
from ...configuration_utils import PretrainedConfig


lowerCamelCase__	=							{
    "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        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: int	=		"tapas"

						def __init__(			self       ,							a=3_05_22       ,							a=7_68       ,							a=12       ,							a=12       ,							a=30_72       ,							a="gelu"       ,							a=0.1       ,							a=0.1       ,							a=10_24       ,							a=[3, 2_56, 2_56, 2, 2_56, 2_56, 10]       ,							a=0.02       ,							a=1e-12       ,							a=0       ,							a=10.0       ,							a=0       ,							a=1.0       ,							a=None       ,							a=1.0       ,							a=False       ,							a=None       ,							a=1.0       ,							a=1.0       ,							a=False       ,							a=False       ,							a="ratio"       ,							a=None       ,							a=None       ,							a=64       ,							a=32       ,							a=False       ,							a=True       ,							a=False       ,							a=False       ,							a=True       ,							a=False       ,							a=None       ,							a=None       ,							**a       ,							)					->      str:


								'''simple docstring'''

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

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

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

								# Aggregation hyperparameters
								_UpperCamelCase						    =	aggregation_labels
								_UpperCamelCase						    =	no_aggregation_label_index

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





def 							__A(lowerCAmelCase )		->				list[list[int]]:

		"""simple docstring"""






		_UpperCamelCase						    =	[]
		if len(lowerCAmelCase ) == 1:
				return [nums.copy()]
		for _ in range(len(lowerCAmelCase ) ):
				_UpperCamelCase						    =	nums.pop(0 )
				_UpperCamelCase						    =	permute(lowerCAmelCase )
				for perm in permutations:
						perm.append(lowerCAmelCase )
				result.extend(lowerCAmelCase )
				nums.append(lowerCAmelCase )
		return result


def 							__A(lowerCAmelCase )		->				Optional[int]:

		"""simple docstring"""






		def backtrack(lowerCAmelCase ):
				if start == len(lowerCAmelCase ) - 1:
						output.append(nums[:] )
				else:
						for i in range(lowerCAmelCase						,  len(lowerCAmelCase ) ):
								_UpperCamelCase    ,	_UpperCamelCase						    =	nums[i], nums[start]
								backtrack(start + 1 )
								_UpperCamelCase    ,	_UpperCamelCase						    =	nums[i], nums[start]  # backtrack

		_UpperCamelCase						    =	[]
		backtrack(0 )
		return output


if __name__ == "__main__":
		import doctest

		# use res to print the data in permute2 function
		lowerCamelCase__	=							permutea([1, 2, 3])
		print(res)
		doctest.testmod()



