code: 
  
  class __UpperCamelCase    :
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					def __init__(							self							,			lowerCamelCase__	):
  						UpperCAmelCase__:  Union[str, Any]		       =   data
  						UpperCAmelCase__:  List[Any]		       =   None
  						UpperCAmelCase__:  Optional[Any]		       =   None
  
  
  
  
  
  					def __str__(							self	):
  						return F"{self.data}"
  
  class __UpperCamelCase    :
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					def __init__(							self	):
  						UpperCAmelCase__:  str		       =   None
  						UpperCAmelCase__:  List[Any]		       =   None
  
  
  
  
  
  					def __iter__(							self	):
  						UpperCAmelCase__:  str		       =   self.head
  						while node:
  							yield node.data
  							UpperCAmelCase__:  Tuple		       =   node.next
  
  
  
  
  
  					def __str__(							self	):
  						return "->".join([str(lowerCamelCase__	) for item in self]	)
  
  
  
  
  
  					def __len__(							self	):
  						return sum(1 for _ in self	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__	):
  						self.insert_at_nth(0							,			lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__	):
  						self.insert_at_nth(len(self	)							,			lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__	):
  						UpperCAmelCase__:  Optional[int]		       =   len(self	)
  
  						if not 0 <= index <= length:
  							raise IndexError("list index out of range"	)
  						UpperCAmelCase__:  List[Any]		       =   Node(lowerCamelCase__	)
  						if self.head is None:
  							UpperCAmelCase__:  List[Any]		       =   new_node
  						elif index == 0:
  							UpperCAmelCase__:  List[Any]		       =   new_node
  							UpperCAmelCase__:  Optional[int]		       =   self.head
  							UpperCAmelCase__:  int		       =   new_node
  						elif index == length:
  							UpperCAmelCase__:  List[Any]		       =   new_node
  							UpperCAmelCase__:  str		       =   self.tail
  							UpperCAmelCase__:  Optional[int]		       =   new_node
  						else:
  							UpperCAmelCase__:  Any		       =   self.head
  							for _ in range(0							,			lowerCamelCase__	):
  								UpperCAmelCase__:  Dict		       =   temp.next
  							UpperCAmelCase__:  int		       =   new_node
  							UpperCAmelCase__:  List[str]		       =   temp.previous
  							UpperCAmelCase__:  Any		       =   temp
  							UpperCAmelCase__:  List[Any]		       =   new_node
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						return self.delete_at_nth(0	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						return self.delete_at_nth(len(self	) - 1	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__	):
  						UpperCAmelCase__:  Any		       =   len(self	)
  
  						if not 0 <= index <= length - 1:
  							raise IndexError("list index out of range"	)
  						UpperCAmelCase__:  Optional[int]		       =   self.head  # default first node
  						if length == 1:
  							UpperCAmelCase__:  int		       =   None
  						elif index == 0:
  							UpperCAmelCase__:  Dict		       =   self.head.next
  							UpperCAmelCase__:  Tuple		       =   None
  						elif index == length - 1:
  							UpperCAmelCase__:  List[str]		       =   self.tail
  							UpperCAmelCase__:  Tuple		       =   self.tail.previous
  							UpperCAmelCase__:  int		       =   None
  						else:
  							UpperCAmelCase__:  str		       =   self.head
  							for _ in range(0							,			lowerCamelCase__	):
  								UpperCAmelCase__:  Any		       =   temp.next
  							UpperCAmelCase__:  List[str]		       =   temp
  							UpperCAmelCase__:  Tuple		       =   temp.previous
  							UpperCAmelCase__:  Union[str, Any]		       =   temp.next
  						return delete_node.data
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__	):
  						UpperCAmelCase__:  Optional[int]		       =   self.head
  
  						while current.data != data:  # Find the position to delete
  							if current.next:
  								UpperCAmelCase__:  str		       =   current.next
  							else:  # We have reached the end an no value matches
  								raise ValueError("No data matching given value"	)
  
  						if current == self.head:
  							self.delete_head()
  
  						elif current == self.tail:
  							self.delete_tail()
  
  						else:  # Before: 1 <--> 2(current) <--> 3
  							UpperCAmelCase__:  Optional[Any]		       =   current.next  # 1 --> 3
  							UpperCAmelCase__:  Dict		       =   current.previous  # 1 <--> 3
  						return data
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						return len(self	) == 0
  
  
  
  def  _A  (			):
  	UpperCAmelCase__:  Optional[int]		       =   DoublyLinkedList()
  	assert linked_list.is_empty() is True
  	assert str(SCREAMING_SNAKE_CASE			) == ""
  
  	try:
  		linked_list.delete_head()
  		raise AssertionError  # This should not happen.
  	except IndexError:
  		assert True  # This should happen.
  
  	try:
  		linked_list.delete_tail()
  		raise AssertionError  # This should not happen.
  	except IndexError:
  		assert True  # This should happen.
  
  	for i in range(1_0			):
  		assert len(SCREAMING_SNAKE_CASE			) == i
  		linked_list.insert_at_nth(SCREAMING_SNAKE_CASE  ,i + 1			)
  	assert str(SCREAMING_SNAKE_CASE			) == "->".join(str(SCREAMING_SNAKE_CASE			) for i in range(1  ,1_1			)			)
  
  	linked_list.insert_at_head(0			)
  	linked_list.insert_at_tail(1_1			)
  	assert str(SCREAMING_SNAKE_CASE			) == "->".join(str(SCREAMING_SNAKE_CASE			) for i in range(0  ,1_2			)			)
  
  	assert linked_list.delete_head() == 0
  	assert linked_list.delete_at_nth(9			) == 1_0
  	assert linked_list.delete_tail() == 1_1
  	assert len(SCREAMING_SNAKE_CASE			) == 9
  	assert str(SCREAMING_SNAKE_CASE			) == "->".join(str(SCREAMING_SNAKE_CASE			) for i in range(1  ,1_0			)			)
  
  
  if __name__ == "__main__":
  					from doctest import testmod
  
  					testmod()
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()