"""simple docstring"""
import random







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->bool:
  a__:					Any      							=     num - 1
  a__:					Union[str, Any]      							=     0

  while s % 2 == 0:
    a__:					str      							=     s // 2
    t += 1

  for _ in range(5       ):
    a__:					List[Any]      							=     random.randrange(2		,					num - 1       )
    a__:					str      							=     pow(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
    if v != 1:
      a__:					Tuple      							=     0
      while v != (num - 1):
        if i == t - 1:
          return False
        else:
          a__:					str      							=     i + 1
          a__:					List[Any]      							=     (v**2) % num
  return True







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->bool:
  if num < 2:
    return False

  a__:					str      							=     [
      2,
      3,
      5,
      7,
      11,
      13,
      17,
      19,
      23,
      29,
      31,
      37,
      41,
      43,
      47,
      53,
      59,
      61,
      67,
      71,
      73,
      79,
      83,
      89,
      97,
      101,
      103,
      107,
      109,
      113,
      127,
      131,
      137,
      139,
      149,
      151,
      157,
      163,
      167,
      173,
      179,
      181,
      191,
      193,
      197,
      199,
      211,
      223,
      227,
      229,
      233,
      239,
      241,
      251,
      257,
      263,
      269,
      271,
      277,
      281,
      283,
      293,
      307,
      311,
      313,
      317,
      331,
      337,
      347,
      349,
      353,
      359,
      367,
      373,
      379,
      383,
      389,
      397,
      401,
      409,
      419,
      421,
      431,
      433,
      439,
      443,
      449,
      457,
      461,
      463,
      467,
      479,
      487,
      491,
      499,
      503,
      509,
      521,
      523,
      541,
      547,
      557,
      563,
      569,
      571,
      577,
      587,
      593,
      599,
      601,
      607,
      613,
      617,
      619,
      631,
      641,
      643,
      647,
      653,
      659,
      661,
      673,
      677,
      683,
      691,
      701,
      709,
      719,
      727,
      733,
      739,
      743,
      751,
      757,
      761,
      769,
      773,
      787,
      797,
      809,
      811,
      821,
      823,
      827,
      829,
      839,
      853,
      857,
      859,
      863,
      877,
      881,
      883,
      887,
      907,
      911,
      919,
      929,
      937,
      941,
      947,
      953,
      967,
      971,
      977,
      983,
      991,
      997,
  ]

  if num in low_primes:
    return True

  for prime in low_primes:
    if (num % prime) == 0:
      return False

  return rabin_miller(_SCREAMING_SNAKE_CASE       )







def 						__a       (     _SCREAMING_SNAKE_CASE = 1024       )     ->int:
  while True:
    a__:					Tuple      							=     random.randrange(2 ** (keysize - 1)		,					2 ** (keysize)       )
    if is_prime_low_num(_SCREAMING_SNAKE_CASE       ):
      return num


if __name__ == "__main__":
 lowercase__ 	=     generate_large_prime()
 print(('Prime number:', num))
 print(('is_prime_low_num:', is_prime_low_num(num)))



"""simple docstring"""
import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin




class        __snake_case	(					__lowerCAmelCase	,				unittest.TestCase    ):
      a__				=		DebertaTokenizer
      a__				=		True
      a__				=		DebertaTokenizerFast



      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        a__:					List[str]      							=     [
            'l',
            'o',
            'w',
            'e',
            'r',
            's',
            't',
            'i',
            'd',
            'n',
            '\u0120',
            '\u0120l',
            '\u0120n',
            '\u0120lo',
            '\u0120low',
            'er',
            '\u0120lowest',
            '\u0120newer',
            '\u0120wider',
            '[UNK]',
        ]
        a__:					List[str]      							=     dict(zip(lowercase   ,      range(len(lowercase))))
        a__:					Any      							=     ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
        a__:					Optional[Any]      							=     {'unk_token': '[UNK]'}

        a__:					Any      							=     os.path.join(self.tmpdirname   ,      VOCAB_FILES_NAMES['vocab_file'])
        a__:					Dict      							=     os.path.join(self.tmpdirname   ,      VOCAB_FILES_NAMES['merges_file'])
        with open(self.vocab_file   ,      'w'   ,      encoding='utf-8') as fp:
          fp.write(json.dumps(lowercase) + '\n')
        with open(self.merges_file   ,      'w'   ,      encoding='utf-8') as fp:
          fp.write('\n'.join(lowercase))



      def   lowerCamelCase_       (						self   ,      **lowercase)     ->      int:





