




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


UpperCAmelCase     :     str							=	logging.get_logger(__name__)







@dataclass
class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : Dict            =      [
          """no_inference""",
          """no_cuda""",
          """no_tpu""",
          """no_speed""",
          """no_memory""",
          """no_env_print""",
          """no_multi_process""",
      ]







      def __init__(   self		,				**lowerCAmelCase__							)       ->						Optional[Any]:



        '''simple docstring'''




        for deprecated_arg in self.deprecated_args:
          if deprecated_arg in kwargs:
            a__     : List[str]  =deprecated_arg[3:]
            a__     : Tuple  =not kwargs.pop(lowerCAmelCase__							)
            logger.warning(
                F'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
                F''' {positive_arg}={kwargs[positive_arg]}'''							)
        a__     : str  =kwargs.pop("tpu_name"		,				self.tpu_name							)
        a__     : Any  =kwargs.pop("device_idx"		,				self.device_idx							)
        a__     : Tuple  =kwargs.pop("eager_mode"		,				self.eager_mode							)
        a__     : List[Any]  =kwargs.pop("use_xla"		,				self.use_xla							)
        super().__init__(**lowerCAmelCase__							)

      _lowercase    : str            =      field(
          default=UpperCamelCase__						,    metadata={"""help""": """Name of TPU"""}						,    )
      _lowercase    : int            =      field(
          default=0						,    metadata={"""help""": """CPU / GPU device index. Defaults to 0."""}						,    )
      _lowercase    : bool            =      field(default=UpperCamelCase__						,    metadata={"""help""": """Benchmark models in eager model."""})
      _lowercase    : bool            =      field(
          default=UpperCamelCase__						,    metadata={
              """help""": """Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."""
          }						,    )







      @cached_property
      def        _lowercase  (   self							)       ->						Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:



        '''simple docstring'''




        requires_backends(self		,				["tf"]							)
        a__     : Optional[int]  =None
        if self.tpu:
          try:
            if self.tpu_name:
              a__     : List[Any]  =tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name							)
            else:
              a__     : Dict  =tf.distribute.cluster_resolver.TPUClusterResolver()
          except ValueError:
            a__     : Optional[int]  =None
        return tpu







      @cached_property
      def        _lowercase  (   self							)       ->						Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:



        '''simple docstring'''




        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							)

          a__     : Any  =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"							)
            a__     : Dict  =tf.distribute.OneDeviceStrategy(device=F'''/gpu:{self.device_idx}'''							)
          else:
            tf.config.set_visible_devices([]		,				"GPU"							)  # disable GPU
            a__     : List[str]  =tf.distribute.OneDeviceStrategy(device=F'''/cpu:{self.device_idx}'''							)

        return strategy







      @property
      def        _lowercase  (   self							)       ->						bool:



        '''simple docstring'''




        requires_backends(self		,				["tf"]							)
        return self._setup_tpu is not None







      @property
      def        _lowercase  (   self							)       ->						"tf.distribute.Strategy":



        '''simple docstring'''




        requires_backends(self		,				["tf"]							)
        return self._setup_strategy







      @property
      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




        requires_backends(self		,				["tf"]							)
        return tf.config.list_physical_devices("GPU"							)







      @property
      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        requires_backends(self		,				["tf"]							)
        if self.cuda:
          return len(self.gpu_list							)
        return 0







      @property
      def        _lowercase  (   self							)       ->						bool:



        '''simple docstring'''




        return self.n_gpu > 0













import os
def 					_A	(		):





  """simple docstring"""



  a__     : Optional[int]  =os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE						)	,	"num.txt"						)
  with open(SCREAMING_SNAKE_CASE						) as file_hand:
    return str(sum(int(SCREAMING_SNAKE_CASE						) for line in file_hand						)						)[:10]


if __name__ == "__main__":
     print(solution())






