




'''simple docstring'''





import math





def       lowerCAmelCase_     (					__A						:							List[str] ):




			'''simple docstring'''


			if not isinstance(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ):
						snake_case:       Tuple        =       f"""Input value of [number={number}] must be an integer"""
						raise TypeError(SCREAMING_SNAKE_CASE_ )

			if number < 1:
						snake_case:       Union[str, Any]        =       f"""Input value of [number={number}] must be > 0"""
						raise ValueError(SCREAMING_SNAKE_CASE_ )
			elif number == 1:
						return 3
			elif number == 2:
						return 5
			else:
						snake_case:       str        =       int(math.log(number // 3 ,			2 ) ) + 2

						snake_case:       str        =       [3, 5]
						snake_case:       Any        =       2
						snake_case:       Union[str, Any]        =       3
						for block in range(1 ,			SCREAMING_SNAKE_CASE_ ):
									for _ in range(SCREAMING_SNAKE_CASE_ ):
												proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1] )
												proth_index += 1
									increment *= 2

			return proth_list[number - 1]


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			for number in range(11):
						__UpperCAmelCase       =		0
						try:
									__UpperCAmelCase       =		proth(number)
						except ValueError:
									print(F'ValueError: there is no {number}th Proth number')
									continue

						print(F'The {number}th Proth number: {value}')
'''simple docstring'''


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						  =		logging.get_logger(__name__)





@dataclass
class       _a    (		__a      ):


  """simple docstring"""

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




  def __init__(		self      :       int   ,				**lowercase_      :       int      ):







    '''simple docstring'''


    for deprecated_arg in self.deprecated_args:
      if deprecated_arg in kwargs:
        lowercase_     =       deprecated_arg[3:]
        setattr(self   ,				lowercase_   ,				not kwargs.pop(lowercase_      )      )
        logger.warning(
            F"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
            F""" {positive_arg}={kwargs[positive_arg]}"""      )

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

  A_    							=						field(default=__a ,				metadata={'''help''': '''Trace the models using torchscript'''}      )
  A_    							=						field(default=__a ,				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 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    requires_backends(self   ,				["""torch"""]      )
    logger.info("""PyTorch: setting up devices"""      )
    if not self.cuda:
      lowercase_     =       torch.device("""cpu"""      )
      lowercase_     =       0
    elif is_torch_tpu_available():
      lowercase_     =       xm.xla_device()
      lowercase_     =       0
    else:
      lowercase_     =       torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""      )
      lowercase_     =       torch.cuda.device_count()
    return device, n_gpu




  @property
  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


    return is_torch_tpu_available() and self.tpu




  @property
  def 		lowerCamelCase__  (		self      :       Tuple      ):







    '''simple docstring'''


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




  @property
  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    requires_backends(self   ,				["""torch"""]      )
    return self._setup_devices[0]




  @property
  def 		lowerCamelCase__  (		self      :       Any      ):







    '''simple docstring'''


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





  @property
  def 		lowerCamelCase__  (		self      :       Optional[int]      ):







    '''simple docstring'''


    return self.n_gpu > 0

