code: 
  '''simple docstring'''
  
  
  
  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
  
  
  __A          =    "1.5.0"
  
  __A          =    ["vision-encoder-decoder"]
  
  
  
  
  
  def  _A		(				lowercase__	):
  						if is_optimum_available():
  												from optimum.version import __version__ as optimum_version
  
  												lowercase__							 =       version.parse(lowercase__	)
  												if parsed_optimum_version < version.parse(lowercase__	):
  																		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]"""	)
  						lowercase__							 =       [
  						    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}''',
  						]
  						lowercase__							 =       subprocess.Popen(""" """.join(lowercase__	)		,		stdout=subprocess.PIPE		,		shell=lowercase__	)
  						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		(				lowercase__	):
  						lowercase__							 =       args.output if args.output.is_file() else args.output.joinpath("""model.onnx"""	)
  						if not args.output.parent.exists():
  												args.output.parent.mkdir(parents=lowercase__	)
  
  						# Allocate the model
  						lowercase__							 =       FeaturesManager.get_model_from_feature(
  						    args.feature		,		args.model		,		framework=args.framework		,		cache_dir=args.cache_dir	)
  
  						lowercase__ ,						lowercase__							 =       FeaturesManager.check_supported_model_or_raise(lowercase__		,		feature=args.feature	)
  						lowercase__							 =       model_onnx_config(model.config	)
  
  						if model_kind in ENCODER_DECODER_MODELS:
  												lowercase__							 =       model.get_encoder()
  												lowercase__							 =       model.get_decoder()
  
  												lowercase__							 =       onnx_config.get_encoder_config(encoder_model.config	)
  												lowercase__							 =       onnx_config.get_decoder_config(
  												    encoder_model.config		,		decoder_model.config		,		feature=args.feature	)
  
  												if args.opset is None:
  																		lowercase__							 =       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.'''	)
  
  												lowercase__							 =       AutoFeatureExtractor.from_pretrained(args.model	)
  
  												lowercase__ ,						lowercase__							 =       export(
  												    lowercase__		,		lowercase__		,		lowercase__		,		args.opset		,		args.output.parent.joinpath("""encoder_model.onnx"""	)		,		)
  
  												validate_model_outputs(
  												    lowercase__		,		lowercase__		,		lowercase__		,		args.output.parent.joinpath("""encoder_model.onnx"""	)		,		lowercase__		,		args.atol if args.atol else encoder_onnx_config.atol_for_validation		,		)
  
  												lowercase__							 =       AutoTokenizer.from_pretrained(args.model	)
  
  												lowercase__ ,						lowercase__							 =       export(
  												    lowercase__		,		lowercase__		,		lowercase__		,		args.opset		,		args.output.parent.joinpath("""decoder_model.onnx"""	)		,		)
  
  												validate_model_outputs(
  												    lowercase__		,		lowercase__		,		lowercase__		,		args.output.parent.joinpath("""decoder_model.onnx"""	)		,		lowercase__		,		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":
  																		lowercase__							 =       get_preprocessor(args.model	)
  												elif args.preprocessor == "tokenizer":
  																		lowercase__							 =       AutoTokenizer.from_pretrained(args.model	)
  												elif args.preprocessor == "image_processor":
  																		lowercase__							 =       AutoImageProcessor.from_pretrained(args.model	)
  												elif args.preprocessor == "feature_extractor":
  																		lowercase__							 =       AutoFeatureExtractor.from_pretrained(args.model	)
  												elif args.preprocessor == "processor":
  																		lowercase__							 =       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:
  																		lowercase__							 =       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.'''	)
  
  												lowercase__ ,						lowercase__							 =       export(
  												    lowercase__		,		lowercase__		,		lowercase__		,		args.opset		,		args.output		,		)
  
  												if args.atol is None:
  																		lowercase__							 =       onnx_config.atol_for_validation
  
  												validate_model_outputs(lowercase__		,		lowercase__		,		lowercase__		,		args.output		,		lowercase__		,		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."""		,		lowercase__		,		)
  
  
  
  
  
  def  _A		(				):
  						lowercase__							 =       ArgumentParser("""Hugging Face Transformers ONNX exporter"""	)
  						parser.add_argument(
  						    """-m"""		,		"""--model"""		,		type=lowercase__		,		required=lowercase__		,		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=lowercase__		,		default=lowercase__		,		help="""ONNX opset version to export the model with."""	)
  						parser.add_argument(
  						    """--atol"""		,		type=lowercase__		,		default=lowercase__		,		help="""Absolute difference tolerance when validating the model."""	)
  						parser.add_argument(
  						    """--framework"""		,		type=lowercase__		,		choices=["""pt""", """tf"""]		,		default=lowercase__		,		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=lowercase__		,		help="""Path indicating where to store generated ONNX model."""	)
  						parser.add_argument("""--cache_dir"""		,		type=lowercase__		,		default=lowercase__		,		help="""Path indicating where to store cache."""	)
  						parser.add_argument(
  						    """--preprocessor"""		,		type=lowercase__		,		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."""
  						    )		,		)
  
  						lowercase__							 =       parser.parse_args()
  						if args.export_with_transformers or not is_optimum_available():
  												export_with_transformers(lowercase__	)
  						else:
  												export_with_optimum(lowercase__	)
  
  
  if __name__ == "__main__":
  				__A          =    logging.get_logger("transformers.onnx")  # pylint: disable=invalid-name
  				logger.setLevel(logging.INFO)
  				main()
  
