code: 
  
  import os
  import warnings
  from pathlib import Path
  from typing import Optional, Union
  
  from .dynamic_module_utils import custom_object_save
  from .tokenization_utils_base import PreTrainedTokenizerBase
  from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging
  
  
  _lowerCAmelCase     :			Optional[int]    =logging.get_logger(__name__)
  
  # Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
  _lowerCAmelCase     :			str    =direct_transformers_import(Path(__file__).parent)
  
  
  _lowerCAmelCase     :			str    ={
      """AutoTokenizer""": """PreTrainedTokenizerBase""",
      """AutoFeatureExtractor""": """FeatureExtractionMixin""",
      """AutoImageProcessor""": """ImageProcessingMixin""",
  }
  
  class __UpperCamelCase    (							lowerCamelCase_		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					__magic_name__     			=      ["""feature_extractor""", """tokenizer"""]
  					# Names need to be attr_class for attr in attributes
  					__magic_name__     			=      None
  					__magic_name__     			=      None
  					__magic_name__     			=      None
  
  
  
  
  
  					def __init__(							self							,			*lowerCamelCase__							,			**lowerCamelCase__	):
  
  
  
  
  
  
  
  						for key in kwargs:
  							if key not in self.attributes:
  								raise TypeError(F"Unexpected keyword argument {key}."	)
  						for arg, attribute_name in zip(lowerCamelCase__							,			self.attributes	):
  							if attribute_name in kwargs:
  								raise TypeError(F"Got multiple values for argument {attribute_name}."	)
  							else:
  								UpperCAmelCase__:  List[Any]		       =   arg
  
  						if len(lowerCamelCase__	) != len(self.attributes	):
  							raise ValueError(
  							    F"This processor requires {len(self.attributes	)} arguments: {', '.join(self.attributes	)}. Got "
  							    F"{len(lowerCamelCase__	)} arguments instead."	)
  
  						# Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
  						for attribute_name, arg in kwargs.items():
  							UpperCAmelCase__:  Tuple		       =   getattr(self							,			F"{attribute_name}_class"	)
  							# Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
  							UpperCAmelCase__:  Optional[Any]		       =   AUTO_TO_BASE_CLASS_MAPPING.get(lowerCamelCase__							,			lowerCamelCase__	)
  							if isinstance(lowerCamelCase__							,			lowerCamelCase__	):
  								UpperCAmelCase__:  List[Any]		       =   tuple(getattr(lowerCamelCase__							,			lowerCamelCase__	) for n in class_name if n is not None	)
  							else:
  								UpperCAmelCase__:  Optional[int]		       =   getattr(lowerCamelCase__							,			lowerCamelCase__	)
  
  							if not isinstance(lowerCamelCase__							,			lowerCamelCase__	):
  								raise ValueError(
  								    F"Received a {type(lowerCamelCase__	).__name__} for argument {attribute_name}, but a {class_name} was expected."	)
  
  							setattr(self							,			lowerCamelCase__							,			lowerCamelCase__	)
  
  
  
  
  
  					def __repr__(							self	):
  						UpperCAmelCase__:  int		       =   [F"- {name}: {repr(getattr(self							,			lowerCamelCase__	)	)}" for name in self.attributes]
  						UpperCAmelCase__:  int		       =   "\n".join(lowerCamelCase__	)
  						return F"{self.__class__.__name__}:\n{attributes_repr}"
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__ = False							,			**lowerCamelCase__	):
  						os.makedirs(lowerCamelCase__							,			exist_ok=lowerCamelCase__	)
  
