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0!==e.Regex?RegExp(t?e.Regex:`(${e.Regex})`,"gu"):void 0!==e.String?e.String:(console.warn("Unknown pattern type:",e),null)}function ey(e){return new Map(Object.entries(e))}function ew(e){return e.replace(/ \./g,".").replace(/ \?/g,"?").replace(/ \!/g,"!").replace(/ ,/g,",").replace(/ \' /g,"'").replace(/ n\'t/g,"n't").replace(/ \'m/g,"'m").replace(/ \'s/g,"'s").replace(/ \'ve/g,"'ve").replace(/ \'re/g,"'re")}function eb(e){return e.replace(/[\u0300-\u036f]/g,"")}let ex="\\p{P}\\u0021-\\u002F\\u003A-\\u0040\\u005B-\\u0060\\u007B-\\u007E";class ek extends h{constructor(e){super(),this.config=e,this.vocab=[],this.tokens_to_ids=new Map,this.unk_token_id=void 0,this.unk_token=void 0,this.end_of_word_suffix=void 0,this.fuse_unk=this.config.fuse_unk??!1}static fromConfig(e,...t){switch(e.type){case"WordPiece":return new ev(e);case"Unigram":return new eA(e,...t);case"BPE":return new eT(e);default:if(e.vocab)return new eS(e,...t);throw Error(`Unknown TokenizerModel type: ${e.type}`)}}_call(e){return this.encode(e)}encode(e){throw Error("encode should be implemented in subclass.")}convert_tokens_to_ids(e){let t=e.map(e=>this.tokens_to_ids.get(e)??this.unk_token_id);return this.fuse_unk&&(t=function(e,t){let r=[],s=0;for(;sthis.vocab[e]??this.unk_token)}}class ev extends ek{constructor(e){for(let[t,r]of(super(e),this.tokens_to_ids=ey(e.vocab),this.unk_token_id=this.tokens_to_ids.get(e.unk_token),this.unk_token=e.unk_token,this.vocab=Array(this.tokens_to_ids.size),this.tokens_to_ids))this.vocab[r]=t}encode(e){let t=[];for(let r of e){let e=[...r],s=!1,n=0,i=[];for(;n0&&(s=this.config.continuing_subword_prefix+s),this.tokens_to_ids.has(s)){r=s;break}--t}if(null===r){s=!0;break}i.push(r),n=t}s?t.push(this.unk_token):t.push(...i)}return t}}class eA extends ek{constructor(e,t){super(e);let r=e.vocab.length;this.vocab=Array(r),this.scores=Array(r);for(let t=0;t[e,t])),this.bosToken=" 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Map(e.merges.map((e,t)=>[e,t])),this.merges=e.merges.map(e=>e.split(this.BPE_SPLIT_TOKEN)),this.end_of_word_suffix=e.end_of_word_suffix,this.byte_fallback=this.config.byte_fallback??!1,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.cache=new Map}bpe(e){if(0===e.length)return[];let t=this.cache.get(e);if(void 0!==t)return t;let r=Array.from(e);this.end_of_word_suffix&&(r[r.length-1]+=this.end_of_word_suffix);let s=[];if(r.length>1){let e=new eu((e,t)=>e.score`<0x${e.toString(16).toUpperCase().padStart(2,"0")}>`)):t.push(this.unk_token);return t}}class eS extends ek{constructor(e,t){for(let[r,s]of(super(e),this.tokens_to_ids=ey(t.target_lang?e.vocab[t.target_lang]:e.vocab),this.bos_token=t.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=t.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=t.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=t.unk_token,this.unk_token_id=this.tokens_to_ids.get(this.unk_token),this.vocab=Array(this.tokens_to_ids.size),this.tokens_to_ids))this.vocab[s]=r}encode(e){return e}}class eM extends h{constructor(e){super(),this.config=e}static fromConfig(e){if(null===e)return null;switch(e.type){case"BertNormalizer":return new ej(e);case"Precompiled":return new e9(e);case"Sequence":return new eR(e);case"Replace":return new eI(e);case"NFC":return new eC(e);case"NFKD":return new eB(e);case"Strip":return new eO(e);case"StripAccents":return new eP(e);case"Lowercase":return new eU(e);case"Prepend":return new eL(e);default:throw Error(`Unknown Normalizer type: ${e.type}`)}}normalize(e){throw Error("normalize should be implemented in subclass.")}_call(e){return this.normalize(e)}}class eI extends eM{normalize(e){let t=eg(this.config.pattern);return null===t?e:e=e.replaceAll(t,this.config.content)}}class eC extends eM{normalize(e){return e=e.normalize("NFC")}}class eB extends eM{normalize(e){return e=e.normalize("NFKD")}}class eO extends eM{normalize(e){return this.config.strip_left&&this.config.strip_right?e=e.trim():(this.config.strip_left&&(e=e.trimStart()),this.config.strip_right&&(e=e.trimEnd())),e}}class eP extends eM{normalize(e){return e=eb(e)}}class eU extends eM{normalize(e){return e=e.toLowerCase()}}class eL extends eM{normalize(e){return e=this.config.prepend+e}}class eR extends eM{constructor(e){super(e),this.normalizers=e.normalizers.map(e=>eM.fromConfig(e))}normalize(e){return this.normalizers.reduce((e,t)=>t.normalize(e),e)}}class ej extends eM{_tokenize_chinese_chars(e){let t=[];for(let r=0;r=19968&&e<=40959||e>=13312&&e<=19903||e>=131072&&e<=173791||e>=173824&&e<=177983||e>=177984&&e<=178207||e>=178208&&e<=183983||e>=63744&&e<=64255||e>=194560&&e<=195103}stripAccents(e){return e.normalize("NFD").replace(/[\u0300-\u036f]/g,"")}normalize(e){return this.config.handle_chinese_chars&&(e=this._tokenize_chinese_chars(e)),this.config.lowercase?(e=e.toLowerCase(),!1!==this.config.strip_accents&&(e=this.stripAccents(e))):this.config.strip_accents&&(e=this.stripAccents(e)),e}}class e$ extends h{static fromConfig(e){if(null===e)return null;switch(e.type){case"BertPreTokenizer":return new eN(e);case"Sequence":return new e7(e);case"WhitespaceSplit":return new te(e);case"Metaspace":return new e8(e);case"ByteLevel":return new eF(e);case"Split":return new eD(e);case"Punctuation":return new eq(e);case"Digits":return new eG(e);default:throw Error(`Unknown PreTokenizer type: ${e.type}`)}}pre_tokenize_text(e){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize(e){return(Array.isArray(e)?e.map(e=>this.pre_tokenize_text(e)):this.pre_tokenize_text(e)).flat()}_call(e){return this.pre_tokenize(e)}}class eN extends e${constructor(e){super(),this.pattern=RegExp(`[^\\s${ex}]+|[${ex}]`,"gu")}pre_tokenize_text(e){return e.trim().match(this.pattern)||[]}}class eF extends e${constructor(e){super(),this.config=e,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=/'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+/gu,this.byte_encoder=eE,this.text_encoder=new TextEncoder}pre_tokenize_text(e){return(this.use_regex?e.match(this.pattern)||[]:[e]).map(e=>(this.add_prefix_space&&!e.startsWith(" ")&&(e=" "+e),e=Array.from(this.text_encoder.encode(e),e=>this.byte_encoder[e]).join("")))}}class eD extends e${constructor(e){super(),this.config=e,this.pattern=eg(this.config.pattern,this.config.invert)}pre_tokenize_text(e){return null===this.pattern?[]:this.config.invert?e.match(this.pattern)||[]:e.split(this.pattern).filter(e=>e)}}class eq extends e${constructor(e){super(),this.config=e,this.pattern=RegExp(`[^${ex}]+|[${ex}]+`,"gu")}pre_tokenize_text(e){return e.match(this.pattern)||[]}}class eG extends e${constructor(e){super(),this.config=e;let t=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=RegExp(t,"gu")}pre_tokenize_text(e){return e.match(this.pattern)||[]}}class eW extends h{constructor(e){super(),this.config=e}static fromConfig(e){if(null===e)return null;switch(e.type){case"TemplateProcessing":return new eX(e);case"ByteLevel":return new eV(e);case"RobertaProcessing":return new eK(e);case"BertProcessing":return new eH(e);default:throw Error(`Unknown PostProcessor type: ${e.type}`)}}post_process(e,...t){throw Error("post_process should be implemented in subclass.")}_call(e,...t){return this.post_process(e,...t)}}class eH extends eW{constructor(e){super(e),this.cls=e.cls[0],this.sep=e.sep[0]}post_process(e,t=null){return e=f([this.cls],e,[this.sep]),null!