diff --git "a/_next/static/chunks/842.3f8418770ac3440a.js" "b/_next/static/chunks/842.3f8418770ac3440a.js"
new file mode 100644--- /dev/null
+++ "b/_next/static/chunks/842.3f8418770ac3440a.js"
@@ -0,0 +1,16 @@
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Got '${p}'`)}}return{data:z,dims:L}}function A(e,t,{periodic:r=!0,frame_length:n=null,center:o=!0}={}){let i;let a=r?e+1:e;switch(t){case"boxcar":i=new Float64Array(a).fill(1);break;case"hann":case"hann_window":i=w(a);break;case"povey":i=w(a).map(e=>Math.pow(e,.85));break;default:throw Error(`Unknown window type ${t}.`)}if(r&&(i=i.subarray(0,e)),null===n)return i;if(e>n)throw Error(`Length of the window (${e}) may not be larger than frame_length (${n})`);return i}function T(e,t=.5,r=null,n=!1){let o=e.logits,i=e.pred_boxes,[a,s,l]=o.dims;if(null!==r&&r.length!==a)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let u=[];for(let e=0;e<a;++e){let a=null!==r?r[e]:null,d={boxes:[],classes:[],scores:[]},f=o[e],h=i[e];for(let e=0;e<s;++e){let r,o=f[e],i=[];if(n){r=o.sigmoid().data;for(let e=0;e<r.length;++e)r[e]>t&&i.push(e)}else{let e=(0,c.Fp)(o.data)[1];if(e===l-1)continue;i.push(e),r=(0,c.XA)(o.data)}for(let t of i){let n=h[e].data;n=function([e,t,r,n]){return[e-r/2,t-n/2,e+r/2,t+n/2]}(n),null!==a&&(n=n.map((e,t)=>e*a[(t+1)%2])),d.boxes.push(n),d.classes.push(t),d.scores.push(r[t])}}u.push(d)}return u}function O(e,t){if(!(e instanceof Float32Array||e instanceof Float64Array))throw Error(`${t} expects input to be a Float32Array or a Float64Array, but got ${e?.constructor?.name??typeof e} instead. 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z{constructor(e){super(e),this.image_mean=this.config.image_mean??this.config.mean,this.image_std=this.config.image_std??this.config.std,this.resample=this.config.resample??2,this.do_rescale=this.config.do_rescale??!0,this.rescale_factor=this.config.rescale_factor??1/255,this.do_normalize=this.config.do_normalize,this.do_resize=this.config.do_resize,this.do_thumbnail=this.config.do_thumbnail,this.size=this.config.size,this.size_divisibility=this.config.size_divisibility??this.config.size_divisor,this.do_center_crop=this.config.do_center_crop,this.crop_size=this.config.crop_size,this.do_convert_rgb=this.config.do_convert_rgb??!0,this.do_crop_margin=this.config.do_crop_margin,this.pad_size=this.config.pad_size,this.do_pad=this.config.do_pad,this.do_pad&&!this.pad_size&&this.size&&void 0!==this.size.width&&void 0!==this.size.height&&(this.pad_size=this.size)}async thumbnail(e,t,r=2){let n=e.height,o=e.width,i=t.height,a=t.width,s=Math.min(n,i),l=Math.min(o,a);return s===n&&l===o?e:(n>o?l=Math.floor(o*s/n):o>n&&(s=Math.floor(n*l/o)),await e.resize(l,s,{resample:r}))}async crop_margin(e,t=200){let r=e.clone().grayscale(),n=(0,c.VV)(r.data)[0],o=(0,c.Fp)(r.data)[0]-n;if(0===o)return e;let i=t/255,a=r.width,s=r.height,l=0,u=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)/o<i&&(a=Math.min(a,c),s=Math.min(s,e),l=Math.max(l,c),u=Math.max(u,e))}return e=await e.crop([a,s,l,u])}pad_image(e,t,r,{mode:n="constant",center:o=!1,constant_values:i=0}={}){let a,s;let[u,c,d]=t;if("number"==typeof r?(a=r,s=r):(a=r.width,s=r.height),a!==c||s!==u){let r=new Float32Array(a*s*d);if(Array.isArray(i))for(let e=0;e<r.length;++e)r[e]=i[e%d];else 0!==i&&r.fill(i);let[f,h]=o?[Math.floor((a-c)/2),Math.floor((s-u)/2)]:[0,0];for(let t=0;t<u;++t){let n=(t+h)*a,o=t*c;for(let t=0;t<c;++t){let i=(n+t+f)*d,a=(o+t)*d;for(let t=0;t<d;++t)r[i+t]=e[a+t]}}if("symmetric"===n){if(o)throw Error("`center` padding is not supported when `mode` is set to `symmetric`.");let t=u-1,n=c-1;for(let o=0;o<s;++o){let i=o*a,s=(0,l.hs)(o,t)*c;for(let t=0;t<a;++t){if(o<u&&t<c)continue;let a=(i+t)*d,f=(s+(0,l.hs)(t,n))*d;for(let t=0;t<d;++t)r[a+t]=e[f+t]}}}e=r,t=[s,a,d]}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;let[o,i]=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/o,r/i),t=o*e,a=i*e,s=void 0===n?1:Math.min(n/t,n/a),l=Math.floor(Number((t*s).toFixed(2))),u=Math.floor(Number((a*s).toFixed(2)));return void 0!