sg,ddlZddlZddlZddlmZmZddlmZddlm Z m Z m Z m Z m Z mZmZmZmZmZddlZddlmZddlmZmZmZmZdd lmZmZmZe rdd lm Z dd l!m"Z"dd l#m$Z$dd l%m&Z&erddl'm(Z(ejRe*Z+dZ,dZ-ej\GddZ/GddeZ0Gdde0eZ1Gdde1Z2y)N)ABCabstractmethod) OrderedDict) TYPE_CHECKINGAnyCallableDictIterableListMappingOptionalTupleUnion)version) TensorTypeis_torch_availableis_vision_availablelogging)ParameterFormat compute_effective_axis_dimension"compute_serialized_parameters_size)PretrainedConfigFeatureExtractionMixinImageProcessingMixinPreTrainedTokenizerBase)Image lcXeZdZUdZeed<eed<eed<dZe eed<dZ e eed<y) 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. It is useful for ops that are class or static methods for instance. oname custom_opNorig_op op_wrapper) __name__ __module__ __qualname____doc__r__annotations__strrr(r r)K/var/www/html/venv/lib/python3.12/site-packages/transformers/onnx/config.pyr$r$/s7  F I"&GXh &%)J")r1r$c2eZdZdZdZdZdZejdZ e dddd ie d ddd ie dddd ie ddd ddd ddd d e dddd ie dddd ie dd diie ddd ddd d e ddd ddd de ddddddie dddd ie dd diie dddd ie dddd ie dddd idZ dAddde de efdZedBddde ddfdZeedee eee fffdZedee eee fffdZedeee effdZedefd Zedefd!Zedefd"Zedefd#Zedefd$Z ede!fd%Z"e#d&ede!fd'Z$ dCd(ed)ed*ed+efd,Z% dDd(ed-ed.ed/efd0Z& dEd1e'd2d(ed3ed4ed5e!d6ee(d)ed+ed*ed-ed.ed/ed7d8dee effd9Z)d:ee efdee effd;Z*d<Z+d=Z,ed>e d?e-ede.e effd@Z/y)F OnnxConfigzv Base class for ONNX exportable model describing metadata on how to export the model through the ONNX format. rz1.8logitsbatchsequencerrlast_hidden_state)r7 pred_boxes pred_masksr)r7r<) 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_specscl||_||jvr(t|d|jj||_g|_||ngD]`}|}|j 5tj|t|j|j}|j j|by)Nz+ is not a supported task, supported tasks: )r() _config_tasks_to_common_outputs ValueErrorkeysrH_patching_specsr( dataclassesreplacegetattrr%r&append)selfrGrHrIspec final_specs r2__init__zOnnxConfig.__init__os t44 4&CDDaDaDfDfDhCij  !&4&@Nb 4DJ||#(00wtvvtyy?YZ  ' ' 3  4r1returnc|||S)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 )rHr0clsrGrHs r2from_model_configzOnnxConfig.from_model_configs6%%r1ct)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 )NotImplementedErrorrTs r2inputszOnnxConfig.inputss "##r1c^|j|j}tj|S)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 )rLrHcopydeepcopy)rTcommon_outputss r2outputszOnnxConfig.outputss'66tyyA}}^,,r1c8t|jdrddiSy)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)hasattrrKr_s r2values_overridezOnnxConfig.values_overrides  4<< -' 'r1c"tjS)zp The default batch size to use if no other indication Returns: Integer > 0 )r4default_fixed_batchr_s r2default_batch_sizezOnnxConfig.default_batch_sizes---r1c"tjS)zu The default sequence length to use if no other indication Returns: Integer > 0 )r4default_fixed_sequencer_s r2default_sequence_lengthz"OnnxConfig.default_sequence_lengths000r1c"tjS)zw The default number of choices to use if no other indication Returns: Integer > 0 )r4default_fixed_num_choicesr_s r2default_num_choiceszOnnxConfig.default_num_choicess333r1ctS)z{ Which onnx opset to use when exporting the model Returns: Integer ONNX Opset version )DEFAULT_ONNX_OPSETr_s r2default_onnx_opsetzOnnxConfig.default_onnx_opsets "!r1cy)z What absolute tolerance value to use during model conversion validation. Returns: Float absolute tolerance value. gh㈵>r0r_s r2atol_for_validationzOnnxConfig.atol_for_validationsr1crtr-ddlm}tj||j k\Sy)z The minimum PyTorch version required to export the model. 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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.zSOverwriting the `preprocessor` argument with `tokenizer` to generate dummmy inputs.r)fixed_dimensionnum_token_to_add0 rF) 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. 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D4144Z^_kZl4  &'9 & &Ua & &$WS#X%6 67$$-gc3h&7!78-- '#s(*;!<  .C..1114S44"C""U D      "fh14HK_bmp  25 NS fi  *.""/3vghvv v  v  vJ'vvvvvvv-v c vp &PSUXPXHY &^efiknfn^o &2 0 ^c^(3-^TXY\^aYaTb^^r1r4cveZdZ ddddedeedeffd ZeddddeddfdZ e de ee e effffd Z e dee eeffd Ze de fd Ze de fd Z dd dde de dedeede eeff fd Z dde ee e effdedefdZdZdedeedeeefffd ZxZS) OnnxConfigWithPastrGrrHrIuse_pastc8t||||||_y)N)rHrI)superrWr)rTrGrHrIrrs r2rWzOnnxConfigWithPast.__init__s  d>J  r1rXc|||dS)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)rHrr0rZs r2 with_pastzOnnxConfigWithPast.with_pasts6t44r1cZt|}|jr|j|d|S)Nre direction)rrerfill_with_past_key_values_)rTrdrs r2rezOnnxConfigWithPast.outputss, ==  + +Ni + Pr1cLt|jdrd|jiSy)Nrg)rhrKrr_s r2riz"OnnxConfigWithPast.values_overrides" 4<< -/ /r1cpt|jds td|jjS)z The number of layers attribute retrieved from the model config. 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