==t&&(e=f(e,[this.sep],t,[this.sep])),e}}class eK extends eH{}class eX extends eW{constructor(e){super(e),this.single=e.single,this.pair=e.pair}post_process(e,t=null){let r=null===t?this.single:this.pair,s=[];for(let n of r)"SpecialToken"in n?s.push(n.SpecialToken.id):"Sequence"in n&&("A"===n.Sequence.id?s=f(s,e):"B"===n.Sequence.id&&(s=f(s,t)));return s}}class eV extends eW{post_process(e){return e}}class eY extends h{constructor(e){super(),this.config=e,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=e.trim_offsets}static fromConfig(e){switch(e.type){case"WordPiece":return new e1(e);case"Metaspace":return new e5(e);case"ByteLevel":return new e2(e);case"Replace":return new eJ(e);case"ByteFallback":return new eZ(e);case"Fuse":return new eQ(e);case"Strip":return new e0(e);case"Sequence":return new e6(e);case"CTC":return new e3(e);case"BPEDecoder":return new e4(e);default:throw Error(`Unknown Decoder type: ${e.type}`)}}_call(e){return this.decode(e)}decode(e){return this.decode_chain(e).join("")}decode_chain(e){throw Error("`decode_chain` should be implemented in subclass.")}}class eJ extends eY{decode_chain(e){let t=eg(this.config.pattern);return null===t?e:e.map(e=>e.replaceAll(t,this.config.content))}}class eZ extends eY{constructor(e){super(e),this.text_decoder=new TextDecoder}decode_chain(e){let t=[],r=[];for(let s of e){let e=null;if(6===s.length&&s.startsWith("<0x")&&s.endsWith(">")){let t=parseInt(s.slice(3,5),16);isNaN(t)||(e=t)}if(null!==e)r.push(e);else{if(r.length>0){let e=this.text_decoder.decode(Uint8Array.from(r));t.push(e),r=[]}t.push(s)}}if(r.length>0){let e=this.text_decoder.decode(Uint8Array.from(r));t.push(e),r=[]}return t}}class eQ extends eY{decode_chain(e){return[e.join("")]}}class e0 extends eY{constructor(e){super(e),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(e){return e.map(e=>{let t=0;for(let r=0;r(0!==t&&(e=e.startsWith(this.config.prefix)?e.replace(this.config.prefix,""):" "+e),this.cleanup&&(e=ew(e)),e))}}class e2 extends eY{constructor(e){super(e),this.byte_decoder=ez,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(e){let t=e.join(""),r=new Uint8Array([...t].map(e=>this.byte_decoder[e]));return this.text_decoder.decode(r)}decode_chain(e){let t=[],r=[];for(let s of e)this.added_tokens.includes(s)?(r.length>0&&(t.push(this.convert_tokens_to_string(r)),r=[]),t.push(s)):r.push(s);return r.length>0&&t.push(this.convert_tokens_to_string(r)),t}}class e3 extends eY{constructor(e){super(e),this.pad_token=this.config.pad_token,this.word_delimiter_token=this.config.word_delimiter_token,this.cleanup=this.config.cleanup}convert_tokens_to_string(e){if(0===e.length)return"";let t=[e[0]];for(let r=1;re!==this.pad_token).join("");return this.cleanup&&(r=ew(r).replaceAll(this.word_delimiter_token," ").trim()),r}decode_chain(e){return[this.convert_tokens_to_string(e)]}}class e6 extends eY{constructor(e){super(e),this.decoders=e.decoders.map(e=>eY.fromConfig(e))}decode_chain(e){return this.decoders.reduce((e,t)=>t.decode_chain(e),e)}}class e4 extends eY{constructor(e){super(e),this.suffix=this.config.suffix}decode_chain(e){return e.map((t,r)=>t.replaceAll(this.suffix,r===e.length-1?"":" "))}}class e8 extends e${constructor(e){super(),this.addPrefixSpace=e.add_prefix_space,this.replacement=e.replacement,this.strRep=e.str_rep||this.replacement}pre_tokenize(e){"string"==typeof e&&(e=e.trimStart().split(/\s+/));let t=[];for(let r of e){let e=r.replaceAll(" ",this.strRep);this.addPrefixSpace&&!e.startsWith(this.replacement)&&(e=this.strRep+e),t.push(e)}return t}}class e5 extends eY{constructor(e){super(e),this.addPrefixSpace=e.add_prefix_space,this.replacement=e.replacement}decode_chain(e){let t=[];for(let r=0;re.normalize("NFKC")).join("～")}else e=e.normalize("NFKC");return e}}class e7 extends e${constructor(e){super(),this.tokenizers=e.pretokenizers.map(e=>e$.fromConfig(e))}pre_tokenize_text(e){return"string"==typeof e&&(e=[e]),this.tokenizers.reduce((e,t)=>t.pre_tokenize(e),e)}}class te extends e${constructor(e){super()}pre_tokenize_text(e){return e.match(/\S+/g)||[]}}class tt extends h{constructor(e,t){for(let r of(super(),this.normalizer=eM.fromConfig(e.normalizer),this.pre_tokenizer=e$.fromConfig(e.pre_tokenizer),this.model=ek.fromConfig(e.model,t),this.post_processor=eW.fromConfig(e.post_processor),this.decoder=eY.fromConfig(e.decoder),this.decoder.end_of_word_suffix=this.model.end_of_word_suffix,this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[],e.added_tokens)){let e=r.id,t=r.content;this.added_tokens.push(t),this.model.tokens_to_ids.set(t,e),this.model.vocab[e]=t,r.special&&(this.special_tokens.push(t),this.all_special_ids.push(e))}this.special_tokens.push(...t.additional_special_tokens??[]),this.special_tokens=[...new Set(this.special_tokens)],this.decoder.added_tokens=this.added_tokens,this.added_tokens_regex=this.added_tokens.length>0?RegExp("("+this.added_tokens.map(l).join("|")+")"):null,this.mask_token=this.getToken(t,"mask_token"),this.mask_token_id=this.model.tokens_to_ids.get(this.mask_token),this.pad_token=this.getToken(t,"pad_token","eos_token"),this.pad_token_id=this.model.tokens_to_ids.get(this.pad_token),this.sep_token=this.getToken(t,"sep_token"),this.sep_token_id=this.model.tokens_to_ids.get(this.sep_token),this.model_max_length=t.model_max_length,this.remove_space=t.remove_space,this.clean_up_tokenization_spaces=t.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=t.do_lowercase_and_remove_accent??!1,this.padding_side="right"}getToken(e,...t){for(let r of t){let t=e[r];if(t){if("object"!=typeof t)return t;if("AddedToken"===t.__type)return t.content;throw Error(`Unknown token: ${t}`)}}return null}static async from_pretrained(e,{progress_callback:t=null,config:r=null,cache_dir:s=null,local_files_only:n=!1,revision:i="main"}={}){let o=await em(e,{progress_callback:t,config:r,cache_dir:s,local_files_only:n,revision:i});return new this(...o)}prepare_model_inputs(e){return e}_call(e,{text_pair:t=null,padding:r=!1,truncation:s=null,max_length:n=null,return_tensor:i=!0}={}){let o;if(Array.isArray(e)){if(0===e.length)throw Error("text array must be non-empty");if(null!==t){if(Array.isArray(t)){if(e.length!==t.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");o=e.map((e,r)=>this.encode(e,t[r]))}else o=e.map(e=>this.encode(e))}else{if(null===e)throw Error("text may not be null");if(Array.isArray(t))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");o=[this.encode(e,t)]}let a=Z(o.map(e=>e.length))[0];null===n&&(n=a),n=Math.min(n,this.model_max_length);let l=[];if(r||s)for(let e=0;en)s&&(o[e]=o[e].slice(0,n)),l.push(Array(o[e].length).fill(1));else if(r){let t=n-o[e].length;"right"===this.padding_side?(l.push(Array(o[e].length).fill(1).concat(Array(t).fill(0))),o[e].push(...Array(t).fill(this.pad_token_id))):(l.push(Array(t).fill(0).concat(Array(o[e].length).fill(1))),o[e].unshift(...Array(t).fill(this.pad_token_id)))}else l.push(Array(o[e].length).fill(1))}else l=o.map(e=>Array(e.length).fill(1));if(i){if(!(r&&s)&&o.some(e=>e.length!==o[0].length))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=true' and 'truncation=true' to have batched tensors with the same length.");let e=[o.length,o[0].length];o=new es("int64",BigInt64Array.from(o.flat().map(BigInt)),e),l=new es("int64",BigInt64Array.from(l.flat().map(BigInt)),e)}else Array.isArray(e)||(o=o[0],l=l[0]);let h={input_ids:o,attention_mask:l};return this.prepare_model_inputs(h)}_encode_text(e){if(null===e)return null;let t=this.added_tokens_regex?e.split(this.added_tokens_regex).filter(e=>e):[e];return t.map(e=>{if(this.added_tokens.includes(e))return e;{!0===this.remove_space&&(e=e.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(e=eb(e.toLowerCase())),null!==this.normalizer&&(e=this.normalizer(e));let t=null!