






"""simple docstring"""





UpperCamelCase					=     [chr(i) for i in range(32, 1_26)]
UpperCamelCase					=     list(range(len(alphabets)))
UpperCamelCase					=     list(range(len(alphabets)))
UpperCamelCase					=     list(range(len(alphabets)))
UpperCamelCase					=     list(reversed(range(len(alphabets))))
UpperCamelCase					=     []
UpperCamelCase					=     UpperCamelCase					=     UpperCamelCase					=     0






def 							A(    ):
       """simple docstring"""





       global gear_one_pos
       global gear_two_pos
       global gear_three_pos
       lowercase__:							int         =     gear_one[0]
       gear_one.append(snake_case_						)
       del gear_one[0]
       gear_one_pos += 1
       if gear_one_pos % int(len(snake_case_						)						) == 0:
              lowercase__:							List[str]         =     gear_two[0]
              gear_two.append(snake_case_						)
              del gear_two[0]
              gear_two_pos += 1
              if gear_two_pos % int(len(snake_case_						)						) == 0:
                     lowercase__:							Dict         =     gear_three[0]
                     gear_three.append(snake_case_						)
                     del gear_three[0]
                     gear_three_pos += 1






def 							A(    snake_case_						):
       """simple docstring"""





       lowercase__:							List[Any]         =     alphabets.index(snake_case_						)
       lowercase__:							Dict         =     gear_one[target]
       lowercase__:							Tuple         =     gear_two[target]
       lowercase__:							str         =     gear_three[target]
       lowercase__:							Any         =     reflector[target]
       lowercase__:							Any         =     gear_three.index(snake_case_						)
       lowercase__:							Tuple         =     gear_two.index(snake_case_						)
       lowercase__:							Union[str, Any]         =     gear_one.index(snake_case_						)
       code.append(alphabets[target]						)
       rotator()


if __name__ == "__main__":
  UpperCamelCase					=     list(input("""Type your message:\n"""))
  while True:
    try:
      UpperCamelCase					=     int(input("""Please set token:(must be only digits)\n"""))
      break
    except Exception as error:
      print(error)
  for _ in range(token):
    rotator()
  for j in decode:
    engine(j)
  print("""\n""" + """""".join(code))
  print(
      F"\nYour Token is {token} please write it down.\nIf you want to decode "
      """this message again you should input same digits as token!"""
  )













from typing import TYPE_CHECKING

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


UpperCAmelCase__    :      int       					={
    '''configuration_flaubert''': ['''FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FlaubertConfig''', '''FlaubertOnnxConfig'''],
    '''tokenization_flaubert''': ['''FlaubertTokenizer'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__    :      int       					=[
							    '''FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''FlaubertForMultipleChoice''',
							    '''FlaubertForQuestionAnswering''',
							    '''FlaubertForQuestionAnsweringSimple''',
							    '''FlaubertForSequenceClassification''',
							    '''FlaubertForTokenClassification''',
							    '''FlaubertModel''',
							    '''FlaubertWithLMHeadModel''',
							    '''FlaubertPreTrainedModel''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__    :      List[str]       					=[
							    '''TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFFlaubertForMultipleChoice''',
							    '''TFFlaubertForQuestionAnsweringSimple''',
							    '''TFFlaubertForSequenceClassification''',
							    '''TFFlaubertForTokenClassification''',
							    '''TFFlaubertModel''',
							    '''TFFlaubertPreTrainedModel''',
							    '''TFFlaubertWithLMHeadModel''',
							]


if TYPE_CHECKING:
							from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
							from .tokenization_flaubert import FlaubertTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flaubert import (
														    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FlaubertForMultipleChoice,
														    FlaubertForQuestionAnswering,
														    FlaubertForQuestionAnsweringSimple,
														    FlaubertForSequenceClassification,
														    FlaubertForTokenClassification,
														    FlaubertModel,
														    FlaubertPreTrainedModel,
														    FlaubertWithLMHeadModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_flaubert import (
														    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFFlaubertForMultipleChoice,
														    TFFlaubertForQuestionAnsweringSimple,
														    TFFlaubertForSequenceClassification,
														    TFFlaubertForTokenClassification,
														    TFFlaubertModel,
														    TFFlaubertPreTrainedModel,
														    TFFlaubertWithLMHeadModel,
														)

else:
							import sys

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





