this.label2id=Object.fromEntries(Object.entries(this.model.config.label2id).map(([e,t])=>[e.toLowerCase(),t])),this.entailment_id=this.label2id.entailment,void 0===this.entailment_id&&(console.warn("Could not find 'entailment' in label2id mapping. Using 2 as entailment_id."),this.entailment_id=2),this.contradiction_id=this.label2id.contradiction??this.label2id.not_entailment,void 0===this.contradiction_id&&(console.warn("Could not find 'contradiction' in label2id mapping. Using 0 as contradiction_id."),this.contradiction_id=0)}async _call(e,t,{hypothesis_template:r="This example is {}.",multi_label:n=!1}={}){let i=Array.isArray(e);i||(e=[e]),Array.isArray(t)||(t=[t]);let a=t.map(e=>r.replace("{}",e)),o=n||1===t.length,s=[];for(let r of e){let e=[];for(let t of a){let n=this.tokenizer(r,{text_pair:t,padding:!0,truncation:!0}
1),i=await this.model(n);o?e.push([i.logits.data[this.contradiction_id],i.logits.data[this.entailment_id]]):e.push(i.logits.data[this.entailment_id])}let n=(o?e.map(e=>(0,c.Vs)(e)[1]):(0,c.Vs)(e)).map((e,t)=>[e,t]).sort((e,t)=>t[0]-e[0]);s.push({sequence:r,labels:n.map(e=>t[e[1]]),scores:n.map(e=>e[0])})}return i?s:s[0]}}class eB extends ex{constructor(e){super(e)}async _call(e,{pooling:t="none",normalize:r=!1,quantize:n=!1,precision:i="binary"}={}){let a=this.tokenizer(e,{padding:!0,truncation:!0}),o=await this.model(a),s=o.last_hidden_state??o.logits??o.token_embeddings;if("none"===t);else if("mean"===t)s=(0,u.X7)(s,a.attention_mask);else if("cls"===t)s=s.slice(null,0);else throw Error(`Pooling method '${t}' not supported.`);return r&&(s=s.normalize(2,-1)),n&&(s=(0,u.J)(s,i)),s}}class eO extends ex{constructor(e){super(e)}async _call(e,{pool:t=null}={}){let r,n=await eb(e),{pixel_values:i}=await this.processor(n),a=await this.model({pixel_values:i});if(t){if(!("pooler_output"in a))throw Error("No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.");r=a.pooler_output}else r=a.last_hidden_state??a.logits??a.image_embeds;return r}}class eL extends ex{constructor(e){super(e)}async _call(e,{topk:t=null}={}){let r=!Array.isArray(e),n=this.processor.feature_extractor.config.sampling_rate,i=await ev(e,n),a=this.model.config.id2label,o=[];for(let e of i){let r=await this.processor(e),n=(await this.model(r)).logits[0],i=(0,c.Em)((0,c.Vs)(n.data),t).map(e=>({label:a[e[0]],score:e[1]}));1===t?o.push(...i):o.push(i)}return r&&1!==t?o[0]:o}}class e$ extends ex{constructor(e){super(e)}async _call(e,t,{hypothesis_template:r="This is a sound of {}."}={}){let n=!Array.isArray(e);n&&(e=[e]);let i=t.map(e=>r.replace("{}",e)),a=this.tokenizer(i,{padding:!0,truncation:!0}),o=this.processor.feature_extractor.config.sampling_rate,s=await ev(e,o),l=[];for(let e of s){let r=await this.processor(e),n=await this.model({...a,...r}),i=(0,c.Vs)(n.logits_per_audio.data);l.push([...i].map((e,r)=>({score:e,label:t[r]})))}return n?l[0]:l}}class ej extends ex{constructor(e){super(e)}async _call(e,t={}){switch(this.model.config.model_type){case"whisper":return this._call_whisper(e,t);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":return this._call_wav2vec2(e,t);default:throw Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(e,t={}){t.language&&console.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'),t.task&&console.