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  
  
  import timeit
  from typing import Callable, Optional
  
  from ..configuration_utils import PretrainedConfig
  from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
  from ..utils import is_pyanvml_available, is_torch_available, logging
  from .benchmark_utils import (
      Benchmark,
      Memory,
      MemorySummary,
      measure_peak_memory_cpu,
      start_memory_tracing,
      stop_memory_tracing,
  )
  
  
  if is_torch_available():
  				import torch
  
  				from .benchmark_args import PyTorchBenchmarkArguments
  
  
  if is_pyanvml_available():
  				import pyanvml.pyanvml as nvml
  
  
  __A          =    logging.get_logger(__name__)
  
  
  
  
  
  class 			A							(       __UpperCAmelCase				):
  			lowerCamelCase							:							PyTorchBenchmarkArguments
  			lowerCamelCase							:							PretrainedConfig
  			lowerCamelCase							:							str       		=  "PyTorch"
  
  
  
  			@property
  			def A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return torch.__version__
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		float:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self._prepare_inference_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									return self._measure_speed(_inference       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		[Memory, Optional[MemorySummary]]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self._prepare_inference_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									return self._measure_memory(_inference       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		float:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self._prepare_train_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									return self._measure_speed(_train       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		[Memory, Optional[MemorySummary]]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self._prepare_train_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									return self._measure_memory(_train       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Callable[[], None]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.config_dict[model_name]
  
  									if self.args.torchscript:
  															lowercase__							 =       True
  
  									lowercase__							 =       (
  									    hasattr(lowerCamelCase__       ,     """architectures"""       )
  									    and isinstance(config.architectures       ,     lowerCamelCase__       )
  									    and len(config.architectures       ) > 0
  									)
  									if not self.args.only_pretrain_model and has_model_class_in_config:
  															try:
  																					lowercase__							 =       config.architectures[0]
  																					lowercase__							 =       __import__("""transformers"""       ,     fromlist=[model_class]       )
  																					lowercase__							 =       getattr(lowerCamelCase__       ,     lowerCamelCase__       )
  																					lowercase__							 =       model_cls(lowerCamelCase__       )
  															except ImportError:
  																					raise ImportError(
  																					    F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
  																					    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""       )
  									else:
  															lowercase__							 =       MODEL_MAPPING[config.__class__](lowerCamelCase__       )
  
  									model.eval()
  									model.to(self.args.device       )
  
  									# encoder-decoder has vocab size saved differently
  									lowercase__							 =       config.vocab_size if hasattr(lowerCamelCase__       ,     """vocab_size"""       ) else config.encoder.vocab_size
  									lowercase__							 =       torch.randint(lowerCamelCase__       ,     (batch_size, sequence_length)       ,     dtype=torch.long       ,     device=self.args.device       )
  
  									if self.args.fpaa:
  															logger.info("""Running training in Mixed Precision..."""       )
  															if not self.args.is_gpu:
  																					raise ValueError("""Mixed precision is possible only for GPU."""       )
  															# amp seems to have memory leaks so that memory usage
  															# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
  															model.half()
  
  									if self.args.torchscript:
  															with torch.no_grad():
  																					lowercase__							 =       torch.jit.trace(lowerCamelCase__       ,     lowerCamelCase__       )
  									else:
  															lowercase__							 =       model
  
  									def encoder_decoder_forward():
  															with torch.no_grad():
  																					lowercase__							 =       inference_model(lowerCamelCase__       ,     decoder_input_ids=lowerCamelCase__       )
  															return outputs
  
  									def encoder_forward():
  															with torch.no_grad():
  																					lowercase__							 =       inference_model(lowerCamelCase__       )
  															return outputs
  
  									lowercase__							 =       encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
  									return _forward
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Callable[[], None]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.config_dict[model_name]
  
  									lowercase__							 =       (
  									    hasattr(lowerCamelCase__       ,     """architectures"""       )
  									    and isinstance(config.architectures       ,     lowerCamelCase__       )
  									    and len(config.architectures       ) > 0
  									)
  									if not self.args.only_pretrain_model and has_model_class_in_config:
  															try:
  																					lowercase__							 =       config.architectures[0]
  																					lowercase__							 =       __import__("""transformers"""       ,     fromlist=[model_class]       )
  																					lowercase__							 =       getattr(lowerCamelCase__       ,     lowerCamelCase__       )
  																					lowercase__							 =       model_cls(lowerCamelCase__       )
  															except ImportError:
  																					raise ImportError(
  																					    F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
  																					    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""       )
  									else:
  															lowercase__							 =       MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCamelCase__       )
  
  									if self.args.torchscript:
  															raise NotImplementedError("""Training for torchscript is currently not implemented"""       )
  									else:
  															lowercase__							 =       model
  
  									model.train()
  									model.to(self.args.device       )
  
