fThaSSKrSSKrSSKrSSKJrJr SSKJr SSKJ r J r J r J r J r JrJrJrJrJr SSKrSSKJr SSKJrJrJrJr SS KJrJrJr \ (aSS KJ r SS K!J"r" SS K#J$r$ SS K%J&r& \"5(aSSK'J(r( \RR"\*5r+Sr,Sr-\R\"SS55r/"SS\5r0"SS\0\5r1"SS\15r2g)N)ABCabstractmethod) OrderedDict) TYPE_CHECKINGAnyCallableDictIterableListMappingOptionalTupleUnion)version) TensorTypeis_torch_availableis_vision_availablelogging)ParameterFormat compute_effective_axis_dimension"compute_serialized_parameters_size)PretrainedConfigFeatureExtractionMixinImageProcessingMixinPreTrainedTokenizerBase)Image lc`\rSrSr%Sr\\S'\\S'\\S'Sr \ \\S'Sr \ \\S'S r g) PatchingSpec/a Data class that holds patching specifications. Args: o: Module / object where the op to patch is located name: Name of the op to monkey patch custom_op: Custom op that patches the original op orig_op: Original op that is being patched op_wrapper: Wrapper (optional) that wraps both the original and custom ops. It is useful for ops that are class or static methods for instance. oname custom_opNorig_op op_wrapper) __name__ __module__ __qualname____firstlineno____doc__r__annotations__strrr)r r*__static_attributes__r+P/var/www/auris/envauris/lib/python3.13/site-packages/transformers/onnx/config.pyr$r$/s7  F I"&GXh &%)J")r4r$ct\rSrSrSrSrSrSr\R"S5r \ "SSS S .05\ "S SS S .05\ "SSS S .05\ "SS S .SS S .SS S .S .5\ "SSS S .05\ "SSS S .05\ "SS S005\ "SS S .SS S .S.5\ "SS S .SS S .S.5\ "SSSSSS.05\ "SSSS .05\ "SS S005\ "SSS S .05\ "SSS S .05\ "SSS S .05S.r SCSSS\ S\\\4Sjjr\SDSSS\ SS4Sjj5r\\S\\ \\\ 444Sj55r\S\\ \\\ 444Sj5r\S\\\ \44S j5r\S\4S!j5r\S\4S"j5r\S\4S#j5r\S\4S$j5r\S\ 4S%j5r!\S\"4S&j5r#\$S'\S\"4S(j5r%SES)\S*\S+\S,\4S-jjr&SFS)\S.\S/\ S0\4S1jjr'SGS2\(S3S)\S4\S5\S6\"S7\\)S*\S,\S+\S.\S/\ S0\S8\S9S\\ \44S:jjr*S;\\ \4S\\ \44S<jr+S=r,S>r-\S?\ S@\.\S\/\ \44SAj5r0SBr1g)H OnnxConfigDzn Base class for ONNX exportable model describing metadata on how to export the model through the ONNX format. rz1.8logitsbatchsequencerrlast_hidden_state)r; pred_boxes pred_masksr)r;r@) start_logits end_logits num_labelsheightwidth)rrrdecoder_sequence)z causal-lmdefaultzimage-classificationzimage-segmentationz masked-imz masked-lmmultiple-choicezobject-detectionzquestion-answeringzsemantic-segmentationz seq2seq-lmzsequence-classificationztoken-classificationz vision2seq-lmz speech2seq-lmNconfigrtaskpatching_specscnXlX R;a([USURR535eX l/UlUbUO/H`nUnUR c3[R"U[URUR5S9nUR RU5 Mb g)Nz+ is not a supported task, supported tasks: )r)) _config_tasks_to_common_outputs ValueErrorkeysrL_patching_specsr) dataclassesreplacegetattrr&r'append)selfrKrLrMspec final_specs r5__init__OnnxConfig.__init__os 44 4&CDDaDaDfDfDhCij  !&4&@NbHDJ||#(00wtvvtyy?YZ  ' ' 3 Ir4returncU"XS9$)z Instantiate a OnnxConfig for a specific model Args: config: The model's configuration to use when exporting to ONNX Returns: OnnxConfig for this model )rLr+clsrKrLs r5from_model_configOnnxConfig.from_model_configs6%%r4c[5e)z Mapping containing the axis definition of the input tensors to provide to the model Returns: For each input: its name associated to the axes symbolic name and the axis position within the tensor )NotImplementedErrorrXs r5inputsOnnxConfig.inputss "##r4c`URURn[R"U5$)z Mapping containing the axis definition of the output tensors to provide to the model Returns: For each output: its name associated to the axes symbolic name and the axis