





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


A_												=     logging.get_logger(__name__)

@dataclass
class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					A__				 =    [
					    "no_inference",
					    "no_cuda",
					    "no_tpu",
					    "no_speed",
					    "no_memory",
					    "no_env_print",
					    "no_multi_process",
					]





					def __init__( self	,  **_lowerCAmelCase	):
										for deprecated_arg in self.deprecated_args:
															if deprecated_arg in kwargs:
																				lowerCamelCase__       =       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]}"	)

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

					A__				 =    field(default=lowercase_							,       metadata={"help": "Trace the models using torchscript"}   )
					A__				 =    field(default=lowercase_							,       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 					__magic_name__ ( self	):
										requires_backends(self	,  ["torch"]	)
										logger.info("PyTorch: setting up devices"	)
										if not self.cuda:
															lowerCamelCase__       =       torch.device("cpu"	)
															lowerCamelCase__       =       0
										elif is_torch_tpu_available():
															lowerCamelCase__       =       xm.xla_device()
															lowerCamelCase__       =       0
										else:
															lowerCamelCase__       =       torch.device("cuda" if torch.cuda.is_available() else "cpu"	)
															lowerCamelCase__       =       torch.cuda.device_count()
										return device, n_gpu





					@property
					def 					__magic_name__ ( self	):
										return is_torch_tpu_available() and self.tpu





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





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





					@property
					def 					__magic_name__ ( self	):
										requires_backends(self	,  ["torch"]	)
										return self._setup_devices[1]






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








def    __UpperCamelCase      (    a) ->str:

					if isinstance(a,     a):
										raise TypeError("'float' object cannot be interpreted as an integer")
					if isinstance(a,     a):
										raise TypeError("'str' object cannot be interpreted as an integer")

					if num == 0:
										return "0b0"

					lowerCamelCase__       =       False

					if num < 0:
										lowerCamelCase__       =       True
										lowerCamelCase__       =       -num

					lowerCamelCase__       =       []
					while num > 0:
										binary.insert(0,     num % 2)
										num >>= 1

					if negative:
										return "-0b" + "".join(str(a) for e in binary)

					return "0b" + "".join(str(a) for e in binary)


if __name__ == "__main__":
						import doctest

						doctest.testmod()