  									# encoder-decoder has vocab size saved differently
  									lowercase__							 =       config.vocab_size if hasattr(lowerCamelCase__       ,     """vocab_size"""       ) else config.encoder.vocab_size
  									lowercase__							 =       torch.randint(lowerCamelCase__       ,     (batch_size, sequence_length)       ,     dtype=torch.long       ,     device=self.args.device       )
  
  									if self.args.fpaa:
  															logger.info("""Running training in Mixed Precision..."""       )
  															if not self.args.is_gpu:
  																					raise ValueError("""Mixed precision is possible only for GPU."""       )
  
  															# amp seems to have memory leaks so that memory usage
  															# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
  															model.half()
  
  									def compute_loss_and_backprob_encoder():
  															lowercase__							 =       train_model(lowerCamelCase__       ,     labels=lowerCamelCase__       )[0]
  															loss.backward()
  															return loss
  
  									def compute_loss_and_backprob_encoder_decoder():
  															lowercase__							 =       train_model(lowerCamelCase__       ,     decoder_input_ids=lowerCamelCase__       ,     labels=lowerCamelCase__       )[0]
  															loss.backward()
  															return loss
  
  									lowercase__							 =       (
  									    compute_loss_and_backprob_encoder_decoder
  									    if config.is_encoder_decoder
  									    else compute_loss_and_backprob_encoder
  									)
  									return _train
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       )			->		float:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									try:
  															if self.args.is_tpu or self.args.torchscript:
  																					# run additional 10 times to stabilize compilation for tpu and torchscript
  																					logger.info("""Do inference on TPU or torchscript. Running model 5 times to stabilize compilation"""       )
  																					timeit.repeat(
  																					    lowerCamelCase__       ,     repeat=1       ,     number=5       ,     )
  
  															# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
  															lowercase__							 =       timeit.repeat(
  															    lowerCamelCase__       ,     repeat=self.args.repeat       ,     number=10       ,     )
  
  															if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
  																					import torch_xla.debug.metrics as met
  
  																					self.print_fn(met.metrics_report()       )
  
  															return min(lowerCamelCase__       ) / 10.0
  									except RuntimeError as e:
  															self.print_fn(F'''Doesn\'t fit on GPU. {e}'''       )
  															return "N/A"
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       )			->		[Memory, MemorySummary]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									try:
  															if self.args.trace_memory_line_by_line:
  																					lowercase__							 =       start_memory_tracing("""transformers"""       )
  
  															if self.args.is_tpu:
  																					# tpu
  																					raise NotImplementedError(
  																					    """Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with"""
  																					    """ `--no-memory` or `args.memory=False`"""       )
  															elif self.args.is_gpu:
  																					if not is_pyanvml_available():
  																											logger.warning(
  																											    """py3nvml not installed, we won't log GPU memory usage. """
  																											    """Install py3nvml (pip install py3nvml) to log information about GPU."""       )
  																											lowercase__							 =       """N/A"""
  																					else:
  																											logger.info(
  																											    """Measuring total GPU usage on GPU device. Make sure to not have additional processes running"""
  																											    """ on the same GPU."""       )
  																											# init nvml
  																											nvml.nvmlInit()
  																											func()
  																											lowercase__							 =       nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx       )
  																											lowercase__							 =       nvml.nvmlDeviceGetMemoryInfo(lowerCamelCase__       )
  																											lowercase__							 =       meminfo.used
  																											lowercase__							 =       Memory(lowerCamelCase__       )
  																											# shutdown nvml
  																											nvml.nvmlShutdown()
  															else:
  																					# cpu
  																					lowercase__							 =       measure_peak_memory_cpu(lowerCamelCase__       )
  																					lowercase__							 =       Memory(lowerCamelCase__       ) if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ) else memory_bytes
  
  															if self.args.trace_memory_line_by_line:
  																					lowercase__							 =       stop_memory_tracing(lowerCamelCase__       )
  															else:
  																					lowercase__							 =       None
  
  															return memory, summary
  									except RuntimeError as e:
  															self.print_fn(F'''Doesn\'t fit on GPU. {e}'''       )
  															return "N/A", None
  
  
  
  
  
  
  