==this.size_divisibility&&([l,u]=I([l,u],this.size_divisibility)),[l,u]}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/i,n=e/o;Math.abs(1-n)<Math.abs(1-t)?t=n:n=t,r=M(t*i,this.config.ensure_multiple_of),e=M(n*o,this.config.ensure_multiple_of)}return[e,r]}if(void 0!==this.size_divisibility)return I([o,i],this.size_divisibility);throw Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(t)}`)}async resize(e){let[t,r]=this.get_resize_output_image_size(e,this.size);return await e.resize(t,r,{resample:this.resample})}async preprocess(e,{do_normalize:t=null,do_pad:r=null,do_convert_rgb:n=null,do_convert_grayscale:o=null}={}){this.do_crop_margin&&(e=await this.crop_margin(e));let[i,a]=e.size;if(n??this.do_convert_rgb?e=e.rgb():o&&(e=e.grayscale()),this.do_resize&&(e=await this.resize(e)),this.do_thumbnail&&(e=await this.thumbnail(e,this.size,this.resample)),this.do_center_crop){let t,r;Number.isInteger(this.crop_size)?(t=this.crop_size,r=this.crop_size):(t=this.crop_size.width,r=this.crop_size.height),e=await e.center_crop(t,r)}let s=[e.height,e.width],l=Float32Array.from(e.data),u=[e.height,e.width,e.channels];if(this.do_rescale&&this.rescale(l),t??this.do_normalize){let t=this.image_mean;Array.isArray(this.image_mean)||(t=Array(e.channels).fill(t));let r=this.image_std;if(Array.isArray(this.image_std)||(r=Array(e.channels).fill(t)),t.length!==e.channels||r.length!==e.channels)throw Error(`When set to arrays, the length of \`image_mean\` (${t.length}) and \`image_std\` (${r.length}) must match the number of channels in the image (${e.channels}).`);for(let n=0;n<l.length;n+=e.channels)for(let o=0;o<e.channels;++o)l[n+o]=(l[n+o]-t[o])/r[o]}if(r??this.do_pad){if(this.pad_size){let t=this.pad_image(l,[e.height,e.width,e.channels],this.pad_size);[l,u]=t}else if(this.size_divisibility){let[e,t]=I([u[1],u[0]],this.size_divisibility);[l,u]=this.pad_image(l,u,{width:e,height:t})}}return{original_size:[a,i],reshaped_input_size:s,pixel_values:new d.es("float32",l,u).permute(2,0,1)}}async _call(e,...t){Array.isArray(e)||(e=[e]);let r=await Promise.all(e.map(e=>this.preprocess(e)));return{pixel_values:(0,d.kn)(r.map(e=>e.pixel_values),0),original_sizes:r.map(e=>e.original_size),reshaped_input_sizes:r.map(e=>e.reshaped_input_size)}}}class P extends L{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 o=[];for(let e=0;e<n;++e){let n=null!==t?t[e]:null,i=r[e];null!==n&&(i=(0,d.sX)(i,n,"bilinear",!1));let[a,s]=n??i.dims.slice(-2),l=new d.es("int32",new Int32Array(a*s),[a,s]),u=i[0].data;for(let e=1;e<i.dims[0];++e){let t=i[e].data;for(let r=0;r<t.length;++r)t[r]>u[r]&&(u[r]=t[r],l.data[r]=e)}let c=Array(i.dims[0]),f=l.data;for(let e=0;e<f.length;++e){let t=f[e];c[t]=t}let h=c.filter(e=>void 0!==e);o.push({segmentation:l,labels:h})}return o}}class B extends L{}class j extends B{}class N extends L{}class $ extends L{}class U extends L{}class F extends L{}class D extends L{}class W extends L{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,o]=this.get_resize_output_image_size(e,{shortest_edge:r});e=await e.resize(n,o,{resample:this.resample}),e=await e.center_crop(t,t)}else e=await e.resize(t,t,{resample:this.resample});return e}}class H extends W{}class V extends L{}class Z extends L{}class q extends L{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 X extends L{}class G extends L{post_process_object_detection(...e){return T(...e)}}class K extends G{}class Y extends L{}class J extends L{}class Q extends L{pad_image(e,t,r,n={}){let[o,i,a]=t,s=this.image_mean;Array.isArray(this.image_mean)||(s=Array(a).fill(s));let l=this.image_std;Array.isArray(l)||(l=Array(a).fill(s));let u=s.map((e,t)=>-e/l[t]);return super.pad_image(e,t,r,{center:!0,constant_values:u,...n})}}class ee extends Q{}class et extends L{async _call(e){let t=await super._call(e),r=[t.pixel_values.dims[0],64,64],n=new d.es("int64",new BigInt64Array(r.reduce((e,t)=>e*t)).fill(1n),r);return{...t,pixel_mask:n}}post_process_object_detection(...e){return T(...e)}remove_low_and_no_objects(e,t,r,n){let o=[],i=[],a=[];for(let s=0;s<e.dims[0];++s){let l=e[s],u=t[s],d=(0,c.Fp)(l.data)[1];if(d===n)continue;let f=(0,c.XA)(l.data)[d];f>r&&(o.push(u),i.push(f),a.push(d))}return[o,i,a]}check_segment_validity(e,t,r,n=.5,o=.8){let i=[],a=0,s=0;for(let o=0;o<e.length;++o)e[o]===r&&(i.push(o),++a),t[r].data[o]>=n&&++s;let l=a>0&&s>0;return l&&(l=a/s>o),[l,i]}compute_segments(e,t,r,n,o,i=null,a=null){let[s,l]=a??e[0].dims,u=new d.es("int32",new Int32Array(s*l),[s,l]),c=[];if(null!