.`i}~UddlmZmZddlmZmZddlmZmZddl m Z ddl m Z m Z ddlmZddlmZmZmZmZmZmZmZmZmZddlZdd lmZdd lmZm Z dd l!m"Z"m#Z#dd l$m%Z%dd l&m'Z'erddl(Z(ddl)Z(ddl*m+Z+ddl,m-Z-ne%de.dZ(e dZ/edej0dfZ1ee2d< ee3dej0de3ej0e3dfZ4ee2d< ee3e5ej0dfZ6ee2d< ee1ddfZ7ee2d< ee4de8e4e9e:efffZ;ee2d< ee6e8ej0e5fdfZGdd eZ?e>e?zZ@ eGd!d"ed#$ZAee:e=efZBee2d%< ee:e3e:dze:zfZCee2d&< e d'(Gd)d*ZDee3d+e3dde8d,fZEee2d+< d-eEd.eEd/eFfd0ZGd1eEd2e(jHjId/eEfd3ZJe9e:eEfZKee2d4< d-eKd.eKd/eFfd5ZLe Gd6d7ZMe Gd8d9ZNe d'd':Gd;deOZPe d'd':Gd?d@eOZQe d'd':GdAdBeOZRe d'(GdCdDZSGdEdFee:eNfZTe dGeTeTdzeTHZUGdIdJee:eeUfZVeVeTeVeTdzzZWee2dK<e9e:e3e:fZX ee:eeDfZYee2dL< GdMdNeZZGdOdPeZZ[dS)Q)ABCabstractmethod)UserDict defaultdict)MappingSequence) dataclass)cached_propertypartial) accumulate) TYPE_CHECKINGAnyLiteralOptional TypeAlias TypedDictUnioncastfinalN)Image) NotRequiredTypeVar) full_groupby is_list_of) LazyLoaderjson_map_leaves) BatchFeature)MediaWithBytestorch_Tr torch.Tensor HfImageItem HfVideoItem HfAudioItemzMediaWithBytes[HfImageItem] ImageItem VideoItem AudioItem ModalityDatacDeZdZUdZeded<eed<edzed<dS)VisionChunkImagez.Represents an image wrapped as a vision chunk.imagetypeNuuid)__name__ __module__ __qualname____doc__r__annotations__rstrj/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/vllm/multimodal/inputs.pyr,r,lsB88 '  LLL *r7r,cdeZdZUdZeded<eeed<edzed<eed<e ed<dS)VisionChunkVideoz'Represents a video chunk with metadata. video_chunkr.Nr/prompt video_idx) r0r1r2r3rr4listrr5intr6r7r8r:r:tsY11 -    e * KKKNNNNNr7r:creZdZUdZeeed< eeed< eeed< ee ed<dS)MultiModalDataBuiltinsz7Type annotations for modality types predefined by vLLM.r-videoaudio vision_chunkN) r0r1r2r3r*r'r4r(r) VisionChunkr6r7r8rArAseAA  """"  """"  """"{++++RRr7rAF)totalMultiModalDataDictMultiModalUUIDDictT)frozenceZdZUdZeed< eed< dZeded< ede j dzfdZ edefd Z d ed ede eeffd Zdee eeffd ZdedefdZdS)PlaceholderRangea Placeholder location information for multi-modal data. Example: Prompt: `AAAA BBBB What is in these images?` Images A and B will have: ``` A: PlaceholderRange(offset=0, length=4) B: PlaceholderRange(offset=5, length=4) ``` offsetlengthNr#is_embedreturncJ|jdn|jdS)Nrdim)rNcumsumselfs r8 embeds_cumsumzPlaceholderRange.embeds_cumsums&},tt$-2F2F12F2M2MMr7cR|j|jSt|jdS)N)rVrMr?rTs r8get_num_embedszPlaceholderRange.get_num_embedss)   %; 4%b)***r7 start_idxend_idxc|j||fS|dkrt|j|dz nd}t|j|dz }||fS)a Returns the starting and ending indices of the embeddings of encoder outputs in the range of [start_idx, end_idx) in the placeholders. For example, given: PlaceholderRange(offset=2, length=5, is_embed=[False, True, False, True, True]) If start_idx=3 and end_idx=5, the output is (1, 3) because we want to get the second and the third embeddings from the encoder output. Nrr)rVr?)rUrZr[embeds_start_idxembeds_end_idxs r8get_embeds_indices_in_rangez,PlaceholderRange.get_embeds_indices_in_rangesj   %g% %7@!mmC"9q=1 2 2 2 T/! <==//r7c6|j|j|j|jzdz fgS|j}t jt j||ddk}t jt j||ddk}t j ||fd|jz}d| DS)aExtract the start and end indices of the embedded region in prompt. For example, given `PlaceholderRange(offset=2, length=5)` and `is_embed = [False, True, False, True, True]`, the output is `[(1 + offset, 1 + offset), (3 + offset, 4 + offset)]`. Returns: A tuple `(start, end)` representing the start and end indices (inclusive) of the embedded region. Returns full placeholder range if `is_embed` is `None`. Nr)prepend)appendrXrQc,g|]}t|Sr6)tuple).0xs r8 z9PlaceholderRange.extract_embeds_range..s222Qa222r7) rNrLrMr?r!nonzerodiff new_zerosflattenstacktolist)rUmask_istartsendsrangess r8extract_embeds_rangez%PlaceholderRange.extract_embeds_ranges = [$+ ";a"?@A A""$$ Jvv'7'7':': ; ; ;q @  ')) } Jvf&6&6q&9&9 : : :b @  ')) fd^333dkA22&--//2222r7otherct||jsdS|j|jf|j|jfksdS|j |jduS|j |jduSt |j|jSNF) isinstance __class__rLrMrNnested_tensors_equal)rUrss r8__eq__zPlaceholderRange.__eq__s{%00 5 T[)elEL-III5 = >T) ) > !=D( (#DM5>BBBr7)r0r1r2r3r?r4rNrr r!TensorrVrYrdr_r>rrobjectboolryr6r7r8rKrKs4   KKK; KKK()-Hh~&--- Nu|d2NNN_N++++_+ 00'*0 sCx0000.3d5c?&;33332 CF Ct C C C C C Cr7rK NestedTensors)r#.abrOcvt|tjr/t|tjotj||St|tjr/t|tjotj||St|trz'nested_tensors_equal.."B+ + -3R R ( (+ + + + + + r7c3<K|]\}}t||VdSrr)rerrs r8rz'nested_tensors_equal..&rr7)rvr!rzequalr>allzipr~rs r8rxrxs3 !U\""A!U\**@u{1a/@/@@ Au| $ $A!U\**@u{1a/@/@@!T !T"" s+ + 7:1ayy+ + + ( (  !T !T"" s+ + 7:1ayy+ + + ( (  6Mr7tensorsdevicec2|Stfd|S)Ncjt|tjr|dn|S)NT)r non_blocking)rvr!rzto)rfrs r8z%_nested_tensors_h2d..7s4!U\**add&td<<<r7r)rrs `r8_nested_tensors_h2dr.s:~         r7BatchedTensorInputsc>tfdDS)zq Equality check between [`BatchedTensorInputs`][vllm.multimodal.inputs.BatchedTensorInputs] objects. c3XK|]$}|vot||V%dSrr)rekr~rs r8rz(batched_tensors_equal..Ks?FFqqAv:.qtQqT::FFFFFFr7)rrs``r8batched_tensors_equalrFs, FFFFFAFFF F FFr7ceZdZUdZeded< eed< eed< eed< dZedzed< e d e dd e efd Z dS) MultiModalFeatureSpeca  Represents a single multimodal input with its processed data and metadata. Used by the V1 engine to track multimodal data through processing and caching. A request containing multiple multimodal items will have one MultiModalFeatureSpec per item. MultiModalKwargsItemdatamodality identifier mm_positionNmm_hashfeatureskeyscttttft}|D]:}|j}|/|D],}||vr&||||j-;t |Sr)rr5r>r}rrbdict)rrkwargsfitemrs r8 gather_kwargsz#MultiModalFeatureSpec.gather_kwargsgs~S$}"556t<< 7 7A6D77ADyyq ((a666F||r7) r0r1r2r3rr4r5rKr staticmethodr>setrr6r7r8rrNs ) *****MMM>OOO6!!!!6GS4ZA %< = SX   \   r7rcTeZdZUdZeed< eed< eed< ded< dedefd Z d S) MultiModalFieldElemz{ Represents a keyword argument inside a [`MultiModalKwargsItem`][vllm.multimodal.inputs.MultiModalKwargsItem]. rkeyrBaseMultiModalFieldfieldrsrOc8t||jsdS|j |jdu}n+|j |jdu}nt|j|j}|j|jf|j|jfko)|o't |jt |juSru)rvrwrrxrrr.r)rUrs data_equals r8ryzMultiModalFieldElem.__eq__s%00 5 9 t+JJ Z d*JJ-diDDJ]DH %%.%))D D 6 6TZ  D$5$55 r7N) r0r1r2r3r5r4r}r{r|ryr6r7r8rrus MMM HHH  !     