

'''simple docstring'''







def    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      str:
       if not all(char in """01""" for char in bin_string					):
              raise ValueError("""Non-binary value was passed to the function"""					)
       if not bin_string:
              raise ValueError("""Empty string was passed to the function"""					)
       A_						       =   """"""
       while len(UpperCAmelCase__					) % 3 != 0:
              A_						       =   """0""" + bin_string
       A_						       =   [
           bin_string[index : index + 3]
           for index in range(len(UpperCAmelCase__					)					)
           if index % 3 == 0
       ]
       for bin_group in bin_string_in_3_list:
              A_						       =   0
              for index, val in enumerate(UpperCAmelCase__					):
                     oct_val += int(2 ** (2 - index) * int(UpperCAmelCase__					)					)
              oct_string += str(UpperCAmelCase__					)
       return oct_string


if __name__ == "__main__":
      from doctest import testmod

      testmod()



'''simple docstring'''







from ...configuration_utils import PretrainedConfig


class        A__   (   _snake_case      ):
    lowercase						       =		"bert-generation"



    def __init__(       self		,					UpperCamelCase__=50358		,					UpperCamelCase__=1024		,					UpperCamelCase__=24		,					UpperCamelCase__=16		,					UpperCamelCase__=4096		,					UpperCamelCase__="gelu"		,					UpperCamelCase__=0.1		,					UpperCamelCase__=0.1		,					UpperCamelCase__=512		,					UpperCamelCase__=0.02		,					UpperCamelCase__=1e-1_2		,					UpperCamelCase__=0		,					UpperCamelCase__=2		,					UpperCamelCase__=1		,					UpperCamelCase__="absolute"		,					UpperCamelCase__=True		,					**UpperCamelCase__		,					)    ->   Any:





           '''simple docstring'''
           super().__init__(pad_token_id=UpperCamelCase__		,					bos_token_id=UpperCamelCase__		,					eos_token_id=UpperCamelCase__		,					**UpperCamelCase__							)

           A_						       =   vocab_size
           A_						       =   hidden_size
           A_						       =   num_hidden_layers
           A_						       =   num_attention_heads
           A_						       =   hidden_act
           A_						       =   intermediate_size
           A_						       =   hidden_dropout_prob
           A_						       =   attention_probs_dropout_prob
           A_						       =   max_position_embeddings
           A_						       =   initializer_range
           A_						       =   layer_norm_eps
           A_						       =   position_embedding_type
           A_						       =   use_cache
