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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}},lP=iP,cP=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()}},uP=cP,pP=class extends cc{constructor(t){super(t),this.tokens_to_ids=Lf(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 uP(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 lP((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}},TE=pP,fP=class extends cc{constructor(t,e){super(t);let r=t.vocab;this.tokens_to_ids=Lf(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}},dP=fP;function _P(t,e){switch(t.type){case"WordPiece":return new EE(t);case"Unigram":return new ME(t,e.eos_token);case"BPE":return new TE(t);default:if(t.vocab)return Array.isArray(t.vocab)?new ME(t,e.eos_token):Object.hasOwn(t,"continuing_subword_prefix")&&Object.hasOwn(t,"unk_token")?Object.hasOwn(t,"merges")?new TE(t):new EE(t):new dP(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 mP=_P,hP=class extends co{constructor(t){super(),this.config=t}_call(t,...e){return this.post_process(t,...e)}},uo=hP,gP=class extends uo{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=Vt(n,t),o=Vt(o,new Array(t.length).fill(a.Sequence.type_id))):a.Sequence.id==="B"&&(n=Vt(n,e),o=Vt(o,new Array(e.length).fill(a.Sequence.type_id))));return{tokens:n,token_type_ids:o}}},wP=gP,xP=class extends uo{post_process(t,e=null){return{tokens:t,tokens_pair:e}}},yP=xP,bP=class extends uo{constructor(t){super(t),this.sep=t.sep,this.cls=t.cls}post_process(t,e=null,r=!0){r&&(t=Vt([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=Vt(t,n,e,o),s=Vt(s,new Array(e.length+n.length+o.length).fill(1))}return{tokens:t,token_type_ids:s}}},vP=bP,kP=class extends uo{constructor(t){super(t),this.sep=t.sep,this.cls=t.cls}post_process(t,e,r=!0){r&&(t=Vt([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=Vt(t,n,e,o),s=Vt(s,new Array(e.length+n.length+o.length).fill(1))}return{tokens:t,token_type_ids:s}}},EP=kP,AP=class extends uo{constructor(t){super(t),this.processors=(t.processors??[]).map(e=>LE(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}},MP=AP;function TP(t){if(t===null)return null;switch(t.type){case"TemplateProcessing":return new wP(t);case"ByteLevel":return new yP(t);case"BertProcessing":return new vP(t);case"RobertaProcessing":return new EP(t);case"Sequence":return new MP(t);default:throw new Error(`Unknown PostProcessor type: ${t.type}`)}}var LE=TP,SP=class extends co{constructor(t){super(),this.config=t,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets="trim_offsets"in t?t.trim_offsets:!1}_call(t){return this.decode(t)}decode(t){return this.decode_chain(t).join("")}},Dt=SP,OP=class extends Dt{constructor(t){super(t),this.byte_decoder=WI,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(t){let e=t.join(""),r=new Uint8Array([...e].map(s=>this.byte_decoder[s]));return this.text_decoder.decode(r)}decode_chain(t){let e=[],r=[];for(let s of t)this.added_tokens.find(n=>n.content===s)!==void 0?(r.length>0&&(e.push(this.convert_tokens_to_string(r)),r=[]),e.push(s)):r.push(s);return r.length>0&&e.push(this.convert_tokens_to_string(r)),e}},IP=OP,CP=class extends Dt{constructor(t){super(t),this.cleanup=t.cleanup}decode_chain(t){return t.map((e,r)=>{if(r!==0){let s=this.config.prefix;s&&e.startsWith(s)?e=e.replace(s,""):e=" "+e}return this.cleanup&&(e=zf(e)),e})}},PP=CP,zP=class extends Dt{constructor(t){super(t),this.replacement=t.replacement??"\u2581"}decode_chain(t){let e=[];for(let r=0;r<t.length;++r){let s=t[r].replaceAll(this.replacement," ");r==0&&s.startsWith(" ")&&(s=s.substring(1)),e.push(s)}return e}},LP=zP,NP=class extends Dt{constructor(t){super(t),this.suffix=t.suffix??""}decode_chain(t){return t.map((e,r)=>e.replaceAll(this.suffix,r===t.length-1?"":" "))}},$P=NP,RP=class extends Dt{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=zf(s).replaceAll(this.word_delimiter_token," ").trim()),s}decode_chain(t){return[this.convert_tokens_to_string(t)]}},DP=RP,FP=class extends Dt{constructor(t){super(t),this.decoders=(t.decoders??[]).map(e=>NE(e))}decode_chain(t){return this.decoders.reduce((e,r)=>r.decode_chain(e),t)}},BP=FP,UP=class extends Dt{decode_chain(t){let e=ic(this.config.pattern),r=this.config.content??"";return e===null?t:t.map(s=>s.replaceAll(e,r))}},jP=UP,GP=class extends Dt{decode_chain(t){return[t.join("")]}},qP=GP,WP=class extends Dt{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)})}},VP=WP,HP=class extends Dt{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}},XP=HP;function KP(t){if(t===null)return null;switch(t.type){case"ByteLevel":return new IP(t);case"WordPiece":return new PP(t);case"Metaspace":return new LP(t);case"BPEDecoder":return new $P(t);case"CTC":return new DP(t);case"Sequence":return new BP(t);case"Replace":return new jP(t);case"Fuse":return new qP(t);case"Strip":return new VP(t);case"ByteFallback":return new XP(t);default:throw new Error(`Unknown Decoder type: ${t.type}`)}}var NE=KP,YP=class{constructor(t,e){let r=kE(t,"Tokenizer",["model","decoder","post_processor","pre_tokenizer","normalizer"]);if(r)throw new Error(r);let s=kE(e,"Config");if(s)throw new Error(s);this.tokenizer=t,this.config=e,this.normalizer=IE(this.tokenizer.normalizer),this.pre_tokenizer=CE(this.tokenizer.pre_tokenizer),this.model=mP(this.tokenizer.model,this.config),this.post_processor=LE(this.tokenizer.post_processor),this.decoder=NE(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 GI(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 n.push(i.content)}(this.config.additional_special_tokens??[]).forEach(a=>{this.special_tokens.includes(a)||this.special_tokens.push(a)}),this.decoder&&(this.decoder.added_tokens=this.added_tokens,this.decoder.end_of_word_suffix=this.model.end_of_word_suffix),this.splitter_unnormalized=new bE(n),this.splitter_normalized=new bE(o),this.remove_space=this.config.remove_space,this.clean_up_tokenization_spaces=this.config.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=this.config.do_lowercase_and_remove_accent??!1}encode(t,{text_pair:e=null,add_special_tokens:r=!0,return_token_type_ids:s=null}={}){let{tokens:n,token_type_ids:o}=this.tokenize_helper(t,{text_pair:e,add_special_tokens:r}),a=n.map(l=>this.added_tokens_map.get(l)?.id??this.model.tokens_to_ids.get(l)??this.model.unk_token_id),i={ids:a,tokens:n,attention_mask:new Array(a.length).fill(1)};return s&&o&&(i.token_type_ids=o),i}decode(t,e={}){if(!Array.isArray(t)||t.length===0||!YI(t[0]))throw Error("token_ids must be a non-empty array of integers.");let r=t.map(n=>this.model.vocab[Number(n)]??this.model.unk_token);e.skip_special_tokens&&(r=r.filter(n=>!this.special_tokens.includes(n)));let s=this.decoder?this.decoder(r):r.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(s=s.replaceAll(this.decoder.end_of_word_suffix," "),e.skip_special_tokens&&(s=s.trim())),(e.clean_up_tokenization_spaces??this.clean_up_tokenization_spaces)&&(s=zf(s)),s}tokenize(t,{text_pair:e=null,add_special_tokens:r=!1}={}){return this.tokenize_helper(t,{text_pair:e,add_special_tokens:r}).tokens}encode_text(t){if(t===null)return null;let e=this.splitter_unnormalized.split(t);return e.forEach((r,s)=>{let n=this.added_tokens_map.get(r);n&&(n.lstrip&&s>0&&(e[s-1]=e[s-1].trimEnd()),n.rstrip&&s<e.length-1&&(e[s+1]=e[s+1].trimStart()))}),e.flatMap((r,s)=>{if(r.length===0)return[];if(this.added_tokens_map.has(r))return[r];if(this.remove_space===!0&&(r=r.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(r=JI(r)),this.normalizer!==null&&(r=this.normalizer(r)),r.length===0)return[];let n=this.splitter_normalized.split(r);return n.forEach((o,a)=>{let i=this.added_tokens_map.get(o);i&&(i.lstrip&&a>0&&(n[a-1]=n[a-1].trimEnd()),i.rstrip&&a<n.length-1&&(n[a+1]=n[a+1].trimStart()))}),n.flatMap(o=>{if(o.length===0)return[];if(this.added_tokens_map.has(o))return[o];let a=this.pre_tokenizer!==null?this.pre_tokenizer(o,{section_index:s}):[o];return this.model(a)})})}tokenize_helper(t,{text_pair:e=null,add_special_tokens:r=!0}){let s=this.encode_text(t),n=this.encode_text(e||null);return this.post_processor?this.post_processor(s,n,r):{tokens:Vt(s??[],n??[])}}token_to_id(t){return this.model.tokens_to_ids.get(t)}id_to_token(t){return this.model.vocab[t]}get_added_tokens_decoder(){let t=new Map;for(let e of this.added_tokens)t.set(e.id,e);return t}get_vocab(t=!0){let e=new Map;for(let r=0;r<this.model.vocab.length;++r){let s=this.model.vocab[r];(t||!this.added_tokens_map.has(s))&&e.set(s,r)}return e}},$E=YP;var D=Object.freeze({Text:"Text",NumericLiteral:"NumericLiteral",StringLiteral:"StringLiteral",Identifier:"Identifier",Equals:"Equals",OpenParen:"OpenParen",CloseParen:"CloseParen",OpenStatement:"OpenStatement",CloseStatement:"CloseStatement",OpenExpression:"OpenExpression",CloseExpression:"CloseExpression",OpenSquareBracket:"OpenSquareBracket",CloseSquareBracket:"CloseSquareBracket",OpenCurlyBracket:"OpenCurlyBracket",CloseCurlyBracket:"CloseCurlyBracket",Comma:"Comma",Dot:"Dot",Colon:"Colon",Pipe:"Pipe",CallOperator:"CallOperator",AdditiveBinaryOperator:"AdditiveBinaryOperator",MultiplicativeBinaryOperator:"MultiplicativeBinaryOperator",ComparisonBinaryOperator:"ComparisonBinaryOperator",UnaryOperator:"UnaryOperator",Comment:"Comment"}),Ft=class{constructor(t,e){this.value=t,this.type=e}};function RE(t){return/\w/.test(t)}function po(t){return/[0-9]/.test(t)}function DE(t){return/\s/.test(t)}var 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s=this.evaluate(e.start,r),n=this.evaluate(e.stop,r),o=this.evaluate(e.step,r);if(!(s instanceof he||s instanceof He))throw new Error("Slice start must be numeric or undefined");if(!(n instanceof he||n instanceof He))throw new Error("Slice stop must be numeric or undefined");if(!(o instanceof he||o instanceof He))throw new Error("Slice step must be numeric or undefined");return t instanceof ye?new ye(jE(t.value,s.value,n.value,o.value)):new te(jE(Array.from(t.value),s.value,n.value,o.value).join(""))}evaluateMemberExpression(t,e){let r=this.evaluate(t.object,e),s;if(t.computed){if(t.property.type==="SliceExpression")return this.evaluateSliceExpression(r,t.property,e);s=this.evaluate(t.property,e)}else s=new te(t.property.value);let n;if(r instanceof ft){if(!(s instanceof te))throw new Error(`Cannot access property with non-string: got ${s.type}`);n=r.value.get(s.value)??r.builtins.get(s.value)}else if(r instanceof ye||r instanceof te)if(s instanceof he)n=r.value.at(s.value),r 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if(t.assignee.type==="MemberExpression"){let s=t.assignee,n=this.evaluate(s.object,e);if(!(n instanceof ft))throw new Error("Cannot assign to member of non-object");if(s.property.type!=="Identifier")throw new Error("Cannot assign to member with non-identifier property");n.value.set(s.property.value,r)}else throw new Error(`Invalid LHS inside assignment expression: ${JSON.stringify(t.assignee)}`);return new Ke}evaluateIf(t,e){let r=this.evaluate(t.test,e);return this.evaluateBlock(r.__bool__().value?t.body:t.alternate,e)}evaluateFor(t,e){let r=new _s(e),s,n;if(t.iterable.type==="SelectExpression"){let c=t.iterable;n=this.evaluate(c.lhs,r),s=c.test}else n=this.evaluate(t.iterable,r);if(!(n instanceof ye||n instanceof ft))throw new Error(`Expected iterable or object type in for loop: got ${n.type}`);n instanceof ft&&(n=n.keys());let o=[],a=[];for(let c=0;c<n.value.length;++c){let p=new _s(r),f=n.value[c],_;if(t.loopvar.type==="Identifier")_=m=>m.setVariable(t.loopvar.value,f);else if(t.loopvar.type==="TupleLiteral"){let m=t.loopvar;if(f.type!=="ArrayValue")throw new Error(`Cannot unpack non-iterable type: ${f.type}`);let w=f;if(m.value.length!==w.value.length)throw new Error(`Too ${m.value.length>w.value.length?"few":"many"} items to unpack`);_=x=>{for(let k=0;k<m.value.length;++k){if(m.value[k].type!=="Identifier")throw new Error(`Cannot unpack non-identifier type: ${m.value[k].type}`);x.setVariable(m.value[k].value,w.value[k])}}}else throw new Error(`Invalid loop variable(s): ${t.loopvar.type}`);s&&(_(p),!this.evaluate(s,p).__bool__().value)||(o.push(f),a.push(_))}let i="",l=!0;for(let c=0;c<o.length;++c){let p=new Map([["index",new he(c+1)],["index0",new he(c)],["revindex",new he(o.length-c)],["revindex0",new he(o.length-c-1)],["first",new de(c===0)],["last",new de(c===o.length-1)],["length",new he(o.length)],["previtem",c>0?o[c-1]:new He],["nextitem",c<o.length-1?o[c+1]:new He]]);r.setVariable("loop",new ft(p)),a[c](r);try{let 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Releasing tensor."),this.releaseTensor()}this.activeUpload?this.activeUpload.set(e):this.activeUpload=new Uint8Array(e)}async download(t){if(this.activeUpload){let e=this.wrapper?.isDataConverted?ed(this.activeUpload,this.wrapper?.type):this.activeUpload;if(t){t instanceof ArrayBuffer?new Uint8Array(t).set(e):new Uint8Array(t.buffer,t.byteOffset,t.byteLength).set(e);return}else return e.buffer}if(!this.wrapper)throw new Error("Tensor has not been created.");return t?this.wrapper.read(t):this.wrapper.read()}},NA=class{constructor(t){this.backend=t,this.tensorTrackersById=new Map,this.freeTensors=[],this.externalTensors=new Set}getMLContext(t){let e=this.backend.getMLContext(t);if(!e)throw new Error("MLContext not found for session.");return e}getMLOpSupportLimits(t){return this.backend.getMLOpSupportLimits(t)}reserveTensorId(){let t=td();return this.tensorTrackersById.set(t,new nd(this)),t}releaseTensorId(t){let e=this.tensorTrackersById.get(t);e&&(this.tensorTrackersById.delete(t),e.tensorWrapper&&this.releaseTensor(e.tensorWrapper))}async ensureTensor(t,e,r,s,n){yt("verbose",()=>`[WebNN] TensorManager.ensureTensor {tensorId: ${e}, dataType: ${r}, shape: ${s}, copyOld: ${n}}`);let o=this.tensorTrackersById.get(e);if(!o)throw new Error("Tensor not found.");return o.ensureTensor(t,r,s,n)}upload(t,e){let r=this.tensorTrackersById.get(t);if(!r)throw new Error("Tensor not found.");r.upload(e)}async download(t,e){yt("verbose",()=>`[WebNN] TensorManager.download {tensorId: ${t}, dstBuffer: ${e?.byteLength}}`);let r=this.tensorTrackersById.get(t);if(!r)throw new Error("Tensor not found.");return r.download(e)}releaseTensorsForSession(t){for(let e of this.freeTensors)e.sessionId===t&&e.destroy();this.freeTensors=this.freeTensors.filter(e=>e.sessionId!==t)}registerTensor(t,e,r,s){let n=this.getMLContext(t),o=td(),a=new sd({sessionId:t,context:n,tensor:e,dataType:r,shape:s});return this.tensorTrackersById.set(o,new nd(this,a)),this.externalTensors.add(a),o}async getCachedTensor(t,e,r,s,n,o,a){let i=this.getMLContext(t);for(let[c,p]of this.freeTensors.entries())if(p.canReuseTensor(i,e,r)){yt("verbose",()=>`[WebNN] Reusing tensor {dataType: ${e}, ${a?`fallbackDataType: ${a},`:""} shape: ${r}`);let f=this.freeTensors.splice(c,1)[0];return f.sessionId=t,f}yt("verbose",()=>`[WebNN] MLContext.createTensor {dataType: ${e}, ${a?`fallbackDataType: ${a},`:""} shape: ${r}}`);let l=await i.createTensor({dataType:a??e,shape:r,dimensions:r,usage:s,writable:n,readable:o});return new sd({sessionId:t,context:i,tensor:l,dataType:e,shape:r,fallbackDataType:a})}releaseTensor(t){this.externalTensors.has(t)&&this.externalTensors.delete(t),this.freeTensors.push(t)}},x2=(...t)=>new NA(...t)}),y2={};So(y2,{WebNNBackend:()=>b2});var ko,$A,b2,EL=be(()=>{"use strict";pn(),Ms(),vL(),kL(),w2(),ko=new Map([[1,"float32"],[10,"float16"],[6,"int32"],[12,"uint32"],[7,"int64"],[13,"uint64"],[22,"int4"],[21,"uint4"],[3,"int8"],[2,"uint8"],[9,"uint8"]]),$A=(t,e)=>{if(t===e)return!0;if(t===void 0||e===void 0)return!1;let r=Object.keys(t).sort(),s=Object.keys(e).sort();return r.length===s.length&&r.every((n,o)=>n===s[o]&&t[n]===e[n])},b2=class{constructor(t){this.tensorManager=x2(this),this.mlContextBySessionId=new Map,this.sessionIdsByMLContext=new Map,this.mlContextCache=[],this.sessionGraphInputs=new Map,this.sessionGraphOutputs=new Map,this.temporaryGraphInputs=[],this.temporaryGraphOutputs=[],this.temporarySessionTensorIds=new Map,this.mlOpSupportLimitsBySessionId=new Map,g2(t.logLevel,!!t.debug)}get currentSessionId(){if(this.activeSessionId===void 0)throw new Error("No active session");return this.activeSessionId}onRunStart(t){yt("verbose",()=>`[WebNN] onRunStart {sessionId: ${t}}`),this.activeSessionId=t}onRunEnd(t){yt("verbose",()=>`[WebNN] onRunEnd {sessionId: ${t}}`);let e=this.temporarySessionTensorIds.get(t);if(e){for(let r of e)yt("verbose",()=>`[WebNN] releasing temporary tensor {tensorId: ${r}}`),this.tensorManager.releaseTensorId(r);this.temporarySessionTensorIds.delete(t),this.activeSessionId=void 0}}async createMLContext(t){if(t instanceof GPUDevice){let r=this.mlContextCache.findIndex(s=>s.gpuDevice===t);if(r!==-1)return this.mlContextCache[r].mlContext;{let s=await navigator.ml.createContext(t);return this.mlContextCache.push({gpuDevice:t,mlContext:s}),s}}else if(t===void 0){let r=this.mlContextCache.findIndex(s=>s.options===void 0&&s.gpuDevice===void 0);if(r!==-1)return this.mlContextCache[r].mlContext;{let s=await navigator.ml.createContext();return this.mlContextCache.push({mlContext:s}),s}}let e=this.mlContextCache.findIndex(r=>$A(r.options,t));if(e!==-1)return this.mlContextCache[e].mlContext;{let r=await navigator.ml.createContext(t);return 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){yt("verbose",()=>`[WebNN] releaseTensorId {tensorId: ${t}}`),this.tensorManager.releaseTensorId(t)}async ensureTensor(t,e,r,s,n){let o=ko.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){yt("verbose",()=>`[WebNN] createTemporaryTensor {onnxDataType: ${e}, shape: ${r}}`);let s=ko.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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workaround)":""}`),s.constant(n,p)}registerGraphInput(t){this.temporaryGraphInputs.push(t)}registerGraphOutput(t){this.temporaryGraphOutputs.push(t)}isGraphInput(t,e){let r=this.sessionGraphInputs.get(t);return r?r.includes(e):!1}isGraphOutput(t,e){let r=this.sessionGraphOutputs.get(t);return r?r.includes(e):!1}isGraphInputOutputTypeSupported(t,e,r=!0){let s=ko.get(bs(e)),n=this.mlOpSupportLimitsBySessionId.get(t);return typeof s>"u"?!1:r?!!n?.input.dataTypes.includes(s):!!n?.output.dataTypes.includes(s)}flush(){}}}),RA,Ed,Ad,Rr,DA,od,Oc,Md,Td,ad,Sd,Od,Id,v2=be(()=>{"use strict";As(),yL(),bL(),pn(),Ms(),yd(),m2(),RA=(t,e)=>{De()._OrtInit(t,e)!==0&&ze("Can't initialize onnxruntime.")},Ed=async t=>{RA(t.wasm.numThreads,Sc(t.logLevel))},Ad=async(t,e)=>{De().asyncInit?.();let r=t.webgpu.adapter;if(e==="webgpu"){if(typeof navigator>"u"||!navigator.gpu)throw new Error("WebGPU is not supported in current environment");if(r){if(typeof r.limits!="object"||typeof r.features!