





'''simple docstring'''







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


_lowercase       						=			logging.get_logger(__name__)
@dataclass
class 			_lowercase						(       __a					):
     _UpperCAmelCase										=					[
         '''no_inference''',
         '''no_cuda''',
         '''no_tpu''',
         '''no_speed''',
         '''no_memory''',
         '''no_env_print''',
         '''no_multi_process''',
     ]







     def __init__( self    ,   **A__    ) ->	Optional[int]:
      for deprecated_arg in self.deprecated_args:
       if deprecated_arg in kwargs:
        snake_case				=			deprecated_arg[3:]
        snake_case				=			not kwargs.pop(A__    )
        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__(**A__    )

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







     @cached_property
     def     UpperCamelCase			( self    ) ->	Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:
      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     UpperCamelCase			( self    ) ->	Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:
      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     UpperCamelCase			( self    ) ->	bool:
      requires_backends(self    ,   ['''tf''']    )
      return self._setup_tpu is not None







     @property
     def     UpperCamelCase			( self    ) ->	"tf.distribute.Strategy":
      requires_backends(self    ,   ['''tf''']    )
      return self._setup_strategy







     @property
     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      requires_backends(self    ,   ['''tf''']    )
      return tf.config.list_physical_devices('''GPU'''    )







     @property
     def     UpperCamelCase			( self    ) ->	int:
      requires_backends(self    ,   ['''tf''']    )
      if self.cuda:
       return len(self.gpu_list    )
      return 0







     @property
     def     UpperCamelCase			( self    ) ->	bool:
      return self.n_gpu > 0








'''simple docstring'''







def    __UpperCamelCase	(	a    : int					,   a    : int     ) ->int:
 while b:
  snake_case				,		snake_case				=			b, a % b
 return a

def    __UpperCamelCase	(	a    : int					,   a    : int     ) ->int:
 return a if b == 0 else euclidean_gcd_recursive(a					,   a % b     )

def    __UpperCamelCase	(	) ->Optional[Any]:
 print(f"""euclidean_gcd(3, 5) = {euclidean_gcd(3					,   5     )}"""     )
 print(f"""euclidean_gcd(5, 3) = {euclidean_gcd(5					,   3     )}"""     )
 print(f"""euclidean_gcd(1, 3) = {euclidean_gcd(1					,   3     )}"""     )
 print(f"""euclidean_gcd(3, 6) = {euclidean_gcd(3					,   6     )}"""     )
 print(f"""euclidean_gcd(6, 3) = {euclidean_gcd(6					,   3     )}"""     )

 print(f"""euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3					,   5     )}"""     )
 print(f"""euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5					,   3     )}"""     )
 print(f"""euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1					,   3     )}"""     )
 print(f"""euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3					,   6     )}"""     )
 print(f"""euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6					,   3     )}"""     )


if __name__ == "__main__":
   main()