==this.pre_tokenizer?this.pre_tokenizer(e):[e];return this.model(t)}}).flat()}encode(e,t=null){let r=this._encode_text(e),s=this._encode_text(t),n=null!==this.post_processor?this.post_processor(r,s):f(r??[],s??[]);return this.model.convert_tokens_to_ids(n)}batch_decode(e,t={}){return e.map(e=>this.decode(e,t))}decode(e,t={}){if(!Array.isArray(e)||0===e.length||!u(e[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(e,t)}decode_single(e,{skip_special_tokens:t=!1,clean_up_tokenization_spaces:r=null}){let s=this.model.convert_ids_to_tokens(e);t&&(s=s.filter(e=>!this.special_tokens.includes(e)));let n=this.decoder(s);return this.decoder.end_of_word_suffix&&(n=n.replaceAll(this.decoder.end_of_word_suffix," "),t&&(n=n.trim())),(r??this.clean_up_tokenization_spaces)&&(n=ew(n)),n}}function tr(e){if(e.input_ids instanceof es)e.token_type_ids=new es("int64",new BigInt64Array(e.input_ids.data.length),e.input_ids.dims);else if(Array.isArray(e.input_ids))Array.isArray(e.input_ids[0])?e.token_type_ids=e.input_ids.map(e=>Array(e.length).fill(0)):e.token_type_ids=Array(e.input_ids.length).fill(0);else throw Error("Input ids must be a Tensor or an Array");return e}class ts extends tt{prepare_model_inputs(e){return tr(e)}}class tn extends tt{prepare_model_inputs(e){return tr(e)}}class ti extends tt{prepare_model_inputs(e){return tr(e)}}class to extends tt{prepare_model_inputs(e){return tr(e)}}class ta extends tt{prepare_model_inputs(e){return tr(e)}}class tl extends tt{prepare_model_inputs(e){return tr(e)}}class th extends tt{prepare_model_inputs(e){return tr(e)}}class tc extends tt{}class tu extends tt{}class td extends tt{constructor(e,t){super(e,t),console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}prepare_model_inputs(e){return tr(e)}}class tf extends tt{}class tp extends tt{}class t_ extends tt{}class tm extends tt{constructor(e,t){super(e,t),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter(e=>this.languageRegex.test(e)),this.lang_to_token=e=>e}_build_translation_inputs(e,t,r){return tz(this,e,t,r)}}class tg extends tm{}class ty extends tt{}class tw extends tt{constructor(e,t){let r=".,!?…。，、।۔،",s=e.pre_tokenizer?.pretokenizers[0]?.pattern;s&&s.Regex===` ?[^(\\s|[${r}])]+`&&(s.Regex=` ?[^\\s${r}]+`),super(e,t)}}class tb extends tt{}class tx extends tt{}class tk extends tt{}class tv extends tt{}class tA extends tt{prepare_model_inputs(e){return tr(e)}}class tE extends tt{}function tz(e,t,r,s){if(!("language_codes"in e)||!Array.isArray(e.language_codes))throw Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in e)||!(e.languageRegex instanceof RegExp))throw Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in e)||"function"!=typeof e.lang_to_token)throw Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");let n=s.src_lang,i=s.tgt_lang;if(!e.language_codes.includes(i))throw Error(`Target language code "${i}" is not valid. Must be one of: {${e.language_codes.join(", ")}}`);if(void 0!==n){if(!e.language_codes.includes(n))throw Error(`Source language code "${n}" is not valid. Must be one of: {${e.language_codes.join(", ")}}`);for(let t of e.post_processor.config.single)if("SpecialToken"in t&&e.languageRegex.test(t.SpecialToken.id)){t.SpecialToken.id=e.lang_to_token(n);break}}return s.forced_bos_token_id=e.model.convert_tokens_to_ids([e.lang_to_token(i)])[0],e._call(t,r)}class tT extends tt{constructor(e,t){super(e,t),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(e=>this.languageRegex.test(e)),this.lang_to_token=e=>e}_build_translation_inputs(e,t,r){return tz(this,e,t,r)}}class tS extends tt{constructor(e,t){super(e,t),this.languageRegex=/^__[a-z]{2,3}__$/,this.language_codes=this.special_tokens.filter(e=>this.languageRegex.test(e)).map(e=>e.slice(2,-2)),this.lang_to_token=e=>`__${e}__`}_build_translation_inputs(e,t,r){return tz(this,e,t,r)}}let tM=[["en","english"],["zh","chinese"],["de","german"],["es","spanish"],["ru","russian"],["ko","korean"],["fr","french"],["ja","japanese"],["pt","portuguese"],["tr","turkish"],["pl","polish"],["ca","catalan"],["nl","dutch"],["ar","arabic"],["sv","swedish"],["it","italian"],["id","indonesian"],["hi","hindi"],["fi","finnish"],["vi","vietnamese"],["he","hebrew"],["uk","ukrainian"],["el","greek"],["ms","malay"],["cs","czech"],["ro","romanian"],["da","danish"],["hu","hungarian"],["ta","tamil"],["no","norwegian"],["th","thai"],["ur","urdu"],["hr","croatian"],["bg","bulgarian"],["lt","lithuanian"],["la","latin"],["mi","maori"],["ml","malayalam"],["cy","welsh"],["sk","slovak"],["te","telugu"],["fa","persian"],["lv","latvian"],["bn","bengali"],["sr","serbian"],["az","azerbaijani"],["sl","slovenian"],["kn","kannada"],["et","estonian"],["mk","macedonian"],["br","breton"],["eu","basque"],["is","icelandic"],["hy","armenian"],["ne","nepali"],["mn","mongolian"],["bs","bosnian"],["kk","kazakh"],["sq","albanian"],["sw","swahili"],["gl","galician"],["mr","marathi"],["pa","punjabi"],["si","sinhala"],["km","khmer"],["sn","shona"],["yo","yoruba"],["so","somali"],["af","afrikaans"],["oc","occitan"],["ka","georgian"],["be","belarusian"],["tg","tajik"],["sd","sindhi"],["gu","gujarati"],["am","amharic"],["yi","yiddish"],["lo","lao"],["uz","uzbek"],["fo","faroese"],["ht","haitian creole"],["ps","pashto"],["tk","turkmen"],["nn","nynorsk"],["mt","maltese"],["sa","sanskrit"],["lb","luxembourgish"],["my","myanmar"],["bo","tibetan"],["tl","tagalog"],["mg","malagasy"],["as","assamese"],["tt","tatar"],["haw","hawaiian"],["ln","lingala"],["ha","hausa"],["ba","bashkir"],["jw","javanese"],["su","sundanese"]],tI=new Map(tM),tC=new Map([...tM.map(([e,t])=>[t,e]),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);class tB extends tt{_decode_asr(e,{return_timestamps:t=!1,return_language:r=!1,time_precision:s=null,force_full_sequences:n=!0}={}){if(null===s)throw Error("Must specify time_precision");let i=null,o="word"===t;function a(){return{language:i,timestamp:[null,null],text:""}}let l=[],h=a(),c=0,u=this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1,d=[],f=[],p=!1,_=null,m=new Set(this.all_special_ids);for(let r of e){let e=r.tokens,n=o?r.token_timestamps:null,g=null,y=u;if("stride"in r){let[t,n,i]=r.stride;if(c-=n,_=t-i,n&&(y=n/s+u),i)for(let t=e.length-1;t>=0;--t){let r=e[t];if(r>=u){if(null!==g&&(r-u)*s<_)break;g=r}}}let w=[],b=[];for(let r=0;r=u){let e=(_-u)*s+c,t=et(e,2);if(null!==g&&_>=g)p=!0;else if(p||d.length>0&&_0?(d.push(w),o&&f.push(b)):d.every(e=>0===e.length)&&(h=a(),d=[],w=[],f=[],b=[])}if(d.length>0){if(n&&t)throw Error("Whisper did not predict an ending timestamp, which can happen if audio is cut off in the middle of a word. Also make sure WhisperTimeStampLogitsProcessor was used during generation.");let[e,r]=this.findLongestCommonSequence(d,f),s=this.decode(e);h.text=s,o&&(h.words=this.collateWordTimestamps(e,r,i)),l.push(h)}let g=Object.create(null),y=l.map(e=>e.text).join("");if(t||r){for(let e=0;e0,o=i?[]:null,a=i?t[0]:null;for(let l=1;le===f[t]).length,_=p/e+t;p>1&&_>c&&(c=_,u=[n,i,a,l])}let[f,p,_,m]=u,g=Math.floor((p+f)/2),y=Math.floor((m+_)/2);n.push(...r.slice(0,g)),s=(r=h.slice(y)).length,i&&(o.push(...a.slice(0,g)),a=t[l].slice(y))}return(n.push(...r),i)?(o.push(...a),[n,o]):[n,[]]}collateWordTimestamps(e,t,r){let[s,n,i]=this.combineTokensIntoWords(e,r),o=[];for(let e=0;e=s){let e=(t-s)*r;e=et(e,2),n.push(`<|${e}|>`),n.push([])}else n[n.length-1].push(t);return(n=n.map(e=>"string"==typeof e?e:super.decode(e,t))).join("")}splitTokensOnUnicode(e){let t=this.decode(e,{decode_with_timestamps:!0}),r=[],s=[],n=[],i=[],o=[],a=0;for(let l=0;l=this.model.tokens_to_ids.get("<|endoftext|>"),d=l.startsWith(" "),f=l.trim(),p=a.test(f);if(u||d||p||0===n.length)n.push(l),i.push(h),o.push(c);else{let e=n.length-1;n[e]+=l,i[e].push(...h),o[e].push(...c)}}return[n,i,o]}mergePunctuations(e,t,r,s,n){let i=structuredClone(e),o=structuredClone(t),a=structuredClone(r),l=i.length-2,h=i.length-1;for(;l>=0;)i[l].startsWith(" ")&&s.includes(i[l].trim())?