position within the tensor )rPrLcopydeepcopy)rXcommon_outputss r5outputsOnnxConfig.outputss'66tyyA}}^,,r4cB[URS5(aSS0$g)z Dictionary of keys to override in the model's config before exporting Returns: Dictionary with the keys (and their corresponding values) to override use_cacheFN)hasattrrOres r5values_overrideOnnxConfig.values_overrides# 4<< - -' 'r4c"[R$)zP The default batch size to use if no other indication Returns: Integer > 0 )r7default_fixed_batchres r5default_batch_sizeOnnxConfig.default_batch_sizes---r4c"[R$)zU The default sequence length to use if no other indication Returns: Integer > 0 )r7default_fixed_sequenceres r5default_sequence_length"OnnxConfig.default_sequence_lengths000r4c"[R$)zW The default number of choices to use if no other indication Returns: Integer > 0 )r7default_fixed_num_choicesres r5default_num_choicesOnnxConfig.default_num_choicess333r4c[$)z[ Which onnx opset to use when exporting the model Returns: Integer ONNX Opset version )DEFAULT_ONNX_OPSETres r5default_onnx_opsetOnnxConfig.default_onnx_opsets "!r4cg)zx What absolute tolerance value to use during model conversion validation. Returns: Float absolute tolerance value. gh㈵>r+res r5atol_for_validationOnnxConfig.atol_for_validationsr4c~[5(a.SSKJn [R"U"55UR :$g)z The minimum PyTorch version required to export the model. Returns: `bool`: Whether the installed version of PyTorch is compatible with the model. r)get_torch_versionF)rtransformers.utilsrrparsetorch_onnx_minimum_version)rXrs r5is_torch_support_available%OnnxConfig.is_torch_support_availables1    <==!2!459X9XX Xr4num_parameterscD[U[R5[:$)z Flag indicating if the model requires using external data format Args: num_parameters: Number of parameter on the model Returns: True if model.num_parameters() * size_of(float32) >= 2Gb False otherwise )rrFloatEXTERNAL_DATA_FORMAT_SIZE_LIMIT)rs r5use_external_data_format#OnnxConfig.use_external_data_formats! /~?T?T U. / r4 batch_size num_channels image_height image_widthc/n[U5Hin[RRX4U5S-nUR [ R "URS55RS55 Mk U$)Nuint8RGB) rangenprandomrandrWr! fromarrayastypeconvert)rXrrrrimages_datas r5_generate_dummy_images!OnnxConfig._generate_dummy_imagessaz"A99>>,\JSPD MM%//$++g*>?GGN O# r4 sampling_rate time_duration frequencyc /n[U5Hdn[R"SU[X2-5SS9nUR S[R "S[R -U-U-5-5 Mf U$)NrF)endpointg?r)rrlinspaceintrWsinpi)rXrrrr audio_datarts r5_generate_dummy_audio OnnxConfig._generate_dummy_audio sm z"A A}c-2O.P[`aA   cBFF1ruu9y+@1+D$EE F #r4 preprocessor)r rr seq_length num_choicesis_pair framework tokenizerr cSSKJn SSKJn SSKJn [ UU5(aU b [S5eU b2[R"S[5 [RS5 U n[ UU5(Ga7[U[RSS 9nUR!U5n[U[R"US 9nUR$b%['UR$5S:a UR$OS nS R)U/5U-/U-nUR*S :Xa[U[R,SS 9nUU-nU"UUS 9nUR/5H6unn[1S['U5U5Vs/sH nUUUU-PM snUU'M8 [3UR5US95$[3U"UUS95$[ X5(aUR6SS:wa2[SUR8R:SUR6S35e[U[RS9nUR=X'X5n[3U"UUS95$[ X5(aMUR6SS:Xa:[U[RS9nUR=X'X5n[3U"UUS95$[ X5(aMUR6SS:Xa:[U[RS9nUR?X*X5n[3U"UUS95$[S5es snf)a Generate inputs to provide to the ONNX exporter for the specific framework Args: preprocessor: ([`PreTrainedTokenizerBase`], [`FeatureExtractionMixin`], or [`ImageProcessingMixin`]): The preprocessor associated with this model configuration. batch_size (`int`, *optional*, defaults to -1): The batch size to export the model for (-1 means dynamic axis). num_choices (`int`, *optional*, defaults to -1): The number of candidate answers provided for multiple