  						if push_to_hub:
  							UpperCAmelCase__:  int		       =   kwargs.pop("commit_message"							,			lowerCamelCase__	)
  							UpperCAmelCase__:  str		       =   kwargs.pop("repo_id"							,			save_directory.split(os.path.sep	)[-1]	)
  							UpperCAmelCase__:  List[Any]		       =   self._create_repo(lowerCamelCase__							,			**lowerCamelCase__	)
  							UpperCAmelCase__:  Union[str, Any]		       =   self._get_files_timestamps(lowerCamelCase__	)
  						# If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
  						# loaded from the Hub.
  						if self._auto_class is not None:
  							UpperCAmelCase__:  int		       =   [getattr(self							,			lowerCamelCase__	) for attribute_name in self.attributes]
  							UpperCAmelCase__:  Optional[int]		       =   [(a.init_kwargs if isinstance(lowerCamelCase__							,			lowerCamelCase__	) else a) for a in attrs]
  							custom_object_save(self							,			lowerCamelCase__							,			config=lowerCamelCase__	)
  
  						for attribute_name in self.attributes:
  							UpperCAmelCase__:  Tuple		       =   getattr(self							,			lowerCamelCase__	)
  							# Include the processor class in the attribute config so this processor can then be reloaded with the
  							# `AutoProcessor` API.
  							if hasattr(lowerCamelCase__							,			"_set_processor_class"	):
  								attribute._set_processor_class(self.__class__.__name__	)
  							attribute.save_pretrained(lowerCamelCase__	)
  
  						if self._auto_class is not None:
  							# We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
  							for attribute_name in self.attributes:
  								UpperCAmelCase__:  Any		       =   getattr(self							,			lowerCamelCase__	)
  								if isinstance(lowerCamelCase__							,			lowerCamelCase__	):
  									del attribute.init_kwargs["auto_map"]
  
  						if push_to_hub:
  							self._upload_modified_files(
  							    lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			commit_message=lowerCamelCase__							,			token=kwargs.get("use_auth_token"	)							,			)
  
  
  
  
  
  					@classmethod
  					def       _UpperCAmelCase						(							cls							,			lowerCamelCase__							,			lowerCamelCase__ = None							,			lowerCamelCase__ = False							,			lowerCamelCase__ = False							,			lowerCamelCase__ = None							,			lowerCamelCase__ = "main"							,			**lowerCamelCase__							,			):
  						UpperCAmelCase__:  List[Any]		       =   cache_dir
  						UpperCAmelCase__:  Dict		       =   force_download
  						UpperCAmelCase__:  int		       =   local_files_only
  						UpperCAmelCase__:  Tuple		       =   revision
  
  						UpperCAmelCase__:  Union[str, Any]		       =   kwargs.pop("use_auth_token"							,			lowerCamelCase__	)
  						if use_auth_token is not None:
  							warnings.warn(
  							    "The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers."							,			lowerCamelCase__	)
  							if token is not None:
  								raise ValueError(
  								    "`token` and `use_auth_token` are both specified. Please set only the argument `token`."	)
  							UpperCAmelCase__:  List[str]		       =   use_auth_token
  
  						if token is not None:
  							# change to `token` in a follow-up PR
  							UpperCAmelCase__:  Any		       =   token
  
  						UpperCAmelCase__:  List[Any]		       =   cls._get_arguments_from_pretrained(lowerCamelCase__							,			**lowerCamelCase__	)
  						return cls(*lowerCamelCase__	)
  
  
  
  
  
  					@classmethod
  					def       _UpperCAmelCase						(							cls							,			lowerCamelCase__="AutoProcessor"	):
  
  
  
  
  
  
  
  						if not isinstance(lowerCamelCase__							,			lowerCamelCase__	):
  							UpperCAmelCase__:  int		       =   auto_class.__name__
  
  						import transformers.models.auto as auto_module
  
  						if not hasattr(lowerCamelCase__							,			lowerCamelCase__	):
  							raise ValueError(F"{auto_class} is not a valid auto class."	)
  
  						UpperCAmelCase__:  str		       =   auto_class
  
  
  
  
  
