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If using the feature extractor directly, remember to use \`read_audio(url, sampling_rate)\` to obtain the raw audio data of the file/url.`)}function T(e,t,r=0,n=null){let i=e/t,o=(0,h.eT)(i)*t;return null!==n&&o>n&&(o=Math.floor(i)*t),oi?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,h.VV)(r.data)[0],i=(0,h.Fp)(r.data)[0]-n;if(0===i)return e;let o=t/255,a=r.width,s=r.height,l=0,u=0;for(let e=0;ethis.preprocess(e)));return{pixel_values:(0,c.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 $ extends j{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;eu[r]&&(u[r]=t[r],l.data[r]=e)}let h=Array(o.dims[0]),f=l.data;for(let e=0;evoid 0!==e);i.push({segmentation:l,labels:d})}return i}}class U extends j{}class L extends U{}class R extends j{}class F extends j{}class N extends j{}class P extends j{}class V extends j{}class D extends j{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 q extends D{}class H extends j{}class W extends j{}class G extends j{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 j{}class K extends X{}class Z extends j{post_process_object_detection(...e){return I(...e)}}class J extends Z{}class Y extends j{}class Q extends j{}class ee extends j{pad_image(e,t,r,n={}){let[i,o,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 et extends ee{}class er extends j{async _call(e){let t=await super._call(e),r=[t.pixel_values.dims[0],64,64],n=new c.es("int64",new BigInt64Array(r.reduce((e,t)=>e*t)).fill(1n),r);return{...t,pixel_mask:n}}post_process_object_detection(...e){return I(...e)}remove_low_and_no_objects(e,t,r,n){let i=[],o=[],a=[];for(let s=0;sr&&(i.push(u),o.push(f),a.push(c))}return[i,o,a]}check_segment_validity(e,t,r,n=.5,i=.8){let o=[],a=0,s=0;for(let i=0;i=n&&++s;let l=a>0&&s>0;return l&&(l=a/s>i),[l,o]}compute_segments(e,t,r,n,i,o=null,a=null){let[s,l]=a??e[0].dims,u=new c.es("int32",new Int32Array(s*l),[s,l]),h=[];if(null!==a)for(let t=0;td[t]&&(f[t]=r,d[t]=e[r].data[t])}let p=0;for(let o=0;oe!==t.dims[r]))throw Error(`The first ${r.length} dimensions of 'input_points' and 'input_labels' must be the same.`);return new c.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:i=!0,pad_size:o=null}={}){let a=[],s=[(o=o??this.pad_size).height,o.width];for(let o=0;on&&(e[t]=1);r=new c.es("bool",e,r.dims)}f.push(r)}a.push((0,c.kn)(f))}return a}}class eo extends j{pad_image(e,t,r,n={}){let[i,o,a]=t;return super.pad_image(e,t,{width:o+(r-o%r)%r,height:i+(r-i%r)%r},{mode:"symmetric",center:!1,constant_values:-1,...n})}}class ea extends j{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,c.kn)(r.map((e,t)=>(0,c.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 es extends B{constructor(e){super(e),this.config.mel_filters??=A(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,8e3,this.config.sampling_rate,"slaney","slaney"),this.window=z(this.config.n_fft,"hann")}_extract_fbank_features(e){let{data:t,dims:r}=S(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,h.Fp)(t)[0];for(let e=0;ethis.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 c.es("float32",r,[1,...n])}}}class el extends B{_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){M(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 c.es("float32",t,r),attention_mask:new c.es("int64",new BigInt64Array(t.length).fill(1n),r)}}}class eu extends B{constructor(e){super(e);let t=this.config.sampling_rate,r=A(256,this.config.num_mel_bins,20,Math.floor(t/2),t,null,"kaldi",!0);for(let e=0;e32768*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 o;M(e,"SeamlessM4TFeatureExtractor");let a=this._extract_fbank_features(e,this.config.max_length);if(n){let[e,t]=a.dims;for(let r=0;r0){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]},i&&(o=new c.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 (${u}).`);let h=new c.es("float32",a.data,a.dims).view(1,Math.floor(s/u),l*u),f={input_features:h};if(i){let e=h.dims[1],t=new c.es("int64",new BigInt64Array(e),[1,e]);if(o)for(let e=1,r=0;e0){if("rand_trunc"===r){o=!0;let r=Math.floor(Math.random()*(a+1));e=e.subarray(r,r+t),(i=this._extract_fbank_features(e,this.mel_filters_slaney,this.config.nb_max_samples)).dims=[1,...i.dims]}else throw Error(`Truncation strategy "${r}" not implemented`)}else{if(a<0){let r=new Float64Array(t);if(r.set(e),"repeat"===n)for(let n=e.length;nw.read(e)))}async function eb(e,t){return Array.isArray(e)||(e=[e]),await Promise.all(e.map(e=>"string"==typeof e||e instanceof URL?