choice task (-1 means dynamic axis). seq_length (`int`, *optional*, defaults to -1): The sequence length to export the model for (-1 means dynamic axis). is_pair (`bool`, *optional*, defaults to `False`): Indicate if the input is a pair (sentence 1, sentence 2) framework (`TensorType`, *optional*, defaults to `None`): The framework (PyTorch or TensorFlow) that the tokenizer will generate tensors for. num_channels (`int`, *optional*, defaults to 3): The number of channels of the generated images. image_width (`int`, *optional*, defaults to 40): The width of the generated images. image_height (`int`, *optional*, defaults to 40): The height of the generated images. sampling_rate (`int`, *optional* defaults to 22050) The sampling rate for audio data generation. time_duration (`float`, *optional* defaults to 5.0) Total seconds of sampling for audio data generation. frequency (`int`, *optional* defaults to 220) The desired natural frequency of generated audio. Returns: Mapping[str, Tensor] holding the kwargs to provide to the model's forward function rrrrzPYou cannot provide both a tokenizer and a preprocessor to generate dummy inputs.ztThe `tokenizer` argument is deprecated and will be removed in version 5 of Transformers. Use `preprocessor` instead.zROverwriting the `preprocessor` argument with `tokenizer` to generate dummy inputs.r)fixed_dimensionnum_token_to_add0 rJ) text_pair) tensor_type)return_tensors pixel_valuesz*The `preprocessor` is an image processor (zC) and expects `model_input_names[0]` to be "pixel_values", but got )r)rrinput_featuresz\Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.) feature_extraction_utilsrimage_processing_utilsrtokenization_utils_baser isinstancerQwarningswarn FutureWarningloggerwarningrr7rtnum_special_tokens_to_addrx unk_tokenlenjoinrLr|itemsrdictconvert_to_tensorsmodel_input_names __class__r,rr)rXrrrrrrrrrrrrrrrr token_to_add input_token dummy_inputtokenized_inputkvis r5generate_dummy_inputs OnnxConfig.generate_dummy_inputss` FAE l$; < <AVop p  MM+  NNo p$L l$; < <9J,J,J]^J(AA'JL9J,M,M`lJ !**63|?U?U;VYZ;Z&&  88[M2Z?@:MKyy--?1U1Uhi *K7 ".{k"R+113DAqJOPQSVWXSY[fJg)hJgQ!AK*@Jg)hOA&4O>>9>UVV [KL L  ; ;--a0NB @AWAWA`A`@abMMYMkMklmMnLoq :*V`VtVtuJ55jP\jK K RS S  = =,B`B`abBcguBu9*V`VtVtuJ55jP\jK K RS S | < <A_A_`aAbfvAv:*V`VtVtuJ44ZP]iK [KL Ln 5*is,Lreference_model_inputscU$)a Generate inputs for ONNX Runtime using the reference model inputs. Override this to run inference with seq2seq models which have the encoder and decoder exported as separate ONNX files. 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Fqu4(4034QYZ^_kZlQm4$ &'9 & &Ua & &$WS#X%6 67$$-gc3h&7!78-- '#s(*;!<  .C..1114S44"C""U D      "fh14HK_bmp  25 NS fi  *.""9=vghvv v  v  vJ'vvvvvvv56v c vp &PSUXPXHY &^efiknfn^o &2 0 ^c^(3-^TXY\^aYaTb^^r4r7c^\rSrSrSSSS\S\\\S\4U4Sjjjr \ SSSS\SS4S jj5r \ S\ \\ \\444U4S jj5r\ S\\ \\44S j5r\ S\4S j5r\ S\4S j5rS SSS\S\S\S\\S\ \\44 U4SjjjrS!S\ \\ \\44S\S\4SjjrSrS\S\\S\\\44U4SjjrSrU=r$)"OnnxConfigWithPastirKrrLrMuse_pastc.>[TU]XUS9 X@lg)N)rLrM)superr[r)rXrKrLrMrrs r5r[OnnxConfigWithPast.