="object"||typeof 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this._nearest_interpolate_4d||(this._nearest_interpolate_4d=Ur([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=Ur([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=Ur([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=Ur([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=Ur([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=Ur([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=Ur([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=Ur([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 G2=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"}),zd=ie.IS_NODE_ENV?"cpu":"wasm";function zc(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 (${zd}).`),zd):zd}var V2=(function(){let t;return async function(){if(t===void 0)if(!ie.IS_WEBGPU_AVAILABLE)t=!1;else try{t=(await navigator.gpu.requestAdapter()).features.has("shader-f16")}catch{t=!1}return t}})(),st=Object.freeze({auto:"auto",fp32:"fp32",fp16:"fp16",q8:"q8",int8:"int8",uint8:"uint8",q4:"q4",bnb4:"bnb4",q4f16:"q4f16",q2:"q2",q2f16:"q2f16",q1:"q1",q1f16:"q1f16"}),q2=st.fp32,W2=Object.freeze({[G2.wasm]:st.q8}),jr=Object.freeze({[st.fp32]:"",[st.fp16]:"_fp16",[st.int8]:"_int8",[st.uint8]:"_uint8",[st.q8]:"_quantized",[st.q4]:"_q4",[st.q2]:"_q2",[st.q1]:"_q1",[st.q4f16]:"_q4f16",[st.q2f16]:"_q2f16",[st.q1f16]:"_q1f16",[st.bnb4]:"_bnb4"});function Lc(t,e,r,{configDtype:s=null,warn:n}={}){let o,a=!1;t&&typeof t!="string"?t.hasOwnProperty(e)?o=t[e]:(o=null,a=!0):o=t;let i;if(o===st.auto){if(s){let l=typeof s=="string"?s:s?.[e];if(l&&l!==st.auto&&st.hasOwnProperty(l))return l}i=W2[r]??q2}else o&&st.hasOwnProperty(o)?i=o:i=W2[r]??q2;return a&&n&&n(`dtype not specified for "${e}". Using the default dtype (${i}) for this device (${r}).`),i}var Gr=Object.freeze({float32:Float32Array,float16:typeof Float16Array<"u"?Float16Array:Uint16Array,float64:Float64Array,string:Array,int8:Int8Array,uint8:Uint8Array,int16:Int16Array,uint16:Uint16Array,int32:Int32Array,uint32:Uint32Array,int64:BigInt64Array,uint64:BigUint64Array,bool:Uint8Array,uint4:Uint8Array,int4:Int8Array});var N=class t{get dims(){return this.ort_tensor.dims}set dims(e){this.ort_tensor.dims=e}get type(){return this.ort_tensor.type}get data(){return this.ort_tensor.data}get size(){return this.ort_tensor.size}get location(){return this.ort_tensor.location}ort_tensor;constructor(...e){return Pc(e[0])?this.ort_tensor=e[0]:this.ort_tensor=new j2(e[0],e[1],e[2]),new Proxy(this,{get:(r,s)=>{if(typeof s=="string"){let n=Number(s);if(Number.isInteger(n))return r._getitem(n)}return r[s]},set:(r,s,n)=>r[s]=n})}dispose(){this.ort_tensor.dispose()}*[Symbol.iterator](){let[e,...r]=this.dims;if(r.length>0){let s=r.reduce((n,o)=>n*o);for(let n=0;n<e;++n)yield this._subarray(n,s,r)}else yield*this.data}_getitem(e){let[r,...s]=this.dims;if(e=cr(e,r),s.length>0){let n=s.reduce((o,a)=>o*a);return this._subarray(e,n,s)}else return new t(this.type,[this.data[e]],s)}indexOf(e){let r=this.data;for(let s=0;s<r.length;++s)if(r[s]==e)return s;return-1}_subarray(e,r,s){let n=e*r,o=(e+1)*r,a="subarray"in this.data?this.data.subarray(n,o):this.data.slice(n,o);return new t(this.type,a,s)}item(){let e=this.data;if(e.length!==1)throw new Error(`a Tensor with ${e.length} elements cannot be converted to Scalar`);return e[0]}tolist(){return LL(this.data,this.dims)}sigmoid(){return this.clone().sigmoid_()}sigmoid_(){let e=this.data;for(let r=0;r<e.length;++r)e[r]=1/(1+Math.exp(-e[r]));return this}map(e){return this.clone().map_(e)}map_(e){let r=this.data;for(let s=0;s<r.length;++s)r[s]=e(r[s],s,r);return this}mul(e){return this.clone().mul_(e)}mul_(e){let r=this.data;for(let s=0;s<r.length;++s)r[s]*=e;return this}div(e){return this.clone().div_(e)}div_(e){let r=this.data;for(let s=0;s<r.length;++s)r[s]/=e;return this}add(e){return this.clone().add_(e)}add_(e){let r=this.data;for(let s=0;s<r.length;++s)r[s]+=e;return this}sub(e){return this.clone().sub_(e)}sub_(e){let r=this.data;for(let s=0;s<r.length;++s)r[s]-=e;return this}clone(){return new t(this.type,this.data.slice(),this.dims.slice())}slice(...e){let r=[],s=[];for(let p=0;p<this.dims.length;++p){let f=e[p];if(f==null)s.push([0,this.dims[p]]),r.push(this.dims[p]);else if(typeof f=="number")f=cr(f,this.dims[p],p),s.push([f,f+1]);else if(Array.isArray(f)&&f.length===2){let[_,m]=f;if(_=_===null?0:cr(_,this.dims[p],p,!1),m=m===null?this.dims[p]:cr(m,this.dims[p],p,!1),_>m)throw new Error(`Invalid slice: ${f}`);let w=[Math.max(_,0),Math.min(m,this.dims[p])];s.push(w),r.push(w[1]-w[0])}else throw new Error(`Invalid slice: ${f}`)}let n=s.map(([p,f])=>f-p),o=n.reduce((p,f)=>p*f),a=this.data,i=new a.constructor(o),l=this.stride(),c=!0;for(let p=1;p<n.length;++p)if(s[p][0]!==0||s[p][1]!==this.dims[p]){c=!1;break}if(c){let p=s[0][0]*l[0],f=s[0][1]*l[0];if(ArrayBuffer.isView(a))i.set(a.subarray(p,f));else if(Array.isArray(a)){let _=a.slice(p,f);for(let m=0;m<_.length;++m)i[m]=_[m]}else throw new Error("Unsupported data type for slicing")}else for(let p=0;p<o;++p){let f=0;for(let _=n.length-1,m=p;_>=0;--_){let w=n[_];f+=(m%w+s[_][0])*l[_],m=Math.floor(m/w)}i[p]=a[f]}return new t(this.type,i,r)}permute(...e){return NL(this,e)}transpose(...e){return this.permute(...e)}sum(e=null,r=!1){return this.norm(1,e,r)}norm(e="fro",r=null,s=!1){if(e==="fro")e=2;else if(typeof e=="string")throw Error(`Unsupported norm: ${e}`);let n=this.data,o=n instanceof BigInt64Array||n instanceof BigUint64Array;if(o&&e!==1)throw Error(`Expected a floating point tensor as input. Got ${this.type}`);let a,i;if(o?(a=(f,_)=>f+_,i=0n):(a=(f,_)=>f+_**e,i=0),r===null){let f=n.reduce(a,i);return e!==1&&(f=f**(1/e)),new t(this.type,[f],[])}let[l,c,p]=Io(a,this,r,s);if(e!==1)for(let f=0;f<c.length;++f)c[f]=c[f]**(1/e);return new t(l,c,p)}normalize_(e=2,r=1){r=cr(r,this.dims.length);let s=this.norm(e,r,!0),n=this.data,o=s.data;for(let a=0;a<n.length;++a){let i=0;for(let l=this.dims.length-1,c=a,p=1;l>=0;--l){let f=this.dims[l];if(l!==r){let _=c%f;i+=_*p,p*=this.dims[l]}c=Math.floor(c/f)}n[a]/=o[i]}return this}normalize(e=2,r=1){return this.clone().normalize_(e,r)}stride(){return Ld(this.dims)}squeeze(e=null){return new t(this.type,this.data,H2(this.dims,e))}squeeze_(e=null){return this.dims=H2(this.dims,e),this}unsqueeze(e){return new t(this.type,this.data,X2(this.dims,e))}unsqueeze_(e){return this.dims=X2(this.dims,e),this}flatten_(e=0,r=-1){r=(r+this.dims.length)%this.dims.length;let s=this.dims.slice(0,e),n=this.dims.slice(e,r+1),o=this.dims.slice(r+1);return this.dims=[...s,n.reduce((a,i)=>a*i,1),...o],this}flatten(e=0,r=-1){return this.clone().flatten_(e,r)}view(...e){let r=-1;for(let n=0;n<e.length;++n)if(e[n]===-1){if(r!==-1)throw new Error("Only one dimension can be inferred");r=n}let s=this.data;if(r!==-1){let n=e.reduce((o,a,i)=>i!==r?o*a:o,1);e[r]=s.length/n}return new t(this.type,s,e)}neg_(){let e=this.data;for(let r=0;r<e.length;++r)e[r]=-e[r];return this}neg(){return this.clone().neg_()}gt(e){let r=new Uint8Array(this.data.length),s=this.data;for(let n=0;n<s.length;++n)r[n]=s[n]>e?1:0;return new t("bool",r,this.dims)}lt(e){let r=new Uint8Array(this.data.length),s=this.data;for(let n=0;n<s.length;++n)r[n]=s[n]<e?1:0;return new t("bool",r,this.dims)}clamp_(e,r){let s=this.data;for(let n=0;n<s.length;++n)s[n]=Math.min(Math.max(s[n],e),r);return this}clamp(e,r){return this.clone().clamp_(e,r)}round_(){let e=this.data;for(let r=0;r<e.length;++r)e[r]=Math.round(e[r]);return this}round(){return this.clone().round_()}mean(e=null,r=!1){return Rc(this,e,r)}min(e=null,r=!1){if(e===null){let a=yo(this.data)[0];return new t(this.type,[a],[])}let[s,n,o]=Io((a,i)=>Math.min(a,i),this,e,r,1/0);return new t(s,n,o)}max(e=null,r=!1){if(e===null){let a=Pe(this.data)[0];return new t(this.type,[a],[])}let[s,n,o]=Io((a,i)=>Math.max(a,i),this,e,r,-1/0);return new t(s,n,o)}argmin(e=null,r=!1){if(e!==null)throw new Error("`dim !== null` not yet implemented.");let s=yo(this.data)[1];return new t("int64",[BigInt(s)],[])}argmax(e=null,r=!1){if(e!==null)throw new Error("`dim !== null` not yet implemented.");let s=Pe(this.data)[1];return new t("int64",[BigInt(s)],[])}repeat(...e){if(e.length<this.dims.length)throw new Error(`Number of dimensions of repeat dims (${e.length}) cannot be smaller than number of dimensions of tensor (${this.dims.length})`);if(e.every(p=>p===1)){if(e.length===this.dims.length)return this.clone();let p=e.length-this.dims.length,f=Array(p).fill(1).concat(this.dims);return new t(this.type,this.data.slice(),f)}let r=e.length-this.dims.length,s=Array(r).fill(1).concat(this.dims),n=s.map((p,f)=>p*e[f]),o=n.reduce((p,f)=>p*f,1),a=this.data,i=new a.constructor(o),l=Ld(s),c=Ld(n);for(let p=0;p<o;++p){let f=p,_=0;for(let m=0;m<n.length;++m){let w=Math.floor(f/c[m]);f=f%c[m];let x=w%s[m];_+=x*l[m]}i[p]=a[_]}return new t(this.type,i,n)}tile(...e){if(e.length<this.dims.length){let r=this.dims.length-e.length;e=Array(r).fill(1).concat(e)}return this.repeat(...e)}to(e){if(this.type===e)return this;if(!Gr.hasOwnProperty(e))throw new Error(`Unsupported type: ${e}`);let r,s=["int64","uint64"].includes(this.type),n=["int64","uint64"].includes(e);if(s&&!n)r=Number;else if(!s&&n)["float16","float32","float64"].includes(this.type)?r=o=>BigInt(Math.floor(o)):r=BigInt;else if(this.type==="float16"&&e=="float32"&&this.data instanceof Uint16Array)return new t(e,dA(this.data),this.dims);return new t(e,Gr[e].from(this.data,r),this.dims)}};function LL(t,e){let r=t.length,s=e.reduce((o,a)=>o*a);if(r!==s)throw Error(`cannot reshape array of size ${r} into shape (${e})`);let n=t;for(let o=e.length-1;o>=0;o--)n=n.reduce((a,i)=>{let l=a[a.length-1];return l.length<e[o]?l.push(i):a.push([i]),a},[[]]);return n[0]}function NL(t,e){let[r,s]=iA(t.data,t.dims,e);return new N(t.type,r,s)}function Nd(t,[e,r],s="bilinear",n=!1){let o=t.dims.at(-3)??1,a=t.dims.at(-2),i=t.dims.at(-1),l=aA(t.data,[o,a,i],[e,r],s,n);return new N(t.type,l,[o,e,r])}async function bt(t,{size:e=null,mode:r="bilinear"}={}){if(t.dims.length!==4)throw new Error("`interpolate_4d` currently only supports 4D input.");if(!e)throw new Error("`interpolate_4d` requires a `size` argument.");let s;if(e.length===2)s=[...t.dims.slice(0,2),...e];else if(e.length===3)s=[t.dims[0],...e];else if(e.length===4)s=e;else throw new Error("`size` must be of length 2, 3, or 4.");let n;if(r==="nearest")n=await lr.nearest_interpolate_4d;else if(r==="bilinear")n=await lr.bilinear_interpolate_4d;else 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Error("`layer_norm` currently only supports 2D input.");let[s,n]=t.dims;if(e.length!==1&&e[0]!==n)throw new Error("`normalized_shape` must be a 1D array with shape `[input.dims[1]]`.");let[o,a]=$d(t,1,0,!0),i=o.data,l=a.data,c=t.data,p=new c.constructor(c.length);for(let f=0;f<s;++f){let _=f*n;for(let m=0;m<n;++m){let w=_+m;p[w]=(c[w]-l[f])/(i[f]+r)}}return new N(t.type,p,t.dims)}function H2(t,e){return t=t.slice(),e===null?t=t.filter(r=>r!==1):typeof e=="number"?t[e]===1&&t.splice(e,1):Array.isArray(e)&&(t=t.filter((r,s)=>r!==1||!e.includes(s))),t}function X2(t,e){return e=cr(e,t.length+1),t=t.slice(),t.splice(e,0,1),t}function cr(t,e,r=null,s=!0){if(t<-e||t>=e){if(s)throw new Error(`IndexError: index ${t} is out of bounds for dimension${r===null?"":" "+r} with size ${e}`);return t<-e?0:e}return t<0&&(t=(t%e+e)%e),t}function ve(t,e=0){e=cr(e,t[0].dims.length);let r=t[0].dims.slice();r[e]=t.reduce((a,i)=>a+i.dims[e],0);let s=r.reduce((a,i)=>a*i,1),n=new 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Ot(t,"tokenizer_config.json",{})).exists?["tokenizer.json","tokenizer_config.json"]:[]}async function Bd(t,e){let r=await _n(t);return await Promise.all(r.map(s=>nt(t,s,!0,e)))}function Po(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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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 XE(o),this._compiled_template_cache.set(o,x));let k=Object.create(null);for(let E of $L){let S=Ts(this.config,E);S&&(k[E]=S)}let A=x.render({messages:e,add_generation_prompt:a,tools:s,documents:n,...k,...w});if(i){let E=this._call(A,{add_special_tokens:!1,padding:l,truncation:c,max_length:p,return_tensor:f,...m});return _?E:E.input_ids}return A}};function mn(t,e,r,s){if(!("language_codes"in t)||!Array.isArray(t.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in t)||!(t.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in t)||typeof t.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");let n=s.src_lang,o=s.tgt_lang;if(!t.language_codes.includes(o))throw new Error(`Target language code "${o}" is not valid. Must be one of: {${t.language_codes.join(", ")}}`);if(n!==void 0){if(!t.language_codes.includes(n))throw new Error(`Source language code "${n}" is not valid. Must be one of: {${t.language_codes.join(", ")}}`);for(let a of t._tokenizer.post_processor.config.single)if("SpecialToken"in a&&t.languageRegex.test(a.SpecialToken.id)){a.SpecialToken.id=t.lang_to_token(n);break}}return s.forced_bos_token_id=t._tokenizer.token_to_id(t.lang_to_token(o)),t._call(e,r)}var P_={};en(P_,{AlbertTokenizer:()=>Ud,AutoTokenizer:()=>ne,BartTokenizer:()=>jd,BertTokenizer:()=>Gd,BlenderbotSmallTokenizer:()=>qd,BlenderbotTokenizer:()=>Wd,BloomTokenizer:()=>Vd,CLIPTokenizer:()=>Xd,CamembertTokenizer:()=>Hd,CodeGenTokenizer:()=>Yd,CodeLlamaTokenizer:()=>Kd,CohereAsrTokenizer:()=>Jd,CohereTokenizer:()=>Qd,ConvBertTokenizer:()=>Zd,DebertaTokenizer:()=>t_,DebertaV2Tokenizer:()=>e_,DistilBertTokenizer:()=>r_,ElectraTokenizer:()=>s_,EsmTokenizer:()=>n_,FalconTokenizer:()=>o_,GPT2Tokenizer:()=>l_,GPTNeoXTokenizer:()=>i_,GemmaTokenizer:()=>a_,HerbertTokenizer:()=>c_,LlamaTokenizer:()=>u_,M2M100Tokenizer:()=>p_,MBart50Tokenizer:()=>d_,MBartTokenizer:()=>zo,MPNetTokenizer:()=>h_,MarianTokenizer:()=>f_,MgpstrTokenizer:()=>__,MobileBertTokenizer:()=>m_,NllbTokenizer:()=>g_,NougatTokenizer:()=>w_,PreTrainedTokenizer:()=>W,Qwen2Tokenizer:()=>x_,RoFormerTokenizer:()=>b_,RobertaTokenizer:()=>y_,SiglipTokenizer:()=>v_,SpeechT5Tokenizer:()=>k_,SqueezeBertTokenizer:()=>E_,T5Tokenizer:()=>A_,TokenizersBackend:()=>W,VitsTokenizer:()=>T_,Wav2Vec2CTCTokenizer:()=>S_,WhisperTokenizer:()=>O_,XLMRobertaTokenizer:()=>I_,XLMTokenizer:()=>C_});var Ud=class extends W{return_token_type_ids=!0};var jd=class extends W{};var Gd=class extends W{return_token_type_ids=!0};var qd=class extends W{};var Wd=class extends W{};var Vd=class extends W{};var Hd=class extends W{};var Xd=class extends W{};var Kd=class extends W{};var Yd=class extends W{};var Qd=class extends W{};var Jd=class extends W{};var Zd=class extends W{return_token_type_ids=!0};var e_=class extends W{return_token_type_ids=!0};var t_=class extends W{return_token_type_ids=!0};var r_=class extends W{};var s_=class extends W{return_token_type_ids=!0};var n_=class extends W{};var o_=class extends W{};var a_=class extends W{};var i_=class extends W{};var l_=class extends W{};var c_=class extends W{return_token_type_ids=!0};var u_=class extends W{padding_side="left"};var p_=class extends W{constructor(e,r){super(e,r),this.languageRegex=/^__[a-z]{2,3}__$/,this.language_codes=this.all_special_tokens.filter(s=>this.languageRegex.test(s)).map(s=>s.slice(2,-2)),this.lang_to_token=s=>`__${s}__`}_build_translation_inputs(e,r,s){return mn(this,e,r,s)}};var f_=class extends W{constructor(e,r){super(e,r),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=Array.from(this.get_vocab().keys()).filter(s=>this.languageRegex.test(s)),ee.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}_encode_text(e){if(e===null)return null;let[r,...s]=e.trim().split(this.languageRegex);if(s.length===0)return super._encode_text(r);if(s.length===2){let[n,o]=s;return this.supported_language_codes.includes(n)||ee.warn(`Unsupported language code "${n}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`),gt([n],super._encode_text(o))}}};var zo=class extends W{constructor(e,r){super(e,r),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,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 mn(this,e,r,s)}};var d_=class extends zo{};var __=class extends W{};var m_=class extends W{return_token_type_ids=!0};var h_=class extends W{};var g_=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 mn(this,e,r,s)}};var w_=class extends W{};var x_=class extends W{};var y_=class extends W{};var b_=class extends W{return_token_type_ids=!0};var v_=class extends W{};var k_=class extends W{};var E_=class extends W{return_token_type_ids=!0};var A_=class extends W{};var M_=class extends Dt{decode_chain(e){let r="";for(let s=1;s<e.length;s+=2)r+=e[s];return[r]}},T_=class extends W{constructor(e,r){super(e,r),this._tokenizer.decoder=new M_({type:"VitsDecoder"})}};var S_=class extends W{};var Z2=[["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"]],Lo=new Map(Z2),BL=new Map([...Z2.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 eM(t){t=t.toLowerCase();let e=BL.get(t);if(e===void 0){let r=t.match(/^<\|([a-z]{2})\|>$/);if(r&&(t=r[1]),Lo.has(t))e=t;else{let n=t.length===2?Lo.keys():Lo.values();throw new Error(`Language "${t}" is not supported. Must be one of: ${JSON.stringify(Array.from(n))}`)}}return e}var UL="\\p{P}\\u0021-\\u002F\\u003A-\\u0040\\u005B-\\u0060\\u007B-\\u007E",tM=new RegExp(`^[${UL}]+$`,"gu"),jL=.1,O_=class extends W{get timestamp_begin(){return this._tokenizer.token_to_id("<|notimestamps|>")+1}_decode_asr(e,{return_timestamps:r=!1,return_language:s=!1,time_precision:n=null,force_full_sequences:o=!0}={}){if(n===null)throw Error("Must specify time_precision");let a=null,i=r==="word";function l(){return{language:a,timestamp:[null,null],text:""}}let c=[],p=l(),f=0,_=this.timestamp_begin,w=_+1500,x=[],k=[],A=!1,E=null,S=new Set(this.all_special_ids);for(let O of e){let b=O.tokens,F=i?O.token_timestamps:null,j=null,U=_;if("stride"in O){let[J,R,C]=O.stride;if(f-=R,E=J-C,R&&(U=R/n+_),C)for(let se=b.length-1;se>=0;--se){let Y=Number(b[se]);if(Y>=_){if(j!==null&&(Y-_)*n<E)break;j=Y}}}let X=[],K=[];for(let J=0;J<b.length;++J){let R=Number(b[J]);if(S.has(R)){let C=this.decode([R]),se=Lo.get(C.slice(2,-2));if(se!==void 0){if(a!==null&&se!==a&&!r){x.push(X);let Y=this.findLongestCommonSequence(x)[0],z=this.decode(Y);p.text=z,c.push(p),x=[],X=[],p=l()}a=p.language=se}}else if(R>=_&&R<=w){let C=(R-_)*n+f,se=gs(C,2);if(j!==null&&R>=j)A=!0;else if(A||x.length>0&&R<U)A=!1;else if(p.timestamp[0]===null)p.timestamp[0]=se;else if(se!==p.timestamp[0]){p.timestamp[1]=se,x.push(X),i&&k.push(K);let[Y,z]=this.findLongestCommonSequence(x,k),$=this.decode(Y);if(p.text=$,i&&(p.words=this.collateWordTimestamps(Y,z,a),p.words.length>0&&p.timestamp[1]!==null))for(let B of p.words)B.timestamp[1]>p.timestamp[1]&&p.timestamp[1]>=B.timestamp[0]&&(B.timestamp[1]=p.timestamp[1]);c.push(p),x=[],X=[],k=[],K=[],p=l()}}else if(X.push(R),i){let C=gs(F[J]+f,2),se;if(J+1<F.length){se=gs(F[J+1]+f,2);let Y=this.decode([R]);tM.test(Y)&&(se=gs(Math.min(C+n,se),2))}else se=null;K.push([C,se])}}if("stride"in O){let[J,R,C]=O.stride;f+=J-C}X.length>0?(x.push(X),i&&k.push(K)):x.every(J=>J.length===0)&&(p=l(),x=[],X=[],k=[],K=[])}if(x.length>0){if(o&&r)throw new Error("Whisper did not predict an ending timestamp, which can happen if audio is cut off in the middle of a word. 