(i[h]=i[l]+i[h],o[h]=f(o[l],o[h]),a[h]=f(a[l],a[h]),i[l]="",o[l]=[],a[l]=[]):h=l,--l;for(l=0,h=1;he),o.filter(e=>e.length>0),a.filter(e=>e.length>0)]}get_decoder_prompt_ids({language:e=null,task:t=null,no_timestamps:r=!0}={}){let s=[];if(e){e=e.toLowerCase();let t=tC.get(e);if(void 0===t){if(tI.has(e))t=e;else{let t=2===e.length,r=t?tI.keys():tI.values();throw Error(`Language "${e}" is not supported. Must be one of: ${JSON.stringify(r)}`)}}let r=this.model.tokens_to_ids.get(`<|${t}|>`);if(void 0===r)throw Error(`Unable to find language "${t}" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.`);s.push(r)}else s.push(null);if(t){if("transcribe"!==(t=t.toLowerCase())&&"translate"!==t)throw Error(`Task "${t}" is not supported. Must be one of: ["transcribe", "translate"]`);let e=this.model.tokens_to_ids.get(`<|${t}|>`);if(void 0===e)throw Error(`Unable to find task "${t}" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.`);s.push(e)}else s.push(null);if(r){let e=this.model.tokens_to_ids.get("<|notimestamps|>");if(void 0===e)throw Error('Unable to find "<|notimestamps|>" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.');s.push(e)}return s.map((e,t)=>[t+1,e]).filter(e=>null!==e[1])}}class tO extends tt{}class tP extends tt{}class tU extends tt{constructor(e,t){super(e,t),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter(e=>this.languageRegex.test(e)),console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}_encode_text(e){if(null===e)return null;let[t,...r]=e.trim().split(this.languageRegex);if(0===r.length)return super._encode_text(t);if(2===r.length){let[e,t]=r;return this.supported_language_codes.includes(e)||console.warn(`Unsupported language code "${e}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`),f([e],super._encode_text(t))}}}class tL extends tt{}class tR{static TOKENIZER_CLASS_MAPPING={T5Tokenizer:tf,DistilBertTokenizer:tc,CamembertTokenizer:tu,DebertaTokenizer:ta,DebertaV2Tokenizer:tl,BertTokenizer:ts,HerbertTokenizer:th,XLMTokenizer:td,MobileBertTokenizer:ti,SqueezeBertTokenizer:to,AlbertTokenizer:tn,GPT2Tokenizer:tp,BartTokenizer:t_,MBartTokenizer:tm,MBart50Tokenizer:tg,RobertaTokenizer:ty,WhisperTokenizer:tB,CodeGenTokenizer:tO,CLIPTokenizer:tP,MarianTokenizer:tU,BloomTokenizer:tw,NllbTokenizer:tT,M2M100Tokenizer:tS,LlamaTokenizer:tb,CodeLlamaTokenizer:tx,XLMRobertaTokenizer:tk,MPNetTokenizer:tv,FalconTokenizer:tA,GPTNeoXTokenizer:tE,Wav2Vec2CTCTokenizer:tL,PreTrainedTokenizer:tt};static async from_pretrained(e,{quantized:t=!0,progress_callback:r=null,config:s=null,cache_dir:n=null,local_files_only:i=!1,revision:o="main"}={}){let[a,l]=await em(e,{quantized:t,progress_callback:r,config:s,cache_dir:n,local_files_only:i,revision:o}),h=l.tokenizer_class.replace(/Fast$/,""),c=this.TOKENIZER_CLASS_MAPPING[h];return c||(console.warn(`Unknown tokenizer class "${h}", attempting to construct from base class.`),c=tt),new c(a,l)}}async function tj(e,t){return await K(e,"config.json",!0,t)}class t${constructor(e){this.model_type=null,this.is_encoder_decoder=!1,Object.assign(this,e)}static async from_pretrained(e,{progress_callback:t=null,config:r=null,cache_dir:s=null,local_files_only:n=!1,revision:i="main"}={}){let o=r??await tj(e,{progress_callback:t,config:r,cache_dir:s,local_files_only:n,revision:i});return new this(o)}}class tN{static async from_pretrained(...e){return t$.from_pretrained(...e)}}class tF extends h{constructor(){super(),this.processors=[]}push(e){this.processors.push(e)}extend(e){this.processors.push(...e)}_call(e,t){for(let r of t)this.processors.forEach(t=>t(e,r))}[Symbol.iterator](){return this.processors.values()}}class tD extends h{_call(e,t){throw Error("`_call` should be implemented in a subclass")}}class tq extends tD{constructor(e){super(),this.force_token_map=Object.fromEntries(e??[])}_call(e,t){let r=this.force_token_map[e.length];return null!=r&&(t.data.fill(-1/0),t.data[r]=0),t}}class tG extends tD{constructor(e){super(),this.bos_token_id=e}_call(e,t){return 1===e.length&&(t.data.fill(-1/0),t.data[this.bos_token_id]=0),t}}class tW extends tD{constructor(e,t){super(),this.max_length=e,this.forced_eos_token_id=t}_call(e,t){}}class tH extends tD{constructor(e,t){super(),this.begin_suppress_tokens=e,this.begin_index=t}_call(e,t){if(e.length===this.begin_index)for(let e of this.begin_suppress_tokens)t.data[e]=-1/0;return t}}class tK extends tD{constructor(e){super(),this.eos_token_id=e.eos_token_id,this.no_timestamps_token_id=e.no_timestamps_token_id,this.timestamp_begin=this.no_timestamps_token_id+1,this.begin_index=(e.forced_decoder_ids||[]).length+2,e.forced_decoder_ids.slice(-1)[0][1]===this.no_timestamps_token_id&&(this.begin_index-=1),this.max_initial_timestamp_index=e.max_initial_timestamp_index}_call(e,t){if(t.data[this.no_timestamps_token_id]=-1/0,e.length===this.begin_index-1)return t.data.fill(-1/0),t.data[this.timestamp_begin]=0,t;let r=e.slice(this.begin_index),s=r.length>=1&&r[r.length-1]>=this.timestamp_begin,n=r.length<2||r[r.length-2]>=this.timestamp_begin;if(s&&(n?t.data.subarray(this.timestamp_begin).fill(-1/0):t.data.subarray(0,this.eos_token_id).fill(-1/0)),e.length===this.begin_index&&null!==this.max_initial_timestamp_index){let e=this.timestamp_begin+this.max_initial_timestamp_index;t.data.subarray(e+1).fill(-1/0)}let i=function(e){let t=Y(e),r=t.map(e=>Math.log(e));return r}(t.data),o=Math.log(i.subarray(this.timestamp_begin).map(Math.exp).reduce((e,t)=>e+t)),a=Z(i.subarray(0,this.timestamp_begin))[0];return o>a&&t.data.subarray(0,this.timestamp_begin).fill(-1/0),t}}class tX extends tD{constructor(e){super(),this.no_repeat_ngram_size=e}getNgrams(e){let t=e.length,r=[];for(let s=0;s0&&(s=s.map(e=>e/this.generation_config.temperature)),s}randomSelect(e){let t=Math.random()*e.reduce((e,t)=>e+t,0);for(let r=0;r1)return new t0(e);if(e.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${e.num_return_sequences}.`);return new tZ(e)}}class tZ extends tJ{sample(e,t=-1){return[[Z(this.getLogits(e,t))[1],0]]}}class tQ extends tJ{sample(e,t=-1){let r=e.dims.at(-1);this.generation_config.top_k>0&&(r=Math.min(this.generation_config.top_k,r));let s=this.getLogits(e,t),n=J(s,r),i=Y(n.map(e=>e[1]));return Array.from({length:this.generation_config.num_beams},()=>{let e=this.randomSelect(i);return[n[e][0],Math.log(i[e])]})}}class t0 extends tJ{sample(e,t=-1){let r=e.dims.at(-1);this.generation_config.top_k>0&&(r=Math.min(this.generation_config.top_k,r));let s=this.getLogits(e,t),n=J(s,r),i=Y(n.map(e=>e[1]));return Array.from({length:this.generation_config.num_beams},(e,t)=>[n[t][0],Math.log(i[t])])}}let{InferenceSession:t1,Tensor:t2}=s,t3={EncoderOnly:0,EncoderDecoder:1,Seq2Seq:2,Vision2Seq:3,DecoderOnly:4},t6=new Map([["CLIPTextModelWithProjection",t3.EncoderOnly],["CLIPVisionModelWithProjection",t3.EncoderOnly]]);async function t4(e,t,r){let s=`onnx/${t}${r.quantized?"_quantized":""}.onnx`,n=await H(e,s,!0,r);try{return await t1.create(n,{executionProviders:E})}catch(e){if(1===E.length&&"wasm"===E[0])throw e;return console.warn(e),console.warn("Something went wrong during model construction (most likely a missing operation). Using `wasm` as a fallback. "),await t1.create(n,{executionProviders:["wasm"]})}}async function t8(e,t){let r={},s=[];for(let n of e.inputNames)void 0===t[n]?s.push(n):r[n]=t[n];if(s.length>0)throw Error(`An error occurred during model execution: "Missing the following inputs: ${s.join(", ")}.