__init__s >J r4r]cU"XSS9$)z Instantiate a OnnxConfig with `use_past` attribute set to True Args: config: The underlying model's config to use when exporting to ONNX Returns: OnnxConfig with `.use_past = True` T)rLrr+r_s r5 with_pastOnnxConfigWithPast.with_pasts6t44r4c^>[TU]nUR(aURUSS9 U$)Nrl direction)rrlrfill_with_past_key_values_)rXrkrs r5rlOnnxConfigWithPast.outputss, ==  + +Ni + Pr4cV[URS5(aSUR0$g)Nro)rprOrres r5rq"OnnxConfigWithPast.values_overrides% 4<< - -/ /r4cz[URS5(d [S5eURR$)z The number of layers attribute retrieved from the model config. 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(,(( (  ( J' ( c ((Vqve!(gc3h.?)?!@eMPeime27 s 8C= UYZ]_bZbUc  r4rc ^\rSrSr\S\\\\\444U4Sjj5r\S\ \4U4Sjj5r \S\ \4U4Sjj5r SS\ SS\S \S \ S \ \S\\\44 U4S jjjrS \\\\\44S\4SjrSrSrU=r$)OnnxSeq2SeqConfigWithPastiPr]c>[[U] nUR5H8up#SU;aSOSnUR5HupRSU;aXCU'MX#U'M M: UR(aUR USS9 U$)Nencoderencoder_sequencerHr=rlr )rrrlrrr )rXrkr' axes_names sequence_nameaxis_idxrs r5rl!OnnxSeq2SeqConfigWithPast.outputsQs14@ . 4 4 6 D2;t2C.I[M","2"2"4%+8x(,0x( #5!7 ==  + +Ni + Pr4c >[TU]nX4nU$![aq [URS5(aJ[URS5(a/URR URR 4nU$[S5ef=f)Nencoder_layersdecoder_layerszcould not find the number of encoder and decoder layers attributes in the model configuration, override the num_layers property of the model OnnxConfig to solve this)rrrrprOrKrL)rXrrs r5r$OnnxSeq2SeqConfigWithPast.num_layersbs +J$1J t||%56674<[TU]nX4nU$![aq [URS5(aJ[URS5(a/URR URR 4nU$[S5ef=f)Nencoder_attention_headsdecoder_attention_headszcould not find the number of attention heads for the encoder and the decoder attributes in the model configuration, override the num_attention_heads property of the model OnnxConfig to solve this)rrrrprOrPrQ)rXrrs r5r-OnnxSeq2SeqConfigWithPast.num_attention_headsrs "''"= #6"L #" t||%>??GDLLZsDtDt'+||'K'KT\\MqMq&r##" %  rNrr rrrrc 8>[[U] XX4US9nUR(dUOSn[[U] XXtUS9nUR 5V V s0sH upSU 3U _M nn n [ S 0UDUD6n UR(Ga[ 5(d [S5eSSKn U SRSn U SRSnU SRSnURunnU UUURRU-4nU UUS-URRU-4n/U S 'URunn[UU5n[UU5U- nUU:aS OS n[!U5HWnU S R#U R%U5U R%U5U R%U5U R%U545 MY US :XaUOUn[!UU5H7nU S R#U R%U5U R%U545 M9 U $s sn n f) Nrrdecoder_rrrdecoder_input_idsrGrrDdecoderr+)rrrrrrrrQr r!rrOr"rminmaxrrWr%)rXrrrrrencoder_inputsdecoder_seq_lengthdecoder_inputsr'tensorr&r r<encoder_seq_lengthnum_encoder_attention_headsnum_decoder_attention_heads encoder_shape decoder_shapenum_encoder_layersnum_decoder_layersmin_num_layersmax_num_layersremaining_side_namerr!rs r5r/OnnxSeq2SeqConfigWithPast.generate_dummy_inputss`14N `iO  04}}Z!14N 9KhqO IWH\H\H^_H^ HTF+V3H^_@~@@ ===%'' !dee!+.44Q7E!.{!;!A!A!!D !./B!C!I!I!!L GKG_G_ D ')D+" ((,GG M+"Q& ((,GG M02M+ ,59__ 2  2 !35GHN !35GH>YN/ADV/V)\e >*/077 M2 M2 M2 M2 +&9I%EM=E>>:/077U9KU[[Y^M_8`a;e`sHr+r c US;a[SUS35eUS:XaSOSnURupE[XE5n[XE5U- nXE:aSOSnS n US:XaS OS n [ U5H7n S U S .XSU S3'S U S .XSU S3'S U S .XSU S3'S U S .XSU S3'M9 [ Xg5H!n US:XaS U S .n OS U S .n XUSU SUS3'M# g)Nr.r/r0rfrr1rDrVpast_encoder_sequencepast_decoder_sequencez past_decoder_sequence + sequencer<r2r .decoder.key.decoder.value .encoder.key.encoder.valuer3)rQrrWrXr) rXr+r r'rbrcrdrerfrErHr axes_infos r5r 4OnnxSeq2SeqConfigWithPast.fill_with_past_key_values_sL 1 1ST]S^^hij j$-$9 y26./D/D~U+=+RiXa26?86K2Qs~&A?FK[;\ as,7 8AHM]=^ as.9 :?FK[;\ as,7 8AHM]=^ as.9 : ' ~6A"i/ ',<= ',<= IRas!,?+@E F 7r4clUSXSUS3'USXSUS3'USXSUS3'USXSUS 3'g) NrrrkrrlrrmrGrnr+r7s r5r93OnnxSeq2SeqConfigWithPast._flatten_past_key_values_sj89!63%|45:;A$63%~6789!63%|45:;A$63%~67r4r+r=)r,r-r.r/rr r2rrlrrrr rrrrr r9r3r?r@s@r5rBrBPs gc3h&7!78  E#J   #U3Z # #$*. 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