


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


UpperCAmelCase_				:			Any   =	logging.get_logger(__name__)
@dataclass
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      snake_case__    :					Optional[Any]					       =						[
          '''no_inference''',
          '''no_cuda''',
          '''no_tpu''',
          '''no_speed''',
          '''no_memory''',
          '''no_env_print''',
          '''no_multi_process''',
      ]
      def __init__(	self    :	Tuple					,							**SCREAMING_SNAKE_CASE__    :	List[Any]					)		->      Optional[Any]:
             for deprecated_arg in self.deprecated_args:
                    if deprecated_arg in kwargs:
                           a_ :		Union[str, Any]							=  deprecated_arg[3:]
                           setattr(self					,							SCREAMING_SNAKE_CASE__					,							not kwargs.pop(SCREAMING_SNAKE_CASE__					)					)
                           logger.warning(
                               F"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
                               F""" {positive_arg}={kwargs[positive_arg]}"""					)

             a_ :		Any							=  kwargs.pop('torchscript'					,							self.torchscript					)
             a_ :		Dict							=  kwargs.pop('torch_xla_tpu_print_metrics'					,							self.torch_xla_tpu_print_metrics					)
             a_ :		Optional[Any]							=  kwargs.pop('fp16_opt_level'					,							self.fpaa_opt_level					)
             super().__init__(**SCREAMING_SNAKE_CASE__					)

      snake_case__    :					bool					       =						field(default=lowercase__						,   metadata={'''help''': '''Trace the models using torchscript'''}      )
      snake_case__    :					bool					       =						field(default=lowercase__						,   metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''}      )
      snake_case__    :					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 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      Tuple["torch.device", int]:
             requires_backends(self					,							['torch']					)
             logger.info('PyTorch: setting up devices'					)
             if not self.cuda:
                    a_ :		List[Any]							=  torch.device('cpu'					)
                    a_ :		Union[str, Any]							=  0
             elif is_torch_tpu_available():
                    a_ :		Dict							=  xm.xla_device()
                    a_ :		Union[str, Any]							=  0
             else:
                    a_ :		List[Any]							=  torch.device('cuda' if torch.cuda.is_available() else 'cpu'					)
                    a_ :		List[Any]							=  torch.cuda.device_count()
             return device, n_gpu
      @property
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      List[Any]:
             return is_torch_tpu_available() and self.tpu
      @property
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      int:
             requires_backends(self					,							['torch']					)
             # TODO(PVP): currently only single GPU is supported
             return torch.cuda.current_device()
      @property
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					)		->      "torch.device":
             requires_backends(self					,							['torch']					)
             return self._setup_devices[0]
      @property
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					)		->      List[str]:
             requires_backends(self					,							['torch']					)
             return self._setup_devices[1]

      @property
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					)		->      Optional[Any]:
             return self.n_gpu > 0






def 	SCREAMING_SNAKE_CASE_						(      __A			:      list    )     ->		bool:

       """simple docstring"""

       if not isinstance(__A  ,   __A    ):
              raise ValueError('Input series is not valid, valid series - [2, 4, 6]'    )
       if len(__A    ) == 0:
              raise ValueError('Input list must be a non empty list'    )
       if len(__A    ) == 1:
              return True
       a_ :		Tuple							=  series[1] - series[0]
       for index in range(len(__A    ) - 1    ):
              if series[index + 1] - series[index] != common_diff:
                     return False
       return True







def 	SCREAMING_SNAKE_CASE_						(      __A			:      list    )     ->		float:

       """simple docstring"""

       if not isinstance(__A  ,   __A    ):
              raise ValueError('Input series is not valid, valid series - [2, 4, 6]'    )
       if len(__A    ) == 0:
              raise ValueError('Input list must be a non empty list'    )
       a_ :		str							=  0
       for val in series:
              answer += val
       return answer / len(__A    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()