`);let n=Object.keys(t).length,i=e.inputNames.length;return n>i&&console.warn(`WARNING: Too many inputs were provided (${n} > ${i}). The following inputs will be ignored: "${Object.keys(t).filter(t=>!e.inputNames.includes(t)).join(", ")}".`),r}async function t5(e,t){let r=await t8(e,t);try{let t=await e.run(r);return t=function e(t){for(let r in t)t[r]instanceof t2?t[r]=new es(t[r]):"object"==typeof t[r]&&e(t[r]);return t}(t)}catch(e){throw console.error(`An error occurred during model execution: "${e}".`),console.error("Inputs given to model:",r),e}}function t9(e,t){let r=e.config.pad_token_id??null,s=e.config.eos_token_id??null;u(s)&&(s=[s]);let n=-1!==t.indexOf(r),i=null===s||!s.includes(r);if(!n||!i)return new es("int64",new BigInt64Array(t.data.length).fill(1n),t.dims);{let e=BigInt64Array.from(t.data.map(e=>e!=r));return new es("int64",e,t.dims)}}function t7(e){return new es("bool",[e],[1])}async function re(e,t){let r=e.add_decoder_pkv??!0,{encoder_outputs:s,past_key_values:n}=t;s||(s=(await rn(e,t)).last_hidden_state);let i={input_ids:t.decoder_input_ids,encoder_hidden_states:s,use_cache_branch:t7(!!n)};e.decoder_merged_session.inputNames.includes("encoder_attention_mask")&&(i.encoder_attention_mask=t.attention_mask),e.addPastKeyValues(i,n,r);let o=await t5(e.decoder_merged_session,i),a=o.logits;n=e.getPastKeyValues(o,n);let l=e.getAttentions(o);return new sp({logits:a,past_key_values:n,encoder_outputs:s,...l})}function rt(e,t,r){let s=[],n=0,i=e.requires_attention_mask??!0,o=e.config.decoder_start_token_id;for(let r of(Array.isArray(o)||(o=[o]),t)){r.dims=[1,...r.dims];let t={inputs:r,encoder_outputs:null,prev_model_outputs:null,output_token_ids:o,done:!1,score:0,id:n++};i&&(t.attention_mask=t9(e,r)),s.push(t)}return s}async function rr(e,t){let r=e.main_input_name,s={[r]:t.inputs,decoder_input_ids:function(e){if(e instanceof es)return e;if(0===e.length)throw Error("items must be non-empty");if(!Array.isArray(e[0]))return new es("int64",BigInt64Array.from(e.map(e=>BigInt(e))),[1,e.length]);if(e.some(t=>t.length!==e[0].length))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' and/or 'truncation=True' to have batched tensors with the same length.");return new es("int64",BigInt64Array.from(e.flat().map(e=>BigInt(e))),[e.length,e[0].length])}(t.output_token_ids.slice(-1)),encoder_outputs:t.encoder_outputs,past_key_values:t.prev_model_outputs?.past_key_values};t.attention_mask&&(s.attention_mask=t.attention_mask);let n=await e.forward(s);return t.prev_model_outputs=n,t.encoder_outputs=n.encoder_outputs,n}function rs(e,t){e.output_token_ids=[...e.output_token_ids,t]}async function rn(e,t){let r={};for(let s of e.session.inputNames)r[s]=t[s];return await t5(e.session,r)}async function ri(e,t){let{input_ids:r,past_key_values:s,attention_mask:n}=t,i={input_ids:r,attention_mask:n??t9(e,r),use_cache_branch:t7(!!s)};e.addPastKeyValues(i,s);let o=await t5(e.session,i);return{logits:o.logits,past_key_values:s=e.getPastKeyValues(o,s)}}function ro(e,t,r,s){let n=[],i=0;for(let o of t){let t,a=o.tolist().map(Number);o.dims=[1,...o.dims],s?(t=s[i]).dims=[1,...t.dims]:t=t9(e,o);let l={input:o,model_input_ids:o,attention_mask:t,prev_model_outputs:null,output_token_ids:a,num_output_tokens:r,done:!1,score:0,id:i++};n.push(l)}return n}async function ra(e,t){let r=new BigInt64Array(t.output_token_ids.length).fill(1n),s={input_ids:t.model_input_ids,attention_mask:new es("int64",r,[1,r.length]),past_key_values:t.prev_model_outputs?.past_key_values},n=await e.forward(s);return t.prev_model_outputs=n,n}function rl(e,t){e.output_token_ids=[...e.output_token_ids,t],e.model_input_ids=new es("int64",[BigInt(t)],[1,1])}class rh extends h{main_input_name="input_ids";constructor(e,t){super(),this.config=e,this.session=t;let r=this.constructor.name,s=t6.get(r);this.can_generate=!1,this._runBeam=null,this._getStartBeams=null,this._updateBeam=null,this._forward=null,s===t3.DecoderOnly?(this.can_generate=!0,this._runBeam=ra,this._getStartBeams=ro,this._updateBeam=rl,this._forward=ri):s===t3.Seq2Seq||s===t3.Vision2Seq?(this.can_generate=!0,this._runBeam=rr,this._getStartBeams=rt,this._updateBeam=rs,this._forward=re):(t3.EncoderDecoder,this._forward=rn)}async dispose(){let e=[];for(let t of Object.keys(this)){let r=this[t];r instanceof t1&&e.push(r.handler.dispose())}return await Promise.all(e)}static async from_pretrained(e,{quantized:t=!0,progress_callback:r=null,config:s=null,cache_dir:n=null,local_files_only:i=!1,revision:o="main",model_file_name:a=null}={}){let l,h={quantized:t,progress_callback:r,config:s,cache_dir:n,local_files_only:i,revision:o,model_file_name:a},c=t6.get(this.name);return c===t3.DecoderOnly?l=await Promise.all([tN.from_pretrained(e,h),t4(e,h.model_file_name??"decoder_model_merged",h)]):c===t3.Seq2Seq||c===t3.Vision2Seq?l=await Promise.all([tN.from_pretrained(e,h),t4(e,"encoder_model",h),t4(e,"decoder_model_merged",h),K(e,"generation_config.json",!1,h)]):c===t3.EncoderDecoder?l=await Promise.all([tN.from_pretrained(e,h),t4(e,"encoder_model",h),t4(e,"decoder_model_merged",h)]):(c!==t3.EncoderOnly&&console.warn(`Model type for ${this.name} not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`),l=await Promise.all([tN.from_pretrained(e,h),t4(e,h.model_file_name??"model",h)])),new this(...l)}async _call(e){return await this.forward(e)}async forward(e){return await this._forward(this,e)}_get_logits_processor(e,t,r=null){let s=new tF;if(null!==e.repetition_penalty&&1!==e.repetition_penalty&&s.push(new tV(e.repetition_penalty)),null!==e.no_repeat_ngram_size&&e.no_repeat_ngram_size>0&&s.push(new tX(e.no_repeat_ngram_size)),null!==e.forced_bos_token_id&&s.push(new tG(e.forced_bos_token_id)),null!==e.forced_eos_token_id&&s.push(new tW(e.max_length,e.forced_eos_token_id)),null!==e.begin_suppress_tokens){let r=t>1||null===e.forced_bos_token_id?t:t+1;null!==e.forced_decoder_ids&&(r+=e.forced_decoder_ids[e.forced_decoder_ids.length-1][0]),s.push(new tH(e.begin_suppress_tokens,r))}return null!==e.forced_decoder_ids&&s.push(new tq(e.forced_decoder_ids)),null!==r&&s.extend(r),s}_get_generation_config(e){let t=new tY;return"generation_config"in this&&Object.assign(t,this.generation_config),null!==e&&Object.assign(t,e),t}async generate(e,t=null,r=null,{inputs_attention_mask:s=null}={}){let n;if(!this.can_generate){let e=se.get(this.config.model_type),t=`The current model class (${this.constructor.name}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw e&&(t+=` Please use one of the following classes instead: {'${e.constructor.name}'}`),Error(t)}if(!(e instanceof es)&&e?.prototype?.__proto__?.constructor?.name!=="TypedArray"&&!Array.isArray(e))throw Error(`\`inputs\` must be a Tensor, TypedArray, or Array, but is "${e.constructor.name}".