F t      r7r)rIkw_onlyc eZdZUdZdZeed< dedefdZe dedede de e fd Z e d ee d ede fd Zd dddee dejjd ede fdZd S)rz Defines how to interpret tensor data belonging to a keyword argument for [`MultiModalKwargsItems`][vllm.multimodal.inputs.MultiModalKwargsItems], and vice versa. F keep_on_cpurrc`tt|||dtdtffd }|S)N)rrrrrOc|S)Nrr6)rrs r8factoryz3BaseMultiModalField._field_factory..factorys1$<<< r7)r rr})rUrrrrs @r8_field_factoryz"BaseMultiModalField._field_factorysW       - ,?      r7rrOct)a  Construct [`MultiModalFieldElem`][vllm.multimodal.inputs.MultiModalFieldElem] instances to represent the provided data. This is the inverse of [`reduce_data`][vllm.multimodal.inputs.BaseMultiModalField.reduce_data]. NotImplementedError)rUrrrs r8 build_elemszBaseMultiModalField.build_elemss "!r7batch pin_memoryctrrrUrrs r8 _reduce_dataz BaseMultiModalField._reduce_datas "!r7Nrrelemsrcd|D}tt|dkrtd|| |jrd}|r |jrd}d|D}|||}t || S) z Merge the data from multiple instances of [`MultiModalFieldElem`][vllm.multimodal.inputs.MultiModalFieldElem]. This is the inverse of [`build_elems`][vllm.multimodal.inputs.BaseMultiModalField.build_elems]. c6g|]}t|jSr6)r.r)rers r8rgz3BaseMultiModalField.reduce_data..s":::DtDJ'':::r7rz#Cannot merge different field_types=NcpuFcg|] }|j Sr6rreelems r8rgz3BaseMultiModalField.reduce_data..s---t---r7)r)r)lenr ValueErrorrrr)rUrrr field_typesrouts r8 reduce_datazBaseMultiModalField.reduce_datas;:E::: s; 1 $ $E{EEFF F  $"2 F  $* J--u---*=="3v6666r7)r0r1r2r3rr|r4r5rrr}rrrr>rr!typesDevicerr6r7r8rrs= K # C    """ " % & """^" "M"" "  """^"&* 777'(7 " 7  7  777777r7rc VeZdZdZdedededeefdZde ede defd Z d S) MultiModalBatchedFieldzo Info: [`MultiModalFieldConfig.batched`][vllm.multimodal.inputs.MultiModalFieldConfig.batched] rrrrOcN|||fd|DS)Nrrc&g|] }|Sr6r6)rer field_factorys r8rgz6MultiModalBatchedField.build_elems.. s#555 d##555r7)rrUrrrrs @r8rz"MultiModalBatchedField.build_elemss7 ++X3+GG 55555555r7rrcBt|dkr t|tjdrt t tj|}t|dkr-|ddS|djtfd|Dr`tj t|g|djR|dj |dj |}tj ||S|S)Nrrcheckrc3.K|]}|jkVdSrshape)rer first_shapes r8rz6MultiModalBatchedField._reduce_data..s*??4:,??????r7dtyperr)r)rrr!rzrr> unsqueeze contiguousrremptyrrrl)rUrrrrs @r8rz#MultiModalBatchedField._reduce_data s u::>>j EJJJ>el+U33E5zzQQx))!,,77999(.K????????? 3kZZ1%(.11(. 8?)  {5c2222 r7N) r0r1r2r3r5r}rrrr>r|rr6r7r8rrs 666 6 % & 6666M"   r7rc eZdZUdZeeeeezed<dZeed<de de de dee fd Z d e e d ede fd Zd S)MultiModalFlatFieldz Info: [`MultiModalFieldConfig.flat`][vllm.multimodal.inputs.MultiModalFieldConfig.flat] [`MultiModalFieldConfig.flat_from_sizes`][vllm.multimodal.inputs.MultiModalFieldConfig.flat_from_sizes] slicesrrRrrrrOc|||t|jtds$t t js Jdfd|jDS)Nrrrz,torch.Tensor is required for multiple slicesc Xg|]&}tt|'Sr6)rslice)resrrs r8rgz3MultiModalFlatField.build_elems..