:jsddlZddlZddlZddlmZmZddlmZddlm 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&ejNe(Z)dZ*dZ+ejXGddZ-GddeZ.Gdde.eZ/Gdde/Z0y)N)ABCabstractmethod) OrderedDict)IterableMapping) TYPE_CHECKINGAnyCallableOptionalUnion)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. 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Returns: Float absolute tolerance value. gh㈵>r-r\s r/atol_for_validationzOnnxConfig.atol_for_validationsr.crtr-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.zROverwriting the `preprocessor` argument with `tokenizer` to generate dummy inputs.r)fixed_dimensionnum_token_to_add0 rC) 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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