


from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
 import tensorflow as tf


_SCREAMING_SNAKE_CASE     :					List[str]      =       logging.get_logger(__name__)

@dataclass
class     A__   (       _a      ):







    """simple docstring"""

    __magic_name__         =						[
        'no_inference',
        'no_cuda',
        'no_tpu',
        'no_speed',
        'no_memory',
        'no_env_print',
        'no_multi_process',
    ]
    def __init__(			self     ,   **__snake_case       ):
         for deprecated_arg in self.deprecated_args:
              if deprecated_arg in kwargs:
                   snake_case											=							deprecated_arg[3:]
                   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]}'''       )
         snake_case											=							kwargs.pop('''tpu_name'''     ,   self.tpu_name       )
         snake_case											=							kwargs.pop('''device_idx'''     ,   self.device_idx       )
         snake_case											=							kwargs.pop('''eager_mode'''     ,   self.eager_mode       )
         snake_case											=							kwargs.pop('''use_xla'''     ,   self.use_xla       )
         super().__init__(**_SCREAMING_SNAKE_CASE       )

    __magic_name__         =						field(
        default=_a			,   metadata={'help': 'Name of TPU'}			,   )
    __magic_name__         =						field(
        default=0			,   metadata={'help': 'CPU / GPU device index. Defaults to 0.'}			,   )
    __magic_name__         =						field(default=_a			,   metadata={'help': 'Benchmark models in eager model.'}      )
    __magic_name__         =						field(
        default=_a			,   metadata={
            'help': 'Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'
        }			,   )
    @cached_property
    def 						a_     (			self       ):
         requires_backends(self     ,   ['''tf''']       )
         snake_case											=							None
         if self.tpu:
              try:
                   if self.tpu_name:
                        snake_case											=							tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name       )
                   else:
                        snake_case											=							tf.distribute.cluster_resolver.TPUClusterResolver()
              except ValueError:
                   snake_case											=							None
         return tpu
    @cached_property
    def 						a_     (			self       ):
         requires_backends(self     ,   ['''tf''']       )
         if self.is_tpu:
              tf.config.experimental_connect_to_cluster(self._setup_tpu       )
              tf.tpu.experimental.initialize_tpu_system(self._setup_tpu       )

              snake_case											=							tf.distribute.TPUStrategy(self._setup_tpu       )
         else:
              # currently no multi gpu is allowed
              if self.is_gpu:
                   # TODO: Currently only single GPU is supported
                   tf.config.set_visible_devices(self.gpu_list[self.device_idx]     ,   '''GPU'''       )
                   snake_case											=							tf.distribute.OneDeviceStrategy(device=F'''/gpu:{self.device_idx}'''       )
              else:
                   tf.config.set_visible_devices([]     ,   '''GPU'''       )  # disable GPU
                   snake_case											=							tf.distribute.OneDeviceStrategy(device=F'''/cpu:{self.device_idx}'''       )

         return strategy
    @property
    def 						a_     (			self       ):
         requires_backends(self     ,   ['''tf''']       )
         return self._setup_tpu is not None
    @property
    def 						a_     (			self       ):
         requires_backends(self     ,   ['''tf''']       )
         return self._setup_strategy
    @property
    def 						a_     (			self       ):
         requires_backends(self     ,   ['''tf''']       )
         return tf.config.list_physical_devices('''GPU'''       )
    @property
    def 						a_     (			self       ):
         requires_backends(self     ,   ['''tf''']       )
         if self.cuda:
              return len(self.gpu_list       )
         return 0




    @property
    def 						a_     (			self       ):
         return self.n_gpu > 0










from functools import reduce

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







def lowerCamelCase__  (     _lowercase = N ):

     '''simple docstring'''


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


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