ޘhiddlZddlZddlZddlmZmZddlmZddlm Z m Z ddl m Z m Z mZmZmZddlZddlmZddlmZmZmZmZd d lmZmZmZe rdd lmZdd lm Z dd l!m"Z"ddl#m$Z$erddl%m&Z&ej'e(Z)dZ*dZ+ej,GddZ-GddeZ.Gdde.eZ/Gdde/Z0dS)N)ABCabstractmethod) OrderedDict)IterableMapping) TYPE_CHECKINGAnyCallableOptionalUnion)version) TensorTypeis_torch_availableis_vision_availablelogging)ParameterFormat compute_effective_axis_dimension"compute_serialized_parameters_size)PretrainedConfigFeatureExtractionMixinImageProcessingMixinPreTrainedTokenizerBase)Image lcfeZdZUdZeed<eed<eed<dZe eed<dZ e eed<dS) PatchingSpeca 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. 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Returns: Float absolute tolerance value. gh㈵>r-r\s r/atol_for_validationzOnnxConfig.atol_for_validations tr.cxtr+ddlm}tj||jkSdS)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.utilsrvr parsetorch_onnx_minimum_version)rQrvs r/is_torch_support_availablez%OnnxConfig.is_torch_support_availablesK     < < < < < <=!2!2!4!4559XX X5r.num_parameterscHt|tjtkS)a 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)r{s r/use_external_data_formatz#OnnxConfig.use_external_data_formats! /~?T U U. / r.r@( batch_size num_channels image_height image_widthcg}t|D]s}tj|||dz}|t j|ddt|S)Nuint8RGB) rangenprandomrandrPr fromarrayastypeconvert)rQrrrrimages_datas r/_generate_dummy_imagesz!OnnxConfig._generate_dummy_imagess|z"" P PA9>>, \JJSPD MM%/$++g*>*>??GGNN O O O O r."V@ sampling_rate time_duration frequencyc g}t|D]g}tjd|t||zd}|dtjdtjz|z|zzh|S)NrF)endpointg?r)rrlinspaceintrPsinpi)rQrrrr audio_datarts r/_generate_dummy_audioz OnnxConfig._generate_dummy_audios z"" G GA A}c--2O.P.P[`aaaA   cBF1ru9y+@1+D$E$EE F F F Fr.F preprocessor)rrr seq_length num_choicesis_pair framework tokenizerrcddlm}ddlm}ddlm}t ||r| td| 6tj dtt d| }t ||rPt|tjd }||}t|tj| }|jt'|jd kr|jnd }d |g|zg|z}|jd krttjd |z}|||}|D]3\}fdt1d t'D||<4t3||St3|||St ||r|jd dkr*td|jjd|jd t|tj}|||| |}t3|||St ||r^|jd dkrMt|tj}|||| |}t3|||St ||r^|jd dkrMt|tj}||| | | }t3|||Std)am 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 rrrrNzPYou 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 rC) text_pairc*g|]}||zSr-r-).0irvs r/ z4OnnxConfig.generate_dummy_inputs..us')h)h)hQ!AK,?*@)h)h)hr.) 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. 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((,(( (  ( J' ( c ((((((Vqvee!(gc3h.?)?!@eMPeimeeee2777 s 8C= UYZ]_bZbUc          r.rc8eZdZedeeeeeffffd Zedeeffd Z edeeffd Z dde d d ed ed e d e e deeeff fd ZdeeeeeffdefdZdZxZS)OnnxSeq2SeqConfigWithPastrUctt|j}|D]6\}}d|vrdnd}|D]\}}d|vr|||<|||<7|jr||d|S)Nencoderencoder_sequencerAr6rbr)rrrbrrr)rQrar# axes_names sequence_nameaxis_idxrs r/rbz!OnnxSeq2SeqConfigWithPast.outputsRs1488@ . 4 4 6 6 0 0 D*2;t2C2C..I[M","2"2"4"4 0 0$%%+8Jx((,0Jx((  0 = Q  + +Ni + P P Pr.c tj}||f}nb#t$rUt|jdr.t|jdr|jj|jjf}ntdYnwxYw|S)Nencoder_layersdecoder_layerszcould not find the number of encoder and decoder layers attributes in the model configuration, override the num_layers property of the model OnnxConfig to solve this)rrrrerHr+r,)rQrrs r/rz$OnnxSeq2SeqConfigWithPast.num_layerscs +J$j1JJ   t|%566 74?? GDLZsDtDt '+|'KT\Mq&r##$$# #"r-rFNrrrrrrc tt||||||}|js|nd}tt||||||}d|D}t d i||} |jrt stdddl} | dj d} | dj d} | dj d}|j \} }| | | |j j | zf}| ||dz|j j |zf}g| d <|j \}}t||}t|||z }||krd nd }t!|D]m}| d | || || || |fn|d kr|n|}t!||D]E}| d | || |fF| S) Nrrc i|] \}}d|| S)decoder_r-)rr#tensors r/rzCOnnxSeq2SeqConfigWithPast.generate_dummy_inputs..s'___ f+T++V___r.rrrdecoder_input_idsr@rr%decoderr-)rrrrrrrrJrrrrHrrminmaxrrPr )rQrrrrrencoder_inputsdecoder_seq_lengthdecoder_inputsr rr5encoder_seq_lengthnum_encoder_attention_headsnum_decoder_attention_heads encoder_shape decoder_shapenum_encoder_layersnum_decoder_layersmin_num_layersmax_num_layersremaining_side_namerrrs r/rz/OnnxSeq2SeqConfigWithPast.generate_dummy_inputss1488NN *W`iO   04}CZZ!1488NN *9KU\hqO  `_H\H\H^H^___@@~@@@ =- b%''  !deee !+.4Q7E!.{!;!A!!D !./B!C!I!!L GKG_ D ')D+" (,GG M+"Q& (,GG M02M+ ,59_ 2  2 !35GHHN !35GHH>YN/ADV/V/V))\e >**  /077 M22 M22 M22 M22 &9I%E%EMM=E>>:: b b/077U9K9KU[[Y^M_M_8`aaaar.rrc |dvrtd|d|dkrdnd}|j\}}t||}t|||z }||krdnd}d } |dkrd nd } t |D]:} d | d ||d| d<d | d ||d| d<d | d ||d| d<d | d ||d| d<;t ||D]!} |dkrd | d } nd | d } | ||d| d|d<"dS)Nrrrr]rrr%r6past_encoder_sequencepast_decoder_sequencez past_decoder_sequence + sequencer5rr .decoder.key.decoder.value .encoder.key.encoder.valuer)rJrr7r8r) rQrrr#rArBrCrDrEr&rAr axes_infos r/rz4OnnxSeq2SeqConfigWithPast.fill_with_past_key_values_s 1 1 1iT]iiijj j$-$9$9  y26../1CDD/1CDD~U+=@R+R+RiiXa26?86K6K22Qs~&& _ _A?FK[;\;\ 77777 8AHM]=^=^ 99999 :?FK[;\;\ 77777 8AHM]=^=^ 99999 : :~~66 S SA"i// ',<== ',<== IR EEEE,?EEE F F  S Sr.c|d||d|d<|d||d|d<|d||d|d<|d||d|d <dS) NrrrIrrJrrKr@rLr-rs r/rz3OnnxSeq2SeqConfigWithPast._flatten_past_key_values_s89!D4434445:;A$D663666789!D4434445:;A$D663666777r.r)r'r(r)rrr,rrbtuplerrr rrr rrrr r!s@r/r#r#Qs gc3h&7!78X  E#J     X  #U3Z # # # # #X #$*. 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