=s0III d4q>>233IIIr7)rrrrrvr!rzrs `@r8rzMultiModalFlatField.build_elems2s ++X3+GG $+uE::: dEL11  >  1JIIIIT[IIIIr7rrc  t|dkrMt|tjdr0t t tj|}t|dkr|dS|j|jdkt|djzzdtjffd |d t fd|Drq \}}tfd|D}tj g|||R|dj |dj | }tj||j| S|jdks Jd d |DS) NrrrrtensorcF|jd|jdzdfSNrr)rrRs r8_shape_before_afterz=MultiModalFlatField._reduce_data.._shape_before_afterOs(|DSD)6<a +BBBr7c36K|]}|kVdSrr6)rerrrs r8rz3MultiModalFlatField._reduce_data..Ts4NN&&t,, ;NNNNNNr7c32K|]}|jVdSrr)rerrRs r8rz3MultiModalFlatField._reduce_data..Vs)"E"Et4:c?"E"E"E"E"E"Er7r)rRrz$dim == 0 is required for nested listcg|] }|D]}| Sr6r6)reres r8rgz4MultiModalFlatField._reduce_data..`s%222dT222222r7)rrr!rzrr>rrRrrsumrrrconcat) rUrr shape_before shape_after shape_concatrrrRrs @@@r8rz MultiModalFlatField._reduce_data?s u::>>j EJJJ>el+U33E5zzQQx**,,,(dhlc%(..A.AAAC CEL C C C C C C.-eAh77KNNNNNNNNNN B,7) k""E"E"E"Eu"E"E"EEE k?l?L?;??(. 8?)  |EtxSAAAAx1}}}D}}}22e2222r7N)r0r1r2r3rrr4rRr?r5r}rrr>r|rr6r7r8rr's UOhx7 7777CLLL J J J J % & J J J J!3M"!3 !3  !3!3!3!3!3!3r7rc beZdZUdZeed<dedededee fdZ de ed e defd Z d S) MultiModalSharedFieldzm Info: [`MultiModalFieldConfig.shared`][vllm.multimodal.inputs.MultiModalFieldConfig.shared] batch_sizerrrrOcX|||}||g|jzS)Nr)rrrs r8rz!MultiModalSharedField.build_elemsls6 ++X3+GG  d##$t66r7rrc|dS)Nrr6rs r8rz"MultiModalSharedField._reduce_dataus Qxr7N)r0r1r2r3r?r4r5r}rrrr>r|rr6r7r8rrcs OOO777 7 % & 7777M"   r7rc "eZdZUedddedefdZe ddddedeeeeezde defd Z e dddded d de defd Z eddded e defdZ e ed<eed<dededeefdZdS)MultiModalFieldConfigFrrrc@tt||S)aN Defines a field where an element in the batch is obtained by indexing into the first dimension of the underlying data. Args: modality: The modality of the multi-modal item that uses this keyword argument. keep_on_cpu: Whether to keep this field on the CPU for the model inputs. Example: ``` Input: Data: [[AAAA] [BBBB] [CCCC]] Output: Element 1: [AAAA] Element 2: [BBBB] Element 3: [CCCC] ``` rrr)rr)rrs r8batchedzMultiModalFieldConfig.batcheds,2%([AAA    r7rrrRcDtt||||S)a Defines a field where an element in the batch is obtained by slicing along the first dimension of the underlying data. Args: modality: The modality of the multi-modal item that uses this keyword argument. slices: For each multi-modal item, a slice (dim=0) or a tuple of slices (dim>0) that is used to extract the data corresponding to it. dim: The dimension to extract data, default to 0. keep_on_cpu: Whether to keep this field on the CPU for the model inputs. Example: ``` Given: slices: [slice(0, 3), slice(3, 7), slice(7, 9)] Input: Data: [AAABBBBCC] Output: Element 1: [AAA] Element 2: [BBBB] Element 3: [CC] ``` ``` Given: slices: [ (slice(None), slice(0, 3)), (slice(None), slice(3, 7)), (slice(None), slice(7, 9))] dim: 1 Input: Data: [[A],[A],[A],[B],[B],[B],[B],[C],[C]] Output: Element 1: [[A],[A],[A]] Element 2: [[B],[B],[B],[B]] Element 3: [[C],[C]] ``` )rrRrr)rr)rrrRrs r8flatzMultiModalFieldConfig.flats<j%%'      r7 size_per_itemr#c|jdkrtd|jdgt|fdt t |D}t |||S)a Defines a field where an element in the batch is obtained by slicing along the first dimension of the underlying data. Args: modality: The modality of the multi-modal