
"""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


snake_case			       =    '1.5.0'

snake_case			       =    ['vision-encoder-decoder']

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_      ):

					if is_optimum_available():
										from optimum.version import __version__ as optimum_version

										SCREAMING_SNAKE_CASE           =     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]'      )
					SCREAMING_SNAKE_CASE           =     [
					    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}''',
					]
					SCREAMING_SNAKE_CASE           =     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   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     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
					SCREAMING_SNAKE_CASE           =     FeaturesManager.get_model_from_feature(
					    args.feature,					args.model,					framework=args.framework,					cache_dir=args.cache_dir      )

					SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     FeaturesManager.check_supported_model_or_raise(lowercase_,					feature=args.feature      )
					SCREAMING_SNAKE_CASE           =     model_onnx_config(model.config      )

					if model_kind in ENCODER_DECODER_MODELS:
										SCREAMING_SNAKE_CASE           =     model.get_encoder()
										SCREAMING_SNAKE_CASE           =     model.get_decoder()

										SCREAMING_SNAKE_CASE           =     onnx_config.get_encoder_config(encoder_model.config      )
										SCREAMING_SNAKE_CASE           =     onnx_config.get_decoder_config(
										    encoder_model.config,					decoder_model.config,					feature=args.feature      )

										if args.opset is None:
															SCREAMING_SNAKE_CASE           =     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.'''      )

										SCREAMING_SNAKE_CASE           =     AutoFeatureExtractor.from_pretrained(args.model      )

										SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     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,					)

										SCREAMING_SNAKE_CASE           =     AutoTokenizer.from_pretrained(args.model      )

										SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     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":
															SCREAMING_SNAKE_CASE           =     get_preprocessor(args.model      )
										elif args.preprocessor == "tokenizer":
															SCREAMING_SNAKE_CASE           =     AutoTokenizer.from_pretrained(args.model      )
										elif args.preprocessor == "image_processor":
															SCREAMING_SNAKE_CASE           =     AutoImageProcessor.from_pretrained(args.model      )
										elif args.preprocessor == "feature_extractor":
															SCREAMING_SNAKE_CASE           =     AutoFeatureExtractor.from_pretrained(args.model      )
										elif args.preprocessor == "processor":
															SCREAMING_SNAKE_CASE           =     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:
															SCREAMING_SNAKE_CASE           =     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.'''      )

										SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     export(
										    lowercase_,					lowercase_,					lowercase_,					args.opset,					args.output,					)

										if args.atol is None:
															SCREAMING_SNAKE_CASE           =     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   UpperCamelCase_  (		):
					SCREAMING_SNAKE_CASE           =     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.'
					    ),					)

					SCREAMING_SNAKE_CASE           =     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__":
	snake_case			       =    logging.get_logger('transformers.onnx')  # pylint: disable=invalid-name
	logger.setLevel(logging.INFO)
	main()


"""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


snake_case			       =		logging.get_logger(__name__)




class 					UpperCamelCase     (	__magic_name__				):

				"""simple docstring"""



				UpperCAmelCase_    :				PyTorchBenchmarkArguments
				UpperCAmelCase_    :				PretrainedConfig
				UpperCAmelCase_    :				str						  =    "PyTorch"


				@property
				def 							A (    self      )   ->   List[str]:
									"""simple docstring"""







									return torch.__version__


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__      )   ->   float:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self._prepare_inference_func(lowercase__		,     lowercase__		,     lowercase__      )
									return self._measure_speed(_inference      )


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__      )   ->   [Memory, Optional[MemorySummary]]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self._prepare_inference_func(lowercase__		,     lowercase__		,     lowercase__      )
									return self._measure_memory(_inference      )


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__      )   ->   float:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self._prepare_train_func(lowercase__		,     lowercase__		,     lowercase__      )
									return self._measure_speed(_train      )


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__      )   ->   [Memory, Optional[MemorySummary]]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self._prepare_train_func(lowercase__		,     lowercase__		,     lowercase__      )
									return self._measure_memory(_train      )


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__      )   ->   Callable[[], None]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.config_dict[model_name]

									if self.args.torchscript:
														SCREAMING_SNAKE_CASE           =     True

									SCREAMING_SNAKE_CASE           =     (
									    hasattr(lowercase__		,     'architectures'      )
									    and isinstance(config.architectures		,     lowercase__      )
									    and len(config.architectures      ) > 0
									)
									if not self.args.only_pretrain_model and has_model_class_in_config:
														try:
																			SCREAMING_SNAKE_CASE           =     config.architectures[0]
																			SCREAMING_SNAKE_CASE           =     __import__('transformers'		,     fromlist=[model_class]      )
																			SCREAMING_SNAKE_CASE           =     getattr(lowercase__		,     lowercase__      )
																			SCREAMING_SNAKE_CASE           =     model_cls(lowercase__      )
														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:
														SCREAMING_SNAKE_CASE           =     MODEL_MAPPING[config.__class__](lowercase__      )

									model.eval()
									model.to(self.args.device      )

