

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


snake_case    :						Optional[Any]  =						logging.get_logger(__name__)




@dataclass
class 							lowerCAmelCase__  (	UpperCamelCase						):
					__A     :    Union[str, Any]           =						[
					    'no_inference',
					    'no_cuda',
					    'no_tpu',
					    'no_speed',
					    'no_memory',
					    'no_env_print',
					    'no_multi_process',
					]







					def __init__(       self      :     str			,		**_A      :     str):
											for deprecated_arg in self.deprecated_args:
																	if deprecated_arg in kwargs:
																							A__	:							Optional[Any]						    = deprecated_arg[3:]
																							setattr(self			,		_A			,		not kwargs.pop(_A))
																							logger.warning(
																							    F'{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'
																							    F' {positive_arg}={kwargs[positive_arg]}')

											A__	:							int						    = kwargs.pop("torchscript"			,		self.torchscript)
											A__	:							str						    = kwargs.pop("torch_xla_tpu_print_metrics"			,		self.torch_xla_tpu_print_metrics)
											A__	:							Union[str, Any]						    = kwargs.pop("fp16_opt_level"			,		self.fpaa_opt_level)
											super().__init__(**_A)

					__A     :    bool           =						field(default=UpperCamelCase					,							metadata={'help': 'Trace the models using torchscript'}						)
					__A     :    bool           =						field(default=UpperCamelCase					,							metadata={'help': 'Print Xla/PyTorch tpu metrics'}						)
					__A     :    str           =						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  _lowercase			(       self      :     Union[str, Any]):
											requires_backends(self			,		["torch"])
											logger.info("PyTorch: setting up devices")
											if not self.cuda:
																	A__	:							int						    = torch.device("cpu")
																	A__	:							Dict						    = 0
											elif is_torch_tpu_available():
																	A__	:							Optional[int]						    = xm.xla_device()
																	A__	:							List[str]						    = 0
											else:
																	A__	:							Optional[int]						    = torch.device("cuda" if torch.cuda.is_available() else "cpu")
																	A__	:							Union[str, Any]						    = torch.cuda.device_count()
											return device, n_gpu







					@property
					def  _lowercase			(       self      :     Optional[int]):
											return is_torch_tpu_available() and self.tpu







					@property
					def  _lowercase			(       self      :     Optional[int]):
											requires_backends(self			,		["torch"])
											# TODO(PVP): currently only single GPU is supported
											return torch.cuda.current_device()







					@property
					def  _lowercase			(       self      :     Optional[Any]):
											requires_backends(self			,		["torch"])
											return self._setup_devices[0]







					@property
					def  _lowercase			(       self      :     Union[str, Any]):
											requires_backends(self			,		["torch"])
											return self._setup_devices[1]







					@property
					def  _lowercase			(       self      :     int):
											return self.n_gpu > 0




from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands
def 	snake_case__       (					)		->		List[Any]:







						"""simple docstring"""

						A__	:							List[Any]						    = ArgumentParser("Transformers CLI tool"							,     usage="transformers-cli <command> [<args>]"       )
						A__	:							int						    = parser.add_subparsers(help="transformers-cli command helpers"       )

						# Register commands
						ConvertCommand.register_subcommand(__lowercase       )
						DownloadCommand.register_subcommand(__lowercase       )
						EnvironmentCommand.register_subcommand(__lowercase       )
						RunCommand.register_subcommand(__lowercase       )
						ServeCommand.register_subcommand(__lowercase       )
						UserCommands.register_subcommand(__lowercase       )
						AddNewModelCommand.register_subcommand(__lowercase       )
						AddNewModelLikeCommand.register_subcommand(__lowercase       )
						LfsCommands.register_subcommand(__lowercase       )
						PTtoTFCommand.register_subcommand(__lowercase       )

						# Let's go
						A__	:							int						    = parser.parse_args()

						if not hasattr(__lowercase							,     "func"       ):
												parser.print_help()
												exit(1       )

						# Run
						A__	:							Any						    = args.func(__lowercase       )
						service.run()


if __name__ == "__main__":
		main()