





'''simple docstring'''




import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

lowercase__       : Optional[int]         =       3






def 			_lowerCAmelCase     (    __snake_case    :					int     )	->	int:
     print('Generating primitive root of p'     )
     while True:
          __A		:					Optional[int]						=			random.randrange(3					,				__snake_case     )
          if pow(__snake_case					,				2					,				__snake_case     ) == 1:
               continue
          if pow(__snake_case					,				__snake_case					,				__snake_case     ) == 1:
               continue
          return g






def 			_lowerCAmelCase     (    __snake_case    :					int     )	->	tuple[tuple[int, int, int, int], tuple[int, int]]:
     print('Generating prime p...'     )
     __A		:					Tuple						=			rabin_miller.generate_large_prime(__snake_case     )  # select large prime number.
     __A		:					List[Any]						=			primitive_root(__snake_case     )  # one primitive root on modulo p.
     __A		:					Optional[Any]						=			random.randrange(3					,				__snake_case     )  # private_key -> have to be greater than 2 for safety.
     __A		:					Any						=			cryptomath.find_mod_inverse(pow(__snake_case					,				__snake_case					,				__snake_case     )					,				__snake_case     )

     __A		:					List[str]						=			(key_size, e_a, e_a, p)
     __A		:					Dict						=			(key_size, d)

     return public_key, private_key






def 			_lowerCAmelCase     (    __snake_case    :					str					,				__snake_case    :					int     )	->	None:
     if os.path.exists(f'{name}_pubkey.txt'     ) or os.path.exists(f'{name}_privkey.txt'     ):
          print('\nWARNING:'     )
          print(
              f'"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'
              'Use a different name or delete these files and re-run this program.'     )
          sys.exit()

     __A					,__A		:					str						=			generate_key(__snake_case     )
     print(f'\nWriting public key to file {name}_pubkey.txt...'     )
     with open(f'{name}_pubkey.txt'					,				'w'     ) as fo:
          fo.write(f'{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}'     )

     print(f'Writing private key to file {name}_privkey.txt...'     )
     with open(f'{name}_privkey.txt'					,				'w'     ) as fo:
          fo.write(f'{private_key[0]},{private_key[1]}'     )






def 			_lowerCAmelCase     (    )	->	None:
     print('Making key files...'     )
     make_key_files('elgamal'					,				20_48     )
     print('Key files generation successful'     )


if __name__ == "__main__":
  main()






'''simple docstring'''




from ...configuration_utils import PretrainedConfig


lowercase__       : Any         =       {
    '''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__				):
     lowerCAmelCase						     =			'''tapas'''


     def __init__(		self		,      _UpperCAmelCase=3_0522		,      _UpperCAmelCase=768		,      _UpperCAmelCase=12		,      _UpperCAmelCase=12		,      _UpperCAmelCase=3072		,      _UpperCAmelCase="gelu"		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=1024		,      _UpperCAmelCase=[3, 256, 256, 2, 256, 256, 10]		,      _UpperCAmelCase=0.02		,      _UpperCAmelCase=1e-1_2		,      _UpperCAmelCase=0		,      _UpperCAmelCase=10.0		,      _UpperCAmelCase=0		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=None		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=False		,      _UpperCAmelCase=None		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=False		,      _UpperCAmelCase=False		,      _UpperCAmelCase="ratio"		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      _UpperCAmelCase=64		,      _UpperCAmelCase=32		,      _UpperCAmelCase=False		,      _UpperCAmelCase=True		,      _UpperCAmelCase=False		,      _UpperCAmelCase=False		,      _UpperCAmelCase=True		,      _UpperCAmelCase=False		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






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

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

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

          # Aggregation hyperparameters
          __A		:					Optional[Any]						=			aggregation_labels
          __A		:					List[str]						=			no_aggregation_label_index

          if isinstance(self.aggregation_labels		,      _UpperCAmelCase):
               __A		:					Optional[Any]						=			{int(_UpperCAmelCase): v for k, v in aggregation_labels.items()}