

from ...configuration_utils import PretrainedConfig


_SCREAMING_SNAKE_CASE  :						Optional[int]         =    {
    '''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  UpperCAmelCase__	( __lowerCamelCase ):






  """simple docstring"""


  a     =      "tapas"






  def __init__(							self		:	List[str]			,     __lowerCamelCase		:	Any=3_0522			,     __lowerCamelCase		:	str=768			,     __lowerCamelCase		:	Dict=12			,     __lowerCamelCase		:	int=12			,     __lowerCamelCase		:	int=3072			,     __lowerCamelCase		:	Dict="gelu"			,     __lowerCamelCase		:	Tuple=0.1			,     __lowerCamelCase		:	Optional[Any]=0.1			,     __lowerCamelCase		:	Dict=1024			,     __lowerCamelCase		:	Tuple=[3, 256, 256, 2, 256, 256, 10]			,     __lowerCamelCase		:	Union[str, Any]=0.02			,     __lowerCamelCase		:	Optional[int]=1e-12			,     __lowerCamelCase		:	Optional[int]=0			,     __lowerCamelCase		:	Any=10.0			,     __lowerCamelCase		:	Tuple=0			,     __lowerCamelCase		:	Dict=1.0			,     __lowerCamelCase		:	Any=None			,     __lowerCamelCase		:	Any=1.0			,     __lowerCamelCase		:	Any=False			,     __lowerCamelCase		:	str=None			,     __lowerCamelCase		:	List[str]=1.0			,     __lowerCamelCase		:	Dict=1.0			,     __lowerCamelCase		:	Dict=False			,     __lowerCamelCase		:	str=False			,     __lowerCamelCase		:	Optional[Any]="ratio"			,     __lowerCamelCase		:	str=None			,     __lowerCamelCase		:	List[str]=None			,     __lowerCamelCase		:	List[str]=64			,     __lowerCamelCase		:	str=32			,     __lowerCamelCase		:	str=False			,     __lowerCamelCase		:	Dict=True			,     __lowerCamelCase		:	Tuple=False			,     __lowerCamelCase		:	Dict=False			,     __lowerCamelCase		:	List[str]=True			,     __lowerCamelCase		:	Optional[Any]=False			,     __lowerCamelCase		:	List[str]=None			,     __lowerCamelCase		:	Dict=None			,     **__lowerCamelCase		:	Any			,     )	->							str:
       super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_      )

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

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

       # Aggregation hyperparameters
       SCREAMING_SNAKE_CASE__					=					aggregation_labels
       SCREAMING_SNAKE_CASE__					=					no_aggregation_label_index

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





import math






class  UpperCAmelCase__	:






  """simple docstring"""


  def    lowercase_	(							self		:	int			,     __lowerCamelCase		:	list[list[float]]			,     __lowerCamelCase		:	list[int]      )	->							int:
       SCREAMING_SNAKE_CASE__					=					0.0
       SCREAMING_SNAKE_CASE__					=					0.0
       for i in range(len(__lowerCamelCase      )      ):
            da += math.pow((sample[i] - weights[0][i])			,     2      )
            da += math.pow((sample[i] - weights[1][i])			,     2      )
            return 0 if da > da else 1
       return 0







  def    lowercase_	(							self		:	Optional[int]			,     __lowerCamelCase		:	list[list[int | float]]			,     __lowerCamelCase		:	list[int]			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	float      )	->							list[list[int | float]]:
       for i in range(len(__lowerCamelCase      )      ):
            weights[j][i] += alpha * (sample[i] - weights[j][i])
       return weights



def        UpperCAmelCase_     (		):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

     # weight initialization ( n, C )
     SCREAMING_SNAKE_CASE__					=					[[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

     # training
     SCREAMING_SNAKE_CASE__					=					SelfOrganizingMap()
     SCREAMING_SNAKE_CASE__					=					3
     SCREAMING_SNAKE_CASE__					=					0.5

     for _ in range(_A       ):
          for j in range(len(_A       )       ):
               # training sample
               SCREAMING_SNAKE_CASE__					=					training_samples[j]

               # Compute the winning vector
               SCREAMING_SNAKE_CASE__					=					self_organizing_map.get_winner(_A   ,	_A       )

               # Update the winning vector
               SCREAMING_SNAKE_CASE__					=					self_organizing_map.update(_A   ,	_A   ,	_A   ,	_A       )

    # classify test sample
     SCREAMING_SNAKE_CASE__					=					[0, 0, 0, 1]
     SCREAMING_SNAKE_CASE__					=					self_organizing_map.get_winner(_A   ,	_A       )

     # results
     print(F'''Clusters that the test sample belongs to : {winner}'''       )
     print(F'''Weights that have been trained : {weights}'''       )


# running the main() function
if __name__ == "__main__":
       main()


