





"""simple docstring"""




import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

lowerCamelCase_     :						Tuple						=					3





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  print('Generating primitive root of p'					)
  while True:
    A_  :       Tuple										=  random.randrange(3 ,					_UpperCAmelCase					)
    if pow(_UpperCAmelCase ,					2 ,					_UpperCAmelCase					) == 1:
      continue
    if pow(_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					) == 1:
      continue
    return g





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  print('Generating prime p...'					)
  A_  :       Dict										=  rabin_miller.generate_large_prime(_UpperCAmelCase					)  # select large prime number.
  A_  :       List[Any]										=  primitive_root(_UpperCAmelCase					)  # one primitive root on modulo p.
  A_  :       Union[str, Any]										=  random.randrange(3 ,					_UpperCAmelCase					)  # private_key -> have to be greater than 2 for safety.
  A_  :       Dict										=  cryptomath.find_mod_inverse(pow(_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					) ,					_UpperCAmelCase					)

  A_  :       int										=  (key_size, e_a, e_a, p)
  A_  :       Dict										=  (key_size, d)

  return public_key, private_key





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  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_  :       Any										=  generate_key(_UpperCAmelCase					)
  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 						UpperCAmelCase__		(     ):






  """simple docstring"""





  print('Making key files...'					)
  make_key_files('elgamal' ,					2048					)
  print('Key files generation successful'					)


if __name__ == "__main__":
   main()






"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase_     :						str						=					logging.get_logger(__name__)

lowerCamelCase_     :						Any						=					{
    'facebook/xmod-base': 'https://huggingface.co/facebook/xmod-base/resolve/main/config.json',
    'facebook/xmod-large-prenorm': 'https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json',
    'facebook/xmod-base-13-125k': 'https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json',
    'facebook/xmod-base-30-125k': 'https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json',
    'facebook/xmod-base-30-195k': 'https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json',
    'facebook/xmod-base-60-125k': 'https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json',
    'facebook/xmod-base-60-265k': 'https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json',
    'facebook/xmod-base-75-125k': 'https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json',
    'facebook/xmod-base-75-269k': 'https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json',
}

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_      : Tuple	    =   """xmod"""

      def __init__(     self					,      snake_case_=3_0_5_2_2					,      snake_case_=7_6_8					,      snake_case_=1_2					,      snake_case_=1_2					,      snake_case_=3_0_7_2					,      snake_case_="gelu"					,      snake_case_=0.1					,      snake_case_=0.1					,      snake_case_=5_1_2					,      snake_case_=2					,      snake_case_=0.02					,      snake_case_=1E-12					,      snake_case_=1					,      snake_case_=0					,      snake_case_=2					,      snake_case_="absolute"					,      snake_case_=True					,      snake_case_=None					,      snake_case_=False					,      snake_case_=2					,      snake_case_=False					,      snake_case_=True					,      snake_case_=True					,      snake_case_=("en_XX",)					,      snake_case_=None					,      **snake_case_					,      ):

        """simple docstring"""

        super().__init__(pad_token_id=snake_case_					,      bos_token_id=snake_case_					,      eos_token_id=snake_case_					,      **snake_case_  )

        A_  :       Union[str, Any]										=  vocab_size
        A_  :       Any										=  hidden_size
        A_  :       List[str]										=  num_hidden_layers
        A_  :       Tuple										=  num_attention_heads
        A_  :       int										=  hidden_act
        A_  :       Any										=  intermediate_size
        A_  :       Any										=  hidden_dropout_prob
        A_  :       Dict										=  attention_probs_dropout_prob
        A_  :       Union[str, Any]										=  max_position_embeddings
        A_  :       List[Any]										=  type_vocab_size
        A_  :       List[str]										=  initializer_range
        A_  :       Any										=  layer_norm_eps
        A_  :       Optional[Any]										=  position_embedding_type
        A_  :       int										=  use_cache
        A_  :       Dict										=  classifier_dropout
        A_  :       int										=  pre_norm
        A_  :       Optional[Any]										=  adapter_reduction_factor
        A_  :       List[Any]										=  adapter_layer_norm
        A_  :       int										=  adapter_reuse_layer_norm
        A_  :       Dict										=  ln_before_adapter
        A_  :       List[str]										=  list(snake_case_  )
        A_  :       Union[str, Any]										=  default_language


class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      @property
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        if self.task == "multiple-choice":
          A_  :       Dict										=  {0: 'batch', 1: 'choice', 2: 'sequence'}
        else:
          A_  :       int										=  {0: 'batch', 1: 'sequence'}
        return OrderedDict(
            [
                ('input_ids', dynamic_axis),
                ('attention_mask', dynamic_axis),
            ]  )