==a)for(let t=0;t<e.length;++t)e[t]=(0,d.sX)(e[t],a,"bilinear",!1);let f=new Int32Array(e[0].data.length),h=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]>h[t]&&(f[t]=r,h[t]=e[r].data[t])}let p=0;for(let i=0;i<r.length;++i){let a=r[i],[s,l]=this.check_segment_validity(f,e,i,n,o);if(s){for(let e of(++p,l))u.data[e]=p;c.push({id:p,label_id:a,score:t[i]})}}return[u,c]}post_process_panoptic_segmentation(e,t=.5,r=.5,n=.8,o=null,i=null){null===o&&(console.warn("`label_ids_to_fuse` unset. No instance will be fused."),o=new Set);let a=e.logits,s=e.pred_masks.sigmoid(),[l,u,c]=a.dims;if(c-=1,null!==i&&i.length!==l)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let f=[];for(let e=0;e<l;++e){let l=null!==i?i[e]:null,u=a[e],h=s[e],[p,m,g]=this.remove_low_and_no_objects(u,h,t,c);if(0===g.length){let[e,t]=l??h.dims.slice(-2),r=new d.es("int32",new Int32Array(e*t).fill(-1),[e,t]);f.push({segmentation:r,segments_info:[]});continue}let[y,v]=this.compute_segments(p,m,g,r,n,o,l);f.push({segmentation:y,segments_info:v})}return f}post_process_instance_segmentation(){throw Error("Not implemented yet")}}class er extends L{post_process_object_detection(...e){return T(...e)}}class en extends L{reshape_input_points(e,t,r){e=structuredClone(e);let n=(0,l.jg)(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 o=t[n],i=r[n],a=[i[0]/o[0],i[1]/o[1]];for(let t=0;t<e[n].length;++t)for(let r=0;r<e[n][t].length;++r)for(let o=0;o<e[n][t][r].length;++o)e[n][t][r][o]*=a[o]}return new d.es("float32",Float32Array.from(e.flat(1/0)),n)}add_input_labels(e,t){let r=(0,l.jg)(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 d.es("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:o=!0,pad_size:i=null}={}){let a=[],s=[(i=i??this.pad_size).height,i.width];for(let i=0;i<t.length;++i){let l=t[i],u=r[i],c=e[i],f=[];for(let e=0;e<c.dims[0];++e){let t=c[e],r=(0,d.sX)(t,s,"bilinear",!1);if(r=r.slice(null,[0,u[0]],[0,u[1]]),r=(0,d.sX)(r,l,"bilinear",!1),o){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 d.es("bool",e,r.dims)}f.push(r)}a.push((0,d.kn)(f))}return a}}class eo extends L{pad_image(e,t,r,n={}){let[o,i,a]=t;return super.pad_image(e,t,{width:i+(r-i%r)%r,height:o+(r-o%r)%r},{mode:"symmetric",center:!1,constant_values:-1,...n})}}class ei extends L{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,d.kn)(r.map((e,t)=>(0,d.d3)([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 ea extends z{constructor(e){super(e),this.config.mel_filters??=k(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}=R(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.Fp)(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;O(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 d.es("float32",r,[1,...n])}}}class es extends z{_zero_mean_unit_var_norm(e){let t=e.reduce((e,t)=>e+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){O(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 d.es("float32",t,r),attention_mask:new d.es("int64",new BigInt64Array(t.length).fill(1n),r)}}}class el extends z{constructor(e){super(e);let t=this.config.sampling_rate,r=k(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 R(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:o=!0}={}){let i;O(e,"SeamlessM4TFeatureExtractor");let a=this._extract_fbank_features(e,this.config.max_length);if(n){let[e,t]=a.dims;for(let r=0;r<t;++r){let n=0;for(let o=0;o<e;++o)n+=a.data[o*t+r];let o=n/e,i=0;for(let n=0;n<e;++n)i+=(a.data[n*t+r]-o)**2;let s=Math.sqrt((i/=e-1)+1e-7);for(let n=0;n<e;++n){let e=n*t+r;a.data[e]=(a.data[e]-o)/s}}}if(t){let[e,t]=a.dims,n=e%r;if(n>0){let r=new Float32Array(t*(e+n));r.set(a.data),r.fill(this.config.padding_value,a.data.length);let s=e+n;a={data:r,dims:[s,t]},o&&(i=new d.es("int64",new BigInt64Array(s),[1,s])).data.fill(1n,0,e)}}let[s,l]=a.dims,u=this.config.stride;if(0!=s%u)throw Error(`The number of frames (${s}) must be a multiple of the stride (${u}).