


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


SCREAMING_SNAKE_CASE :   Any							      =				logging.get_logger(__name__)






@dataclass
class _lowerCamelCase(		_a     ):
       lowercase_				:			List[Any]									=    [
           """no_inference""",
           """no_cuda""",
           """no_tpu""",
           """no_speed""",
           """no_memory""",
           """no_env_print""",
           """no_multi_process""",
       ]
       def __init__(      self,   **lowerCamelCase) ->     Dict:




           """simple docstring"""

           for deprecated_arg in self.deprecated_args:
               if deprecated_arg in kwargs:
                   _lowercase :							Optional[int]   	=	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 :							Optional[Any]   	=	kwargs.pop('torchscript',   self.torchscript)
           _lowercase :							str   	=	kwargs.pop('torch_xla_tpu_print_metrics',   self.torch_xla_tpu_print_metrics)
           _lowercase :							List[str]   	=	kwargs.pop('fp16_opt_level',   self.fpaa_opt_level)
           super().__init__(**lowerCamelCase)

       lowercase_				:			bool									=    field(default=_a,				metadata={"""help""": """Trace the models using torchscript"""}     )
       lowercase_				:			bool									=    field(default=_a,				metadata={"""help""": """Print Xla/PyTorch tpu metrics"""}     )
       lowercase_				:			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     UpperCamelCase  (      self) ->     Tuple["torch.device", int]:




           """simple docstring"""

           requires_backends(self,   ['torch'])
           logger.info('PyTorch: setting up devices')
           if not self.cuda:
               _lowercase :							int   	=	torch.device('cpu')
               _lowercase :							List[Any]   	=	0
           elif is_torch_tpu_available():
               _lowercase :							Optional[Any]   	=	xm.xla_device()
               _lowercase :							Dict   	=	0
           else:
               _lowercase :							Any   	=	torch.device('cuda' if torch.cuda.is_available() else 'cpu')
               _lowercase :							List[str]   	=	torch.cuda.device_count()
           return device, n_gpu
       @property
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           return is_torch_tpu_available() and self.tpu
       @property
       def     UpperCamelCase  (      self) ->     int:




           """simple docstring"""

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




           """simple docstring"""

           requires_backends(self,   ['torch'])
           return self._setup_devices[0]
       @property
       def     UpperCamelCase  (      self) ->     List[Any]:




           """simple docstring"""

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




       @property
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           return self.n_gpu > 0






def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		bool:
    return numa ^ numa < 0


if __name__ == "__main__":
 import doctest

 doctest.testmod()


