




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCAmelCase_						=			{
    'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
    'tokenization_xlm': ['XLMTokenizer'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_						=			[
     'XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
     'XLMForMultipleChoice',
     'XLMForQuestionAnswering',
     'XLMForQuestionAnsweringSimple',
     'XLMForSequenceClassification',
     'XLMForTokenClassification',
     'XLMModel',
     'XLMPreTrainedModel',
     'XLMWithLMHeadModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_						=			[
     'TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFXLMForMultipleChoice',
     'TFXLMForQuestionAnsweringSimple',
     'TFXLMForSequenceClassification',
     'TFXLMForTokenClassification',
     'TFXLMMainLayer',
     'TFXLMModel',
     'TFXLMPreTrainedModel',
     'TFXLMWithLMHeadModel',
 ]


if TYPE_CHECKING:
 from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
 from .tokenization_xlm import XLMTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_xlm import (
      XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      XLMForMultipleChoice,
      XLMForQuestionAnswering,
      XLMForQuestionAnsweringSimple,
      XLMForSequenceClassification,
      XLMForTokenClassification,
      XLMModel,
      XLMPreTrainedModel,
      XLMWithLMHeadModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_xlm import (
      TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFXLMForMultipleChoice,
      TFXLMForQuestionAnsweringSimple,
      TFXLMForSequenceClassification,
      TFXLMForTokenClassification,
      TFXLMMainLayer,
      TFXLMModel,
      TFXLMPreTrainedModel,
      TFXLMWithLMHeadModel,
  )

else:
 import sys

 lowerCAmelCase_						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)









from string import ascii_uppercase

lowerCAmelCase_						=			{char: i for i, char in enumerate(ascii_uppercase)}
lowerCAmelCase_						=			dict(enumerate(ascii_uppercase))






def  snake_case(    __magic_name__			,		__magic_name__   )			->							str:





     '''simple docstring'''


     lowercase :       Optional[Any]       =			len(__magic_name__   )
     lowercase :       Any       =			0
     while True:
          if x == i:
               lowercase :       Any       =			0
          if len(__magic_name__   ) == len(__magic_name__   ):
               break
          key += key[i]
          i += 1
     return key






def  snake_case(    __magic_name__			,		__magic_name__   )			->							str:





     '''simple docstring'''


     lowercase :       str       =			''''''
     lowercase :       Dict       =			0
     for letter in message:
          if letter == " ":
               cipher_text += " "
          else:
               lowercase :       Dict       =			(dicta[letter] - dicta[key_new[i]]) % 26
               i += 1
               cipher_text += dicta[x]
     return cipher_text






def  snake_case(    __magic_name__			,		__magic_name__   )			->							str:





     '''simple docstring'''


     lowercase :       Any       =			''''''
     lowercase :       str       =			0
     for letter in cipher_text:
          if letter == " ":
               or_txt += " "
          else:
               lowercase :       Any       =			(dicta[letter] + dicta[key_new[i]] + 26) % 26
               i += 1
               or_txt += dicta[x]
     return or_txt






def  snake_case(    )			->							None:





     '''simple docstring'''


     lowercase :       Dict       =			'''THE GERMAN ATTACK'''
     lowercase :       Dict       =			'''SECRET'''
     lowercase :       Union[str, Any]       =			generate_key(__magic_name__			,		__magic_name__   )
     lowercase :       List[str]       =			cipher_text(__magic_name__			,		__magic_name__   )
     print(F"""Encrypted Text = {s}"""   )
     print(F"""Original Text = {original_text(__magic_name__			,		__magic_name__   )}"""   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()