  					@classmethod
  					def       _UpperCAmelCase						(							cls							,			lowerCamelCase__							,			**lowerCamelCase__	):
  						UpperCAmelCase__:  Tuple		       =   []
  						for attribute_name in cls.attributes:
  							UpperCAmelCase__:  Dict		       =   getattr(cls							,			F"{attribute_name}_class"	)
  							if isinstance(lowerCamelCase__							,			lowerCamelCase__	):
  								UpperCAmelCase__:  Tuple		       =   tuple(getattr(lowerCamelCase__							,			lowerCamelCase__	) if n is not None else None for n in class_name	)
  								UpperCAmelCase__:  List[str]		       =   kwargs.get("use_fast"							,			lowerCamelCase__	)
  								if use_fast and classes[1] is not None:
  									UpperCAmelCase__:  int		       =   classes[1]
  								else:
  									UpperCAmelCase__:  Any		       =   classes[0]
  							else:
  								UpperCAmelCase__:  Optional[Any]		       =   getattr(lowerCamelCase__							,			lowerCamelCase__	)
  
  							args.append(attribute_class.from_pretrained(lowerCamelCase__							,			**lowerCamelCase__	)	)
  						return args
  
  
  
  
  
  					@property
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  str		       =   getattr(self							,			self.attributes[0]	)
  						return getattr(lowerCamelCase__							,			"model_input_names"							,			lowerCamelCase__	)
  _lowerCAmelCase     :			List[Any]    =copy_func(ProcessorMixin.push_to_hub)
  if ProcessorMixin.push_to_hub.__doc__ is not None:
  					_lowerCAmelCase     :			List[Any]    =ProcessorMixin.push_to_hub.__doc__.format(
  					    object="""processor""", object_class="""AutoProcessor""", object_files="""processor files"""
  					)
style_context: 
  
  import subprocess
  import sys
  import warnings
  from argparse import ArgumentParser
  from pathlib import Path
  
  from packaging import version
  
  from .. import AutoFeatureExtractor, AutoImageProcessor, AutoProcessor, AutoTokenizer
  from ..utils import logging
  from ..utils.import_utils import is_optimum_available
  from .convert import export, validate_model_outputs
  from .features import FeaturesManager
  from .utils import get_preprocessor
  
  
  _lowerCAmelCase     :			int    ="""1.5.0"""
  
  _lowerCAmelCase     :			Optional[Any]    =["""vision-encoder-decoder"""]
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE			):
  	if is_optimum_available():
  		from optimum.version import __version__ as optimum_version
  
  		UpperCAmelCase__:  Optional[int]		       =   version.parse(SCREAMING_SNAKE_CASE			)
  		if parsed_optimum_version < version.parse(SCREAMING_SNAKE_CASE			):
  			raise RuntimeError(
  			    f"transformers.onnx requires optimum >= {MIN_OPTIMUM_VERSION} but {optimum_version} is installed. You "
  			    "can upgrade optimum by running: pip install -U optimum[exporters]"			)
  	else:
  		raise RuntimeError(
  		    "transformers.onnx requires optimum to run, you can install the library by running: pip install "
  		    "optimum[exporters]"			)
  	UpperCAmelCase__:  Any		       =   [
  	    sys.executable,
  	    "-m",
  	    "optimum.exporters.onnx",
  	    f"--model {args.model}",
  	    f"--task {args.feature}",
  	    f"--framework {args.framework}" if args.framework is not None else "",
  	    f"{args.output}",
  	]
  	UpperCAmelCase__:  str		       =   subprocess.Popen(" ".join(SCREAMING_SNAKE_CASE			)  ,stdout=subprocess.PIPE  ,shell=SCREAMING_SNAKE_CASE			)
  	proc.wait()
  
  	logger.info(
  	    "The export was done by optimum.exporters.onnx. We recommend using to use this package directly in future, as "
  	    "transformers.onnx is deprecated, and will be removed in v5. You can find more information here: "
  	    "https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model."			)
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE			):
  	UpperCAmelCase__:  Optional[int]		       =   args.output if args.output.is_file() else args.output.joinpath("model.onnx"			)
  	if not args.output.parent.exists():
  		args.output.parent.mkdir(parents=SCREAMING_SNAKE_CASE			)
  
