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t.trim().match(this.pattern)||[]}},aP=oP,iP=class extends Kt{constructor(t){super(),this.config=t,this.pattern=ap(this.config.pattern??{}),this.content=this.config.content??""}pre_tokenize_text(t){return this.pattern===null?[t]:[t.replaceAll(this.pattern,this.config.content??"")]}},lP=iP,cP=class extends Kt{constructor(t){super(),this.tokenizers=(t.pretokenizers??[]).map(e=>rA(e))}pre_tokenize_text(t,e){return this.tokenizers.reduce((r,s)=>s?s.pre_tokenize(r,e):r,[t])}},uP=cP,pP=class extends Kt{pre_tokenize_text(t){return gC(t)}},fP=pP,dP=class extends Kt{constructor(t){super(),this.config=t,this._length=t.length}pre_tokenize_text(t){let e=[];for(let r=0;r<t.length;r+=this._length)e.push(t.slice(r,r+this._length));return e}},_P=dP;function mP(t){if(t===null)return null;switch(t.type){case"BertPreTokenizer":return new aP;case"Sequence":return new uP(t);case"Whitespace":return new YC;case"WhitespaceSplit":return new fP;case"Metaspace":return new JC(t);case"ByteLevel":return new XC(t);case"Split":return new eP(t);case"Punctuation":return new rP(t);case"Digits":return new nP(t);case"Replace":return new lP(t);case"FixedLength":return new _P(t);default:throw new Error(`Unknown PreTokenizer type: ${t.type}`)}}var rA=mP,hP=class extends gl{constructor(t){super(),this.config=t,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}_call(t){let e=this.encode(t);return this.fuse_unk&&(e=pC(e,this.tokens_to_ids,this.unk_token_id)),e}},lp=hP,gP=class extends lp{constructor(t){super(t),this.max_input_chars_per_word=100,this.tokens_to_ids=Ub(t.vocab),this.unk_token_id=this.tokens_to_ids.get(t.unk_token),this.unk_token=t.unk_token,this.max_input_chars_per_word=t.max_input_chars_per_word??100,this.vocab=new Array(this.tokens_to_ids.size);for(let[e,r]of this.tokens_to_ids)this.vocab[r]=e}encode(t){let e=[];for(let r of t){let s=[...r];if(s.length>this.max_input_chars_per_word){e.push(this.unk_token);continue}let n=!1,o=0,a=[];for(;o<s.length;){let i=s.length,l=null;for(;o<i;){let c=s.slice(o,i).join("");if(o>0&&(c=this.config.continuing_subword_prefix+c),this.tokens_to_ids.has(c)){l=c;break}--i}if(l===null){n=!0;break}a.push(l),o=i}n?e.push(this.unk_token):e.push(...a)}return e}},KE=gP,YE=class sA{constructor(e,r){this.is_leaf=e,this.children=r}static default(){return new sA(!1,new Map)}},wP=class{constructor(){this.root=YE.default()}extend(t){for(let e of t)this.push(e)}push(t){let e=this.root;for(let r of t){let s=e.children.get(r);s===void 0&&(s=YE.default(),e.children.set(r,s)),e=s}e.is_leaf=!0}*common_prefix_search(t){let e=this.root;if(e===void 0)return;let r="";for(let s of t){if(r+=s,e=e.children.get(s),e===void 0)return;e.is_leaf&&(yield r)}}},xP=wP,Fb=class nA{constructor(e,r,s,n,o){this.token_id=e,this.node_id=r,this.pos=s,this.length=n,this.score=o,this.prev=null,this.backtrace_score=0}clone(){let e=new nA(this.token_id,this.node_id,this.pos,this.length,this.score);return e.prev=this.prev,e.backtrace_score=this.backtrace_score,e}},yP=class{constructor(t,e,r){this.chars=Array.from(t),this.len=this.chars.length,this.bos_token_id=e,this.eos_token_id=r,this.nodes=[],this.begin_nodes=Array.from({length:this.len+1},()=>[]),this.end_nodes=Array.from({length:this.len+1},()=>[]);let s=new Fb(this.bos_token_id??0,0,0,0,0),n=new Fb(this.eos_token_id??0,1,this.len,0,0);this.nodes.push(s.clone()),this.nodes.push(n.clone()),this.begin_nodes[this.len].push(n),this.end_nodes[0].push(s)}insert(t,e,r,s){let n=this.nodes.length,o=new Fb(s,n,t,e,r);this.begin_nodes[t].push(o),this.end_nodes[t+e].push(o),this.nodes.push(o)}viterbi(){let t=this.len,e=0;for(;e<=t;){if(this.begin_nodes[e].length==0)return[];for(let a of this.begin_nodes[e]){a.prev=null;let i=0,l=null;for(let c of this.end_nodes[e]){let p=c.backtrace_score+a.score;(l===null||p>i)&&(l=c.clone(),i=p)}if(l!==null)a.prev=l,a.backtrace_score=i;else return[]}++e}let r=[],n=this.begin_nodes[t][0].prev;if(n===null)return[];let o=n.clone();for(;o.prev!==null;)r.push(o.clone()),o=o.clone().prev.clone();return r.reverse(),r}piece(t){return this.chars.slice(t.pos,t.pos+t.length).join("")}tokens(){return this.viterbi().map(e=>this.piece(e))}token_ids(){return this.viterbi().map(e=>e.token_id)}},bP=yP;function vP(t){if(t.length===0)throw new Error("Array must not be empty");let e=t[0],r=0;for(let s=1;s<t.length;++s)t[s]<e&&(e=t[s],r=s);return[e,r]}var kP=class extends lp{constructor(t,e){super(t);let r=t.vocab.length;this.vocab=new Array(r),this.scores=new Array(r);for(let s=0;s<r;++s)[this.vocab[s],this.scores[s]]=t.vocab[s];this.unk_token_id=t.unk_id,this.unk_token=this.vocab[t.unk_id],this.tokens_to_ids=new Map(this.vocab.map((s,n)=>[s,n])),this.bos_token=" ",this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=e,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.unk_token=this.vocab[this.unk_token_id],this.min_score=vP(this.scores)[0],this.unk_score=this.min_score-10,this.scores[this.unk_token_id]=this.unk_score,this.trie=new xP,this.trie.extend(this.vocab),this.fuse_unk=!0}populate_nodes(t){let e=t.chars,r=1,s=0;for(;s<e.length;){let n=!1,o=[],a=e.slice(s).join(""),i=this.trie.common_prefix_search(a);for(let l of i){o.push(l);let c=this.tokens_to_ids.get(l),p=this.scores[c],f=_C(l);t.insert(s,f,p,c),!n&&f===r&&(n=!0)}n||t.insert(s,r,this.unk_score,this.unk_token_id),s+=r}}tokenize(t){let e=new bP(t,this.bos_token_id,this.eos_token_id);return this.populate_nodes(e),e.tokens()}encode(t){let e=[];for(let r of t){let s=this.tokenize(r);e.push(...s)}return e}},QE=kP,EP=class{constructor(t=(r,s)=>r>s,e=1/0){this._heap=[],this._comparator=t,this._max_size=e}get size(){return this._heap.length}is_empty(){return this.size===0}peek(){return this._heap[0]}push(...t){return this.extend(t)}extend(t){for(let e of t)if(this.size<this._max_size)this._heap.push(e),this._sift_up();else{let r=this._smallest();this._comparator(e,this._heap[r])&&(this._heap[r]=e,this._sift_up_from(r))}return this.size}pop(){let t=this.peek(),e=this.size-1;return e>0&&this._swap(0,e),this._heap.pop(),this._sift_down(),t}replace(t){let e=this.peek();return this._heap[0]=t,this._sift_down(),e}_parent(t){return(t+1>>>1)-1}_left(t){return(t<<1)+1}_right(t){return t+1<<1}_greater(t,e){return this._comparator(this._heap[t],this._heap[e])}_swap(t,e){let r=this._heap[t];this._heap[t]=this._heap[e],this._heap[e]=r}_sift_up(){this._sift_up_from(this.size-1)}_sift_up_from(t){for(;t>0&&this._greater(t,this._parent(t));)this._swap(t,this._parent(t)),t=this._parent(t)}_sift_down(){let t=0;for(;this._left(t)<this.size&&this._greater(this._left(t),t)||this._right(t)<this.size&&this._greater(this._right(t),t);){let e=this._right(t)<this.size&&this._greater(this._right(t),this._left(t))?this._right(t):this._left(t);this._swap(t,e),t=e}}_smallest(){return 2**Math.floor(Math.log2(this.size))-1}},AP=EP,MP=class{constructor(t){this.capacity=t,this.cache=new Map}get(t){if(!this.cache.has(t))return;let e=this.cache.get(t);return this.cache.delete(t),this.cache.set(t,e),e}put(t,e){this.cache.has(t)&&this.cache.delete(t),this.cache.set(t,e),this.cache.size>this.capacity&&this.cache.delete(this.cache.keys().next().value)}clear(){this.cache.clear()}},TP=MP,SP=class extends lp{constructor(t){super(t),this.tokens_to_ids=Ub(t.vocab),this.unk_token_id=this.tokens_to_ids.get(t.unk_token),this.unk_token=t.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(let[r,s]of this.tokens_to_ids)this.vocab[s]=r;let e=Array.isArray(t.merges[0]);this.merges=e?t.merges:t.merges.map(r=>r.split(" ",2)),this.bpe_ranks=new Map(this.merges.map((r,s)=>[JSON.stringify(r),s])),this.end_of_word_suffix=t.end_of_word_suffix,this.continuing_subword_suffix=t.continuing_subword_suffix??null,this.byte_fallback=this.config.byte_fallback??!1,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.ignore_merges=this.config.ignore_merges??!1,this.max_length_to_cache=256,this.cache_capacity=1e4,this.cache=new TP(this.cache_capacity)}clear_cache(){this.cache.clear()}bpe(t){if(t.length===0)return[];let e=this.cache.get(t);if(e!==void 0)return e;let r=Array.from(t);this.end_of_word_suffix&&(r[r.length-1]+=this.end_of_word_suffix);let s=[];if(r.length>1){let n=new AP((i,l)=>i.score<l.score),o={token:r[0],bias:0,prev:null,next:null},a=o;for(let i=1;i<r.length;++i){let l={bias:i/r.length,token:r[i],prev:a,next:null};a.next=l,this.add_node(n,a),a=l}for(;!n.is_empty();){let i=n.pop();if(i.deleted||!i.next||i.next.deleted)continue;if(i.deleted=!0,i.next.deleted=!0,i.prev){let c={...i.prev};i.prev.deleted=!0,i.prev=c,c.prev?c.prev.next=c:o=c}let l={token:i.token+i.next.token,bias:i.bias,prev:i.prev,next:i.next.next};l.prev?(l.prev.next=l,this.add_node(n,l.prev)):o=l,l.next&&(l.next.prev=l,this.add_node(n,l))}for(let i=o;i!==null;i=i.next)s.push(i.token)}else s=r;if(this.continuing_subword_suffix)for(let n=0;n<s.length-1;++n)s[n]+=this.continuing_subword_suffix;return t.length<this.max_length_to_cache&&this.cache.put(t,s),s}add_node(t,e){let r=this.bpe_ranks.get(JSON.stringify([e.token,e.next.token]));r!==void 0&&(e.score=r+e.bias,t.push(e))}encode(t){let e=[];for(let r of t){if(this.ignore_merges&&this.tokens_to_ids.has(r)){e.push(r);continue}let s=this.bpe(r);for(let n of s)if(this.tokens_to_ids.has(n))e.push(n);else if(this.byte_fallback){let o=Array.from(this.text_encoder.encode(n)).map(a=>`<0x${a.toString(16).toUpperCase().padStart(2,"0")}>`);o.every(a=>this.tokens_to_ids.has(a))?e.push(...o):this.unk_token!=null&&e.push(this.unk_token)}else this.unk_token!=null&&e.push(this.unk_token)}return e}},JE=SP,OP=class extends lp{constructor(t,e){super(t);let r=t.vocab;this.tokens_to_ids=Ub(e.target_lang?r[e.target_lang]:r),this.bos_token=e.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=e.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=e.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=e.unk_token,this.unk_token_id=this.tokens_to_ids.get(this.unk_token),this.vocab=new Array(this.tokens_to_ids.size);for(let[s,n]of this.tokens_to_ids)this.vocab[n]=s}encode(t){return t}},IP=OP;function CP(t,e){switch(t.type){case"WordPiece":return new KE(t);case"Unigram":return new QE(t,e.eos_token);case"BPE":return new JE(t);default:if(t.vocab)return Array.isArray(t.vocab)?new QE(t,e.eos_token):Object.hasOwn(t,"continuing_subword_prefix")&&Object.hasOwn(t,"unk_token")?Object.hasOwn(t,"merges")?new JE(t):new KE(t):new IP(t,{target_lang:e.target_lang,bos_token:e.bos_token,eos_token:e.eos_token,pad_token:e.pad_token,unk_token:e.unk_token});throw new Error(`Unknown TokenizerModel type: ${t?.type}`)}}var PP=CP,zP=class extends gl{constructor(t){super(),this.config=t}_call(t,...e){return this.post_process(t,...e)}},wl=zP,LP=class extends wl{post_process(t,e=null,r=!0){let s=e===null?this.config.single:this.config.pair,n=[],o=[];for(let a of s)"SpecialToken"in a?r&&(n.push(a.SpecialToken.id),o.push(a.SpecialToken.type_id)):"Sequence"in a&&(a.Sequence.id==="A"?(n=Xt(n,t),o=Xt(o,new Array(t.length).fill(a.Sequence.type_id))):a.Sequence.id==="B"&&(n=Xt(n,e),o=Xt(o,new Array(e.length).fill(a.Sequence.type_id))));return{tokens:n,token_type_ids:o}}},NP=LP,$P=class extends wl{post_process(t,e=null){return{tokens:t,tokens_pair:e}}},RP=$P,DP=class extends wl{constructor(t){super(t),this.sep=t.sep,this.cls=t.cls}post_process(t,e=null,r=!0){r&&(t=Xt([this.cls[0]],t,[this.sep[0]]));let s=new Array(t.length).fill(0);if(e){let n=[],o=r?[this.sep[0]]:[];t=Xt(t,n,e,o),s=Xt(s,new Array(e.length+n.length+o.length).fill(1))}return{tokens:t,token_type_ids:s}}},FP=DP,BP=class extends wl{constructor(t){super(t),this.sep=t.sep,this.cls=t.cls}post_process(t,e,r=!0){r&&(t=Xt([this.cls[0]],t,[this.sep[0]]));let s=new Array(t.length).fill(0);if(e){let n=r?[this.sep[0]]:[],o=r?[this.sep[0]]:[];t=Xt(t,n,e,o),s=Xt(s,new Array(e.length+n.length+o.length).fill(1))}return{tokens:t,token_type_ids:s}}},UP=BP,jP=class extends wl{constructor(t){super(t),this.processors=(t.processors??[]).map(e=>oA(e))}post_process(t,e=null,r=!0){let s={tokens:t,tokens_pair:e};for(let n of this.processors)s=n.post_process(s.tokens,s.tokens_pair,r);return s}},GP=jP;function qP(t){if(t===null)return null;switch(t.type){case"TemplateProcessing":return new NP(t);case"ByteLevel":return new RP(t);case"BertProcessing":return new FP(t);case"RobertaProcessing":return new UP(t);case"Sequence":return new GP(t);default:throw new Error(`Unknown PostProcessor type: ${t.type}`)}}var oA=qP,WP=class extends gl{constructor(t){super(),this.config=t,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets="trim_offsets"in t?t.trim_offsets:!1}_call(t){return this.decode(t)}decode(t){return this.decode_chain(t).join("")}},Ft=WP,VP=class extends Ft{constructor(t){super(t),this.byte_decoder=lC,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(t){let e=t.join(""),r=new Uint8Array([...e].map(s=>this.byte_decoder[s]));return this.text_decoder.decode(r)}decode_chain(t){let e=[],r=[];for(let s of t)this.added_tokens.find(n=>n.content===s)!==void 0?(r.length>0&&(e.push(this.convert_tokens_to_string(r)),r=[]),e.push(s)):r.push(s);return r.length>0&&e.push(this.convert_tokens_to_string(r)),e}},HP=VP,XP=class extends Ft{constructor(t){super(t),this.cleanup=t.cleanup}decode_chain(t){return t.map((e,r)=>{if(r!==0){let s=this.config.prefix;s&&e.startsWith(s)?e=e.replace(s,""):e=" "+e}return this.cleanup&&(e=Bb(e)),e})}},KP=XP,YP=class extends Ft{constructor(t){super(t),this.replacement=t.replacement??"\u2581"}decode_chain(t){let e=[];for(let r=0;r<t.length;++r){let s=t[r].replaceAll(this.replacement," ");r==0&&s.startsWith(" ")&&(s=s.substring(1)),e.push(s)}return e}},QP=YP,JP=class extends Ft{constructor(t){super(t),this.suffix=t.suffix??""}decode_chain(t){return t.map((e,r)=>e.replaceAll(this.suffix,r===t.length-1?"":" "))}},ZP=JP,ez=class extends Ft{constructor(t){super(t),this.pad_token=t.pad_token??"",this.word_delimiter_token=t.word_delimiter_token??"",this.cleanup=t.cleanup}convert_tokens_to_string(t){if(t.length===0)return"";let e=[t[0]];for(let n=1;n<t.length;++n)t[n]!==e.at(-1)&&e.push(t[n]);let s=e.filter(n=>n!==this.pad_token).join("");return this.cleanup&&(s=Bb(s).replaceAll(this.word_delimiter_token," ").trim()),s}decode_chain(t){return[this.convert_tokens_to_string(t)]}},tz=ez,rz=class extends Ft{constructor(t){super(t),this.decoders=(t.decoders??[]).map(e=>aA(e))}decode_chain(t){return this.decoders.reduce((e,r)=>r.decode_chain(e),t)}},sz=rz,nz=class extends Ft{decode_chain(t){let e=ap(this.config.pattern),r=this.config.content??"";return e===null?t:t.map(s=>s.replaceAll(e,r))}},oz=nz,az=class extends Ft{decode_chain(t){return[t.join("")]}},iz=az,lz=class extends Ft{constructor(t){super(t),this.content=t.content??"",this.start=t.start??0,this.stop=t.stop??0}decode_chain(t){return t.map(e=>{let r=0;for(let n=0;n<this.start&&e[n]===this.content;++n){r=n+1;continue}let s=e.length;for(let n=0;n<this.stop;++n){let o=e.length-n-1;if(e[o]===this.content){s=o;continue}else break}return e.slice(r,s)})}},cz=lz,uz=class extends Ft{constructor(t){super(t),this.text_decoder=new TextDecoder}decode_chain(t){let e=[],r=[];for(let s of t){let n=null;if(s.length===6&&s.startsWith("<0x")&&s.endsWith(">")){let o=parseInt(s.slice(3,5),16);isNaN(o)||(n=o)}if(n!==null)r.push(n);else{if(r.length>0){let o=this.text_decoder.decode(Uint8Array.from(r));e.push(o),r=[]}e.push(s)}}if(r.length>0){let s=this.text_decoder.decode(Uint8Array.from(r));e.push(s),r=[]}return e}},pz=uz;function fz(t){if(t===null)return null;switch(t.type){case"ByteLevel":return new HP(t);case"WordPiece":return new KP(t);case"Metaspace":return new QP(t);case"BPEDecoder":return new ZP(t);case"CTC":return new tz(t);case"Sequence":return new sz(t);case"Replace":return new oz(t);case"Fuse":return new iz(t);case"Strip":return new cz(t);case"ByteFallback":return new pz(t);default:throw new Error(`Unknown Decoder type: ${t.type}`)}}var aA=fz,dz=class{constructor(t,e){let r=XE(t,"Tokenizer",["model","decoder","post_processor","pre_tokenizer","normalizer"]);if(r)throw new Error(r);let s=XE(e,"Config");if(s)throw new Error(s);this.tokenizer=t,this.config=e,this.normalizer=tA(this.tokenizer.normalizer),this.pre_tokenizer=rA(this.tokenizer.pre_tokenizer),this.model=PP(this.tokenizer.model,this.config),this.post_processor=oA(this.tokenizer.post_processor),this.decoder=aA(this.tokenizer.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];let n=[],o=[];this.added_tokens_map=new Map;for(let a of this.tokenizer.added_tokens){let i=new aC(a);if(this.added_tokens.push(i),this.model.tokens_to_ids.set(i.content,i.id),this.model.vocab[i.id]=i.content,i.special&&(this.special_tokens.push(i.content),this.all_special_ids.push(i.id)),this.added_tokens_map.set(i.content,i),i.normalized&&this.normalizer!==null){let l=this.normalizer(i.content);o.push(l),this.added_tokens_map.set(l,i)}else 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this.mlContextCache.push({options:t,mlContext:r}),r}}registerMLContext(t,e){this.mlContextBySessionId.set(t,e);let r=this.sessionIdsByMLContext.get(e);r||(r=new Set,this.sessionIdsByMLContext.set(e,r)),r.add(t),this.mlOpSupportLimitsBySessionId.has(t)||this.mlOpSupportLimitsBySessionId.set(t,e.opSupportLimits()),this.temporaryGraphInputs.length>0&&(this.sessionGraphInputs.set(t,this.temporaryGraphInputs),this.temporaryGraphInputs=[]),this.temporaryGraphOutputs.length>0&&(this.sessionGraphOutputs.set(t,this.temporaryGraphOutputs),this.temporaryGraphOutputs=[])}onReleaseSession(t){this.sessionGraphInputs.delete(t),this.sessionGraphOutputs.delete(t);let e=this.mlContextBySessionId.get(t);if(!e)return;this.tensorManager.releaseTensorsForSession(t),this.mlContextBySessionId.delete(t),this.mlOpSupportLimitsBySessionId.delete(t);let r=this.sessionIdsByMLContext.get(e);if(r.delete(t),r.size===0){this.sessionIdsByMLContext.delete(e);let s=this.mlContextCache.findIndex(n=>n.mlContext===e);s!==-1&&this.mlContextCache.splice(s,1)}}getMLContext(t){return this.mlContextBySessionId.get(t)}getMLOpSupportLimits(t){return this.mlOpSupportLimitsBySessionId.get(t)}reserveTensorId(){return this.tensorManager.reserveTensorId()}releaseTensorId(t){vt("verbose",()=>`[WebNN] releaseTensorId {tensorId: ${t}}`),this.tensorManager.releaseTensorId(t)}async ensureTensor(t,e,r,s,n){let o=Il.get(r);if(!o)throw new Error(`Unsupported ONNX data type: ${r}`);return this.tensorManager.ensureTensor(t??this.currentSessionId,e,o,s,n)}async createTemporaryTensor(t,e,r){vt("verbose",()=>`[WebNN] createTemporaryTensor {onnxDataType: ${e}, shape: ${r}}`);let s=Il.get(e);if(!s)throw new Error(`Unsupported ONNX data type: ${e}`);let n=this.tensorManager.reserveTensorId();await this.tensorManager.ensureTensor(t,n,s,r,!1);let o=this.temporarySessionTensorIds.get(t);return 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Remove the useWasmCache override.");return Gn=Promise.resolve(),Gn}return Gn=(async()=>{let e=it.wasm.wasmPaths,r=!1;await Promise.all([e.wasm&&!qb(e.wasm)?(async()=>{try{let s=await aM(Wb(e.wasm));s&&(it.wasm.wasmBinary=s,r=!0)}catch(s){ee.warn("Failed to pre-load WASM binary:",s)}})():Promise.resolve(),e.mjs&&!qb(e.mjs)?(async()=>{try{let s=await iM(Wb(e.mjs));s&&(it.wasm.wasmPaths.mjs=s)}catch(s){ee.warn("Failed to pre-load WASM factory:",s)}})():Promise.resolve()]),r||(it.wasm.wasmPaths.mjs=e.mjs)})(),Gn}async function Op(t,e,r){await KL();let s=pM(_e.logLevel??St.WARNING),n=()=>XL.create(t,{logSeverityLevel:s,...e}),o=await(ie.IS_WEB_ENV?cM=cM.then(n):n());return o.config=r,o}var uM=Promise.resolve();async function Ip(t,e){let r=()=>t.run(e);return ie.IS_WEB_ENV?uM=uM.then(r):r()}function Cp(t){return t instanceof qn.Tensor}var it=qn?.env;function $l(){return it?.wasm?.proxy}if(it){let t=function(e){let r=pM(e);it.logLevel=HL[r]};if(it.wasm){if(!(typeof ServiceWorkerGlobalScope<"u"&&self instanceof ServiceWorkerGlobalScope)&&it.versions?.web&&!it.wasm.wasmPaths){let e=`https://cdn.jsdelivr.net/npm/onnxruntime-web@${it.versions.web}/dist/`;it.wasm.wasmPaths=ie.IS_SAFARI?{mjs:`${e}ort-wasm-simd-threaded.mjs`,wasm:`${e}ort-wasm-simd-threaded.wasm`}:{mjs:`${e}ort-wasm-simd-threaded.asyncify.mjs`,wasm:`${e}ort-wasm-simd-threaded.asyncify.wasm`}}it.wasm.proxy=!1}it.webgpu&&(it.webgpu.powerPreference="high-performance"),t(_e.logLevel??St.WARNING),_e.backends.onnx={...it,setLogLevel:t}}var as=async(t,e,r)=>{let s=await Op(new Uint8Array(t),e);return(async n=>{let o=$l(),a=Object.fromEntries(Object.entries(n).map(([l,c])=>[l,(o?c.clone():c).ort_tensor])),i=await Ip(s,a);return Array.isArray(r)?r.map(l=>new N(i[l])):new N(i[r])})},pr=class{static session_options={};static get nearest_interpolate_4d(){return this._nearest_interpolate_4d||(this._nearest_interpolate_4d=as([8,10,18,0,58,129,1,10,41,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,18,10,4,109,111,100,101,34,7,110,101,97,114,101,115,116,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,21],this.session_options,"y")),this._nearest_interpolate_4d}static get bilinear_interpolate_4d(){return this._bilinear_interpolate_4d||(this._bilinear_interpolate_4d=as([8,9,18,0,58,128,1,10,40,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,17,10,4,109,111,100,101,34,6,108,105,110,101,97,114,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20],this.session_options,"y")),this._bilinear_interpolate_4d}static get bicubic_interpolate_4d(){return this._bicubic_interpolate_4d||(this._bicubic_interpolate_4d=as([8,9,18,0,58,127,10,39,10,1,120,10,0,10,0,10,1,115,18,1,121,34,6,82,101,115,105,122,101,42,16,10,4,109,111,100,101,34,5,99,117,98,105,99,160,1,3,18,1,114,90,31,10,1,120,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,90,15,10,1,115,18,10,10,8,8,7,18,4,10,2,8,4,98,31,10,1,121,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,99,10,3,18,1,104,10,3,18,1,119,66,2,16,20],this.session_options,"y")),this._bicubic_interpolate_4d}static get matmul(){return this._matmul||(this._matmul=as([8,9,18,0,58,55,10,17,10,1,97,10,1,98,18,1,99,34,6,77,97,116,77,117,108,18,1,114,90,9,10,1,97,18,4,10,2,8,1,90,9,10,1,98,18,4,10,2,8,1,98,9,10,1,99,18,4,10,2,8,1,66,2,16,20],this.session_options,"c")),this._matmul}static get stft(){return this._stft||(this._stft=as([8,7,18,0,58,148,1,10,38,10,1,115,10,1,106,10,1,119,10,1,108,18,1,111,34,4,83,84,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,115,90,26,10,1,115,18,21,10,19,8,1,18,15,10,3,18,1,98,10,3,18,1,115,10,3,18,1,99,90,11,10,1,106,18,6,10,4,8,7,18,0,90,16,10,1,119,18,11,10,9,8,1,18,5,10,3,18,1,119,90,11,10,1,108,18,6,10,4,8,7,18,0,98,31,10,1,111,18,26,10,24,8,1,18,20,10,3,18,1,98,10,3,18,1,102,10,3,18,1,100,10,3,18,1,99,66,2,16,17],this.session_options,"o")),this._stft}static get rfft(){return this._rfft||(this._rfft=as([8,9,18,0,58,97,10,33,10,1,120,10,0,10,1,97,18,1,121,34,3,68,70,84,42,15,10,8,111,110,101,115,105,100,101,100,24,1,160,1,2,18,1,100,90,21,10,1,120,18,16,10,14,8,1,18,10,10,3,18,1,115,10,3,18,1,99,90,11,10,1,97,18,6,10,4,8,7,18,0,98,21,10,1,121,18,16,10,14,8,1,18,10,10,3,18,1,115,10,3,18,1,99,66,2,16,20],this.session_options,"y")),this._rfft}static get top_k(){return this._top_k||(this._top_k=as([8,10,18,0,58,73,10,18,10,1,120,10,1,107,18,1,118,18,1,105,34,4,84,111,112,75,18,1,116,90,9,10,1,120,18,4,10,2,8,1,90,15,10,1,107,18,10,10,8,8,7,18,4,10,2,8,1,98,9,10,1,118,18,4,10,2,8,1,98,9,10,1,105,18,4,10,2,8,7,66,2,16,21],this.session_options,["v","i"])),this._top_k}static get slice(){return this._slice||(this._slice=as([8,7,18,0,58,96,10,25,10,1,120,10,1,115,10,1,101,10,1,97,10,1,116,18,1,121,34,5,83,108,105,99,101,18,1,114,90,9,10,1,120,18,4,10,2,8,1,90,9,10,1,115,18,4,10,2,8,7,90,9,10,1,101,18,4,10,2,8,7,90,9,10,1,97,18,4,10,2,8,7,90,9,10,1,116,18,4,10,2,8,7,98,9,10,1,121,18,4,10,2,8,1,66,2,16,13],this.session_options,"y")),this._slice}};var dM=Object.freeze({auto:"auto",gpu:"gpu",cpu:"cpu",wasm:"wasm",webgpu:"webgpu",cuda:"cuda",dml:"dml",coreml:"coreml",webnn:"webnn","webnn-npu":"webnn-npu","webnn-gpu":"webnn-gpu","webnn-cpu":"webnn-cpu"}),j1=ie.IS_NODE_ENV?"cpu":"wasm";function Pp(t,e,{warn:r}={}){return t?typeof t=="string"?t:t.hasOwnProperty(e)?t[e]:(r&&r(`device not specified for "${e}". Using the default device (${j1}).`),j1):j1}var hM=(function(){let t;return async function(){if(t===void 0)if(!ie.IS_WEBGPU_AVAILABLE)t=!1;else try{t=(await navigator.gpu.requestAdapter()).features.has("shader-f16")}catch{t=!1}return t}})(),st=Object.freeze({auto:"auto",fp32:"fp32",fp16:"fp16",q8:"q8",int8:"int8",uint8:"uint8",q4:"q4",bnb4:"bnb4",q4f16:"q4f16",q2:"q2",q2f16:"q2f16",q1:"q1",q1f16:"q1f16"}),_M=st.fp32,mM=Object.freeze({[dM.wasm]:st.q8}),is=Object.freeze({[st.fp32]:"",[st.fp16]:"_fp16",[st.int8]:"_int8",[st.uint8]:"_uint8",[st.q8]:"_quantized",[st.q4]:"_q4",[st.q2]:"_q2",[st.q1]:"_q1",[st.q4f16]:"_q4f16",[st.q2f16]:"_q2f16",[st.q1f16]:"_q1f16",[st.bnb4]:"_bnb4"});function zp(t,e,r,{configDtype:s=null,warn:n}={}){let o,a=!1;t&&typeof t!="string"?t.hasOwnProperty(e)?o=t[e]:(o=null,a=!0):o=t;let i;if(o===st.auto){if(s){let l=typeof s=="string"?s:s?.[e];if(l&&l!==st.auto&&st.hasOwnProperty(l))return l}i=mM[r]??_M}else o&&st.hasOwnProperty(o)?i=o:i=mM[r]??_M;return a&&n&&n(`dtype not specified for "${e}". 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Ot(t,"tokenizer_config.json",{})).exists?["tokenizer.json","tokenizer_config.json"]:[]}async function K1(t,e){let r=await Vn(t);return await Promise.all(r.map(s=>nt(t,s,!0,e)))}function Ul(t){let e=t.dims;switch(e.length){case 1:return t.tolist();case 2:if(e[0]!==1)throw new Error("Unable to decode tensor with `batch size !== 1`. 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To pad to max length, use `padding: 'max_length'`."),l=this.model_max_length):o===!1&&(ee.warn("Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation: true` to explicitly truncate examples to max length."),i=!0)),o===!0&&(l=Math.min(ze(f.map(m=>m.input_ids.length))[0],l??1/0)),l=Math.min(l,this.model_max_length??1/0),o||i)for(let m=0;m<f.length;++m)f[m].input_ids.length!==l&&(f[m].input_ids.length>l?i&&rN(f[m],l):o&&tN(f[m],l,w=>w==="input_ids"?this.pad_token_id:0,this.padding_side));let _={};if(c){if(!(o&&i)&&f.some(w=>{for(let x of Object.keys(w))if(w[x].length!