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');let r=!Array.isArray(e);r&&(e=[e]);let n=this.processor.feature_extractor.config.sampling_rate,i=await ev(e,n),a=[];for(let e of i){let t=await this.processor(e),r=(await this.model(t)).logits[0],n=[];for(let e of r)n.push((0,c.T9)(e.data)[1]);let i=this.tokenizer.decode(n);a.push({text:i})}return r?a[0]:a}async _call_whisper(e,t={}){let r=t.return_timestamps??!1,n=t.chunk_length_s??0,i=t.chunk_callback??null,a=t.force_full_sequences??!1,o=t.stride_length_s??null;"word"===r&&(t.return_token_timestamps=!0);let s=(0,l.uY)(t,"language",null),h=(0,l.uY)(t,"task",null);if(s||h||r){if(t.forced_decoder_ids)throw Error("Cannot specify `language`/`task`/`return_timestamps` and `forced_decoder_ids` at the same time.");let e=this.tokenizer.get_decoder_prompt_ids({language:s,task:h,no_timestamps:!r});e.length>0&&(t.forced_decoder_ids=e)}let u=!Array.isArray(e);u&&(e=[e]);let d=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,f=this.processor.feature_extractor.config.hop_length,p=this.processor.feature_extractor.config.sampling_rate,m=await ev(e,p),g=[];for(let e of m){let s=[];if(n>0){if(null===o)o=n/6;else if(n<=o)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");let t=p*n,r=p*o,i=t-2*r,a=0;for(;a<e.length;){let n=e.subarray(a,a+t),o=await this.processor(n),l=0===a,h=a+i>=e.length;s.push({stride:[n.length,l?0:r,h?0:r],input_features:o.input_features,is_last:h}),a+=i}}else s=[{stride:[e.length,0,0],input_features:(await this.processor(e)).input_features,is_last:!0}];for(let e of s){t.num_frames=Math.floor(e.stride[0]/f);let n=await this.model.generate(e.input_features,t);"word"===r?(e.tokens=n.sequences[0],e.token_timestamps=n.token_timestamps.tolist()[0].map(e=>(0,c.LI)(e,2))):e.tokens=n[0],e.stride=e.stride.map(e=>e/p),null!==i&&i(e)}let[l,h]=this.tokenizer._decode_asr(s,{time_precision:d,return_timestamps:r,force_full_sequences:a});g.push({text:l,...h})}return u?g[0]:g}}class eU extends ex{constructor(e){super(e)}async _call(e,t={}){let r=Array.isArray(e),n=await eb(e),{pixel_values:i}=await this.processor(n),a=[];for(let e of i){e.dims=[1,...e.dims];let r=await this.model.generate(e,t),n=this.tokenizer.batch_decode(r,{skip_special_tokens:!0}).map(e=>({generated_text:e.trim()}));a.push(n)}return r?a:a[0]}}class eN extends ex{constructor(e){super(e)}async _call(e,{topk:t=1}={}){let r=Array.isArray(e),n=await eb(e),{pixel_values:i}=await this.processor(n),a=await this.model({pixel_values:i}),o=this.model.config.id2label,s=[];for(let e of a.logits){let r=(0,c.Em)((0,c.Vs)(e.data),t).map(e=>({label:o[e[0]],score:e[1]}));1===t?s.push(...r):s.push(r)}return r||1===t?s:s[0]}}class eR extends ex{constructor(e){super(e),this.subtasks_mapping={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"}}async _call(e,{threshold:t=.5,mask_threshold:r=.5,overlap_mask_area_threshold:n=.8,label_ids_to_fuse:i=null,target_sizes:a=null,subtask:o=null}={}){if(Array.isArray(e)&&1!==e.length)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");let s=await eb(e),l=s.map(e=>[e.height,e.width]),{pixel_values:h,pixel_mask:c}=await this.processor(s),u=await this.model({pixel_values:h,pixel_mask:c}),d=null;
1if(null!==o)d=this.subtasks_mapping[o];else for(let[e,t]of Object.entries(this.subtasks_mapping))if(t in this.processor.feature_extractor){d=this.processor.feature_extractor[t].bind(this.processor.feature_extractor),o=e;break}let f=this.model.config.id2label,p=[];if("panoptic"===o||"instance"===o){let e=d(u,t,r,n,i,a??l)[0],o=e.segmentation;for(let t of e.segments_info){let e=new Uint8ClampedArray(o.data.length);for(let r=0;r<o.data.length;++r)o.data[r]===t.id&&(e[r]=255);let r=new w(e,o.dims[1],o.dims[0],1);p.push({score:t.score,label:f[t.label_id],mask:r})}}else if("semantic"===o){let{segmentation:e,labels:t}=d(u,a??l)[0];for(let r of t){let t=new Uint8ClampedArray(e.data.length);for(let n=0;n<e.data.length;++n)e.data[n]===r&&(t[n]=255);let n=new w(t,e.dims[1],e.dims[0],1);p.push({score:null,label:f[r],mask:n})}}else throw Error(`Subtask ${o} not supported.