`);if(this.config.is_encoder_decoder)n=0;else if(0===(n=e instanceof es?e.dims[0]:e.length))throw Error("Must supply a non-empty array of input token ids.");t=this._get_generation_config(t),r=r??new tF,r=this._get_logits_processor(t,n,r);let i=1,o=i+(t.max_new_tokens??1/0),a=Number.isInteger(t.max_length)&&(t.max_new_tokens??null)===null,l=tJ.getSampler(t),h=this.getStartBeams(e,i,s);for(;h.some(e=>!e.done)&&i=t.max_length){s.done=!0,e.push(s);continue}let n=await this.runBeam(s);t.output_attentions&&this.addAttentionsToBeam(s,n),t.output_scores;let i=n.logits.slice(null,-1,null);for(let[t,n]of(r(s.output_token_ids,i),l(i))){let r={...s};this.updateBeam(r,t),r.score+=n,t===this.config.eos_token_id&&(r.done=!0),e.push(r)}}++i,h=(e=this.groupBeams(e).map(e=>e.sort((e,t)=>t.score-e.score).slice(0,t.num_beams))).flat(),t.callback_function&&t.callback_function(h)}let c=this.groupBeams(h),u=e=>c.map(r=>t.num_return_sequences>1?r.slice(0,t.num_return_sequences).map(t=>t[e]):[r[0][e]]).flat(),d=u("output_token_ids");if(!t.return_dict_in_generate)return d;{let e=u("decoder_attentions"),t=u("cross_attentions");return{sequences:d,decoder_attentions:e,cross_attentions:t}}}addAttentionsToBeam(e,t){if(this.config.is_encoder_decoder){if(!t.cross_attentions||0===t.cross_attentions.length)throw Error("`output_attentions` is true, but the model did not produce cross-attentions. This is most likely because the model was not exported with `output_attentions=True`.");e.cross_attentions||(e.cross_attentions=[]),e.cross_attentions.push(t.cross_attentions)}if(!t.decoder_attentions||0===t.decoder_attentions.length)throw Error("`output_attentions` is true, but the model did not produce decoder-attentions. This is most likely because the model was not exported with `output_attentions=True`.");e.decoder_attentions||(e.decoder_attentions=[]),e.decoder_attentions.push(t.decoder_attentions)}groupBeams(e){let t=Object.create(null);for(let r of e)void 0===t[r.id]?t[r.id]=[r]:t[r.id].push(r);return Object.values(t)}getPastKeyValues(e,t){let r=Object.create(null);for(let s in e)if(s.startsWith("present")){let n=s.replace("present","past_key_values");t&&s.includes("encoder")?r[n]=t[n]:r[n]=e[s]}return r}getAttentions(e){let t=Object.create(null);for(let r of["cross_attentions","decoder_attentions"]){let s=[];for(let t in e)if(t.startsWith(r)){let r=t.split(".").pop();s[r]=e[t]}t[r]=s}return t}addPastKeyValues(e,t,r=!1){if(t)Object.assign(e,t);else if(r){let t=[1,this.num_encoder_heads,0,this.encoder_dim_kv];for(let r=0;r{let s=Array.from({length:this.config.decoder_layers},(t,r)=>eh(e.map(e=>e[r]),2)),i=function(e,t=0){return eh(e.map(e=>e.unsqueeze(t)),t)}(t.map(([e,t])=>r?s[e].slice(null,t,null,[0,r]):s[e].slice(null,t))),[o,a]=function(e,t=null,r=1,s=!1){if(null===t){let t=e.data.reduce((e,t)=>e+t,0),s=t/e.data.length,n=Math.sqrt(e.data.reduce((e,t)=>e+(t-s)**2,0)/(e.data.length-r)),i=new es(e.type,[s],[]),o=new es(e.type,[n],[]);return[o,i]}t=el(t,e.dims.length);let n=ec(e,t,s),i=e.dims.slice();i[t]=1;let o=new e.data.constructor(e.data.length/e.dims[t]);for(let r=0;r=0;--n){let r=e.dims[n];if(n!==t){let e=o%r;s+=e*a,a*=i[n]}o=Math.floor(o/r)}o[s]+=(e.data[r]-n.data[s])**2}for(let s=0;s0||a>0;){l.push(o-1),h.push(a-1);let e=i[o][a].item();switch(e){case 0:--o,--a;break;case 1:--o;break;case 2:--a;break;default:throw Error(`Internal error in dynamic time warping. Unexpected trace[${o}, ${a}]. Please file a bug report.`)}}return l.reverse(),h.reverse(),[l,h]}(t),o=f([1],Array.from({length:r.length-1},(e,t)=>r[t+1]-r[t])).map(e=>!!e),l=[];for(let e=0;e{if(!self.OffscreenCanvas)throw Error("OffscreenCanvas not supported by this browser.");return new self.OffscreenCanvas(e,t)},o=self.createImageBitmap,i=self.ImageData;else if(sb)o=async e=>{let t=await e.metadata(),r=t.channels,{data:s,info:n}=await e.raw().toBuffer({resolveWithObject:!0}),i=new sv(new Uint8ClampedArray(s),n.width,n.height,n.channels);return void 0!==r&&r!==n.channels&&i.convert(r),i};else throw Error("Unable to load image processing library.");let sk={0:"nearest",1:"lanczos",2:"bilinear",3:"bicubic",4:"box",5:"hamming"};class sv{_CONTENT_TYPE_MAP={png:"image/png",jpg:"image/jpeg",jpeg:"image/jpeg",gif:"image/gif"};constructor(e,t,r,s){this._update(e,t,r,s)}static async read(e){if(e instanceof sv)return e;if(c(e)||e instanceof URL)return await this.fromURL(e);throw Error(`Unsupported input type: ${typeof e}`)}static async fromURL(e){let t=await D(e);if(200!==t.status)throw Error(`Unable to read image from "${e}" (${t.status} ${t.statusText})`);let r=await t.blob();return this.fromBlob(r)}static async fromBlob(e){if(sx){let t=await o(e),r=n(t.width,t.height).getContext("2d");return r.drawImage(t,0,0),new this(r.getImageData(0,0,t.width,t.height).data,t.width,t.height,4)}{let t=sb(await e.arrayBuffer());return await o(t)}}grayscale(){if(1===this.channels)return this;let e=new Uint8ClampedArray(this.width*this.height*1);switch(this.channels){case 3:case 4:for(let t=0,r=0;t=0?l=r:c=-r,s>=0?h=s:u=-s,a.drawImage(o,l,h,e,t,c,u,e,t),new sv(a.getImageData(0,0,e,t).data,e,t,4).convert(i)}{let n=this.toSharp();if(r>=0&&s>=0)n=n.extract({left:Math.floor(r),top:Math.floor(s),width:e,height:t});else if(r<=0&&s<=0){let i=Math.floor(-s),o=Math.floor(-r);n=n.extend({top:i,left:o,right:e-this.width-o,bottom:t-this.height-i})}else{let i=[0,0],o=0;s<0?(i[0]=Math.floor(-s),i[1]=t-this.height-i[0]):o=Math.floor(s);let a=[0,0],l=0;r<0?(a[0]=Math.floor(-r),a[1]=e-this.width-a[0]):l=Math.floor(r),n=n.extend({top:i[0],bottom:i[1],left:a[0],right:a[1]}).extract({left:l,top:o,width:e,height:t})}return await o(n)}}toCanvas(){if(!sx)throw Error("toCanvas() is only supported in browser environments.");let e=this.clone().rgba(),t=n(e.width,e.height),r=new i(e.data,e.width,e.height);return t.getContext("2d").putImageData(r,0,0),t}_update(e,t,r,s=null){return this.data=e,this.width=t,this.height=r,null!==s&&(this.channels=s),this}clone(){return new sv(this.data.slice(),this.width,this.height,this.channels)}convert(e){if(this.channels===e)return this;switch(e){case 1:this.grayscale();break;case 3:this.rgb();break;case 4:this.rgba();break;default:throw Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this}save(e){if(sx){let t=e.split(".").pop().toLowerCase(),r=this._CONTENT_TYPE_MAP[t]??"image/png",s=this.toCanvas(),n=s.toDataURL(r),i=document.createElement("a");i.href=n,i.download=e,i.click(),i.remove()}else if(L.useFS){let t=this.toSharp();t.toFile(e)}else throw Error("Unable to save the image because filesystem is disabled in this environment.")}toSharp(){if(sx)throw Error("toSharp() is only supported in server-side environments.");return sb(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}})}}async function sA(e,t){let r;if("undefined"==typeof AudioContext)throw Error("Unable to load audio from path/URL since `AudioContext` is not available in your environment. Instead, audio data should be passed directly to the pipeline/processor. For more information and some example code, see https://huggingface.co/docs/transformers.js/guides/node-audio-processing.");let s=await (await D(e)).arrayBuffer(),n=new AudioContext({sampleRate:t});void 0===t&&console.warn(`No sampling rate provided, using default of ${n.sampleRate}Hz.`);let i=await n.decodeAudioData(s);if(2===i.numberOfChannels){let e=Math.sqrt(2),t=i.getChannelData(0),s=i.getChannelData(1);r=new Float32Array(t.length);for(let n=0;nt){let t=u[e].data;t=function([e,t,r,s]){return[e-r/2,t-s/2,e+r/2,t+s/2]}(t),null!==i&&(t=t.map((e,t)=>e*i[(t+1)%2])),h.boxes.push(t),h.classes.push(s),h.scores.push(n)}}l.push(h)}return l}class sz extends h{constructor(e){super(),this.config=e}}class sT extends sz{constructor(e){super(e),this.image_mean=this.config.image_mean,this.image_std=this.config.image_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.size=this.config.size,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.pad_size=this.config.pad_size,this.do_pad=(this.config.do_pad??!1)&&this.pad_size}async preprocess(e){this.do_convert_rgb&&(e=e.rgb());let t=e.width,r=e.height;if(this.do_resize){let s,n;if(Number.isInteger(this.size)?(s=this.size,n=this.config.max_size??s):(s=this.size.shortest_edge,n=this.size.longest_edge),void 0!