






'''simple docstring'''







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 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				"tapas"






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

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

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

							# Aggregation hyperparameters
							a_		:  List[str]											=      aggregation_labels
							a_		:  Dict											=      no_aggregation_label_index

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












'''simple docstring'''







from __future__ import annotations





def 		_UpperCAmelCase   (							__A							:   list[int]      ):
				a_		:  int											=      len(__A      ) // 2

				# choose the middle 3 elements
				a_		:  Dict											=      lst[m - 1 : m + 2]

				# if middle element is peak
				if three[1] > three[0] and three[1] > three[2]:
								return three[1]

				# if increasing, recurse on right
				elif three[0] < three[2]:
								if len(lst[:m]      ) == 2:
												m -= 1
								return peak(lst[m:]      )

				# decreasing
				else:
								if len(lst[:m]      ) == 2:
												m += 1
								return peak(lst[:m]      )


if __name__ == "__main__":
		import doctest

		doctest.testmod()




