'''simple docstring'''
from ...configuration_utils import PretrainedConfig


a=  {
    '''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 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						'''tapas'''







     def __init__(		self     ,			_lowerCamelCase=3_0_5_2_2     ,			_lowerCamelCase=7_6_8     ,			_lowerCamelCase=1_2     ,			_lowerCamelCase=1_2     ,			_lowerCamelCase=3_0_7_2     ,			_lowerCamelCase="gelu"     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=1_0_2_4     ,			_lowerCamelCase=[3, 2_5_6, 2_5_6, 2, 2_5_6, 2_5_6, 1_0]     ,			_lowerCamelCase=0.0_2     ,			_lowerCamelCase=1E-1_2     ,			_lowerCamelCase=0     ,			_lowerCamelCase=1_0.0     ,			_lowerCamelCase=0     ,			_lowerCamelCase=1.0     ,			_lowerCamelCase=None     ,			_lowerCamelCase=1.0     ,			_lowerCamelCase=False     ,			_lowerCamelCase=None     ,			_lowerCamelCase=1.0     ,			_lowerCamelCase=1.0     ,			_lowerCamelCase=False     ,			_lowerCamelCase=False     ,			_lowerCamelCase="ratio"     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase=6_4     ,			_lowerCamelCase=3_2     ,			_lowerCamelCase=False     ,			_lowerCamelCase=True     ,			_lowerCamelCase=False     ,			_lowerCamelCase=False     ,			_lowerCamelCase=True     ,			_lowerCamelCase=False     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			**_lowerCamelCase     ,			):
         super().__init__(pad_token_id=_lowerCamelCase     ,			**_lowerCamelCase    )

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

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

         # Aggregation hyperparameters
         __UpperCamelCase	:	Dict		    =  aggregation_labels
         __UpperCamelCase	:	Union[str, Any]		    =  no_aggregation_label_index

         if isinstance(self.aggregation_labels     ,			_lowerCamelCase    ):
             __UpperCamelCase	:	Tuple		    =  {int(_lowerCamelCase    ): v for k, v in aggregation_labels.items()}




'''simple docstring'''
def  _UpperCamelCase (     _a	:	int			):




    """simple docstring"""



    if bit_count < 0:
        raise ValueError('The given input must be positive'			)

    # get the generated string sequence
    __UpperCamelCase	:	Dict		    =  gray_code_sequence_string(_a			)
    #
    # convert them to integers
    for i in range(len(_a			)			):
        __UpperCamelCase	:	int		    =  int(sequence[i]				,     2			)

    return sequence







def  _UpperCamelCase (     _a	:	int			):




    """simple docstring"""



    if bit_count == 0:
        return ["0"]

    if bit_count == 1:
        return ["0", "1"]

    __UpperCamelCase	:	Dict		    =  1 << bit_count  # defines the length of the sequence
    # 1<< n is equivalent to 2^n

    # recursive answer will generate answer for n-1 bits
    __UpperCamelCase	:	Tuple		    =  gray_code_sequence_string(bit_count - 1			)

    __UpperCamelCase	:	str		    =  []

    # append 0 to first half of the smaller sequence generated
    for i in range(seq_len // 2			):
        __UpperCamelCase	:	Optional[Any]		    =  '0' + smaller_sequence[i]
        sequence.append(_a			)

    # append 1 to second half ... start from the end of the list
    for i in reversed(range(seq_len // 2			)			):
        __UpperCamelCase	:	List[Any]		    =  '1' + smaller_sequence[i]
        sequence.append(_a			)

    return sequence


if __name__ == "__main__":
  import doctest

  doctest.testmod()



