





from ...configuration_utils import PretrainedConfig


_A :   Tuple  			=  {
    '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        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):
							_UpperCAmelCase				:  Optional[int]			   =   "tapas"







							def __init__(				self					:     Union[str, Any]		, A					:     Optional[Any]=3_0_5_2_2		, A					:     int=7_6_8		, A					:     Union[str, Any]=1_2		, A					:     Optional[Any]=1_2		, A					:     int=3_0_7_2		, A					:     List[Any]="gelu"		, A					:     Tuple=0.1		, A					:     List[Any]=0.1		, A					:     Optional[Any]=1_0_2_4		, A					:     int=[3, 2_5_6, 2_5_6, 2, 2_5_6, 2_5_6, 1_0]		, A					:     Optional[int]=0.02		, A					:     List[str]=1e-12		, A					:     Tuple=0		, A					:     List[str]=10.0		, A					:     Optional[Any]=0		, A					:     Tuple=1.0		, A					:     Union[str, Any]=None		, A					:     Dict=1.0		, A					:     Union[str, Any]=False		, A					:     Any=None		, A					:     int=1.0		, A					:     int=1.0		, A					:     Tuple=False		, A					:     Tuple=False		, A					:     Tuple="ratio"		, A					:     Dict=None		, A					:     int=None		, A					:     List[Any]=6_4		, A					:     Optional[int]=3_2		, A					:     List[str]=False		, A					:     Optional[int]=True		, A					:     List[Any]=False		, A					:     List[Any]=False		, A					:     Union[str, Any]=True		, A					:     str=False		, A					:     Union[str, Any]=None		, A					:     Tuple=None		, **A					:     Optional[Any]		, )       ->List[str]:
										super().__init__(pad_token_id=A		, **A      )

										# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
										lowerCamelCase__       :						List[Any]        =							vocab_size
										lowerCamelCase__       :						Optional[Any]        =							hidden_size
										lowerCamelCase__       :						Optional[Any]        =							num_hidden_layers
										lowerCamelCase__       :						Optional[Any]        =							num_attention_heads
										lowerCamelCase__       :						str        =							hidden_act
										lowerCamelCase__       :						int        =							intermediate_size
										lowerCamelCase__       :						Tuple        =							hidden_dropout_prob
										lowerCamelCase__       :						int        =							attention_probs_dropout_prob
										lowerCamelCase__       :						Optional[int]        =							max_position_embeddings
										lowerCamelCase__       :						Union[str, Any]        =							type_vocab_sizes
										lowerCamelCase__       :						Union[str, Any]        =							initializer_range
										lowerCamelCase__       :						Optional[Any]        =							layer_norm_eps

										# Fine-tuning task hyperparameters
										lowerCamelCase__       :						int        =							positive_label_weight
										lowerCamelCase__       :						str        =							num_aggregation_labels
										lowerCamelCase__       :						List[str]        =							aggregation_loss_weight
										lowerCamelCase__       :						Tuple        =							use_answer_as_supervision
										lowerCamelCase__       :						List[str]        =							answer_loss_importance
										lowerCamelCase__       :						Tuple        =							use_normalized_answer_loss
										lowerCamelCase__       :						Optional[Any]        =							huber_loss_delta
										lowerCamelCase__       :						List[Any]        =							temperature
										lowerCamelCase__       :						Dict        =							aggregation_temperature
										lowerCamelCase__       :						Tuple        =							use_gumbel_for_cells
										lowerCamelCase__       :						Union[str, Any]        =							use_gumbel_for_aggregation
										lowerCamelCase__       :						Union[str, Any]        =							average_approximation_function
										lowerCamelCase__       :						Union[str, Any]        =							cell_selection_preference
										lowerCamelCase__       :						Any        =							answer_loss_cutoff
										lowerCamelCase__       :						List[Any]        =							max_num_rows
										lowerCamelCase__       :						Tuple        =							max_num_columns
										lowerCamelCase__       :						Dict        =							average_logits_per_cell
										lowerCamelCase__       :						List[str]        =							select_one_column
										lowerCamelCase__       :						Optional[Any]        =							allow_empty_column_selection
										lowerCamelCase__       :						List[str]        =							init_cell_selection_weights_to_zero
										lowerCamelCase__       :						Optional[int]        =							reset_position_index_per_cell
										lowerCamelCase__       :						Tuple        =							disable_per_token_loss

										# Aggregation hyperparameters
										lowerCamelCase__       :						Tuple        =							aggregation_labels
										lowerCamelCase__       :						Optional[int]        =							no_aggregation_label_index

										if isinstance(self.aggregation_labels		, A      ):
													lowerCamelCase__       :						Dict        =							{int(A      ): v for k, v in aggregation_labels.items()}










def 	_a						(   UpperCAmelCase					)    ->    int:







   """simple docstring"""

   if not isinstance(UpperCAmelCase      ,  UpperCAmelCase					):
      raise TypeError('''only integers accepted as input'''					)
   else:
      lowerCamelCase__       :						Any        =							str(abs(UpperCAmelCase					)					)
      lowerCamelCase__       :						Union[str, Any]        =							[list(UpperCAmelCase					) for char in range(len(UpperCAmelCase					)					)]
      for index in range(len(UpperCAmelCase					)					):
         num_transpositions[index].pop(UpperCAmelCase					)
      return max(
          int(''''''.join(list(UpperCAmelCase					)					)					) for transposition in num_transpositions					)


if __name__ == "__main__":
       __import__('doctest').testmod()