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Therefore, you may experience slightly inaccurate results.')}};var ne=class{static async from_pretrained(e,{progress_callback:r=null,config:s=null,cache_dir:n=null,local_files_only:o=!1,revision:a="main"}={}){let[i,l]=await Bd(e,{progress_callback:r,config:s,cache_dir:n,local_files_only:o,revision:a}),c=l.tokenizer_class?.replace(/Fast$/,"")??"PreTrainedTokenizer",p=P_[c];return p||(ee.warn(`Unknown tokenizer class "${c}", attempting to construct from base class.`),p=W),new p(i,l)}};var qr="https://github.com/huggingface/transformers.js/issues/new/choose";var No="preprocessor_config.json",yr=No,Fc="processor_config.json",Bc="chat_template.jinja";var re=class extends We{static classes=["image_processor_class","tokenizer_class","feature_extractor_class"];static uses_processor_config=!1;static uses_chat_template_file=!1;constructor(e,r,s){super(),this.config=e,this.components=r,this.chat_template=s}get image_processor(){return this.components.image_processor}get tokenizer(){return this.components.tokenizer}get feature_extractor(){return this.components.feature_extractor}apply_chat_template(e,r={}){if(!this.tokenizer)throw new Error("Unable to apply chat template without a tokenizer.");return this.tokenizer.apply_chat_template(e,{tokenize:!1,chat_template:this.chat_template??void 0,...r})}batch_decode(...e){if(!this.tokenizer)throw new Error("Unable to decode without a tokenizer.");return this.tokenizer.batch_decode(...e)}decode(...e){if(!this.tokenizer)throw new Error("Unable to decode without a tokenizer.");return this.tokenizer.decode(...e)}async _call(e,...r){for(let s of[this.image_processor,this.feature_extractor,this.tokenizer])if(s)return s(e,...r);throw new Error("No image processor, feature extractor, or tokenizer found.")}static async from_pretrained(e,r={}){let[s,n,o]=await Promise.all([this.uses_processor_config?nt(e,Fc,!0,r):{},Promise.all(this.classes.filter(a=>a in this).map(async a=>{let i=await this[a].from_pretrained(e,r);return[a.replace(/_class$/,""),i]})).then(Object.fromEntries),this.uses_chat_template_file?xo(e,Bc,!0,r):null]);return new this(s,n,o)}};var pu={};en(pu,{ChatterboxProcessor:()=>X_,CohereAsrProcessor:()=>K_,Florence2Processor:()=>jm,Gemma3Processor:()=>Gm,Gemma3nProcessor:()=>qm,Gemma4Processor:()=>Wm,Glm46VProcessor:()=>Vm,GraniteSpeechProcessor:()=>Hm,GroundingDinoProcessor:()=>Xm,Idefics3Processor:()=>iu,JinaCLIPProcessor:()=>Ym,Lfm2VlProcessor:()=>Qm,LlavaProcessor:()=>Jm,MgpstrProcessor:()=>Zm,MoonshineProcessor:()=>eh,OwlViTProcessor:()=>th,PaliGemmaProcessor:()=>rh,Phi3VProcessor:()=>sh,PixtralProcessor:()=>nh,Processor:()=>re,PyAnnoteProcessor:()=>oh,Qwen2VLProcessor:()=>Os,Qwen2_5_VLProcessor:()=>Ho,Qwen3VLProcessor:()=>ah,Sam2Processor:()=>lu,Sam2VideoProcessor:()=>ih,SamProcessor:()=>Xo,SmolVLMProcessor:()=>iu,SpeechT5Processor:()=>lh,UltravoxProcessor:()=>ch,VLChatProcessor:()=>Km,VoxtralProcessor:()=>uh,VoxtralRealtimeProcessor:()=>fh,Wav2Vec2Processor:()=>dh,Wav2Vec2ProcessorWithLM:()=>_h,WhisperProcessor:()=>mh});var Ee=class extends We{constructor(e){super(),this.config=e}static async from_pretrained(e,r={}){let s=await nt(e,No,!0,r);return new this(s)}};function Ae(t,e){if(!(t instanceof Float32Array||t instanceof Float64Array))throw new Error(`${e} expects input to be a Float32Array or a Float64Array, but got ${t?.constructor?.name??typeof t} instead. If using the feature extractor directly, remember to use \`read_audio(url, sampling_rate)\` to obtain the raw audio data of the file/url.`)}var Go={};en(Go,{ASTFeatureExtractor:()=>N_,ChatterboxFeatureExtractor:()=>$_,ClapFeatureExtractor:()=>R_,CohereAsrFeatureExtractor:()=>D_,DacFeatureExtractor:()=>Fo,EncodecFeatureExtractor:()=>Ro,FeatureExtractor:()=>Ee,Gemma3nAudioFeatureExtractor:()=>Bo,Gemma4AudioFeatureExtractor:()=>Uo,GraniteSpeechFeatureExtractor:()=>F_,MoonshineFeatureExtractor:()=>B_,ParakeetFeatureExtractor:()=>Do,PyAnnoteFeatureExtractor:()=>jo,SeamlessM4TFeatureExtractor:()=>U_,SnacFeatureExtractor:()=>j_,SpeechT5FeatureExtractor:()=>G_,VoxtralRealtimeFeatureExtractor:()=>V_,Wav2Vec2FeatureExtractor:()=>q_,WeSpeakerFeatureExtractor:()=>W_,WhisperFeatureExtractor:()=>H_});import GL from"fs";import{Readable as qL}from"stream";import{pipeline as WL}from"stream/promises";async function Uc(t,e){if(ie.IS_BROWSER_ENV){if(ie.IS_WEBWORKER_ENV)throw new Error("Unable to save a file from a Web Worker.");let r=URL.createObjectURL(e),s=document.createElement("a");s.href=r,s.download=t,s.click(),s.remove(),URL.revokeObjectURL(r)}else if(ie.IS_FS_AVAILABLE){let r=e.stream(),s=qL.fromWeb(r),n=GL.createWriteStream(t);await WL(s,n)}else throw new Error("Unable to save because filesystem is disabled in this environment.")}async function nM(t,e){if(typeof AudioContext>"u")throw Error("Unable to load audio from path/URL since `AudioContext` is not available in your environment. Instead, audio data should be passed directly to the pipeline/processor. For more information and some example code, see https://huggingface.co/docs/transformers.js/guides/node-audio-processing.");let r=await(await Pr(t)).arrayBuffer(),s=new AudioContext({sampleRate:e});typeof e>"u"&&ee.warn(`No sampling rate provided, using default of ${s.sampleRate}Hz.`);let n=await s.decodeAudioData(r),o;if(n.numberOfChannels===2){let a=Math.sqrt(2),i=n.getChannelData(0),l=n.getChannelData(1);o=new Float32Array(i.length);for(let c=0;c<n.length;++c)o[c]=a*(i[c]+l[c])/2}else o=n.getChannelData(0);return o}var L_=nM;function oM(t,e){if(t<1)return new Float64Array;if(t===1)return new Float64Array([1]);let r=1-e,s=2*Math.PI/(t-1),n=new Float64Array(t);for(let o=0;o<t;++o)n[o]=e-r*Math.cos(o*s);return n}function rM(t){return oM(t,.5)}function VL(t){return oM(t,.54)}var HL={htk:t=>2595*Math.log10(1+t/700),kaldi:t=>1127*Math.log(1+t/700),slaney:(t,e=1e3,r=15,s=27/Math.log(6.4))=>t>=e?r+Math.log(t/e)*s:3*t/200};function z_(t,e="htk"){let r=HL[e];if(!r)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof t=="number"?r(t):t.map(s=>r(s))}var XL={htk:t=>700*(10**(t/2595)-1),kaldi:t=>700*(Math.exp(t/1127)-1),slaney:(t,e=1e3,r=15,s=Math.log(6.4)/27)=>t>=r?e*Math.exp(s*(t-r)):200*t/3};function KL(t,e="htk"){let r=XL[e];if(!r)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof t=="number"?r(t):t.map(s=>r(s))}function YL(t,e){let r=Float64Array.from({length:e.length-1},(a,i)=>e[i+1]-e[i]),s=Array.from({length:t.length},()=>new Array(e.length));for(let a=0;a<t.length;++a){let i=s[a];for(let l=0;l<e.length;++l)i[l]=e[l]-t[a]}let n=e.length-2,o=Array.from({length:n},()=>new Array(t.length));for(let a=0;a<t.length;++a){let i=s[a];for(let l=0;l<n;++l){let c=-i[l]/r[l],p=i[l+2]/r[l+1];o[l][a]=Math.max(0,Math.min(c,p))}}return o}function sM(t,e,r){let s=(e-t)/(r-1);return Float64Array.from({length:r},(n,o)=>t+s*o)}function lt(t,e,r,s,n,o=null,a="htk",i=!1){if(o!==null&&o!=="slaney")throw new Error('norm must be one of null or "slaney"');if(t<2)throw new Error(`Require num_frequency_bins: ${t} >= 2`);if(r>s)throw new Error(`Require min_frequency: ${r} <= max_frequency: ${s}`);let l=z_(r,a),c=z_(s,a),p=sM(l,c,e+2),f=KL(p,a),_;if(i){let w=n/((t-1)*2);_=z_(Float64Array.from({length:t},(x,k)=>k*w),a),f=p}else _=sM(0,Math.floor(n/2),t);let m=YL(_,f);if(o!==null&&o==="slaney")for(let w=0;w<e;++w){let x=m[w],k=2/(f[w+2]-f[w]);for(let A=0;A<t;++A)x[A]*=k}return m}function QL(t,e,r){let s=new t.constructor(t.length+e+r),n=t.length-1;for(let o=0;o<t.length;++o)s[e+o]=t[o];for(let o=1;o<=e;++o)s[e-o]=t[sn(o,n)];for(let o=1;o<=r;++o)s[n+e+o]=t[sn(n-o,n)];return s}function aM(t,e,r,s,n){if(r<=0)throw new Error("reference must be greater than zero");if(s<=0)throw new Error("min_value must be greater than zero");r=Math.max(s,r);let o=Math.log10(r);for(let a=0;a<t.length;++a)t[a]=e*Math.log10(Math.max(s,t[a])-o);if(n!==null){if(n<=0)throw new Error("db_range must be greater than zero");let a=Pe(t)[0]-n;for(let i=0;i<t.length;++i)t[i]=Math.max(t[i],a)}return t}function JL(t,e=1,r=1e-5,s=null){return aM(t,20,e,r,s)}function ZL(t,e=1,r=1e-10,s=null){return aM(t,10,e,r,s)}async function ot(t,e,r,s,{fft_length:n=null,power:o=1,center:a=!0,pad_mode:i="reflect",onesided:l=!0,preemphasis:c=null,preemphasis_htk_flavor:p=!0,mel_filters:f=null,mel_floor:_=1e-10,log_mel:m=null,max_log_mel:w=null,reference:x=1,min_value:k=1e-10,db_range:A=null,remove_dc_offset:E=null,min_num_frames:S=null,max_num_frames:T=null,do_pad:I=!0,transpose:O=!1,mel_offset:b=0,mel_floor_mode:F="clamp"}={}){let j=e.length;if(n===null&&(n=r),r>n)throw Error(`frame_length (${r}) may not be larger than fft_length (${n})`);if(j!==r)throw new Error(`Length of the window (${j}) must equal frame_length (${r})`);if(s<=0)throw new Error("hop_length must be greater than zero");if(o===null&&f!==null)throw new Error("You have provided `mel_filters` but `power` is `None`. Mel spectrogram computation is not yet supported for complex-valued spectrogram. Specify `power` to fix this issue.");if(!p)throw new Error("`preemphasis_htk_flavor=false` is not currently supported.");if(a){let Q=Math.floor(r/2);switch(i){case"reflect":{t=QL(t,Q,Q);break}case"constant":{let le=new t.constructor(t.length+2*Q);le.set(t,Q),t=le;break}case"semicausal":{let le=new t.constructor(t.length+Q);le.set(t,Q),t=le;break}default:throw new Error(`pad_mode="${i}" not implemented yet.`)}}let U=Math.floor(1+Math.floor((t.length-r)/s));S!==null&&U<S&&(U=S);let X=l?Math.floor(n/2)+1:n,K=U,J=U;T!==null&&(T>U?I&&(J=T):J=K=T);let R=new gc(n),C=new Float64Array(n),se=new Float64Array(R.outputBufferSize),Y=new Float32Array(X*J);for(let Q=0;Q<K;++Q){let le=Q*s,Ge=Math.min(t.length-le,r);Ge!==r&&C.fill(0,0,r);for(let ce=0;ce<Ge;++ce)C[ce]=t[le+ce];if(E){let ce=0;for(let qe=0;qe<Ge;++qe)ce+=C[qe];let Et=ce/Ge;for(let qe=0;qe<Ge;++qe)C[qe]-=Et}if(c!==null){for(let ce=Ge-1;ce>=1;--ce)C[ce]-=c*C[ce-1];C[0]*=1-c}for(let ce=0;ce<e.length;++ce)C[ce]*=e[ce];R.realTransform(se,C);for(let ce=0;ce<X;++ce){let Et=ce<<1;Y[ce*J+Q]=se[Et]**2+se[Et+1]**2}}if(o!==null&&o!==2){let Q=o/2;for(let le=0;le<Y.length;++le)Y[le]**=Q}let z=f.length,$=await K2(new N("float32",f.flat(),[z,X]),new N("float32",Y,[X,J]));O&&($=$.transpose(1,0));let B=$.data;if(F==="add")for(let Q=0;Q<B.length;++Q)B[Q]=b+B[Q]+_;else for(let Q=0;Q<B.length;++Q)B[Q]=b+Math.max(_,B[Q]);if(o!==null&&m!==null){let Q=Math.min(B.length,K*z);switch(m){case"log":for(let le=0;le<Q;++le)B[le]=Math.log(B[le]);break;case"log10":for(let le=0;le<Q;++le)B[le]=Math.log10(B[le]);break;case"log10_max_norm":{for(let ce=0;ce<Q;++ce)B[ce]=Math.log10(B[ce]);let Ge=(w??Pe(B)[0])-8;for(let ce=0;ce<Q;++ce)B[ce]=(Math.max(B[ce],Ge)+4)/4;break}case"dB":if(o===1)JL(B,x,k,A);else if(o===2)ZL(B,x,k,A);else throw new Error(`Cannot use log_mel option '${m}' with power ${o}`);break;default:throw new Error(`log_mel must be one of null, 'log', 'log10', 'log10_max_norm', or 'dB'. Got '${m}'`)}}return $}function _t(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=rM(o);break;case"hamming":a=VL(o);break;case"povey":a=rM(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 eN(t,e){let r=t.reduce((o,a)=>o+a.length,0),s=new ArrayBuffer(44),n=new DataView(s);return jc(n,0,"RIFF"),n.setUint32(4,36+r*4,!0),jc(n,8,"WAVE"),jc(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),jc(n,36,"data"),n.setUint32(40,r*4,!0),new Blob([s,...t.map(o=>o.buffer)],{type:"audio/wav"})}function jc(t,e,r){for(let s=0;s<r.length;++s)t.setUint8(e+s,r.charCodeAt(s))}var $o=class{constructor(e,r){this.audio=e,this.sampling_rate=r}get data(){if(Array.isArray(this.audio)){if(this.audio.length===0)return new Float32Array(0);if(this.audio.length===1)return this.audio[0];let e=this.audio.reduce((n,o)=>n+o.length,0),r=new Float32Array(e),s=0;for(let n of this.audio)r.set(n,s),s+=n.length;return r}else return this.audio}toBlob(){let e=this.audio;return e instanceof Float32Array&&(e=[e]),eN(e,this.sampling_rate)}async save(e){return Uc(e,this.toBlob())}};var N_=class extends Ee{constructor(e){super(e);let r=this.config.sampling_rate,s=lt(257,this.config.num_mel_bins,20,Math.floor(r/2),r,null,"kaldi",!0);this.mel_filters=s,this.window=_t(400,"hann",{periodic:!1}),this.mean=this.config.mean,this.std=this.config.std}async _extract_fbank_features(e,r){return ot(e,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:r,transpose:!0})}async _call(e){Ae(e,"ASTFeatureExtractor");let r=await this._extract_fbank_features(e,this.config.max_length);if(this.config.do_normalize){let s=this.std*2,n=r.data;for(let o=0;o<n.length;++o)n[o]=(n[o]-this.mean)/s}return{input_values:r.unsqueeze_(0)}}};var Ro=class extends Ee{async _call(e){Ae(e,"EncodecFeatureExtractor"),e instanceof Float64Array&&(e=new Float32Array(e));let r=this.config.feature_size;if(e.length%r!==0)throw new Error(`The length of the audio data must be a multiple of the number of channels (${r}).`);let s=[1,r,e.length/r];return{input_values:new N("float32",e,s)}}};var $_=class extends Ee{async _call(e){Ae(e,"ChatterboxFeatureExtractor"),e instanceof Float64Array&&(e=new Float32Array(e));let r=[1,e.length];return{input_values:new N("float32",e,r)}}};var R_=class extends Ee{constructor(e){super(e),this.mel_filters=lt(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,null,"htk"),this.mel_filters_slaney=lt(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,"slaney","slaney"),this.window=_t(this.config.fft_window_size,"hann")}async _get_input_mel(e,r,s,n){let o,a=!1,i=e.length-r;if(i>0)if(s==="rand_trunc"){a=!0;let l=Math.floor(hs.random()*(i+1));e=e.subarray(l,l+r),o=await this._extract_fbank_features(e,this.mel_filters_slaney,this.config.nb_max_samples)}else throw new Error(`Truncation strategy "${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 Ae(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 tN=1e-5,Do=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=_t(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){Ae(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)+tN);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 D_=class extends Do{_apply_dither(e){let r=this.config.dither??0;if(r<=0)return e;let s=new Ir(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){Ae(e,"CohereAsrFeatureExtractor");let r=new Float64Array(e);return this._apply_dither(r),super._call(r)}};var Fo=class extends Ro{};var Bo=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=_t(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(Ae(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=Qe([1,a.dims[0]],!0);return{input_features:a.unsqueeze_(0),input_features_mask:i}}};var Uo=class extends Bo{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={}){Ae(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 F_=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=_t(s,"hann");this.window=new Float64Array(r);let i=Math.floor((r-s)/2);this.window.set(a,i)}async _call(e){Ae(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 B_=class extends Ee{async _call(e){Ae(e,"MoonshineFeatureExtractor"),e instanceof Float64Array&&(e=new Float32Array(e));let r=[1,e.length];return{input_values:new N("float32",e,r)}}};var jo=class extends Ee{async _call(e){Ae(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=$e(o[l]),[p,f]=Pe(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 U_=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=_t(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}={}){Ae(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 j_=class extends Fo{};var G_=class extends Ee{};var q_=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){Ae(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 W_=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=_t(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){Ae(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 V_=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=_t(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 Ae(e,"VoxtralRealtimeFeatureExtractor"),{input_features:(await this._extract_fbank_features(e,{center:r})).unsqueeze_(0)}}};var H_=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=_t(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}={}){Ae(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,No,!0,r),n=s.feature_extractor_type,o=Go[n];if(!o)throw new Error(`Unknown feature_extractor_type: '${n}'. Please report this at ${qr}.`);return new o(s)}};var X_=class extends re{static tokenizer_class=ne;static feature_extractor_class=je;async _call(e,r=null){let s=this.tokenizer(e),n=r?await this.feature_extractor(r):{};return{...s,...n}}};var rN=new Set(["ja","zh"]),K_=class extends re{static tokenizer_class=ne;static feature_extractor_class=je;static uses_processor_config=!0;get_decoder_prompt_ids(e="en"){let r=["\u2581","<|startofcontext|>","<|startoftranscript|>","<|emo:undefined|>",`<|${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=rN.has(r)?"":" ";return[s[0].trimEnd(),...s.slice(1).map(a=>a.trim())].join(n)}async _call(e){return await this.feature_extractor(e)}};import Y_ from"sharp";var Ss,iM,Wr;if(ie.IS_WEB_ENV)Ss=(t,e)=>{if(!self.OffscreenCanvas)throw new Error("OffscreenCanvas not supported by this environment.");return new self.OffscreenCanvas(t,e)},Wr=self.createImageBitmap,iM=self.ImageData;else if(Y_)Wr=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 sN={0:"nearest",1:"lanczos",2:"bilinear",3:"bicubic",4:"box",5:"hamming"},nN=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: ${typeof e}`)}static fromCanvas(e){if(!ie.IS_WEB_ENV)throw new Error("fromCanvas() is only supported in browser environments.");let s=e.getContext("2d").getImageData(0,0,e.width,e.height).data;return new t(s,e.width,e.height,4)}static async fromURL(e){let r=await Pr(e);if(r.status!==200)throw new Error(`Unable to read image from "${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 Wr(e),s=Ss(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=Y_(await e.arrayBuffer());return await Wr(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=sN[s]??s,o=If(e),a=If(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=Ss(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 Wr(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=Ss(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 Wr(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=Ss(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 Wr(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=Ss(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 Wr(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=Ss(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: ${this.channels}`)}return this}async save(e){if(ie.IS_WEB_ENV){if(ie.IS_WEBWORKER_ENV)throw new Error("Unable to save an image from a Web Worker.");let r=e.split(".").pop().toLowerCase(),s=nN.get(r)??"image/png",n=await this.toBlob(s);return Uc(e,n)}else if(ie.IS_FS_AVAILABLE)await this.toSharp().toFile(e);else throw new Error("Unable to save the image because filesystem is disabled in this environment.")