'''simple docstring'''

import random







def      UpperCamelCase_						(					snake_case_       :       Dict      )					->     Optional[int]:






 '''simple docstring'''

 __lowerCAmelCase											=						num - 1
 __lowerCAmelCase											=						0

 while s % 2 == 0:
  __lowerCAmelCase											=						s // 2
  t += 1

 for _ in range(5      ):
  __lowerCAmelCase											=						random.randrange(2						,		num - 1      )
  __lowerCAmelCase											=						pow(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase      )
  if v != 1:
   __lowerCAmelCase											=						0
   while v != (num - 1):
    if i == t - 1:
     return False
    else:
     __lowerCAmelCase											=						i + 1
     __lowerCAmelCase											=						(v**2) % num
 return True







def      UpperCamelCase_						(					snake_case_       :       List[str]      )					->     Optional[int]:






 '''simple docstring'''

 if num < 2:
  return False

 __lowerCAmelCase											=						[
     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,
     1_01,
     1_03,
     1_07,
     1_09,
     1_13,
     1_27,
     1_31,
     1_37,
     1_39,
     1_49,
     1_51,
     1_57,
     1_63,
     1_67,
     1_73,
     1_79,
     1_81,
     1_91,
     1_93,
     1_97,
     1_99,
     2_11,
     2_23,
     2_27,
     2_29,
     2_33,
     2_39,
     2_41,
     2_51,
     2_57,
     2_63,
     2_69,
     2_71,
     2_77,
     2_81,
     2_83,
     2_93,
     3_07,
     3_11,
     3_13,
     3_17,
     3_31,
     3_37,
     3_47,
     3_49,
     3_53,
     3_59,
     3_67,
     3_73,
     3_79,
     3_83,
     3_89,
     3_97,
     4_01,
     4_09,
     4_19,
     4_21,
     4_31,
     4_33,
     4_39,
     4_43,
     4_49,
     4_57,
     4_61,
     4_63,
     4_67,
     4_79,
     4_87,
     4_91,
     4_99,
     5_03,
     5_09,
     5_21,
     5_23,
     5_41,
     5_47,
     5_57,
     5_63,
     5_69,
     5_71,
     5_77,
     5_87,
     5_93,
     5_99,
     6_01,
     6_07,
     6_13,
     6_17,
     6_19,
     6_31,
     6_41,
     6_43,
     6_47,
     6_53,
     6_59,
     6_61,
     6_73,
     6_77,
     6_83,
     6_91,
     7_01,
     7_09,
     7_19,
     7_27,
     7_33,
     7_39,
     7_43,
     7_51,
     7_57,
     7_61,
     7_69,
     7_73,
     7_87,
     7_97,
     8_09,
     8_11,
     8_21,
     8_23,
     8_27,
     8_29,
     8_39,
     8_53,
     8_57,
     8_59,
     8_63,
     8_77,
     8_81,
     8_83,
     8_87,
     9_07,
     9_11,
     9_19,
     9_29,
     9_37,
     9_41,
     9_47,
     9_53,
     9_67,
     9_71,
     9_77,
     9_83,
     9_91,
     9_97,
 ]

 if num in low_primes:
  return True

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

 return rabin_miller(_UpperCAmelCase      )







def      UpperCamelCase_						(					snake_case_       :       Union[str, Any] = 10_24      )					->     Optional[Any]:






 '''simple docstring'''

 while True:
  __lowerCAmelCase											=						random.randrange(2 ** (keysize - 1)						,		2 ** (keysize)      )
  if is_prime_low_num(_UpperCAmelCase      ):
   return num


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













"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_     :						List[str]						=					{'configuration_xlnet': ['XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLNetConfig']}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						str						=					['XLNetTokenizer']

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						List[str]						=					['XLNetTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						int						=					[
       'XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
       'XLNetForMultipleChoice',
       'XLNetForQuestionAnswering',
       'XLNetForQuestionAnsweringSimple',
       'XLNetForSequenceClassification',
       'XLNetForTokenClassification',
       'XLNetLMHeadModel',
       'XLNetModel',
       'XLNetPreTrainedModel',
       'load_tf_weights_in_xlnet',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						Union[str, Any]						=					[
       'TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFXLNetForMultipleChoice',
       'TFXLNetForQuestionAnsweringSimple',
       'TFXLNetForSequenceClassification',
       'TFXLNetForTokenClassification',
       'TFXLNetLMHeadModel',
       'TFXLNetMainLayer',
       'TFXLNetModel',
       'TFXLNetPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xlnet import XLNetTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xlnet_fast import XLNetTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xlnet import (
          XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          XLNetForMultipleChoice,
          XLNetForQuestionAnswering,
          XLNetForQuestionAnsweringSimple,
          XLNetForSequenceClassification,
          XLNetForTokenClassification,
          XLNetLMHeadModel,
          XLNetModel,
          XLNetPreTrainedModel,
          load_tf_weights_in_xlnet,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_xlnet import (
          TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXLNetForMultipleChoice,
          TFXLNetForQuestionAnsweringSimple,
          TFXLNetForSequenceClassification,
          TFXLNetForTokenClassification,
          TFXLNetLMHeadModel,
          TFXLNetMainLayer,
          TFXLNetModel,
          TFXLNetPreTrainedModel,
      )

else:
   import sys

   lowerCamelCase_     :						List[Any]						=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)