`);let c=new d.es("float32",a.data,a.dims).view(1,Math.floor(s/u),l*u),f={input_features:c};if(o){let e=c.dims[1],t=new d.es("int64",new BigInt64Array(e),[1,e]);if(i)for(let e=1,r=0;e<s;e+=u,++r)t.data[r]=i.data[e];else t.data.fill(1n);f.attention_mask=t}return f}}class eu extends z{constructor(e){super(e);let t=this.config.sampling_rate,r=k(256,this.config.num_mel_bins,20,Math.floor(t/2),t,null,"kaldi",!0);for(let e=0;e<r.length;++e)r[e].push(0);this.mel_filters=r,this.window=A(400,"hann",{periodic:!1}),this.mean=this.config.mean,this.std=this.config.std}_extract_fbank_features(e,t){return R(e,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:t,transpose:!0})}async _call(e){O(e,"ASTFeatureExtractor");let t=this._extract_fbank_features(e,this.config.max_length);if(this.config.do_normalize){let e=2*this.std;for(let r=0;r<t.data.length;++r)t.data[r]=(t.data[r]-this.mean)/e}return{input_values:new d.es("float32",t.data,[1,...t.dims])}}}class ec extends z{constructor(e){super(e),this.mel_filters=k(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,null,"htk"),this.mel_filters_slaney=k(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,"slaney","slaney"),this.window=A(this.config.fft_window_size,"hann")}_get_input_mel(e,t,r,n){let o;let i=!1,a=e.length-t;if(a>0){if("rand_trunc"===r){i=!0;let r=Math.floor(Math.random()*(a+1));e=e.subarray(r,r+t),(o=this._extract_fbank_features(e,this.mel_filters_slaney,this.config.nb_max_samples)).dims=[1,...o.dims]}else throw Error(`Truncation strategy "${r}" not implemented`)}else{if(a<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<-a;t+=e.length)r.set(e,t);e=r}if("fusion"===r)throw Error(`Truncation strategy "${r}" not implemented`);(o=this._extract_fbank_features(e,this.mel_filters_slaney,this.config.nb_max_samples)).dims=[1,...o.dims]}return{...o,longer:i}}_extract_fbank_features(e,t,r=null){return R(e,this.window,this.config.fft_window_size,this.config.hop_length,{power:2,mel_filters:t,log_mel:"dB",max_num_frames:r,do_pad:!1,transpose:!0})}async _call(e,{max_length:t=null}={}){O(e,"ClapFeatureExtractor");let r=this._get_input_mel(e,t??this.config.nb_max_samples,this.config.truncation,this.config.padding);return{input_features:new d.es("float32",r.data,[1,...r.dims])}}}class ed extends z{}class ef extends l.Ag{constructor(e){super(),this.feature_extractor=e}async _call(e,...t){return await this.feature_extractor(e,...t)}}class eh extends ef{async _call(...e){return await this.feature_extractor(...e)}post_process_masks(...e){return this.feature_extractor.post_process_masks(...e)}reshape_input_points(...e){return this.feature_extractor.reshape_input_points(...e)}}class ep extends ef{async _call(e){return await this.feature_extractor(e)}}class em extends ef{async _call(e){return await this.feature_extractor(e)}}class eg extends ef{async _call(e){return await this.feature_extractor(e)}}class ey extends ef{}class ev{static FEATURE_EXTRACTOR_CLASS_MAPPING={ImageFeatureExtractor:L,WhisperFeatureExtractor:ea,ViTFeatureExtractor:V,MobileViTFeatureExtractor:X,OwlViTFeatureExtractor:G,Owlv2ImageProcessor:K,CLIPFeatureExtractor:U,ChineseCLIPFeatureExtractor:F,SiglipImageProcessor:D,ConvNextFeatureExtractor:W,ConvNextImageProcessor:H,SegformerFeatureExtractor:P,BitImageProcessor:N,DPTImageProcessor:j,DPTFeatureExtractor:B,GLPNFeatureExtractor:$,BeitFeatureExtractor:J,DeiTFeatureExtractor:Y,DetrFeatureExtractor:et,YolosFeatureExtractor:er,DonutFeatureExtractor:Q,NougatImageProcessor:ee,EfficientNetImageProcessor:q,ViTImageProcessor:Z,VitMatteImageProcessor:ei,SamImageProcessor:en,Swin2SRImageProcessor:eo,Wav2Vec2FeatureExtractor:es,SeamlessM4TFeatureExtractor:el,SpeechT5FeatureExtractor:ed,ASTFeatureExtractor:eu,ClapFeatureExtractor:ec};static PROCESSOR_CLASS_MAPPING={WhisperProcessor:ep,Wav2Vec2ProcessorWithLM:em,SamProcessor:eh,SpeechT5Processor:eg,OwlViTProcessor:ey};static async from_pretrained(e,{progress_callback:t=null,config:r=null,cache_dir:n=null,local_files_only:o=!1,revision:i="main"}={}){let a=r??await (0,u.yM)(e,"preprocessor_config.json",!0,{progress_callback:t,config:r,cache_dir:n,local_files_only:o,revision:i}),s=a.feature_extractor_type??a.image_processor_type,l=this.FEATURE_EXTRACTOR_CLASS_MAPPING[s];if(!l){if(void 0!