}toSharp(){if(ie.IS_WEB_ENV)throw new Error("toSharp() is only supported in server-side environments.");return Y_(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}})}},oN=Je.read.bind(Je);function lM(t,e,r=0,s=null){let n=t/e,o=pA(n)*e;return s!==null&&o>s&&(o=Math.floor(n)*e),o<r&&(o=Math.ceil(n)*e),o}function cM([t,e],r){return[Math.max(Math.floor(t/r),1)*r,Math.max(Math.floor(e/r),1)*r]}function Q_([t,e,r,s]){return[t-r/2,e-s/2,t+r/2,e+s/2]}function Vr(t,e=.5,r=null,s=!1){let n=t.logits,o=t.pred_boxes,[a,i,l]=n.dims;if(r!==null&&r.length!==a)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let c=[];for(let p=0;p<a;++p){let f=r!==null?r[p]:null,_={boxes:[],classes:[],scores:[]},m=n[p],w=o[p];for(let x=0;x<i;++x){let k=m[x],A=[],E;if(s){E=k.sigmoid().data;for(let S=0;S<E.length;++S)E[S]>e&&A.push(S)}else{let S=Pe(k.data)[1];if(S===l-1||(E=$e(k.data),E[S]<e))continue;A.push(S)}for(let S of A){let T=w[x].data;T=Q_(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 Gc(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=Nd(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 aN(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=Pe(l.data)[1];if(p===s)continue;let _=$e(l.data)[p];_>r&&(n.push(c),o.push(_),a.push(p))}return[n,o,a]}function iN(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 lN(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]=Nd(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]=iN(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 hn(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 qc(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]=aN(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]=lN(A,E,S,r,s,n,w);_.push({segmentation:T,segments_info:I})}return _}function Wc(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=yo(s.data)[0],a=Pe(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=sn(A,x)*l;for(let T=0;T<p;++T){if(A<i&&T<l)continue;let I=(E+T)*c,O=(S+sn(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,_]=cM([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=lM(c*n,this.config.ensure_multiple_of),i=lM(p*s,this.config.ensure_multiple_of)}return[i,l]}else{if(this.size_divisibility!==void 0)return cM([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:xt(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,yr,!0,r);return new this(s)}};var bn={};en(bn,{BeitFeatureExtractor:()=>J_,BitImageProcessor:()=>Z_,CHMv2ImageProcessor:()=>tm,CLIPFeatureExtractor:()=>rm,CLIPImageProcessor:()=>Vc,ChineseCLIPFeatureExtractor:()=>em,ConvNextFeatureExtractor:()=>sm,ConvNextImageProcessor:()=>Hc,DINOv3ViTImageProcessor:()=>am,DPTFeatureExtractor:()=>lm,DPTImageProcessor:()=>Yc,DeiTFeatureExtractor:()=>nm,DeiTImageProcessor:()=>Xc,DetrFeatureExtractor:()=>om,DetrImageProcessor:()=>Kc,DonutFeatureExtractor:()=>im,DonutImageProcessor:()=>gn,EfficientNetImageProcessor:()=>cm,GLPNFeatureExtractor:()=>fm,Gemma3ImageProcessor:()=>um,Gemma4ImageProcessor:()=>qo,Glm46VImageProcessor:()=>pm,GroundingDinoImageProcessor:()=>dm,Idefics3ImageProcessor:()=>Qc,ImageFeatureExtractor:()=>H,ImageProcessor:()=>H,JinaCLIPImageProcessor:()=>mm,Lfm2VlImageProcessor:()=>hm,LlavaOnevisionImageProcessor:()=>gm,Mask2FormerImageProcessor:()=>xm,MaskFormerFeatureExtractor:()=>wm,MaskFormerImageProcessor:()=>wn,MobileNetV1FeatureExtractor:()=>ym,MobileNetV1ImageProcessor:()=>Jc,MobileNetV2FeatureExtractor:()=>bm,MobileNetV2ImageProcessor:()=>Zc,MobileNetV3FeatureExtractor:()=>vm,MobileNetV3ImageProcessor:()=>eu,MobileNetV4FeatureExtractor:()=>km,MobileNetV4ImageProcessor:()=>tu,MobileViTFeatureExtractor:()=>Em,MobileViTImageProcessor:()=>ru,NougatImageProcessor:()=>Am,OwlViTFeatureExtractor:()=>Mm,OwlViTImageProcessor:()=>xn,Owlv2ImageProcessor:()=>Tm,Phi3VImageProcessor:()=>Im,PixtralImageProcessor:()=>Cm,PvtImageProcessor:()=>Pm,Qwen2VLImageProcessor:()=>Wo,RTDetrImageProcessor:()=>zm,Sam2ImageProcessor:()=>Vo,Sam3ImageProcessor:()=>Vo,SamImageProcessor:()=>Vo,SapiensFeatureExtractor:()=>Lm,SapiensImageProcessor:()=>su,SegformerFeatureExtractor:()=>Nm,SegformerImageProcessor:()=>nu,SiglipImageProcessor:()=>$m,SmolVLMImageProcessor:()=>Qc,Swin2SRImageProcessor:()=>Rm,VLMImageProcessor:()=>_m,ViTFeatureExtractor:()=>Dm,ViTImageProcessor:()=>ou,VitMatteImageProcessor:()=>Fm,VitPoseImageProcessor:()=>Bm,YolosFeatureExtractor:()=>Um,YolosImageProcessor:()=>au});var J_=class extends H{};var Z_=class extends H{};var em=class extends H{};var tm=class extends H{};var Vc=class extends H{},rm=class extends Vc{};var Hc=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}},sm=class extends Hc{};var Xc=class extends H{},nm=class extends Xc{};var Kc=class extends H{async _call(e){let r=await super._call(e),s=[r.pixel_values.dims[0],64,64],n=Qe(s,1n);return{...r,pixel_mask:n}}post_process_object_detection(...e){return Vr(...e)}post_process_panoptic_segmentation(...e){return qc(...e)}post_process_instance_segmentation(...e){return Wc(...e)}},om=class extends Kc{};var am=class extends H{};var gn=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})}},im=class extends gn{};var Yc=class extends H{},lm=class extends Yc{};var cm=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 um=class extends H{};function cN(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 uN(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 qo=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]=cN(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:_}=uN(c,l.height,l.width,l.channels,r,n,s);o.push(p),a.push(f),i.push(_)}return{pixel_values:xt(o,0),image_position_ids:xt(a,0),num_soft_tokens_per_image:i}}};var Wo=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 hn(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=ve(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 pm=class extends Wo{get_resize_output_image_size(e,r){let s=this.patch_size*this.merge_size,n=this.config.temporal_patch_size??2;return hn(e.height,e.width,s,this.min_pixels,this.max_pixels,n)}};var fm=class extends H{};var dm=class extends H{async _call(e){let r=await super._call(e),s=r.pixel_values.dims,n=et([s[0],s[2],s[3]]);return{...r,pixel_mask:n}}};var Qc=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 bt(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,ve(X,0)})),a.push(I),i.push(O)}else{let I=[S,S];T=await Promise.all(E.map(O=>bt(O.pixel_values,{size:I}))),a.push(new Array(E.length).fill(0)),i.push(new Array(E.length).fill(0))}o.push(ve(T,0))}let p=o.length,[f,_,m,w]=o[0].dims,x,k;if(p===1)x=o[0].unsqueeze_(0),k=Qe([p,f,m,w],!0);else{let A=Math.max(...o.map(T=>T.dims.at(0)));k=Qe([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]=ve([o[T],Qe([A-I,_,m,w],0)],0);let O=T*S+I*m*w,b=(T+1)*S;E.fill(!1,O,b)}}x=xt(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 $c(e,[A,k],[S,E],[2,3]);o.push(O)}let _=s,m=n;(a!==_||i!==m)&&(e=await bt(e,{size:[_,m]}))}return o.push(e),{frames:o,num_splits_h:l,num_splits_w:c}}};var _m=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 mm=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 uM(t,e){return Math.round(t/e)*e}function pN(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 fN(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 dN(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 _N(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 hm=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,uM(e,s)),o=Math.max(this.encoder_patch_size,uM(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=fN(this.min_tiles,this.max_tiles),[n,o]=pN(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]=hn(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 bt(k,{size:[J,K]});I=[];for(let C=0;C<X;++C)for(let se=0;se<U;++se){let Y=C*this.tile_size,z=se*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 bt(k,{size:[T,S]}))}else I=[await bt(k,{size:[T,S]})];for(let U of I){let[,,X,K]=U.dims,J=dN(U,this.encoder_patch_size),{padded:R,mask:C}=_N(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:ve(n,0),pixel_attention_mask:xt(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 gm=class extends H{};var wn=class extends H{post_process_panoptic_segmentation(...e){return qc(...e)}post_process_instance_segmentation(...e){return Wc(...e)}},wm=class extends wn{};var xm=class extends wn{};var Jc=class extends H{},ym=class extends Jc{};var Zc=class extends H{},bm=class extends Zc{};var eu=class extends H{},vm=class extends eu{};var tu=class extends H{},km=class extends tu{};var ru=class extends H{},Em=class extends ru{};var Am=class extends gn{};var xn=class extends H{post_process_object_detection(...e){return Vr(...e)}},Mm=class extends xn{};var Tm=class extends xn{};var Nt=336,mN=[2,3],{ceil:Sm,floor:yn,sqrt:Om}=Math,Im=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 yn((yn(r/Nt)*yn(e/Nt)+1)*s+1+(yn(r/Nt)+1)*Om(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=Nt*Sm(o/Nt),l=Nt*Sm(a/Nt),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||Om(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 bt(_,{size:[Nt,Nt],mode:"bicubic"});if(r>0){let k=[],A=Om(r),E=yn(w/A),S=yn(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 $c(_,[F,b],[U,j],mN);k.push(J)}let T=await bt(ve(k,0),{size:[Nt,Nt],mode:"bicubic"});i.push(ve([x,T],0))}else i.push(x)}let l=xt(i,0),c=a.map(_=>_.map(m=>Nt*Sm(m/Nt))),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 Cm=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 Pm=class extends H{};var zm=class extends H{post_process_object_detection(...e){return Vr(...e)}};var Vo=class extends H{reshape_input_points(e,r,s,n=!1){e=structuredClone(e);let o=Cf(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=Cf(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 bt(e[c],{mode:"bilinear",size:l});if(_=_.slice(null,null,[0,f[0]],[0,f[1]]),_=await bt(_,{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 su=class extends H{post_process_semantic_segmentation(...e){return Gc(...e)}},Lm=class extends su{};var nu=class extends H{post_process_semantic_segmentation(...e){return Gc(...e)}},Nm=class extends nu{};var $m=class extends H{};var Rm=class extends H{pad_image(e,r,s,n={}){let[o,a,i]=r;return super.pad_image(e,r,{width:a+(s-a%s)%s,height:o+(s-o%s)%s},{mode:"symmetric",center:!1,constant_values:-1,...n})}};var ou=class extends H{},Dm=class extends ou{};var Fm=class extends H{async _call(e,r){Array.isArray(e)||(e=[e]),Array.isArray(r)||(r=[r]);let s=await Promise.all(e.map(a=>this.preprocess(a))),n=await Promise.all(r.map(a=>this.preprocess(a,{do_normalize:!1,do_convert_rgb:!1,do_convert_grayscale:!0})));return{pixel_values:xt(s.map((a,i)=>ve([a.pixel_values,n[i].pixel_values],0)),0),original_sizes:s.map(a=>a.original_size),reshaped_input_sizes:s.map(a=>a.reshaped_input_size)}}};var Bm=class extends H{post_process_pose_estimation(e,r,{threshold:s=null}={}){let n=e.tolist(),[o,a,i,l]=e.dims,c=[];for(let p=0;p<o;++p){let f=n[p],_=r[p],m=[];for(let w=0;w<_.length;++w){let x=_[w],k=[],A=[],E=[],S=x.at(-2)/l,T=x.at(-1)/i;for(let I=0;I<f.length;++I){let[O,b]=[0,0],F=0,j=-1/0,U=f[I];for(let K=0;K<U.length;++K){let J=U[K];for(let R=0;R<J.length;++R){let C=J[R];F+=C,j=Math.max(j,C),O+=(R+.5)*C,b+=K*C}}if(s!=null&&j<s)continue;let X=[S*O/F,T*b/F];k.push(X),E.push(I),A.push(j)}m.push({bbox:x,scores:A,labels:E,keypoints:k})}c.push(m)}return c}};var au=class extends H{post_process_object_detection(...e){return Vr(...e)}},Um=class extends au{};var Me=class{static async from_pretrained(e,r={}){let s=await nt(e,yr,!0,r),n=s.image_processor_type??s.feature_extractor_type,o=bn[n?.replace(/Fast$/,"")];return o||(n!==void 0&&ee.warn(`Image processor type '${n}' not found, assuming base ImageProcessor. Please report this at ${qr}.`),o=H),new o(s)}};var jm=class extends re{static tokenizer_class=ne;static image_processor_class=Me;constructor(e,r,s){super(e,r,s);let{tasks_answer_post_processing_type:n,task_prompts_without_inputs:o,task_prompts_with_input:a}=this.image_processor.config;this.tasks_answer_post_processing_type=new Map(Object.entries(n??{})),this.task_prompts_without_inputs=new Map(Object.entries(o??{})),this.task_prompts_with_input=new Map(Object.entries(a??{})),this.regexes={quad_boxes:/(.+?)<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>/gm,bboxes:/([^<]+)?<loc_(\d+)><loc_(\d+)><loc_(\d+)><loc_(\d+)>/gm},this.size_per_bin=1e3}construct_prompts(e){typeof e=="string"&&(e=[e]);let r=[];for(let s of e)if(this.task_prompts_without_inputs.has(s))r.push(this.task_prompts_without_inputs.get(s));else{for(let[n,o]of this.task_prompts_with_input)if(s.includes(n)){r.push(o.replaceAll("{input}",s).replaceAll(n,""));break}r.length!==e.length&&r.push(s)}return r}post_process_generation(e,r,s){let n=this.tasks_answer_post_processing_type.get(r)??"pure_text";e=e.replaceAll("<s>","").replaceAll("</s>","");let o;switch(n){case"pure_text":o=e;break;case"description_with_bboxes":case"bboxes":case"phrase_grounding":case"ocr":let a=n==="ocr"?"quad_boxes":"bboxes",i=e.matchAll(this.regexes[a]),l=[],c=[];for(let[p,f,..._]of i)l.push(f?f.trim():l.at(-1)??""),c.push(_.map((m,w)=>(Number(m)+.5)/this.size_per_bin*s[w%2]));o={labels:l,[a]:c};break;default:throw new Error(`Task "${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 Gm=class extends re{static tokenizer_class=ne;static image_processor_class=Me;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 qm=class extends re{static image_processor_class=Me;static feature_extractor_class=je;static tokenizer_class=ne;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 Wm=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,Fc,!0,r),ne.from_pretrained(e,r),xo(e,Bc,!1,r)]),a={tokenizer:n};return s.image_processor&&(a.image_processor=new qo(s.image_processor)),s.feature_extractor&&(a.feature_extractor=new Uo(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 Os=class extends re{static image_processor_class=Me;static tokenizer_class=ne;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 Vm=class extends Os{static image_token="<|image|>"};var Hm=class extends re{static tokenizer_class=ne;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 ${c}.`);e=e.replaceAll(c,c.repeat(i))}return{...this.tokenizer(e,{add_special_tokens:!1,...s}),...n}}};function hN(t,e){let s=t.dims.at(-1)-1,n=t.tolist();n.fill(!1,0,1),n.fill(!1,s);let o=e.tolist();return n.map((a,i)=>a?i:null).filter(a=>a!==null).map(a=>o[a])}var Xm=class extends re{static tokenizer_class=ne;static image_processor_class=Me;async _call(e,r,s={}){let n=e?await this.image_processor(e,s):{};return{...r?this.tokenizer(r,s):{},...n}}post_process_grounded_object_detection(e,r,{box_threshold:s=.25,text_threshold:n=.25,target_sizes:o=null}={}){let{logits:a,pred_boxes:i}=e,l=a.dims[0];if(o!==null&&o.length!==l)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let c=a.dims.at(1),p=a.sigmoid(),f=p.max(-1).tolist(),_=i.tolist().map(w=>w.map(x=>Q_(x))),m=[];for(let w=0;w<l;++w){let x=o!==null?o[w]:null;x!==null&&(_[w]=_[w].map(T=>T.map((I,O)=>I*x[(O+1)%2])));let k=f[w],A=[],E=[],S=[];for(let T=0;T<c;++T){let I=k[T];if(I<=s)continue;let O=_[w][T],b=p[w][T];A.push(I),S.push(O);let F=hN(b.gt(n),r[w]);E.push(F)}m.push({scores:A,boxes:S,labels:this.batch_decode(E)})}return m}};function gN(t,e,r,s,n,o){let a="";for(let i=0;i<e;++i){for(let l=0;l<r;++l)a+=s+`<row_${i+1}_col_${l+1}>`+n.repeat(t);a+=`
`}return a+=`
${s}${o}`+n.repeat(t)+`${s}`,a}function wN(t,e,r,s){return`${e}${s}`+r.repeat(t)+`${e}`}function xN(t,e,r,s,n,o){return t===0&&e===0?wN(r,s,n,o):gN(r,t,e,s,n,o)}var iu=class extends re{static image_processor_class=Me;static tokenizer_class=ne;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(yE(_,this.image_token));let x=m.map((E,S)=>xN(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 Km=class extends re{static image_processor_class=Me;static tokenizer_class=ne;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 (${l})`);let[c,p,f]=n.convert_tokens_to_ids([this.image_tag,this.image_start_tag,this.image_end_tag]),_=a(i[0]),m=new Array(_.length).fill(!1);for(let k=1;k<i.length;++k){let A=new Array(this.num_image_tokens).fill(c),E=a(i[k]);_=gt(_,[p],A,[f],E);let S=new Array(this.num_image_tokens).fill(!0);m=gt(m,[!1],S,[!1],new Array(E.length).fill(!1))}let w=[1,_.length],x={input_ids:new N("int64",_,w),attention_mask:new N("int64",new Array(_.length).fill(1),w),images_seq_mask:new N("bool",m,w),images_emb_mask:new N("bool",new Array(l*this.num_image_tokens).fill(!0),[1,l,this.num_image_tokens])};if(r&&r.length>0){let k=await this.image_processor(r);return k.pixel_values.unsqueeze_(0),{...x,...k}}return x}};var Ym=class extends re{static tokenizer_class=ne;static image_processor_class=Me;async _call(e=null,r=null,s={}){if(!e&&!r)throw new Error("Either text or images must be provided");let n=e?this.tokenizer(e,s):{},o=r?await this.image_processor(r,s):{};return{...n,...o}}};var Qm=class extends re{static tokenizer_class=ne;static image_processor_class=Me;async _call(e,r=null,s={}){let{image_rows:n,image_cols:o,image_sizes:a,...i}=await this.image_processor(e,{...s,return_row_col_info:!0});if(r){let l=this.config.image_token??"<image>",{tile_size:c=512,downsample_factor:p=2,encoder_patch_size:f=16,use_thumbnail:_=!0}=this.image_processor.config,m=S=>Math.ceil(Math.floor(S/f)/p),w=m(c)**2,x=this.config.image_start_token??"<|image_start|>",k=this.config.image_end_token??"<|image_end|>",A=this.config.image_thumbnail??"<|img_thumbnail|>";Array.isArray(r)||(r=[r]);let E=0;r=r.map(S=>{let T=S.split(l);return T[0]+T.slice(1).map(I=>{let O=E++,[b,F]=a[O],j=n[O],U=o[O],X=m(b)*m(F),K=x;if(j>1||U>1){let J=l.repeat(w);for(let R=0;R<j;++R)for(let C=0;C<U;++C)K+=`<|img_row_${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 Jm=class extends re{static tokenizer_class=ne;static image_processor_class=Me;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 pM={char:["char_decode",1],bpe:["bpe_decode",2],wp:["wp_decode",102]},Zm=class extends re{static tokenizer_class=ne;static image_processor_class=Me;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(!pM.hasOwnProperty(r))throw new Error(`Format ${r} is not supported.`);let[s,n]=pM[r],o=this[s].bind(this),[a,i]=e.dims,l=[],c=[],p=e.tolist();for(let _=0;_<a;++_){let m=p[_],w=[],x=[];for(let A=1;A<i;++A){let[E,S]=Pe($e(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]=Pe([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 ne.from_pretrained("Xenova/gpt2"),n=await ne.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 eh=class extends re{static tokenizer_class=ne;static feature_extractor_class=je;async _call(e){return await this.feature_extractor(e)}};var th=class extends re{static tokenizer_class=ne;static image_processor_class=Me};var vn="<image>";function yN(t,e,r,s,n){return`${s.repeat(r*n)}${e}${t}