        '''simple docstring'''





        kwargs.update(self.special_tokens_map)
        return self.tokenizer_class.from_pretrained(self.tmpdirname   ,      **lowercase)



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      Dict:





        '''simple docstring'''





        a__:					Tuple      							=     'lower newer'
        a__:					int      							=     'lower newer'
        return input_text, output_text



      def   lowerCamelCase_       (						self)     ->      Optional[int]:





        '''simple docstring'''





        a__:					Optional[int]      							=     self.get_tokenizer()
        a__:					List[Any]      							=     'lower newer'
        a__:					Optional[int]      							=     ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
        a__:					Optional[Any]      							=     tokenizer.tokenize(lowercase)
        self.assertListEqual(lowercase   ,      lowercase)

        a__:					int      							=     tokens + [tokenizer.unk_token]
        a__:					Optional[int]      							=     [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase)   ,      lowercase)



      def   lowerCamelCase_       (						self)     ->      List[str]:





        '''simple docstring'''





        a__:					int      							=     self.get_tokenizer()
        a__:					Any      							=     tokenizer('Hello'   ,      'World')
        a__:					Optional[Any]      							=     [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
        self.assertListEqual(tokd['token_type_ids']   ,      lowercase)



      @slow
      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





        a__:					Dict      							=     self.tokenizer_class.from_pretrained('microsoft/deberta-base')

        a__:					Union[str, Any]      							=     tokenizer.encode('sequence builders'   ,      add_special_tokens=lowercase)
        a__:					Optional[Any]      							=     tokenizer.encode('multi-sequence build'   ,      add_special_tokens=lowercase)

        a__:					List[str]      							=     tokenizer.encode(
            'sequence builders'   ,      add_special_tokens=lowercase   ,      add_prefix_space=lowercase)
        a__:					Any      							=     tokenizer.encode(
            'sequence builders'   ,      'multi-sequence build'   ,      add_special_tokens=lowercase   ,      add_prefix_space=lowercase)

        a__:					Optional[int]      							=     tokenizer.build_inputs_with_special_tokens(lowercase)
        a__:					Union[str, Any]      							=     tokenizer.build_inputs_with_special_tokens(lowercase   ,      lowercase)

        assert encoded_sentence == encoded_text_from_decode
        assert encoded_pair == encoded_pair_from_decode



      @slow
      def   lowerCamelCase_       (						self)     ->      Any:





        '''simple docstring'''





        a__:					List[Any]      							=     [self.tokenizer_class]
        if self.test_rust_tokenizer:
          tokenizer_classes.append(self.rust_tokenizer_class)

        for tokenizer_class in tokenizer_classes:
          a__:					int      							=     tokenizer_class.from_pretrained('microsoft/deberta-base')

          a__:					List[str]      							=     [
              'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
              'ALBERT incorporates two parameter reduction techniques',
              'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
              ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
              ' vocabulary embedding.',
          ]

          a__:					Dict      							=     tokenizer(lowercase   ,      padding=lowercase)
          a__:					Union[str, Any]      							=     [tokenizer.decode(lowercase   ,      skip_special_tokens=lowercase) for seq in encoding['input_ids']]

          # fmt: off
          a__:					Any      							=     {
              'input_ids': [
                  [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                  [1, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                  [1, 1_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 4, 2]
              ],
              'token_type_ids': [
                  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
              ],
              'attention_mask': [
                  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
              ]
          }
          # fmt: on

          a__:					str      							=     [
              'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
              'ALBERT incorporates two parameter reduction techniques',
              'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
              ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
              ' vocabulary embedding.',
          ]

          self.assertDictEqual(encoding.data   ,      lowercase)

          for expected, decoded in zip(lowercase   ,      lowercase):
            self.assertEqual(lowercase   ,      lowercase)