==a.size)console.warn(`Feature extractor type "${s}" not found, assuming ImageFeatureExtractor due to size parameter in config.`),l=L;else throw Error(`Unknown Feature Extractor type: ${s}`)}return new(this.PROCESSOR_CLASS_MAPPING[a.processor_class]??ef)(new l(a))}}async function eb(e){return Array.isArray(e)||(e=[e]),await Promise.all(e.map(e=>v.read(e)))}async function ew(e,t){return Array.isArray(e)||(e=[e]),await Promise.all(e.map(e=>"string"==typeof e||e instanceof URL?b(e,t):e instanceof Float64Array?new Float32Array(e):e))}function ex(e,t){t&&(e=e.map(e=>0|e));let[r,n,o,i]=e;return{xmin:r,ymin:n,xmax:o,ymax:i}}class e_ extends l.Ag{constructor({task:e,model:t,tokenizer:r=null,processor:n=null}){super(),this.task=e,this.model=t,this.tokenizer=r,this.processor=n}async dispose(){await this.model.dispose()}}class eS extends e_{constructor(e){super(e)}async _call(e,{topk:t=1}={}){let r=this.tokenizer(e,{padding:!0,truncation:!0}),n=await this.model(r),o="multi_label_classification"===this.model.config.problem_type?e=>e.sigmoid().data:e=>(0,c.XA)(e.data),i=this.model.config.id2label,a=[];for(let e of n.logits){let r=o(e),n=(0,c.em)(r,t).map(e=>({label:i[e[0]],score:e[1]}));1===t?a.push(...n):a.push(n)}return Array.isArray(e)||1===t?a:a[0]}}class eE extends e_{constructor(e){super(e)}async _call(e,{ignore_labels:t=["O"]}={}){let r=Array.isArray(e),n=this.tokenizer(r?e:[e],{padding:!0,truncation:!0}),o=(await this.model(n)).logits,i=this.model.config.id2label,a=[];for(let e=0;e<o.dims[0];++e){let r=n.input_ids[e],s=o[e],l=[];for(let e=0;e<s.dims[0];++e){let n=s[e],o=(0,c.Fp)(n.data)[1],a=i?i[o]:`LABEL_${o}`;if(t.includes(a))continue;let u=this.tokenizer.decode([r[e].item()],{skip_special_tokens:!0});if(""===u)continue;let d=(0,c.XA)(n.data);l.push({entity:a,score:d[o],index:e,word:u,start:null,end:null})}a.push(l)}return r?a:a[0]}}class ek extends e_{constructor(e){super(e)}async _call(e,t,{topk:r=1}={}){let n=this.tokenizer(e,{text_pair:t,padding:!0,truncation:!0}),o=await this.model(n),i=[];for(let e=0;e<o.start_logits.dims[0];++e){let t=n.input_ids[e],a=t.indexOf(this.tokenizer.sep_token_id),s=Array.from((0,c.XA)(o.start_logits[e].data)).map((e,t)=>[e,t]).filter(e=>e[1]>a),u=Array.from((0,c.XA)(o.end_logits[e].data)).map((e,t)=>[e,t]).filter(e=>e[1]>a),d=(0,l.O7)(s,u).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(d.length,r);++e){let[r,n,o]=d[e],a=[...t].slice(r,n+1),s=this.tokenizer.decode(a,{skip_special_tokens:!0});i.push({answer:s,score:o})}}return 1===r?i[0]:i}}class eC extends e_{constructor(e){super(e)}async _call(e,{topk:t=5}={}){let r=this.tokenizer(e,{padding:!0,truncation:!0}),n=await this.model(r),o=[];for(let e=0;e<r.input_ids.dims[0];++e){let i=r.input_ids[e],a=i.indexOf(this.tokenizer.mask_token_id);if(-1===a)throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`);let s=n.logits[e][a],l=(0,c.em)((0,c.XA)(s.data),t);o.push(l.map(e=>{let t=[...i];return t[a]=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)?o:o[0]}}class eR extends e_{_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 o=this.tokenizer,i={padding:!0,truncation:!0};r=this instanceof eT&&"_build_translation_inputs"in o?o._build_translation_inputs(e,i,t).input_ids:o(e,i).input_ids;let a=await this.model.generate(r,t);return o.batch_decode(a,{skip_special_tokens:!0}).map(e=>({[this._key]:e}))}}class eA extends eR{_key="summary_text";constructor(e){super(e)}}class eT extends eR{_key="translation_text";constructor(e){super(e)}}function eO(e){return Array.isArray(e)&&e.every(e=>"role"in e&&"content"in e)}class eM extends e_{constructor(e){super(e)}async _call(e,t={}){let r,n,o=!1,i=!1;if("string"==typeof e)r=e=[e];else if(Array.isArray(e)&&e.every(e=>"string"==typeof e))o=!0,r=e;else{if(eO(e))e=[e];else if(Array.isArray(e)&&e.every(eO))o=!0;else throw Error("Input must be a string, an array of strings, a Chat, or an array of Chats");i=!0,r=e.map(e=>this.tokenizer.apply_chat_template(e,{tokenize:!1,add_generation_prompt:!0}))}let a=t.add_special_tokens??!1,s=!i&&(t.return_full_text??!0);this.tokenizer.padding_side="left";let{input_ids:l,attention_mask:u}=this.tokenizer(r,{add_special_tokens:a,padding:!0,truncation:!0}),c=await this.model.generate(l,t,null,{inputs_attention_mask:u}),d=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 f=Array.from({length:e.length},e=>[]);for(let t=0;t<d.length;++t){let r=Math.floor(t/c.length*e.length);n&&(d[t]=d[t].slice(n[r])),f[r].push({generated_text:i?