  	# Allocate the model
  	UpperCAmelCase__:  int		       =   FeaturesManager.get_model_from_feature(
  	    args.feature  ,args.model  ,framework=args.framework  ,cache_dir=args.cache_dir			)
  
  	UpperCAmelCase__       ,	UpperCAmelCase__:  List[str]		       =   FeaturesManager.check_supported_model_or_raise(SCREAMING_SNAKE_CASE  ,feature=args.feature			)
  	UpperCAmelCase__:  str		       =   model_onnx_config(model.config			)
  
  	if model_kind in ENCODER_DECODER_MODELS:
  		UpperCAmelCase__:  List[str]		       =   model.get_encoder()
  		UpperCAmelCase__:  Optional[int]		       =   model.get_decoder()
  
  		UpperCAmelCase__:  List[str]		       =   onnx_config.get_encoder_config(encoder_model.config			)
  		UpperCAmelCase__:  Any		       =   onnx_config.get_decoder_config(
  		    encoder_model.config  ,decoder_model.config  ,feature=args.feature			)
  
  		if args.opset is None:
  			UpperCAmelCase__:  Tuple		       =   max(encoder_onnx_config.default_onnx_opset  ,decoder_onnx_config.default_onnx_opset			)
  
  		if args.opset < min(encoder_onnx_config.default_onnx_opset  ,decoder_onnx_config.default_onnx_opset			):
  			raise ValueError(
  			    f"Opset {args.opset} is not sufficient to export {model_kind}. At least "
  			    f" {min(encoder_onnx_config.default_onnx_opset  ,decoder_onnx_config.default_onnx_opset			)} is required."			)
  
  		UpperCAmelCase__:  str		       =   AutoFeatureExtractor.from_pretrained(args.model			)
  
  		UpperCAmelCase__       ,	UpperCAmelCase__:  Tuple		       =   export(
  		    SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,args.opset  ,args.output.parent.joinpath("encoder_model.onnx"			)  ,)
  
  		validate_model_outputs(
  		    SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,args.output.parent.joinpath("encoder_model.onnx"			)  ,SCREAMING_SNAKE_CASE  ,args.atol if args.atol else encoder_onnx_config.atol_for_validation  ,)
  
  		UpperCAmelCase__:  Dict		       =   AutoTokenizer.from_pretrained(args.model			)
  
  		UpperCAmelCase__       ,	UpperCAmelCase__:  str		       =   export(
  		    SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,args.opset  ,args.output.parent.joinpath("decoder_model.onnx"			)  ,)
  
  		validate_model_outputs(
  		    SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,args.output.parent.joinpath("decoder_model.onnx"			)  ,SCREAMING_SNAKE_CASE  ,args.atol if args.atol else decoder_onnx_config.atol_for_validation  ,)
  		logger.info(
  		    f"All good, model saved at: {args.output.parent.joinpath('encoder_model.onnx'			).as_posix()},"
  		    f" {args.output.parent.joinpath('decoder_model.onnx'			).as_posix()}"			)
  
  	else:
  		# Instantiate the appropriate preprocessor
  		if args.preprocessor == "auto":
  			UpperCAmelCase__:  str		       =   get_preprocessor(args.model			)
  		elif args.preprocessor == "tokenizer":
  			UpperCAmelCase__:  Any		       =   AutoTokenizer.from_pretrained(args.model			)
  		elif args.preprocessor == "image_processor":
  			UpperCAmelCase__:  Optional[int]		       =   AutoImageProcessor.from_pretrained(args.model			)
  		elif args.preprocessor == "feature_extractor":
  			UpperCAmelCase__:  int		       =   AutoFeatureExtractor.from_pretrained(args.model			)
  		elif args.preprocessor == "processor":
  			UpperCAmelCase__:  Optional[int]		       =   AutoProcessor.from_pretrained(args.model			)
  		else:
  			raise ValueError(f"Unknown preprocessor type '{args.preprocessor}'"			)
  
