





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


lowerCAmelCase_						   =    logging.get_logger(__name__)



@dataclass
class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       Dict      					=[
      """no_inference""",
      """no_cuda""",
      """no_tpu""",
      """no_speed""",
      """no_memory""",
      """no_env_print""",
      """no_multi_process""",
  ]







  def __init__(      self      :					Union[str, Any]      ,				**UpperCamelCase      :					Optional[int]							):


    '''simple docstring'''





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

    _snake_case   :				Union[str, Any]														=	kwargs.pop('torchscript'      ,				self.torchscript							)
    _snake_case   :				Dict														=	kwargs.pop('torch_xla_tpu_print_metrics'      ,				self.torch_xla_tpu_print_metrics							)
    _snake_case   :				Any														=	kwargs.pop('fp16_opt_level'      ,				self.fpaa_opt_level							)
    super().__init__(**UpperCamelCase							)

  a_							:       bool      					=field(default=UpperCAmelCase_							,    metadata={"""help""": """Trace the models using torchscript"""}							)
  a_							:       bool      					=field(default=UpperCAmelCase_							,    metadata={"""help""": """Print Xla/PyTorch tpu metrics"""}							)
  a_							:       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							):


    '''simple docstring'''





    requires_backends(self      ,				['torch']							)
    logger.info('PyTorch: setting up devices'							)
    if not self.cuda:
      _snake_case   :				List[str]														=	torch.device('cpu'							)
      _snake_case   :				Dict														=	0
    elif is_torch_tpu_available():
      _snake_case   :				str														=	xm.xla_device()
      _snake_case   :				Optional[Any]														=	0
    else:
      _snake_case   :				Optional[Any]														=	torch.device('cuda' if torch.cuda.is_available() else 'cpu'							)
      _snake_case   :				Any														=	torch.cuda.device_count()
    return device, n_gpu







  @property
  def UpperCamelCase_ (      self      :					Optional[Any]							):


    '''simple docstring'''





    return is_torch_tpu_available() and self.tpu







  @property
  def UpperCamelCase_ (      self      :					str							):


    '''simple docstring'''





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







  @property
  def UpperCamelCase_ (      self      :					Tuple							):


    '''simple docstring'''





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







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


    '''simple docstring'''





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







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


    '''simple docstring'''





    return self.n_gpu > 0











from functools import reduce

lowerCAmelCase_						   =    (
    """73167176531330624919225119674426574742355349194934"""
    """96983520312774506326239578318016984801869478851843"""
    """85861560789112949495459501737958331952853208805511"""
    """12540698747158523863050715693290963295227443043557"""
    """66896648950445244523161731856403098711121722383113"""
    """62229893423380308135336276614282806444486645238749"""
    """30358907296290491560440772390713810515859307960866"""
    """70172427121883998797908792274921901699720888093776"""
    """65727333001053367881220235421809751254540594752243"""
    """52584907711670556013604839586446706324415722155397"""
    """53697817977846174064955149290862569321978468622482"""
    """83972241375657056057490261407972968652414535100474"""
    """82166370484403199890008895243450658541227588666881"""
    """16427171479924442928230863465674813919123162824586"""
    """17866458359124566529476545682848912883142607690042"""
    """24219022671055626321111109370544217506941658960408"""
    """07198403850962455444362981230987879927244284909188"""
    """84580156166097919133875499200524063689912560717606"""
    """05886116467109405077541002256983155200055935729725"""
    """71636269561882670428252483600823257530420752963450"""
)


def     lowerCamelCase_	(      lowerCAmelCase:	str = N				)->		int:
  return max(
      # mypy cannot properly interpret reduce
      int(reduce(lambda lowerCAmelCase    ,							lowerCAmelCase				: str(int(lowerCAmelCase				) * int(lowerCAmelCase				)				)    ,							n[i : i + 13]				)				)
      for i in range(len(lowerCAmelCase				) - 12				)				)


if __name__ == "__main__":
      print(F"""{solution() = }""")