[...e[r],{role:"assistant",content:d[t]}]:d[t]})}return o||1!==f.length?f:f[0]}}class eI extends e_{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 o=Array.isArray(e);o||(e=[e]),Array.isArray(t)||(t=[t]);let i=t.map(e=>r.replace("{}",e)),a=n||1===t.length,s=[];for(let r of e){let e=[];for(let t of i){let n=this.tokenizer(r,{text_pair:t,padding:!0,truncation:!0}),o=await this.model(n);a?e.push([o.logits.data[this.contradiction_id],o.logits.data[this.entailment_id]]):e.push(o.logits.data[this.entailment_id])}let n=(a?e.map(e=>(0,c.XA)(e)[1]):(0,c.XA)(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 o?s:s[0]}}class ez extends e_{constructor(e){super(e)}async _call(e,{pooling:t="none",normalize:r=!1,quantize:n=!1,precision:o="binary"}={}){let i=this.tokenizer(e,{padding:!0,truncation:!0}),a=await this.model(i),s=a.last_hidden_state??a.logits;if("none"===t);else if("mean"===t)s=(0,d.v6)(s,i.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,d.e)(s,o)),s}}class eL extends e_{constructor(e){super(e)}async _call(e,{pool:t=null}={}){let r;let n=await eb(e),{pixel_values:o}=await this.processor(n),i=await this.model({pixel_values:o});if(t){if(!("pooler_output"in i))throw Error("No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.");r=i.pooler_output}else r=i.last_hidden_state??i.logits??i.image_embeds;return r}}class eP extends e_{constructor(e){super(e)}async _call(e,{topk:t=null}={}){let r=!Array.isArray(e),n=this.processor.feature_extractor.config.sampling_rate,o=await ew(e,n),i=this.model.config.id2label,a=[];for(let e of o){let r=await this.processor(e),n=(await this.model(r)).logits[0],o=(0,c.em)((0,c.XA)(n.data),t).map(e=>({label:i[e[0]],score:e[1]}));1===t?a.push(...o):a.push(o)}return r&&1!==t?a[0]:a}}class eB extends e_{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 o=t.map(e=>r.replace("{}",e)),i=this.tokenizer(o,{padding:!0,truncation:!0}),a=this.processor.feature_extractor.config.sampling_rate,s=await ew(e,a),l=[];for(let e of s){let r=await this.processor(e),n=await this.model({...i,...r}),o=(0,c.XA)(n.logits_per_audio.data);l.push([...o].map((e,r)=>({score:e,label:t[r]})))}return n?l[0]:l}}class ej extends e_{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,o=await ew(e,n),i=[];for(let e of o){let t=await this.processor(e),r=(await this.model(t)).logits[0],n=[];for(let e of r)n.push((0,c.Fp)(e.data)[1]);let o=this.tokenizer.decode(n);i.push({text:o})}return r?i[0]:i}async _call_whisper(e,t={}){let r=t.return_timestamps??!1,n=t.chunk_length_s??0,o=t.chunk_callback??null,i=t.force_full_sequences??!1,a=t.stride_length_s??null;"word"===r&&(t.return_token_timestamps=!0);let s=(0,l.Sw)(t,"language",null),u=(0,l.Sw)(t,"task",null);if(s||u||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:u,no_timestamps:!r});e.length>0&&(t.forced_decoder_ids=e)}let d=!Array.isArray(e);d&&(e=[e]);let f=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,h=this.processor.feature_extractor.config.hop_length,p=this.processor.feature_extractor.config.sampling_rate,m=await ew(e,p),g=[];for(let e of m){let s=[];if(n>0){if(null===a)a=n/6;else if(n<=a)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");let t=p*n,r=p*a,o=t-2*r,i=0;for(;i<e.length;){let n=e.subarray(i,i+t),a=await this.processor(n),l=0===i,u=i+o>=e.length;s.push({stride:[n.length,l?0:r,u?0:r],input_features:a.input_features,is_last:u}),i+=o}}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]/h);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.NM)(e,2))):e.tokens=n[0],e.stride=e.stride.map(e=>e/p),null!