==s||void 0!==n){let i=void 0===s?1:Math.max(s/t,s/r),o=t*i,a=r*i,l=void 0===n?1:Math.min(n/o,n/a),h=Math.floor(Number((o*l).toPrecision(3))),c=Math.floor(Number((a*l).toPrecision(3)));e=await e.resize(h,c,{resample:this.resample})}else if(void 0!==this.size.width&&void 0!==this.size.height)e=await e.resize(this.size.width,this.size.height,{resample:this.resample});else throw Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(this.size)}`)}if(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];if(this.do_pad){let t=this.pad_size.width-e.width,r=this.pad_size.height-e.height;e=await e.pad([0,t,0,r])}let n=Float32Array.from(e.data);if(this.do_rescale)for(let e=0;ethis.preprocess(e)));return t.forEach(e=>e.pixel_values.dims=[1,...e.pixel_values.dims]),{pixel_values:eh(t.map(e=>e.pixel_values)),original_sizes:t.map(e=>e.original_size),reshaped_input_sizes:t.map(e=>e.reshaped_input_size)}}}class sS extends sT{}class sM extends sT{}class sI extends sT{}class sC extends sT{}class sB extends sT{}class sO extends sT{async _call(e){let t=await super._call(e),r=[t.pixel_values.dims[0],64,64];return t.pixel_mask=new es("int64",new BigInt64Array(r.reduce((e,t)=>e*t)).fill(1n),r),t}post_process_object_detection(...e){return sE(...e)}remove_low_and_no_objects(e,t,r,s){let n=[],i=[],o=[];for(let a=0;ar&&(n.push(h),i.push(u),o.push(c))}return[n,i,o]}check_segment_validity(e,t,r,s=.5,n=.8){let i=[],o=0,a=0;for(let n=0;n=s&&++a;let l=o>0&&a>0;return l&&(l=o/a>n),[l,i]}compute_segments(e,t,r,s,n,i=null,o=null){let[a,l]=o??e[0].dims,h=new es("int32",new Int32Array(a*l),[a,l]),c=[];if(null!==o)for(let t=0;td[t]&&(u[t]=r,d[t]=e[r].data[t])}let f=0;for(let i=0;iBigInt(Math.round(e)))),i)}}post_process_masks(e,t,r,{mask_threshold:s=0,binarize:n=!0,pad_size:i=null}={}){let o=[],a=[(i=i??this.pad_size).height,i.width];for(let i=0;ie>s),t.dims)),t.dims=[1,...t.dims],u.push(t)}let d=eh(u);o.push(d)}return o}}class sL extends sz{constructor(e){super(e),this.config.mel_filters??=function(e,t,r=128){r=Math.floor(r);let s=Math.floor(1+t/2),n=Array(r),i=function(e,t=1){if(!Number.isInteger(e))throw TypeError(`n should be an integer, but ${e} given.`);let r=1/(e*t),s=Math.floor(e/2)+1,n=Array(s);for(let e=0;e=h?l[e]=1e3*Math.exp(c*(t-h)):l[e]=0+a*t,u[e]=i.map(t=>l[e]-t)}let d=l.slice(1).map((e,t)=>1/(e-l[t]));for(let e=0;e>1;++e){let t=(e+1-r)**2/2,s=Math.sqrt(m**2+g**2)**t,n=t*Math.atan2(g,m),i=2*e;l[i]=s*Math.cos(n),l[i+1]=s*Math.sin(n),h[i]=l[i],h[i+1]=-l[i+1]}let y=l.subarray(s,n),w=new Q(i>>1);w.transform(d,h);for(let r=0;r>1,i=n[s]*t[s];c[e]=i*y[e],c[r]=i*y[r]}w.transform(f,c);for(let e=0;es?i-s:0,r=i>1,l=new Float32Array(o*a);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`.");let t=e.slice(0,this.config.n_samples),r=this._extract_fbank_features(t);return{input_features:new es("float32",r.data,[1,...r.dims])}}}class sR extends sz{_zero_mean_unit_var_norm(e){let t=e.reduce((e,t)=>e+t,0),r=t/e.length,s=e.reduce((e,t)=>e+(t-r)**2,0)/e.length;return e.map(e=>(e-r)/Math.sqrt(s+1e-7))}async _call(e){if(!(e instanceof Float32Array||e instanceof Float64Array))throw Error(`Wav2Vec2FeatureExtractor expects input to be a Float32Array or a Float64Array, but got ${e?.constructor?.name??typeof e} instead.If using the feature extractor directly, remember to use \`read_audio(url, sampling_rate)\` to obtain the raw audio data of the file/url.`);e instanceof Float64Array&&(e=new Float32Array(e));let t=e;this.config.do_normalize&&(t=this._zero_mean_unit_var_norm(t));let r=[1,t.length];return{input_values:new es("float32",t,r),attention_mask:new es("int64",new BigInt64Array(t.length).fill(1n),r)}}}class sj extends h{constructor(e){super(),this.feature_extractor=e}async _call(e){return await this.feature_extractor(e)}}class s$ extends sj{async _call(e,t){return await this.feature_extractor(e,t)}post_process_masks(...e){return this.feature_extractor.post_process_masks(...e)}}class sN extends sj{async _call(e){return await this.feature_extractor(e)}}class sF extends sj{async _call(e){return await this.feature_extractor(e)}}class sD{static FEATURE_EXTRACTOR_CLASS_MAPPING={WhisperFeatureExtractor:sL,ViTFeatureExtractor:sM,MobileViTFeatureExtractor:sI,ConvNextFeatureExtractor:sS,BeitFeatureExtractor:sB,DeiTFeatureExtractor:sC,DetrFeatureExtractor:sO,YolosFeatureExtractor:sP,SamImageProcessor:sU,Wav2Vec2FeatureExtractor:sR};static PROCESSOR_CLASS_MAPPING={WhisperProcessor:sN,Wav2Vec2ProcessorWithLM:sF,SamProcessor:s$};static async from_pretrained(e,{progress_callback:t=null,config:r=null,cache_dir:s=null,local_files_only:n=!1,revision:i="main"}={}){let o=r??await K(e,"preprocessor_config.json",!0,{progress_callback:t,config:r,cache_dir:s,local_files_only:n,revision:i}),a=o.feature_extractor_type??o.image_processor_type,l=this.FEATURE_EXTRACTOR_CLASS_MAPPING[a];if(!l){if(void 0!==o.size)console.warn("Feature extractor type not specified, assuming ImageFeatureExtractor due to size parameter in config."),l=sT;else throw Error(`Unknown Feature Extractor type: ${o.feature_extractor_type}`)}let h=this.PROCESSOR_CLASS_MAPPING[o.processor_class]??sj,c=new l(o);return new h(c)}}async function sq(e){return Array.isArray(e)||(e=[e]),e=await Promise.all(e.map(e=>sv.read(e)))}class sG extends h{constructor({task:e,model:t,tokenizer:r=null,processor:s=null}){super(),this.task=e,this.model=t,this.tokenizer=r,this.processor=s}async dispose(){await this.model.dispose()}async _call(e,...t){let r=this.tokenizer(e,{padding:!0,truncation:!0}),s=await this.model(r);return[r,s]}}class sW extends sG{_key=null;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 s=this.model.config.task_specific_params;s&&s[this.task]&&s[this.task].prefix&&(e=e.map(e=>s[this.task].prefix+e));let n={padding:!0,truncation:!0};r=this instanceof sH&&"_build_translation_inputs"in this.tokenizer?this.tokenizer._build_translation_inputs(e,n,t).input_ids:this.tokenizer(e,n).input_ids;let i=await this.model.generate(r,t),o=this.tokenizer.batch_decode(i,{skip_special_tokens:!0});return null!==this._key&&(o=o.map(e=>null===this._key?e:{[this._key]:e})),o}}class sH extends sW{_key="translation_text"}let sK={"text-classification":{tokenizer:tR,pipeline:class extends sG{async _call(e,{topk:t=1}={}){let r="multi_label_classification"===this.model.config.problem_type?e=>e.sigmoid().data:e=>Y(e.data),[s,n]=await super._call(e),i=this.model.config.id2label,o=[];for(let e of n.logits){let s=J(r(e),t).map(function(e){return{label:i[e[0]],score:e[1]}});1===t?o.push(...s):o.push(s)}return Array.isArray(e)||1===t?o:o[0]}},model:sd,default:{model:"Xenova/distilbert-base-uncased-finetuned-sst-2-english"},type:"text"},"token-classification":{tokenizer:tR,pipeline:class extends sG{async _call(e,{ignore_labels:t=["O"]}={}){let r=Array.isArray(e);r||(e=[e]);let s=this.tokenizer,[n,i]=await super._call(e),o=i.logits,a=this.model.config.id2label,l=[];for(let e=0;ee.flatMap(e=>t.map(t=>[e,t])))})(Array.from(Y(n.start_logits[e].data)).map((e,t)=>[e,t]).filter(e=>e[1]>o),Array.from(Y(n.end_logits[e].data)).map((e,t)=>[e,t]).filter(e=>e[1]>o)).