



'''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 __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			Union[str, Any]		=			[
     'no_inference',
     'no_cuda',
     'no_tpu',
     'no_speed',
     'no_memory',
     'no_env_print',
     'no_multi_process',
 ]



 def __init__(self					,  **A			)       ->	int:


      """simple docstring"""



      for deprecated_arg in self.deprecated_args:
           if deprecated_arg in kwargs:
                _a           =    deprecated_arg[3:]
                _a           =    not kwargs.pop(A			)
                logger.warning(
                    f'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
                    f''' {positive_arg}={kwargs[positive_arg]}'''			)
      _a           =    kwargs.pop('''tpu_name'''					,  self.tpu_name			)
      _a           =    kwargs.pop('''device_idx'''					,  self.device_idx			)
      _a           =    kwargs.pop('''eager_mode'''					,  self.eager_mode			)
      _a           =    kwargs.pop('''use_xla'''					,  self.use_xla			)
      super().__init__(**A			)

 __lowerCamelCase       :			str		=			field(
     default=A  ,  metadata={'help': 'Name of TPU'}  ,  )
 __lowerCamelCase       :			int		=			field(
     default=0  ,  metadata={'help': 'CPU / GPU device index. Defaults to 0.'}  ,  )
 __lowerCamelCase       :			bool		=			field(default=A  ,  metadata={'help': 'Benchmark models in eager model.'}		)
 __lowerCamelCase       :			bool		=			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			)       ->	Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:


      """simple docstring"""



      requires_backends(self					,  ['''tf''']			)
      _a           =    None
      if self.tpu:
           try:
                if self.tpu_name:
                     _a           =    tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name			)
                else:
                     _a           =    tf.distribute.cluster_resolver.TPUClusterResolver()
           except ValueError:
                _a           =    None
      return tpu



 @cached_property
 def    a__    (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           =    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           =    tf.distribute.OneDeviceStrategy(device=f'''/gpu:{self.device_idx}'''			)
           else:
                tf.config.set_visible_devices([]					,  '''GPU'''			)  # disable GPU
                _a           =    tf.distribute.OneDeviceStrategy(device=f'''/cpu:{self.device_idx}'''			)

      return strategy



 @property
 def    a__    (self			)       ->	bool:


      """simple docstring"""



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



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


      """simple docstring"""



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



 @property
 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



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



 @property
 def    a__    (self			)       ->	int:


      """simple docstring"""



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



 @property
 def    a__    (self			)       ->	bool:


      """simple docstring"""



      return self.n_gpu > 0








'''simple docstring'''





def lowerCAmelCase    (__A):



     """simple docstring"""



     if not isinstance(__A    ,      __A):
          raise ValueError('''multiplicative_persistence() only accepts integral values''')
     if num < 0:
          raise ValueError('''multiplicative_persistence() does not accept negative values''')

     _a           =    0
     _a           =    str(__A)

     while len(__A) != 1:
          _a           =    [int(__A) for i in num_string]

          _a           =    1
          for i in range(0    ,      len(__A)):
               total *= numbers[i]

          _a           =    str(__A)

          steps += 1
     return steps




def lowerCAmelCase    (__A):



     """simple docstring"""



     if not isinstance(__A    ,      __A):
          raise ValueError('''additive_persistence() only accepts integral values''')
     if num < 0:
          raise ValueError('''additive_persistence() does not accept negative values''')

     _a           =    0
     _a           =    str(__A)

     while len(__A) != 1:
          _a           =    [int(__A) for i in num_string]

          _a           =    0
          for i in range(0    ,      len(__A)):
               total += numbers[i]

          _a           =    str(__A)

          steps += 1
     return steps


if __name__ == "__main__":
  import doctest

  doctest.testmod()