`}var rh=class extends re{static tokenizer_class=ne;static image_processor_class=Me;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(vn))?a=r.map(c=>{let p=c.replaceAll(vn,vn.repeat(o)),f=p.lastIndexOf(vn),_=f===-1?0:f+vn.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=>yN(c,n,o,vn,e.length)));let i=this.tokenizer(a,s);return{...await this.image_processor(e,s),...i}}};var fM="<|image|>",bN=/<\|image_\d+\|>/g,sh=class extends re{static image_processor_class=Me;static tokenizer_class=ne;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(bN).join(fM.repeat(l[_])));a=this.tokenizer(c,{padding:s,truncation:n});let p=this.tokenizer._tokenizer.token_to_id(fM);a.input_ids.map_(f=>f==p?-f:f)}else a=this.tokenizer(e);return{...a,...i}}};var nh=class extends re{static tokenizer_class=ne;static image_processor_class=Me;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 oh=class extends re{static feature_extractor_class=jo;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 Ho=class extends Os{};var ah=class extends Ho{};var Xo=class extends re{static image_processor_class=Me;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 lu=class extends Xo{},ih=class extends lu{};var lh=class extends re{static tokenizer_class=ne;static feature_extractor_class=je;async _call(e){return await this.feature_extractor(e)}};var ch=class extends re{static tokenizer_class=ne;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 cu="[AUDIO]",vN="[BEGIN_AUDIO]",kN=375;function EN(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 uh=class extends re{static tokenizer_class=ne;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(cu))throw new Error(`The input text does not contain the audio token ${cu}.`);Array.isArray(r)||(r=[r]);let a=e.split(cu),i=a.length-1;if(i!==r.length)throw new Error(`The number of audio inputs (${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=>EN(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?ve(_,0):_[0];let m=a[0];for(let w=0;w<p.length;++w){m+=vN;for(let x=0;x<p[w];++x)m+=cu.repeat(kN);m+=a[w+1]}e=m}return{...this.tokenizer(e,{add_special_tokens:!1,...s}),...n}}};var dM=32,ph=6,uu=8,AN=10,MN=32,fh=class extends re{static tokenizer_class=ne;static feature_extractor_class=je;static uses_processor_config=!1;get num_mel_frames_first_audio_chunk(){return(ph+1)*uu}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 uu*e+r}get num_right_pad_tokens(){return ph+1+AN}get audio_length_per_tok(){return uu}get raw_audio_length_per_tok(){return uu*this.feature_extractor.config.hop_length}async _call(e,{is_streaming:r=!1,is_first_audio_chunk:s=!0}={}){if(Ae(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=dM*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+(dM+ph),c=new BigInt64Array(l).fill(BigInt(MN));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 dh=class extends re{static tokenizer_class=ne;static feature_extractor_class=je;async _call(e){return await this.feature_extractor(e)}};var _h=class extends re{static tokenizer_class=ne;static feature_extractor_class=je;async _call(e){return await this.feature_extractor(e)}};var mh=class extends re{static tokenizer_class=ne;static feature_extractor_class=je;async _call(e){return await this.feature_extractor(e)}};var fu=class{static async from_pretrained(e,r={}){let s=await nt(e,yr,!0,r),{image_processor_type:n,feature_extractor_type:o,processor_class:a}=s;if(a&&pu[a])return pu[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=bn[n.replace(/Fast$/,"")];if(!c)throw new Error(`Unknown image_processor_type: '${n}'.`);i.image_processor=new c(s)}if(o){let c=bn[o];if(c)i.image_processor=new c(s);else{let p=Go[o];if(!p)throw new Error(`Unknown feature_extractor_type: '${o}'.`);i.feature_extractor=new p(s)}}let l={};return new re(l,i,null)}};async function TN(t,e){return await nt(t,"config.json",!0,e)}function kn(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=kn(t.text_config);break;case"moondream1":r=kn(t.phi_config);break;case"musicgen":r=kn(t.decoder);break;case"multi_modality":r=kn(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 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Mn=class{max_length=20;max_new_tokens=null;min_length=0;min_new_tokens=null;early_stopping=!1;max_time=null;do_sample=!1;num_beams=1;num_beam_groups=1;penalty_alpha=null;use_cache=!0;temperature=1;top_k=50;top_p=1;typical_p=1;epsilon_cutoff=0;eta_cutoff=0;diversity_penalty=0;repetition_penalty=1;encoder_repetition_penalty=1;length_penalty=1;no_repeat_ngram_size=0;bad_words_ids=null;force_words_ids=null;renormalize_logits=!1;constraints=null;forced_bos_token_id=null;forced_eos_token_id=null;remove_invalid_values=!1;exponential_decay_length_penalty=null;suppress_tokens=null;streamer=null;begin_suppress_tokens=null;forced_decoder_ids=null;guidance_scale=null;num_return_sequences=1;output_attentions=!1;output_hidden_states=!1;output_scores=!1;return_dict_in_generate=!1;pad_token_id=null;bos_token_id=null;eos_token_id=null;encoder_no_repeat_ngram_size=0;decoder_start_token_id=null;generation_kwargs={};constructor(e){Object.assign(this,Ve(e,Object.getOwnPropertyNames(this)))}};var Hr=class extends We{_call(e,r){throw Error("StoppingCriteria needs to be subclassed")}},Tn=class t extends We{constructor(){super(),this.criteria=[]}push(e){this.criteria.push(e)}extend(e){e instanceof t?e=e.criteria:e instanceof Hr&&(e=[e]),this.criteria.push(...e)}_call(e,r){let s=new Array(e.length).fill(!1);for(let n of this.criteria){let o=n(e,r);for(let a=0;a<s.length;++a)s[a]||=o[a]}return s}[Symbol.iterator](){return this.criteria.values()}},Au=class extends Hr{constructor(e,r=null){super(),this.max_length=e,this.max_position_embeddings=r}_call(e){return e.map(r=>r.length>=this.max_length)}},Mu=class extends Hr{constructor(e){super(),Array.isArray(e)||(e=[e]),this.eos_token_id=e}_call(e,r){return e.map(s=>{let n=s.at(-1);return this.eos_token_id.some(o=>n==o)})}},yM=class extends Hr{constructor(){super(),this.interrupted=!1}interrupt(){this.interrupted=!0}reset(){this.interrupted=!1}_call(e,r){return new Array(e.length).fill(this.interrupted)}};var Ps=class extends We{constructor(e){super(),this.generation_config=e}async _call(e){return this.sample(e)}async sample(e){throw Error("sample should be implemented in subclasses.")}getLogits(e,r){let s=e.dims.at(-1),n=e.data;if(r===-1)n=n.slice(-s);else{let o=r*s;n=n.slice(o,o+s)}return n}randomSelect(e){return YE(e)}static getSampler(e){if(e.do_sample)return new xh(e);if(e.num_beams>1)return new yh(e);if(e.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${e.num_return_sequences}.`);return new wh(e)}},wh=class extends Ps{async sample(e){let r=Pe(e.data)[1];return[[BigInt(r),0]]}},xh=class extends Ps{async sample(e){let r=e.dims.at(-1);this.generation_config.top_k>0&&(r=Math.min(this.generation_config.top_k,r));let[s,n]=await Jt(e,r),o=$e(s.data);return Array.from({length:this.generation_config.num_beams},()=>{let a=this.randomSelect(o);return[n.data[a],Math.log(o[a])]})}},yh=class extends Ps{async sample(e){let r=e.dims.at(-1);this.generation_config.top_k>0&&(r=Math.min(this.generation_config.top_k,r));let[s,n]=await Jt(e,r),o=$e(s.data);return Array.from({length:this.generation_config.num_beams},(a,i)=>[n.data[i],Math.log(o[i])])}};var bh=class{constructor(e){if(e)for(let r in e){if(r in this)throw new TypeError(`Key "${r}" conflicts with an existing property on DynamicCache`);let s=e[r];if(!(s instanceof N))throw new TypeError(`Expected a Tensor for key "${r}", got ${typeof s}`);this[r]=s}}get_seq_length(){let e=this;if(Object.keys(e).length===0)return 0;for(let r in e)if(r.startsWith("past_key_values."))return e[r].dims.at(-2);throw new Error("Unable to determine sequence length from the cache.")}update(e){for(let r in e){let s=this[r],n=e[r];s&&s!==n&&s.location==="gpu-buffer"&&s.dispose(),this[r]=n}}async dispose(){let e=[];for(let r of Object.values(this))r.location==="gpu-buffer"&&e.push(r.dispose());await Promise.all(e)}},Sn=bh;var 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},ur={[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={...ur[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={...ur[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={...ur[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={...ur[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 vh(t){return ur[t]?.text_only_sessions??null}function Yo(t,e,r={}){let s=ur[t]??ur.default;return{sessions:s.sessions(e,r,r.textOnly??!1),cache_sessions:s.cache_sessions,optional_configs:s.optional_configs}}function On(t,{warn:e=!0}={}){let r=t.architectures||[];for(let s of r){let n=Zt.get(s);if(n!==void 0)return n}if(t.model_type){let s=Zt.get(t.model_type);if(s!==void 0)return s;for(let n of Object.values(zs))if(n.has(t.model_type)){let o=Zt.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: ${qr}`)}return V.EncoderOnly}function Qo(t,{config:e=null,cache_dir:r=null,local_files_only:s=!1,revision:n="main"}={}){if(e!==null)return jt.from_pretrained(t,{config:e,cache_dir:r,local_files_only:s,revision:n});let o=JSON.stringify([t,r,s,n]);return _c(o,()=>jt.from_pretrained(t,{config:e,cache_dir:r,local_files_only:s,revision:n}))}async function In(t,{config:e=null,dtype:r=null,device:s=null,model_file_name:n=null}={}){e=await Qo(t,{config:e});let o=["config.json"],a=e["transformers.js_config"]??{},i=a.use_external_data_format,l="onnx",c=s??a.device,p=r??a.dtype,f=On(e),_=(x,k=null)=>{k=k??x;let A=zc(c,x),E=Lc(p,x,A),S=jr[E]??"",T=`${k}${S}.onnx`,I=l?`${l}/${T}`:T;o.push(I);let O=hh(i,T,x);for(let b of gh(T,O)){let F=l?`${l}/${b}`:b;o.push(F)}},{sessions:m,optional_configs:w}=Yo(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 zs=null;function kM(t){zs=t}function kh(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 Eh(t){return new N("bool",[t],[1])}var bM={[V.DecoderOnly]:{can_generate:!0,forward:It,prepare_inputs:Cn},[V.DecoderOnlyWithoutHead]:{can_generate:!1,forward:It,prepare_inputs:Cn},[V.Seq2Seq]:{can_generate:!0,forward:Tu,prepare_inputs:Jo},[V.Vision2Seq]:{can_generate:!0,forward:Tu,prepare_inputs:Jo},[V.Musicgen]:{can_generate:!0,forward:Tu},[V.EncoderDecoder]:{can_generate:!1,forward:Tu},[V.ImageTextToText]:{can_generate:!0,forward:LN,prepare_inputs:Su},[V.AudioTextToText]:{can_generate:!0,forward:zN,prepare_inputs:Su},[V.ImageAudioTextToText]:{can_generate:!0,prepare_inputs:Su},[V.Phi3V]:{can_generate:!0,prepare_inputs:Su},[V.MultiModality]:{can_generate:!0},[V.AutoEncoder]:{can_generate:!1,forward:CN},[V.Chatterbox]:{can_generate:!0,forward:Gt},[V.VoxtralRealtime]:{can_generate:!0,prepare_inputs:Cn},default:{can_generate:!1,forward:Gt}};function vM(t,e){let r=Zt.get(t),s=!1,n=e?.architectures?.[0];if(n&&n!==t&&t?.endsWith("ForCausalLM")&&n.endsWith("ForConditionalGeneration")){let i=Zt.get(n);i!==void 0&&(r=i,s=!0)}let o=bM[r]??bM.default,a=ur[r]??ur.default;return{typeConfig:{...o,...a},textOnly:s,modelType:r}}var Zt=new Map,Iu=new Map,Ls=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=Ls.get(this.constructor),{typeConfig:o}=vM(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=Ls.get(this);s=m.config=await jt.from_pretrained(e,m);let{typeConfig:x,textOnly:k,modelType:A}=vM(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. 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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 Gt(this,r);if(n.guidance_scale!==null&&n.guidance_scale>1)o=ve([o,Co(o,0)],0),"attention_mask"in r&&(r.attention_mask=ve([r.attention_mask,Fd(r.attention_mask)],0));else if(r.decoder_input_ids){let a=kh(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=ve(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=kh(i)}return l.decoder_attention_mask=Dc(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=Ps.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=PN(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],se=await k(C);for(let[Y,z]of se){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=Ou(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 Tu(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 Gt(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 It(t,a,!0)}async function Gt(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=Fd(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=et([n[0],n[2],n[3]])}return await ue(r,s)}async function CN(t,e){let r=await t.encode(e);return await t.decode(r)}function Ou(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 Sn(r)}function PN(t){let e={};for(let r of["cross_attentions","encoder_attentions","decoder_attentions"])for(let s in t)s.startsWith(r)&&(r in e||(e[r]=[]),e[r].push(t[s]));return e}function Ah(t,e){return t.map(r=>typeof r=="number"?r:e[r]??0)}function Zo(t,e,r){if(r&&Object.keys(r).length>0)return Object.assign(e,r),r;let s=t.sessions.decoder_model_merged??t.sessions.model,n=(e[t.main_input_name]??e.attention_mask)?.dims?.[0]??1,o=Is(t.config),a=t.config?.normalized_config?.num_heads,i={batch_size:n};typeof a=="number"&&(i["batch_size x num_heads"]=n*a);let l=Object.create(null);for(let c of s.inputMetadata){if(!o.has(c.name))continue;let p=Ah(c.shape,i),f=p.reduce((w,x)=>w*x,1),_=Gr[c.type],m=new N(c.type,new _(f),p);e[c.name]=m,l[c.name]=m}return r?(r.update(l),r):new Sn(l)}async function It(t,e,r=!1){let s=t.sessions[r?"decoder_model_merged":"model"],{past_key_values:n,...o}=e;if(s.inputNames.includes("use_cache_branch")&&(o.use_cache_branch=Eh(n!=null&&Object.keys(n).length>0)),s.inputNames.includes("position_ids")&&o.attention_mask&&!o.position_ids){let i=["paligemma","gemma3_text","gemma3"].includes(t.config.model_type)?1:0;o.position_ids=NN(o,n,i)}s.inputNames.includes("num_logits_to_keep")&&!o.num_logits_to_keep&&(o.num_logits_to_keep=new N("int64",[0n],[])),Zo(t,o,n);let a=Ve(o,s.inputNames);return await ue(s,a)}async function EM(t,{encode_function:e,merge_function:r,modality_input_names:s,modality_output_name:n,input_ids:o=null,attention_mask:a=null,position_ids:i=null,inputs_embeds:l=null,past_key_values:c=null,generation_config:p=null,logits_processor:f=null,..._}){if(!l){l=await t.encode_text({input_ids:o,..._});let w=Ve(_,s);if(Object.keys(w).length>0){if(o.dims[1]!==1){let x=await e({...w,..._});({inputs_embeds:l,attention_mask:a}=r({[n]:x,inputs_embeds:l,input_ids:o,attention_mask:a}))}else if(c&&o.dims[1]===1){let x=o.dims[1],k=c.get_seq_length();a=ve([et([o.dims[0],k]),a.slice(null,[a.dims[1]-x,a.dims[1]])],1)}}}if(!i&&["qwen2_vl","qwen2_vl_text","qwen2_5_vl","qwen2_5_vl_text","qwen3_vl","qwen3_vl_text","qwen3_vl_moe","qwen3_vl_moe_text","qwen3_5","qwen3_5_text","qwen3_5_moe","qwen3_5_moe_text","glm_ocr","glm_ocr_text"].includes(t.config.model_type)){let{image_grid_thw:w,video_grid_thw:x}=_;[i]=t.get_rope_index(o,w,x,a)}return await It(t,{inputs_embeds:l,past_key_values:c,attention_mask:a,position_ids:i,generation_config:p,logits_processor:f},!0)}async function zN(t,e){return await EM(t,{...e,modality_input_names:["audio_values","input_features"],modality_output_name:"audio_features",encode_function:t.encode_audio.bind(t),merge_function:t._merge_input_ids_with_audio_features.bind(t)})}async function LN(t,e){return await EM(t,{...e,modality_input_names:["pixel_values"],modality_output_name:"image_features",encode_function:t.encode_image.bind(t),merge_function:t._merge_input_ids_with_image_features.bind(t)})}function Mh(t,e=0){let[r,s]=t.dims,n=t.data,o=new BigInt64Array(n.length);for(let a=0;a<r;++a){let i=a*s,l=BigInt(e);for(let c=0;c<s;++c){let p=i+c;n[p]===0n?o[p]=BigInt(1):(o[p]=l,l+=n[p])}}return{data:o,dims:t.dims}}function NN(t,e=null,r=0){let{input_ids:s,inputs_embeds:n,attention_mask:o}=t,{data:a,dims:i}=Mh(o,r),l=new N("int64",a,i);if(e){let c=-(s??n).dims.at(1);l=l.slice(null,[c,null])}return l}function Cn(t,e,r,s){let n=r.past_key_values?r.past_key_values.get_seq_length():0;if((t.sessions.decoder_model_merged??t.sessions.model)?.inputNames.includes("num_logits_to_keep")&&!r.num_logits_to_keep&&(r.num_logits_to_keep=new N("int64",[1n],[])),!r.attention_mask){let a;for(let i of["input_ids","inputs_embeds","position_ids"])if(r[i]){a=r[i].dims;break}if(!a)throw new Error("attention_mask is not provided, and unable to infer its shape from model inputs.");r.attention_mask=et([a[0],n+a[1]])}if(r.past_key_values){let{input_ids:a,attention_mask:i}=r;i&&i.dims[1]>a.dims[1]||n<a.dims[1]&&(r.input_ids=a.slice(null,[n,null]))}return r}function Jo(t,e,r,s){return r.past_key_values&&(e=e.map(n=>[n.at(-1)])),{...r,decoder_input_ids:kh(e)}}function Su(t,...e){return t.config.is_encoder_decoder?Jo(t,...e):Cn(t,...e)}function AM({modality_token_id:t,inputs_embeds:e,modality_features:r,input_ids:s,attention_mask:n}){let o=s.tolist().map(c=>c.reduce((p,f,_)=>(f==t&&p.push(_),p),[])),a=o.reduce((c,p)=>c+p.length,0),i=r.dims[0];if(a!