
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[str]									=			logging.get_logger(__name__)






@dataclass
class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

       """simple docstring"""






       _snake_case     		=  [
           'no_inference',
           'no_cuda',
           'no_tpu',
           'no_speed',
           'no_memory',
           'no_env_print',
           'no_multi_process',
       ]



       def __init__(  self     ,	**SCREAMING_SNAKE_CASE_			)->   Tuple:





             '''simple docstring'''






             for deprecated_arg in self.deprecated_args:
                   if deprecated_arg in kwargs:
                         __UpperCamelCase       =			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]}"			)

             __UpperCamelCase       =			kwargs.pop('''torchscript'''     ,	self.torchscript			)
             __UpperCamelCase       =			kwargs.pop('''torch_xla_tpu_print_metrics'''     ,	self.torch_xla_tpu_print_metrics			)
             __UpperCamelCase       =			kwargs.pop('''fp16_opt_level'''     ,	self.fpaa_opt_level			)
             super().__init__(**SCREAMING_SNAKE_CASE_			)

       _snake_case     		=  field(default=SCREAMING_SNAKE_CASE_   ,		metadata={'help': 'Trace the models using torchscript'}    )
       _snake_case     		=  field(default=SCREAMING_SNAKE_CASE_   ,		metadata={'help': 'Print Xla/PyTorch tpu metrics'}    )
       _snake_case     		=  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:
                   __UpperCamelCase       =			torch.device('''cpu'''			)
                   __UpperCamelCase       =			0
             elif is_torch_tpu_available():
                   __UpperCamelCase       =			xm.xla_device()
                   __UpperCamelCase       =			0
             else:
                   __UpperCamelCase       =			torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''			)
                   __UpperCamelCase       =			torch.cuda.device_count()
             return device, n_gpu



       @property
       def     A__		(  self			)->   int:





             '''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			)->   List[Any]:





             '''simple docstring'''






             return self.n_gpu > 0






def 	A_ (							snake_case							:   float )      ->    float:



						'''simple docstring'''

						if edge <= 0 or not isinstance(snake_case ,      snake_case ):
												raise ValueError('''Length must be a positive.''' )
						return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)




def 	A_ (							snake_case							:   float )      ->    float:



						'''simple docstring'''

						if edge <= 0 or not isinstance(snake_case ,      snake_case ):
												raise ValueError('''Length must be a positive.''' )
						return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)


if __name__ == "__main__":
				import doctest

				doctest.testmod()




