

"""simple docstring"""



import random
import sys

from . import cryptomath_module as cryptomath

SCREAMING_SNAKE_CASE_       :						Dict					=    (
    r' !"#$%&\'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`'
    r'abcdefghijklmnopqrstuvwxyz{|}~'
)





def 			_snake_case  (     UpperCAmelCase_	:       int       , UpperCAmelCase_	:       int       , UpperCAmelCase_	:       str      ):
     if mode == "encrypt":
          if key_a == 1:
               sys.exit(
                   """The affine cipher becomes weak when key """
                   """A is set to 1. Choose different key"""      )
          if key_b == 0:
               sys.exit(
                   """The affine cipher becomes weak when key """
                   """B is set to 0. Choose different key"""      )
     if key_a < 0 or key_b < 0 or key_b > len(UpperCAmelCase_      ) - 1:
          sys.exit(
              """Key A must be greater than 0 and key B must """
              F"""be between 0 and {len(UpperCAmelCase_      ) - 1}."""      )
     if cryptomath.gcd(UpperCAmelCase_       , len(UpperCAmelCase_      )      ) != 1:
          sys.exit(
              F"""Key A {key_a} and the symbol set size {len(UpperCAmelCase_      )} """
              """are not relatively prime. Choose a different key."""      )





def 			_snake_case  (     UpperCAmelCase_	:       int       , UpperCAmelCase_	:       str      ):
     A__      ,					A__				      =	divmod(UpperCAmelCase_       , len(UpperCAmelCase_      )      )
     check_keys(UpperCAmelCase_       , UpperCAmelCase_       , """encrypt"""      )
     A__				      =	""""""
     for symbol in message:
          if symbol in SYMBOLS:
               A__				      =	SYMBOLS.find(UpperCAmelCase_      )
               cipher_text += SYMBOLS[(sym_index * key_a + key_b) % len(UpperCAmelCase_      )]
          else:
               cipher_text += symbol
     return cipher_text





def 			_snake_case  (     UpperCAmelCase_	:       int       , UpperCAmelCase_	:       str      ):
     A__      ,					A__				      =	divmod(UpperCAmelCase_       , len(UpperCAmelCase_      )      )
     check_keys(UpperCAmelCase_       , UpperCAmelCase_       , """decrypt"""      )
     A__				      =	""""""
     A__				      =	cryptomath.find_mod_inverse(UpperCAmelCase_       , len(UpperCAmelCase_      )      )
     for symbol in message:
          if symbol in SYMBOLS:
               A__				      =	SYMBOLS.find(UpperCAmelCase_      )
               plain_text += SYMBOLS[
                   (sym_index - key_b) * mod_inverse_of_key_a % len(UpperCAmelCase_      )
               ]
          else:
               plain_text += symbol
     return plain_text





def 			_snake_case  (     ):
     while True:
          A__				      =	random.randint(2       , len(UpperCAmelCase_      )      )
          A__				      =	random.randint(2       , len(UpperCAmelCase_      )      )
          if cryptomath.gcd(UpperCAmelCase_       , len(UpperCAmelCase_      )      ) == 1 and key_b % len(UpperCAmelCase_      ) != 0:
               return key_b * len(UpperCAmelCase_      ) + key_b





def 			_snake_case  (     ):
     A__				      =	input("""Enter message: """      ).strip()
     A__				      =	int(input("""Enter key [2000 - 9000]: """      ).strip()      )
     A__				      =	input("""Encrypt/Decrypt [E/D]: """      ).strip().lower()

     if mode.startswith("""e"""      ):
          A__				      =	"""encrypt"""
          A__				      =	encrypt_message(UpperCAmelCase_       , UpperCAmelCase_      )
     elif mode.startswith("""d"""      ):
          A__				      =	"""decrypt"""
          A__				      =	decrypt_message(UpperCAmelCase_       , UpperCAmelCase_      )
     print(F"""\n{mode.title()}ed text: \n{translated}"""      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 # main()









"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_       :						Union[str, Any]					=    {'configuration_gptj': ['GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTJConfig', 'GPTJOnnxConfig']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_       :						Optional[int]					=    [
     'GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST',
     'GPTJForCausalLM',
     'GPTJForQuestionAnswering',
     'GPTJForSequenceClassification',
     'GPTJModel',
     'GPTJPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_       :						List[str]					=    [
     'TFGPTJForCausalLM',
     'TFGPTJForQuestionAnswering',
     'TFGPTJForSequenceClassification',
     'TFGPTJModel',
     'TFGPTJPreTrainedModel',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_       :						Tuple					=    [
     'FlaxGPTJForCausalLM',
     'FlaxGPTJModel',
     'FlaxGPTJPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gptj import (
      GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTJForCausalLM,
      GPTJForQuestionAnswering,
      GPTJForSequenceClassification,
      GPTJModel,
      GPTJPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_gptj import (
      TFGPTJForCausalLM,
      TFGPTJForQuestionAnswering,
      TFGPTJForSequenceClassification,
      TFGPTJModel,
      TFGPTJPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
 import sys

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