==f[0][x]?.length)return!0;return!1}))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 m=[f.length,f[0].input_ids.length];for(let w of Object.keys(f[0]))_[w]=new N("int64",BigInt64Array.from(f.flatMap(x=>x[w]).map(BigInt)),m)}else{for(let m of Object.keys(f[0]))_[m]=f.map(w=>w[m]);if(!p)for(let m of Object.keys(_))_[m]=_[m][0]}return _}_encode_text(e){return e===null?null:this._tokenizer.encode(e).tokens}_encode_plus(e,{text_pair:r=null,add_special_tokens:s=!0,return_token_type_ids:n=null}={}){let{ids:o,attention_mask:a,token_type_ids:i}=this._tokenizer.encode(e,{text_pair:r,add_special_tokens:s,return_token_type_ids:n??this.return_token_type_ids});return{input_ids:o,attention_mask:a,...i?{token_type_ids:i}:{}}}tokenize(e,{pair:r=null,add_special_tokens:s=!1}={}){return this._tokenizer.tokenize(e,{text_pair:r,add_special_tokens:s})}encode(e,{text_pair:r=null,add_special_tokens:s=!0,return_token_type_ids:n=null}={}){return this._tokenizer.encode(e,{text_pair:r,add_special_tokens:s,return_token_type_ids:n}).ids}batch_decode(e,r={}){return e instanceof N&&(e=e.tolist()),e.map(s=>this.decode(s,r))}decode(e,r={}){if(e instanceof N&&(e=Ul(e)),!Array.isArray(e)||e.length===0||!GE(e[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(e,r)}decode_single(e,{skip_special_tokens:r=!1,clean_up_tokenization_spaces:s=null}){return this._tokenizer.decode(e,{skip_special_tokens:r,clean_up_tokenization_spaces:s})}get_chat_template({chat_template:e=null,tools:r=null}={}){if(this.chat_template&&typeof this.chat_template=="object"){let s=this.chat_template;if(e!==null&&Object.hasOwn(s,e))e=s[e];else if(e===null)if(r!==null&&"tool_use"in s)e=s.tool_use;else if("default"in s)e=s.default;else throw Error(`This model has multiple chat templates with no default specified! Please either pass a chat template or the name of the template you wish to use to the 'chat_template' argument. Available template names are ${Object.keys(s).sort()}.`)}else if(e===null)if(this.chat_template)e=this.chat_template;else throw Error("Cannot use apply_chat_template() because tokenizer.chat_template is not set and no template argument was passed! For information about writing templates and setting the tokenizer.chat_template attribute, please see the documentation at https://huggingface.co/docs/transformers/main/en/chat_templating");return e}apply_chat_template(e,r={}){let{tools:s=null,documents:n=null,chat_template:o=null,add_generation_prompt:a=!1,tokenize:i=!0,padding:l=!1,truncation:c=!1,max_length:p=null,return_tensor:f=!0,return_dict:_=!0,tokenizer_kwargs:m={},...w}=r;if(o=this.get_chat_template({chat_template:o,tools:s}),typeof o!="string")throw Error(`chat_template must be a string, but got ${typeof o}`);let x=this._compiled_template_cache.get(o);x===void 0&&(x=new xA(o),this._compiled_template_cache.set(o,x));let k=Object.create(null);for(let E of eN){let S=Ws(this.config,E);S&&(k[E]=S)}let A=x.render({messages:e,add_generation_prompt:a,tools:s,documents:n,...k,...w});if(i){let E=this._call(A,{add_special_tokens:!1,padding:l,truncation:c,max_length:p,return_tensor:f,...m});return _?E:E.input_ids}return A}};function Hn(t,e,r,s){if(!("language_codes"in t)||!Array.isArray(t.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in t)||!(t.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in t)||typeof t.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");let n=s.src_lang,o=s.tgt_lang;if(!t.language_codes.includes(o))throw new Error(`Target language code "${o}" is not valid. 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Therefore, you may experience slightly inaccurate results.')}_encode_text(e){if(e===null)return null;let[r,...s]=e.trim().split(this.languageRegex);if(s.length===0)return super._encode_text(r);if(s.length===2){let[n,o]=s;return this.supported_language_codes.includes(n)||ee.warn(`Unsupported language code "${n}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`),wt([n],super._encode_text(o))}}};var Xn=class extends W{constructor(e,r){super(e,r),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.all_special_tokens.filter(s=>this.languageRegex.test(s)).map(s=>s),this.lang_to_token=s=>s}_build_translation_inputs(e,r,s){return Hn(this,e,r,s)}};var ff=class extends Xn{};var df=class extends W{};var _f=class extends W{return_token_type_ids=!0};var mf=class extends W{};var hf=class extends W{constructor(e,r){super(e,r),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.all_special_tokens.filter(s=>this.languageRegex.test(s)),this.lang_to_token=s=>s}_build_translation_inputs(e,r,s){return Hn(this,e,r,s)}};var gf=class extends W{};var wf=class extends W{};var xf=class extends W{};var yf=class extends W{return_token_type_ids=!0};var bf=class extends W{};var vf=class extends W{};var kf=class extends W{return_token_type_ids=!0};var Ef=class extends W{};var Y1=class extends Ft{decode_chain(e){let r="";for(let s=1;s<e.length;s+=2)r+=e[s];return[r]}},Af=class extends W{constructor(e,r){super(e,r),this._tokenizer.decoder=new Y1({type:"VitsDecoder"})}};var Mf=class extends W{};var yM=[["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"]],jl=new Map(yM),nN=new Map([...yM.map(([t,e])=>[e,t]),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);function bM(t){t=t.toLowerCase();let e=nN.get(t);if(e===void 0){let r=t.match(/^<\|([a-z]{2})\|>$/);if(r&&(t=r[1]),jl.has(t))e=t;else{let n=t.length===2?jl.keys():jl.values();throw new Error(`Language "${t}" is not supported. 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Got '${m}'`)}}return $}function mt(t,e,{periodic:r=!0,frame_length:s=null,center:n=!0}={}){let o=r?t+1:t,a;switch(e){case"boxcar":a=new Float64Array(o).fill(1);break;case"hann":case"hann_window":a=MM(o);break;case"hamming":a=iN(o);break;case"povey":a=MM(o).map(c=>Math.pow(c,.85));break;default:throw new Error(`Unknown window type ${e}.`)}if(r&&(a=a.subarray(0,t)),s===null||t===s)return a;if(t>s)throw new Error(`Length of the window (${t}) may not be larger than frame_length (${s})`);let i=new Float64Array(s),l=n?Math.floor((s-t)/2):0;return i.set(a,l),i}function mN(t,e){let r=t.reduce((o,a)=>o+a.length,0),s=new ArrayBuffer(44),n=new DataView(s);return zf(n,0,"RIFF"),n.setUint32(4,36+r*4,!0),zf(n,8,"WAVE"),zf(n,12,"fmt "),n.setUint32(16,16,!0),n.setUint16(20,3,!0),n.setUint16(22,1,!0),n.setUint32(24,e,!0),n.setUint32(28,e*4,!0),n.setUint16(32,4,!0),n.setUint16(34,32,!0),zf(n,36,"data"),n.setUint32(40,r*4,!0),new Blob([s,...t.map(o=>o.buffer)],{type:"audio/wav"})}function zf(t,e,r){for(let s=0;s<r.length;++s)t.setUint8(e+s,r.charCodeAt(s))}var Kn=class{constructor(e,r){this.audio=e,this.sampling_rate=r}get data(){if(Array.isArray(this.audio)){if(this.audio.length===0)return new Float32Array(0);if(this.audio.length===1)return this.audio[0];let e=this.audio.reduce((n,o)=>n+o.length,0),r=new Float32Array(e),s=0;for(let n of this.audio)r.set(n,s),s+=n.length;return r}else return this.audio}toBlob(){let e=this.audio;return e instanceof Float32Array&&(e=[e]),mN(e,this.sampling_rate)}async save(e){return Pf(e,this.toBlob())}};var Nf=class extends Ee{constructor(e){super(e);let r=this.config.sampling_rate,s=lt(257,this.config.num_mel_bins,20,Math.floor(r/2),r,null,"kaldi",!0);this.mel_filters=s,this.window=mt(400,"hann",{periodic:!1}),this.mean=this.config.mean,this.std=this.config.std}async _extract_fbank_features(e,r){return ot(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:r,transpose:!0})}async _call(e){Me(e,"ASTFeatureExtractor");let r=await this._extract_fbank_features(e,this.config.max_length);if(this.config.do_normalize){let s=this.std*2,n=r.data;for(let o=0;o<n.length;++o)n[o]=(n[o]-this.mean)/s}return{input_values:r.unsqueeze_(0)}}};var Yn=class extends Ee{async _call(e){Me(e,"EncodecFeatureExtractor"),e instanceof Float64Array&&(e=new Float32Array(e));let r=this.config.feature_size;if(e.length%r!==0)throw new Error(`The length of the audio data must be a multiple of the number of channels (${r}).`);let s=[1,r,e.length/r];return{input_values:new N("float32",e,s)}}};var $f=class extends Ee{async _call(e){Me(e,"ChatterboxFeatureExtractor"),e instanceof Float64Array&&(e=new Float32Array(e));let r=[1,e.length];return{input_values:new N("float32",e,r)}}};var Rf=class extends Ee{constructor(e){super(e),this.mel_filters=lt(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,null,"htk"),this.mel_filters_slaney=lt(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,"slaney","slaney"),this.window=mt(this.config.fft_window_size,"hann")}async _get_input_mel(e,r,s,n){let o,a=!1,i=e.length-r;if(i>0)if(s==="rand_trunc"){a=!0;let l=Math.floor(Kr.random()*(i+1));e=e.subarray(l,l+r),o=await this._extract_fbank_features(e,this.mel_filters_slaney,this.config.nb_max_samples)}else throw new Error(`Truncation strategy "${s}" not implemented`);else{if(i<0){let l=new Float64Array(r);if(l.set(e),n==="repeat")for(let c=e.length;c<r;c+=e.length)l.set(e.subarray(0,Math.min(e.length,r-c)),c);else if(n==="repeatpad")for(let c=e.length;c<-i;c+=e.length)l.set(e,c);e=l}if(s==="fusion")throw new Error(`Truncation strategy "${s}" not implemented`);o=await this._extract_fbank_features(e,this.mel_filters_slaney,this.config.nb_max_samples)}return o.unsqueeze_(0)}async _extract_fbank_features(e,r,s=null){return ot(e,this.window,this.config.fft_window_size,this.config.hop_length,{power:2,mel_filters:r,log_mel:"dB",max_num_frames:s,do_pad:!1,transpose:!0})}async _call(e,{max_length:r=null}={}){return Me(e,"ClapFeatureExtractor"),{input_features:(await this._get_input_mel(e,r??this.config.nb_max_samples,this.config.truncation,this.config.padding)).unsqueeze_(0)}}};var hN=1e-5,Qn=class extends Ee{constructor(e){super(e),this.config.mel_filters??=lt(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,this.config.sampling_rate/2,this.config.sampling_rate,"slaney","slaney");let r=mt(this.config.win_length,"hann",{periodic:!1});this.window=new Float64Array(this.config.n_fft);let s=Math.floor((this.config.n_fft-this.config.win_length)/2);this.window.set(r,s)}async _extract_fbank_features(e){let r=this.config.preemphasis;e=new Float64Array(e);for(let n=e.length-1;n>=1;--n)e[n]-=r*e[n-1];return await ot(e,this.window,this.window.length,this.config.hop_length,{fft_length:this.config.n_fft,power:2,mel_filters:this.config.mel_filters,log_mel:"log",mel_floor:-1/0,pad_mode:"constant",center:!0,transpose:!0,mel_offset:2**-24})}async _call(e){Me(e,"ParakeetFeatureExtractor");let r=await this._extract_fbank_features(e),s=Math.floor((e.length+Math.floor(this.config.n_fft/2)*2-this.config.n_fft)/this.config.hop_length),n=r.data;n.fill(0,s*r.dims[1]);let[o,a]=r.dims,i=new Float64Array(a),l=new Float64Array(a);for(let f=0;f<s;++f){let _=f*a;for(let m=0;m<a;++m){let w=n[_+m];i[m]+=w,l[m]+=w*w}}let c=s>1?s-1:1;for(let f=0;f<a;++f){let _=i[f]/s,m=(l[f]-s*_*_)/c,x=1/(Math.sqrt(m)+hN);for(let k=0;k<s;++k){let A=k*a+f;n[A]=(n[A]-_)*x}}let p=new BigInt64Array(o);return p.fill(1n,0,s),{input_features:r.unsqueeze_(0),attention_mask:new N("int64",p,[1,o])}}};var Df=class extends Qn{_apply_dither(e){let r=this.config.dither??0;if(r<=0)return e;let s=new Xr(e.length);for(let n=0;n<e.length;++n)e[n]+=r*s.gauss();return e}split_audio(e){let r=this.config.max_audio_clip_s??35,s=this.config.overlap_chunk_second??5,n=this.config.min_energy_window_samples??1600,o=this.config.sampling_rate,a=Math.max(1,Math.round(r*o)),i=Math.max(1,Math.round(s*o));if(e.length<=a)return[e];let l=[],c=0,p=e.length;for(;c<p;){if(c+a>=p){l.push(e.slice(c,p));break}let f=Math.max(c,c+a-i),_=Math.min(c+a,p),m;_<=f?m=c+a:m=this._find_split_point_energy(e,f,_,n),m=Math.max(c+1,Math.min(m,p)),l.push(e.slice(c,m)),c=m}return l}_find_split_point_energy(e,r,s,n){let o=s-r;if(o<=n)return Math.floor((r+s)/2);let a=1/0,i=r,l=o-n;for(let c=0;c<=l;c+=n){let p=0;for(let f=0;f<n;++f){let _=e[r+c+f];p+=_*_}p=Math.sqrt(p/n),p<a&&(a=p,i=r+c)}return i}async _call(e){Me(e,"CohereAsrFeatureExtractor");let r=new Float64Array(e);return this._apply_dither(r),super._call(r)}};var Jn=class extends Yn{};var Zn=class extends Ee{constructor(e){super(e);let{fft_length:r,feature_size:s,min_frequency:n,max_frequency:o,sampling_rate:a,frame_length:i}=this.config,l=lt(Math.floor(1+r/2),s,n,o,a,null,"htk",!1);this.mel_filters=l,this.window=mt(i,"hann")}async _extract_fbank_features(e,r){return ot(e,this.window,this.config.frame_length,this.config.hop_length,{fft_length:this.config.fft_length,center:!1,onesided:!0,preemphasis:this.config.preemphasis,preemphasis_htk_flavor:this.config.preemphasis_htk_flavor,mel_filters:this.mel_filters,log_mel:"log",mel_floor:this.config.mel_floor,remove_dc_offset:!1,transpose:!0})}async _call(e,{max_length:r=48e4,truncation:s=!0,padding:n=!0,pad_to_multiple_of:o=128}={}){if(Me(e,"Gemma3nAudioFeatureExtractor"),s&&e.length>r&&(e=e.slice(0,r)),n&&e.length%o!==0){let l=o-e.length%o,c=new Float64Array(e.length+l);c.set(e),this.config.padding_value!==0&&c.fill(this.config.padding_value,e.length),e=c}let a=await this._extract_fbank_features(e,this.config.max_length),i=Ke([1,a.dims[0]],!0);return{input_features:a.unsqueeze_(0),input_features_mask:i}}};var eo=class extends Zn{async _extract_fbank_features(e,r){let{frame_length:s,hop_length:n,fft_length:o}=this.config,a=Math.floor(s/2),i=Math.floor((e.length+a-(s+1))/n)+1;return ot(e,this.window,s,n,{fft_length:o,center:!0,pad_mode:"semicausal",onesided:!0,preemphasis:this.config.preemphasis,preemphasis_htk_flavor:this.config.preemphasis_htk_flavor,mel_filters:this.mel_filters,log_mel:"log",mel_floor:this.config.mel_floor,mel_floor_mode:"add",remove_dc_offset:!1,transpose:!0,max_num_frames:i})}async _call(e,r={}){Me(e,"Gemma4AudioFeatureExtractor");let s=e.length,n=await super._call(e,r),{input_features:o}=n,[,a,i]=o.dims,{frame_length:l,hop_length:c}=this.config,p=Math.floor(l/2),f=l+1,_=new Uint8Array(s+p+(r.pad_to_multiple_of??128));_.fill(1,p,p+s);let m=new Uint8Array(a);for(let x=0;x<a;++x)m[x]=_[x*c+f-1]?1:0;let w=o.data;for(let x=0;x<a;++x)m[x]||w.fill(0,x*i,(x+1)*i);return n.input_features_mask=new N("bool",m,[1,a]),n}};var Ff=class extends Ee{constructor(e){super(e);let{n_fft:r,win_length:s,n_mels:n,sample_rate:o}=e.melspec_kwargs;this.mel_filters=lt(Math.floor(1+r/2),n,0,o/2,o,null,"htk");let a=mt(s,"hann");this.window=new Float64Array(r);let i=Math.floor((r-s)/2);this.window.set(a,i)}async _call(e){Me(e,"GraniteSpeechFeatureExtractor");let{n_fft:r,hop_length:s,n_mels:n}=this.config.melspec_kwargs,o=1+Math.floor((e.length-1)/s),a=o-o%2;return{input_features:(await ot(e,this.window,r,s,{power:2,mel_filters:this.mel_filters,log_mel:"log10_max_norm",transpose:!0,max_num_frames:a,do_pad:!1})).view(-1,2*n).unsqueeze_(0)}}};var Bf=class extends Ee{async _call(e){Me(e,"MoonshineFeatureExtractor"),e instanceof Float64Array&&(e=new Float32Array(e));let r=[1,e.length];return{input_values:new N("float32",e,r)}}};var to=class extends Ee{async _call(e){Me(e,"PyAnnoteFeatureExtractor"),e instanceof Float64Array&&(e=new Float32Array(e));let r=[1,1,e.length];return{input_values:new N("float32",e,r)}}samples_to_frames(e){return(e-this.config.offset)/this.config.step}post_process_speaker_diarization(e,r){let s=r/this.samples_to_frames(r)/this.config.sampling_rate,n=[];for(let o of e.tolist()){let a=[],i=-1;for(let l=0;l<o.length;++l){let c=Pe(o[l]),[p,f]=ze(c),[_,m]=[l,l+1];f!==i?(i=f,a.push({id:f,start:_,end:m,score:p})):(a.at(-1).end=m,a.at(-1).score+=p)}n.push(a.map(({id:l,start:c,end:p,score:f})=>({id:l,start:c*s,end:p*s,confidence:f/(p-c)})))}return n}};var Uf=class extends Ee{constructor(e){super(e);let r=this.config.sampling_rate,s=lt(257,this.config.num_mel_bins,20,Math.floor(r/2),r,null,"kaldi",!0);this.mel_filters=s,this.window=mt(400,"povey",{periodic:!1})}async _extract_fbank_features(e,r){return e=e.map(s=>s*32768),ot(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:r,transpose:!0})}async _call(e,{padding:r=!0,pad_to_multiple_of:s=2,do_normalize_per_mel_bins:n=!0,return_attention_mask:o=!0}={}){Me(e,"SeamlessM4TFeatureExtractor");let a=await this._extract_fbank_features(e,this.config.max_length);if(n){let[w,x]=a.dims,k=a.data;for(let A=0;A<x;++A){let E=0;for(let O=0;O<w;++O)E+=k[O*x+A];let S=E/w,T=0;for(let O=0;O<w;++O)T+=(k[O*x+A]-S)**2;T/=w-1;let I=Math.sqrt(T+1e-7);for(let O=0;O<w;++O){let b=O*x+A;k[b]=(k[b]-S)/I}}}let i;if(r){let[w,x]=a.dims,k=a.data,A=w%s;if(A>0){let E=new Float32Array(x*(w+A));E.set(k),E.fill(this.config.padding_value,k.length);let S=w+A;a=new N(a.type,E,[S,x]),o&&(i=new N("int64",new BigInt64Array(S),[1,S]),i.data.fill(1n,0,w))}}let[l,c]=a.dims,p=this.config.stride;if(l%p!==0)throw new Error(`The number of frames (${l}) must be a multiple of the stride (${p}).`);let _=a.view(1,Math.floor(l/p),c*p),m={input_features:_};if(o){let w=_.dims[1],x=new BigInt64Array(w);if(i){let k=i.data;for(let A=1,E=0;A<l;A+=p,++E)x[E]=k[A]}else x.fill(1n);m.attention_mask=new N("int64",x,[1,w])}return m}};var jf=class extends Jn{};var Gf=class extends Ee{};var qf=class extends Ee{_zero_mean_unit_var_norm(e){let s=e.reduce((o,a)=>o+a,0)/e.length,n=e.reduce((o,a)=>o+(a-s)**2,0)/e.length;return e.map(o=>(o-s)/Math.sqrt(n+1e-7))}async _call(e){Me(e,"Wav2Vec2FeatureExtractor"),e instanceof Float64Array&&(e=new Float32Array(e));let r=e;this.config.do_normalize&&(r=this._zero_mean_unit_var_norm(r));let s=[1,r.length];return{input_values:new N("float32",r,s),attention_mask:new N("int64",new BigInt64Array(r.length).fill(1n),s)}}};var Wf=class extends Ee{constructor(e){super(e);let r=this.config.sampling_rate,s=lt(257,this.config.num_mel_bins,20,Math.floor(r/2),r,null,"kaldi",!0);this.mel_filters=s,this.window=mt(400,"hamming",{periodic:!1}),this.min_num_frames=this.config.min_num_frames}async _extract_fbank_features(e){return e=e.map(r=>r*32768),ot(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,transpose:!0,min_num_frames:this.min_num_frames})}async _call(e){Me(e,"WeSpeakerFeatureExtractor");let r=(await this._extract_fbank_features(e)).unsqueeze_(0);if(this.config.fbank_centering_span===null){let s=r.mean(1).data,n=r.data,[o,a,i]=r.dims;for(let l=0;l<o;++l){let c=l*a*i,p=l*i;for(let f=0;f<a;++f){let _=c+f*i;for(let m=0;m<i;++m)n[_+m]-=s[p+m]}}}return{input_features:r}}};var Vf=class extends Ee{constructor(e){super(e),this.config.mel_filters??=lt(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,8e3,this.config.sampling_rate,"slaney","slaney"),this.window=mt(this.config.n_fft,"hann")}async _extract_fbank_features(e,{center:r=!0}={}){let{n_fft:s,hop_length:n,mel_filters:o,global_log_mel_max:a}=this.config,i=Math.floor(r?e.length/n:(e.length-s)/n);return await ot(e,this.window,s,n,{power:2,mel_filters:o,log_mel:"log10_max_norm",max_log_mel:a,center:r,max_num_frames:i,do_pad:!1})}async _call(e,{center:r=!0}={}){return Me(e,"VoxtralRealtimeFeatureExtractor"),{input_features:(await this._extract_fbank_features(e,{center:r})).unsqueeze_(0)}}};var Hf=class extends Ee{constructor(e){super(e),this.config.mel_filters??=lt(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,8e3,this.config.sampling_rate,"slaney","slaney"),this.window=mt(this.config.n_fft,"hann")}async _extract_fbank_features(e){return await ot(e,this.window,this.config.n_fft,this.config.hop_length,{power:2,mel_filters:this.config.mel_filters,log_mel:"log10_max_norm",max_num_frames:Math.min(Math.floor(e.length/this.config.hop_length),this.config.nb_max_frames)})}async _call(e,{max_length:r=null}={}){Me(e,"WhisperFeatureExtractor");let s,n=r??this.config.n_samples;return e.length>n?(e.length>this.config.n_samples&&ee.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`."),s=e.slice(0,n)):(s=new Float32Array(n),s.set(e)),{input_features:(await this._extract_fbank_features(s)).unsqueeze_(0)}}};var je=class{static async from_pretrained(e,r={}){let s=await nt(e,Gl,!0,r),n=s.feature_extractor_type,o=ql[n];if(!o)throw new Error(`Unknown feature_extractor_type: '${n}'. Please report this at ${cs}.`);return new o(s)}};var Xf=class extends re{static tokenizer_class=se;static feature_extractor_class=je;async _call(e,r=null){let s=this.tokenizer(e),n=r?await this.feature_extractor(r):{};return{...s,...n}}};var gN=new Set(["ja","zh"]),Kf=class extends re{static tokenizer_class=se;static feature_extractor_class=je;static uses_processor_config=!0;get_decoder_prompt_ids(e="en"){let r=["\u2581","<|startofcontext|>","<|startoftranscript|>","<|emo:undefined|>",`<|${e}|>`,`<|${e}|>`,"<|pnc|>","<|noitn|>","<|notimestamp|>","<|nodiarize|>"];return this.tokenizer.convert_tokens_to_ids(r)}static join_chunks(e,r="en"){let s=e.filter(a=>a&&a.trim());if(s.length===0)return"";let n=gN.has(r)?"":" ";return[s[0].trimEnd(),...s.slice(1).map(a=>a.trim())].join(n)}async _call(e){return await this.feature_extractor(e)}};var Yf=vr(require("sharp"),1);var Vs,IM,us;if(ie.IS_WEB_ENV)Vs=(t,e)=>{if(!self.OffscreenCanvas)throw new Error("OffscreenCanvas not supported by this environment.");return new self.OffscreenCanvas(t,e)},us=self.createImageBitmap,IM=self.ImageData;else if(Yf.default)us=async t=>{let r=(await t.metadata()).channels,{data:s,info:n}=await t.rotate().raw().toBuffer({resolveWithObject:!0}),o=new Je(new Uint8ClampedArray(s),n.width,n.height,n.channels);return r!==void 0&&r!==n.channels&&o.convert(r),o};else throw new Error("Unable to load image processing library.");var wN={0:"nearest",1:"lanczos",2:"bilinear",3:"bicubic",4:"box",5:"hamming"},xN=new Map([["png","image/png"],["jpg","image/jpeg"],["jpeg","image/jpeg"],["gif","image/gif"]]),Je=class t{constructor(e,r,s,n){this.data=e,this.width=r,this.height=s,this.channels=n}get size(){return[this.width,this.height]}static async read(e){if(e instanceof t)return e;if(typeof e=="string"||e instanceof URL)return await this.fromURL(e);if(e instanceof Blob)return await this.fromBlob(e);if(typeof HTMLCanvasElement<"u"&&e instanceof HTMLCanvasElement||typeof OffscreenCanvas<"u"&&e instanceof OffscreenCanvas)return this.fromCanvas(e);throw new Error(`Unsupported input type: ${typeof e}`)}static fromCanvas(e){if(!ie.IS_WEB_ENV)throw new Error("fromCanvas() is only supported in browser environments.");let s=e.getContext("2d").getImageData(0,0,e.width,e.height).data;return new t(s,e.width,e.height,4)}static async fromURL(e){let r=await Qr(e);if(r.status!==200)throw new Error(`Unable to read image from "${e}" (${r.status} ${r.statusText})`);let s=await r.blob();return this.fromBlob(s)}static async fromBlob(e){if(ie.IS_WEB_ENV){let r=await us(e),s=Vs(r.width,r.height).getContext("2d");return s.drawImage(r,0,0),new this(s.getImageData(0,0,r.width,r.height).data,r.width,r.height,4)}else{let r=(0,Yf.default)(await e.arrayBuffer());return await us(r)}}static fromTensor(e,r="CHW"){if(e.dims.length!==3)throw new Error(`Tensor should have 3 dimensions, but has ${e.dims.length} dimensions.`);if(r==="CHW")e=e.transpose(1,2,0);else if(r!=="HWC")throw new Error(`Unsupported channel format: ${r}`);if(!(e.data instanceof Uint8ClampedArray||e.data instanceof Uint8Array))throw new Error(`Unsupported tensor type: ${e.type}`);switch(e.dims[2]){case 1:case 2:case 3:case 4:return new t(e.data,e.dims[1],e.dims[0],e.dims[2]);default:throw new Error(`Unsupported number of channels: ${e.dims[2]}`)}}grayscale(){if(this.channels===1)return this;let e=new Uint8ClampedArray(this.width*this.height*1);switch(this.channels){case 3:case 4:for(let r=0,s=0;r<this.data.length;r+=this.channels){let n=this.data[r],o=this.data[r+1],a=this.data[r+2];e[s++]=Math.round(.2989*n+.587*o+.114*a)}break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(e,this.width,this.height,1)}rgb(){if(this.channels===3)return this;let e=new Uint8ClampedArray(this.width*this.height*3);switch(this.channels){case 1:for(let r=0,s=0;r<this.data.length;++r)e[s++]=this.data[r],e[s++]=this.data[r],e[s++]=this.data[r];break;case 4:for(let r=0,s=0;r<this.data.length;r+=4)e[s++]=this.data[r],e[s++]=this.data[r+1],e[s++]=this.data[r+2];break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(e,this.width,this.height,3)}rgba(){if(this.channels===4)return this;let e=new Uint8ClampedArray(this.width*this.height*4);switch(this.channels){case 1:for(let r=0,s=0;r<this.data.length;++r)e[s++]=this.data[r],e[s++]=this.data[r],e[s++]=this.data[r],e[s++]=255;break;case 3:for(let r=0,s=0;r<this.data.length;r+=3)e[s++]=this.data[r],e[s++]=this.data[r+1],e[s++]=this.data[r+2],e[s++]=255;break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(e,this.width,this.height,4)}putAlpha(e){if(e.width!==this.width||e.height!==this.height)throw new Error(`Expected mask size to be ${this.width}x${this.height}, but got ${e.width}x${e.height}`);if(e.channels!==1)throw new Error(`Expected mask to have 1 channel, but got ${e.channels}`);let r=this.data,s=e.data,n=this.width*this.height;if(this.channels===3){let o=new Uint8ClampedArray(n*4);for(let a=0,i=0,l=0;a<n;++a)o[l++]=r[i++],o[l++]=r[i++],o[l++]=r[i++],o[l++]=s[a];return this._update(o,this.width,this.height,4)}else if(this.channels===4){for(let o=0;o<n;++o)r[4*o+3]=s[o];return this}throw new Error(`Expected image to have 3 or 4 channels, but got ${this.channels}`)}async resize(e,r,{resample:s=2}={}){if(this.width===e&&this.height===r)return this;let n=wN[s]??s,o=Rb(e),a=Rb(r);if(o&&a)return this;if(o?e=r/this.height*this.width:a&&(r=e/this.width*this.height),ie.IS_WEB_ENV){let i=this.channels,l=this.toCanvas(),c=Vs(e,r).getContext("2d");return c.drawImage(l,0,0,e,r),new t(c.getImageData(0,0,e,r).data,e,r,4).convert(i)}else{let i=this.toSharp();switch(n){case"box":case"hamming":(n==="box"||n==="hamming")&&(ee.warn(`Resampling method ${n} is not yet supported. Using bilinear instead.`),n="bilinear");case"nearest":case"bilinear":case"bicubic":i=i.affine([e/this.width,0,0,r/this.height],{interpolator:n});break;case"lanczos":i=i.resize({width:e,height:r,fit:"fill",kernel:"lanczos3"});break;default:throw new Error(`Resampling method ${n} is not supported.