


'''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


A__		:					Dict												=  logging.get_logger(__name__)





@dataclass
class    UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  :					List[Any]							     =			[
        'no_inference',
        'no_cuda',
        'no_tpu',
        'no_speed',
        'no_memory',
        'no_env_print',
        'no_multi_process',
    ]







    def __init__(							self			,     **SCREAMING_SNAKE_CASE_	)				->     Optional[Any]:
         for deprecated_arg in self.deprecated_args:
              if deprecated_arg in kwargs:
                   __lowerCamelCase   :	int								=		deprecated_arg[3:]
                   __lowerCamelCase   :	Optional[Any]								=		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]}'	)
         __lowerCamelCase   :	Dict								=		kwargs.pop('tpu_name'			,     self.tpu_name	)
         __lowerCamelCase   :	int								=		kwargs.pop('device_idx'			,     self.device_idx	)
         __lowerCamelCase   :	Optional[int]								=		kwargs.pop('eager_mode'			,     self.eager_mode	)
         __lowerCamelCase   :	Optional[Any]								=		kwargs.pop('use_xla'			,     self.use_xla	)
         super().__init__(**SCREAMING_SNAKE_CASE_	)

    lowerCamelCase  :					str							     =			field(
        default=_UpperCAmelCase					,		metadata={'help': 'Name of TPU'}					,		)
    lowerCamelCase  :					int							     =			field(
        default=0					,		metadata={'help': 'CPU / GPU device index. Defaults to 0.'}					,		)
    lowerCamelCase  :					bool							     =			field(default=_UpperCAmelCase					,		metadata={'help': 'Benchmark models in eager model.'}  )
    lowerCamelCase  :					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"]:
         requires_backends(self			,     ['tf']	)
         __lowerCamelCase   :	Dict								=		None
         if self.tpu:
              try:
                   if self.tpu_name:
                        __lowerCamelCase   :	Any								=		tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name	)
                   else:
                        __lowerCamelCase   :	List[str]								=		tf.distribute.cluster_resolver.TPUClusterResolver()
              except ValueError:
                   __lowerCamelCase   :	List[str]								=		None
         return tpu







    @cached_property
    def       lowercase_				(							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	)

              __lowerCamelCase   :	Optional[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'	)
                   __lowerCamelCase   :	Optional[int]								=		tf.distribute.OneDeviceStrategy(device=f'/gpu:{self.device_idx}'	)
              else:
                   tf.config.set_visible_devices([]			,     'GPU'	)  # disable GPU
                   __lowerCamelCase   :	List[Any]								=		tf.distribute.OneDeviceStrategy(device=f'/cpu:{self.device_idx}'	)

         return strategy







    @property
    def       lowercase_				(							self	)				->     bool:
         requires_backends(self			,     ['tf']	)
         return self._setup_tpu is not None







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







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







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







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










'''simple docstring'''



A__		:					Optional[int]												=  """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				bytes							)     ->		bytes:
     # Make sure the supplied data is a bytes-like object
     if not isinstance(UpperCAmelCase_      ,    UpperCAmelCase_							):
          __lowerCamelCase   :	Dict								=		F'a bytes-like object is required, not \'{data.__class__.__name__}\''
          raise TypeError(UpperCAmelCase_							)

     __lowerCamelCase   :	Optional[int]								=		''.join(bin(UpperCAmelCase_							)[2:].zfill(8							) for byte in data							)

     __lowerCamelCase   :	Any								=		len(UpperCAmelCase_							) % 6 != 0

     if padding_needed:
          # The padding that will be added later
          __lowerCamelCase   :	int								=		B'=' * ((6 - len(UpperCAmelCase_							) % 6) // 2)

          # Append binary_stream with arbitrary binary digits (0's by default) to make its
          # length a multiple of 6.
          binary_stream += "0" * (6 - len(UpperCAmelCase_							) % 6)
     else:
          __lowerCamelCase   :	str								=		B''

     # Encode every 6 binary digits to their corresponding Base64 character
     return (
         "".join(
             B64_CHARSET[int(binary_stream[index : index + 6]      ,    2							)]
             for index in range(0      ,    len(UpperCAmelCase_							)      ,    6							)							).encode()
         + padding
     )



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				str							)     ->		bytes:
     # Make sure encoded_data is either a string or a bytes-like object
     if not isinstance(UpperCAmelCase_      ,    UpperCAmelCase_							) and not isinstance(UpperCAmelCase_      ,    UpperCAmelCase_							):
          __lowerCamelCase   :	Dict								=		(
              'argument should be a bytes-like object or ASCII string, '
              F'not \'{encoded_data.__class__.__name__}\''
          )
          raise TypeError(UpperCAmelCase_							)

     # In case encoded_data is a bytes-like object, make sure it contains only
     # ASCII characters so we convert it to a string object
     if isinstance(UpperCAmelCase_      ,    UpperCAmelCase_							):
          try:
               __lowerCamelCase   :	int								=		encoded_data.decode('utf-8'							)
          except UnicodeDecodeError:
               raise ValueError('base64 encoded data should only contain ASCII characters'							)

     __lowerCamelCase   :	Union[str, Any]								=		encoded_data.count('='							)

     # Check if the encoded string contains non base64 characters
     if padding:
          assert all(
              char in B64_CHARSET for char in encoded_data[:-padding]							), "Invalid base64 character(s) found."
     else:
          assert all(
              char in B64_CHARSET for char in encoded_data							), "Invalid base64 character(s) found."

     # Check the padding
     assert len(UpperCAmelCase_							) % 4 == 0 and padding < 3, "Incorrect padding"

     if padding:
          # Remove padding if there is one
          __lowerCamelCase   :	Any								=		encoded_data[:-padding]

          __lowerCamelCase   :	Optional[Any]								=		''.join(
              bin(B64_CHARSET.index(UpperCAmelCase_							)							)[2:].zfill(6							) for char in encoded_data							)[: -padding * 2]
     else:
          __lowerCamelCase   :	Any								=		''.join(
              bin(B64_CHARSET.index(UpperCAmelCase_							)							)[2:].zfill(6							) for char in encoded_data							)

     __lowerCamelCase   :	str								=		[
         int(binary_stream[index : index + 8]      ,    2							)
         for index in range(0      ,    len(UpperCAmelCase_							)      ,    8							)
     ]

     return bytes(UpperCAmelCase_							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()