									# encoder-decoder has vocab size saved differently
									SCREAMING_SNAKE_CASE           =     config.vocab_size if hasattr(lowercase__		,     'vocab_size'      ) else config.encoder.vocab_size
									SCREAMING_SNAKE_CASE           =     torch.randint(lowercase__		,     (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():
																			SCREAMING_SNAKE_CASE           =     torch.jit.trace(lowercase__		,     lowercase__      )
									else:
														SCREAMING_SNAKE_CASE           =     model

									def encoder_decoder_forward():
														with torch.no_grad():
																			SCREAMING_SNAKE_CASE           =     inference_model(lowercase__		,     decoder_input_ids=lowercase__      )
														return outputs

									def encoder_forward():
														with torch.no_grad():
																			SCREAMING_SNAKE_CASE           =     inference_model(lowercase__      )
														return outputs

									SCREAMING_SNAKE_CASE           =     encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
									return _forward


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__      )   ->   Callable[[], None]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.config_dict[model_name]

									SCREAMING_SNAKE_CASE           =     (
									    hasattr(lowercase__		,     'architectures'      )
									    and isinstance(config.architectures		,     lowercase__      )
									    and len(config.architectures      ) > 0
									)
									if not self.args.only_pretrain_model and has_model_class_in_config:
														try:
																			SCREAMING_SNAKE_CASE           =     config.architectures[0]
																			SCREAMING_SNAKE_CASE           =     __import__('transformers'		,     fromlist=[model_class]      )
																			SCREAMING_SNAKE_CASE           =     getattr(lowercase__		,     lowercase__      )
																			SCREAMING_SNAKE_CASE           =     model_cls(lowercase__      )
														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:
														SCREAMING_SNAKE_CASE           =     MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowercase__      )

									if self.args.torchscript:
														raise NotImplementedError('Training for torchscript is currently not implemented'      )
									else:
														SCREAMING_SNAKE_CASE           =     model

									model.train()
									model.to(self.args.device      )

									# encoder-decoder has vocab size saved differently
									SCREAMING_SNAKE_CASE           =     config.vocab_size if hasattr(lowercase__		,     'vocab_size'      ) else config.encoder.vocab_size
									SCREAMING_SNAKE_CASE           =     torch.randint(lowercase__		,     (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():
														SCREAMING_SNAKE_CASE           =     train_model(lowercase__		,     labels=lowercase__      )[0]
														loss.backward()
														return loss

									def compute_loss_and_backprob_encoder_decoder():
														SCREAMING_SNAKE_CASE           =     train_model(lowercase__		,     decoder_input_ids=lowercase__		,     labels=lowercase__      )[0]
														loss.backward()
														return loss

									SCREAMING_SNAKE_CASE           =     (
									    compute_loss_and_backprob_encoder_decoder
									    if config.is_encoder_decoder
									    else compute_loss_and_backprob_encoder
									)
									return _train


				def 							A (    self		,     lowercase__      )   ->   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(
																			    lowercase__		,     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
														SCREAMING_SNAKE_CASE           =     timeit.repeat(
														    lowercase__		,     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(lowercase__      ) / 10.0
									except RuntimeError as e:
														self.print_fn(f'''Doesn\'t fit on GPU. {e}'''      )
														return "N/A"




				def 							A (    self		,     lowercase__      )   ->   [Memory, MemorySummary]:
									"""simple docstring"""







									try:
														if self.args.trace_memory_line_by_line:
																			SCREAMING_SNAKE_CASE           =     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.'      )
																								SCREAMING_SNAKE_CASE           =     '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()
																								SCREAMING_SNAKE_CASE           =     nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx      )
																								SCREAMING_SNAKE_CASE           =     nvml.nvmlDeviceGetMemoryInfo(lowercase__      )
																								SCREAMING_SNAKE_CASE           =     meminfo.used
																								SCREAMING_SNAKE_CASE           =     Memory(lowercase__      )
																								# shutdown nvml
																								nvml.nvmlShutdown()
														else:
																			# cpu
																			SCREAMING_SNAKE_CASE           =     measure_peak_memory_cpu(lowercase__      )
																			SCREAMING_SNAKE_CASE           =     Memory(lowercase__      ) if isinstance(lowercase__		,     lowercase__      ) else memory_bytes

														if self.args.trace_memory_line_by_line:
																			SCREAMING_SNAKE_CASE           =     stop_memory_tracing(lowercase__      )
														else:
																			SCREAMING_SNAKE_CASE           =     None

														return memory, summary
									except RuntimeError as e:
														self.print_fn(f'''Doesn\'t fit on GPU. {e}'''      )
														return "N/A", None