`)}return await us(i)}}async pad([e,r,s,n]){if(e=Math.max(e,0),r=Math.max(r,0),s=Math.max(s,0),n=Math.max(n,0),e===0&&r===0&&s===0&&n===0)return this;if(ie.IS_WEB_ENV){let o=this.channels,a=this.toCanvas(),i=this.width+e+r,l=this.height+s+n,c=Vs(i,l).getContext("2d");return c.drawImage(a,0,0,this.width,this.height,e,s,this.width,this.height),new t(c.getImageData(0,0,i,l).data,i,l,4).convert(o)}else{let o=this.toSharp().extend({left:e,right:r,top:s,bottom:n});return await us(o)}}async crop([e,r,s,n]){if(e=Math.max(e,0),r=Math.max(r,0),s=Math.min(s,this.width-1),n=Math.min(n,this.height-1),e===0&&r===0&&s===this.width-1&&n===this.height-1)return this;let o=s-e+1,a=n-r+1;if(ie.IS_WEB_ENV){let i=this.channels,l=this.toCanvas(),c=Vs(o,a).getContext("2d");return c.drawImage(l,e,r,o,a,0,0,o,a),new t(c.getImageData(0,0,o,a).data,o,a,4).convert(i)}else{let i=this.toSharp().extract({left:e,top:r,width:o,height:a});return await us(i)}}async center_crop(e,r){if(this.width===e&&this.height===r)return this;let s=(this.width-e)/2,n=(this.height-r)/2;if(ie.IS_WEB_ENV){let o=this.channels,a=this.toCanvas(),i=Vs(e,r).getContext("2d"),l=0,c=0,p=0,f=0;return s>=0?l=s:p=-s,n>=0?c=n:f=-n,i.drawImage(a,l,c,e,r,p,f,e,r),new t(i.getImageData(0,0,e,r).data,e,r,4).convert(o)}else{let o=this.toSharp();if(s>=0&&n>=0)o=o.extract({left:Math.floor(s),top:Math.floor(n),width:e,height:r});else if(s<=0&&n<=0){let a=Math.floor(-n),i=Math.floor(-s);o=o.extend({top:a,left:i,right:e-this.width-i,bottom:r-this.height-a})}else{let a=[0,0],i=0;n<0?(a[0]=Math.floor(-n),a[1]=r-this.height-a[0]):i=Math.floor(n);let l=[0,0],c=0;s<0?(l[0]=Math.floor(-s),l[1]=e-this.width-l[0]):c=Math.floor(s),o=o.extend({top:a[0],bottom:a[1],left:l[0],right:l[1]}).extract({left:c,top:i,width:e,height:r})}return await us(o)}}async toBlob(e="image/png",r=1){if(!ie.IS_WEB_ENV)throw new Error("toBlob() is only supported in browser environments.");return await this.toCanvas().convertToBlob({type:e,quality:r})}toTensor(e="CHW"){let r=new N("uint8",new Uint8Array(this.data),[this.height,this.width,this.channels]);if(e!=="HWC")if(e==="CHW")r=r.permute(2,0,1);else throw new Error(`Unsupported channel format: ${e}`);return r}toCanvas(){if(!ie.IS_WEB_ENV)throw new Error("toCanvas() is only supported in browser environments.");let e=this.clone().rgba(),r=Vs(e.width,e.height),s=new IM(e.data,e.width,e.height);return r.getContext("2d").putImageData(s,0,0),r}split(){let{data:e,width:r,height:s,channels:n}=this,o=e.constructor,a=e.length/n,i=Array.from({length:n},()=>new o(a));for(let l=0;l<a;++l){let c=n*l;for(let p=0;p<n;++p)i[p][l]=e[c+p]}return i.map(l=>new t(l,r,s,1))}_update(e,r,s,n=null){return this.data=e,this.width=r,this.height=s,n!==null&&(this.channels=n),this}clone(){return new t(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 new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this}async save(e){if(ie.IS_WEB_ENV){if(ie.IS_WEBWORKER_ENV)throw new Error("Unable to save an image from a Web Worker.");let r=e.split(".").pop().toLowerCase(),s=xN.get(r)??"image/png",n=await this.toBlob(s);return Pf(e,n)}else if(ie.IS_FS_AVAILABLE)await this.toSharp().toFile(e);else throw new Error("Unable to save the image because filesystem is disabled in this environment.")}toSharp(){if(ie.IS_WEB_ENV)throw new Error("toSharp() is only supported in server-side environments.");return(0,Yf.default)(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}})}},CM=Je.read.bind(Je);function PM(t,e,r=0,s=null){let n=t/e,o=$A(n)*e;return s!==null&&o>s&&(o=Math.floor(n)*e),o<r&&(o=Math.ceil(n)*e),o}function zM([t,e],r){return[Math.max(Math.floor(t/r),1)*r,Math.max(Math.floor(e/r),1)*r]}function ev([t,e,r,s]){return[t-r/2,e-s/2,t+r/2,e+s/2]}function ps(t,e=.5,r=null,s=!1){let n=t.logits,o=t.pred_boxes,[a,i,l]=n.dims;if(r!==null&&r.length!==a)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let c=[];for(let p=0;p<a;++p){let f=r!==null?r[p]:null,_={boxes:[],classes:[],scores:[]},m=n[p],w=o[p];for(let x=0;x<i;++x){let k=m[x],A=[],E;if(s){E=k.sigmoid().data;for(let S=0;S<E.length;++S)E[S]>e&&A.push(S)}else{let S=ze(k.data)[1];if(S===l-1||(E=Pe(k.data),E[S]<e))continue;A.push(S)}for(let S of A){let T=w[x].data;T=ev(T),f!==null&&(T=T.map((I,O)=>I*f[(O+1)%2])),_.boxes.push(T),_.classes.push(S),_.scores.push(E[S])}}c.push(_)}return c}function Qf(t,e=null){let r=t.logits,s=r.dims[0];if(e!==null&&e.length!==s)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let n=[];for(let o=0;o<s;++o){let a=e!==null?e[o]:null,i=r[o];a!==null&&(i=Np(i,a,"bilinear",!1));let[l,c]=a??i.dims.slice(-2),p=new N("int32",new Int32Array(l*c),[l,c]),f=i[0].data,_=p.data;for(let x=1;x<i.dims[0];++x){let k=i[x].data;for(let A=0;A<k.length;++A)k[A]>f[A]&&(f[A]=k[A],_[A]=x)}let m=new Array(i.dims[0]);for(let x=0;x<_.length;++x){let k=_[x];m[k]=k}let w=m.filter(x=>x!==void 0);n.push({segmentation:p,labels:w})}return n}function yN(t,e,r,s){let n=[],o=[],a=[];for(let i=0;i<t.dims[0];++i){let l=t[i],c=e[i],p=ze(l.data)[1];if(p===s)continue;let _=Pe(l.data)[p];_>r&&(n.push(c),o.push(_),a.push(p))}return[n,o,a]}function bN(t,e,r,s=.5,n=.8){let o=[],a=0,i=0,l=e[r].data;for(let p=0;p<t.length;++p)t[p]===r&&(o.push(p),++a),l[p]>=s&&++i;let c=a>0&&i>0;return c&&(c=a/i>n),[c,o]}function vN(t,e,r,s,n,o=null,a=null){let[i,l]=a??t[0].dims,c=new N("int32",new Int32Array(i*l),[i,l]),p=[];if(a!==null)for(let x=0;x<t.length;++x)t[x]=Np(t[x],a,"bilinear",!1);let f=new Int32Array(t[0].data.length),_=new Float32Array(t[0].data.length);for(let x=0;x<t.length;++x){let k=e[x],A=t[x].data;for(let E=0;E<A.length;++E)A[E]*=k,A[E]>_[E]&&(f[E]=x,_[E]=A[E])}let m=0,w=c.data;for(let x=0;x<r.length;++x){let k=r[x],[A,E]=bN(f,t,x,s,n);if(A){++m;for(let S of E)w[S]=m;p.push({id:m,label_id:k,score:e[x]})}}return[c,p]}function ro(t,e,r=28,s=3136,n=784*1280,o=1){if(t<r||e<r){let l=Math.max(r/t,r/e);t=Math.round(t*l),e=Math.round(e*l)}if(Math.max(t,e)/Math.min(t,e)>200)throw new Error(`absolute aspect ratio must be smaller than 200, got ${Math.max(t,e)/Math.min(t,e)}`);let a=Math.round(t/r)*r,i=Math.round(e/r)*r;if(o*a*i>n){let l=Math.sqrt(o*t*e/n);a=Math.max(r,Math.floor(t/l/r)*r),i=Math.max(r,Math.floor(e/l/r)*r)}else if(o*a*i<s){let l=Math.sqrt(s/(o*t*e));a=Math.ceil(t*l/r)*r,i=Math.ceil(e*l/r)*r}return[i,a]}function Jf(t,e=.5,r=.5,s=.8,n=null,o=null){n===null&&(ee.warn("`label_ids_to_fuse` unset. No instance will be fused."),n=new Set);let a=t.class_queries_logits??t.logits,l=(t.masks_queries_logits??t.pred_masks).sigmoid(),[c,p,f]=a.dims;if(f-=1,o!==null&&o.length!==c)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let _=[];for(let m=0;m<c;++m){let w=o!==null?o[m]:null,x=a[m],k=l[m],[A,E,S]=yN(x,k,e,f);if(S.length===0){let[O,b]=w??k.dims.slice(-2),F=new N("int32",new Int32Array(O*b).fill(-1),[O,b]);_.push({segmentation:F,segments_info:[]});continue}let[T,I]=vN(A,E,S,r,s,n,w);_.push({segmentation:T,segments_info:I})}return _}function Zf(t,e=.5,r=null){throw new Error("`post_process_instance_segmentation` is not yet implemented.")}var H=class extends We{constructor(e){super(),this.image_mean=e.image_mean??e.mean,this.image_std=e.image_std??e.std,this.resample=e.resample??2,this.do_rescale=e.do_rescale??!0,this.rescale_factor=e.rescale_factor??1/255,this.do_normalize=e.do_normalize,this.do_thumbnail=e.do_thumbnail,this.size=e.size??e.image_size,this.do_resize=e.do_resize??this.size!==void 0,this.size_divisibility=e.size_divisibility??e.size_divisor,this.do_center_crop=e.do_center_crop,this.crop_size=e.crop_size,this.do_convert_rgb=e.do_convert_rgb??!0,this.do_crop_margin=e.do_crop_margin,this.pad_size=e.pad_size,this.do_pad=e.do_pad,this.min_pixels=e.min_pixels,this.max_pixels=e.max_pixels,this.do_pad&&!this.pad_size&&!this.size_divisibility&&this.size&&this.size.width!==void 0&&this.size.height!==void 0&&(this.pad_size=this.size),this.do_flip_channel_order=e.do_flip_channel_order??!1,this.config=e}async thumbnail(e,r,s=2){let n=e.height,o=e.width,a=r.height,i=r.width,l=Math.min(n,a),c=Math.min(o,i);return l===n&&c===o?e:(n>o?c=Math.floor(o*l/n):o>n&&(l=Math.floor(n*c/o)),await e.resize(c,l,{resample:s}))}async crop_margin(e,r=200){let s=e.clone().grayscale(),n=Tl(s.data)[0],a=ze(s.data)[0]-n;if(a===0)return e;let i=r/255,l=s.width,c=s.height,p=0,f=0,_=s.data;for(let m=0;m<s.height;++m){let w=m*s.width;for(let x=0;x<s.width;++x)(_[w+x]-n)/a<i&&(l=Math.min(l,x),c=Math.min(c,m),p=Math.max(p,x),f=Math.max(f,m))}return e=await e.crop([l,c,p,f]),e}pad_image(e,r,s,{mode:n="constant",center:o=!1,constant_values:a=0}={}){let[i,l,c]=r,p,f;if(typeof s=="number"?(p=s,f=s):s==="square"?p=f=Math.max(i,l):(p=s.width,f=s.height),p!==l||f!==i){let _=new Float32Array(p*f*c);if(Array.isArray(a))for(let x=0;x<_.length;++x)_[x]=a[x%c];else a!==0&&_.fill(a);let[m,w]=o?[Math.floor((p-l)/2),Math.floor((f-i)/2)]:[0,0];for(let x=0;x<i;++x){let k=(x+w)*p,A=x*l;for(let E=0;E<l;++E){let S=(k+E+m)*c,T=(A+E)*c;for(let I=0;I<c;++I)_[S+I]=e[T+I]}}if(n==="symmetric"){if(o)throw new Error("`center` padding is not supported when `mode` is set to `symmetric`.");let x=i-1,k=l-1;for(let A=0;A<f;++A){let E=A*p,S=Nn(A,x)*l;for(let T=0;T<p;++T){if(A<i&&T<l)continue;let I=(E+T)*c,O=(S+Nn(T,k))*c;for(let b=0;b<c;++b)_[I+b]=e[O+b]}}}e=_,r=[f,p,c]}return[e,r]}rescale(e){for(let r=0;r<e.length;++r)e[r]=this.rescale_factor*e[r]}get_resize_output_image_size(e,r){let[s,n]=e.size,o,a;if(this.do_thumbnail){let{height:i,width:l}=r;o=Math.min(i,l)}else Number.isInteger(r)?(o=r,a=this.config.max_size??o):r!==void 0&&(o=r.shortest_edge,a=r.longest_edge);if(o!==void 0||a!==void 0){let i=o===void 0?1:Math.max(o/s,o/n),l=s*i,c=n*i,p=a===void 0?1:Math.min(a/l,a/c),f=Math.floor(Number((l*p).toFixed(2))),_=Math.floor(Number((c*p).toFixed(2)));return this.size_divisibility!==void 0&&([f,_]=zM([f,_],this.size_divisibility)),[f,_]}else if(r!==void 0&&r.width!==void 0&&r.height!==void 0){let i=r.width,l=r.height;if(this.config.keep_aspect_ratio&&this.config.ensure_multiple_of){let c=l/n,p=i/s;Math.abs(1-p)<Math.abs(1-c)?c=p:p=c,l=PM(c*n,this.config.ensure_multiple_of),i=PM(p*s,this.config.ensure_multiple_of)}return[i,l]}else{if(this.size_divisibility!==void 0)return zM([s,n],this.size_divisibility);throw new Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(r)}`)}}async resize(e){let[r,s]=this.get_resize_output_image_size(e,this.size);return await e.resize(r,s,{resample:this.resample})}async preprocess(e,{do_normalize:r=null,do_pad:s=null,do_convert_rgb:n=null,do_convert_grayscale:o=null,do_flip_channel_order:a=null}={}){this.do_crop_margin&&(e=await this.crop_margin(e));let[i,l]=e.size;if(n??this.do_convert_rgb?e=e.rgb():o&&(e=e.grayscale()),this.do_resize&&(e=await this.resize(e)),this.do_thumbnail&&(e=await this.thumbnail(e,this.size,this.resample)),this.do_center_crop){let m,w;Number.isInteger(this.crop_size)?(m=this.crop_size,w=this.crop_size):(m=this.crop_size.width,w=this.crop_size.height),e=await e.center_crop(m,w)}let c=[e.height,e.width],p=Float32Array.from(e.data),f=[e.height,e.width,e.channels];if(this.do_rescale&&this.rescale(p),r??this.do_normalize){let m=this.image_mean;Array.isArray(this.image_mean)||(m=new Array(e.channels).fill(m));let w=this.image_std;if(Array.isArray(this.image_std)||(w=new Array(e.channels).fill(w)),m.length!==e.channels||w.length!==e.channels)throw new Error(`When set to arrays, the length of \`image_mean\` (${m.length}) and \`image_std\` (${w.length}) must match the number of channels in the image (${e.channels}).`);for(let x=0;x<p.length;x+=e.channels)for(let k=0;k<e.channels;++k)p[x+k]=(p[x+k]-m[k])/w[k]}if(s??this.do_pad){if(this.pad_size)[p,f]=this.pad_image(p,[e.height,e.width,e.channels],this.pad_size);else if(this.size_divisibility){let m=Math.ceil(f[1]/this.size_divisibility)*this.size_divisibility,w=Math.ceil(f[0]/this.size_divisibility)*this.size_divisibility;[p,f]=this.pad_image(p,f,{width:m,height:w})}}if(a??this.do_flip_channel_order){if(f[2]!==3)throw new Error("Flipping channel order is only supported for RGB images.");for(let m=0;m<p.length;m+=3){let w=p[m];p[m]=p[m+2],p[m+2]=w}}let _=new N("float32",p,f).permute(2,0,1);return{original_size:[l,i],reshaped_input_size:c,pixel_values:_}}async _call(e,...r){Array.isArray(e)||(e=[e]);let s=await Promise.all(e.map(o=>this.preprocess(o)));return{pixel_values:_t(s.map(o=>o.pixel_values),0),original_sizes:s.map(o=>o.original_size),reshaped_input_sizes:s.map(o=>o.reshaped_input_size)}}static async from_pretrained(e,r={}){let s=await nt(e,Er,!0,r);return new this(s)}};var io={};Os(io,{BeitFeatureExtractor:()=>ed,BitImageProcessor:()=>td,CHMv2ImageProcessor:()=>sd,CLIPFeatureExtractor:()=>nd,CLIPImageProcessor:()=>Wl,ChineseCLIPFeatureExtractor:()=>rd,ConvNextFeatureExtractor:()=>od,ConvNextImageProcessor:()=>Vl,DINOv3ViTImageProcessor:()=>ld,DPTFeatureExtractor:()=>ud,DPTImageProcessor:()=>Kl,DeiTFeatureExtractor:()=>ad,DeiTImageProcessor:()=>Hl,DetrFeatureExtractor:()=>id,DetrImageProcessor:()=>Xl,DonutFeatureExtractor:()=>cd,DonutImageProcessor:()=>Hs,EfficientNetImageProcessor:()=>pd,GLPNFeatureExtractor:()=>_d,Gemma3ImageProcessor:()=>fd,Gemma4ImageProcessor:()=>so,Glm46VImageProcessor:()=>dd,GroundingDinoImageProcessor:()=>md,Idefics3ImageProcessor:()=>Yl,ImageFeatureExtractor:()=>H,ImageProcessor:()=>H,JinaCLIPImageProcessor:()=>gd,Lfm2VlImageProcessor:()=>wd,LlavaOnevisionImageProcessor:()=>xd,Mask2FormerImageProcessor:()=>bd,MaskFormerFeatureExtractor:()=>yd,MaskFormerImageProcessor:()=>Xs,MobileNetV1FeatureExtractor:()=>vd,MobileNetV1ImageProcessor:()=>Ql,MobileNetV2FeatureExtractor:()=>kd,MobileNetV2ImageProcessor:()=>Jl,MobileNetV3FeatureExtractor:()=>Ed,MobileNetV3ImageProcessor:()=>Zl,MobileNetV4FeatureExtractor:()=>Ad,MobileNetV4ImageProcessor:()=>ec,MobileViTFeatureExtractor:()=>Md,MobileViTImageProcessor:()=>tc,NougatImageProcessor:()=>Td,OwlViTFeatureExtractor:()=>Sd,OwlViTImageProcessor:()=>Ks,Owlv2ImageProcessor:()=>Od,Phi3VImageProcessor:()=>Id,PixtralImageProcessor:()=>Cd,PvtImageProcessor:()=>Pd,Qwen2VLImageProcessor:()=>no,RTDetrImageProcessor:()=>zd,Sam2ImageProcessor:()=>ao,Sam3ImageProcessor:()=>ao,SamImageProcessor:()=>ao,SapiensFeatureExtractor:()=>Ld,SapiensImageProcessor:()=>rc,SegformerFeatureExtractor:()=>Nd,SegformerImageProcessor:()=>sc,SiglipImageProcessor:()=>$d,SmolVLMImageProcessor:()=>Yl,Swin2SRImageProcessor:()=>Rd,VLMImageProcessor:()=>hd,ViTFeatureExtractor:()=>Dd,ViTImageProcessor:()=>nc,VitMatteImageProcessor:()=>Fd,VitPoseImageProcessor:()=>Bd,YolosFeatureExtractor:()=>Ud,YolosImageProcessor:()=>oc});var ed=class extends H{};var td=class extends H{};var rd=class extends H{};var sd=class extends H{};var Wl=class extends H{},nd=class extends Wl{};var Vl=class extends H{constructor(e){super(e),this.crop_pct=this.config.crop_pct??224/256}async resize(e){let r=this.size?.shortest_edge;if(r===void 0)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(r<384){let s=Math.floor(r/this.crop_pct),[n,o]=this.get_resize_output_image_size(e,{shortest_edge:s});e=await e.resize(n,o,{resample:this.resample}),e=await e.center_crop(r,r)}else e=await e.resize(r,r,{resample:this.resample});return e}},od=class extends Vl{};var Hl=class extends H{},ad=class extends Hl{};var Xl=class extends H{async _call(e){let r=await super._call(e),s=[r.pixel_values.dims[0],64,64],n=Ke(s,1n);return{...r,pixel_mask:n}}post_process_object_detection(...e){return ps(...e)}post_process_panoptic_segmentation(...e){return Jf(...e)}post_process_instance_segmentation(...e){return Zf(...e)}},id=class extends Xl{};var ld=class extends H{};var Hs=class extends H{pad_image(e,r,s,n={}){let[o,a,i]=r,l=this.image_mean;Array.isArray(this.image_mean)||(l=new Array(i).fill(l));let c=this.image_std;Array.isArray(c)||(c=new Array(i).fill(l));let p=l.map((f,_)=>-f/c[_]);return super.pad_image(e,r,s,{center:!0,constant_values:p,...n})}},cd=class extends Hs{};var Kl=class extends H{},ud=class extends Kl{};var pd=class extends H{constructor(e){super(e),this.include_top=this.config.include_top??!0,this.include_top&&(this.image_std=this.image_std.map(r=>r*r))}};var fd=class extends H{};function kN(t,e,r,s,n){let o=s*r**2,a=Math.sqrt(o/(t*e)),i=n*r,l=Math.floor(a*t/i)*i,c=Math.floor(a*e/i)*i;if(l===0&&c===0)throw new Error(`Attempting to resize to a 0 x 0 image. Resized height should be divisible by \`pooling_kernel_size * patch_size\`=${i}.`);let p=Math.floor(s/n**2)*i;return l===0?(l=i,c=Math.min(Math.floor(e/t)*i,p)):c===0&&(c=i,l=Math.min(Math.floor(t/e)*i,p)),[l,c]}function EN(t,e,r,s,n,o,a){let i=Math.floor(e/n),l=Math.floor(r/n),c=i*l,p=n*n*s,f=new Float32Array(o*p),_=0;for(let x=0;x<i;++x)for(let k=0;k<l;++k)for(let A=0;A<n;++A){let E=(x*n+A)*r*s+k*n*s;for(let S=0;S<n;++S){let T=E+S*s;for(let I=0;I<s;++I)f[_++]=t[T+I]}}let m=new BigInt64Array(o*2).fill(-1n),w=0;for(let x=0;x<i;++x)for(let k=0;k<l;++k)m[w++]=BigInt(k),m[w++]=BigInt(x);return{patches:new N("float32",f,[o,p]),positions:new N("int64",m,[o,2]),num_soft_tokens:Math.floor(c/a**2)}}var so=class extends We{constructor(e){super(),this.config=e,this.patch_size=e.patch_size??16,this.max_soft_tokens=e.max_soft_tokens??280,this.pooling_kernel_size=e.pooling_kernel_size??3,this.resample=e.resample??3,this.rescale_factor=e.rescale_factor??1/255,this.do_rescale=e.do_rescale??!0,this.do_resize=e.do_resize??!0,this.do_convert_rgb=e.do_convert_rgb??!0}async _call(e){Array.isArray(e)||(e=[e]);let{patch_size:r,pooling_kernel_size:s}=this,n=this.max_soft_tokens*s**2,o=[],a=[],i=[];for(let l of e){if(this.do_convert_rgb&&(l=l.rgb()),this.do_resize){let[m,w]=kN(l.height,l.width,r,n,s);(m!==l.height||w!==l.width)&&(l=await l.resize(w,m,{resample:this.resample}))}let c=Float32Array.from(l.data);if(this.do_rescale)for(let m=0;m<c.length;++m)c[m]*=this.rescale_factor;let{patches:p,positions:f,num_soft_tokens:_}=EN(c,l.height,l.width,l.channels,r,n,s);o.push(p),a.push(f),i.push(_)}return{pixel_values:_t(o,0),image_position_ids:_t(a,0),num_soft_tokens_per_image:i}}};var no=class extends H{constructor(e){super(e),this.min_pixels=e.min_pixels??e.size?.shortest_edge,this.max_pixels=e.max_pixels??e.size?.longest_edge,this.patch_size=e.patch_size,this.merge_size=e.merge_size}get_resize_output_image_size(e,r){let s=this.patch_size*this.merge_size;return ro(e.height,e.width,s,this.min_pixels,this.max_pixels)}async _call(e,...r){let{pixel_values:s,original_sizes:n,reshaped_input_sizes:o}=await super._call(e,...r),a=s,{temporal_patch_size:i,merge_size:l,patch_size:c}=this.config;a.dims[0]===1&&(a=ye(Array.from({length:i},()=>a),0));let p=a.dims[0]/i,f=a.dims[1],_=Math.floor(a.dims[2]/c),m=Math.floor(a.dims[3]/c),w=a.view(p,i,f,Math.floor(_/l),l,c,Math.floor(m/l),l,c).permute(0,3,6,4,7,2,1,5,8).view(p*_*m,f*i*c*c),x=new N("int64",[p,_,m],[1,3]);return{pixel_values:w,image_grid_thw:x,original_sizes:n,reshaped_input_sizes:o}}};var dd=class extends no{get_resize_output_image_size(e,r){let s=this.patch_size*this.merge_size,n=this.config.temporal_patch_size??2;return ro(e.height,e.width,s,this.min_pixels,this.max_pixels,n)}};var _d=class extends H{};var md=class extends H{async _call(e){let r=await super._call(e),s=r.pixel_values.dims,n=Ze([s[0],s[2],s[3]]);return{...r,pixel_mask:n}}};var Yl=class extends H{constructor(e){super(e),this.do_image_splitting=e.do_image_splitting??!0,this.max_image_size=e.max_image_size}get_resize_for_vision_encoder(e,r){let[s,n]=e.dims.slice(-2),o=n/s;return n>=s?(n=Math.ceil(n/r)*r,s=Math.floor(n/o),s=Math.ceil(s/r)*r):(s=Math.ceil(s/r)*r,n=Math.floor(s*o),n=Math.ceil(n/r)*r),{height:s,width:n}}async _call(e,{do_image_splitting:r=null,return_row_col_info:s=!1}={}){let n;if(!Array.isArray(e))n=[[e]];else{if(e.length===0||!e[0])throw new Error("No images provided.");Array.isArray(e[0])?n=e:n=[e]}let o=[],a=[],i=[],l=[],c=[];for(let A of n){let E=await Promise.all(A.map(I=>this.preprocess(I)));l.push(...E.map(I=>I.original_size)),c.push(...E.map(I=>I.reshaped_input_size)),E.forEach(I=>I.pixel_values.unsqueeze_(0));let{longest_edge:S}=this.max_image_size,T;if(r??this.do_image_splitting){let I=new Array(E.length),O=new Array(E.length);T=await Promise.all(E.map(async(b,F)=>{let j=this.get_resize_for_vision_encoder(b.pixel_values,S),U=await yt(b.pixel_values,{size:[j.height,j.width]}),{frames:X,num_splits_h:K,num_splits_w:J}=await this.split_image(U,this.max_image_size);return I[F]=K,O[F]=J,ye(X,0)})),a.push(I),i.push(O)}else{let I=[S,S];T=await Promise.all(E.map(O=>yt(O.pixel_values,{size:I}))),a.push(new Array(E.length).fill(0)),i.push(new Array(E.length).fill(0))}o.push(ye(T,0))}let p=o.length,[f,_,m,w]=o[0].dims,x,k;if(p===1)x=o[0].unsqueeze_(0),k=Ke([p,f,m,w],!0);else{let A=Math.max(...o.map(T=>T.dims.at(0)));k=Ke([p,A,m,w],!0);let E=k.data,S=A*m*w;for(let T=0;T<p;++T){let I=o[T].dims[0];if(I<A){o[T]=ye([o[T],Ke([A-I,_,m,w],0)],0);let O=T*S+I*m*w,b=(T+1)*S;E.fill(!1,O,b)}}x=_t(o,0)}return{pixel_values:x,pixel_attention_mask:k,original_sizes:l,reshaped_input_sizes:c,...s?{rows:a,cols:i}:{}}}async split_image(e,{longest_edge:r}){let s=r,n=r,o=[],[a,i]=e.dims.slice(-2),l=0,c=0;if(a>s||i>n){l=Math.ceil(a/s),c=Math.ceil(i/n);let p=Math.ceil(a/l),f=Math.ceil(i/c);for(let w=0;w<l;++w)for(let x=0;x<c;++x){let k,A,E,S;w===l-1?(A=a-p,S=a):(A=w*p,S=(w+1)*p),x===c-1?(k=i-f,E=i):(k=x*f,E=(x+1)*f);let O=await Dl(e,[A,k],[S,E],[2,3]);o.push(O)}let _=s,m=n;(a!==_||i!==m)&&(e=await yt(e,{size:[_,m]}))}return o.push(e),{frames:o,num_splits_h:l,num_splits_w:c}}};var hd=class extends H{constructor(e){super({do_pad:!0,pad_size:{width:e.image_size,height:e.image_size},...e}),this.constant_values=this.config.background_color.map(r=>r*this.rescale_factor)}pad_image(e,r,s,n){return super.pad_image(e,r,s,{constant_values:this.constant_values,center:!0,...n})}};var gd=class extends H{constructor(e){let{resize_mode:r,fill_color:s,interpolation:n,size:o,...a}=e,i=r==="squash"?{width:o,height:o}:r==="shortest"?{shortest_edge:o}:{longest_edge:o},l=n==="bicubic"?3:2;super({...a,size:i,resample:l,do_center_crop:!0,crop_size:o,do_normalize:!0})}};function LM(t,e){return Math.round(t/e)*e}function AN(t,e,r,s,n){let o=1/0,a=[1,1],i=r*s;for(let l of e){let c=Math.abs(t-l[0]/l[1]);c<o?(o=c,a=l):c===o&&i>.5*n*n*l[0]*l[1]&&(a=l)}return a}function MN(t,e){let r=[],s=new Set;for(let n=t;n<=e;++n)for(let o=1;o<=n;++o)for(let a=1;a<=n;++a){let i=o*a;if(i>=t&&i<=e){let l=o<<16|a;s.has(l)||(s.add(l),r.push([o,a]))}}return r.sort((n,o)=>n[0]*n[1]-o[0]*o[1])}function TN(t,e){let[r,s,n,o]=t.dims,a=Math.floor(n/e),i=Math.floor(o/e),l=e*e*s,c=t.data,p=new Float32Array(r*a*i*l),f=n*o;for(let _=0;_<r;++_){let m=_*s*f,w=_*a*i*l;for(let x=0;x<a;++x)for(let k=0;k<i;++k){let A=w+(x*i+k)*l;for(let E=0;E<e;++E){let S=(x*e+E)*o+k*e;for(let T=0;T<e;++T){let I=S+T;for(let O=0;O<s;++O)p[A++]=c[m+O*f+I]}}}}return new N("float32",p,[r,a*i,l])}function SN(t,e){let[,r,s]=t.dims,n=new BigInt64Array(e);n.fill(1n,0,r);let o=t;if(r<e){let a=new Float32Array(e*s);a.set(t.data),o=new N("float32",a,[1,e,s])}return{padded:o,mask:new N("int64",n,[e])}}var wd=class extends H{constructor(e){super(e),this.downsample_factor=e.downsample_factor??2,this.do_image_splitting=e.do_image_splitting??!0,this.min_tiles=e.min_tiles??2,this.max_tiles=e.max_tiles??10,this.use_thumbnail=e.use_thumbnail??!0,this.min_image_tokens=e.min_image_tokens??64,this.max_image_tokens=e.max_image_tokens??256,this.encoder_patch_size=e.encoder_patch_size??e.patch_size??16,this.tile_size=e.tile_size??512,this.max_pixels_tolerance=e.max_pixels_tolerance??2,this.return_row_col_info=e.return_row_col_info??!1;let r=this.max_image_tokens*this.downsample_factor**2,s=this.do_image_splitting?(this.tile_size/this.encoder_patch_size)**2:0;this.max_num_patches=Math.max(r,s)}_is_image_too_large(e,r){let s=this.encoder_patch_size*this.downsample_factor,n=Math.max(this.encoder_patch_size,LM(e,s)),o=Math.max(this.encoder_patch_size,LM(r,s));return n*o>this.max_image_tokens*(this.encoder_patch_size*this.downsample_factor)**2*this.max_pixels_tolerance}_get_grid_layout(e,r){let s=MN(this.min_tiles,this.max_tiles),[n,o]=AN(r/e,s,r,e,this.tile_size);return{grid_width:n,grid_height:o,target_width:this.tile_size*n,target_height:this.tile_size*o}}async _call(e,{return_row_col_info:r=null}={}){let s;Array.isArray(e)?Array.isArray(e[0])?s=e:s=[e]:s=[[e]];let n=[],o=[],a=[],i=[],l=[],c=[];for(let f of s){let _=await Promise.all(f.map(m=>this.preprocess(m,{do_pad:!1})));for(let{pixel_values:m}of _){let[,w,x]=m.dims,k=m.unsqueeze_(0),A=this.encoder_patch_size*this.downsample_factor,E=A**2,[S,T]=ro(Math.max(A,w),Math.max(A,x),A,this.min_image_tokens*E,this.max_image_tokens*E).map(U=>Math.max(A,U)),I,O=1,b=1,F=this._is_image_too_large(w,x),j=this.do_image_splitting&&!(this.min_tiles===1&&this.max_tiles===1);if(F&&j){let{grid_width:U,grid_height:X,target_width:K,target_height:J}=this._get_grid_layout(w,x);O=X,b=U;let R=await yt(k,{size:[J,K]});I=[];for(let C=0;C<X;++C)for(let ne=0;ne<U;++ne){let Y=C*this.tile_size,z=ne*this.tile_size;I.push(R.slice(null,null,[Y,Y+this.tile_size],[z,z+this.tile_size]))}this.use_thumbnail&&U*X!==1&&I.push(await yt(k,{size:[T,S]}))}else I=[await yt(k,{size:[T,S]})];for(let U of I){let[,,X,K]=U.dims,J=TN(U,this.encoder_patch_size),{padded:R,mask:C}=SN(J,this.max_num_patches);n.push(R),o.push(C),a.push([Math.floor(X/this.encoder_patch_size),Math.floor(K/this.encoder_patch_size)])}i.push(O),l.push(b),c.push([T,S])}}let p={pixel_values:ye(n,0),pixel_attention_mask:_t(o,0),spatial_shapes:new N("int64",BigInt64Array.from(a.flat(),BigInt),[a.length,2])};return(r??this.return_row_col_info)&&(p.image_rows=i,p.image_cols=l,p.image_sizes=c),p}};var xd=class extends H{};var Xs=class extends H{post_process_panoptic_segmentation(...e){return Jf(...e)}post_process_instance_segmentation(...e){return Zf(...e)}},yd=class extends Xs{};var bd=class extends Xs{};var Ql=class extends H{},vd=class extends Ql{};var Jl=class extends H{},kd=class extends Jl{};var Zl=class extends H{},Ed=class extends Zl{};var ec=class extends H{},Ad=class extends ec{};var tc=class extends H{},Md=class extends tc{};var Td=class extends Hs{};var Ks=class extends H{post_process_object_detection(...e){return ps(...e)}},Sd=class extends Ks{};var Od=class extends Ks{};var $t=336,ON=[2,3],{ceil:tv,floor:oo,sqrt:rv}=Math,Id=class extends H{constructor(e){super({...e,do_normalize:!0,do_pad:!0,pad_size:"custom",do_convert_rgb:!0,do_resize:!0}),this._num_crops=e.num_crops}calc_num_image_tokens_from_image_size(e,r){let{num_img_tokens:s}=this.config;return oo((oo(r/$t)*oo(e/$t)+1)*s+1+(oo(r/$t)+1)*rv(s))}get_resize_output_image_size(e,r){let s=this._num_crops,[n,o]=e.size,a=n/o,i=1;for(;i*Math.ceil(i/a)<=s;)i+=1;i-=1;let l=Math.floor(i*336),c=Math.floor(l/a);return[l,c]}pad_image(e,r,s,n={}){let[o,a]=r,i=$t*tv(o/$t),l=$t*tv(a/$t),c=[1,1,1].map((p,f)=>(p-this.image_mean[f])/this.image_std[f]);return super.pad_image(e,r,{width:l,height:i},{center:!0,constant_values:c,...n})}async _call(e,{num_crops:r=null}={}){if(this._num_crops=r??