==i)throw new Error(`Number of tokens and features do not match: tokens: ${a}, features ${i}`);let l=0;for(let c=0;c<o.length;++c){let p=o[c],f=e[c];for(let _=0;_<p.length;++_)f[p[_]].data.set(r[l++].data)}return{inputs_embeds:e,attention_mask:n}}function Pn({image_token_id:t,inputs_embeds:e,image_features:r,input_ids:s,attention_mask:n}){return 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Xu{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 It(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 Pn({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 Cu({audio_token_id:this.config.audio_token_id,...e,audio_features:s})}},Qw=class extends ts{};var Dn=class extends ts{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})}},Jw=class extends Dn{};var $a=class extends y{},Zw=class extends $a{},ex=class extends $a{};var Ra=class extends y{},tx=class extends Ra{},rx=class extends Ra{};var Ku=class extends y{forward_params=["input_ids","attention_mask","position_ids","past_key_values","pixel_values","image_grid_thw"]},Fn=class extends Ku{image_grid_thw_name="grid_thw";_get_text_only_rope_index(e,r){if(r){let{data:s,dims:n}=Mh(r),o=BigInt64Array.from({length:3*s.length},(i,l)=>s[l%s.length]),a=Array.from({length:n[0]},(i,l)=>Pe(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]),Dd([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 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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 Pn({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 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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 Ba=class extends y{},nx=class extends Ba{},ox=class extends Ba{};var Ua=class extends y{},ax=class extends Ua{},ix=class extends Ua{};var ja=class extends y{},lx=class extends ja{},cx=class extends ja{};var Ga=class extends y{},ux=class extends Ga{},px=class extends 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y{},Gx=class extends Bn{async forward(e){let r=!e.input_ids,s=!e.pixel_values;if(r&&s)throw new Error("Either `input_ids` or `pixel_values` should be provided.");if(r&&(e.input_ids=et([e.pixel_values.dims[0],1])),s){let{image_size:c}=this.config.vision_config;e.pixel_values=Qe([0,3,c,c],0)}let{text_embeddings:n,image_embeddings:o,l2norm_text_embeddings:a,l2norm_image_embeddings:i}=await super.forward(e),l={};return r||(l.text_embeddings=n,l.l2norm_text_embeddings=a),s||(l.image_embeddings=o,l.l2norm_image_embeddings=i),l}},ti=class extends Bn{static async from_pretrained(e,r={}){return super.from_pretrained(e,{...r,model_file_name:r.model_file_name??"text_model"})}},qx=class extends Bn{static async from_pretrained(e,r={}){return super.from_pretrained(e,{...r,model_file_name:r.model_file_name??"vision_model"})}};var ri=class extends y{},Wx=class extends ri{},Vx=class extends ri{};var Hx=class extends kt{};var si=class extends y{},Xx=class extends si{},Kx=class extends si{};var Yx=class 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q(await super._call(e))}};var mi=class extends y{},By=class extends mi{},Uy=class extends mi{async _call(e){return new q(await super._call(e))}};var js=class extends y{},jy=class extends js{},Gy=class extends js{async _call(e){return new we(await super._call(e))}},qy=class extends js{async _call(e){return new q(await super._call(e))}},Wy=class extends js{async _call(e){return new ge(await super._call(e))}};var hi=class extends y{},Vy=class extends hi{},Hy=class extends hi{};var gi=class extends y{requires_attention_mask=!1;main_input_name="input_values";forward_params=["input_values","decoder_input_ids","past_key_values"]},Xy=class extends gi{},Ky=class extends gi{};var rs=class extends y{},Yy=class extends rs{},Qy=class extends rs{async _call(e){return new we(await super._call(e))}},Jy=class extends rs{async _call(e){return new q(await super._call(e))}},Zy=class extends rs{async _call(e){return new ge(await super._call(e))}},e0=class extends rs{async _call(e){return new Te(await super._call(e))}};var wi=class extends y{},t0=class extends wi{},r0=class extends wi{};var xi=class extends y{},s0=class extends xi{},n0=class extends xi{};var ap=class extends y{},o0=class extends ap{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 It(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)),Jo(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 vi=class extends y{},l0=class extends vi{},c0=class extends vi{};var ki=class extends y{},u0=class extends ki{},p0=class extends ki{};var ss=class extends y{},f0=class extends ss{},d0=class extends ss{async _call(e){return new we(await super._call(e))}},_0=class extends ss{async _call(e){return new q(await super._call(e))}},m0=class extends ss{async _call(e){return new ge(await super._call(e))}},h0=class extends ss{async _call(e){return new Te(await super._call(e))}};var ip=class extends y{},g0=class extends ip{};var Ei=class extends y{},w0=class extends Ei{},x0=class extends Ei{};var Ai=class extends y{},y0=class extends Ai{},b0=class extends Ai{};var Mi=class extends y{},v0=class extends Mi{},k0=class extends Mi{};var Ti=class extends y{},E0=class extends Ti{},A0=class extends Ti{};var Si=class extends y{},M0=class extends Si{},T0=class extends Si{async _call(e){return new q(await super._call(e))}};var Oi=class extends y{},S0=class extends Oi{},O0=class extends Oi{};var Ii=class extends y{},I0=class extends Ii{},C0=class extends Ii{};var Ci=class extends y{},P0=class extends Ci{},z0=class extends Ci{};var Pi=class extends y{},L0=class extends Pi{},N0=class extends Pi{};var $0=class extends kt{};var lp=class extends y{},R0=class extends lp{async _call(e){return new vt(await super._call(e))}};var zi=class extends y{},D0=class extends zi{},F0=class extends zi{};var Li=class extends y{},B0=class extends Li{},U0=class extends Li{};var Ni=class extends y{},j0=class extends Ni{},G0=class extends Ni{};var $i=class extends y{},q0=class extends $i{},W0=class extends $i{};var cp=class extends y{forward_params=["input_ids","inputs_embeds","attention_mask","position_ids","pixel_values","image_sizes","past_key_values"]},Ri=class extends cp{async forward({input_ids:e=null,attention_mask:r=null,pixel_values:s=null,image_sizes:n=null,position_ids:o=null,inputs_embeds:a=null,past_key_values:i=null,generation_config:l=null,logits_processor:c=null,...p}){if(!a){let _;if(s&&e.dims[1]!==1){if(!n)throw new 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 It(this,{inputs_embeds:a,past_key_values:i,attention_mask:r,position_ids:o,generation_config:l,logits_processor:c},!1)}};var Di=class extends y{},V0=class extends Di{},H0=class extends Di{async _call(e){return new q(await super._call(e))}};var Fi=class extends y{},X0=class extends Fi{},K0=class extends Fi{async _call(e){return new ge(await super._call(e))}};var Bi=class extends y{},Y0=class extends Bi{},Q0=class extends Bi{};var Ui=class extends y{},J0=class extends Ui{},Z0=class extends Ui{};var ji=class extends y{},eb=class extends ji{},tb=class extends ji{};var Gi=class extends y{},rb=class extends Gi{},sb=class extends Gi{};var qi=class extends y{},nb=class extends qi{},ob=class extends qi{};var Gs=class extends Ds{},Wi=class extends Fa{};var ab=class extends Gs{},ib=class extends Wi{};var Vn=class extends Gs{},Vi=class extends Vn{};var lb=class extends Vn{},cb=class extends Vi{};var Hi=class extends y{},ub=class extends Hi{},pb=class extends Hi{async _call(e){return new q(await super._call(e))}};var Xi=class extends y{},fb=class extends Xi{},db=class extends Xi{async _call(e){return new up(await super._call(e))}},up=class extends fr{};var ns=class extends y{},_b=class extends ns{},mb=class extends ns{async _call(e){return new we(await super._call(e))}},hb=class extends ns{async _call(e){return new q(await super._call(e))}},gb=class extends ns{async _call(e){return new ge(await super._call(e))}},wb=class extends ns{async _call(e){return new Te(await super._call(e))}};var os=class extends y{},xb=class extends os{},yb=class extends os{async _call(e){return new we(await super._call(e))}},bb=class extends os{async _call(e){return new q(await super._call(e))}},vb=class extends os{async _call(e){return new ge(await super._call(e))}},kb=class extends os{async _call(e){return new Te(await super._call(e))}};var Ki=class extends y{},Eb=class extends Ki{},Ab=class extends Ki{async _call(e){return new pp(await super._call(e))}},pp=class extends fr{};var fp=class extends Re{constructor({iou_scores:e,pred_masks:r}){super(),this.iou_scores=e,this.pred_masks=r}},dp=class extends y{},Mb=class extends dp{async get_image_embeddings({pixel_values:e}){return await Gt(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??=et(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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E=w[A].neg().squeeze_(0),[S,T]=fA(E.tolist()),I=Array.from({length:S.length-1},(j,U)=>S[U+1]-S[U]),O=gt([1],I).map(j=>!!j),b=[];for(let j=0;j<O.length;++j)O[j]&&b.push(T[j]*n);let F=new Array(o).fill(0);F.push(...b),b.length>0&&F.push(b.at(-1)),k[A].data.set(F)}return k}},G1=class extends Op{};var is=class extends y{},q1=class extends is{},W1=class extends is{async _call(e){return new we(await super._call(e))}},V1=class extends is{async _call(e){return new q(await super._call(e))}},H1=class extends is{async _call(e){return new ge(await super._call(e))}},X1=class extends is{async _call(e){return new Te(await super._call(e))}};var ls=class extends y{},K1=class extends ls{},Y1=class extends ls{async _call(e){return new we(await super._call(e))}},Q1=class extends ls{async _call(e){return new q(await super._call(e))}},J1=class extends ls{async _call(e){return new ge(await super._call(e))}},Z1=class extends ls{async _call(e){return new Te(await super._call(e))}};var _l=class extends y{},ev=class extends _l{},tv=class extends _l{async _call(e){return new Ip(await super._call(e))}},Ip=class extends Re{constructor({logits:e,pred_boxes:r}){super(),this.logits=e,this.pred_boxes=r}};var ml=class extends y{},rv=class extends ml{},sv=class extends ml{};var GN=new Map([["bert","BertModel"],["eurobert","EuroBertModel"],["neobert","NeoBertModel"],["modernbert","ModernBertModel"],["nomic_bert","NomicBertModel"],["roformer","RoFormerModel"],["electra","ElectraModel"],["esm","EsmModel"],["convbert","ConvBertModel"],["camembert","CamembertModel"],["deberta","DebertaModel"],["deberta-v2","DebertaV2Model"],["mpnet","MPNetModel"],["albert","AlbertModel"],["distilbert","DistilBertModel"],["roberta","RobertaModel"],["xlm","XLMModel"],["xlm-roberta","XLMRobertaModel"],["clap","ClapModel"],["clip","CLIPModel"],["clipseg","CLIPSegModel"],["chinese_clip","ChineseCLIPModel"],["siglip","SiglipModel"],["jina_clip","JinaCLIPModel"],["mobilebert","MobileBertModel"],["squeezebert","SqueezeBertModel"],["wav2vec2","Wav2Vec2Model"],["wav2vec2-bert","Wav2Vec2BertModel"],["unispeech","UniSpeechModel"],["unispeech-sat","UniSpeechSatModel"],["hubert","HubertModel"],["wavlm","WavLMModel"],["audio-spectrogram-transformer","ASTModel"],["vits","VitsModel"],["pyannote","PyAnnoteModel"],["wespeaker-resnet","WeSpeakerResNetModel"],["detr","DetrModel"],["rt_detr","RTDetrModel"],["rt_detr_v2","RTDetrV2Model"],["rf_detr","RFDetrModel"],["d_fine","DFineModel"],["table-transformer","TableTransformerModel"],["vit","ViTModel"],["ijepa","IJepaModel"],["pvt","PvtModel"],["vit_msn","ViTMSNModel"],["vit_mae","ViTMAEModel"],["groupvit","GroupViTModel"],["fastvit","FastViTModel"],["mobilevit","MobileViTModel"],["mobilevitv2","MobileViTV2Model"],["owlvit","OwlViTModel"],["owlv2","Owlv2Model"],["beit","BeitModel"],["deit","DeiTModel"],["hiera","HieraModel"],["convnext","ConvNextModel"],["convnextv2","ConvNextV2Model"],["dinov2","Dinov2Model"],["dinov2_with_registers","Dinov2WithRegistersModel"],["dinov3_vit","DINOv3ViTModel"],["dinov3_convnext","DINOv3ConvNextModel"],["resnet","ResNetModel"],["swin","SwinModel"],["swin2sr","Swin2SRModel"],["donut-swin","DonutSwinModel"],["yolos","YolosModel"],["dpt","DPTModel"],["glpn","GLPNModel"],["hifigan","SpeechT5HifiGan"],["efficientnet","EfficientNetModel"],["decision_transformer","DecisionTransformerModel"],["patchtst","PatchTSTModel"],["patchtsmixer","PatchTSMixerModel"],["mobilenet_v1","MobileNetV1Model"],["mobilenet_v2","MobileNetV2Model"],["mobilenet_v3","MobileNetV3Model"],["mobilenet_v4","MobileNetV4Model"],["maskformer","MaskFormerModel"],["mgp-str","MgpstrForSceneTextRecognition"],["style_text_to_speech_2","StyleTextToSpeech2Model"],["openai_privacy_filter","OpenAIPrivacyFilterModel"]]),qN=new Map([["t5","T5Model"],["longt5","LongT5Model"],["mt5","MT5Model"],["bart","BartModel"],["mbart","MBartModel"],["marian","MarianModel"],["whisper","WhisperModel"],["cohere_asr","CohereAsrModel"],["m2m_100","M2M100Model"],["blenderbot","BlenderbotModel"],["blenderbot-small","BlenderbotSmallModel"]]),WN=new Map([["mimi","MimiModel"],["dac","DacModel"],["snac","SnacModel"]]),VN=new 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"]]),SM=new Map([["speecht5","SpeechT5ForSpeechToText"],["whisper","WhisperForConditionalGeneration"],["lite-whisper","LiteWhisperForConditionalGeneration"],["moonshine","MoonshineForConditionalGeneration"],["cohere_asr","CohereAsrForConditionalGeneration"]]),OM=new Map([["speecht5","SpeechT5ForTextToSpeech"]]),IM=new Map([["vits","VitsModel"],["musicgen","MusicgenForConditionalGeneration"],["supertonic","SupertonicForConditionalGeneration"]]),CM=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"]]),PM=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"]]),zM=new Map([["t5","T5ForConditionalGeneration"],["longt5","LongT5ForConditionalGeneration"],["mt5","MT5ForConditionalGeneration"],["bart","BartForConditionalGeneration"],["mbart","MBartForConditionalGeneration"],["marian","MarianMTModel"],["m2m_100","M2M100ForConditionalGeneration"],["blenderbot","BlenderbotForConditionalGeneration"],["blenderbot-small","BlenderbotSmallForConditionalGeneration"]]),LM=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"]]),HN=new Map([["multi_modality","MultiModalityCausalLM"]]),NM=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"]]),$M=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"]]),RM=new Map([["vision-encoder-decoder","VisionEncoderDecoderModel"],["idefics3","Idefics3ForConditionalGeneration"],["smolvlm","SmolVLMForConditionalGeneration"]]),DM=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"]]),FM=new Map([["granite_speech","GraniteSpeechForConditionalGeneration"],["ultravox","UltravoxModel"],["voxtral","VoxtralForConditionalGeneration"],["voxtral_realtime","VoxtralRealtimeForConditionalGeneration"]]),XN=new Map([["vision-encoder-decoder","VisionEncoderDecoderModel"]]),BM=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"]]),UM=new Map([["detr","DetrForObjectDetection"],["rt_detr","RTDetrForObjectDetection"],["rt_detr_v2","RTDetrV2ForObjectDetection"],["rf_detr","RFDetrForObjectDetection"],["d_fine","DFineForObjectDetection"],["table-transformer","TableTransformerForObjectDetection"],["yolos","YolosForObjectDetection"]]),jM=new Map([["owlvit","OwlViTForObjectDetection"],["owlv2","Owlv2ForObjectDetection"],["grounding-dino","GroundingDinoForObjectDetection"]]),eo=new Map([["detr","DetrForSegmentation"],["clipseg","CLIPSegForImageSegmentation"]]),GM=new Map([["segformer","SegformerForSemanticSegmentation"],["sapiens","SapiensForSemanticSegmentation"],["swin","SwinForSemanticSegmentation"],["mobilenet_v1","MobileNetV1ForSemanticSegmentation"],["mobilenet_v2","MobileNetV2ForSemanticSegmentation"],["mobilenet_v3","MobileNetV3ForSemanticSegmentation"],["mobilenet_v4","MobileNetV4ForSemanticSegmentation"]]),qM=new Map([["detr","DetrForSegmentation"],["maskformer","MaskFormerForInstanceSegmentation"]]),WM=new Map([["sam","SamModel"],["sam2","Sam2Model"],["edgetam","EdgeTamModel"],["sam3_tracker","Sam3TrackerModel"]]),VM=new Map([["wav2vec2","Wav2Vec2ForCTC"],["wav2vec2-bert","Wav2Vec2BertForCTC"],["unispeech","UniSpeechForCTC"],["unispeech-sat","UniSpeechSatForCTC"],["wavlm","WavLMForCTC"],["hubert","HubertForCTC"],["parakeet_ctc","ParakeetForCTC"]]),HM=new Map([["wav2vec2","Wav2Vec2ForSequenceClassification"],["wav2vec2-bert","Wav2Vec2BertForSequenceClassification"],["unispeech","UniSpeechForSequenceClassification"],["unispeech-sat","UniSpeechSatForSequenceClassification"],["wavlm","WavLMForSequenceClassification"],["hubert","HubertForSequenceClassification"],["audio-spectrogram-transformer","ASTForAudioClassification"]]),XM=new Map([["wavlm","WavLMForXVector"]]),KM=new Map([["unispeech-sat","UniSpeechSatForAudioFrameClassification"],["wavlm","WavLMForAudioFrameClassification"],["wav2vec2","Wav2Vec2ForAudioFrameClassification"],["pyannote","PyAnnoteForAudioFrameClassification"]]),YM=new Map([["vitmatte","VitMatteForImageMatting"]]),KN=new Map([["patchtst","PatchTSTForPrediction"],["patchtsmixer","PatchTSMixerForPrediction"]]),QM=new Map([["swin2sr","Swin2SRForImageSuperResolution"]]),JM=new Map([["chmv2","CHMv2ForDepthEstimation"],["dpt","DPTForDepthEstimation"],["depth_anything","DepthAnythingForDepthEstimation"],["glpn","GLPNForDepthEstimation"],["sapiens","SapiensForDepthEstimation"],["depth_pro","DepthProForDepthEstimation"],["metric3d","Metric3DForDepthEstimation"],["metric3dv2","Metric3Dv2ForDepthEstimation"]]),ZM=new Map([["sapiens","SapiensForNormalEstimation"]]),eT=new Map([["vitpose","VitPoseForPoseEstimation"]]),tT=new Map([["clip","CLIPVisionModelWithProjection"],["siglip","SiglipVisionModel"],["jina_clip","JinaCLIPVisionModel"]]),nv=[[GN,V.EncoderOnly],[qN,V.EncoderDecoder],[VN,V.DecoderOnlyWithoutHead],[WN,V.AutoEncoder],[CM,V.EncoderOnly],[PM,V.EncoderOnly],[zM,V.Seq2Seq],[SM,V.Seq2Seq],[LM,V.DecoderOnly],[HN,V.MultiModality],[NM,V.EncoderOnly],[$M,V.EncoderOnly],[RM,V.Vision2Seq],[DM,V.ImageTextToText],[FM,V.AudioTextToText],[BM,V.EncoderOnly],[eo,V.EncoderOnly],[qM,V.EncoderOnly],[GM,V.EncoderOnly],[YM,V.EncoderOnly],[KN,V.EncoderOnly],[QM,V.EncoderOnly],[JM,V.EncoderOnly],[ZM,V.EncoderOnly],[eT,V.EncoderOnly],[UM,V.EncoderOnly],[jM,V.EncoderOnly],[WM,V.MaskGeneration],[VM,V.EncoderOnly],[HM,V.EncoderOnly],[OM,V.Seq2Seq],[IM,V.EncoderOnly],[XM,V.EncoderOnly],[KM,V.EncoderOnly],[tT,V.EncoderOnly]];for(let[t,e]of nv)for(let r of t.values()){Zt.set(r,e);let s=hl[r];Ls.set(s,r),Iu.set(r,s)}var YN=[["MusicgenForConditionalGeneration",bi,V.Musicgen],["Phi3VForCausalLM",Ri,V.Phi3V],["CLIPTextModelWithProjection",pa,V.EncoderOnly],["SiglipTextModel",Ji,V.EncoderOnly],["JinaCLIPTextModel",ti,V.EncoderOnly],["ClapTextModelWithProjection",ca,V.EncoderOnly],["ClapAudioModelWithProjection",ua,V.EncoderOnly],["DacEncoderModel",ba,V.EncoderOnly],["DacDecoderModel",va,V.EncoderOnly],["MimiEncoderModel",ci,V.EncoderOnly],["MimiDecoderModel",ui,V.EncoderOnly],["SnacEncoderModel",el,V.EncoderOnly],["SnacDecoderModel",tl,V.EncoderOnly],["Gemma3nForConditionalGeneration",ts,V.ImageAudioTextToText],["Gemma4ForConditionalGeneration",Dn,V.ImageAudioTextToText],["SupertonicForConditionalGeneration",ol,V.Supertonic],["ChatterboxModel",la,V.Chatterbox],["VoxtralRealtimeForConditionalGeneration",fl,V.VoxtralRealtime]];for(let[t,e,r]of YN)Zt.set(t,r),Ls.set(e,t),Iu.set(t,e);var rT=new Map([["modnet",eo],["birefnet",eo],["isnet",eo],["ben",eo]]);for(let[t,e]of rT.entries())e.set(t,"PreTrainedModel"),Zt.set(t,V.EncoderOnly),Iu.set(t,y);var sT=new Set(rT.keys());Zt.set("PreTrainedModel",V.EncoderOnly);Ls.set(y,"PreTrainedModel");var Le={MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES:CM,MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES:PM,MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES:OM,MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING_NAMES:IM,MODEL_FOR_MASKED_LM_MAPPING_NAMES:NM,MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES:$M,MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES:BM,MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES:eo,MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES:GM,MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING_NAMES:qM,MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES:UM,MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES:jM,MODEL_FOR_MASK_GENERATION_MAPPING_NAMES:WM,MODEL_FOR_CTC_MAPPING_NAMES:VM,MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES:HM,MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES:XM,MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES:KM,MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES:XN,MODEL_FOR_IMAGE_MATTING_MAPPING_NAMES:YM,MODEL_FOR_IMAGE_TO_IMAGE_MAPPING_NAMES:QM,MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES:JM,MODEL_FOR_NORMAL_ESTIMATION_MAPPING_NAMES:ZM,MODEL_FOR_POSE_ESTIMATION_MAPPING_NAMES:eT,MODEL_FOR_IMAGE_FEATURE_EXTRACTION_MAPPING_NAMES:tT,MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES:DM,MODEL_FOR_AUDIO_TEXT_TO_TEXT_MAPPING_NAMES:FM,MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES:zM,MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES:SM,MODEL_FOR_CAUSAL_LM_MAPPING_NAMES:LM,MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES:RM};kM(Le);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 jt.