==o&&o(e)}let[l,u]=this.tokenizer._decode_asr(s,{time_precision:f,return_timestamps:r,force_full_sequences:i});g.push({text:l,...u})}return d?g[0]:g}}class eN extends e_{constructor(e){super(e)}async _call(e,t={}){let r=Array.isArray(e),n=await eb(e),{pixel_values:o}=await this.processor(n),i=[];for(let e of o){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()}));i.push(n)}return r?i:i[0]}}class e$ extends e_{constructor(e){super(e)}async _call(e,{topk:t=1}={}){let r=Array.isArray(e),n=await eb(e),{pixel_values:o}=await this.processor(n),i=await this.model({pixel_values:o}),a=this.model.config.id2label,s=[];for(let e of i.logits){let r=(0,c.em)((0,c.XA)(e.data),t).map(e=>({label:a[e[0]],score:e[1]}));1===t?s.push(...r):s.push(r)}return r||1===t?s:s[0]}}class eU extends e_{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:o=null,target_sizes:i=null,subtask:a=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:u,pixel_mask:c}=await this.processor(s),d=await this.model({pixel_values:u,pixel_mask:c}),f=null;if(null!==a)f=this.subtasks_mapping[a];else for(let[e,t]of Object.entries(this.subtasks_mapping))if(t in this.processor.feature_extractor){f=this.processor.feature_extractor[t].bind(this.processor.feature_extractor),a=e;break}let h=this.model.config.id2label,p=[];if("panoptic"===a||"instance"===a){let e=f(d,t,r,n,o,i??l)[0],a=e.segmentation;for(let t of e.segments_info){let e=new Uint8ClampedArray(a.data.length);for(let r=0;r<a.data.length;++r)a.data[r]===t.id&&(e[r]=255);let r=new v(e,a.dims[1],a.dims[0],1);p.push({score:t.score,label:h[t.label_id],mask:r})}}else if("semantic"===a){let{segmentation:e,labels:t}=f(d,i??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 v(t,e.dims[1],e.dims[0],1);p.push({score:null,label:h[r],mask:n})}}else throw Error(`Subtask ${a} not supported.`);return p}}class eF extends e_{constructor(e){super(e)}async _call(e,t,{hypothesis_template:r="This is a photo of {}"}={}){let n=Array.isArray(e),o=await eb(e),i=t.map(e=>r.replace("{}",e)),a=this.tokenizer(i,{padding:"siglip"!==this.model.config.model_type||"max_length",truncation:!0}),{pixel_values:s}=await this.processor(o),l=await this.model({...a,pixel_values:s}),u="siglip"===this.model.config.model_type?e=>e.sigmoid().data:e=>(0,c.XA)(e.data),d=[];for(let e of l.logits_per_image){let r=[...u(e)].map((e,r)=>({score:e,label:t[r]}));r.sort((e,t)=>t.score-e.score),d.push(r)}return n?d:d[0]}}class eD extends e_{constructor(e){super(e)}async _call(e,{threshold:t=.9,percentage:r=!1}={}){let n=Array.isArray(e);if(n&&1!==e.length)throw Error("Object detection pipeline currently only supports a batch size of 1.");let o=await eb(e),i=r?null:o.map(e=>[e.height,e.width]),{pixel_values:a,pixel_mask:s}=await this.processor(o),l=await this.model({pixel_values:a,pixel_mask:s}),u=this.processor.feature_extractor.post_process_object_detection(l,t,i),c=this.model.config.id2label,d=u.map(e=>e.boxes.map((t,n)=>({score:e.scores[n],label:c[e.classes[n]],box:ex(t,!r)})));return n?d:d[0]}}class eW extends e_{constructor(e){super(e)}async _call(e,t,{threshold:r=.1,topk:n=null,percentage:o=!1}={}){let i=Array.isArray(e),a=await eb(e),s=this.tokenizer(t,{padding:!0,truncation:!0}),l=await this.processor(a),u=[];for(let e=0;e<a.length;++e){let i=a[e],c=o?null:[[i.height,i.width]],d=l.pixel_values[e].unsqueeze_(0),f=await this.model({...s,pixel_values:d}),h=this.processor.feature_extractor.post_process_object_detection(f,r,c,!0)[0],p=h.boxes.map((e,r)=>({score:h.scores[r],label:t[h.classes[r]],box:ex(e,!o)})).sort((e,t)=>t.score-e.score);null!==n&&(p=p.slice(0,n)),u.push(p)}return i?u:u[0]}}class eH extends e_{constructor(e){super(e)}async _call(e,t,r={}){let n=(await eb(e))[0],{pixel_values:o}=await this.processor(n),i=`<s_docvqa><s_question>${t}</s_question><s_answer>`,a=this.tokenizer(i,{add_special_tokens:!1,padding:!0,truncation:!0}).input_ids,s=await this.model.generate(o,{...r,decoder_input_ids:a,max_length:this.model.config.decoder.max_position_embeddings}),l=this.tokenizer.batch_decode(s)[0].match(/<s_answer>(.*?)<\/s_answer>/),u=null;return l&&l.length>=2&&(u=l[1].trim()),[{answer:u}]}}class eV extends e_{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.$Sz.from_pretrained(this.DEFAULT_VOCODER_ID,{quantized:!1})),("string"==typeof t||t instanceof URL)&&(t=new Float32Array(await (await fetch(t)).arrayBuffer())),t instanceof Float32Array)t=new d.es("float32",t,[1,t.length]);else if(!