=this.config.num_crops,r<4||rv(r)%1!==0)throw new Error("num_crops must be a square number >= 4");Array.isArray(e)||(e=[e]);let s=e.length,n=await Promise.all(e.map(_=>this.preprocess(_))),o=n.map(_=>_.original_size),a=n.map(_=>_.reshaped_input_size),i=[];for(let{pixel_values:_}of n){_.unsqueeze_(0);let[m,w]=_.dims.slice(-2),x=await yt(_,{size:[$t,$t],mode:"bicubic"});if(r>0){let k=[],A=rv(r),E=oo(w/A),S=oo(m/A);for(let I=0;I<A;++I)for(let O=0;O<A;++O){let b,F,j,U;I===A-1?(F=m-S,U=m):(F=I*S,U=(I+1)*S),O===A-1?(b=w-E,j=w):(b=O*E,j=(O+1)*E);let J=await Dl(_,[F,b],[U,j],ON);k.push(J)}let T=await yt(ye(k,0),{size:[$t,$t],mode:"bicubic"});i.push(ye([x,T],0))}else i.push(x)}let l=_t(i,0),c=a.map(_=>_.map(m=>$t*tv(m/$t))),p=new N("int64",c.flat(),[s,2]),f=c.map(([_,m])=>this.calc_num_image_tokens_from_image_size(m,_));return{pixel_values:l,original_sizes:o,reshaped_input_sizes:a,image_sizes:p,num_img_tokens:f}}};var Cd=class extends H{get_resize_output_image_size(e,r){let{longest_edge:s}=r;if(s===void 0)throw new Error("size must contain 'longest_edge'");let[n,o]=e.size,a=Math.max(n,o)/s,i=n,l=o;a>1&&(i=Math.floor(n/a),l=Math.floor(o/a));let{patch_size:c,spatial_merge_size:p}=this.config;if(!p)throw new Error("config must contain 'spatial_merge_size'");let f=c*p,_=Math.floor((i-1)/f)+1,m=Math.floor((l-1)/f)+1;return[_*f,m*f]}};var Pd=class extends H{};var zd=class extends H{post_process_object_detection(...e){return ps(...e)}};var ao=class extends H{reshape_input_points(e,r,s,n=!1){e=structuredClone(e);let o=Db(e);if(o.length===3)n||(o=[1,...o]),e=[e];else if(o.length!==4)throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.");for(let a=0;a<e.length;++a){let[i,l]=r[a],[c,p]=s[a],f=[p/l,c/i];for(let _=0;_<e[a].length;++_)for(let m=0;m<e[a][_].length;++m)for(let w=0;w<e[a][_][m].length;++w)e[a][_][m][w]*=f[w%2]}return new N("float32",Float32Array.from(e.flat(1/0)),o)}add_input_labels(e,r){let s=Db(e);if(s.length===2)s=[1,...s],e=[e];else if(s.length!==3)throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.");if(s.some((n,o)=>n!==r.dims[o]))throw Error(`The first ${s.length} dimensions of 'input_points' and 'input_labels' must be the same.`);return new N("int64",e.flat(1/0).map(BigInt),s)}async _call(e,{input_points:r=null,input_labels:s=null,input_boxes:n=null}={}){let o=await super._call(e);if(r&&(o.input_points=this.reshape_input_points(r,o.original_sizes,o.reshaped_input_sizes)),s){if(!o.input_points)throw Error("`input_points` must be provided if `input_labels` are provided.");o.input_labels=this.add_input_labels(s,o.input_points)}return n&&(o.input_boxes=this.reshape_input_points(n,o.original_sizes,o.reshaped_input_sizes,!0)),o}async post_process_masks(e,r,s,{mask_threshold:n=0,binarize:o=!0,pad_size:a=null}={}){let i=[];a=a??this.pad_size??this.size;let l=[a.height,a.width];for(let c=0;c<r.length;++c){let p=r[c],f=s[c],_=await yt(e[c],{mode:"bilinear",size:l});if(_=_.slice(null,null,[0,f[0]],[0,f[1]]),_=await yt(_,{mode:"bilinear",size:p}),o){let m=_.data,w=new Uint8Array(m.length);for(let x=0;x<m.length;++x)m[x]>n&&(w[x]=1);_=new N("bool",w,_.dims)}i.push(_)}return i}generate_crop_boxes(e,r,{crop_n_layers:s=0,overlap_ratio:n=512/1500,points_per_crop:o=32,crop_n_points_downscale_factor:a=1}={}){}};var rc=class extends H{post_process_semantic_segmentation(...e){return Qf(...e)}},Ld=class extends rc{};var sc=class extends H{post_process_semantic_segmentation(...e){return Qf(...e)}},Nd=class extends sc{};var $d=class extends H{};var Rd=class extends H{pad_image(e,r,s,n={}){let[o,a,i]=r;return super.pad_image(e,r,{width:a+(s-a%s)%s,height:o+(s-o%s)%s},{mode:"symmetric",center:!1,constant_values:-1,...n})}};var nc=class extends H{},Dd=class extends nc{};var Fd=class extends H{async _call(e,r){Array.isArray(e)||(e=[e]),Array.isArray(r)||(r=[r]);let s=await Promise.all(e.map(a=>this.preprocess(a))),n=await Promise.all(r.map(a=>this.preprocess(a,{do_normalize:!1,do_convert_rgb:!1,do_convert_grayscale:!0})));return{pixel_values:_t(s.map((a,i)=>ye([a.pixel_values,n[i].pixel_values],0)),0),original_sizes:s.map(a=>a.original_size),reshaped_input_sizes:s.map(a=>a.reshaped_input_size)}}};var Bd=class extends H{post_process_pose_estimation(e,r,{threshold:s=null}={}){let n=e.tolist(),[o,a,i,l]=e.dims,c=[];for(let p=0;p<o;++p){let f=n[p],_=r[p],m=[];for(let w=0;w<_.length;++w){let x=_[w],k=[],A=[],E=[],S=x.at(-2)/l,T=x.at(-1)/i;for(let I=0;I<f.length;++I){let[O,b]=[0,0],F=0,j=-1/0,U=f[I];for(let K=0;K<U.length;++K){let J=U[K];for(let R=0;R<J.length;++R){let C=J[R];F+=C,j=Math.max(j,C),O+=(R+.5)*C,b+=K*C}}if(s!=null&&j<s)continue;let X=[S*O/F,T*b/F];k.push(X),E.push(I),A.push(j)}m.push({bbox:x,scores:A,labels:E,keypoints:k})}c.push(m)}return c}};var oc=class extends H{post_process_object_detection(...e){return ps(...e)}},Ud=class extends oc{};var Ae=class{static async from_pretrained(e,r={}){let s=await nt(e,Er,!0,r),n=s.image_processor_type??s.feature_extractor_type,o=io[n?.replace(/Fast$/,"")];return o||(n!==void 0&&ee.warn(`Image processor type '${n}' not found, assuming base ImageProcessor. Please report this at ${cs}.`),o=H),new o(s)}};var jd=class extends re{static tokenizer_class=se;static image_processor_class=Ae;constructor(e,r,s){super(e,r,s);let{tasks_answer_post_processing_type:n,task_prompts_without_inputs:o,task_prompts_with_input:a}=this.image_processor.config;this.tasks_answer_post_processing_type=new Map(Object.entries(n??{})),this.task_prompts_without_inputs=new Map(Object.entries(o??{})),this.task_prompts_with_input=new Map(Object.entries(a??{})),this.regexes={quad_boxes:/(.+?)<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>/gm,bboxes:/([^<]+)?<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>/gm},this.size_per_bin=1e3}construct_prompts(e){typeof e=="string"&&(e=[e]);let r=[];for(let s of e)if(this.task_prompts_without_inputs.has(s))r.push(this.task_prompts_without_inputs.get(s));else{for(let[n,o]of this.task_prompts_with_input)if(s.includes(n)){r.push(o.replaceAll("{input}",s).replaceAll(n,""));break}r.length!==e.length&&r.push(s)}return r}post_process_generation(e,r,s){let n=this.tasks_answer_post_processing_type.get(r)??"pure_text";e=e.replaceAll("<s>","").replaceAll("</s>","");let o;switch(n){case"pure_text":o=e;break;case"description_with_bboxes":case"bboxes":case"phrase_grounding":case"ocr":let a=n==="ocr"?"quad_boxes":"bboxes",i=e.matchAll(this.regexes[a]),l=[],c=[];for(let[p,f,..._]of i)l.push(f?f.trim():l.at(-1)??""),c.push(_.map((m,w)=>(Number(m)+.5)/this.size_per_bin*s[w%2]));o={labels:l,[a]:c};break;default:throw new Error(`Task "${r}" (of type "${n}") not yet implemented.`)}return{[r]:o}}async _call(e,r=null,s={}){if(!e&&!r)throw new Error("Either text or images must be provided");let n=await this.image_processor(e,s),o=r?this.tokenizer(this.construct_prompts(r),s):{};return{...n,...o}}};var Gd=class extends re{static tokenizer_class=se;static image_processor_class=Ae;static uses_processor_config=!0;static uses_chat_template_file=!0;constructor(e,r,s){super(e,r,s),this.image_seq_length=this.config.image_seq_length;let{boi_token:n,image_token:o,eoi_token:a}=this.tokenizer.config;this.boi_token=n,this.image_token=o,this.eoi_token=a;let i=o.repeat(this.image_seq_length);this.full_image_sequence=`

${n}${i}${a}

`}async _call(e,r=null,s={}){typeof e=="string"&&(e=[e]);let n;return r&&(n=await this.image_processor(r,s),e=e.map(a=>a.replaceAll(this.boi_token,this.full_image_sequence))),{...this.tokenizer(e,s),...n}}};var qd=class extends re{static image_processor_class=Ae;static feature_extractor_class=je;static tokenizer_class=se;static uses_processor_config=!0;static uses_chat_template_file=!0;constructor(e,r,s){super(e,r,s),this.audio_seq_length=this.config.audio_seq_length,this.image_seq_length=this.config.image_seq_length;let{audio_token_id:n,boa_token:o,audio_token:a,eoa_token:i,image_token_id:l,boi_token:c,image_token:p,eoi_token:f}=this.tokenizer.config;this.audio_token_id=n,this.boa_token=o,this.audio_token=a;let _=a.repeat(this.audio_seq_length);this.full_audio_sequence=`

${o}${_}${i}

`,this.image_token_id=l,this.boi_token=c,this.image_token=p;let m=p.repeat(this.image_seq_length);this.full_image_sequence=`

${c}${m}${f}

`}async _call(e,r=null,s=null,n={}){typeof e=="string"&&(e=[e]);let o;s&&(o=await this.feature_extractor(s,n),e=e.map(l=>l.replaceAll(this.audio_token,this.full_audio_sequence)));let a;return r&&(a=await this.image_processor(r,n),e=e.map(l=>l.replaceAll(this.image_token,this.full_image_sequence))),{...this.tokenizer(e,n),...a,...o}}};var Wd=class extends re{static uses_processor_config=!0;static uses_chat_template_file=!0;constructor(e,r,s){super(e,r,s),this.audio_ms_per_token=this.config.audio_ms_per_token??40,this.audio_seq_length=this.config.audio_seq_length??750,this.image_seq_length=this.config.image_seq_length??280;let{audio_token:n,boa_token:o,eoa_token:a,image_token:i,boi_token:l,eoi_token:c}=this.tokenizer.config;this.audio_token=n,this.boa_token=o,this.eoa_token=a,this.image_token=i,this.boi_token=l,this.eoi_token=c}static async from_pretrained(e,r={}){let[s,n,o]=await Promise.all([nt(e,If,!0,r),se.from_pretrained(e,r),Ml(e,Cf,!1,r)]),a={tokenizer:n};return s.image_processor&&(a.image_processor=new so(s.image_processor)),s.feature_extractor&&(a.feature_extractor=new eo(s.feature_extractor)),new this(s,a,o)}_compute_audio_num_tokens(e,r){let s=Math.round(r*20/1e3),n=Math.round(r*10/1e3),o=Math.floor(s/2),a=Math.floor((e+o-s-1)/n)+1;if(a<=0)return 0;for(let i=0;i<2;++i)a=Math.floor((a-1)/2)+1;return Math.min(a,this.audio_seq_length)}async _call(e,r=null,s=null,n={}){typeof e=="string"&&(e=[e]);let o;if(r){o=await this.image_processor(r,n);let i=o.num_soft_tokens_per_image,l=0;e=e.map(c=>c.replaceAll(this.image_token,()=>`

${this.boi_token}${this.image_token.repeat(i[l++])}${this.eoi_token}

`))}let a;if(s){let i=Array.isArray(s)?s:[s];a=await this.feature_extractor(i[0],n);let l=this.feature_extractor.config.sampling_rate??16e3,c=0;e=e.map(p=>p.replaceAll(this.audio_token,()=>`

${this.boa_token}${this.audio_token.repeat(this._compute_audio_num_tokens(i[c++].length,l))}${this.eoa_token}

`))}return{...this.tokenizer(e,n),...o,...a}}};var fs=class extends re{static image_processor_class=Ae;static tokenizer_class=se;static image_token="<|image_pad|>";async _call(e,r=null,...s){Array.isArray(e)||(e=[e]);let n,o;if(r&&(n=await this.image_processor(r),o=n.image_grid_thw),o){let i=this.image_processor.config.merge_size**2,l=0,c=this.constructor.image_token,p=o.tolist();e=e.map(f=>{for(;f.includes(c);){let _=Number(p[l++].reduce((m,w)=>m*w,1n));f=f.replace(c,"<|placeholder|>".repeat(Math.floor(_/i)))}return f.replaceAll("<|placeholder|>",c)})}return{...this.tokenizer(e),...n}}};var Vd=class extends fs{static image_token="<|image|>"};var Hd=class extends re{static tokenizer_class=se;static feature_extractor_class=je;static uses_processor_config=!0;_get_num_audio_features(e){let{hop_length:r}=this.feature_extractor.config.melspec_kwargs,{projector_window_size:s,projector_downsample_rate:n}=this.feature_extractor.config,o=Math.floor(s/n),a=Math.floor(e/r)+1,i=Math.floor(a/2);return Math.ceil(i/s)*o}async _call(e,r=null,s={}){if(Array.isArray(e))throw new Error("Batched inputs are not supported yet.");let n={};if(r){let{input_features:a}=await this.feature_extractor(r);n.input_features=a;let i=this._get_num_audio_features(r.length),l=new Uint8Array(i).fill(1);n.input_features_mask=new N("bool",l,[1,i]);let c=this.config.audio_token??"<|audio|>";if(!e.includes(c))throw new Error(`The input text does not contain the audio token ${c}.`);e=e.replaceAll(c,c.repeat(i))}return{...this.tokenizer(e,{add_special_tokens:!1,...s}),...n}}};function IN(t,e){let s=t.dims.at(-1)-1,n=t.tolist();n.fill(!1,0,1),n.fill(!1,s);let o=e.tolist();return n.map((a,i)=>a?i:null).filter(a=>a!==null).map(a=>o[a])}var Xd=class extends re{static tokenizer_class=se;static image_processor_class=Ae;async _call(e,r,s={}){let n=e?await this.image_processor(e,s):{};return{...r?this.tokenizer(r,s):{},...n}}post_process_grounded_object_detection(e,r,{box_threshold:s=.25,text_threshold:n=.25,target_sizes:o=null}={}){let{logits:a,pred_boxes:i}=e,l=a.dims[0];if(o!==null&&o.length!==l)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let c=a.dims.at(1),p=a.sigmoid(),f=p.max(-1).tolist(),_=i.tolist().map(w=>w.map(x=>ev(x))),m=[];for(let w=0;w<l;++w){let x=o!==null?o[w]:null;x!==null&&(_[w]=_[w].map(T=>T.map((I,O)=>I*x[(O+1)%2])));let k=f[w],A=[],E=[],S=[];for(let T=0;T<c;++T){let I=k[T];if(I<=s)continue;let O=_[w][T],b=p[w][T];A.push(I),S.push(O);let F=IN(b.gt(n),r[w]);E.push(F)}m.push({scores:A,boxes:S,labels:this.batch_decode(E)})}return m}};function CN(t,e,r,s,n,o){let a="";for(let i=0;i<e;++i){for(let l=0;l<r;++l)a+=s+`<row_${i+1}_col_${l+1}>`+n.repeat(t);a+=`
`}return a+=`
${s}${o}`+n.repeat(t)+`${s}`,a}function PN(t,e,r,s){return`${e}${s}`+r.repeat(t)+`${e}`}function zN(t,e,r,s,n,o){return t===0&&e===0?PN(r,s,n,o):CN(r,t,e,s,n,o)}var ac=class extends re{static image_processor_class=Ae;static tokenizer_class=se;static uses_processor_config=!0;fake_image_token="<fake_token_around_image>";image_token="<image>";global_img_token="<global-img>";async _call(e,r=null,s={}){s.return_row_col_info??=!0;let n;r&&(n=await this.image_processor(r,s)),Array.isArray(e)||(e=[e]);let o=n.rows??[new Array(e.length).fill(0)],a=n.cols??[new Array(e.length).fill(0)],i=this.config.image_seq_len,l=[],c=[];for(let f=0;f<e.length;++f){let _=e[f],m=o[f],w=a[f];l.push(WE(_,this.image_token));let x=m.map((E,S)=>zN(E,w[S],i,this.fake_image_token,this.image_token,this.global_img_token)),k=_.split(this.image_token);if(k.length===0)throw new Error("The image token should be present in the text.");let A=k[0];for(let E=0;E<x.length;++E)A+=x[E]+k[E+1];c.push(A)}return{...this.tokenizer(c),...n}}};var Kd=class extends re{static image_processor_class=Ae;static tokenizer_class=se;static uses_processor_config=!0;constructor(e,r,s){super(e,r,s),this.image_tag=this.config.image_tag,this.image_start_tag=this.config.image_start_tag,this.image_end_tag=this.config.image_end_tag,this.num_image_tokens=this.config.num_image_tokens}async _call(e,{images:r=null,chat_template:s="default"}={}){r?Array.isArray(r)||(r=[r]):r=await Promise.all(e.filter(k=>k.images).flatMap(k=>k.images).map(k=>Je.read(k)));let n=this.tokenizer,o=n.apply_chat_template(e,{tokenize:!1,add_generation_prompt:!0,chat_template:s}),a=k=>n.encode(k,{add_special_tokens:!1}),i=o.split(this.image_tag),l=i.length-1;if(r.length!==l)throw new Error(`Number of images provided (${r.length}) does not match number of "${this.image_tag}" image tags (${l})`);let[c,p,f]=n.convert_tokens_to_ids([this.image_tag,this.image_start_tag,this.image_end_tag]),_=a(i[0]),m=new Array(_.length).fill(!1);for(let k=1;k<i.length;++k){let A=new Array(this.num_image_tokens).fill(c),E=a(i[k]);_=wt(_,[p],A,[f],E);let S=new Array(this.num_image_tokens).fill(!0);m=wt(m,[!1],S,[!1],new Array(E.length).fill(!1))}let w=[1,_.length],x={input_ids:new N("int64",_,w),attention_mask:new N("int64",new Array(_.length).fill(1),w),images_seq_mask:new N("bool",m,w),images_emb_mask:new N("bool",new Array(l*this.num_image_tokens).fill(!0),[1,l,this.num_image_tokens])};if(r&&r.length>0){let k=await this.image_processor(r);return k.pixel_values.unsqueeze_(0),{...x,...k}}return x}};var Yd=class extends re{static tokenizer_class=se;static image_processor_class=Ae;async _call(e=null,r=null,s={}){if(!e&&!r)throw new Error("Either text or images must be provided");let n=e?this.tokenizer(e,s):{},o=r?await this.image_processor(r,s):{};return{...n,...o}}};var Qd=class extends re{static tokenizer_class=se;static image_processor_class=Ae;async _call(e,r=null,s={}){let{image_rows:n,image_cols:o,image_sizes:a,...i}=await this.image_processor(e,{...s,return_row_col_info:!0});if(r){let l=this.config.image_token??"<image>",{tile_size:c=512,downsample_factor:p=2,encoder_patch_size:f=16,use_thumbnail:_=!0}=this.image_processor.config,m=S=>Math.ceil(Math.floor(S/f)/p),w=m(c)**2,x=this.config.image_start_token??"<|image_start|>",k=this.config.image_end_token??"<|image_end|>",A=this.config.image_thumbnail??"<|img_thumbnail|>";Array.isArray(r)||(r=[r]);let E=0;r=r.map(S=>{let T=S.split(l);return T[0]+T.slice(1).map(I=>{let O=E++,[b,F]=a[O],j=n[O],U=o[O],X=m(b)*m(F),K=x;if(j>1||U>1){let J=l.repeat(w);for(let R=0;R<j;++R)for(let C=0;C<U;++C)K+=`<|img_row_${R+1}_col_${C+1}|>`+J;_&&(K+=A+l.repeat(X))}else K+=l.repeat(X);return K+k+I}).join("")})}return{...i,...r?this.tokenizer(r,s):{}}}};var Jd=class extends re{static tokenizer_class=se;static image_processor_class=Ae;static uses_processor_config=!0;async _call(e,r=null,s={}){let n=await this.image_processor(e,s);if(r){let[a,i]=n.pixel_values.dims.slice(-2),{image_token:l,patch_size:c,num_additional_image_tokens:p}=this.config,f=Math.floor(a/c)*Math.floor(i/c)+p;r=structuredClone(r),Array.isArray(r)||(r=[r]);for(let _=0;_<r.length;++_)r[_]=r[_].replace(l,l.repeat(f))}let o=r?this.tokenizer(r,s):{};return{...n,...o}}};var NM={char:["char_decode",1],bpe:["bpe_decode",2],wp:["wp_decode",102]},Zd=class extends re{static tokenizer_class=se;static image_processor_class=Ae;get char_tokenizer(){return this.components.char_tokenizer}get bpe_tokenizer(){return this.components.bpe_tokenizer}get wp_tokenizer(){return this.components.wp_tokenizer}_decode_helper(e,r){if(!NM.hasOwnProperty(r))throw new Error(`Format ${r} is not supported.`);let[s,n]=NM[r],o=this[s].bind(this),[a,i]=e.dims,l=[],c=[],p=e.tolist();for(let _=0;_<a;++_){let m=p[_],w=[],x=[];for(let A=1;A<i;++A){let[E,S]=ze(Pe(m[A]));if(x.push(E),S==n)break;w.push(S)}let k=x.length>0?x.reduce((A,E)=>A*E,1):0;c.push(w),l.push(k)}return[o(c),l]}char_decode(e){return this.char_tokenizer.batch_decode(e).map(r=>r.replaceAll(" ",""))}bpe_decode(e){return this.bpe_tokenizer.batch_decode(e)}wp_decode(e){return this.wp_tokenizer.batch_decode(e).map(r=>r.replaceAll(" ",""))}batch_decode([e,r,s]){let[n,o]=this._decode_helper(e,"char"),[a,i]=this._decode_helper(r,"bpe"),[l,c]=this._decode_helper(s,"wp"),p=[],f=[];for(let _=0;_<n.length;++_){let[m,w]=ze([o[_],i[_],c[_]]);p.push([n[_],a[_],l[_]][w]),f.push(m)}return{generated_text:p,scores:f,char_preds:n,bpe_preds:a,wp_preds:l}}static async from_pretrained(...e){let r=await super.from_pretrained(...e),s=await se.from_pretrained("Xenova/gpt2"),n=await se.from_pretrained("Xenova/bert-base-uncased");return r.components={image_processor:r.image_processor,char_tokenizer:r.tokenizer,bpe_tokenizer:s,wp_tokenizer:n},r}async _call(e,r=null){let s=await this.image_processor(e);return r&&(s.labels=this.tokenizer(r).input_ids),s}};var e_=class extends re{static tokenizer_class=se;static feature_extractor_class=je;async _call(e){return await this.feature_extractor(e)}};var t_=class extends re{static tokenizer_class=se;static image_processor_class=Ae};var lo="<image>";function LN(t,e,r,s,n){return`${s.repeat(r*n)}${e}${t}
`}var r_=class extends re{static tokenizer_class=se;static image_processor_class=Ae;static uses_processor_config=!1;async _call(e,r=null,s={}){r||(ee.warn("You are using PaliGemma without a text prefix. It will perform as a picture-captioning model."),r=""),Array.isArray(e)||(e=[e]),Array.isArray(r)||(r=[r]);let n=this.tokenizer.bos_token,o=this.image_processor.config.image_seq_length,a;r.some(c=>c.includes(lo))?a=r.map(c=>{let p=c.replaceAll(lo,lo.repeat(o)),f=p.lastIndexOf(lo),_=f===-1?0:f+lo.length;return p.slice(0,_)+n+p.slice(_)+`
`}):(ee.warn("You are passing both `text` and `images` to `PaliGemmaProcessor`. The processor expects special image tokens in the text, as many tokens as there are images per each text. It is recommended to add `<image>` tokens in the very beginning of your text. For this call, we will infer how many images each text has and add special tokens."),a=r.map(c=>LN(c,n,o,lo,e.length)));let i=this.tokenizer(a,s);return{...await this.image_processor(e,s),...i}}};var $M="<|image|>",NN=/<\|image_\d+\|>/g,s_=class extends re{static image_processor_class=Ae;static tokenizer_class=se;async _call(e,r=null,{padding:s=!0,truncation:n=!0,num_crops:o=null}={}){Array.isArray(e)||(e=[e]);let a,i;if(r){i=await this.image_processor(r,{num_crops:o});let{num_img_tokens:l}=i,c=e.map((f,_)=>f.split(NN).join($M.repeat(l[_])));a=this.tokenizer(c,{padding:s,truncation:n});let p=this.tokenizer._tokenizer.token_to_id($M);a.input_ids.map_(f=>f==p?-f:f)}else a=this.tokenizer(e);return{...a,...i}}};var n_=class extends re{static tokenizer_class=se;static image_processor_class=Ae;static uses_processor_config=!0;async _call(e,r=null,s={}){let n=await this.image_processor(e,s);if(r){let[a,i]=n.pixel_values.dims.slice(-2),{image_token:l,image_break_token:c,image_end_token:p,patch_size:f,spatial_merge_size:_}=this.config,m=f*_,w=Math.floor(a/m),x=Math.floor(i/m);r=structuredClone(r),Array.isArray(r)||(r=[r]);for(let k=0;k<r.length;++k){let A=l.repeat(x),E=A+c,S=A+p,T=E.repeat(w-1)+S;r[k]=r[k].replace(l,T)}}let o=r?this.tokenizer(r,s):{};return{...n,...o}}};var o_=class extends re{static feature_extractor_class=to;async _call(e){return await this.feature_extractor(e)}post_process_speaker_diarization(...e){return this.feature_extractor.post_process_speaker_diarization(...e)}get sampling_rate(){return this.feature_extractor.config.sampling_rate}};var co=class extends fs{};var a_=class extends co{};var uo=class extends re{static image_processor_class=Ae;async _call(...e){return await this.image_processor(...e)}post_process_masks(...e){return this.image_processor.post_process_masks(...e)}reshape_input_points(...e){return this.image_processor.reshape_input_points(...e)}};var ic=class extends uo{},i_=class extends ic{};var l_=class extends re{static tokenizer_class=se;static feature_extractor_class=je;async _call(e){return await this.feature_extractor(e)}};var c_=class extends re{static tokenizer_class=se;static feature_extractor_class=je;static uses_processor_config=!0;async _call(e,r=null,s={}){if(Array.isArray(e))throw new Error("Batched inputs are not supported yet.");let n={};if(r){let a=r.length,{input_features:i}=await this.feature_extractor(r,{...s,max_length:a}),l=Math.round(a/this.config.encoder_ds_factor+1e-4),c=1+Math.ceil(l/this.config.stack_factor);n.audio_token_len=[c],n.audio_values=i;let p=this.config.audio_placeholder;if(!e.includes(p))throw new Error(`The input text does not contain the image token ${p}.`);e=e.replaceAll(p,p.repeat(c))}return{...this.tokenizer(e,{add_special_tokens:!1,...s}),...n}}};var u_="[AUDIO]",$N="[BEGIN_AUDIO]",RN=375;function DN(t,e){let r=[];for(let s=0;s<t.length;s+=e)r.push(t.subarray(s,Math.min(s+e,t.length)));return r}var p_=class extends re{static tokenizer_class=se;static feature_extractor_class=je;static uses_processor_config=!1;async _call(e,r=null,s={}){if(Array.isArray(e))throw new Error("Batched inputs are not supported yet.");let n={};if(r){if(!e.includes(u_))throw new Error(`The input text does not contain the audio token ${u_}.`);Array.isArray(r)||(r=[r]);let a=e.split(u_),i=a.length-1;if(i!==r.length)throw new Error(`The number of audio inputs (${r.length}) does not match the number of audio tokens in the text (${i}).`);let l=this.feature_extractor.config.n_samples,c=r.map(w=>DN(w,l)),p=c.map(w=>w.length),f=c.flat(),_=(await Promise.all(f.map(w=>this.feature_extractor(w,s)))).map(w=>w.input_features);n.audio_values=_.length>1?ye(_,0):_[0];let m=a[0];for(let w=0;w<p.length;++w){m+=$N;for(let x=0;x<p[w];++x)m+=u_.repeat(RN);m+=a[w+1]}e=m}return{...this.tokenizer(e,{add_special_tokens:!1,...s}),...n}}};var RM=32,sv=6,f_=8,FN=10,BN=32,d_=class extends re{static tokenizer_class=se;static feature_extractor_class=je;static uses_processor_config=!1;get num_mel_frames_first_audio_chunk(){return(sv+1)*f_}get num_samples_first_audio_chunk(){let{hop_length:e,n_fft:r}=this.feature_extractor.config;return(this.num_mel_frames_first_audio_chunk-1)*e+Math.floor(r/2)}get num_samples_per_audio_chunk(){let{hop_length:e,n_fft:r}=this.feature_extractor.config;return f_*e+r}get num_right_pad_tokens(){return sv+1+FN}get audio_length_per_tok(){return f_}get raw_audio_length_per_tok(){return f_*this.feature_extractor.config.hop_length}async _call(e,{is_streaming:r=!1,is_first_audio_chunk:s=!0}={}){if(Me(e,"VoxtralRealtimeProcessor"),!r&&!s)throw new Error("In non-streaming mode (`is_streaming=false`), `is_first_audio_chunk` must be `true`.");if(s)if(r){let n=RM*this.raw_audio_length_per_tok,o=new Float32Array(n+e.length);o.set(e,n);let a=await this.feature_extractor(o,{center:!0}),l=1+(RM+sv),c=new BigInt64Array(l).fill(BigInt(BN));return c[0]=1n,{input_ids:new N("int64",c,[1,l]),...a}}else{let n=this.num_right_pad_tokens*this.raw_audio_length_per_tok,o=new Float32Array(e.length+n);return o.set(e),await this.feature_extractor(o,{center:!0})}else return await this.feature_extractor(e,{center:!1})}};var __=class extends re{static tokenizer_class=se;static feature_extractor_class=je;async _call(e){return await this.feature_extractor(e)}};var m_=class extends re{static tokenizer_class=se;static feature_extractor_class=je;async _call(e){return await this.feature_extractor(e)}};var h_=class extends re{static tokenizer_class=se;static feature_extractor_class=je;async _call(e){return await this.feature_extractor(e)}};var lc=class{static async from_pretrained(e,r={}){let s=await nt(e,Er,!0,r),{image_processor_type:n,feature_extractor_type:o,processor_class:a}=s;if(a&&g_[a])return g_[a].from_pretrained(e,r);if(!n&&!o)throw new Error("No `image_processor_type` or `feature_extractor_type` found in the config.");let i={};if(n){let c=io[n.replace(/Fast$/,"")];if(!c)throw new Error(`Unknown image_processor_type: '${n}'.