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 hl[k].from_pretrained(e,m)}if(this.BASE_IF_FAIL)return sT.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: ${w}`)}},br=class extends Ce{static MODEL_CLASS_MAPPINGS=nv.map(e=>e[0]);static BASE_IF_FAIL=!0},gl=class extends Ce{static MODEL_CLASS_MAPPINGS=[Le.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES]},Cp=class extends Ce{static MODEL_CLASS_MAPPINGS=[Le.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES]},to=class extends Ce{static MODEL_CLASS_MAPPINGS=[Le.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES]},Pp=class extends Ce{static MODEL_CLASS_MAPPINGS=[Le.MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES]},zp=class extends Ce{static MODEL_CLASS_MAPPINGS=[Le.MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES]},Lp=class extends Ce{static MODEL_CLASS_MAPPINGS=[Le.MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING_NAMES]},Np=class extends Ce{static MODEL_CLASS_MAPPINGS=[Le.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES]},$p=class extends Ce{static MODEL_CLASS_MAPPINGS=[Le.MODEL_FOR_MASKED_LM_MAPPING_NAMES]},Rp=class extends Ce{static MODEL_CLASS_MAPPINGS=[Le.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES]},Dp=class extends Ce{static MODEL_CLASS_MAPPINGS=[Le.MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES]},Fp=class extends Ce{static MODEL_CLASS_MAPPINGS=[Le.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES]},wl=class extends Ce{static MODEL_CLASS_MAPPINGS=[Le.MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES]},xl=class extends Ce{static MODEL_CLASS_MAPPINGS=[Le.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES]},yl=class extends Ce{static MODEL_CLASS_MAPPINGS=[Le.MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING_NAMES]},Bp=class extends Ce{static MODEL_CLASS_MAPPINGS=[Le.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES]},Up=class extends Ce{static MODEL_CLASS_MAPPINGS=[Le.MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES]},nT=class extends Ce{static MODEL_CLASS_MAPPINGS=[Le.MODEL_FOR_MASK_GENERATION_MAPPING_NAMES]},jp=class extends Ce{static MODEL_CLASS_MAPPINGS=[Le.MODEL_FOR_CTC_MAPPING_NAMES]},Gp=class extends Ce{static MODEL_CLASS_MAPPINGS=[Le.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES]},oT=class extends Ce{static MODEL_CLASS_MAPPINGS=[Le.MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES]},aT=class extends Ce{static MODEL_CLASS_MAPPINGS=[Le.MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES]},qp=class extends Ce{static MODEL_CLASS_MAPPINGS=[Le.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES]},iT=class extends Ce{static MODEL_CLASS_MAPPINGS=[Le.MODEL_FOR_IMAGE_MATTING_MAPPING_NAMES]},Wp=class extends Ce{static MODEL_CLASS_MAPPINGS=[Le.MODEL_FOR_IMAGE_TO_IMAGE_MAPPING_NAMES]},Vp=class extends Ce{static MODEL_CLASS_MAPPINGS=[Le.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES]},lT=class extends Ce{static MODEL_CLASS_MAPPINGS=[Le.MODEL_FOR_NORMAL_ESTIMATION_MAPPING_NAMES]},cT=class extends Ce{static MODEL_CLASS_MAPPINGS=[Le.MODEL_FOR_POSE_ESTIMATION_MAPPING_NAMES]},Hp=class extends Ce{static MODEL_CLASS_MAPPINGS=[Le.MODEL_FOR_IMAGE_FEATURE_EXTRACTION_MAPPING_NAMES]},uT=class extends Ce{static MODEL_CLASS_MAPPINGS=[Le.MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES]},pT=class extends Ce{static MODEL_CLASS_MAPPINGS=[Le.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 vr(t,e){return Array.isArray(t)||(t=[t]),await Promise.all(t.map(r=>typeof r=="string"||r instanceof URL?L_(r,e):r instanceof Float64Array?new Float32Array(r):r))}function bl(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 _e=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 vl=class extends _e{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",$e(c.data),c.dims),l=[];for(let c of n.logits){let p=i(c),f=await Jt(p,r),_=f[0].tolist(),w=f[1].tolist().map((x,k)=>({label:a?a[x]:`LABEL_${x}`,score:_[k]}));r===1?l.push(...w):l.push(w)}return Array.isArray(e)||r===1?l:l[0]}};var kl=class extends _e{async _call(e,{ignore_labels:r=["O"],aggregation_strategy:s="none"}={}){if(s!=="none"&&s!=="simple")throw new Error(`Invalid aggregation_strategy: "${s}". Must be one of "none" or "simple".`);let n=Array.isArray(e),o=this.tokenizer(n?e:[e],{padding:!0,truncation:!0}),i=(await this.model(o)).logits,l=this.model.config.id2label,c=[];for(let p=0;p<i.dims[0];++p){let f=o.input_ids[p].tolist(),_=i[p],m=[];for(let w=0;w<_.dims[0];++w){let x=_[w],k=Pe(x.data)[1],A=l?l[k]:`LABEL_${k}`;if(r.includes(A))continue;let E=this.tokenizer.decode([f[w]],{skip_special_tokens:!0});if(E==="")continue;let S=$e(x.data);m.push({entity:A,score:S[k],index:w,word:E})}c.push(s==="simple"?JN(m,f,this.tokenizer):m)}return n?c:c[0]}};function QN(t){let e=t[0];return t[1]==="-"&&(e==="B"||e==="I"||e==="E"||e==="S")?[e,t.slice(2)]:["I",t]}function JN(t,e,r){let s=[],n=null;for(let o=0;o<t.length;++o){let[a,i]=QN(t[o].entity);n===i&&a!=="B"&&a!=="S"?(s[s.length-1].end=o+1,a==="E"&&(n=null)):(s.push({tag:i,start:o,end:o+1}),n=a==="S"?null:i)}return s.map(({tag:o,start:a,end:i})=>{let l=0,c=[];for(let p=a;p<i;++p)l+=t[p].score,c.push(e[t[p].index]);return{entity_group:o,score:l/(i-a),word:r.decode(c,{skip_special_tokens:!0})}})}var El=class extends _e{async _call(e,r,{top_k:s=1}={}){let n=this.tokenizer(e,{text_pair:r,padding:!0,truncation:!0}),o=Array.isArray(e),{start_logits:a,end_logits:i}=await this.model(n),l=n.input_ids.tolist(),c=n.attention_mask.tolist(),{all_special_ids:p,sep_token_id:f}=this.tokenizer,_=[];for(let m=0;m<a.dims[0];++m){let w=l[m],x=w.findIndex(O=>O==f),k=a[m].tolist(),A=i[m].tolist();for(let O=1;O<k.length;++O)(c[m]==0||O<=x||p.findIndex(b=>b==w[O])!==-1)&&(k[O]=-1/0,A[O]=-1/0);let E=$e(k).map((O,b)=>[O,b]),S=$e(A).map((O,b)=>[O,b]);E[0][0]=0,S[0][0]=0;let T=xE(E,S).filter(O=>O[0][1]<=O[1][1]).map(O=>[O[0][1],O[1][1],O[0][0]*O[1][0]]).sort((O,b)=>b[2]-O[2]),I=[];for(let O=0;O<Math.min(T.length,s);++O){let[b,F,j]=T[O],U=w.slice(b,F+1),X=this.tokenizer.decode(U,{skip_special_tokens:!0});I.push({answer:X,score:j})}s===1?_.push(...I):_.push(I)}return o?_:_[0]}};var Al=class extends _e{async _call(e,{top_k:r=5}={}){let{mask_token_id:s,mask_token:n}=this.tokenizer,o=this.tokenizer(e,{padding:!0,truncation:!0}),{logits:a}=await this.model(o),i=[],l=o.input_ids.tolist();for(let c=0;c<l.length;++c){let p=l[c],f=p.findIndex(k=>k==s);if(f===-1)throw Error(`Mask token (${n}) not found in text.`);let _=a[c][f],m=await Jt(new N("float32",$e(_.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 kr=class extends _e{_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 Ml=class extends kr{_key="summary_text"};var Tl=class extends kr{_key="translation_text"};function fT(t){return Array.isArray(t)&&t.every(e=>"role"in e&&"content"in e)}var Sl=class extends _e{_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(fT(e))e=[e];else if(Array.isArray(e)&&e.every(fT))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 Ol=class extends _e{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=>$e(m)[1]):$e(p)).map((m,w)=>[m,w]).sort((m,w)=>w[0]-m[0]);l.push({sequence:c,labels:_.map(m=>r[m[1]]),scores:_.map(m=>m[0])})}return o?l:l[0]}};var Il=class extends _e{async _call(e,{top_k:r=5}={}){let s=this.processor.feature_extractor.config.sampling_rate,n=await vr(e,s),o=this.model.config.id2label,a=[];for(let i of n){let l=await this.processor(i),p=(await this.model(l)).logits[0],f=await Jt(new N("float32",$e(p.data),p.dims),r),_=f[0].tolist(),w=f[1].tolist().map((x,k)=>({label:o?o[x]:`LABEL_${x}`,score:_[k]}));a.push(w)}return Array.isArray(e)?a:a[0]}};var Cl=class extends _e{async _call(e,r,{hypothesis_template:s="This is a sound of {}."}={}){let n=!Array.isArray(e);n&&(e=[e]);let o=r.map(p=>s.replace("{}",p)),a=this.tokenizer(o,{padding:!0,truncation:!0}),i=this.processor.feature_extractor.config.sampling_rate,l=await vr(e,i),c=[];for(let p of l){let f=await this.processor(p),_=await this.model({...a,...f}),m=$e(_.logits_per_audio.data);c.push([...m].map((w,x)=>({score:w,label:r[x]})))}return n?c[0]:c}};var Pl=class extends _e{_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 vr(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(Pe(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 vr(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=>gs(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 vr(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 vr(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 zl=class extends _e{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 me.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 $o(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 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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 $l=class extends Vs{async _call(e,r={}){let s=await tt(e),n=await super._call(e,r),o=s.map((a,i)=>{let l=a.clone();return l.putAlpha(n[i].mask),l});return Array.isArray(e)?o:o[0]}};var Rl=class extends _e{async _call(e,r,{hypothesis_template:s="This is a photo of {}"}={}){let n=Array.isArray(e),o=await tt(e),a=r.map(_=>s.replace("{}",_)),i=this.tokenizer(a,{padding:this.model.config.model_type==="siglip"?"max_length":!0,truncation:!0}),{pixel_values:l}=await this.processor(o),c=await this.model({...i,pixel_values:l}),p=this.model.config.model_type==="siglip"?_=>_.sigmoid().data:_=>$e(_.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 Dl=class extends _e{async _call(e,{threshold:r=.9,percentage:s=!1}={}){let n=Array.isArray(e);if(n&&e.length!==1)throw Error("Object detection pipeline currently only supports a batch size of 1.");let o=await tt(e),a=s?null:o.map(m=>[m.height,m.width]),{pixel_values:i,pixel_mask:l}=await this.processor(o),c=await this.model({pixel_values:i,pixel_mask:l}),p=this.processor.image_processor.post_process_object_detection(c,r,a),{id2label:f}=this.model.config,_=p.map(m=>m.boxes.map((w,x)=>({score:m.scores[x],label:f[m.classes[x]],box:bl(w,!s)})));return n?_:_[0]}};var Fl=class extends _e{async _call(e,r,{threshold:s=.1,top_k:n=null,percentage:o=!1}={}){let a=Array.isArray(e),i=await tt(e),l=this.tokenizer(r,{padding:!0,truncation:!0}),c=await this.processor(i),p=[];for(let f=0;f<i.length;++f){let _=i[f],m=o?null:[[_.height,_.width]],w=c.pixel_values[f].unsqueeze_(0),x=await this.model({...l,pixel_values:w}),k;if("post_process_grounded_object_detection"in this.processor){let A=this.processor.post_process_grounded_object_detection(x,l.input_ids,{box_threshold:s,text_threshold:s,target_sizes:m})[0];k=A.boxes.map((E,S)=>({score:A.scores[S],label:A.labels[S],box:bl(E,!o)}))}else{let A=this.processor.image_processor.post_process_object_detection(x,s,m,!0)[0];k=A.boxes.map((E,S)=>({score:A.scores[S],label:r[A.classes[S]],box:bl(E,!o)}))}k.sort((A,E)=>E.score-A.score),n!==null&&(k=k.slice(0,n)),p.push(k)}return a?p:p[0]}};var Bl=class extends _e{_default_generation_config={max_new_tokens:256};async _call(e,r,s={}){if(Array.isArray(e)){if(e.length!==1)throw Error("Document Question Answering pipeline currently only supports a batch size of 1.");e=e[0]}let n=(await tt(e))[0],{pixel_values:o}=await this.processor(n),a=`<s_docvqa><s_question>${r}</s_question><s_answer>`,i=this.tokenizer(a,{add_special_tokens:!1,padding:!0,truncation:!0}).input_ids,l=await 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Array.isArray(e)?o:o[0]}};var Gl=class extends _e{async _call(e,{pooling:r="none",normalize:s=!1,quantize:n=!1,precision:o="binary"}={}){let a=this.tokenizer(e,{padding:!0,truncation:!0}),i=await this.model(a),l=i.last_hidden_state??i.logits??i.token_embeddings;switch(r){case"none":break;case"mean":l=Y2(l,a.attention_mask);break;case"first_token":case"cls":l=l.slice(null,0);break;case"last_token":case"eos":l=l.slice(null,-1);break;default:throw Error(`Pooling method '${r}' not supported.`)}return s&&(l=l.normalize(2,-1)),n&&(l=J2(l,o)),l}};var ql=class extends _e{async _call(e,{pool:r=null}={}){let s=await tt(e),{pixel_values:n}=await this.processor(s),o=await this.model({pixel_values:n}),a;if(r){if(!("pooler_output"in o))throw Error("No pooled output was returned. 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as AfmoeForCausalLM,zh as AfmoeModel,ta as AfmoePreTrainedModel,Ih as AlbertForMaskedLM,Oh as AlbertForQuestionAnswering,Sh as AlbertForSequenceClassification,Th as AlbertModel,Ns as AlbertPreTrainedModel,Ud as AlbertTokenizer,Ph as ApertusForCausalLM,Ch as ApertusModel,ea as ApertusPreTrainedModel,$h as ArceeForCausalLM,Nh as ArceeModel,ra as ArceePreTrainedModel,Il as AudioClassificationPipeline,jt as AutoConfig,je as AutoFeatureExtractor,Me as AutoImageProcessor,br as AutoModel,Gp as AutoModelForAudioClassification,aT as AutoModelForAudioFrameClassification,pT as AutoModelForAudioTextToText,jp as AutoModelForCTC,Np as AutoModelForCausalLM,Vp as AutoModelForDepthEstimation,qp as AutoModelForDocumentQuestionAnswering,Fp as AutoModelForImageClassification,Hp as AutoModelForImageFeatureExtraction,iT as AutoModelForImageMatting,wl as AutoModelForImageSegmentation,uT as AutoModelForImageTextToText,Wp as AutoModelForImageToImage,nT as AutoModelForMaskGeneration,$p as AutoModelForMaskedLM,lT as AutoModelForNormalEstimation,Bp as AutoModelForObjectDetection,cT as AutoModelForPoseEstimation,Rp as AutoModelForQuestionAnswering,xl as AutoModelForSemanticSegmentation,to as AutoModelForSeq2SeqLM,gl as AutoModelForSequenceClassification,Pp as AutoModelForSpeechSeq2Seq,zp as AutoModelForTextToSpectrogram,Lp as AutoModelForTextToWaveform,Cp as AutoModelForTokenClassification,yl as AutoModelForUniversalSegmentation,Dp as AutoModelForVision2Seq,oT as AutoModelForXVector,Up as AutoModelForZeroShotObjectDetection,fu as AutoProcessor,ne as AutoTokenizer,Pl as AutomaticSpeechRecognitionPipeline,$l as BackgroundRemovalPipeline,Bh as BartForConditionalGeneration,Uh as BartForSequenceClassification,Fh as BartModel,zn as BartPretrainedModel,jd as BartTokenizer,ov as BaseStreamer,J_ as BeitFeatureExtractor,Gh as BeitForImageClassification,jh as BeitModel,na as BeitPreTrainedModel,Wh as BertForMaskedLM,Xh as BertForQuestionAnswering,Vh as BertForSequenceClassification,Hh as BertForTokenClassification,qh as BertModel,Xr as BertPreTrainedModel,Gd as BertTokenizer,Z_ as BitImageProcessor,Yh as BlenderbotForConditionalGeneration,Kh as BlenderbotModel,oa as BlenderbotPreTrainedModel,Jh as BlenderbotSmallForConditionalGeneration,Qh as BlenderbotSmallModel,aa as BlenderbotSmallPreTrainedModel,qd as BlenderbotSmallTokenizer,Wd as BlenderbotTokenizer,eg as BloomForCausalLM,Zh as BloomModel,ia as BloomPreTrainedModel,Vd as BloomTokenizer,ig as CHMv2ForDepthEstimation,tm as CHMv2ImageProcessor,Lu as CHMv2PreTrainedModel,rm as CLIPFeatureExtractor,Vc as CLIPImageProcessor,cg as CLIPModel,pr as CLIPPreTrainedModel,_g as CLIPSegForImageSegmentation,dg as CLIPSegModel,fa as CLIPSegPreTrainedModel,ug as CLIPTextModel,pa as CLIPTextModelWithProjection,Xd as CLIPTokenizer,pg as CLIPVisionModel,fg as CLIPVisionModelWithProjection,rg as CamembertForMaskedLM,og as CamembertForQuestionAnswering,sg as CamembertForSequenceClassification,ng as CamembertForTokenClassification,tg as CamembertModel,Kr as CamembertPreTrainedModel,Hd as CamembertTokenizer,$_ as ChatterboxFeatureExtractor,la as ChatterboxModel,Pu as ChatterboxPreTrainedModel,X_ as ChatterboxProcessor,em as ChineseCLIPFeatureExtractor,ag as ChineseCLIPModel,zu as ChineseCLIPPreTrainedModel,ua as ClapAudioModelWithProjection,R_ as ClapFeatureExtractor,lg as ClapModel,Ln as ClapPreTrainedModel,ca as ClapTextModelWithProjection,ku as ClassifierFreeGuidanceLogitsProcessor,hg as CodeGenForCausalLM,mg as CodeGenModel,da as CodeGenPreTrainedModel,Yd as CodeGenTokenizer,Kd as CodeLlamaTokenizer,yg as Cohere2ForCausalLM,xg as Cohere2Model,ma as Cohere2PreTrainedModel,D_ as CohereAsrFeatureExtractor,vg as CohereAsrForConditionalGeneration,bg as CohereAsrModel,ha as CohereAsrPreTrainedModel,K_ as CohereAsrProcessor,Jd as CohereAsrTokenizer,wg as CohereForCausalLM,gg as CohereModel,_a as CoherePreTrainedModel,Qd as CohereTokenizer,Eg as ConvBertForMaskedLM,Tg as ConvBertForQuestionAnswering,Ag as ConvBertForSequenceClassification,Mg as ConvBertForTokenClassification,kg as ConvBertModel,Yr as ConvBertPreTrainedModel,Zd as ConvBertTokenizer,sm as ConvNextFeatureExtractor,Og as ConvNextForImageClassification,Hc as ConvNextImageProcessor,Sg as ConvNextModel,ga as ConvNextPreTrainedModel,Cg as ConvNextV2ForImageClassification,Ig as ConvNextV2Model,wa as ConvNextV2PreTrainedModel,Ng as DFineForObjectDetection,Lg as DFineModel,ya as DFinePreTrainedModel,iw as DINOv3ConvNextModel,Uu as DINOv3ConvNextPreTrainedModel,am as DINOv3ViTImageProcessor,lw as DINOv3ViTModel,ju as DINOv3ViTPreTrainedModel,lm as DPTFeatureExtractor,hw as DPTForDepthEstimation,Yc as DPTImageProcessor,mw as DPTModel,Ta as DPTPreTrainedModel,va as DacDecoderModel,$u as DacDecoderOutput,ba as DacEncoderModel,Nu as DacEncoderOutput,Fo as DacFeatureExtractor,$g as DacModel,Nn as DacPreTrainedModel,Dg as DebertaForMaskedLM,Ug as DebertaForQuestionAnswering,Fg as DebertaForSequenceClassification,Bg as DebertaForTokenClassification,Rg as DebertaModel,Qr as DebertaPreTrainedModel,t_ as DebertaTokenizer,Wg as DebertaV2ForMaskedLM,Xg as DebertaV2ForQuestionAnswering,Vg as DebertaV2ForSequenceClassification,Hg as DebertaV2ForTokenClassification,qg as DebertaV2Model,Jr as DebertaV2PreTrainedModel,e_ as DebertaV2Tokenizer,Kg as DecisionTransformerModel,Ru as DecisionTransformerPreTrainedModel,Gg as DeepseekV3ForCausalLM,jg as DeepseekV3Model,ka as DeepseekV3PreTrainedModel,nm as DeiTFeatureExtractor,Qg as DeiTForImageClassification,Xc as DeiTImageProcessor,Yg as DeiTModel,Ea as DeiTPreTrainedModel,Jg as DepthAnythingForDepthEstimation,Du as DepthAnythingPreTrainedModel,jl as DepthEstimationPipeline,Zg as DepthProForDepthEstimation,Fu as DepthProPreTrainedModel,om as DetrFeatureExtractor,tw as DetrForObjectDetection,rw as DetrForSegmentation,Kc as DetrImageProcessor,ew as DetrModel,Rn as DetrObjectDetectionOutput,$n as DetrPreTrainedModel,Bu as DetrSegmentationOutput,nw as Dinov2ForImageClassification,sw as Dinov2Model,Aa as Dinov2PreTrainedModel,aw as Dinov2WithRegistersForImageClassification,ow as Dinov2WithRegistersModel,Ma as Dinov2WithRegistersPreTrainedModel,dw as DistilBertForMaskedLM,fw as DistilBertForQuestionAnswering,uw as DistilBertForSequenceClassification,pw as DistilBertForTokenClassification,cw as DistilBertModel,Zr as DistilBertPreTrainedModel,r_ as DistilBertTokenizer,Bl as DocumentQuestionAnsweringPipeline,im as DonutFeatureExtractor,gn as DonutImageProcessor,_w as DonutSwinModel,Gu as DonutSwinPreTrainedModel,Sn as DynamicCache,Tb as EdgeTamModel,ww as EfficientNetForImageClassification,cm as EfficientNetImageProcessor,gw as