_(e,t):e instanceof Float64Array?new Float32Array(e):e))}function ex(e,t){t&&(e=e.map(e=>0|e));let[r,n,i,o]=e;return{xmin:r,ymin:n,xmax:i,ymax:o}}class eE 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 ek extends eE{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,h.XA)(e.data),o=this.model.config.id2label,a=[];for(let e of n.logits){let r=i(e),n=(0,h.em)(r,t).map(e=>({label:o[e[0]],score:e[1]}));1===t?a.push(...n):a.push(n)}return Array.isArray(e)||1===t?a:a[0]}}class eA extends eE{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,o=this.model.config.id2label,a=[];for(let e=0;e[e,t]).filter(e=>e[1]>a),u=Array.from((0,h.XA)(i.end_logits[e].data)).map((e,t)=>[e,t]).filter(e=>e[1]>a),c=(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{let t=[...o];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)?i:i[0]}}class ez extends eE{_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,o={padding:!0,truncation:!0};r=this instanceof eM&&"_build_translation_inputs"in i?i._build_translation_inputs(e,o,t).input_ids:i(e,o).input_ids;let a=await this.model.generate(r,t);return i.batch_decode(a,{skip_special_tokens:!0}).map(e=>({[this._key]:e}))}}class eI extends ez{_key="summary_text";constructor(e){super(e)}}class eM extends ez{_key="translation_text";constructor(e){super(e)}}function eT(e){return Array.isArray(e)&&e.every(e=>"role"in e&&"content"in e)}class eO extends eE{constructor(e){super(e)}async _call(e,t={}){let r,n,i=!1,o=!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(eT(e))e=[e];else if(Array.isArray(e)&&e.every(eT))i=!0;else throw Error("Input must be a string, an array of strings, a Chat, or an array of Chats");o=!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=!o&&(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}),h=await this.model.generate(l,t,null,{inputs_attention_mask:u}),c=this.tokenizer.batch_decode(h,{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[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 o=t.map(e=>r.replace("{}",e)),a=n||1===t.length,s=[];for(let r of e){let e=[];for(let t of o){let n=this.tokenizer(r,{text_pair:t,padding:!0,truncation:!0}),i=await this.model(n);a?e.push([i.logits.data[this.contradiction_id],i.logits.data[this.entailment_id]]):e.push(i.logits.data[this.entailment_id])}let n=(a?e.map(e=>(0,h.XA)(e)[1]):(0,h.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 i?s:s[0]}}class ej extends eE{constructor(e){super(e)}async _call(e,{pooling:t="none",normalize:r=!1,quantize:n=!1,precision:i="binary"}={}){let o=this.tokenizer(e,{padding:!0,truncation:!0}),a=await this.model(o),s=a.last_hidden_state??a.logits??a.token_embeddings;if("none"===t);else if("mean"===t)s=(0,c.v6)(s,o.attention_mask);else if("cls"===t)s=s.slice(null,0);else throw Error(`Pooling method '${t}' not supported.`);return r&&(s=s.normalize(2,-1)),n&&(s=(0,c.e)(s,i)),s}}class e$ extends eE{constructor(e){super(e)}async _call(e,{pool:t=null}={}){let r;let n=await ev(e),{pixel_values:i}=await this.processor(n),o=await this.model({pixel_values:i});if(t){if(!("pooler_output"in o))throw Error("No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.");r=o.pooler_output}else r=o.last_hidden_state??o.logits??o.image_embeds;return r}}class eU extends eE{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 eb(e,n),o=this.model.config.id2label,a=[];for(let e of i){let r=await this.processor(e),n=(await this.model(r)).logits[0],i=(0,h.em)((0,h.XA)(n.data),t).map(e=>({label:o[e[0]],score:e[1]}));1===t?a.push(...i):a.push(i)}return r&&1!==t?a[0]:a}}class eL extends eE{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)),o=this.tokenizer(i,{padding:!0,truncation:!0}),a=this.processor.feature_extractor.config.sampling_rate,s=await eb(e,a),l=[];for(let e of s){let r=await this.processor(e),n=await this.model({...o,...r}),i=(0,h.XA)(n.logits_per_audio.data);l.push([...i].map((e,r)=>({score:e,label:t[r]})))}return n?l[0]:l}}class eR extends eE{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 eb(e,n),o=[];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,h.Fp)(e.data)[1]);let i=this.tokenizer.decode(n);o.push({text:i})}return r?o[0]:o}async _call_whisper(e,t={}){let r=t.return_timestamps??!1,n=t.chunk_length_s??0,i=t.chunk_callback??null,o=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 c=!Array.isArray(e);c&&(e=[e]);let f=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,d=this.processor.feature_extractor.config.hop_length,p=this.processor.feature_extractor.config.sampling_rate,m=await eb(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,i=t-2*r,o=0;for(;o=e.length;s.push({stride:[n.length,l?0:r,u?0:r],input_features:a.input_features,is_last:u}),o+=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]/d);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,h.NM)(e,2))):e.tokens=n[0],e.stride=e.stride.map(e=>e/p),null!==i&&i(e)}let[l,u]=this.tokenizer._decode_asr(s,{time_precision:f,return_timestamps:r,force_full_sequences:o});g.push({text:l,...u})}return c?g[0]:g}}class eF extends eE{constructor(e){super(e)}async _call(e,t={}){let r=Array.isArray(e),n=await ev(e),{pixel_values:i}=await this.processor(n),o=[];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()}));o.push(n)}return r?o:o[0]}}class eN extends eE{constructor(e){super(e)}async _call(e,{topk:t=1}={}){let r=Array.isArray(e),n=await ev(e),{pixel_values:i}=await this.processor(n),o=await this.model({pixel_values:i}),a=this.model.config.id2label,s=[];for(let e of o.logits){let r=(0,h.em)((0,h.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 eP extends eE{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:o=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 ev(e),l=s.map(e=>[e.height,e.width]),{pixel_values:u,pixel_mask:h}=await this.processor(s),c=await 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