`);i.image_processor=new c(s)}if(o){let c=io[o];if(c)i.image_processor=new c(s);else{let p=ql[o];if(!p)throw new Error(`Unknown feature_extractor_type: '${o}'.`);i.feature_extractor=new p(s)}}let l={};return new re(l,i,null)}};async function UN(t,e){return await nt(t,"config.json",!0,e)}function po(t){let e={},r={};switch(t.model_type){case"llava":case"paligemma":case"gemma3":case"florence2":case"llava_onevision":case"idefics3":case"granite_speech":case"ultravox":case"voxtral":case"voxtral_realtime":case"smolvlm":case"gemma3n":case"gemma4":case"lfm2_vl":case"chatterbox":case"lighton_ocr":case"glm_ocr":case"mistral3":case"qwen2_5_vl":case"qwen3_vl":case"qwen3_vl_moe":r=po(t.text_config);break;case"moondream1":r=po(t.phi_config);break;case"musicgen":r=po(t.decoder);break;case"multi_modality":r=po(t.language_config);break;case"gpt2":case"gptj":case"jais":case"codegen":case"gpt_bigcode":e.num_heads="n_head",e.num_layers="n_layer",e.hidden_size="n_embd";break;case"gpt_neox":case"stablelm":case"opt":case"falcon":case"modernbert-decoder":e.num_heads="num_attention_heads",e.num_layers="num_hidden_layers",e.hidden_size="hidden_size";break;case"gpt_oss":case"llama":case"llama4_text":case"nanochat":case"apertus":case"arcee":case"afmoe":case"lfm2":case"lfm2_moe":case"smollm3":case"olmo":case"olmo2":case"olmo3":case"mobilellm":case"granite":case"granitemoehybrid":case"cohere":case"cohere2":case"mistral":case"voxtral_realtime_text":case"voxtral_realtime_encoder":case"starcoder2":case"qwen2":case"qwen2_moe":case"qwen2_vl":case"qwen2_vl_text":case"qwen2_5_vl_text":case"qwen3_moe":case"qwen3_vl_text":case"qwen3_vl_moe_text":case"phi":case"phi3":case"phi3_v":case"llava_qwen2":e.num_heads="num_key_value_heads",e.num_layers="num_hidden_layers",e.hidden_size="hidden_size",e.num_attention_heads="num_attention_heads",e.dim_kv="head_dim";break;case"qwen3":case"solar_open":case"glm_ocr_text":case"gemma":case"gemma2":case"vaultgemma":case"gemma3_text":case"gemma3n_text":case"gemma4_text":case"glm":case"helium":case"ernie4_5":case"hunyuan_v1_dense":case"falcon_h1":case"nemotron_h":case"ministral":case"ministral3":e.num_heads="num_key_value_heads",e.num_layers="num_hidden_layers",e.dim_kv="head_dim";break;case"openelm":e.num_heads="num_kv_heads",e.num_layers="num_transformer_layers",e.dim_kv="head_dim";break;case"gpt_neo":case"donut-swin":e.num_heads="num_heads",e.num_layers="num_layers",e.hidden_size="hidden_size";break;case"bloom":e.num_heads="n_head",e.num_layers="n_layer",e.hidden_size="hidden_size";break;case"mpt":e.num_heads="n_heads",e.num_layers="n_layers",e.hidden_size="d_model";break;case"exaone":e.num_heads="num_key_value_heads",e.num_layers="num_layers",e.dim_kv="head_dim",e.num_attention_heads="num_attention_heads";break;case"youtu":case"deepseek_v3":case"glm_moe_dsa":case"mistral4":e.num_heads="num_key_value_heads",e.num_layers="num_hidden_layers",e.dim_kv="qk_head_dim",e.num_attention_heads="num_attention_heads";break;case"t5":case"mt5":case"longt5":e.num_decoder_layers="num_decoder_layers",e.num_decoder_heads="num_heads",e.decoder_dim_kv="d_kv",e.num_encoder_layers="num_layers",e.num_encoder_heads="num_heads",e.encoder_dim_kv="d_kv";break;case"bart":case"mbart":case"marian":case"whisper":case"lite-whisper":case"m2m_100":case"blenderbot":case"blenderbot-small":case"florence2_language":e.num_decoder_layers="decoder_layers",e.num_decoder_heads="decoder_attention_heads",e.decoder_hidden_size="d_model",e.num_encoder_layers="encoder_layers",e.num_encoder_heads="encoder_attention_heads",e.encoder_hidden_size="d_model";break;case"speecht5":e.num_decoder_layers="decoder_layers",e.num_decoder_heads="decoder_attention_heads",e.decoder_hidden_size="hidden_size",e.num_encoder_layers="encoder_layers",e.num_encoder_heads="encoder_attention_heads",e.encoder_hidden_size="hidden_size";break;case"trocr":e.num_encoder_layers=e.num_decoder_layers="decoder_layers",e.num_encoder_heads=e.num_decoder_heads="decoder_attention_heads",e.encoder_hidden_size=e.decoder_hidden_size="d_model";break;case"musicgen_decoder":e.num_encoder_layers=e.num_decoder_layers="num_hidden_layers",e.num_encoder_heads=e.num_decoder_heads="num_attention_heads",e.encoder_hidden_size=e.decoder_hidden_size="hidden_size";break;case"moonshine":e.num_decoder_layers="decoder_num_hidden_layers",e.num_decoder_heads="decoder_num_key_value_heads",e.num_encoder_layers="encoder_num_hidden_layers",e.num_encoder_heads="encoder_num_key_value_heads",e.encoder_hidden_size=e.decoder_hidden_size="hidden_size";break;case"cohere_asr":e.num_decoder_layers="num_hidden_layers",e.num_decoder_heads="num_key_value_heads",e.decoder_hidden_size="hidden_size",e.decoder_dim_kv="head_dim";let{num_hidden_layers:n,num_attention_heads:o,hidden_size:a}=t.encoder_config;r={num_encoder_layers:n,num_encoder_heads:o,encoder_hidden_size:a,encoder_dim_kv:t.head_dim};break;case"vision-encoder-decoder":let i=po(t.decoder),l="num_decoder_layers"in i,c=Ve(t,["model_type","is_encoder_decoder"]);return l?(c.num_decoder_layers=i.num_decoder_layers,c.num_decoder_heads=i.num_decoder_heads,c.decoder_hidden_size=i.decoder_hidden_size,c.num_encoder_layers=i.num_encoder_layers,c.num_encoder_heads=i.num_encoder_heads,c.encoder_hidden_size=i.encoder_hidden_size):(c.num_layers=i.num_layers,c.num_heads=i.num_heads,c.hidden_size=i.hidden_size),c}let s={...r,...Ve(t,["model_type","multi_query","is_encoder_decoder"])};for(let n in e)s[n]=t[e[n]];return s}function Qs(t,e){t instanceof Ys||(t=new Ys(t));let r=e?.prefix??"past_key_values",s=r==="present"?"present":"past",n=new Set;if(["lfm2","lfm2_moe"].includes(t.model_type)){let{layer_types:o}=t;for(let a=0;a<o.length;++a)if(o[a]==="full_attention")n.add(`${r}.${a}.key`),n.add(`${r}.${a}.value`);else if(o[a]==="conv")n.add(`${s}_conv.${a}`);else throw new Error(`Unsupported layer type: ${o[a]}`);return n}else if(["granitemoehybrid","falcon_h1","nemotron_h"].includes(t.model_type)){let o=t,a=o.layer_types??o.layers_block_type,i=o.num_hidden_layers??a?.length;for(let l=0;l<i;++l)(!a||a[l]==="mamba")&&(n.add(`${s}_conv.${l}`),n.add(`${s}_ssm.${l}`)),(!a||a[l]==="attention")&&(n.add(`${r}.${l}.key`),n.add(`${r}.${l}.value`));return n}else if(["qwen3_next","qwen3_5_text","qwen3_5_moe_text","olmo_hybrid"].includes(t.model_type)){let{layer_types:o}=t;for(let a=0;a<o.length;++a)if(o[a]==="full_attention")n.add(`${r}.${a}.key`),n.add(`${r}.${a}.value`);else if(o[a]==="linear_attention")t.model_type==="olmo_hybrid"?(n.add(`${s}_conv.${a}.key`),n.add(`${s}_conv.${a}.value`),n.add(`${s}_conv.${a}.query`)):n.add(`${s}_conv.${a}`),n.add(`${s}_recurrent.${a}`);else throw new Error(`Unsupported layer type: ${o[a]}`);return n}else if(["gemma4","gemma4_text"].includes(t.model_type)){let o=t.model_type==="gemma4"?t.text_config:t,a=o.num_hidden_layers,i=o.num_kv_shared_layers??0,l=a-i;for(let 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V={EncoderOnly:0,EncoderDecoder:1,Seq2Seq:2,Vision2Seq:3,DecoderOnly:4,DecoderOnlyWithoutHead:5,MaskGeneration:6,ImageTextToText:7,Musicgen:8,MultiModality:9,Phi3V:10,AudioTextToText:11,AutoEncoder:12,ImageAudioTextToText:13,Supertonic:14,Chatterbox:15,VoxtralRealtime:16},dr={[V.DecoderOnly]:{sessions:(t,e)=>({model:e.model_file_name??"model"}),cache_sessions:{model:!0},optional_configs:{generation_config:"generation_config.json"}},[V.DecoderOnlyWithoutHead]:{sessions:(t,e)=>({model:e.model_file_name??"model"})},[V.Seq2Seq]:{sessions:()=>({model:"encoder_model",decoder_model_merged:"decoder_model_merged"}),cache_sessions:{decoder_model_merged:!0},optional_configs:{generation_config:"generation_config.json"}},[V.Vision2Seq]:{sessions:()=>({model:"encoder_model",decoder_model_merged:"decoder_model_merged"}),cache_sessions:{decoder_model_merged:!0},optional_configs:{generation_config:"generation_config.json"}},[V.Musicgen]:{sessions:()=>({model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"}),cache_sessions:{decoder_model_merged:!0},optional_configs:{generation_config:"generation_config.json"}},[V.EncoderDecoder]:{sessions:()=>({model:"encoder_model",decoder_model_merged:"decoder_model_merged"}),cache_sessions:{decoder_model_merged:!0}},[V.MaskGeneration]:{sessions:()=>({model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"})},[V.ImageTextToText]:{text_only_sessions:{embed_tokens:"embed_tokens",decoder_model_merged:"decoder_model_merged"},sessions:(t,e,r)=>{let s={...dr[V.ImageTextToText].text_only_sessions};return r||(s.vision_encoder="vision_encoder"),t.is_encoder_decoder&&(s.model="encoder_model"),s},cache_sessions:{decoder_model_merged:!0},optional_configs:{generation_config:"generation_config.json"}},[V.AudioTextToText]:{text_only_sessions:{embed_tokens:"embed_tokens",decoder_model_merged:"decoder_model_merged"},sessions:(t,e,r)=>{let s={...dr[V.AudioTextToText].text_only_sessions};return r||(s.audio_encoder="audio_encoder"),s},cache_sessions:{decoder_model_merged:!0},optional_configs:{generation_config:"generation_config.json"}},[V.ImageAudioTextToText]:{text_only_sessions:{embed_tokens:"embed_tokens",decoder_model_merged:"decoder_model_merged"},sessions:(t,e,r)=>{let s={...dr[V.ImageAudioTextToText].text_only_sessions};return r||(s.audio_encoder="audio_encoder",s.vision_encoder="vision_encoder"),s},optional_configs:{generation_config:"generation_config.json"}},[V.Phi3V]:{sessions:()=>({prepare_inputs_embeds:"prepare_inputs_embeds",model:"model",vision_encoder:"vision_encoder"}),cache_sessions:{model:!0},optional_configs:{generation_config:"generation_config.json"}},[V.MultiModality]:{sessions:()=>({prepare_inputs_embeds:"prepare_inputs_embeds",model:"language_model",lm_head:"lm_head",gen_head:"gen_head",gen_img_embeds:"gen_img_embeds",image_decode:"image_decode"}),cache_sessions:{model:!0},optional_configs:{generation_config:"generation_config.json"}},[V.AutoEncoder]:{sessions:()=>({encoder_model:"encoder_model",decoder_model:"decoder_model"})},[V.Supertonic]:{sessions:()=>({text_encoder:"text_encoder",latent_denoiser:"latent_denoiser",voice_decoder:"voice_decoder"})},[V.Chatterbox]:{sessions:()=>({embed_tokens:"embed_tokens",speech_encoder:"speech_encoder",model:"language_model",conditional_decoder:"conditional_decoder"}),cache_sessions:{model:!0},optional_configs:{generation_config:"generation_config.json"}},[V.VoxtralRealtime]:{text_only_sessions:{embed_tokens:"embed_tokens",decoder_model_merged:"decoder_model_merged"},sessions:(t,e,r)=>{let s={...dr[V.VoxtralRealtime].text_only_sessions};return r||(s.audio_encoder="audio_encoder"),s},cache_sessions:{decoder_model_merged:!0,audio_encoder:!0},optional_configs:{generation_config:"generation_config.json"}},default:{sessions:(t,e)=>({model:e.model_file_name??"model"})}};function dv(t){return dr[t]?.text_only_sessions??null}function vc(t,e,r={}){let s=dr[t]??dr.default;return{sessions:s.sessions(e,r,r.textOnly??!1),cache_sessions:s.cache_sessions,optional_configs:s.optional_configs}}function mo(t,{warn:e=!0}={}){let r=t.architectures||[];for(let s of r){let n=tr.get(s);if(n!==void 0)return n}if(t.model_type){let s=tr.get(t.model_type);if(s!==void 0)return s;for(let n of Object.values(rn))if(n.has(t.model_type)){let o=tr.get(n.get(t.model_type));if(o!==void 0)return o}}if(e){let s=r.length>0?r.join(", "):"(none)";ee.warn(`[resolve_model_type] Architecture(s) not found in MODEL_TYPE_MAPPING: [${s}] for model type '${t.model_type}'. Falling back to EncoderOnly (single model.onnx file). If you encounter issues, please report at: ${cs}`)}return V.EncoderOnly}function kc(t,{config:e=null,cache_dir:r=null,local_files_only:s=!1,revision:n="main"}={}){if(e!==null)return Rt.from_pretrained(t,{config:e,cache_dir:r,local_files_only:s,revision:n});let o=JSON.stringify([t,r,s,n]);return fp(o,()=>Rt.from_pretrained(t,{config:e,cache_dir:r,local_files_only:s,revision:n}))}async function ho(t,{config:e=null,dtype:r=null,device:s=null,model_file_name:n=null}={}){e=await kc(t,{config:e});let o=["config.json"],a=e["transformers.js_config"]??{},i=a.use_external_data_format,l="onnx",c=s??a.device,p=r??a.dtype,f=mo(e),_=(x,k=null)=>{k=k??x;let A=Pp(c,x),E=zp(p,x,A),S=is[E]??"",T=`${k}${S}.onnx`,I=l?`${l}/${T}`:T;o.push(I);let O=nv(i,T,x);for(let b of ov(T,O)){let F=l?`${l}/${b}`:b;o.push(F)}},{sessions:m,optional_configs:w}=vc(f,e,{model_file_name:n});for(let[x,k]of Object.entries(m))_(x,k);if(w)for(let x of Object.values(w))o.push(x);return o}var rn=null;function qM(t){rn=t}function _v(t){if(t instanceof N)return t;if(t.length===0)throw Error("items must be non-empty");if(Array.isArray(t[0])){if(t.some(e=>e.length!==t[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 N("int64",BigInt64Array.from(t.flat().map(e=>BigInt(e))),[t.length,t[0].length])}else return new N("int64",BigInt64Array.from(t.map(e=>BigInt(e))),[1,t.length])}function mv(t){return new N("bool",[t],[1])}var jM={[V.DecoderOnly]:{can_generate:!0,forward:Ct,prepare_inputs:go},[V.DecoderOnlyWithoutHead]:{can_generate:!1,forward:Ct,prepare_inputs:go},[V.Seq2Seq]:{can_generate:!0,forward:x_,prepare_inputs:Ec},[V.Vision2Seq]:{can_generate:!0,forward:x_,prepare_inputs:Ec},[V.Musicgen]:{can_generate:!0,forward:x_},[V.EncoderDecoder]:{can_generate:!1,forward:x_},[V.ImageTextToText]:{can_generate:!0,forward:XN,prepare_inputs:y_},[V.AudioTextToText]:{can_generate:!0,forward:HN,prepare_inputs:y_},[V.ImageAudioTextToText]:{can_generate:!0,prepare_inputs:y_},[V.Phi3V]:{can_generate:!0,prepare_inputs:y_},[V.MultiModality]:{can_generate:!0},[V.AutoEncoder]:{can_generate:!1,forward:WN},[V.Chatterbox]:{can_generate:!0,forward:qt},[V.VoxtralRealtime]:{can_generate:!0,prepare_inputs:go},default:{can_generate:!1,forward:qt}};function GM(t,e){let r=tr.get(t),s=!1,n=e?.architectures?.[0];if(n&&n!==t&&t?.endsWith("ForCausalLM")&&n.endsWith("ForConditionalGeneration")){let i=tr.get(n);i!==void 0&&(r=i,s=!0)}let o=jM[r]??jM.default,a=dr[r]??dr.default;return{typeConfig:{...o,...a},textOnly:s,modelType:r}}var tr=new Map,v_=new Map,sn=new Map,y=class extends We{main_input_name="input_ids";forward_params=["input_ids","attention_mask"];_return_dict_in_generate_keys=null;constructor(e,r,s){super(),this.config=e,this.sessions=r,this.configs=s;let n=sn.get(this.constructor),{typeConfig:o}=GM(n,e);this.can_generate=o.can_generate,this._forward=o.forward,this._prepare_inputs_for_generation=o.prepare_inputs,this.can_generate&&this.forward_params.push("past_key_values"),this.custom_config=this.config["transformers.js_config"]??{}}async dispose(){let e=[];for(let r of Object.values(this.sessions))e.push(r.release?.());return await Promise.all(e)}static async from_pretrained(e,{progress_callback:r=null,config:s=null,cache_dir:n=null,local_files_only:o=!1,revision:a="main",model_file_name:i=null,subfolder:l="onnx",device:c=null,dtype:p=null,use_external_data_format:f=null,session_options:_={}}={}){let m={progress_callback:r,config:s,cache_dir:n,local_files_only:o,revision:a,model_file_name:i,subfolder:l,device:c,dtype:p,use_external_data_format:f,session_options:_},w=sn.get(this);s=m.config=await Rt.from_pretrained(e,m);let{typeConfig:x,textOnly:k,modelType:A}=GM(w,s);if(A===void 0){let I=w??s?.model_type;I!=="custom"&&ee.warn(`Model type for '${I}' not found, assuming encoder-only architecture. Please report this at ${cs}.`)}if(r&&!(r instanceof Cs)){let I={};try{let O=await ho(e,{config:s,dtype:p,device:c,model_file_name:i});(await Promise.all(O.map(F=>Ot(e,F,m)))).forEach((F,j)=>{if(F.exists){let U=O[j]==="config.json";I[O[j]]={loaded:U?F.size??0:0,total:F.size??0}}})}catch(O){ee.warn(`Unable to fetch model file metadata for total progress tracking: ${O}`)}Object.keys(I).length>0&&(m.progress_callback=new Cs(r,I))}let E=x.sessions(s,m,k),S=[BM(e,E,m,x.cache_sessions)];x.optional_configs&&S.push(YN(e,x.optional_configs,m));let T=await Promise.all(S);return new this(s,...T)}async _call(e){return await this.forward(e)}async forward(e){return await this._forward(this,e)}get generation_config(){return this.configs?.generation_config??null}_get_logits_processor(e,r,s=null){let n=new ds;if(e.repetition_penalty!==null&&e.repetition_penalty!==1&&n.push(new _c(e.repetition_penalty)),e.no_repeat_ngram_size!==null&&e.no_repeat_ngram_size>0&&n.push(new dc(e.no_repeat_ngram_size)),e.bad_words_ids!==null&&n.push(new gc(e.bad_words_ids,e.eos_token_id)),e.min_length!==null&&e.eos_token_id!==null&&e.min_length>0&&n.push(new mc(e.min_length,e.eos_token_id)),e.min_new_tokens!==null&&e.eos_token_id!==null&&e.min_new_tokens>0&&n.push(new hc(r,e.min_new_tokens,e.eos_token_id)),e.forced_bos_token_id!==null&&n.push(new cc(e.forced_bos_token_id)),e.forced_eos_token_id!==null&&n.push(new uc(e.max_length,e.forced_eos_token_id)),e.suppress_tokens!==null&&n.push(new pc(e.suppress_tokens)),e.begin_suppress_tokens!==null){let o=r>1||e.forced_bos_token_id===null?r:r+1;n.push(new Js(e.begin_suppress_tokens,o))}return e.guidance_scale!==null&&e.guidance_scale>1&&n.push(new wc(e.guidance_scale)),e.temperature===0&&e.do_sample&&(ee.warn("`do_sample` changed to false because `temperature: 0` implies greedy sampling (always selecting the most likely token), which is incompatible with `do_sample: true`."),e.do_sample=!1),e.do_sample&&e.temperature!==null&&e.temperature!==1&&n.push(new xc(e.temperature)),s!==null&&n.extend(s),n}_prepare_generation_config(e,r,s=_o){let n={...this.config};for(let a of["decoder","generator","text_config"])a in n&&Object.assign(n,n[a]);let o=new s(n);return Object.assign(o,this.generation_config??{}),e&&Object.assign(o,e),r&&Object.assign(o,Ve(r,Object.getOwnPropertyNames(o))),o}_get_stopping_criteria(e,r=null){let s=new Zs;return e.max_length!==null&&s.push(new yc(e.max_length,this.config.max_position_embeddings??null)),e.eos_token_id!==null&&s.push(new bc(e.eos_token_id)),r&&s.extend(r),s}_validate_model_class(){if(!this.can_generate){let e=[rn.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,rn.MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES,rn.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,rn.MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES].filter(Boolean),r=sn.get(this.constructor),s=new Set,n=this.config.model_type;for(let a of e){let i=a?.get(n);i&&s.add(i)}let o=`The current model class (${r}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw s.size>0&&(o+=` Please use the following class instead: ${[...s].join(", ")}`),Error(o)}}prepare_inputs_for_generation(...e){if(!this._prepare_inputs_for_generation)throw new Error("prepare_inputs_for_generation is not implemented for this model.");return this._prepare_inputs_for_generation(this,...e)}_update_model_kwargs_for_generation({generated_input_ids:e,outputs:r,model_inputs:s,is_encoder_decoder:n}){return s.past_key_values=b_(r,s.past_key_values),s.input_ids=new N("int64",e.flat(),[e.length,1]),n?"decoder_attention_mask"in s&&(s.decoder_attention_mask=ye([s.decoder_attention_mask,Ze([s.decoder_attention_mask.dims[0],1])],1)):s.attention_mask=ye([s.attention_mask,Ze([s.attention_mask.dims[0],1])],1),s.position_ids=null,s}_prepare_model_inputs({inputs:e,bos_token_id:r,model_kwargs:s}){let n=Ve(s,this.forward_params),o=this.main_input_name;if(o in n){if(e)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else n[o]=e;return{inputs_tensor:n[o],model_inputs:n,model_input_name:o}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:e,model_inputs:r,model_input_name:s,generation_config:n}){if(this.sessions.model.inputNames.includes("inputs_embeds")&&!r.inputs_embeds&&"_prepare_inputs_embeds"in this){let{input_ids:a,pixel_values:i,attention_mask:l,...c}=r,p=await this._prepare_inputs_embeds(r);r={...c,...Ve(p,["inputs_embeds","attention_mask"])}}let{last_hidden_state:o}=await qt(this,r);if(n.guidance_scale!==null&&n.guidance_scale>1)o=ye([o,Wn(o,0)],0),"attention_mask"in r&&(r.attention_mask=ye([r.attention_mask,Dp(r.attention_mask)],0));else if(r.decoder_input_ids){let a=_v(r.decoder_input_ids).dims[0];if(a!==o.dims[0]){if(o.dims[0]!==1)throw new Error(`The encoder outputs have a different batch size (${o.dims[0]}) than the decoder inputs (${a}).`);o=ye(Array.from({length:a},()=>o),0)}}return r.encoder_outputs=o,r}_prepare_decoder_input_ids_for_generation({batch_size:e,model_input_name:r,model_kwargs:s,decoder_start_token_id:n,bos_token_id:o,generation_config:a}){let{decoder_input_ids:i,...l}=s;if(!(i instanceof N)){if(i)Array.isArray(i[0])||(i=Array.from({length:e},()=>i));else if(n??=o,this.config.model_type==="musicgen")i=Array.from({length:e*this.config.decoder.num_codebooks},()=>[n]);else if(Array.isArray(n)){if(n.length!==e)throw new Error(`\`decoder_start_token_id\` expcted to have length ${e} but got ${n.length}`);i=n}else i=Array.from({length:e},()=>[n]);i=_v(i)}return l.decoder_attention_mask=Bl(i),{input_ids:i,model_inputs:l}}async generate({inputs:e=null,generation_config:r=null,logits_processor:s=null,stopping_criteria:n=null,streamer:o=null,...a}){this._validate_model_class(),r=this._prepare_generation_config(r,a);let{inputs_tensor:i,model_inputs:l,model_input_name:c}=this._prepare_model_inputs({inputs:e,model_kwargs:a}),p=this.config.is_encoder_decoder;p&&("encoder_outputs"in l||(l=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:i,model_inputs:l,model_input_name:c,generation_config:r})));let f;p?{input_ids:f,model_inputs:l}=this._prepare_decoder_input_ids_for_generation({batch_size:l[c].dims.at(0),model_input_name:c,model_kwargs:l,decoder_start_token_id:r.decoder_start_token_id,bos_token_id:r.bos_token_id,generation_config:r}):f=l[c];let _=f.dims.at(-1);r.max_new_tokens!==null&&(r.max_length=_+r.max_new_tokens);let m=this._get_logits_processor(r,_,s),w=this._get_stopping_criteria(r,n),x=l[c].dims.at(0),k=en.getSampler(r),A=new Array(x).fill(0),E=f.tolist();o&&o.put(E);let S,T={},I={};for(;;){if(l=this.prepare_inputs_for_generation(E,l,r),S=await this.forward(l),r.return_dict_in_generate)if(r.output_attentions){let R=VN(S);for(let C in R)C in T||(T[C]=[]),T[C].push(R[C])}else this._return_dict_in_generate_keys&&Object.assign(I,Ve(S,this._return_dict_in_generate_keys));let U=S.logits.slice(null,-1,null).to("float32"),X=m(E,U),K=[];for(let R=0;R<X.dims.at(0);++R){let C=X[R],ne=await k(C);for(let[Y,z]of ne){let $=BigInt(Y);A[R]+=z,E[R].push($),K.push([$]);break}}if(o&&o.put(K),w(E).every(R=>R))break;l=this._update_model_kwargs_for_generation({generated_input_ids:K,outputs:S,model_inputs:l,is_encoder_decoder:p})}o&&o.end();let O=new N("int64",E.flat(),[E.length,E[0].length]),b=b_(S,l.past_key_values),F=new Set(Object.values(b));for(let U of Object.values(S))U.location==="gpu-buffer"&&!F.has(U)&&U.dispose();return"past_key_values"in a||r.return_dict_in_generate||await b.dispose(),r.return_dict_in_generate?{sequences:O,past_key_values:b,...T,...I}:O}async _encode_input(e,r,s){if(!Object.hasOwn(this.sessions,e))throw new Error(`Model does not have a ${e} session.`);let n=this.sessions[e];return(await ue(n,Ve(r,n.inputNames)))[s]}async encode_image(e){return this._encode_input("vision_encoder",e,"image_features")}async encode_text(e){return this._encode_input("embed_tokens",e,"inputs_embeds")}async encode_audio(e){return this._encode_input("audio_encoder",e,"audio_features")}};async function x_(t,e){let{encoder_outputs:r,input_ids:s,decoder_input_ids:n,decoder_attention_mask:o,...a}=e;if(!r){let i=Ve(e,t.sessions.model.inputNames);r=(await qt(t,i)).last_hidden_state}return a.input_ids=n,a.encoder_hidden_states=r,t.sessions.decoder_model_merged.inputNames.includes("encoder_attention_mask")&&(a.encoder_attention_mask=e.attention_mask),o&&!a.attention_mask&&(a.attention_mask=o),await Ct(t,a,!0)}async function qt(t,e){let r=t.sessions.model,s=Ve(e,r.inputNames);if(r.inputNames.includes("inputs_embeds")&&!s.inputs_embeds){if(!e.input_ids)throw new Error("Both `input_ids` and `inputs_embeds` are missing in the model inputs.");s.inputs_embeds=await t.encode_text({input_ids:e.input_ids})}if(r.inputNames.includes("token_type_ids")&&!s.token_type_ids){if(!s.input_ids)throw new Error("Both `input_ids` and `token_type_ids` are missing in the model inputs.");s.token_type_ids=Dp(s.input_ids)}if(r.inputNames.includes("pixel_mask")&&!s.pixel_mask){if(!s.pixel_values)throw new Error("Both `pixel_values` and `pixel_mask` are missing in the model inputs.");let n=s.pixel_values.dims;s.pixel_mask=Ze([n[0],n[2],n[3]])}return await ue(r,s)}async function WN(t,e){let r=await t.encode(e);return await t.decode(r)}function b_(t,e){let r=Object.create(null);for(let s in t)if(s.startsWith("present")){let n=s.replace("present_ssm","past_ssm").replace("present_conv","past_conv").replace("present_recurrent","past_recurrent").replace("present","past_key_values");s.includes("encoder")&&e?r[n]=e[n]:r[n]=t[s]}return e?(e.update(r),e):new tn(r)}function 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y{forward_params=["input_ids","inputs_embeds","attention_mask","position_ids","audio_values","exaggeration","audio_features","audio_tokens","speaker_embeddings","speaker_features","past_key_values"];main_input_name="input_ids";_return_dict_in_generate_keys=["audio_tokens","speaker_embeddings","speaker_features"]},To=class extends Mc{async encode_speech(e){return ue(this.sessions.speech_encoder,{audio_values:e})}async forward({input_ids:e=null,attention_mask:r=null,audio_values:s=null,exaggeration:n=null,position_ids:o=null,inputs_embeds:a=null,past_key_values:i=null,generation_config:l=null,logits_processor:c=null,audio_features:p=null,audio_tokens:f=null,speaker_embeddings:_=null,speaker_features:m=null,...w}){let x;if(!a){let A=this.sessions.embed_tokens.inputNames,E={input_ids:e};if(A.includes("exaggeration")){if(!(n instanceof N)){let S=e.dims[0];if(n==null)n=Ke([S],.5);else if(typeof n=="number")n=Ke([S],n);else if(Array.isArray(n))n=new N("float32",n,[S]);else throw new Error("Unsupported type for `exaggeration` input")}E.exaggeration=n}if(A.includes("position_ids")&&(E.position_ids=o),{inputs_embeds:a}=await ue(this.sessions.embed_tokens,E),p&&f&&_&&m&&(x={audio_features:p,audio_tokens:f,speaker_embeddings:_,speaker_features:m}),x||s)x??