"""simple docstring"""


from ...configuration_utils import PretrainedConfig


lowerCAmelCase_:					Any     =					{
    'google/tapas-base-finetuned-sqa': (
        'https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'
    ),
    'google/tapas-base-finetuned-wtq': (
        'https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json'
    ),
    'google/tapas-base-finetuned-wikisql-supervised': (
        'https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json'
    ),
    'google/tapas-base-finetuned-tabfact': (
        'https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json'
    ),
}






class    a__    (   lowerCAmelCase__	):
   snake_case_							=      "tapas"


   def __init__(     self,				_UpperCAmelCase=3_0522,				_UpperCAmelCase=768,				_UpperCAmelCase=12,				_UpperCAmelCase=12,				_UpperCAmelCase=3072,				_UpperCAmelCase="gelu",				_UpperCAmelCase=0.1,				_UpperCAmelCase=0.1,				_UpperCAmelCase=1024,				_UpperCAmelCase=[3, 256, 256, 2, 256, 256, 10],				_UpperCAmelCase=0.02,				_UpperCAmelCase=1E-12,				_UpperCAmelCase=0,				_UpperCAmelCase=10.0,				_UpperCAmelCase=0,				_UpperCAmelCase=1.0,				_UpperCAmelCase=None,				_UpperCAmelCase=1.0,				_UpperCAmelCase=False,				_UpperCAmelCase=None,				_UpperCAmelCase=1.0,				_UpperCAmelCase=1.0,				_UpperCAmelCase=False,				_UpperCAmelCase=False,				_UpperCAmelCase="ratio",				_UpperCAmelCase=None,				_UpperCAmelCase=None,				_UpperCAmelCase=64,				_UpperCAmelCase=32,				_UpperCAmelCase=False,				_UpperCAmelCase=True,				_UpperCAmelCase=False,				_UpperCAmelCase=False,				_UpperCAmelCase=True,				_UpperCAmelCase=False,				_UpperCAmelCase=None,				_UpperCAmelCase=None,				**_UpperCAmelCase,				):
        '''simple docstring'''



        super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE,				**_SCREAMING_SNAKE_CASE  )

        # BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
        lowercase__				 =							vocab_size
        lowercase__				 =							hidden_size
        lowercase__				 =							num_hidden_layers
        lowercase__				 =							num_attention_heads
        lowercase__				 =							hidden_act
        lowercase__				 =							intermediate_size
        lowercase__				 =							hidden_dropout_prob
        lowercase__				 =							attention_probs_dropout_prob
        lowercase__				 =							max_position_embeddings
        lowercase__				 =							type_vocab_sizes
        lowercase__				 =							initializer_range
        lowercase__				 =							layer_norm_eps

        # Fine-tuning task hyperparameters
        lowercase__				 =							positive_label_weight
        lowercase__				 =							num_aggregation_labels
        lowercase__				 =							aggregation_loss_weight
        lowercase__				 =							use_answer_as_supervision
        lowercase__				 =							answer_loss_importance
        lowercase__				 =							use_normalized_answer_loss
        lowercase__				 =							huber_loss_delta
        lowercase__				 =							temperature
        lowercase__				 =							aggregation_temperature
        lowercase__				 =							use_gumbel_for_cells
        lowercase__				 =							use_gumbel_for_aggregation
        lowercase__				 =							average_approximation_function
        lowercase__				 =							cell_selection_preference
        lowercase__				 =							answer_loss_cutoff
        lowercase__				 =							max_num_rows
        lowercase__				 =							max_num_columns
        lowercase__				 =							average_logits_per_cell
        lowercase__				 =							select_one_column
        lowercase__				 =							allow_empty_column_selection
        lowercase__				 =							init_cell_selection_weights_to_zero
        lowercase__				 =							reset_position_index_per_cell
        lowercase__				 =							disable_per_token_loss

        # Aggregation hyperparameters
        lowercase__				 =							aggregation_labels
        lowercase__				 =							no_aggregation_label_index

        if isinstance(self.aggregation_labels,				_SCREAMING_SNAKE_CASE  ):
             lowercase__				 =							{int(_SCREAMING_SNAKE_CASE  ): v for k, v in aggregation_labels.items()}





"""simple docstring"""


lowerCAmelCase_:					Any     =					"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"







def 					__a   (		A      ):






     '''simple docstring'''



     if not isinstance(A    ,				A      ):
          lowercase__				 =							f'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
          raise TypeError(A      )

     lowercase__				 =							"".join(bin(A      )[2:].zfill(8      ) for byte in data      )

     lowercase__				 =							len(A      ) % 6 != 0

     if padding_needed:
          # The padding that will be added later
          lowercase__				 =							b"=" * ((6 - len(A      ) % 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(A      ) % 6)
     else:
          lowercase__				 =							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(A      )    ,				6      )      ).encode()
         + padding
     )







def 					__a   (		A      ):






     '''simple docstring'''



     if not isinstance(A    ,				A      ) and not isinstance(A    ,				A      ):
          lowercase__				 =							(
              "argument should be a bytes-like object or ASCII string, "
              f'''not \'{encoded_data.__class__.__name__}\''''
          )
          raise TypeError(A      )

     # 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(A    ,				A      ):
          try:
               lowercase__				 =							encoded_data.decode("utf-8"      )
          except UnicodeDecodeError:
               raise ValueError("base64 encoded data should only contain ASCII characters"      )

     lowercase__				 =							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(A      ) % 4 == 0 and padding < 3, "Incorrect padding"

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

          lowercase__				 =							"".join(
              bin(B64_CHARSET.index(A      )      )[2:].zfill(6      ) for char in encoded_data      )[: -padding * 2]
     else:
          lowercase__				 =							"".join(
              bin(B64_CHARSET.index(A      )      )[2:].zfill(6      ) for char in encoded_data      )

     lowercase__				 =							[
         int(binary_stream[index : index + 8]    ,				2      )
         for index in range(0    ,				len(A      )    ,				8      )
     ]

     return bytes(A      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()