  		# Ensure the requested opset is sufficient
  		if args.opset is None:
  			UpperCAmelCase__:  List[str]		       =   onnx_config.default_onnx_opset
  
  		if args.opset < onnx_config.default_onnx_opset:
  			raise ValueError(
  			    f"Opset {args.opset} is not sufficient to export {model_kind}. "
  			    f"At least  {onnx_config.default_onnx_opset} is required."			)
  
  		UpperCAmelCase__       ,	UpperCAmelCase__:  Tuple		       =   export(
  		    SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,args.opset  ,args.output  ,)
  
  		if args.atol is None:
  			UpperCAmelCase__:  List[Any]		       =   onnx_config.atol_for_validation
  
  		validate_model_outputs(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,args.output  ,SCREAMING_SNAKE_CASE  ,args.atol			)
  		logger.info(f"All good, model saved at: {args.output.as_posix()}"			)
  		warnings.warn(
  		    "The export was done by transformers.onnx which is deprecated and will be removed in v5. We recommend"
  		    " using optimum.exporters.onnx in future. You can find more information here:"
  		    " https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model."  ,SCREAMING_SNAKE_CASE  ,)
  
  
  
  def  _A  (			):
  	UpperCAmelCase__:  str		       =   ArgumentParser("Hugging Face Transformers ONNX exporter"			)
  	parser.add_argument(
  	    "-m"  ,"--model"  ,type=SCREAMING_SNAKE_CASE  ,required=SCREAMING_SNAKE_CASE  ,help="Model ID on huggingface.co or path on disk to load model from."			)
  	parser.add_argument(
  	    "--feature"  ,default="default"  ,help="The type of features to export the model with."  ,)
  	parser.add_argument("--opset"  ,type=SCREAMING_SNAKE_CASE  ,default=SCREAMING_SNAKE_CASE  ,help="ONNX opset version to export the model with."			)
  	parser.add_argument(
  	    "--atol"  ,type=SCREAMING_SNAKE_CASE  ,default=SCREAMING_SNAKE_CASE  ,help="Absolute difference tolerance when validating the model."			)
  	parser.add_argument(
  	    "--framework"  ,type=SCREAMING_SNAKE_CASE  ,choices=["pt", "tf"]  ,default=SCREAMING_SNAKE_CASE  ,help=(
  	        "The framework to use for the ONNX export."
  	        " If not provided, will attempt to use the local checkpoint's original framework"
  	        " or what is available in the environment."
  	    )  ,)
  	parser.add_argument("output"  ,type=SCREAMING_SNAKE_CASE  ,help="Path indicating where to store generated ONNX model."			)
  	parser.add_argument("--cache_dir"  ,type=SCREAMING_SNAKE_CASE  ,default=SCREAMING_SNAKE_CASE  ,help="Path indicating where to store cache."			)
  	parser.add_argument(
  	    "--preprocessor"  ,type=SCREAMING_SNAKE_CASE  ,choices=["auto", "tokenizer", "feature_extractor", "image_processor", "processor"]  ,default="auto"  ,help="Which type of preprocessor to use. 'auto' tries to automatically detect it."  ,)
  	parser.add_argument(
  	    "--export_with_transformers"  ,action="store_true"  ,help=(
  	        "Whether to use transformers.onnx instead of optimum.exporters.onnx to perform the ONNX export. It can be "
  	        "useful when exporting a model supported in transformers but not in optimum, otherwise it is not "
  	        "recommended."
  	    )  ,)
  
  	UpperCAmelCase__:  Optional[int]		       =   parser.parse_args()
  	if args.export_with_transformers or not is_optimum_available():
  		export_with_transformers(SCREAMING_SNAKE_CASE			)
  	else:
  		export_with_optimum(SCREAMING_SNAKE_CASE			)
  
  
  if __name__ == "__main__":
  					_lowerCAmelCase     :			int    =logging.get_logger("""transformers.onnx""")  # pylint: disable=invalid-name
  					logger.setLevel(logging.INFO)
  					main()