=await this.encode_speech(s),a=ye([x.audio_features,a],1),r=Ze([a.dims[0],a.dims[1]]);else{let S=a.dims[1];if(!i||S!==1)throw new Error("Incorrect state encountered during generation.");let T=i.get_seq_length();r=Ze([a.dims[0],T+S])}}return{...await Ct(this,{inputs_embeds:a,past_key_values:i,attention_mask:r,generation_config:l,logits_processor:c},!1),...x}}prepare_inputs_for_generation(e,r,s){if(!r.position_ids&&this.sessions.embed_tokens.inputNames.includes("position_ids"))if(r.input_ids.dims[1]===1){let n=Array.from({length:e.length},(o,a)=>e[a].length-e[a].findLastIndex(i=>i==HM)-1);r.position_ids=new N("int64",n,[e.length,1])}else{let o=r.input_ids.tolist().map(a=>{let i=0;return 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Re{constructor({logits:e,pred_boxes:r}){super(),this.logits=e,this.pred_boxes=r}};var Fo=class extends y{},Cm=class extends Fo{},Pm=class extends Fo{async _call(e){return new sr(await super._call(e))}};var Oc=class extends Re{constructor({audio_codes:e}){super(),this.audio_codes=e}},Ic=class extends Re{constructor({audio_values:e}){super(),this.audio_values=e}},an=class extends y{main_input_name="input_values";forward_params=["input_values"]},zm=class extends an{async encode(e){return new Oc(await ue(this.sessions.encoder_model,e))}async decode(e){return new Ic(await ue(this.sessions.decoder_model,e))}},Bo=class extends an{static async from_pretrained(e,r={}){return super.from_pretrained(e,{...r,model_file_name:r.model_file_name??"encoder_model"})}},Uo=class extends an{static async from_pretrained(e,r={}){return super.from_pretrained(e,{...r,model_file_name:r.model_file_name??"decoder_model"})}};var Or=class extends y{},Lm=class extends Or{},Nm=class extends Or{async _call(e){return 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_call(e){return new we(await super._call(e))}},Oh=class extends hs{async _call(e){return new q(await super._call(e))}},Ih=class extends hs{async _call(e){return new ge(await super._call(e))}};var Ko=class extends y{},Ch=class extends Ko{},Ph=class extends Ko{};var Yo=class extends y{},zh=class extends Yo{},Lh=class extends Yo{};var Qo=class extends y{},Nh=class extends Qo{},$h=class extends Qo{};var Jo=class extends y{},Rh=class extends Jo{},Dh=class extends Jo{async _call(e){return new q(await super._call(e))}};var Dc=class extends y{forward_params=["input_ids","inputs_embeds","attention_mask","pixel_values","encoder_outputs","decoder_input_ids","decoder_inputs_embeds","decoder_attention_mask","past_key_values"];main_input_name="inputs_embeds"},Fh=class extends Dc{_merge_input_ids_with_image_features({inputs_embeds:e,image_features:r,input_ids:s,attention_mask:n}){return{inputs_embeds:ye([r,e],1),attention_mask:ye([Ze(r.dims.slice(0,2)),n],1)}}async _prepare_inputs_embeds({input_ids:e,pixel_values:r,inputs_embeds:s,attention_mask:n}){if(!e&&!r)throw new Error("Either `input_ids` or `pixel_values` should be provided.");let o,a;return e&&(o=await this.encode_text({input_ids:e})),r&&(a=await this.encode_image({pixel_values:r})),o&&a?{inputs_embeds:s,attention_mask:n}=this._merge_input_ids_with_image_features({inputs_embeds:o,image_features:a,input_ids:e,attention_mask:n}):s=o||a,{inputs_embeds:s,attention_mask:n}}async forward({input_ids:e,pixel_values:r,attention_mask:s,decoder_input_ids:n,decoder_attention_mask:o,encoder_outputs:a,past_key_values:i,inputs_embeds:l,decoder_inputs_embeds:c}){if(l||({inputs_embeds:l,attention_mask:s}=await this._prepare_inputs_embeds({input_ids:e,pixel_values:r,inputs_embeds:l,attention_mask:s})),!a){let{last_hidden_state:f}=await qt(this,{inputs_embeds:l,attention_mask:s});a=f}if(!c){if(!n)throw new Error("Either `decoder_input_ids` or `decoder_inputs_embeds` should be provided.");c=await this.encode_text({input_ids:n})}return await Ct(this,{inputs_embeds:c,attention_mask:o,encoder_attention_mask:s,encoder_hidden_states:a,past_key_values:i},!0)}};var Zo=class extends y{},Bh=class extends Zo{},Uh=class extends Zo{};var ea=class extends y{},jh=class extends ea{},Gh=class extends ea{};var Fc=class extends y{forward_params=["input_ids","attention_mask","pixel_values","position_ids","past_key_values"]},bt=class extends Fc{_merge_input_ids_with_image_features(e){let r=e.image_features.dims.at(-1),s=e.image_features.view(-1,r);return wo({image_token_id:this.config.image_token_index??this.config.image_token_id,...e,image_features:s})}},qh=class extends bt{},Wh=class extends bt{};var Bc=class extends y{},Vh=class extends Bc{},Uc=class extends bt{},Hh=class extends Uc{};var jc=class extends y{forward_params=["input_ids","attention_mask","inputs_embeds","per_layer_inputs","position_ids","pixel_values","input_features","input_features_mask","past_key_values"]},zr=class extends jc{async forward({input_ids:e=null,attention_mask:r=null,pixel_values:s=null,input_features:n=null,input_features_mask:o=null,position_ids:a=null,inputs_embeds:i=null,per_layer_inputs:l=null,past_key_values:c=null,generation_config:p=null,logits_processor:f=null,..._}){if((!i||!l)&&({inputs_embeds:i,per_layer_inputs:l}=await ue(this.sessions.embed_tokens,{input_ids:e}),e.dims[1]!==1)){if(s){let{image_features:w}=await this._encode_vision({pixel_values:s,..._});({inputs_embeds:i,attention_mask:r}=this._merge_input_ids_with_image_features({image_features:w,inputs_embeds:i,input_ids:e,attention_mask:r}))}if(n){let{audio_features:w}=await ue(this.sessions.audio_encoder,{input_features:n,input_features_mask:o});({inputs_embeds:i,attention_mask:r}=this._merge_input_ids_with_audio_features({audio_features:w,inputs_embeds:i,input_ids:e,attention_mask:r}))}}return await Ct(this,{inputs_embeds:i,per_layer_inputs:l,past_key_values:c,attention_mask:r,position_ids:a,generation_config:p,logits_processor:f},!0)}_encode_vision(e){return ue(this.sessions.vision_encoder,{pixel_values:e.pixel_values})}_merge_input_ids_with_image_features(e){let r=e.image_features.dims.at(-1),s=e.image_features.view(-1,r);return wo({image_token_id:this.config.image_token_id,...e,image_features:s})}_merge_input_ids_with_audio_features(e){let r=e.audio_features.dims.at(-1),s=e.audio_features.view(-1,r);return k_({audio_token_id:this.config.audio_token_id,...e,audio_features:s})}},Xh=class extends zr{};var un=class extends zr{forward_params=["input_ids","attention_mask","inputs_embeds","per_layer_inputs","position_ids","pixel_values","image_position_ids","input_features","input_features_mask","past_key_values"];_encode_vision(e){return ue(this.sessions.vision_encoder,{pixel_values:e.pixel_values,pixel_position_ids:e.image_position_ids})}},Kh=class extends un{};var ta=class extends y{},Yh=class extends ta{},Qh=class extends ta{};var ra=class extends y{},Jh=class extends ra{},Zh=class extends ra{};var Gc=class extends y{forward_params=["input_ids","attention_mask","position_ids","past_key_values","pixel_values","image_grid_thw"]},pn=class extends Gc{image_grid_thw_name="grid_thw";_get_text_only_rope_index(e,r){if(r){let{data:s,dims:n}=gv(r),o=BigInt64Array.from({length:3*s.length},(i,l)=>s[l%s.length]),a=Array.from({length:n[0]},(i,l)=>ze(s.subarray(n[1]*l,n[1]*(l+1)))[0]+1n+BigInt(n[1]));return[new N("int64",o,[3,...n]),new N("int64",a,[a.length,1])]}else{let[s,n]=e.dims,o=BigInt64Array.from({length:3*s*n},(a,i)=>BigInt(Math.floor(i%n/s)));return[new N("int64",o,[3,...e.dims]),Rp([s,1])]}}_reorder_and_write_positions(e,r,s,n){let o=e.reduce((c,p)=>c+p.length,0),a=new Array(o),i=0;for(let c=0;c<3;++c)for(let p of e){let f=p.length/3;for(let _=c*f;_<(c+1)*f;++_)a[i++]=p[_]}let l=0;for(let c=0;c<r.length;++c)if(r[c]==1){for(let p=0;p<3;++p)s[p][n][c]=a[p*o/3+l];++l}return a}_get_multimodal_rope_positions({filtered_ids:e,image_grid_thw_list:r,video_grid_thw_list:s,spatial_merge_size:n,state:o}){let{image_token_id:a,video_token_id:i,vision_start_token_id:l}=this.config,c=e,f=c.reduce((E,S,T)=>(S==l&&E.push(T),E),[]).map(E=>c[E+1]),_=f.filter(E=>E==a).length,m=f.filter(E=>E==i).length,w=[],x=0,k=_,A=m;for(let E=0;E<f.length;++E){let S=c.findIndex((Q,le)=>le>x&&Q==a),T=c.findIndex((Q,le)=>le>x&&Q==i),I=k>0&&S!==-1?S:c.length+1,O=A>0&&T!==-1?T:c.length+1,b,F,j,U;I<O?([F,j,U]=r[o.image_index],++o.image_index,--k,b=I):([F,j,U]=s[o.video_index],++o.video_index,--A,b=O);let[X,K,J]=[Number(F),Math.floor(Number(j)/n),Math.floor(Number(U)/n)],R=b-x,C=w.length>0?ze(w.at(-1))[0]+1:0;w.push(Array.from({length:3*R},(Q,le)=>C+le%R));let ne=R+C,Y=X*K*J,z=Array.from({length:Y},(Q,le)=>ne+Math.floor(le/(K*J))),$=Array.from({length:Y},(Q,le)=>ne+Math.floor(le/J)%K),B=Array.from({length:Y},(Q,le)=>ne+le%J);w.push([z,$,B].flat()),x=b+Y}if(x<c.length){let E=w.length>0?ze(w.at(-1))[0]+1:0,S=c.length-x;w.push(Array.from({length:3*S},(T,I)=>E+I%S))}return w}get_rope_index(e,r,s,n){let{vision_config:o}=this.config,a=o.spatial_merge_size??2;if(r||s){let i=e.tolist();n||(n=Bl(e));let l=n.tolist(),c=Array.from({length:3},()=>Array.from({length:e.dims[0]},()=>Array.from({length:e.dims[1]},()=>0))),p=r?r.tolist():[],f=s?s.tolist():[],_={image_index:0,video_index:0},m=[];for(let w=0;w<i.length;++w){let x=i[w].filter((E,S)=>l[w][S]==1),k=this._get_multimodal_rope_positions({filtered_ids:x,image_grid_thw_list:p,video_grid_thw_list:f,spatial_merge_size:a,state:_}),A=this._reorder_and_write_positions(k,l[w],c,w);m.push(ze(A)[0]+1-i[w].length)}return[new N("int64",c.flat(1/0),[3,e.dims[0],e.dims[1]]),new N("int64",m,[m.length,1])]}else return this._get_text_only_rope_index(e,n)}async encode_image({pixel_values:e,image_grid_thw:r}){return(await ue(this.sessions.vision_encoder,{pixel_values:e,[this.image_grid_thw_name]:r})).image_features}_merge_input_ids_with_image_features(e){return wo({image_token_id:this.config.image_token_id,...e})}prepare_inputs_for_generation(e,r,s){if(!r.attention_mask||r.position_ids||!(this.sessions.decoder_model_merged??this.sessions.model).inputNames.includes("position_ids"))return r;if(!r.past_key_values)[r.position_ids,r.rope_deltas]=this.get_rope_index(r.input_ids,r.image_grid_thw,r.video_grid_thw,r.attention_mask);else{r.pixel_values=null;let o=r.past_key_values.get_seq_length();if(o<r.input_ids.dims[1]){let[a,i]=this.get_rope_index(r.input_ids,r.image_grid_thw,r.video_grid_thw,r.attention_mask);r.rope_deltas=i,r.position_ids=a.slice(null,null,[o,null]),r.input_ids=r.input_ids.slice(null,[o,null])}else{r.rope_deltas||([,r.rope_deltas]=this.get_rope_index(r.input_ids,r.image_grid_thw,r.video_grid_thw,r.attention_mask));let a=BigInt(o),i=r.rope_deltas.map(l=>a+l);r.position_ids=_t([i,i,i],0)}}return r}},sa=class extends pn{};var gs=class extends pn{image_grid_thw_name="image_grid_thw"},na=class extends sa{image_grid_thw_name="image_grid_thw"};var eg=class extends gs{get_vision_position_ids(e,r,s,n){let o=Math.floor(r[0]/s),a=Math.floor(r[1]/n),i=Math.floor(r[2]/n),l=a*i*o,c=Array.from({length:l},()=>e),p=Array.from({length:l},(_,m)=>e+Math.floor(m/(i*o))),f=Array.from({length:l},(_,m)=>e+m%i);return[...c,...p,...f]}_get_multimodal_rope_positions({filtered_ids:e,image_grid_thw_list:r,video_grid_thw_list:s,spatial_merge_size:n,state:o}){let{image_token_id:a}=this.config,i=[],l=0,c=e[0]==a?1:0;for(let _=1;_<=e.length;++_){let m=_<e.length?e[_]==a?1:0:-1;m!==c&&(i.push([c,l,_]),l=_,c=m)}let p=0,f=[];for(let[_,m,w]of i)if(_===0){let x=w-m;f.push(Array.from({length:3*x},(k,A)=>p+A%x)),p+=x}else{let x=r[o.image_index++].map(Number),k=x[0];f.push(this.get_vision_position_ids(p,x,k,n)),p+=Math.max(x[1],x[2])/n}return f}};var oa=class extends y{},tg=class extends oa{},rg=class extends oa{};var aa=class extends y{},sg=class extends aa{},ng=class extends aa{};var ia=class extends y{},og=class extends ia{},ag=class extends ia{};var la=class extends y{},ig=class extends la{},lg=class extends 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super._call(e))}};var Ra=class extends y{},Jw=class extends Ra{},Zw=class extends Ra{};var Da=class extends y{},ex=class extends Da{},tx=class extends Da{};var eu=class extends y{},rx=class extends eu{forward_params=["input_ids","pixel_values","images_seq_mask","images_emb_mask","attention_mask","position_ids","past_key_values"];constructor(...e){super(...e),this._generation_mode="text"}async forward(e){let r=this._generation_mode??"text",s;if(r==="text"||!e.past_key_values){let l=this.sessions.prepare_inputs_embeds,c=Ve(e,l.inputNames);s=await ue(l,c)}else{let l=this.sessions.gen_img_embeds,c=Ve({image_ids:e.input_ids},l.inputNames);s=await ue(l,c)}let n={...e,...s},o=await Ct(this,n),a=this.sessions[r==="text"?"lm_head":"gen_head"];if(!a)throw new Error(`Unable to find "${a}" generation head`);let i=await ue(a,Ve(o,a.inputNames));return{...s,...o,...i}}prepare_inputs_for_generation(e,r,s){let n=!!r.past_key_values;return 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y{forward_params=["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"];_apply_and_filter_by_delay_pattern_mask(e){let[r,s]=e.dims,n=this.config.decoder.num_codebooks,o=s-n,a=0;for(let c=0;c<e.size;++c){if(e.data[c]==this.config.decoder.pad_token_id)continue;let p=c%s,f=Math.floor(c/s)%n,_=p-f;_>0&&_<=o&&(e.data[a++]=e.data[c])}let i=Math.floor(r/n),l=a/(i*n);return new N(e.type,e.data.slice(0,a),[i,n,l])}prepare_inputs_for_generation(e,r,s){let n=BigInt(this.config.decoder.pad_token_id),o=structuredClone(e);for(let a=0;a<o.length;++a)for(let i=0;i<o[a].length;++i)a%this.config.decoder.num_codebooks>=i&&(o[a][i]=n);return s.guidance_scale!==null&&s.guidance_scale>1&&(o=o.concat(o)),Ec(this,o,r,s)}async generate(e){let r=await super.generate(e),s=this._apply_and_filter_by_delay_pattern_mask(r).unsqueeze_(0),{audio_values:n}=await ue(this.sessions.encodec_decode,{audio_codes:s});return n}};var Ua=class extends y{},ox=class extends 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Error("`image_sizes` must be provided when `pixel_values` is provided.");({image_features:_}=await ue(this.sessions.vision_encoder,{pixel_values:s,image_sizes:n}))}else{let m=this.config.normalized_config.hidden_size;_=new N("float32",[],[0,m])}({inputs_embeds:a}=await ue(this.sessions.prepare_inputs_embeds,{input_ids:e,image_features:_}))}return await Ct(this,{inputs_embeds:a,past_key_values:i,attention_mask:r,position_ids:o,generation_config:l,logits_processor:c},!1)}};var si=class extends y{},Gx=class extends si{},qx=class extends si{async _call(e){return new q(await super._call(e))}};var ni=class extends y{},Wx=class extends ni{},Vx=class extends ni{async _call(e){return new ge(await super._call(e))}};var oi=class extends y{},Hx=class extends oi{},Xx=class extends oi{};var ai=class extends y{},Kx=class extends ai{},Yx=class extends ai{};var ii=class extends y{},Qx=class extends ii{},Jx=class extends ii{};var li=class extends y{},Zx=class extends li{},ey=class extends li{};var ci=class extends y{},ty=class extends ci{},ry=class extends ci{};var vs=class extends gs{},ui=class extends na{};var sy=class extends vs{},ny=class extends ui{};var wn=class extends vs{},pi=class extends wn{};var oy=class extends wn{},ay=class extends pi{};var fi=class extends y{},iy=class extends fi{},ly=class extends fi{async _call(e){return new q(await super._call(e))}};var di=class extends y{},cy=class extends di{},uy=class extends di{async _call(e){return new nu(await super._call(e))}},nu=class extends sr{};var $r=class extends y{},py=class extends $r{},fy=class extends $r{async _call(e){return new we(await super._call(e))}},dy=class extends $r{async _call(e){return new q(await super._call(e))}},_y=class extends $r{async _call(e){return new ge(await super._call(e))}},my=class extends $r{async _call(e){return new Te(await super._call(e))}};var Rr=class extends y{},hy=class extends Rr{},gy=class extends Rr{async _call(e){return new we(await super._call(e))}},wy=class extends Rr{async _call(e){return new q(await super._call(e))}},xy=class extends Rr{async _call(e){return new ge(await super._call(e))}},yy=class extends Rr{async _call(e){return new Te(await super._call(e))}};var _i=class extends y{},by=class extends _i{},vy=class extends _i{async _call(e){return new ou(await super._call(e))}},ou=class extends sr{};var au=class extends Re{constructor({iou_scores:e,pred_masks:r}){super(),this.iou_scores=e,this.pred_masks=r}},iu=class extends y{},ky=class extends iu{async get_image_embeddings({pixel_values:e}){return await qt(this,{pixel_values:e})}async forward(e){!e.image_embeddings||!e.image_positional_embeddings?e={...e,...await this.get_image_embeddings(e)}:e={...e},e.input_labels??=Ze(e.input_points.dims.slice(0,-1));let r={image_embeddings:e.image_embeddings,image_positional_embeddings:e.image_positional_embeddings};return 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a=e.input_points.dims;e.input_labels??=Ze(a.slice(0,-1)),e.input_boxes??=Ke([a[0],0,4],0)}else if(e.input_boxes){let a=e.input_boxes.dims;e.input_labels=Ke([a[0],a[1],0],-1n),e.input_points=Ke([a[0],1,0,2],0)}else throw new Error("At least one of `input_points` or `input_boxes` must be provided.");let n=this.sessions.prompt_encoder_mask_decoder,o=Ve(e,n.inputNames);return await ue(n,o)}async _call(e){return new lu(await super._call(e))}},Ey=class extends mi{},Ay=class extends mi{};var xn=class extends y{},My=class extends xn{},Ty=class extends xn{},Sy=class extends xn{};var yn=class extends y{},Oy=class extends yn{},Iy=class extends yn{},Cy=class extends yn{};var hi=class extends y{},Py=class extends hi{},gi=class extends hi{static async from_pretrained(e,r={}){return super.from_pretrained(e,{...r,model_file_name:r.model_file_name??"text_model"})}},zy=class extends rr{static async from_pretrained(e,r={}){return super.from_pretrained(e,{...r,model_file_name:r.model_file_name??"vision_model"})}};var wi=class extends y{},Ly=class extends wi{},Ny=class extends wi{};var $y=class extends ga{};var bn=class extends y{main_input_name="input_values";forward_params=["input_values"]},Ry=class extends bn{async encode(e){return await ue(this.sessions.encoder_model,e)}async decode(e){return await ue(this.sessions.decoder_model,e)}},xi=class extends bn{static async from_pretrained(e,r={}){return super.from_pretrained(e,{...r,model_file_name:r.model_file_name??"encoder_model"})}},yi=class extends bn{static async from_pretrained(e,r={}){return super.from_pretrained(e,{...r,model_file_name:r.model_file_name??"decoder_model"})}};var bi=class extends y{},Dy=class extends bi{},Fy=class extends bi{};var vn=class extends y{},By=class extends vn{},Uy=class extends vn{},jy=class extends vn{async generate_speech(e,r,{threshold:s=.5,minlenratio:n=0,maxlenratio:o=20,vocoder:a=null}={}){let i={input_ids:e},{encoder_outputs:l,encoder_attention_mask:c}=await qt(this,i),p=l.dims[1]/this.config.reduction_factor,f=Math.floor(p*o),_=Math.floor(p*n),m=this.config.num_mel_bins,w=[],x=null,k=null,A=0;for(;;){++A;let T=mv(!!k),I;k?I=k.output_sequence_out:I=new N("float32",new Float32Array(m),[1,1,m]);let O={use_cache_branch:T,output_sequence:I,encoder_attention_mask:c,speaker_embeddings:r,encoder_hidden_states:l};Ac(this,O,x),k=await ue(this.sessions.decoder_model_merged,O),x=b_(k,x);let{prob:b,spectrum:F}=k;if(w.push(F),A>=_&&(Array.from(b.data).filter(j=>j>=s).length>0||A>=f))break}let E=ye(w),{waveform:S}=await ue(a.sessions.model,{spectrogram:E});return{spectrogram:E,waveform:S}}},Gy=class extends y{main_input_name="spectrogram"};var ks=class extends y{},qy=class extends ks{},Wy=class extends ks{async _call(e){return new we(await super._call(e))}},Vy=class extends ks{async _call(e){return new q(await super._call(e))}},Hy=class extends ks{async _call(e){return new Te(await 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Re{constructor({waveform:e,spectrogram:r}){super(),this.waveform=e,this.spectrogram=r}},wu=class extends y{},M0=class extends wu{async _call(e){return new gu(await super._call(e))}};var T0=class extends ws{};var XM=2,JN=1,wv=new WeakMap;function ZN(t,e){let{text_config:r,audio_config:s}=t.config,n=t.sessions.audio_encoder,{num_mel_bins:o,hidden_size:a}=s,i=o+a,l=new tn,c=Qs(s),p={batch_size:1},f="float32";for(let x of n.inputMetadata){if(x.name==="past_padding_cache"){f=x.type;continue}if(!c.has(x.name))continue;let k=hv(x.shape,p),A=k.reduce((S,T)=>S*T,1),E=ls[x.type];l[x.name]=new N(x.type,new E(A),k)}let _=ls[f],m=new N(f,new _(i*XM),[1,i,XM]),w=e[Symbol.asyncIterator]?.()??e[Symbol.iterator]?.();if(!w)throw new Error("input_features must be iterable or async 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y{},Y0=class extends zi{},Q0=class extends zi{async _call(e){return new ku(await super._call(e))}},ku=class extends Re{constructor({logits:e,pred_boxes:r}){super(),this.logits=e,this.pred_boxes=r}};var Li=class extends y{},J0=class extends Li{},Z0=class extends Li{};var s3=new 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Map([["bloom","BloomModel"],["jais","JAISModel"],["gpt2","GPT2Model"],["gpt_oss","GptOssModel"],["gptj","GPTJModel"],["gpt_bigcode","GPTBigCodeModel"],["gpt_neo","GPTNeoModel"],["gpt_neox","GPTNeoXModel"],["codegen","CodeGenModel"],["llama","LlamaModel"],["apertus","ApertusModel"],["nanochat","NanoChatModel"],["arcee","ArceeModel"],["afmoe","AfmoeModel"],["lfm2","Lfm2Model"],["lfm2_moe","Lfm2MoeModel"],["smollm3","SmolLM3Model"],["exaone","ExaoneModel"],["olmo","OlmoModel"],["olmo2","Olmo2Model"],["olmo3","Olmo3Model"],["olmo_hybrid","OlmoHybridModel"],["mobilellm","MobileLLMModel"],["granite","GraniteModel"],["granitemoehybrid","GraniteMoeHybridModel"],["cohere","CohereModel"],["cohere2","Cohere2Model"],["gemma","GemmaModel"],["gemma2","Gemma2Model"],["vaultgemma","VaultGemmaModel"],["gemma3_text","Gemma3Model"],["helium","HeliumModel"],["glm","GlmModel"],["glm_moe_dsa","GlmMoeDsaModel"],["openelm","OpenELMModel"],["qwen2","Qwen2Model"],["qwen2_moe","Qwen2MoeModel"],["qwen3","Qwen3Model"],["qwen3_moe","Qwen3MoeModel"],["qwen3_next","Qwen3NextModel"],["phi","PhiModel"],["phi3","Phi3Model"],["mpt","MptModel"],["opt","OPTModel"],["mistral","MistralModel"],["mistral4","Mistral4Model"],["ministral","MinistralModel"],["ministral3","Ministral3Model"],["ernie4_5","Ernie4_5ForCausalLM"],["starcoder2","Starcoder2Model"],["deepseek_v3","DeepseekV3Model"],["falcon","FalconModel"],["falcon_h1","FalconH1Model"],["nemotron_h","NemotronHModel"],["solar_open","SolarOpenModel"],["stablelm","StableLmModel"],["modernbert-decoder","ModernBertDecoderModel"],["hunyuan_v1_dense","HunYuanDenseV1Model"],["youtu","YoutuModel"]]),KM=new Map([["speecht5","SpeechT5ForSpeechToText"],["whisper","WhisperForConditionalGeneration"],["lite-whisper","LiteWhisperForConditionalGeneration"],["moonshine","MoonshineForConditionalGeneration"],["cohere_asr","CohereAsrForConditionalGeneration"]]),YM=new Map([["speecht5","SpeechT5ForTextToSpeech"]]),QM=new Map([["vits","VitsModel"],["musicgen","MusicgenForConditionalGeneration"],["supertonic","SupertonicForConditionalGeneration"]]),JM=new Map([["bert","BertForSequenceClassification"],["eurobert","EuroBertForSequenceClassification"],["neobert","NeoBertForSequenceClassification"],["modernbert","ModernBertForSequenceClassification"],["roformer","RoFormerForSequenceClassification"],["electra","ElectraForSequenceClassification"],["esm","EsmForSequenceClassification"],["convbert","ConvBertForSequenceClassification"],["camembert","CamembertForSequenceClassification"],["deberta","DebertaForSequenceClassification"],["deberta-v2","DebertaV2ForSequenceClassification"],["mpnet","MPNetForSequenceClassification"],["albert","AlbertForSequenceClassification"],["distilbert","DistilBertForSequenceClassification"],["roberta","RobertaForSequenceClassification"],["xlm","XLMForSequenceClassification"],["xlm-roberta","XLMRobertaForSequenceClassification"],["bart","BartForSequenceClassification"],["mbart","MBartForSequenceClassification"],["mobilebert","MobileBertForSequenceClassification"],["squeezebert","SqueezeBertForSequenceClassification"]]),ZM=new Map([["bert","BertForTokenClassification"],["eurobert","EuroBertForTokenClassification"],["neobert","NeoBertForTokenClassification"],["modernbert","ModernBertForTokenClassification"],["roformer","RoFormerForTokenClassification"],["electra","ElectraForTokenClassification"],["esm","EsmForTokenClassification"],["convbert","ConvBertForTokenClassification"],["camembert","CamembertForTokenClassification"],["deberta","DebertaForTokenClassification"],["deberta-v2","DebertaV2ForTokenClassification"],["mpnet","MPNetForTokenClassification"],["distilbert","DistilBertForTokenClassification"],["roberta","RobertaForTokenClassification"],["xlm","XLMForTokenClassification"],["xlm-roberta","XLMRobertaForTokenClassification"],["openai_privacy_filter","OpenAIPrivacyFilterForTokenClassification"]]),eT=new Map([["t5","T5ForConditionalGeneration"],["longt5","LongT5ForConditionalGeneration"],["mt5","MT5ForConditionalGeneration"],["bart","BartForConditionalGeneration"],["mbart","MBartForConditionalGeneration"],["marian","MarianMTModel"],["m2m_100","M2M100ForConditionalGeneration"],["blenderbot","BlenderbotForConditionalGeneration"],["blenderbot-small","BlenderbotSmallForConditionalGeneration"]]),tT=new