item that uses this keyword argument. size_per_item: For each multi-modal item, the size of the slice that is used to extract the data corresponding to it. dim: The dimension to slice, default to 0. keep_on_cpu: Whether to keep this field on the CPU for the model inputs. Example: ``` Given: size_per_item: [3, 4, 2] Input: Data: [AAABBBBCC] Output: Element 1: [AAA] Element 2: [BBBB] Element 3: [CC] ``` ``` Given: size_per_item: [3, 4, 2] dim: 1 Input: Data: [[A],[A],[A],[B],[B],[B],[B],[C],[C]] Output: Element 1: [[A],[A],[A]] Element 2: [[B],[B],[B],[B]] Element 3: [[C],[C]] ``` Info: [`MultiModalFieldConfig.flat`][vllm.multimodal.inputs.MultiModalFieldConfig.flat] rz7size_per_item should be a 1-D tensor, but found shape: rc |g|]8}tdddfzt||dzfz9Sr)r)reirR slice_idxss r8rgz9MultiModalFieldConfig.flat_from_sizes..sa   4t $ $ & ,Z]Jq1u$5668 9   r7)rRr)ndimrrr rangerrr)rrrRrrr s ` @r8flat_from_sizesz%MultiModalFieldConfig.flat_from_sizessj   " ":$1$7::  4*]334      3}--..    %))  # *   r7rcBtt|||S)a Defines a field where an element in the batch is obtained by taking the entirety of the underlying data. This means that the data is the same for each element in the batch. Args: modality: The modality of the multi-modal item that uses this keyword argument. batch_size: The number of multi-modal items which share this data. keep_on_cpu: Whether to keep this field on the CPU for the model inputs. Example: ``` Given: batch_size: 4 Input: Data: [XYZ] Output: Element 1: [XYZ] Element 2: [XYZ] Element 3: [XYZ] Element 4: [XYZ] ``` )rrr)rr)rrrs r8sharedzMultiModalFieldConfig.shared%s9F%'%'     r7rrrrOcD|j|j||Sr)rrr)rUrrs r8rz!MultiModalFieldConfig.build_elemsSs z%%dmS%@@@r7N)r)r0r1r2rr5r|rrrr?rrrrr4r}rrr6r7r8rr~s6;   # t   \ :; " ; ; ; ; (8E?";;; ;  ; ; ; \; zF " F F F F %F F  F F F \F P " ( ( ( ( (  ( ( ( \( T MMMA AA % & AAAAAAr7rceZdZdZeddedefdZedee fdZ ifde ee fd d ffd Z e d efd Zd eeeffd ZxZS)rz A collection of [`MultiModalFieldElem`][vllm.multimodal.inputs.MultiModalFieldElem] corresponding to a data item in [`MultiModalDataItems`][vllm.multimodal.parse.MultiModalDataItems]. rrnbytesct|dtj|tjt d}t |gS)zConvenience class for testing.dummy)rr)r)rrrr)rr!ruint8rr from_elems)rrmm_elems r8rzMultiModalKwargsItem.dummycsX&V5;777'1555     $..y999r7rc4td|DS)Nci|] }|j| Sr6rrs r8 z3MultiModalKwargsItem.from_elems..ps$F$F$FTXt$F$F$Fr7)r)rs r8rzMultiModalKwargsItem.from_elemsns#$F$F$F$F$FGGGr7rrONct|d|D}t|dks Jd|t t ||_dS)Nch|] }|j Sr6rrs r8 z0MultiModalKwargsItem.__init__..us>>>dm>>>r7rzFound different modalities=)super__init__valuesrnextiter _modality)rUr modalitiesrws r8r#zMultiModalKwargsItem.__init__rsy >> >>> :!###%O:%O%O###d:..//r7c|jSr)r'rTs r8rzMultiModalKwargsItem.modalityys ~r7c>d|DS)Nc$i|] \}}||jSr6r)rerrs r8rz1MultiModalKwargsItem.get_data..