code: 
  
  import inspect
  import warnings
  
  import torch
  
  from .state import AcceleratorState, GradientState
  from .utils import DistributedType, honor_type, is_tpu_available
  
  
  if is_tpu_available(check_device=False):
  					import torch_xla.core.xla_model as xm
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	if isinstance(SCREAMING_SNAKE_CASE  ,(list, tuple)			):
  		return honor_type(SCREAMING_SNAKE_CASE  ,(move_to_device(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			) for t in state)			)
  	elif isinstance(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  		return type(SCREAMING_SNAKE_CASE			)({k: move_to_device(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			) for k, v in state.items()}			)
  	elif isinstance(SCREAMING_SNAKE_CASE  ,torch.Tensor			):
  		return state.to(SCREAMING_SNAKE_CASE			)
  	return state
  
  class __UpperCamelCase    (							torch.optim.Optimizer		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					def __init__(							self							,			lowerCamelCase__							,			lowerCamelCase__=True							,			lowerCamelCase__=None	):
  						UpperCAmelCase__:  List[str]		       =   optimizer
  						UpperCAmelCase__:  str		       =   scaler
  						UpperCAmelCase__:  Dict		       =   AcceleratorState()
  						UpperCAmelCase__:  Optional[int]		       =   GradientState()
  						UpperCAmelCase__:  Optional[Any]		       =   device_placement
  						UpperCAmelCase__:  Optional[int]		       =   False
  						UpperCAmelCase__:  Any		       =   None
  
  						# Handle device placement
  						if device_placement:
  							UpperCAmelCase__:  int		       =   self.optimizer.state_dict()
  							if self.accelerator_state.distributed_type == DistributedType.TPU:
  								xm.send_cpu_data_to_device(lowerCamelCase__							,			self.accelerator_state.device	)
  							else:
  								UpperCAmelCase__:  List[str]		       =   move_to_device(lowerCamelCase__							,			self.accelerator_state.device	)
  							self.optimizer.load_state_dict(lowerCamelCase__	)
  
  
  
  
  
  					@property
  					def       _UpperCAmelCase						(							self	):
  						return self.optimizer.state
  
  
  
  
  
  					@state.setter
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__	):
  						UpperCAmelCase__:  Tuple		       =   state
  
  
  
  
  
  					@property
  					def       _UpperCAmelCase						(							self	):
  						return self.optimizer.param_groups
  
  
  
  
  
  					@param_groups.setter
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__	):
  						UpperCAmelCase__:  Tuple		       =   param_groups
  
  
  
  
  
  					@property
  					def       _UpperCAmelCase						(							self	):
  						return self.optimizer.defaults
  
  
  
  
  
  					@defaults.setter
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__	):
  						UpperCAmelCase__:  int		       =   defaults
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__	):
  						self.optimizer.add_param_group(lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__	):
  						if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
  							xm.send_cpu_data_to_device(lowerCamelCase__							,			self.accelerator_state.device	)
  						self.optimizer.load_state_dict(lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						return self.optimizer.state_dict()
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__=None	):
  						if self.gradient_state.sync_gradients:
  							UpperCAmelCase__:  int		       =   "set_to_none" in inspect.signature(self.optimizer.zero_grad	).parameters
  							if accept_arg:
  								if set_to_none is None:
  									UpperCAmelCase__:  Dict		       =   False
  								self.optimizer.zero_grad(set_to_none=lowerCamelCase__	)
  							else:
  								if set_to_none is not None:
  									raise ValueError("`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer."	)
  								self.optimizer.zero_grad()
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__=None	):
  						if self.gradient_state.sync_gradients:
  							if self.accelerator_state.distributed_type == DistributedType.TPU:
  								UpperCAmelCase__:  Union[str, Any]		       =   {"closure": closure} if closure is not None else {}
  								xm.optimizer_step(self.optimizer							,			optimizer_args=lowerCamelCase__	)
  							elif self.scaler is not None:
  								UpperCAmelCase__:  Any		       =   False
  								if self._last_scale is None:
  									# `get_scale` is an async operation requiring full synchronization
  									# on CPU and GPUs before finishing. As a result, we store away
  									# the prior one to reduce the call overhead
  									UpperCAmelCase__:  Optional[int]		       =   self.scaler.get_scale()
  									UpperCAmelCase__:  Optional[Any]		       =   True
  								self.scaler.step(self.optimizer							,			lowerCamelCase__	)
  								self.scaler.update()
  								UpperCAmelCase__:  Tuple		       =   self.scaler.get_scale()
  								if not new_scale:
  									# If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
  									UpperCAmelCase__:  Optional[Any]		       =   scale_after < self._last_scale
  								UpperCAmelCase__:  Dict		       =   scale_after
  
  							else:
  								self.optimizer.step(lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__	):
  						for param_group in self.optimizer.param_groups:
  							UpperCAmelCase__:  List[str]		       =   [parameters_map.get(lowerCamelCase__							,			lowerCamelCase__	) for p in param_group["params"]]
  
  
  
  
  
  					@property
  					def       _UpperCAmelCase						(							self	):
  						warnings.warn(
  						    "The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use "
  						    "`optimizer.step_was_skipped` instead."							,			lowerCamelCase__							,			)
  						return self._is_overflow
  
  
  
  
  
  					@property
  					def       _UpperCAmelCase						(							self	):
  						return self._is_overflow
  
  
  
  
  
  					def __getstate__(							self	):
  						return self.__dict__.copy()
  
  
  
  
  
  					def __setstate__(							self							,			lowerCamelCase__	):
  						self.__dict__.update(lowerCamelCase__	)
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()