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:n.model.vocab[e[0]],sequence:n.decode(t,{skip_special_tokens:!0})}}))}return Array.isArray(e)?i:i[0]}},model:class extends r3{static MODEL_CLASS_MAPPINGS=[st]},default:{model:"Xenova/bert-base-uncased"},type:"text"},summarization:{tokenizer:tR,pipeline:class extends sW{_key="summary_text"},model:sf,default:{model:"Xenova/distilbart-cnn-6-6"},type:"text"},translation:{tokenizer:tR,pipeline:sH,model:sf,default:{model:"Xenova/t5-small"},type:"text"},"text2text-generation":{tokenizer:tR,pipeline:sW,model:sf,default:{model:"Xenova/flan-t5-small"},type:"text"},"text-generation":{tokenizer:tR,pipeline:class extends sG{async _call(e,t={}){let r="string"==typeof e||e instanceof String;r&&(e=[e]),this.tokenizer.padding_side="left";let s=this.tokenizer(e,{padding:!0,truncation:!0}),n=s.input_ids,i=s.attention_mask,o=await this.model.generate(n,t,null,{inputs_attention_mask:i}),a=this.tokenizer.batch_decode(o,{skip_special_tokens:!0}),l=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:s=!1}={}){let n=Array.isArray(e);n||(e=[e]),Array.isArray(t)||(t=[t]);let i=t.map(e=>r.replace("{}",e)),o=s||1===t.length,a=[];for(let r of e){let e=[];for(let t of i){let s=this.tokenizer(r,{text_pair:t,padding:!0,truncation:!0}),n=await this.model(s);o?e.push([n.logits.data[this.contradiction_id],n.logits.data[this.entailment_id]]):e.push(n.logits.data[this.entailment_id])}let s=(o?e.map(e=>Y(e)[1]):Y(e)).map((e,t)=>[e,t]).sort((e,t)=>t[0]-e[0]);a.push({sequence:r,labels:s.map(e=>t[e[1]]),scores:s.map(e=>e[0])})}return n?a:a[0]}},model:sd,default:{model:"Xenova/distilbert-base-uncased-mnli"},type:"text"},"audio-classification":{pipeline:class extends sG{constructor(e){super(e)}async _preprocess(e,t){return c(e)&&(e=await sA(e,t)),e}async _call(e,{topk:t=5}={}){let r=!Array.isArray(e);r&&(e=[e]);let s=this.model.config.id2label,n=this.processor.feature_extractor.config.sampling_rate,i=[];for(let r of e){r=await this._preprocess(r,n);let e=await this.processor(r),o=await this.model(e),a=o.logits[0],l=J(Y(a.data),t).map(function(e){return{label:s[e[0]],score:e[1]}});1===t?i.push(...l):i.push(l)}return r&&1!==t?i[0]:i}},model:class extends r3{static MODEL_CLASS_MAPPINGS=[sh]},processor:sD,default:{model:"Xenova/wav2vec2-base-superb-ks"},type:"audio"},"automatic-speech-recognition":{tokenizer:tR,pipeline:class extends sG{constructor(e){super(e)}async _preprocess(e,t){return c(e)&&(e=await sA(e,t)),e}async _call(e,t={}){switch(this.model.config.model_type){case"whisper":return this._call_whisper(e,t);case"wav2vec2":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 s=this.processor.feature_extractor.config.sampling_rate,n=[];for(let t of e){t=await this._preprocess(t,s);let e=await this.processor(t),r=await this.model(e),i=r.logits[0],o=[];for(let e of i)o.push(Z(e.data)[1]);let a=this.tokenizer.decode(o);n.push({text:a})}return r?n[0]:n}async _call_whisper(e,t={}){let r=t.return_timestamps??!1,s=t.chunk_length_s??0,n=t.stride_length_s??null,i=t.chunk_callback??null,o=t.force_full_sequences??!1;"word"===r&&(t.return_token_timestamps=!0);let a=d(t,"language",null),l=d(t,"task",null);if(a||l||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:a,task:l,no_timestamps:!r});e.length>0&&(t.forced_decoder_ids=e)}let h=!Array.isArray(e);h&&(e=[e]);let c=this.processor.feature_extractor.config.sampling_rate,u=this.processor.feature_extractor.config.chunk_length/this.model.config.max_source_positions,f=this.processor.feature_extractor.config.hop_length,p=[];for(let a of e){a=await this._preprocess(a,c);let e=[];if(s>0){if(null===n)n=s/6;else if(s<=n)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");let t=c*s,r=c*n,i=t-2*r,o=0;for(;o=a.length;e.push({stride:[s.length,l?0:r,h?0:r],input_features:n.input_features,is_last:h}),o+=i}}else e=[{stride:[a.length,0,0],input_features:(await this.processor(a)).input_features,is_last:!0}];for(let s of e){t.num_frames=Math.floor(s.stride[0]/f);let e=await this.model.generate(s.input_features,t);"word"===r?(s.tokens=e.sequences[0],s.token_timestamps=e.token_timestamps.tolist()[0].map(e=>et(e,2))):s.tokens=e[0],s.stride=s.stride.map(e=>e/c),null!==i&&i(s)}let[l,h]=this.tokenizer._decode_asr(e,{time_precision:u,return_timestamps:r,force_full_sequences:o});p.push({text:l,...h})}return h?p[0]:p}},model:[sf,class extends r3{static MODEL_CLASS_MAPPINGS=[sl]}],processor:sD,default:{model:"Xenova/whisper-tiny.en"},type:"multimodal"},"image-to-text":{tokenizer:tR,pipeline:class extends sG{constructor(e){super(e)}async _call(e,t={}){let r=Array.isArray(e);e=await sq(e);let{pixel_values:s}=await this.processor(e),n=[];for(let e of s){e.dims=[1,...e.dims];let r=await this.model.generate(e,t),s=this.tokenizer.batch_decode(r,{skip_special_tokens:!0}).map(e=>({generated_text:e.trim()}));n.push(s)}return r?n:n[0]}},model:class extends r3{static MODEL_CLASS_MAPPINGS=[ss]},processor:sD,default:{model:"Xenova/vit-gpt2-image-captioning"},type:"multimodal"},"image-classification":{pipeline:class extends sG{constructor(e){super(e)}async _call(e,{topk:t=1}={}){let r=Array.isArray(e);e=await sq(e);let{pixel_values:s}=await this.processor(e),n=await this.model({pixel_values:s}),i=this.model.config.id2label,o=[];for(let e of n.logits){let r=J(Y(e.data),t).map(function(e){return{label:i[e[0]],score:e[1]}});1===t?o.push(...r):o.push(r)}return r||1===t?o:o[0]}},model:class extends r3{static MODEL_CLASS_MAPPINGS=[sn]},processor:sD,default:{model:"Xenova/vit-base-patch16-224"},type:"multimodal"},"image-segmentation":{pipeline:class extends sG{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:s=.8,label_ids_to_fuse:n=null,target_sizes:i=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 a=(e=await sq(e)).map(e=>[e.height,e.width]),{pixel_values:l,pixel_mask:h}=await this.processor(e),c=await this.model({pixel_values:l,pixel_mask:h}),u=null;if(null!==o)u=this.subtasks_mapping[o];else for(let[e,t]of Object.entries(this.subtasks_mapping))if(t in this.processor.feature_extractor){u=this.processor.feature_extractor[t].bind(this.processor.feature_extractor),o=e;break}let d=[];if("panoptic"===o||"instance"===o){let e=u(c,t,r,s,n,i??a)[0],o=e.segmentation,l=this.model.config.id2label;for(let t of e.segments_info){let e=new Uint8ClampedArray(o.data.length);for(let r=0;rr.replace("{}",e)),i=this.tokenizer(n,{padding:!0,truncation:!0}),{pixel_values:o}=await this.processor(e),a=await this.model({...i,pixel_values:o}),l=[];for(let e of a.logits_per_image){let r=Y(e.data);l.push([...r].map((e,r)=>({score:e,label:t[r]})))}return s?l:l[0]}},model:su,processor:sD,default:{model:"Xenova/clip-vit-base-patch32"},type:"multimodal"},"object-detection":{pipeline:class extends sG{constructor(e){super(e)}async _call(e,{threshold:t=.9,percentage:r=!1}={}){let s=Array.isArray(e);if(s&&1!==e.length)throw Error("Object detection pipeline currently only supports a batch size of 1.");e=await sq(e);let n=r?null:e.map(e=>[e.height,e.width]),{pixel_values:i,pixel_mask:o}=await this.processor(e),a=await this.model({pixel_values:i,pixel_mask:o}),l=this.processor.feature_extractor.post_process_object_detection(a,t,n),h=this.model.config.id2label,c=l.map(e=>e.boxes.map((t,s)=>({score:e.scores[s],label:h[e.classes[s]],box:this._get_bounding_box(t,!r)})));return s?c:c[0]}_get_bounding_box(e,t){t&&(e=e.map(e=>0|e));let[r,s,n,i]=e;return{xmin:r,ymin:s,xmax:n,ymax:i}}},model:class extends r3{static MODEL_CLASS_MAPPINGS=[si]},processor:sD,default:{model:"Xenova/detr-resnet-50"},type:"multimodal"},"feature-extraction":{tokenizer:tR,pipeline:class extends sG{async _call(e,{pooling:t="none",normalize:r=!1}={}){let[s,n]=await super._call(e),i=n.last_hidden_state??n.logits;if("none"===t);else if("mean"===t)i=function(e,t){let r=[e.dims[0],e.dims[2]],s=new e.data.constructor(r[0]*r[1]),[n,i,o]=e.dims,a=0;for(let r=0;r{let n;for(let s of o)try{e(await s.from_pretrained(t,r));return}catch(e){n=e}s(n)}):o.from_pretrained(t,r),s[i]=e,n.push(e))}for(let[e,t]of(await Promise.all(n),Object.entries(s)))s[e]=await t;return s}}}]); \ No newline at end of file