(t instanceof d.es))throw Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");let{input_ids:r}=this.tokenizer(e,{padding:!0,truncation:!0}),{waveform:n}=await this.model.generate_speech(r,t,{vocoder:this.vocoder}),o=this.processor.feature_extractor.config.sampling_rate;return{audio:n.data,sampling_rate:o}}}class eZ extends e_{constructor(e){super(e)}async _call(e){let t=await eb(e),r=await this.processor(t),n=await this.model(r),o=[];for(let e of n.reconstruction){let t=e.squeeze().clamp_(0,1).mul_(255).round_().to("uint8");o.push(v.fromTensor(t))}return o.length>1?o:o[0]}}class eq extends e_{constructor(e){super(e)}async _call(e){let t=await eb(e),r=await this.processor(t),{predicted_depth:n}=await this.model(r),o=[];for(let e=0;e<t.length;++e){let r=(0,d.sX)(n[e],t[e].size.reverse(),"bilinear",!1),i=r.mul_(255/(0,c.Fp)(r.data)[0]).to("uint8");o.push({predicted_depth:n[e],depth:v.fromTensor(i)})}return o.length>1?o:o[0]}}let eX=Object.freeze({"text-classification":{tokenizer:a.t2,pipeline:eS,model:s.o$X,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:a.t2,pipeline:eE,model:s.OjJ,default:{model:"Xenova/bert-base-multilingual-cased-ner-hrl"},type:"text"},"question-answering":{tokenizer:a.t2,pipeline:ek,model:s.wiU,default:{model:"Xenova/distilbert-base-cased-distilled-squad"},type:"text"},"fill-mask":{tokenizer:a.t2,pipeline:eC,model:s.t78,default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:a.t2,pipeline:eA,model:s.Kf0,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:a.t2,pipeline:eT,model:s.Kf0,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:a.t2,pipeline:eR,model:s.Kf0,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:a.t2,pipeline:eM,model:s.Hqk,default:{model:"Xenova/gpt2"},type:"text"},"zero-shot-classification":{tokenizer:a.t2,pipeline:eI,model:s.o$X,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"audio-classification":{pipeline:eP,model:s.K2m,processor:ev,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"zero-shot-audio-classification":{tokenizer:a.t2,pipeline:eB,model:s.$Sz,processor:ev,default:{model:"Xenova/clap-htsat-unfused"},type:"multimodal"},"automatic-speech-recognition":{tokenizer:a.t2,pipeline:ej,model:[s.hZO,s.ENH],processor:ev,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"text-to-audio":{tokenizer:a.t2,pipeline:eV,model:[s.z6E,s.lbf],processor:[ev,null],default:{model:"Xenova/speecht5_tts"},type:"text"},"image-to-text":{tokenizer:a.t2,pipeline:eN,model:s.tLj,processor:ev,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:e$,model:s.En$,processor:ev,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:eU,model:[s.U$$,s.$Bv],processor:ev,default:{model:"Xenova/detr-resnet-50-panoptic"},type:"multimodal"},"zero-shot-image-classification":{tokenizer:a.t2,pipeline:eF,model:s.$Sz,processor:ev,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:eD,model:s.Zn,processor:ev,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"zero-shot-object-detection":{tokenizer:a.t2,pipeline:eW,model:s.LdW,processor:ev,default:{model:"Xenova/owlvit-base-patch32"},type:"multimodal"},"document-question-answering":{tokenizer:a.t2,pipeline:eH,model:s.DcG,processor:ev,default:{model:"Xenova/donut-base-finetuned-docvqa"},type:"multimodal"},"image-to-image":{pipeline:eZ,model:s.S2d,processor:ev,default:{model:"Xenova/swin2SR-classical-sr-x2-64"},type:"image"},"depth-estimation":{pipeline:eq,model:s.hY6,processor:ev,default:{model:"Xenova/dpt-large"},type:"image"},"feature-extraction":{tokenizer:a.t2,pipeline:ez,model:s.$Sz,default:{model:"Xenova/all-MiniLM-L6-v2"},type:"text"},"image-feature-extraction":{processor:ev,pipeline:eL,model:[s.IFL,s.$Sz],default:{model:"Xenova/vit-base-patch16-224-in21k"},type:"image"}}),eG=Object.freeze({"sentiment-analysis":"text-classification",ner:"token-classification",asr:"automatic-speech-recognition","text-to-speech":"text-to-audio",embeddings:"feature-extraction"});async function eK(e,t=null,{quantized:r=!0,progress_callback:n=null,config:o=null,cache_dir:i=null,local_files_only:a=!1,revision:s="main"}={}){let u=eX[(e=eG[e]??e).split("_",1)[0]];if(!u)throw Error(`Unsupported pipeline: ${e}. 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