

_a	 =     [chr(i) for i in range(3_2, 1_2_6)]
_a	 =     list(range(len(alphabets)))
_a	 =     list(range(len(alphabets)))
_a	 =     list(range(len(alphabets)))
_a	 =     list(reversed(range(len(alphabets))))
_a	 =     []
_a	 =     _a	 =     _a	 =     0







def _a				( )					->					Optional[int]:

 """simple docstring"""






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







def _a				( SCREAMING_SNAKE_CASE	:    Union[str, Any]	)					->					Optional[int]:

 """simple docstring"""






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


if __name__ == "__main__":
  _a	 =     list(input('''Type your message:\n'''))
  while True:
    try:
      _a	 =     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!'''
  )








# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


_a	 =     {
    '''configuration_tvlt''': ['''TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TvltConfig'''],
    '''feature_extraction_tvlt''': ['''TvltFeatureExtractor'''],
    '''processing_tvlt''': ['''TvltProcessor'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a	 =     [
      '''TVLT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TvltModel''',
      '''TvltForPreTraining''',
      '''TvltForAudioVisualClassification''',
      '''TvltPreTrainedModel''',
  ]

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a	 =     ['''TvltImageProcessor''']


if TYPE_CHECKING:
  from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
  from .processing_tvlt import TvltProcessor
  from .feature_extraction_tvlt import TvltFeatureExtractor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tvlt import (
        TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TvltForAudioVisualClassification,
        TvltForPreTraining,
        TvltModel,
        TvltPreTrainedModel,
    )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_tvlt import TvltImageProcessor


else:
  import sys

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