EfficientNetModel,Sa as EfficientNetPreTrainedModel,yw as ElectraForMaskedLM,kw as ElectraForQuestionAnswering,bw as ElectraForSequenceClassification,vw as ElectraForTokenClassification,xw as ElectraModel,es as ElectraPreTrainedModel,s_ as ElectraTokenizer,Ro as EncodecFeatureExtractor,Mu as EosTokenCriteria,Aw as Ernie4_5ForCausalLM,Ew as Ernie4_5Model,Oa as Ernie4_5PretrainedModel,Tw as EsmForMaskedLM,Sw as EsmForSequenceClassification,Ow as EsmForTokenClassification,Mw as EsmModel,$s as EsmPreTrainedModel,n_ as EsmTokenizer,Cw as EuroBertForMaskedLM,Pw as EuroBertForSequenceClassification,zw as EuroBertForTokenClassification,Iw as EuroBertModel,Rs as EuroBertPreTrainedModel,Nw as ExaoneForCausalLM,Lw as ExaoneModel,Ia as ExaonePreTrainedModel,Rw as FalconForCausalLM,Fw as FalconH1ForCausalLM,Dw as FalconH1Model,Pa as FalconH1PreTrainedModel,$w as FalconModel,Ca as FalconPreTrainedModel,o_ as FalconTokenizer,Uw as FastViTForImageClassification,Bw as FastViTModel,za as FastViTPreTrainedModel,Gl as FeatureExtractionPipeline,Ee as FeatureExtractor,Al as FillMaskPipeline,jw as Florence2ForConditionalGeneration,qu as Florence2PreTrainedModel,jm as Florence2Processor,_u as ForcedBOSTokenLogitsProcessor,mu as ForcedEOSTokenLogitsProcessor,fm as GLPNFeatureExtractor,ox as GLPNForDepthEstimation,nx as GLPNModel,Ba as GLPNPreTrainedModel,mx as GPT2LMHeadModel,_x as GPT2Model,Wa as GPT2PreTrainedModel,l_ as GPT2Tokenizer,ix as GPTBigCodeForCausalLM,ax as GPTBigCodeModel,Ua as GPTBigCodePreTrainedModel,gx as GPTJForCausalLM,hx as GPTJModel,Va as GPTJPreTrainedModel,cx as GPTNeoForCausalLM,lx as GPTNeoModel,ja as GPTNeoPreTrainedModel,px as GPTNeoXForCausalLM,ux as GPTNeoXModel,Ga as GPTNeoXPreTrainedModel,i_ as GPTNeoXTokenizer,Vw as Gemma2ForCausalLM,Ww as Gemma2Model,Na as Gemma2PreTrainedModel,Yw as Gemma3ForCausalLM,Hu as Gemma3ForConditionalGeneration,um as Gemma3ImageProcessor,Kw as Gemma3Model,Vu as Gemma3PreTrainedModel,Gm as Gemma3Processor,Bo as Gemma3nAudioFeatureExtractor,Qw as Gemma3nForCausalLM,ts as Gemma3nForConditionalGeneration,Xu as Gemma3nPreTrainedModel,qm as Gemma3nProcessor,Uo as Gemma4AudioFeatureExtractor,Jw as Gemma4ForCausalLM,Dn as Gemma4ForConditionalGeneration,qo as Gemma4ImageProcessor,Wm as Gemma4Processor,qw as GemmaForCausalLM,Gw as GemmaModel,La as GemmaPreTrainedModel,a_ as GemmaTokenizer,pm as Glm46VImageProcessor,Vm as Glm46VProcessor,ex as GlmForCausalLM,Zw as GlmModel,rx as GlmMoeDsaForCausalLM,tx as GlmMoeDsaModel,Ra as GlmMoeDsaPreTrainedModel,sx as GlmOcrForConditionalGeneration,$a as GlmPreTrainedModel,dx as GptOssForCausalLM,fx as GptOssModel,qa as GptOssPreTrainedModel,xx as GraniteForCausalLM,wx as GraniteModel,bx as GraniteMoeHybridForCausalLM,yx as GraniteMoeHybridModel,Xa as GraniteMoeHybridPreTrainedModel,Ha as GranitePreTrainedModel,F_ as GraniteSpeechFeatureExtractor,vx as GraniteSpeechForConditionalGeneration,Hm as GraniteSpeechProcessor,kx as GroundingDinoForObjectDetection,dm as GroundingDinoImageProcessor,Qu as GroundingDinoPreTrainedModel,Xm as GroundingDinoProcessor,Ex as GroupViTModel,Ju as GroupViTPreTrainedModel,Mx as HeliumForCausalLM,Ax as HeliumModel,Ka as HeliumPreTrainedModel,c_ as HerbertTokenizer,Sx as HieraForImageClassification,Tx as HieraModel,Ya as HieraPreTrainedModel,Nx as HubertForCTC,$x as HubertForSequenceClassification,Lx as HubertModel,zx as HubertPreTrainedModel,Dx as HunYuanDenseV1ForCausalLM,Rx as HunYuanDenseV1Model,Qa as HunYuanDenseV1PreTrainedModel,Bx as IJepaForImageClassification,Fx as IJepaModel,Za as IJepaPreTrainedModel,Ja as Idefics3ForConditionalGeneration,Qc as Idefics3ImageProcessor,iu as Idefics3Processor,Nl as ImageClassificationPipeline,ql as ImageFeatureExtractionPipeline,H as ImageFeatureExtractor,H as ImageProcessor,Vs as ImageSegmentationPipeline,Ul as ImageToImagePipeline,Ll as ImageToTextPipeline,yM as InterruptableStoppingCriteria,jx as JAISLMHeadModel,Ux as JAISModel,ei as JAISPreTrainedModel,mm as JinaCLIPImageProcessor,Gx as JinaCLIPModel,Bn as JinaCLIPPreTrainedModel,Ym as JinaCLIPProcessor,ti as JinaCLIPTextModel,qx as JinaCLIPVisionModel,Vx as Lfm2ForCausalLM,Wx as Lfm2Model,Kx as Lfm2MoeForCausalLM,Xx as Lfm2MoeModel,si as Lfm2MoePreTrainedModel,ri as Lfm2PreTrainedModel,Yx as Lfm2VlForConditionalGeneration,hm as Lfm2VlImageProcessor,Qm as Lfm2VlProcessor,Hx as LightOnOcrForConditionalGeneration,G1 as LiteWhisperForConditionalGeneration,Zx as Llama4ForCausalLM,Zu as Llama4PreTrainedModel,Jx as LlamaForCausalLM,Qx as LlamaModel,ni as LlamaPreTrainedModel,u_ as LlamaTokenizer,kt as LlavaForConditionalGeneration,kt as LlavaOnevisionForConditionalGeneration,gm as LlavaOnevisionImageProcessor,Wu as LlavaPreTrainedModel,Jm as LlavaProcessor,Xw as LlavaQwen2ForCausalLM,St as LogLevel,$t as LogitsProcessor,Cs as LogitsProcessorList,Ko as LogitsWarper,ty as LongT5ForConditionalGeneration,ey as LongT5Model,oi as LongT5PreTrainedModel,sy as M2M100ForConditionalGeneration,ry as M2M100Model,ai as M2M100PreTrainedModel,p_ as M2M100Tokenizer,d_ as MBart50Tokenizer,py as MBartForCausalLM,cy as MBartForConditionalGeneration,uy as MBartForSequenceClassification,ly as MBartModel,Bs as MBartPreTrainedModel,zo as MBartTokenizer,Qy as MPNetForMaskedLM,e0 as MPNetForQuestionAnswering,Jy as MPNetForSequenceClassification,Zy as MPNetForTokenClassification,Yy as MPNetModel,rs as MPNetPreTrainedModel,h_ as MPNetTokenizer,n0 as MT5ForConditionalGeneration,s0 as MT5Model,xi as MT5PreTrainedModel,oy as MarianMTModel,ny as MarianModel,ii as MarianPreTrainedModel,f_ as MarianTokenizer,xm as Mask2FormerImageProcessor,wm as MaskFormerFeatureExtractor,iy as MaskFormerForInstanceSegmentation,wn as MaskFormerImageProcessor,ay as MaskFormerModel,li as MaskFormerPreTrainedModel,Au as MaxLengthCriteria,fy as Metric3DForDepthEstimation,ep as Metric3DPreTrainedModel,dy as Metric3Dv2ForDepthEstimation,tp as Metric3Dv2PreTrainedModel,_y as MgpstrForSceneTextRecognition,rp as MgpstrModelOutput,sp as MgpstrPreTrainedModel,Zm as MgpstrProcessor,__ as MgpstrTokenizer,ui as MimiDecoderModel,op as MimiDecoderOutput,ci as MimiEncoderModel,np as MimiEncoderOutput,my as MimiModel,Un as MimiPreTrainedModel,yu as MinLengthLogitsProcessor,bu as MinNewTokensLengthLogitsProcessor,xy as Mistral4ForCausalLM,wy as Mistral4Model,fi as Mistral4PreTrainedModel,gy as MistralForCausalLM,hy as MistralModel,pi as MistralPreTrainedModel,by as MobileBertForMaskedLM,ky as MobileBertForQuestionAnswering,vy as MobileBertForSequenceClassification,yy as MobileBertModel,Us as MobileBertPreTrainedModel,m_ as MobileBertTokenizer,Ay as MobileLLMForCausalLM,Ey as MobileLLMModel,di as MobileLLMPreTrainedModel,ym as MobileNetV1FeatureExtractor,Ty as MobileNetV1ForImageClassification,Sy as MobileNetV1ForSemanticSegmentation,Jc as MobileNetV1ImageProcessor,My as MobileNetV1Model,jn as MobileNetV1PreTrainedModel,bm as MobileNetV2FeatureExtractor,Iy as MobileNetV2ForImageClassification,Cy as MobileNetV2ForSemanticSegmentation,Zc as MobileNetV2ImageProcessor,Oy as MobileNetV2Model,Gn as MobileNetV2PreTrainedModel,vm as MobileNetV3FeatureExtractor,zy as MobileNetV3ForImageClassification,Ly as MobileNetV3ForSemanticSegmentation,eu as MobileNetV3ImageProcessor,Py as MobileNetV3Model,qn as MobileNetV3PreTrainedModel,km as MobileNetV4FeatureExtractor,$y as MobileNetV4ForImageClassification,Ry as MobileNetV4ForSemanticSegmentation,tu as MobileNetV4ImageProcessor,Ny as MobileNetV4Model,Wn as MobileNetV4PreTrainedModel,Em as MobileViTFeatureExtractor,Fy as MobileViTForImageClassification,ru as MobileViTImageProcessor,Dy as MobileViTModel,_i as MobileViTPreTrainedModel,Uy as MobileViTV2ForImageClassification,By as MobileViTV2Model,mi as MobileViTV2PreTrainedModel,iv as ModelRegistry,Hy as ModernBertDecoderForCausalLM,Vy as ModernBertDecoderModel,hi as ModernBertDecoderPreTrainedModel,Gy as ModernBertForMaskedLM,qy as ModernBertForSequenceClassification,Wy as ModernBertForTokenClassification,jy as ModernBertModel,js as ModernBertPreTrainedModel,Hw as Moondream1ForConditionalGeneration,B_ as MoonshineFeatureExtractor,Ky as MoonshineForConditionalGeneration,Xy as MoonshineModel,gi as MoonshinePreTrainedModel,eh as MoonshineProcessor,r0 as MptForCausalLM,t0 as MptModel,wi as MptPreTrainedModel,o0 as MultiModalityCausalLM,ap as MultiModalityPreTrainedModel,i0 as MusicgenForCausalLM,bi as MusicgenForConditionalGeneration,a0 as MusicgenModel,yi as MusicgenPreTrainedModel,c0 as NanoChatForCausalLM,l0 as NanoChatModel,vi as NanoChatPreTrainedModel,p0 as NemotronHForCausalLM,u0 as NemotronHModel,ki as NemotronHPreTrainedModel,d0 as NeoBertForMaskedLM,h0 as NeoBertForQuestionAnswering,_0 as NeoBertForSequenceClassification,m0 as NeoBertForTokenClassification,f0 as NeoBertModel,ss as NeoBertPreTrainedModel,g_ as NllbTokenizer,vu as NoBadWordsLogitsProcessor,wu as NoRepeatNGramLogitsProcessor,g0 as NomicBertModel,ip as NomicBertPreTrainedModel,Am as NougatImageProcessor,w_ as NougatTokenizer,C0 as OPTForCausalLM,I0 as OPTModel,Ii as OPTPreTrainedModel,Dl as ObjectDetectionPipeline,b0 as Olmo2ForCausalLM,y0 as Olmo2Model,Ai as Olmo2PreTrainedModel,k0 as Olmo3ForCausalLM,v0 as Olmo3Model,Mi as Olmo3PreTrainedModel,x0 as OlmoForCausalLM,A0 as OlmoHybridForCausalLM,E0 as OlmoHybridModel,Ti as OlmoHybridPreTrainedModel,w0 as OlmoModel,Ei as OlmoPreTrainedModel,T0 as OpenAIPrivacyFilterForTokenClassification,M0 as OpenAIPrivacyFilterModel,Si as OpenAIPrivacyFilterPreTrainedModel,O0 as OpenELMForCausalLM,S0 as OpenELMModel,Oi as OpenELMPreTrainedModel,Mm as OwlViTFeatureExtractor,N0 as OwlViTForObjectDetection,xn as OwlViTImageProcessor,L0 as OwlViTModel,Pi as OwlViTPreTrainedModel,th as OwlViTProcessor,z0 as Owlv2ForObjectDetection,Tm as Owlv2ImageProcessor,P0 as Owlv2Model,Ci as Owlv2PreTrainedModel,$0 as PaliGemmaForConditionalGeneration,rh as PaliGemmaProcessor,Do as ParakeetFeatureExtractor,R0 as ParakeetForCTC,lp as ParakeetPreTrainedModel,F0 as PatchTSMixerForPrediction,D0 as PatchTSMixerModel,zi as PatchTSMixerPreTrainedModel,U0 as PatchTSTForPrediction,B0 as PatchTSTModel,Li as PatchTSTPreTrainedModel,W0 as Phi3ForCausalLM,q0 as Phi3Model,$i as Phi3PreTrainedModel,Ri as Phi3VForCausalLM,Im as Phi3VImageProcessor,cp as Phi3VPreTrainedModel,sh as Phi3VProcessor,G0 as PhiForCausalLM,j0 as PhiModel,Ni as PhiPreTrainedModel,Cm as PixtralImageProcessor,nh as PixtralProcessor,y as PreTrainedModel,W as PreTrainedTokenizer,En as PretrainedConfig,re as Processor,H0 as PvtForImageClassification,Pm as PvtImageProcessor,V0 as PvtModel,Di as PvtPreTrainedModel,jo as PyAnnoteFeatureExtractor,K0 as PyAnnoteForAudioFrameClassification,X0 as PyAnnoteModel,Fi as PyAnnotePreTrainedModel,oh as PyAnnoteProcessor,El as QuestionAnsweringPipeline,Q0 as Qwen2ForCausalLM,Y0 as Qwen2Model,Z0 as Qwen2MoeForCausalLM,J0 as Qwen2MoeModel,Ui as Qwen2MoePreTrainedModel,Bi as Qwen2PreTrainedModel,x_ as Qwen2Tokenizer,Da as Qwen2VLForCausalLM,Fn as Qwen2VLForConditionalGeneration,Wo as Qwen2VLImageProcessor,Ku as Qwen2VLPreTrainedModel,Os as Qwen2VLProcessor,Fa as Qwen2_5_VLForCausalLM,Ds as Qwen2_5_VLForConditionalGeneration,Ho as Qwen2_5_VLProcessor,tb as Qwen3ForCausalLM,eb as Qwen3Model,sb as Qwen3MoeForCausalLM,rb as Qwen3MoeModel,Gi as Qwen3MoePreTrainedModel,ob as Qwen3NextForCausalLM,nb as Qwen3NextModel,qi as Qwen3NextPreTrainedModel,ji as Qwen3PreTrainedModel,Wi as Qwen3VLForCausalLM,Gs as Qwen3VLForConditionalGeneration,ib as Qwen3VLMoeForCausalLM,ab as Qwen3VLMoeForConditionalGeneration,ah as Qwen3VLProcessor,Vi as Qwen3_5ForCausalLM,Vn as Qwen3_5ForConditionalGeneration,cb as Qwen3_5MoeForCausalLM,lb as Qwen3_5MoeForConditionalGeneration,db as RFDetrForObjectDetection,fb as RFDetrModel,up as RFDetrObjectDetectionOutput,Xi as RFDetrPreTrainedModel,zg as RTDetrForObjectDetection,zm as RTDetrImageProcessor,Pg as RTDetrModel,fr as RTDetrObjectDetectionOutput,xa as RTDetrPreTrainedModel,Ab as RTDetrV2ForObjectDetection,Eb as RTDetrV2Model,pp as RTDetrV2ObjectDetectionOutput,Ki as RTDetrV2PreTrainedModel,$o as RawAudio,Je as RawImage,Yp as RawVideo,Wl as RawVideoFrame,xu as RepetitionPenaltyLogitsProcessor,pb as ResNetForImageClassification,ub as ResNetModel,Hi as ResNetPreTrainedModel,yb as RoFormerForMaskedLM,kb as RoFormerForQuestionAnswering,bb as RoFormerForSequenceClassification,vb as RoFormerForTokenClassification,xb as RoFormerModel,os as RoFormerPreTrainedModel,b_ as RoFormerTokenizer,mb as RobertaForMaskedLM,wb as RobertaForQuestionAnswering,hb as RobertaForSequenceClassification,gb as RobertaForTokenClassification,_b as RobertaModel,ns as RobertaPreTrainedModel,y_ as RobertaTokenizer,Vo as Sam2ImageProcessor,_p as Sam2ImageSegmentationOutput,Yi as Sam2Model,mp as Sam2PreTrainedModel,lu as Sam2Processor,ih as Sam2VideoProcessor,Vo as Sam3ImageProcessor,Sb as Sam3TrackerModel,Vo as SamImageProcessor,fp as SamImageSegmentationOutput,Mb as SamModel,dp as SamPreTrainedModel,Xo as SamProcessor,Lm as SapiensFeatureExtractor,Ib as SapiensForDepthEstimation,Cb as SapiensForNormalEstimation,Ob as SapiensForSemanticSegmentation,su as SapiensImageProcessor,Hn as SapiensPreTrainedModel,U_ as SeamlessM4TFeatureExtractor,Nm as SegformerFeatureExtractor,zb as SegformerForImageClassification,Lb as SegformerForSemanticSegmentation,nu as SegformerImageProcessor,Pb as SegformerModel,Xn as SegformerPreTrainedModel,$m as SiglipImageProcessor,Nb as SiglipModel,Qi as SiglipPreTrainedModel,Ji as SiglipTextModel,v_ as SiglipTokenizer,$b as SiglipVisionModel,Db as SmolLM3ForCausalLM,Rb as SmolLM3Model,Zi as SmolLM3PreTrainedModel,Fb as SmolVLMForConditionalGeneration,Qc as SmolVLMImageProcessor,iu as SmolVLMProcessor,tl as SnacDecoderModel,el as SnacEncoderModel,j_ as SnacFeatureExtractor,Bb as SnacModel,Kn as SnacPreTrainedModel,jb as SolarOpenForCausalLM,Ub as SolarOpenModel,rl as SolarOpenPreTrainedModel,G_ as SpeechT5FeatureExtractor,qb as SpeechT5ForSpeechToText,Wb as SpeechT5ForTextToSpeech,Vb as SpeechT5HifiGan,Gb as SpeechT5Model,Yn as SpeechT5PreTrainedModel,lh as SpeechT5Processor,k_ as SpeechT5Tokenizer,Xb as SqueezeBertForMaskedLM,Yb as SqueezeBertForQuestionAnswering,Kb as SqueezeBertForSequenceClassification,Hb as SqueezeBertModel,qs as SqueezeBertPreTrainedModel,E_ as SqueezeBertTokenizer,Jb as StableLmForCausalLM,Qb as StableLmModel,sl as StableLmPreTrainedModel,e1 as Starcoder2ForCausalLM,Zb as Starcoder2Model,nl as Starcoder2PreTrainedModel,Hr as StoppingCriteria,Tn as StoppingCriteriaList,t1 as StyleTextToSpeech2Model,hp as StyleTextToSpeech2PreTrainedModel,Ml as SummarizationPipeline,ol as SupertonicForConditionalGeneration,gp as SupertonicPreTrainedModel,An as SuppressTokensAtBeginLogitsProcessor,hu as SuppressTokensLogitsProcessor,a1 as Swin2SRForImageSuperResolution,Rm as Swin2SRImageProcessor,o1 as Swin2SRModel,al as Swin2SRPreTrainedModel,s1 as SwinForImageClassification,n1 as SwinForSemanticSegmentation,r1 as SwinModel,Qn as SwinPreTrainedModel,l1 as T5ForConditionalGeneration,i1 as T5Model,il as T5PreTrainedModel,A_ as T5Tokenizer,u1 as TableTransformerForObjectDetection,c1 as TableTransformerModel,wp as TableTransformerObjectDetectionOutput,ll as TableTransformerPreTrainedModel,Eu as TemperatureLogitsWarper,N as Tensor,kr as Text2TextGenerationPipeline,vl as TextClassificationPipeline,Sl as TextGenerationPipeline,av as TextStreamer,zl as TextToAudioPipeline,kl as TokenClassificationPipeline,W as TokenizersBackend,xM as TopKLogitsWarper,wM as TopPLogitsWarper,p1 as TrOCRForCausalLM,xp as TrOCRPreTrainedModel,Tl as TranslationPipeline,Fs as UltravoxModel,Yu as UltravoxPreTrainedModel,ch as UltravoxProcessor,d1 as UniSpeechForCTC,_1 as UniSpeechForSequenceClassification,f1 as UniSpeechModel,Jn as UniSpeechPreTrainedModel,w1 as UniSpeechSatForAudioFrameClassification,h1 as UniSpeechSatForCTC,g1 as UniSpeechSatForSequenceClassification,m1 as UniSpeechSatModel,Ws as UniSpeechSatPreTrainedModel,Km as VLChatProcessor,_m as VLMImageProcessor,y1 as VaultGemmaForCausalLM,x1 as VaultGemmaModel,cl as VaultGemmaPreTrainedModel,Dm as ViTFeatureExtractor,k1 as ViTForImageClassification,ou as ViTImageProcessor,E1 as ViTMAEModel,yp as ViTMAEPreTrainedModel,M1 as ViTMSNForImageClassification,A1 as ViTMSNModel,pl as ViTMSNPreTrainedModel,v1 as ViTModel,ul as ViTPreTrainedModel,b1 as VisionEncoderDecoderModel,T1 as VitMatteForImageMatting,Fm as VitMatteImageProcessor,bp as VitMattePreTrainedModel,S1 as VitPoseForPoseEstimation,Bm as VitPoseImageProcessor,vp as VitPosePreTrainedModel,O1 as VitsModel,kp as VitsModelOutput,Ep as VitsPreTrainedModel,T_ as VitsTokenizer,I1 as VoxtralForConditionalGeneration,uh as VoxtralProcessor,V_ as VoxtralRealtimeFeatureExtractor,fl as VoxtralRealtimeForConditionalGeneration,Ap as VoxtralRealtimePreTrainedModel,fh as VoxtralRealtimeProcessor,L1 as Wav2Vec2BertForCTC,N1 as Wav2Vec2BertForSequenceClassification,z1 as Wav2Vec2BertModel,Zn as Wav2Vec2BertPreTrainedModel,S_ as Wav2Vec2CTCTokenizer,q_ as Wav2Vec2FeatureExtractor,Px as Wav2Vec2ForAudioFrameClassification,Ix as Wav2Vec2ForCTC,Cx as Wav2Vec2ForSequenceClassification,Ox as Wav2Vec2Model,er as Wav2Vec2PreTrainedModel,dh as Wav2Vec2Processor,_h as Wav2Vec2ProcessorWithLM,B1 as WavLMForAudioFrameClassification,R1 as WavLMForCTC,D1 as WavLMForSequenceClassification,F1 as WavLMForXVector,$1 as WavLMModel,as as WavLMPreTrainedModel,W_ as WeSpeakerFeatureExtractor,U1 as WeSpeakerResNetModel,Tp as WeSpeakerResNetPreTrainedModel,H_ as WhisperFeatureExtractor,Op as WhisperForConditionalGeneration,j1 as WhisperModel,dl as WhisperPreTrainedModel,mh as WhisperProcessor,mT as WhisperTextStreamer,gu as WhisperTimeStampLogitsProcessor,O_ as WhisperTokenizer,X1 as XLMForQuestionAnswering,V1 as XLMForSequenceClassification,H1 as XLMForTokenClassification,q1 as XLMModel,is as XLMPreTrainedModel,Y1 as XLMRobertaForMaskedLM,Z1 as XLMRobertaForQuestionAnswering,Q1 as XLMRobertaForSequenceClassification,J1 as XLMRobertaForTokenClassification,K1 as XLMRobertaModel,ls as XLMRobertaPreTrainedModel,I_ as XLMRobertaTokenizer,C_ as XLMTokenizer,W1 as XLMWithLMHeadModel,Mp as XVectorOutput,Um as YolosFeatureExtractor,tv as YolosForObjectDetection,au as YolosImageProcessor,ev as YolosModel,Ip as YolosObjectDetectionOutput,_l as YolosPreTrainedModel,sv as YoutuForCausalLM,rv as YoutuModel,ml as YoutuPreTrainedModel,Cl as ZeroShotAudioClassificationPipeline,Ol as ZeroShotClassificationPipeline,Rl as ZeroShotImageClassificationPipeline,Fl as ZeroShotObjectDetectionPipeline,ve as cat,Zz as cos_sim,lA as dot,me as env,Qe as full,Co as full_like,Nd as interpolate,bt as interpolate_4d,z4 as layer_norm,nM as load_audio,oN as load_image,e3 as load_video,Uf as log_softmax,K2 as matmul,Rc as mean,Y2 as mean_pooling,et as ones,Dc as ones_like,NL as permute,Rle as pipeline,J2 as quantize_embeddings,L4 as rand,Q2 as randn,hs as random,L_ as read_audio,P4 as rfft,$c as slice,$e as softmax,xt as stack,$d as std_mean,Jt as topk,Dd as zeros,Fd as zeros_like};