Map([["bloom","BloomForCausalLM"],["gpt2","GPT2LMHeadModel"],["gpt_oss","GptOssForCausalLM"],["jais","JAISLMHeadModel"],["gptj","GPTJForCausalLM"],["gpt_bigcode","GPTBigCodeForCausalLM"],["gpt_neo","GPTNeoForCausalLM"],["gpt_neox","GPTNeoXForCausalLM"],["codegen","CodeGenForCausalLM"],["llama","LlamaForCausalLM"],["nanochat","NanoChatForCausalLM"],["apertus","ApertusForCausalLM"],["llama4_text","Llama4ForCausalLM"],["arcee","ArceeForCausalLM"],["afmoe","AfmoeForCausalLM"],["lfm2","Lfm2ForCausalLM"],["lfm2_moe","Lfm2MoeForCausalLM"],["smollm3","SmolLM3ForCausalLM"],["exaone","ExaoneForCausalLM"],["olmo","OlmoForCausalLM"],["olmo2","Olmo2ForCausalLM"],["olmo3","Olmo3ForCausalLM"],["olmo_hybrid","OlmoHybridForCausalLM"],["mobilellm","MobileLLMForCausalLM"],["granite","GraniteForCausalLM"],["granitemoehybrid","GraniteMoeHybridForCausalLM"],["cohere","CohereForCausalLM"],["cohere2","Cohere2ForCausalLM"],["gemma","GemmaForCausalLM"],["gemma2","Gemma2ForCausalLM"],["vaultgemma","VaultGemmaForCausalLM"],["gemma3_text","Gemma3ForCausalLM"],["gemma3","Gemma3ForCausalLM"],["helium","HeliumForCausalLM"],["glm","GlmForCausalLM"],["glm_moe_dsa","GlmMoeDsaForCausalLM"],["openelm","OpenELMForCausalLM"],["qwen2","Qwen2ForCausalLM"],["qwen2_moe","Qwen2MoeForCausalLM"],["qwen3","Qwen3ForCausalLM"],["qwen3_moe","Qwen3MoeForCausalLM"],["qwen3_next","Qwen3NextForCausalLM"],["qwen2_vl","Qwen2VLForCausalLM"],["qwen2_5_vl","Qwen2_5_VLForCausalLM"],["qwen3_vl","Qwen3VLForCausalLM"],["qwen3_vl_moe","Qwen3VLMoeForCausalLM"],["qwen3_5","Qwen3_5ForCausalLM"],["qwen3_5_text","Qwen3_5ForCausalLM"],["qwen3_5_moe","Qwen3_5MoeForCausalLM"],["gemma3n","Gemma3nForCausalLM"],["gemma4","Gemma4ForCausalLM"],["phi","PhiForCausalLM"],["phi3","Phi3ForCausalLM"],["mpt","MptForCausalLM"],["opt","OPTForCausalLM"],["mbart","MBartForCausalLM"],["mistral","MistralForCausalLM"],["mistral4","Mistral4ForCausalLM"],["ministral","MinistralForCausalLM"],["ministral3","Ministral3ForCausalLM"],["ernie4_5","Ernie4_5ForCausalLM"],["starcoder2","Starcoder2ForCausalLM"],["deepseek_v3","DeepseekV3ForCausalLM"],["falcon","FalconForCausalLM"],["falcon_h1","FalconH1ForCausalLM"],["nemotron_h","NemotronHForCausalLM"],["trocr","TrOCRForCausalLM"],["solar_open","SolarOpenForCausalLM"],["stablelm","StableLmForCausalLM"],["modernbert-decoder","ModernBertDecoderForCausalLM"],["hunyuan_v1_dense","HunYuanDenseV1ForCausalLM"],["youtu","YoutuForCausalLM"],["phi3_v","Phi3VForCausalLM"]]),i3=new Map([["multi_modality","MultiModalityCausalLM"]]),rT=new Map([["bert","BertForMaskedLM"],["eurobert","EuroBertForMaskedLM"],["neobert","NeoBertForMaskedLM"],["modernbert","ModernBertForMaskedLM"],["roformer","RoFormerForMaskedLM"],["electra","ElectraForMaskedLM"],["esm","EsmForMaskedLM"],["convbert","ConvBertForMaskedLM"],["camembert","CamembertForMaskedLM"],["deberta","DebertaForMaskedLM"],["deberta-v2","DebertaV2ForMaskedLM"],["mpnet","MPNetForMaskedLM"],["albert","AlbertForMaskedLM"],["distilbert","DistilBertForMaskedLM"],["roberta","RobertaForMaskedLM"],["xlm","XLMWithLMHeadModel"],["xlm-roberta","XLMRobertaForMaskedLM"],["mobilebert","MobileBertForMaskedLM"],["squeezebert","SqueezeBertForMaskedLM"]]),sT=new Map([["bert","BertForQuestionAnswering"],["neobert","NeoBertForQuestionAnswering"],["roformer","RoFormerForQuestionAnswering"],["electra","ElectraForQuestionAnswering"],["convbert","ConvBertForQuestionAnswering"],["camembert","CamembertForQuestionAnswering"],["deberta","DebertaForQuestionAnswering"],["deberta-v2","DebertaV2ForQuestionAnswering"],["mpnet","MPNetForQuestionAnswering"],["albert","AlbertForQuestionAnswering"],["distilbert","DistilBertForQuestionAnswering"],["roberta","RobertaForQuestionAnswering"],["xlm","XLMForQuestionAnswering"],["xlm-roberta","XLMRobertaForQuestionAnswering"],["mobilebert","MobileBertForQuestionAnswering"],["squeezebert","SqueezeBertForQuestionAnswering"]]),nT=new Map([["vision-encoder-decoder","VisionEncoderDecoderModel"],["idefics3","Idefics3ForConditionalGeneration"],["smolvlm","SmolVLMForConditionalGeneration"]]),oT=new Map([["llava","LlavaForConditionalGeneration"],["llava_onevision","LlavaOnevisionForConditionalGeneration"],["moondream1","Moondream1ForConditionalGeneration"],["florence2","Florence2ForConditionalGeneration"],["qwen2_vl","Qwen2VLForConditionalGeneration"],["qwen2_5_vl","Qwen2_5_VLForConditionalGeneration"],["qwen3_vl","Qwen3VLForConditionalGeneration"],["qwen3_vl_moe","Qwen3VLMoeForConditionalGeneration"],["qwen3_5","Qwen3_5ForConditionalGeneration"],["qwen3_5_moe","Qwen3_5MoeForConditionalGeneration"],["lfm2_vl","Lfm2VlForConditionalGeneration"],["idefics3","Idefics3ForConditionalGeneration"],["smolvlm","SmolVLMForConditionalGeneration"],["paligemma","PaliGemmaForConditionalGeneration"],["llava_qwen2","LlavaQwen2ForCausalLM"],["gemma3","Gemma3ForConditionalGeneration"],["gemma3n","Gemma3nForConditionalGeneration"],["gemma4","Gemma4ForConditionalGeneration"],["mistral3","Mistral3ForConditionalGeneration"],["lighton_ocr","LightOnOcrForConditionalGeneration"],["glm_ocr","GlmOcrForConditionalGeneration"]]),aT=new Map([["granite_speech","GraniteSpeechForConditionalGeneration"],["ultravox","UltravoxModel"],["voxtral","VoxtralForConditionalGeneration"],["voxtral_realtime","VoxtralRealtimeForConditionalGeneration"]]),l3=new Map([["vision-encoder-decoder","VisionEncoderDecoderModel"]]),iT=new Map([["vit","ViTForImageClassification"],["ijepa","IJepaForImageClassification"],["pvt","PvtForImageClassification"],["vit_msn","ViTMSNForImageClassification"],["fastvit","FastViTForImageClassification"],["mobilevit","MobileViTForImageClassification"],["mobilevitv2","MobileViTV2ForImageClassification"],["beit","BeitForImageClassification"],["deit","DeiTForImageClassification"],["hiera","HieraForImageClassification"],["convnext","ConvNextForImageClassification"],["convnextv2","ConvNextV2ForImageClassification"],["dinov2","Dinov2ForImageClassification"],["dinov2_with_registers","Dinov2WithRegistersForImageClassification"],["resnet","ResNetForImageClassification"],["swin","SwinForImageClassification"],["segformer","SegformerForImageClassification"],["efficientnet","EfficientNetForImageClassification"],["mobilenet_v1","MobileNetV1ForImageClassification"],["mobilenet_v2","MobileNetV2ForImageClassification"],["mobilenet_v3","MobileNetV3ForImageClassification"],["mobilenet_v4","MobileNetV4ForImageClassification"]]),lT=new Map([["detr","DetrForObjectDetection"],["rt_detr","RTDetrForObjectDetection"],["rt_detr_v2","RTDetrV2ForObjectDetection"],["rf_detr","RFDetrForObjectDetection"],["d_fine","DFineForObjectDetection"],["table-transformer","TableTransformerForObjectDetection"],["yolos","YolosForObjectDetection"]]),cT=new Map([["owlvit","OwlViTForObjectDetection"],["owlv2","Owlv2ForObjectDetection"],["grounding-dino","GroundingDinoForObjectDetection"]]),Ni=new Map([["detr","DetrForSegmentation"],["clipseg","CLIPSegForImageSegmentation"]]),uT=new Map([["segformer","SegformerForSemanticSegmentation"],["sapiens","SapiensForSemanticSegmentation"],["swin","SwinForSemanticSegmentation"],["mobilenet_v1","MobileNetV1ForSemanticSegmentation"],["mobilenet_v2","MobileNetV2ForSemanticSegmentation"],["mobilenet_v3","MobileNetV3ForSemanticSegmentation"],["mobilenet_v4","MobileNetV4ForSemanticSegmentation"]]),pT=new Map([["detr","DetrForSegmentation"],["maskformer","MaskFormerForInstanceSegmentation"]]),fT=new Map([["sam","SamModel"],["sam2","Sam2Model"],["edgetam","EdgeTamModel"],["sam3_tracker","Sam3TrackerModel"]]),dT=new Map([["wav2vec2","Wav2Vec2ForCTC"],["wav2vec2-bert","Wav2Vec2BertForCTC"],["unispeech","UniSpeechForCTC"],["unispeech-sat","UniSpeechSatForCTC"],["wavlm","WavLMForCTC"],["hubert","HubertForCTC"],["parakeet_ctc","ParakeetForCTC"]]),_T=new Map([["wav2vec2","Wav2Vec2ForSequenceClassification"],["wav2vec2-bert","Wav2Vec2BertForSequenceClassification"],["unispeech","UniSpeechForSequenceClassification"],["unispeech-sat","UniSpeechSatForSequenceClassification"],["wavlm","WavLMForSequenceClassification"],["hubert","HubertForSequenceClassification"],["audio-spectrogram-transformer","ASTForAudioClassification"]]),mT=new Map([["wavlm","WavLMForXVector"]]),hT=new Map([["unispeech-sat","UniSpeechSatForAudioFrameClassification"],["wavlm","WavLMForAudioFrameClassification"],["wav2vec2","Wav2Vec2ForAudioFrameClassification"],["pyannote","PyAnnoteForAudioFrameClassification"]]),gT=new Map([["vitmatte","VitMatteForImageMatting"]]),c3=new Map([["patchtst","PatchTSTForPrediction"],["patchtsmixer","PatchTSMixerForPrediction"]]),wT=new Map([["swin2sr","Swin2SRForImageSuperResolution"]]),xT=new Map([["chmv2","CHMv2ForDepthEstimation"],["dpt","DPTForDepthEstimation"],["depth_anything","DepthAnythingForDepthEstimation"],["glpn","GLPNForDepthEstimation"],["sapiens","SapiensForDepthEstimation"],["depth_pro","DepthProForDepthEstimation"],["metric3d","Metric3DForDepthEstimation"],["metric3dv2","Metric3Dv2ForDepthEstimation"]]),yT=new Map([["sapiens","SapiensForNormalEstimation"]]),bT=new Map([["vitpose","VitPoseForPoseEstimation"]]),vT=new Map([["clip","CLIPVisionModelWithProjection"],["siglip","SiglipVisionModel"],["jina_clip","JinaCLIPVisionModel"]]),yv=[[s3,V.EncoderOnly],[n3,V.EncoderDecoder],[a3,V.DecoderOnlyWithoutHead],[o3,V.AutoEncoder],[JM,V.EncoderOnly],[ZM,V.EncoderOnly],[eT,V.Seq2Seq],[KM,V.Seq2Seq],[tT,V.DecoderOnly],[i3,V.MultiModality],[rT,V.EncoderOnly],[sT,V.EncoderOnly],[nT,V.Vision2Seq],[oT,V.ImageTextToText],[aT,V.AudioTextToText],[iT,V.EncoderOnly],[Ni,V.EncoderOnly],[pT,V.EncoderOnly],[uT,V.EncoderOnly],[gT,V.EncoderOnly],[c3,V.EncoderOnly],[wT,V.EncoderOnly],[xT,V.EncoderOnly],[yT,V.EncoderOnly],[bT,V.EncoderOnly],[lT,V.EncoderOnly],[cT,V.EncoderOnly],[fT,V.MaskGeneration],[dT,V.EncoderOnly],[_T,V.EncoderOnly],[YM,V.Seq2Seq],[QM,V.EncoderOnly],[mT,V.EncoderOnly],[hT,V.EncoderOnly],[vT,V.EncoderOnly]];for(let[t,e]of yv)for(let r of t.values()){tr.set(r,e);let s=Eu[r];sn.set(s,r),v_.set(r,s)}var u3=[["MusicgenForConditionalGeneration",Ba,V.Musicgen],["Phi3VForCausalLM",ri,V.Phi3V],["CLIPTextModelWithProjection",Io,V.EncoderOnly],["SiglipTextModel",gi,V.EncoderOnly],["JinaCLIPTextModel",ya,V.EncoderOnly],["ClapTextModelWithProjection",So,V.EncoderOnly],["ClapAudioModelWithProjection",Oo,V.EncoderOnly],["DacEncoderModel",Bo,V.EncoderOnly],["DacDecoderModel",Uo,V.EncoderOnly],["MimiEncoderModel",Sa,V.EncoderOnly],["MimiDecoderModel",Oa,V.EncoderOnly],["SnacEncoderModel",xi,V.EncoderOnly],["SnacDecoderModel",yi,V.EncoderOnly],["Gemma3nForConditionalGeneration",zr,V.ImageAudioTextToText],["Gemma4ForConditionalGeneration",un,V.ImageAudioTextToText],["SupertonicForConditionalGeneration",Ei,V.Supertonic],["ChatterboxModel",To,V.Chatterbox],["VoxtralRealtimeForConditionalGeneration",Ci,V.VoxtralRealtime]];for(let[t,e,r]of u3)tr.set(t,r),sn.set(e,t),v_.set(t,e);var kT=new Map([["modnet",Ni],["birefnet",Ni],["isnet",Ni],["ben",Ni]]);for(let[t,e]of kT.entries())e.set(t,"PreTrainedModel"),tr.set(t,V.EncoderOnly),v_.set(t,y);var ET=new Set(kT.keys());tr.set("PreTrainedModel",V.EncoderOnly);sn.set(y,"PreTrainedModel");var Ne={MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES:JM,MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES:ZM,MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES:YM,MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING_NAMES:QM,MODEL_FOR_MASKED_LM_MAPPING_NAMES:rT,MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES:sT,MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES:iT,MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES:Ni,MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES:uT,MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING_NAMES:pT,MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES:lT,MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES:cT,MODEL_FOR_MASK_GENERATION_MAPPING_NAMES:fT,MODEL_FOR_CTC_MAPPING_NAMES:dT,MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES:_T,MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES:mT,MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES:hT,MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES:l3,MODEL_FOR_IMAGE_MATTING_MAPPING_NAMES:gT,MODEL_FOR_IMAGE_TO_IMAGE_MAPPING_NAMES:wT,MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES:xT,MODEL_FOR_NORMAL_ESTIMATION_MAPPING_NAMES:yT,MODEL_FOR_POSE_ESTIMATION_MAPPING_NAMES:bT,MODEL_FOR_IMAGE_FEATURE_EXTRACTION_MAPPING_NAMES:vT,MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES:oT,MODEL_FOR_AUDIO_TEXT_TO_TEXT_MAPPING_NAMES:aT,MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES:eT,MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES:KM,MODEL_FOR_CAUSAL_LM_MAPPING_NAMES:tT,MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES:nT};qM(Ne);var Ce=class{static MODEL_CLASS_MAPPINGS=null;static BASE_IF_FAIL=!1;static supports(e){if(!this.MODEL_CLASS_MAPPINGS)return!1;for(let r of this.MODEL_CLASS_MAPPINGS)if(r.has(e))return!0;return this.BASE_IF_FAIL}static async from_pretrained(e,{progress_callback:r=null,config:s=null,cache_dir:n=null,local_files_only:o=!1,revision:a="main",model_file_name:i=null,subfolder:l="onnx",device:c=null,dtype:p=null,use_external_data_format:f=null,session_options:_={}}={}){let m={progress_callback:r,config:s,cache_dir:n,local_files_only:o,revision:a,model_file_name:i,subfolder:l,device:c,dtype:p,use_external_data_format:f,session_options:_};if(m.config=await Rt.from_pretrained(e,m),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);let{model_type:w}=m.config;for(let x of this.MODEL_CLASS_MAPPINGS){let k=x.get(w);if(!k){for(let A of x.values())if(A[0]===w){k=A;break}if(!k)continue}return await Eu[k].from_pretrained(e,m)}if(this.BASE_IF_FAIL)return ET.has(w)||ee.warn(`Unknown model class "${w}", attempting to construct from base class.`),await y.from_pretrained(e,m);throw Error(`Unsupported model type: ${w}`)}},_r=class extends Ce{static MODEL_CLASS_MAPPINGS=yv.map(e=>e[0]);static BASE_IF_FAIL=!0},$i=class extends Ce{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES]},Au=class extends Ce{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES]},Mn=class extends Ce{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES]},Mu=class extends Ce{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES]},Tu=class extends Ce{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES]},Su=class extends Ce{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING_NAMES]},Ou=class extends Ce{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES]},Iu=class extends Ce{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_MASKED_LM_MAPPING_NAMES]},Cu=class extends Ce{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES]},Pu=class extends Ce{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES]},zu=class extends Ce{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES]},Ri=class extends Ce{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES]},Di=class extends Ce{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES]},Fi=class extends Ce{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING_NAMES]},Lu=class extends Ce{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES]},Nu=class extends Ce{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES]},bv=class extends Ce{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_MASK_GENERATION_MAPPING_NAMES]},$u=class extends Ce{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_CTC_MAPPING_NAMES]},Ru=class extends Ce{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES]},vv=class extends Ce{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES]},kv=class extends Ce{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES]},Du=class extends Ce{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES]},Ev=class extends Ce{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_IMAGE_MATTING_MAPPING_NAMES]},Fu=class extends Ce{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_IMAGE_TO_IMAGE_MAPPING_NAMES]},Bu=class extends Ce{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES]},Av=class extends Ce{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_NORMAL_ESTIMATION_MAPPING_NAMES]},Mv=class extends Ce{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_POSE_ESTIMATION_MAPPING_NAMES]},Uu=class extends Ce{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_IMAGE_FEATURE_EXTRACTION_MAPPING_NAMES]},Tv=class extends Ce{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES]},Sv=class extends Ce{static MODEL_CLASS_MAPPINGS=[Ne.MODEL_FOR_AUDIO_TEXT_TO_TEXT_MAPPING_NAMES]};async function tt(t){return Array.isArray(t)||(t=[t]),await Promise.all(t.map(e=>Je.read(e)))}async function Ur(t,e){return Array.isArray(t)||(t=[t]),await Promise.all(t.map(r=>typeof r=="string"||r instanceof URL?Lf(r,e):r instanceof Float64Array?new Float32Array(r):r))}function ju(t,e){e&&(t=t.map(a=>a|0));let[r,s,n,o]=t;return{xmin:r,ymin:s,xmax:n,ymax:o}}var me=class extends We{constructor({task:e,model:r,tokenizer:s=null,processor:n=null}){super(),this.task=e,this.model=r,this.tokenizer=s,this.processor=n}async dispose(){await this.model.dispose()}};var Bi=class extends me{async _call(e,{top_k:r=1}={}){let s=this.tokenizer(e,{padding:!0,truncation:!0}),n=await this.model(s),{problem_type:o,id2label:a}=this.model.config,i=o==="multi_label_classification"?c=>c.sigmoid():c=>new N("float32",Pe(c.data),c.dims),l=[];for(let c of n.logits){let p=i(c),f=await Gt(p,r),_=f[0].tolist(),w=f[1].tolist().map((x,k)=>({label:a?a[x]:`LABEL_${x}`,score:_[k]}));r===1?l.push(...w):l.push(w)}return Array.isArray(e)||r===1?l:l[0]}};var Ui=class extends me{async _call(e,{ignore_labels:r=["O"],aggregation_strategy:s="none"}={}){if(s!=="none"&&s!=="simple")throw new Error(`Invalid aggregation_strategy: "${s}". Must be one of "none" or "simple".`);let n=Array.isArray(e),o=this.tokenizer(n?e:[e],{padding:!0,truncation:!0}),i=(await this.model(o)).logits,l=this.model.config.id2label,c=[];for(let p=0;p<i.dims[0];++p){let f=o.input_ids[p].tolist(),_=i[p],m=[];for(let w=0;w<_.dims[0];++w){let x=_[w],k=ze(x.data)[1],A=l?l[k]:`LABEL_${k}`;if(r.includes(A))continue;let E=this.tokenizer.decode([f[w]],{skip_special_tokens:!0});if(E==="")continue;let S=Pe(x.data);m.push({entity:A,score:S[k],index:w,word:E})}c.push(s==="simple"?f3(m,f,this.tokenizer):m)}return n?c:c[0]}};function p3(t){let e=t[0];return t[1]==="-"&&(e==="B"||e==="I"||e==="E"||e==="S")?[e,t.slice(2)]:["I",t]}function f3(t,e,r){let s=[],n=null;for(let o=0;o<t.length;++o){let[a,i]=p3(t[o].entity);n===i&&a!=="B"&&a!=="S"?(s[s.length-1].end=o+1,a==="E"&&(n=null)):(s.push({tag:i,start:o,end:o+1}),n=a==="S"?null:i)}return s.map(({tag:o,start:a,end:i})=>{let l=0,c=[];for(let p=a;p<i;++p)l+=t[p].score,c.push(e[t[p].index]);return{entity_group:o,score:l/(i-a),word:r.decode(c,{skip_special_tokens:!0})}})}var ji=class extends me{async _call(e,r,{top_k:s=1}={}){let n=this.tokenizer(e,{text_pair:r,padding:!0,truncation:!0}),o=Array.isArray(e),{start_logits:a,end_logits:i}=await this.model(n),l=n.input_ids.tolist(),c=n.attention_mask.tolist(),{all_special_ids:p,sep_token_id:f}=this.tokenizer,_=[];for(let m=0;m<a.dims[0];++m){let w=l[m],x=w.findIndex(O=>O==f),k=a[m].tolist(),A=i[m].tolist();for(let O=1;O<k.length;++O)(c[m]==0||O<=x||p.findIndex(b=>b==w[O])!==-1)&&(k[O]=-1/0,A[O]=-1/0);let E=Pe(k).map((O,b)=>[O,b]),S=Pe(A).map((O,b)=>[O,b]);E[0][0]=0,S[0][0]=0;let T=qE(E,S).filter(O=>O[0][1]<=O[1][1]).map(O=>[O[0][1],O[1][1],O[0][0]*O[1][0]]).sort((O,b)=>b[2]-O[2]),I=[];for(let O=0;O<Math.min(T.length,s);++O){let[b,F,j]=T[O],U=w.slice(b,F+1),X=this.tokenizer.decode(U,{skip_special_tokens:!0});I.push({answer:X,score:j})}s===1?_.push(...I):_.push(I)}return o?_:_[0]}};var Gi=class extends me{async _call(e,{top_k:r=5}={}){let{mask_token_id:s,mask_token:n}=this.tokenizer,o=this.tokenizer(e,{padding:!0,truncation:!0}),{logits:a}=await this.model(o),i=[],l=o.input_ids.tolist();for(let c=0;c<l.length;++c){let p=l[c],f=p.findIndex(k=>k==s);if(f===-1)throw Error(`Mask token (${n}) not found in text.`);let _=a[c][f],m=await Gt(new N("float32",Pe(_.data),_.dims),r),w=m[0].tolist(),x=m[1].tolist();i.push(x.map((k,A)=>{let E=p.slice();return E[f]=k,{score:w[A],token:Number(k),token_str:this.tokenizer.decode([k]),sequence:this.tokenizer.decode(E,{skip_special_tokens:!0})}}))}return Array.isArray(e)?i:i[0]}};var mr=class extends me{_default_generation_config={max_new_tokens:256};_key="generated_text";async _call(e,r={}){Array.isArray(e)||(e=[e]),this.model.config.prefix&&(e=e.map(l=>this.model.config.prefix+l));let s=this.model.config.task_specific_params;s&&s[this.task]&&s[this.task].prefix&&(e=e.map(l=>s[this.task].prefix+l));let n=this.tokenizer,o={padding:!0,truncation:!0},a;this.task==="translation"&&"_build_translation_inputs"in n?a=n._build_translation_inputs(e,o,r):a=n(e,o);let i=await this.model.generate({...a,...this._default_generation_config,...r});return n.batch_decode(i,{skip_special_tokens:!0}).map(l=>({[this._key]:l}))}};var qi=class extends mr{_key="summary_text"};var Wi=class extends mr{_key="translation_text"};function AT(t){return Array.isArray(t)&&t.every(e=>"role"in e&&"content"in e)}var Vi=class extends me{_default_generation_config={max_new_tokens:256};async _call(e,r={}){let{add_special_tokens:s,return_full_text:n,tools:o,documents:a,chat_template:i,tokenizer_encode_kwargs:l,...c}=r,p=!1,f=!1,_=s??(this.tokenizer.add_bos_token||this.tokenizer.add_eos_token)??!1,m=l,w;if(typeof e=="string")w=e=[e];else if(Array.isArray(e)&&e.every(I=>typeof I=="string"))p=!0,w=e;else{if(AT(e))e=[e];else if(Array.isArray(e)&&e.every(AT))p=!0;else throw new Error("Input must be a string, an array of strings, a Chat, or an array of Chats");f=!0;let I={tokenize:!1,add_generation_prompt:!0,...Ve({tools:o,documents:a,chat_template:i},["tools","documents","chat_template"]),...m};w=e.map(O=>this.tokenizer.apply_chat_template(O,I)),_=!1,m=void 0}let x=f?!1:n??!0;this.tokenizer.padding_side="left";let k=this.tokenizer(w,{add_special_tokens:_,padding:!0,truncation:!0,...m}),A=await this.model.generate({...k,...this._default_generation_config,...c}),E=this.tokenizer.batch_decode(A,{skip_special_tokens:!0}),S;!x&&k.input_ids.dims.at(-1)>0&&(S=this.tokenizer.batch_decode(k.input_ids,{skip_special_tokens:!0}).map(I=>I.length));let T=Array.from({length:e.length},I=>[]);for(let I=0;I<E.length;++I){let O=Math.floor(I/A.dims[0]*e.length);S&&(E[I]=E[I].slice(S[O])),T[O].push({generated_text:f?[...e[O],{role:"assistant",content:E[I]}]:E[I]})}return!p&&T.length===1?T[0]:T}};var Hi=class extends me{constructor(e){super(e),this.label2id=Object.fromEntries(Object.entries(this.model.config.label2id).map(([r,s])=>[r.toLowerCase(),s])),this.entailment_id=this.label2id.entailment,this.entailment_id===void 0&&(ee.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,this.contradiction_id===void 0&&(ee.warn("Could not find 'contradiction' in label2id mapping. Using 0 as contradiction_id."),this.contradiction_id=0)}async _call(e,r,{hypothesis_template:s="This example is {}.",multi_label:n=!1}={}){let o=Array.isArray(e);o||(e=[e]),Array.isArray(r)||(r=[r]);let a=r.map(c=>s.replace("{}",c)),i=n||r.length===1,l=[];for(let c of e){let p=[];for(let m of a){let w=this.tokenizer(c,{text_pair:m,padding:!0,truncation:!0}),x=await this.model(w);i?p.push([x.logits.data[this.contradiction_id],x.logits.data[this.entailment_id]]):p.push(x.logits.data[this.entailment_id])}let _=(i?p.map(m=>Pe(m)[1]):Pe(p)).map((m,w)=>[m,w]).sort((m,w)=>w[0]-m[0]);l.push({sequence:c,labels:_.map(m=>r[m[1]]),scores:_.map(m=>m[0])})}return o?l:l[0]}};var Xi=class extends me{async _call(e,{top_k:r=5}={}){let s=this.processor.feature_extractor.config.sampling_rate,n=await Ur(e,s),o=this.model.config.id2label,a=[];for(let i of n){let l=await this.processor(i),p=(await this.model(l)).logits[0],f=await Gt(new N("float32",Pe(p.data),p.dims),r),_=f[0].tolist(),w=f[1].tolist().map((x,k)=>({label:o?o[x]:`LABEL_${x}`,score:_[k]}));a.push(w)}return Array.isArray(e)?a:a[0]}};var Ki=class extends me{async _call(e,r,{hypothesis_template:s="This is a sound of {}."}={}){let n=!Array.isArray(e);n&&(e=[e]);let o=r.map(p=>s.replace("{}",p)),a=this.tokenizer(o,{padding:!0,truncation:!0}),i=this.processor.feature_extractor.config.sampling_rate,l=await Ur(e,i),c=[];for(let p of l){let f=await this.processor(p),_=await this.model({...a,...f}),m=Pe(_.logits_per_audio.data);c.push([...m].map((w,x)=>({score:w,label:r[x]})))}return n?c[0]:c}};var Yi=class extends me{_default_generation_config={};async _call(e,r={}){switch(r={...this._default_generation_config,...r},this.model.config.model_type){case"whisper":case"lite-whisper":return this._call_whisper(e,r);case"wav2vec2":case"wav2vec2-bert":case"unispeech":case"unispeech-sat":case"hubert":case"parakeet_ctc":return this._call_wav2vec2(e,r);case"moonshine":return this._call_moonshine(e,r);case"cohere_asr":return this._call_cohere_asr(e,r);default:throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`)}}async _call_wav2vec2(e,r){r.language&&ee.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'),r.task&&ee.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".');let s=!Array.isArray(e),n=s?[e]:e,o=this.processor.feature_extractor.config.sampling_rate,a=await Ur(n,o),i=[];for(let l of a){let c=await this.processor(l),f=(await this.model(c)).logits[0],_=[];for(let w of f)_.push(ze(w.data)[1]);let m=this.tokenizer.decode(_,{skip_special_tokens:!0}).trim();i.push({text:m})}return s?i[0]:i}async _call_whisper(e,r){let s=r.return_timestamps??!1,n=r.chunk_length_s??0,o=r.force_full_sequences??!1,a=r.stride_length_s??null,i={...r};s==="word"&&(i.return_token_timestamps=!0,i.return_timestamps=!0);let l=!Array.isArray(e),c=l?[e]:e,p=this.processor.feature_extractor.config,f=p.chunk_length/this.model.config.max_source_positions,_=p.hop_length,m=p.sampling_rate,w=await Ur(c,m),x=[];for(let k of w){let A=[];if(n>0){if(a===null)a=n/6;else if(n<=a)throw Error("`chunk_length_s` must be larger than `stride_length_s`.");let T=m*n,I=m*a,O=T-2*I,b=0;for(;;){let F=b+T,j=k.subarray(b,F),U=await this.processor(j),X=b===0,K=F>=k.length;if(A.push({stride:[j.length,X?0:I,K?0:I],input_features:U.input_features,is_last:K}),K)break;b+=O}}else A=[{stride:[k.length,0,0],input_features:(await this.processor(k)).input_features,is_last:!0}];for(let T of A){i.num_frames=Math.floor(T.stride[0]/_);let I=await this.model.generate({inputs:T.input_features,...i});if(s==="word"){let O=I.sequences.tolist()[0],b=I.token_timestamps.tolist()[0],F=this.tokenizer.timestamp_begin,j=Math.max(O.findIndex(U=>Number(U)>=F),0);T.tokens=O.slice(j),T.token_timestamps=b.slice(j).map(U=>Ns(U,2))}else T.tokens=I[0].tolist();T.stride=T.stride.map(O=>O/m)}let[E,S]=this.tokenizer._decode_asr(A,{time_precision:f,return_timestamps:s,force_full_sequences:o});x.push({text:E,...S})}return l?x[0]:x}async _call_moonshine(e,r){let s=!Array.isArray(e),n=s?[e]:e,o=this.processor.feature_extractor.config.sampling_rate,a=await Ur(n,o),i=[];for(let l of a){let c=await this.processor(l),p=Math.floor(l.length/o)*6,f=await this.model.generate({max_new_tokens:p,...r,...c}),_=this.processor.batch_decode(f,{skip_special_tokens:!0})[0];i.push({text:_})}return s?i[0]:i}async _call_cohere_asr(e,r){let s=!Array.isArray(e),n=s?