



'''simple docstring'''


from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
						import torch

if is_torch_tpu_available(check_device=False):
						import torch_xla.core.xla_model as xm


lowercase_      :				List[Any]   					=    logging.get_logger(__name__)




@dataclass
class      __UpperCamelCase       (_UpperCAmelCase			):
	__A			  =					[
	    '''no_inference''',
	    '''no_cuda''',
	    '''no_tpu''',
	    '''no_speed''',
	    '''no_memory''',
	    '''no_env_print''',
	    '''no_multi_process''',
	]







	def __init__(    self ,				**_lowerCAmelCase						)    ->    Union[str, Any]:
							'''simple docstring'''






							for deprecated_arg in self.deprecated_args:
													if deprecated_arg in kwargs:
																			lowercase        =							deprecated_arg[3:]
																			setattr(self ,				_lowerCAmelCase ,				not kwargs.pop(_lowerCAmelCase						)						)
																			logger.warning(
																			    F"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
																			    F""" {positive_arg}={kwargs[positive_arg]}"""						)

							lowercase        =							kwargs.pop("""torchscript""" ,				self.torchscript						)
							lowercase        =							kwargs.pop("""torch_xla_tpu_print_metrics""" ,				self.torch_xla_tpu_print_metrics						)
							lowercase        =							kwargs.pop("""fp16_opt_level""" ,				self.fpaa_opt_level						)
							super().__init__(**_lowerCAmelCase						)

	__A			  =					field(default=_UpperCAmelCase			,   metadata={'''help''': '''Trace the models using torchscript'''}			)
	__A			  =					field(default=_UpperCAmelCase			,   metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''}			)
	__A			  =					field(
	    default='''O1'''			,   metadata={
	        '''help''': (
	            '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
	            '''See details at https://nvidia.github.io/apex/amp.html'''
	        )
	    }			,   )







	@cached_property
	def   _a   (    self						)    ->    Tuple["torch.device", int]:
							'''simple docstring'''






							requires_backends(self ,				["""torch"""]						)
							logger.info("""PyTorch: setting up devices"""						)
							if not self.cuda:
													lowercase        =							torch.device("""cpu"""						)
													lowercase        =							0
							elif is_torch_tpu_available():
													lowercase        =							xm.xla_device()
													lowercase        =							0
							else:
													lowercase        =							torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""						)
													lowercase        =							torch.cuda.device_count()
							return device, n_gpu







	@property
	def   _a   (    self						)    ->    Tuple:
							'''simple docstring'''






							return is_torch_tpu_available() and self.tpu







	@property
	def   _a   (    self						)    ->    int:
							'''simple docstring'''






							requires_backends(self ,				["""torch"""]						)
							# TODO(PVP): currently only single GPU is supported
							return torch.cuda.current_device()







	@property
	def   _a   (    self						)    ->    "torch.device":
							'''simple docstring'''






							requires_backends(self ,				["""torch"""]						)
							return self._setup_devices[0]







	@property
	def   _a   (    self						)    ->    int:
							'''simple docstring'''






							requires_backends(self ,				["""torch"""]						)
							return self._setup_devices[1]







	@property
	def   _a   (    self						)    ->    Tuple:
							'''simple docstring'''






							return self.n_gpu > 0







'''simple docstring'''


from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments







def      SCREAMING_SNAKE_CASE				(				):
						lowercase        =							HfArgumentParser(lowercase_						)
						lowercase        =							parser.parse_args_into_dataclasses()[0]
						lowercase        =							TensorFlowBenchmark(args=lowercase_						)
						try:
												lowercase        =							parser.parse_args_into_dataclasses()[0]
						except ValueError as e:
												lowercase        =							"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
												lowercase        =							""" """.join(str(lowercase_						).split(""" """						)[:-1]						)
												lowercase        =							""""""
												lowercase        =							eval(str(lowercase_						).split(""" """						)[-1]						)
												lowercase        =							[]
												for arg in depreciated_args:
																		# arg[2:] removes '--'
																		if arg[2:] in TensorFlowBenchmark.deprecated_args:
																								# arg[5:] removes '--no_'
																								full_error_msg += arg_error_msg.format(arg[5:]						)
																		else:
																								wrong_args.append(lowercase_						)
												if len(lowercase_						) > 0:
																		lowercase        =							full_error_msg + begin_error_msg + str(lowercase_						)
												raise ValueError(lowercase_						)
						benchmark.run()


if __name__ == "__main__":
						main()