~s ===93TY===r7)itemsrTs r8get_datazMultiModalKwargsItem.get_data}s== ====r7)r)r0r1r2r3rr5r?rrrrrr#propertyrrr}r- __classcell__rws@r8rr[s:::S:::\:H(#67HHH\HBD00WS*=%=>0000000#X>$sM12>>>>>>>>r7r_I)defaultceZdZdZedddeeeffdZede e fdZ ded e e ffd Z dd Zd dddejjded efdZxZS)MultiModalKwargsItemszu A dictionary of [`MultiModalKwargsItem`][vllm.multimodal.inputs.MultiModalKwargsItem]s by modality. hf_inputsr config_by_keycZ ttttf t tt tft }|D]j\}}||}|N|||}t|dkr%| |<||j  |ktt}|D]\}} fd| D} d| D} tt | dkrtd|d| t!t#| } t%| D]Ofd| D}|t|Pt*|S)Nrc"i|] }|| Sr6r6)rer elems_by_keys r8rz8MultiModalKwargsItems.from_hf_inputs..s B B BLO B B Br7c4i|]\}}|t|Sr6)r)rervs r8rz8MultiModalKwargsItems.from_hf_inputs..s$KKKA1c!ffKKKr7rz0Cannot merge different batch sizes for modality=z! Found: batch_sizes=c g|] }| Sr6r6)rer;item_idxs r8rgz8MultiModalKwargsItems.from_hf_inputs..sIII8IIIr7)rr5rrrrr,getrrraddr>rr$rr%r&rrbrr4from_seq)r5r6keys_by_modalityrconfigrrr,rrelems_in_modality batch_sizesrr9r=s @@r8from_hf_inputsz$MultiModalKwargsItems.from_hf_inputss!C*=!>>?AA &sCH}5c::(..00 ? ?KCMM#&&E **366u::>>(-L%$V_599#>>>)*,,.4466 E ENHd B B B BT B B B KK1B1H1H1J1JKKKK3{))++,,--11 -h--)-- d;#5#5#7#78899J!*-- E EIIII.?.F.F.H.HIII 1<.sajr7r)rr4)r,items_by_modalitys r8r@zMultiModalKwargsItems.from_seqs((4H4HIII$%6777r7rrOc ||vr4td|dt|t|S)Nz Modality z" not found. Available modalities: )KeyErrorrrr" __getitem__)rUrrws r8rKz!MultiModalKwargsItems.__getitem__sh 4  .sU   U q++%,   r7)rr5r>rr,rPrQrb) rUrrr9rr,r rrrrs `` r8r-zMultiModalKwargsItems.get_datas #3-@(A#AB4HH #zz|| 3 3OHe$U++ 3 34<&QQQQQQQ"&33IC %,,T22223  3     +0022     r7)rOrL)r0r1r2r3rrr5rrErrr@r1rKrRr!rrr|rr-r/r0s@r8r4r4s  5! 5s$99: 5 5 5\ 5D8!56888\8-C-HRL------&*  "   r7r4MultiModalKwargsOptionalItemsMultiModalPlaceholderDictc~eZdZUdZeded< eeed< eed< e ed< e ed< e e ed<d S) MultiModalInputsz Represents the outputs of [`BaseMultiModalProcessor`][vllm.multimodal.processing.BaseMultiModalProcessor], ready to be passed to vLLM internals. multimodalr.prompt_token_ids mm_kwargs mm_hashesmm_placeholders cache_saltN) r0r1r2r3rr4r>r?rUMultiModalHashesrVrr5r6r7r8rXrXs , 3iD,,,,N-.... C    r7rXc*eZdZUdZeeed<dS)MultiModalEncDecInputsz Represents the outputs of [`EncDecMultiModalProcessor`][vllm.multimodal.processing.EncDecMultiModalProcessor] ready to be passed to vLLM internals. encoder_prompt_token_idsN)r0r1r2r3r>r?r4r6r7r8raras. #3i'''88r7ra)\abcrr collectionsrrcollections.abcrr dataclassesr functoolsr r itertoolsr typingr rrrrrrrrnumpynp PIL.Imagertyping_extensionsrrvllm.utils.collection_utilsrrvllm.utils.import_utilsrvllm.utils.jsontreerr! torch.types%transformers.feature_extraction_utilsrmediar globalsr"ndarrayr$r4r>r%floatr&r'rdrr5r(r)r*r,r:rErArGrHrKr}r|rxrrrrrrrrrrrrrr1r4rUr_rVrXrar6r7r8rws$########----------------!!!!!!........                       22222222@@@@@@@@......//////4LLLBBBBBB%%%%%%% Jw 7 3 3E WT]]w NBC YCCC M2:~tBJ/?nAUU Y tE{BJFG YGGG [.:WWX 9XXX{DcN'B!CC 9[% E0A*BNRS 9SSS tBI.5 i555yy!11 9 S S S S SYe S S S S !(\#->(> ?I???!(T#*-=-C(C DIDDD $bCbCbCbCbCbCbCbCJ!   y  Mm.  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