[e]:e,o=this.processor.feature_extractor,a=o.config.sampling_rate,i=await Ur(n,a),l=r.language??"en",c=this.processor.get_decoder_prompt_ids(l),p=[];for(let f of i){let _=o.split_audio(f),m=[];for(let x of _){let k=await this.processor(x),A=await this.model.generate({...k,decoder_input_ids:c,...r}),E=this.tokenizer.decode(A[0].tolist(),{skip_special_tokens:!0}).trim();m.push(E)}let w=this.processor.constructor.join_chunks(m,l);p.push({text:w})}return s?p[0]:p}};var Qi=class extends me{DEFAULT_VOCODER_ID="Xenova/speecht5_hifigan";constructor(e){super(e),this.vocoder=e.vocoder??null}async _prepare_speaker_embeddings(e,r){if((typeof e=="string"||e instanceof URL)&&(e=new Float32Array(await(await _e.fetch(e)).arrayBuffer())),e instanceof Float32Array)e=new N("float32",e,[e.length]);else if(!(e instanceof N))throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.");if(r>1){if(e.dims[0]===1)e=e.repeat(r,1);else if(e.dims[0]!==r)throw new Error(`Expected speaker embeddings batch size to be 1 or ${r}, but got ${e.dims[0]}.`)}return e}_postprocess_waveform(e,r,s,n=null){let o=r.data,[a,i]=r.dims,l=n?n.data:null,c=[];for(let p=0;p<a;++p){let f=l?Math.min(Math.ceil(l[p]),i):i,_=p*i;c.push(new Kn(o.slice(_,_+f),s))}return Array.isArray(e)?c:c[0]}async _call(e,r){return this.processor?this._call_text_to_spectrogram(e,r):this.model.config.model_type==="supertonic"?this._call_supertonic(e,r):this._call_text_to_waveform(e)}async _call_supertonic(e,{speaker_embeddings:r,num_inference_steps:s,speed:n}){if(!r)throw new Error("Speaker embeddings must be provided for Supertonic models.");let{sampling_rate:o,style_dim:a}=this.model.config,i=this.tokenizer(e,{padding:!0,truncation:!0}),l=i.input_ids.dims[0];r=await this._prepare_speaker_embeddings(r,l),r=r.view(l,-1,a);let{waveform:c,durations:p}=await this.model.generate_speech({...i,style:r,num_inference_steps:s,speed:n});return this._postprocess_waveform(e,c,o,p)}async _call_text_to_waveform(e){let r=this.tokenizer(e,{padding:!0,truncation:!0}),{waveform:s}=await this.model(r),n=this.model.config.sampling_rate;return this._postprocess_waveform(e,s,n)}async _call_text_to_spectrogram(e,{speaker_embeddings:r}){this.vocoder||(ee.info("No vocoder specified, using default HifiGan vocoder."),this.vocoder=await _r.from_pretrained(this.DEFAULT_VOCODER_ID,{dtype:"fp32"}));let{input_ids:s}=this.tokenizer(e,{padding:!0,truncation:!0}),n=s.dims[0];r=await this._prepare_speaker_embeddings(r,n),r=r.view(n,-1);let{waveform:o}=await this.model.generate_speech(s,r,{vocoder:this.vocoder}),a=this.processor.feature_extractor.config.sampling_rate;return this._postprocess_waveform(e,o,a)}};var Ji=class extends me{async _call(e,r={}){let s=Array.isArray(e),n=await tt(e),{pixel_values:o}=await this.processor(n),a=[];for(let i of o){i.dims=[1,...i.dims];let l=await this.model.generate({inputs:i,...r}),c=this.tokenizer.batch_decode(l,{skip_special_tokens:!0}).map(p=>({generated_text:p.trim()}));a.push(c)}return s?a:a[0]}};var Zi=class extends me{async _call(e,{top_k:r=5}={}){let s=await tt(e),{pixel_values:n}=await this.processor(s),o=await this.model({pixel_values:n}),{id2label:a}=this.model.config,i=[];for(let l of o.logits){let c=await Gt(new N("float32",Pe(l.data),l.dims),r),p=c[0].tolist(),_=c[1].tolist().map((m,w)=>({label:a?a[m]:`LABEL_${m}`,score:p[w]}));i.push(_)}return Array.isArray(e)?i:i[0]}};var MT={panoptic:"post_process_panoptic_segmentation",instance:"post_process_instance_segmentation",semantic:"post_process_semantic_segmentation"},As=class extends me{async _call(e,{threshold:r=.5,mask_threshold:s=.5,overlap_mask_area_threshold:n=.8,label_ids_to_fuse:o=null,target_sizes:a=null,subtask:i=null}={}){if(Array.isArray(e)&&e.length!==1)throw Error("Image segmentation pipeline currently only supports a batch size of 1.");let c=await tt(e),p=c.map(E=>[E.height,E.width]),f=await this.processor(c),{inputNames:_,outputNames:m}=this.model.sessions.model;if(!_.includes("pixel_values")){if(_.length!==1)throw Error(`Expected a single input name, but got ${_.length} inputs: ${_}.`);let E=_[0];if(E in f)throw Error(`Input name ${E} already exists in the inputs.`);f[E]=f.pixel_values}let w=await this.model(f),x=null;if(i!==null)x=MT[i];else if(this.processor.image_processor){for(let[E,S]of Object.entries(MT))if(S in this.processor.image_processor){x=this.processor.image_processor[S].bind(this.processor.image_processor),i=E;break}}let k=this.model.config.id2label,A=[];if(i)if(i==="panoptic"||i==="instance"){let E=x(w,r,s,n,o,a??p)[0],S=E.segmentation;for(let T of E.segments_info){let I=new Uint8ClampedArray(S.data.length);for(let b=0;b<S.data.length;++b)S.data[b]===T.id&&(I[b]=255);let O=new Je(I,S.dims[1],S.dims[0],1);A.push({score:T.score,label:k[T.label_id],mask:O})}}else if(i==="semantic"){let{segmentation:E,labels:S}=x(w,a??p)[0];for(let T of S){let I=new Uint8ClampedArray(E.data.length);for(let b=0;b<E.data.length;++b)E.data[b]===T&&(I[b]=255);let O=new Je(I,E.dims[1],E.dims[0],1);A.push({score:null,label:k[T],mask:O})}}else throw Error(`Subtask ${i} not supported.`);else{let S=w[m[0]];for(let T=0;T<p.length;++T){let I=p[T],O=S[T];O.data.some(F=>F<-1e-5||F>1+1e-5)&&O.sigmoid_();let b=await Je.fromTensor(O.mul_(255).to("uint8")).resize(I[1],I[0]);A.push({label:null,score:null,mask:b})}}return A}};var el=class extends As{async _call(e,r={}){let s=await tt(e),n=await super._call(e,r),o=s.map((a,i)=>{let l=a.clone();return l.putAlpha(n[i].mask),l});return Array.isArray(e)?o:o[0]}};var tl=class extends me{async _call(e,r,{hypothesis_template:s="This is a photo of {}"}={}){let n=Array.isArray(e),o=await tt(e),a=r.map(_=>s.replace("{}",_)),i=this.tokenizer(a,{padding:this.model.config.model_type==="siglip"?"max_length":!0,truncation:!0}),{pixel_values:l}=await this.processor(o),c=await this.model({...i,pixel_values:l}),p=this.model.config.model_type==="siglip"?_=>_.sigmoid().data:_=>Pe(_.data),f=[];for(let _ of c.logits_per_image){let w=[...p(_)].map((x,k)=>({score:x,label:r[k]}));w.sort((x,k)=>k.score-x.score),f.push(w)}return n?f:f[0]}};var rl=class extends me{async 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$T(e,r,s)}};0&&(module.exports={ASTFeatureExtractor,ASTForAudioClassification,ASTModel,ASTPreTrainedModel,AfmoeForCausalLM,AfmoeModel,AfmoePreTrainedModel,AlbertForMaskedLM,AlbertForQuestionAnswering,AlbertForSequenceClassification,AlbertModel,AlbertPreTrainedModel,AlbertTokenizer,ApertusForCausalLM,ApertusModel,ApertusPreTrainedModel,ArceeForCausalLM,ArceeModel,ArceePreTrainedModel,AudioClassificationPipeline,AutoConfig,AutoFeatureExtractor,AutoImageProcessor,AutoModel,AutoModelForAudioClassification,AutoModelForAudioFrameClassification,AutoModelForAudioTextToText,AutoModelForCTC,AutoModelForCausalLM,AutoModelForDepthEstimation,AutoModelForDocumentQuestionAnswering,AutoModelForImageClassification,AutoModelForImageFeatureExtraction,AutoModelForImageMatting,AutoModelForImageSegmentation,AutoModelForImageTextToText,AutoModelForImageToImage,AutoModelForMaskGeneration,AutoModelForMaskedLM,AutoModelForNormalEstimation,AutoModelForObjectDetection,AutoModelForPoseEstimation,AutoModelForQuestionAnswering,AutoModelForSemanticSegmentation,AutoModelForSeq2SeqLM,AutoModelForSequenceClassification,AutoModelForSpeechSeq2Seq,AutoModelForTextToSpectrogram,AutoModelForTextToWaveform,AutoModelForTokenClassification,AutoModelForUniversalSegmentation,AutoModelForVision2Seq,AutoModelForXVector,AutoModelForZeroShotObjectDetection,AutoProcessor,AutoTokenizer,AutomaticSpeechRecognitionPipeline,BackgroundRemovalPipeline,BartForConditionalGeneration,BartForSequenceClassification,BartModel,BartPretrainedModel,BartTokenizer,BaseStreamer,BeitFeatureExtractor,BeitForImageClassification,BeitModel,BeitPreTrainedModel,BertForMaskedLM,BertForQuestionAnswering,BertForSequenceClassification,BertForTokenClassification,BertModel,BertPreTrainedModel,BertTokenizer,BitImageProcessor,BlenderbotForConditionalGeneration,BlenderbotModel,BlenderbotPreTrainedModel,BlenderbotSmallForConditionalGeneration,BlenderbotSmallModel,BlenderbotSmallPreTrainedModel,BlenderbotSmallTokenizer,BlenderbotTokenizer,BloomForCausalLM,BloomModel,BloomPreTrainedModel,BloomTokenizer,CHMv2ForDepthEstimation,CHMv2ImageProcessor,CHMv2PreTrainedModel,CLIPFeatureExtractor,CLIPImageProcessor,CLIPModel,CLIPPreTrainedModel,CLIPSegForImageSegmentation,CLIPSegModel,CLIPSegPreTrainedModel,CLIPTextModel,CLIPTextModelWithProjection,CLIPTokenizer,CLIPVisionModel,CLIPVisionModelWithProjection,CamembertForMaskedLM,CamembertForQuestionAnswering,CamembertForSequenceClassification,CamembertForTokenClassification,CamembertModel,CamembertPreTrainedModel,CamembertTokenizer,ChatterboxFeatureExtractor,ChatterboxModel,ChatterboxPreTrainedModel,ChatterboxProcessor,ChineseCLIPFeatureExtractor,ChineseCLIPModel,ChineseCLIPPreTrainedModel,ClapAudioModelWithProjection,ClapFeatureExtractor,ClapModel,ClapPreTrainedModel,ClapTextModelWithProjection,ClassifierFreeGuidanceLogitsProcessor,CodeGenForCausalLM,CodeGenModel,CodeGenPreTrainedModel,CodeGenTokenizer,CodeLlamaTokenizer,Cohere2ForCausalLM,Cohere2Model,Cohere2PreTrainedModel,CohereAsrFeatureExtractor,CohereAsrForConditionalGeneration,CohereAsrModel,CohereAsrPreTrainedModel,CohereAsrProcessor,CohereAsrTokenizer,CohereForCausalLM,CohereModel,CoherePreTrainedModel,CohereTokenizer,ConvBertForMaskedLM,ConvBertForQuestionAnswering,ConvBertForSequenceClassification,ConvBertForTokenClassification,ConvBertModel,ConvBertPreTrainedModel,ConvBertTokenizer,ConvNextFeatureExtractor,ConvNextForImageClassification,ConvNextImageProcessor,ConvNextModel,ConvNextPreTrainedModel,ConvNextV2ForImageClassification,ConvNextV2Model,ConvNextV2PreTrainedModel,DFineForObjectDetection,DFineModel,DFinePreTrainedModel,DINOv3ConvNextModel,DINOv3ConvNextPreTrainedModel,DINOv3ViTImageProcessor,DINOv3ViTModel,DINOv3ViTPreTrainedModel,DPTFeatureExtractor,DPTForDepthEstimation,DPTImageProcessor,DPTModel,DPTPreTrainedModel,DacDecoderModel,DacDecoderOutput,DacEncoderModel,DacEncoderOutput,DacFeatureExtractor,DacModel,DacPreTrainedModel,DebertaForMaskedLM,DebertaForQuestionAnswering,DebertaForSequenceClassification,DebertaForTokenClassification,DebertaModel,DebertaPreTrainedModel,DebertaTokenizer,DebertaV2ForMaskedLM,DebertaV2ForQuestionAnswering,DebertaV2ForSequenceClassification,DebertaV2ForTokenClassification,DebertaV2Model,DebertaV2PreTrainedModel,DebertaV2Tokenizer,DecisionTransformerModel,DecisionTransformerPreTrainedModel,DeepseekV3ForCausalLM,DeepseekV3Model,DeepseekV3PreTrainedModel,DeiTFeatureExtractor,DeiTForImageClassification,DeiTImageProcessor,DeiTModel,DeiTPreTrainedModel,DepthAnythingForDepthEstimation,DepthAnythingPreTrainedModel,DepthEstimationPipeline,DepthProForDepthEstimation,DepthProPreTrainedModel,DetrFeatureExtractor,DetrForObjectDetection,DetrForSegmentation,DetrImageProcessor,DetrModel,DetrObjectDetectionOutput,DetrPreTrainedModel,DetrSegmentationOutput,Dinov2ForImageClassification,Dinov2Model,Dinov2PreTrainedModel,Dinov2WithRegistersForImageClassification,Dinov2WithRegistersModel,Dinov2WithRegistersPreTrainedModel,DistilBertForMaskedLM,DistilBertForQuestionAnswering,DistilBertForSequenceClassification,DistilBertForTokenClassification,DistilBertModel,DistilBertPreTrainedModel,DistilBertTokenizer,DocumentQuestionAnsweringPipeline,DonutFeatureExtractor,DonutImageProcessor,DonutSwinModel,DonutSwinPreTrainedModel,DynamicCache,EdgeTamModel,EfficientNetForImageClassification,EfficientNetImageProcessor,EfficientNetModel,EfficientNetPreTrainedModel,ElectraForMaskedLM,ElectraForQuestionAnswering,ElectraForSequenceClassification,ElectraForTokenClassification,ElectraModel,ElectraPreTrainedModel,ElectraTokenizer,EncodecFeatureExtractor,EosTokenCriteria,Ernie4_5ForCausalLM,Ernie4_5Model,Ernie4_5PretrainedModel,EsmForMaskedLM,EsmForSequenceClassification,EsmForTokenClassification,EsmModel,EsmPreTrainedModel,EsmTokenizer,EuroBertForMaskedLM,EuroBertForSequenceClassification,EuroBertForTokenClassification,EuroBertModel,EuroBertPreTrainedModel,ExaoneForCausalLM,ExaoneModel,ExaonePreTrainedModel,FalconForCausalLM,FalconH1ForCausalLM,FalconH1Model,FalconH1PreTrainedModel,FalconModel,FalconPreTrainedModel,FalconTokenizer,FastViTForImageClassification,FastViTModel,FastViTPreTrainedModel,FeatureExtractionPipeline,FeatureExtractor,FillMaskPipeline,Florence2ForConditionalGeneration,Florence2PreTrainedModel,Florence2Processor,ForcedBOSTokenLogitsProcessor,ForcedEOSTokenLogitsProcessor,GLPNFeatureExtractor,GLPNForDepthEstimation,GLPNModel,GLPNPreTrainedModel,GPT2LMHeadModel,GPT2Model,GPT2PreTrainedModel,GPT2Tokenizer,GPTBigCodeForCausalLM,GPTBigCodeModel,GPTBigCodePreTrainedModel,GPTJForCausalLM,GPTJModel,GPTJPreTrainedModel,GPTNeoForCausalLM,GPTNeoModel,GPTNeoPreTrainedModel,GPTNeoXForCausalLM,GPTNeoXModel,GPTNeoXPreTrainedModel,GPTNeoXTokenizer,Gemma2ForCausalLM,Gemma2Model,Gemma2PreTrainedModel,Gemma3ForCausalLM,Gemma3ForConditionalGeneration,Gemma3ImageProcessor,Gemma3Model,Gemma3PreTrainedModel,Gemma3Processor,Gemma3nAudioFeatureExtractor,Gemma3nForCausalLM,Gemma3nForConditionalGeneration,Gemma3nPreTrainedModel,Gemma3nProcessor,Gemma4AudioFeatureExtractor,Gemma4ForCausalLM,Gemma4ForConditionalGeneration,Gemma4ImageProcessor,Gemma4Processor,GemmaForCausalLM,GemmaModel,GemmaPreTrainedModel,GemmaTokenizer,Glm46VImageProcessor,Glm46VProcessor,GlmForCausalLM,GlmModel,GlmMoeDsaForCausalLM,GlmMoeDsaModel,GlmMoeDsaPreTrainedModel,GlmOcrForConditionalGeneration,GlmPreTrainedModel,GptOssForCausalLM,GptOssModel,GptOssPreTrainedModel,GraniteForCausalLM,GraniteModel,GraniteMoeHybridForCausalLM,GraniteMoeHybridModel,GraniteMoeHybridPreTrainedModel,GranitePreTrainedModel,GraniteSpeechFeatureExtractor,GraniteSpeechForConditionalGeneration,GraniteSpeechProcessor,GroundingDinoForObjectDetection,GroundingDinoImageProcessor,GroundingDinoPreTrainedModel,GroundingDinoProcessor,GroupViTModel,GroupViTPreTrainedModel,HeliumForCausalLM,HeliumModel,HeliumPreTrainedModel,HerbertTokenizer,HieraForImageClassification,HieraModel,HieraPreTrainedModel,HubertForCTC,HubertForSequenceClassification,HubertModel,HubertPreTrainedModel,HunYuanDenseV1ForCausalLM,HunYuanDenseV1Model,HunYuanDenseV1PreTrainedModel,IJepaForImageClassification,IJepaModel,IJepaPreTrainedModel,Idefics3ForConditionalGeneration,Idefics3ImageProcessor,Idefics3Processor,ImageClassificationPipeline,ImageFeatureExtractionPipeline,ImageFeatureExtractor,ImageProcessor,ImageSegmentationPipeline,ImageToImagePipeline,ImageToTextPipeline,InterruptableStoppingCriteria,JAISLMHeadModel,JAISModel,JAISPreTrainedModel,JinaCLIPImageProcessor,JinaCLIPModel,JinaCLIPPreTrainedModel,JinaCLIPProcessor,JinaCLIPTextModel,JinaCLIPVisionModel,Lfm2ForCausalLM,Lfm2Model,Lfm2MoeForCausalLM,Lfm2MoeModel,Lfm2MoePreTrainedModel,Lfm2PreTrainedModel,Lfm2VlForConditionalGeneration,Lfm2VlImageProcessor,Lfm2VlProcessor,LightOnOcrForConditionalGeneration,LiteWhisperForConditionalGeneration,Llama4ForCausalLM,Llama4PreTrainedModel,LlamaForCausalLM,LlamaModel,LlamaPreTrainedModel,LlamaTokenizer,LlavaForConditionalGeneration,LlavaOnevisionForConditionalGeneration,LlavaOnevisionImageProcessor,LlavaPreTrainedModel,LlavaProcessor,LlavaQwen2ForCausalLM,LogLevel,LogitsProcessor,LogitsProcessorList,LogitsWarper,LongT5ForConditionalGeneration,LongT5Model,LongT5PreTrainedModel,M2M100ForConditionalGeneration,M2M100Model,M2M100PreTrainedModel,M2M100Tokenizer,MBart50Tokenizer,MBartForCausalLM,MBartForConditionalGeneration,MBartForSequenceClassification,MBartModel,MBartPreTrainedModel,MBartTokenizer,MPNetForMaskedLM,MPNetForQuestionAnswering,MPNetForSequenceClassification,MPNetForTokenClassification,MPNetModel,MPNetPreTrainedModel,MPNetTokenizer,MT5ForConditionalGeneration,MT5Model,MT5PreTrainedModel,MarianMTModel,MarianModel,MarianPreTrainedModel,MarianTokenizer,Mask2FormerImageProcessor,MaskFormerFeatureExtractor,MaskFormerForInstanceSegmentation,MaskFormerImageProcessor,MaskFormerModel,MaskFormerPreTrainedModel,MaxLengthCriteria,Metric3DForDepthEstimation,Metric3DPreTrainedModel,Metric3Dv2ForDepthEstimation,Metric3Dv2PreTrainedModel,MgpstrForSceneTextRecognition,MgpstrModelOutput,MgpstrPreTrainedModel,MgpstrProcessor,MgpstrTokenizer,MimiDecoderModel,MimiDecoderOutput,MimiEncoderModel,MimiEncoderOutput,MimiModel,MimiPreTrainedModel,MinLengthLogitsProcessor,MinNewTokensLengthLogitsProcessor,Mistral4ForCausalLM,Mistral4Model,Mistral4PreTrainedModel,MistralForCausalLM,MistralModel,MistralPreTrainedModel,MobileBertForMaskedLM,MobileBertForQuestionAnswering,MobileBertForSequenceClassification,MobileBertModel,MobileBertPreTrainedModel,MobileBertTokenizer,MobileLLMForCausalLM,MobileLLMModel,MobileLLMPreTrainedModel,MobileNetV1FeatureExtractor,MobileNetV1ForImageClassification,MobileNetV1ForSemanticSegmentation,MobileNetV1ImageProcessor,MobileNetV1Model,MobileNetV1PreTrainedModel,MobileNetV2FeatureExtractor,MobileNetV2ForImageClassification,MobileNetV2ForSemanticSegmentation,MobileNetV2ImageProcessor,MobileNetV2Model,MobileNetV2PreTrainedModel,MobileNetV3FeatureExtractor,MobileNetV3ForImageClassification,MobileNetV3ForSemanticSegmentation,MobileNetV3ImageProcessor,MobileNetV3Model,MobileNetV3PreTrainedModel,MobileNetV4FeatureExtractor,MobileNetV4ForImageClassification,MobileNetV4ForSemanticSegmentation,MobileNetV4ImageProcessor,MobileNetV4Model,MobileNetV4PreTrainedModel,MobileViTFeatureExtractor,MobileViTForImageClassification,MobileViTImageProcessor,MobileViTModel,MobileViTPreTrainedModel,MobileViTV2ForImageClassification,MobileViTV2Model,MobileViTV2PreTrainedModel,ModelRegistry,ModernBertDecoderForCausalLM,ModernBertDecoderModel,ModernBertDecoderPreTrainedModel,ModernBertForMaskedLM,ModernBertForSequenceClassification,ModernBertForTokenClassification,ModernBertModel,ModernBertPreTrainedModel,Moondream1ForConditionalGeneration,MoonshineFeatureExtractor,MoonshineForConditionalGeneration,MoonshineModel,MoonshinePreTrainedModel,MoonshineProcessor,MptForCausalLM,MptModel,MptPreTrainedModel,MultiModalityCausalLM,MultiModalityPreTrainedModel,MusicgenForCausalLM,MusicgenForConditionalGeneration,MusicgenModel,MusicgenPreTrainedModel,NanoChatForCausalLM,NanoChatModel,NanoChatPreTrainedModel,NemotronHForCausalLM,NemotronHModel,NemotronHPreTrainedModel,NeoBertForMaskedLM,NeoBertForQuestionAnswering,NeoBertForSequenceClassification,NeoBertForTokenClassification,NeoBertModel,NeoBertPreTrainedModel,NllbTokenizer,NoBadWordsLogitsProcessor,NoRepeatNGramLogitsProcessor,NomicBertModel,NomicBertPreTrainedModel,NougatImageProcessor,NougatTokenizer,OPTForCausalLM,OPTModel,OPTPreTrainedModel,ObjectDetectionPipeline,Olmo2ForCausalLM,Olmo2Model,Olmo2PreTrainedModel,Olmo3ForCausalLM,Olmo3Model,Olmo3PreTrainedModel,OlmoForCausalLM,OlmoHybridForCausalLM,OlmoHybridModel,OlmoHybridPreTrainedModel,OlmoModel,OlmoPreTrainedModel,OpenAIPrivacyFilterForTokenClassification,OpenAIPrivacyFilterModel,OpenAIPrivacyFilterPreTrainedModel,OpenELMForCausalLM,OpenELMModel,OpenELMPreTrainedModel,OwlViTFeatureExtractor,OwlViTForObjectDetection,OwlViTImageProcessor,OwlViTModel,OwlViTPreTrainedModel,OwlViTProcessor,Owlv2ForObjectDetection,Owlv2ImageProcessor,Owlv2Model,Owlv2PreTrainedModel,PaliGemmaForConditionalGeneration,PaliGemmaProcessor,ParakeetFeatureExtractor,ParakeetForCTC,ParakeetPreTrainedModel,PatchTSMixerForPrediction,PatchTSMixerModel,PatchTSMixerPreTrainedModel,PatchTSTForPrediction,PatchTSTModel,PatchTSTPreTrainedModel,Phi3ForCausalLM,Phi3Model,Phi3PreTrainedModel,Phi3VForCausalLM,Phi3VImageProcessor,Phi3VPreTrainedModel,Phi3VProcessor,PhiForCausalLM,PhiModel,PhiPreTrainedModel,PixtralImageProcessor,PixtralProcessor,PreTrainedModel,PreTrainedTokenizer,PretrainedConfig,Processor,PvtForImageClassification,PvtImageProcessor,PvtModel,PvtPreTrainedModel,PyAnnoteFeatureExtractor,PyAnnoteForAudioFrameClassification,PyAnnoteModel,PyAnnotePreTrainedModel,PyAnnoteProcessor,QuestionAnsweringPipeline,Qwen2ForCausalLM,Qwen2Model,Qwen2MoeForCausalLM,Qwen2MoeModel,Qwen2MoePreTrainedModel,Qwen2PreTrainedModel,Qwen2Tokenizer,Qwen2VLForCausalLM,Qwen2VLForConditionalGeneration,Qwen2VLImageProcessor,Qwen2VLPreTrainedModel,Qwen2VLProcessor,Qwen2_5_VLForCausalLM,Qwen2_5_VLForConditionalGeneration,Qwen2_5_VLProcessor,Qwen3ForCausalLM,Qwen3Model,Qwen3MoeForCausalLM,Qwen3MoeModel,Qwen3MoePreTrainedModel,Qwen3NextForCausalLM,Qwen3NextModel,Qwen3NextPreTrainedModel,Qwen3PreTrainedModel,Qwen3VLForCausalLM,Qwen3VLForConditionalGeneration,Qwen3VLMoeForCausalLM,Qwen3VLMoeForConditionalGeneration,Qwen3VLProcessor,Qwen3_5ForCausalLM,Qwen3_5ForConditionalGeneration,Qwen3_5MoeForCausalLM,Qwen3_5MoeForConditionalGeneration,RFDetrForObjectDetection,RFDetrModel,RFDetrObjectDetectionOutput,RFDetrPreTrainedModel,RTDetrForObjectDetection,RTDetrImageProcessor,RTDetrModel,RTDetrObjectDetectionOutput,RTDetrPreTrainedModel,RTDetrV2ForObjectDetection,RTDetrV2Model,RTDetrV2ObjectDetectionOutput,RTDetrV2PreTrainedModel,RawAudio,RawImage,RawVideo,RawVideoFrame,RepetitionPenaltyLogitsProcessor,ResNetForImageClassification,ResNetModel,ResNetPreTrainedModel,RoFormerForMaskedLM,RoFormerForQuestionAnswering,RoFormerForSequenceClassification,RoFormerForTokenClassification,RoFormerModel,RoFormerPreTrainedModel,RoFormerTokenizer,RobertaForMaskedLM,RobertaForQuestionAnswering,RobertaForSequenceClassification,RobertaForTokenClassification,RobertaModel,RobertaPreTrainedModel,RobertaTokenizer,Sam2ImageProcessor,Sam2ImageSegmentationOutput,Sam2Model,Sam2PreTrainedModel,Sam2Processor,Sam2VideoProcessor,Sam3ImageProcessor,Sam3TrackerModel,SamImageProcessor,SamImageSegmentationOutput,SamModel,SamPreTrainedModel,SamProcessor,SapiensFeatureExtractor,SapiensForDepthEstimation,SapiensForNormalEstimation,SapiensForSemanticSegmentation,SapiensImageProcessor,SapiensPreTrainedModel,SeamlessM4TFeatureExtractor,SegformerFeatureExtractor,SegformerForImageClassification,SegformerForSemanticSegmentation,SegformerImageProcessor,SegformerModel,SegformerPreTrainedModel,SiglipImageProcessor,SiglipModel,SiglipPreTrainedModel,SiglipTextModel,SiglipTokenizer,SiglipVisionModel,SmolLM3ForCausalLM,SmolLM3Model,SmolLM3PreTrainedModel,SmolVLMForConditionalGeneration,SmolVLMImageProcessor,SmolVLMProcessor,SnacDecoderModel,SnacEncoderModel,SnacFeatureExtractor,SnacModel,SnacPreTrainedModel,SolarOpenForCausalLM,SolarOpenModel,SolarOpenPreTrainedModel,SpeechT5FeatureExtractor,SpeechT5ForSpeechToText,SpeechT5ForTextToSpeech,SpeechT5HifiGan,SpeechT5Model,SpeechT5PreTrainedModel,SpeechT5Processor,SpeechT5Tokenizer,SqueezeBertForMaskedLM,SqueezeBertForQuestionAnswering,SqueezeBertForSequenceClassification,SqueezeBertModel,SqueezeBertPreTrainedModel,SqueezeBertTokenizer,StableLmForCausalLM,StableLmModel,StableLmPreTrainedModel,Starcoder2ForCausalLM,Starcoder2Model,Starcoder2PreTrainedModel,StoppingCriteria,StoppingCriteriaList,StyleTextToSpeech2Model,StyleTextToSpeech2PreTrainedModel,SummarizationPipeline,SupertonicForConditionalGeneration,SupertonicPreTrainedModel,SuppressTokensAtBeginLogitsProcessor,SuppressTokensLogitsProcessor,Swin2SRForImageSuperResolution,Swin2SRImageProcessor,Swin2SRModel,Swin2SRPreTrainedModel,SwinForImageClassification,SwinForSemanticSegmentation,SwinModel,SwinPreTrainedModel,T5ForConditionalGeneration,T5Model,T5PreTrainedModel,T5Tokenizer,TableTransformerForObjectDetection,TableTransformerModel,TableTransformerObjectDetectionOutput,TableTransformerPreTrainedModel,TemperatureLogitsWarper,Tensor,Text2TextGenerationPipeline,TextClassificationPipeline,TextGenerationPipeline,TextStreamer,TextToAudioPipeline,TokenClassificationPipeline,TokenizersBackend,TopKLogitsWarper,TopPLogitsWarper,TrOCRForCausalLM,TrOCRPreTrainedModel,TranslationPipeline,UltravoxModel,UltravoxPreTrainedModel,UltravoxProcessor,UniSpeechForCTC,UniSpeechForSequenceClassification,UniSpeechModel,UniSpeechPreTrainedModel,UniSpeechSatForAudioFrameClassification,UniSpeechSatForCTC,UniSpeechSatForSequenceClassification,UniSpeechSatModel,UniSpeechSatPreTrainedModel,VLChatProcessor,VLMImageProcessor,VaultGemmaForCausalLM,VaultGemmaModel,VaultGemmaPreTrainedModel,ViTFeatureExtractor,ViTForImageClassification,ViTImageProcessor,ViTMAEModel,ViTMAEPreTrainedModel,ViTMSNForImageClassification,ViTMSNModel,ViTMSNPreTrainedModel,ViTModel,ViTPreTrainedModel,VisionEncoderDecoderModel,VitMatteForImageMatting,VitMatteImageProcessor,VitMattePreTrainedModel,VitPoseForPoseEstimation,VitPoseImageProcessor,VitPosePreTrainedModel,VitsModel,VitsModelOutput,VitsPreTrainedModel,VitsTokenizer,VoxtralForConditionalGeneration,VoxtralProcessor,VoxtralRealtimeFeatureExtractor,VoxtralRealtimeForConditionalGeneration,VoxtralRealtimePreTrainedModel,VoxtralRealtimeProcessor,Wav2Vec2BertForCTC,Wav2Vec2BertForSequenceClassification,Wav2Vec2BertModel,Wav2Vec2BertPreTrainedModel,Wav2Vec2CTCTokenizer,Wav2Vec2FeatureExtractor,Wav2Vec2ForAudioFrameClassification,Wav2Vec2ForCTC,Wav2Vec2ForSequenceClassification,Wav2Vec2Model,Wav2Vec2PreTrainedModel,Wav2Vec2Processor,Wav2Vec2ProcessorWithLM,WavLMForAudioFrameClassification,WavLMForCTC,WavLMForSequenceClassification,WavLMForXVector,WavLMModel,WavLMPreTrainedModel,WeSpeakerFeatureExtractor,WeSpeakerResNetModel,WeSpeakerResNetPreTrainedModel,WhisperFeatureExtractor,WhisperForConditionalGeneration,WhisperModel,WhisperPreTrainedModel,WhisperProcessor,WhisperTextStreamer,WhisperTimeStampLogitsProcessor,WhisperTokenizer,XLMForQuestionAnswering,XLMForSequenceClassification,XLMForTokenClassification,XLMModel,XLMPreTrainedModel,XLMRobertaForMaskedLM,XLMRobertaForQuestionAnswering,XLMRobertaForSequenceClassification,XLMRobertaForTokenClassification,XLMRobertaModel,XLMRobertaPreTrainedModel,XLMRobertaTokenizer,XLMTokenizer,XLMWithLMHeadModel,XVectorOutput,YolosFeatureExtractor,YolosForObjectDetection,YolosImageProcessor,YolosModel,YolosObjectDetectionOutput,YolosPreTrainedModel,YoutuForCausalLM,YoutuModel,YoutuPreTrainedModel,ZeroShotAudioClassificationPipeline,ZeroShotClassificationPipeline,ZeroShotImageClassificationPipeline,ZeroShotObjectDetectionPipeline,cat,cos_sim,dot,env,full,full_like,interpolate,interpolate_4d,layer_norm,load_audio,load_image,load_video,log_softmax,matmul,mean,mean_pooling,ones,ones_like,permute,pipeline,quantize_embeddings,rand,randn,random,read_audio,rfft,slice,softmax,stack,std_mean,topk,zeros,zeros_like});