`);return p}}class eV extends ex{constructor(e){super(e)}async _call(e,t,{hypothesis_template:r="This is a photo of {}"}={}){let n=Array.isArray(e),i=await eb(e),a=t.map(e=>r.replace("{}",e)),o=this.tokenizer(a,{padding:"siglip"!==this.model.config.model_type||"max_length",truncation:!0}),{pixel_values:s}=await this.processor(i),l=await this.model({...o,pixel_values:s}),h="siglip"===this.model.config.model_type?e=>e.sigmoid().data:e=>(0,c.Vs)(e.data),u=[];for(let e of l.logits_per_image){let r=[...h(e)].map((e,r)=>({score:e,label:t[r]}));r.sort((e,t)=>t.score-e.score),u.push(r)}return n?u:u[0]}}class eq extends ex{constructor(e){super(e)}async _call(e,{threshold:t=.9,percentage:r=!1}={}){let n=Array.isArray(e);if(n&&1!==e.length)throw Error("Object detection pipeline currently only supports a batch size of 1.");let i=await eb(e),a=r?null:i.map(e=>[e.height,e.width]),{pixel_values:o,pixel_mask:s}=await this.processor(i),l=await this.model({pixel_values:o,pixel_mask:s}),h=this.processor.feature_extractor.post_process_object_detection(l,t,a),c=this.model.config.id2label,u=h.map(e=>e.boxes.map((t,n)=>({score:e.scores[n],label:c[e.classes[n]],box:eM(t,!r)})));return n?u:u[0]}}class eD extends ex{constructor(e){super(e)}async _call(e,t,{threshold:r=.1,topk:n=null,percentage:i=!1}={}){let a=Array.isArray(e),o=await eb(e),s=this.tokenizer(t,{padding:!0,truncation:!0}),l=await this.processor(o),h=[];for(let e=0;e<o.length;++e){let a=o[e],c=i?null:[[a.height,a.width]],u=l.pixel_values[e].unsqueeze_(0),d=await this.model({...s,pixel_values:u}),f=this.processor.feature_extractor.post_process_object_detection(d,r,c,!0)[0],p=f.boxes.map((e,r)=>({score:f.scores[r],label:t[f.classes[r]],box:eM(e,!i)})).sort((e,t)=>t.score-e.score);null!==n&&(p=p.slice(0,n)),h.push(p)}return a?h:h[0]}}class eG extends ex{constructor(e){super(e)}async _call(e,t,r={}){let n=(await eb(e))[0],{pixel_values:i}=await this.processor(n),a=`<s_docvqa><s_question>${t}</s_question><s_answer>`,o=this.tokenizer(a,{add_special_tokens:!1,padding:!0,truncation:!0}).input_ids,s=await this.model.generate(i,{...r,decoder_input_ids:o,max_length:this.model.config.decoder.max_position_embeddings}),l=this.tokenizer.batch_decode(s)[0].match(/<s_answer>(.*?)<\/s_answer>/),h=null;return l&&l.length>=2&&(h=l[1].trim()),[{answer:h}]}}class eW extends ex{DEFAULT_VOCODER_ID="Xenova/speecht5_hifigan";constructor(e){super(e),this.vocoder=e.vocoder??null}async _call(e,{speaker_embeddings:t=null}={}){return this.processor?this._call_text_to_spectrogram(e,{speaker_embeddings:t}):this._call_text_to_waveform(e)}async _call_text_to_waveform(e){let t=this.tokenizer(e,{padding:!0,truncation:!0}),{waveform:r}=await this.model(t),n=this.model.config.sampling_rate;return{audio:r.data,sampling_rate:n}}async _call_text_to_spectrogram(e,{speaker_embeddings:t}){if(this.vocoder||(console.log("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await s.rKH.from_pretrained(this.DEFAULT_VOCODER_ID,{quantized:!1})),("string"==typeof t||t instanceof URL)&&(t=new Float32Array(await (await fetch(t)).arrayBuffer())),t instanceof Float32Array)t=new u.qY("float32",t,[1,t.length]);else if(!(t instanceof u.qY))throw Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");let{input_ids:r}=this.tokenizer(e,{padding:!0,truncation:!0}
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