

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 _UpperCamelCase				(				__A ):
			'''simple docstring'''







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

				"""simple docstring"""

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

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

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

				# Aggregation hyperparameters
				SCREAMING_SNAKE_CASE :   str					  =							aggregation_labels
				SCREAMING_SNAKE_CASE :   Any					  =							no_aggregation_label_index

				if isinstance(self.aggregation_labels   ,   a						):
					SCREAMING_SNAKE_CASE :   str					  =							{int(a						): v for k, v in aggregation_labels.items()}


def 		UpperCamelCase   (lowercase_:					str   )	-> Optional[Any]:
	A__				:       Optional[int]	  =						[]
	A__				:       Optional[int]	  =						set({"""(""", """[""", """{"""}   )
	A__				:       str	  =						set({""")""", """]""", """}"""}   )
	A__				:       Optional[Any]	  =						{"""{""": """}""", """[""": """]""", """(""": """)"""}

	for i in range(len(lowercase_   )   ):
		if s[i] in open_brackets:
			stack.append(s[i]   )

		elif s[i] in closed_brackets and (
		    len(lowercase_   ) == 0 or (len(lowercase_   ) > 0 and open_to_closed[stack.pop()] != s[i])
		):
			return False

	return len(lowercase_   ) == 0

def 		UpperCamelCase   ()	-> List[str]:
	A__				:       Optional[Any]	  =						input("""Enter sequence of brackets: """   )
	if is_balanced(lowercase_   ):
		print(lowercase_       , """is balanced"""   )
	else:
		print(lowercase_       , """is not balanced"""   )


if __name__ == "__main__":
	main()



