






'''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					(					__A       ):
			snake_case__  =				"tapas"






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

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

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

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

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












'''simple docstring'''







import sys

__lowerCAmelCase       		=      (
    '73167176531330624919225119674426574742355349194934'
    '96983520312774506326239578318016984801869478851843'
    '85861560789112949495459501737958331952853208805511'
    '12540698747158523863050715693290963295227443043557'
    '66896648950445244523161731856403098711121722383113'
    '62229893423380308135336276614282806444486645238749'
    '30358907296290491560440772390713810515859307960866'
    '70172427121883998797908792274921901699720888093776'
    '65727333001053367881220235421809751254540594752243'
    '52584907711670556013604839586446706324415722155397'
    '53697817977846174064955149290862569321978468622482'
    '83972241375657056057490261407972968652414535100474'
    '82166370484403199890008895243450658541227588666881'
    '16427171479924442928230863465674813919123162824586'
    '17866458359124566529476545682848912883142607690042'
    '24219022671055626321111109370544217506941658960408'
    '07198403850962455444362981230987879927244284909188'
    '84580156166097919133875499200524063689912560717606'
    '05886116467109405077541002256983155200055935729725'
    '71636269561882670428252483600823257530420752963450'
)





def 		_UpperCAmelCase   (							__A							:   str      ):
				a_		:  Tuple											=      1
				for digit in s:
								product *= int(__A      )
				return product





def 		_UpperCAmelCase   (							__A							:   str = N      ):
				a_		:  Dict											=      -sys.maxsize - 1
				a_		:  Optional[int]											=      n[:13]
				a_		:  str											=      13
				while cur_index < len(__A      ) - 13:
								if int(n[cur_index]      ) >= int(substr[0]      ):
												a_		:  Tuple											=      substr[1:] + n[cur_index]
												cur_index += 1
								else:
												a_		:  Dict											=      max(__A						,		str_eval(__A      )      )
												a_		:  List[str]											=      n[cur_index : cur_index + 13]
												cur_index += 13
				return largest_product


if __name__ == "__main__":
		print(F"""{solution() = }""")




