


from typing import TYPE_CHECKING

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


__snake_case   							=						{
    '''configuration_tapas''': ['''TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TapasConfig'''],
    '''tokenization_tapas''': ['''TapasTokenizer'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case   							=						[
        '''TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TapasForMaskedLM''',
        '''TapasForQuestionAnswering''',
        '''TapasForSequenceClassification''',
        '''TapasModel''',
        '''TapasPreTrainedModel''',
        '''load_tf_weights_in_tapas''',
    ]
try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case   							=						[
        '''TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TFTapasForMaskedLM''',
        '''TFTapasForQuestionAnswering''',
        '''TFTapasForSequenceClassification''',
        '''TFTapasModel''',
        '''TFTapasPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
    from .tokenization_tapas import TapasTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tapas import (
            TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
            TapasForMaskedLM,
            TapasForQuestionAnswering,
            TapasForSequenceClassification,
            TapasModel,
            TapasPreTrainedModel,
            load_tf_weights_in_tapas,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_tapas import (
            TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFTapasForMaskedLM,
            TFTapasForQuestionAnswering,
            TFTapasForSequenceClassification,
            TFTapasModel,
            TFTapasPreTrainedModel,
        )


else:
    import sys

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






import string






def 		_A		(		_lowercase )					-> None:


     """simple docstring"""


     for key in range(len(string.ascii_uppercase ) ):
          __UpperCamelCase =			''
          for symbol in message:
               if symbol in string.ascii_uppercase:
                    __UpperCamelCase =			string.ascii_uppercase.find(_lowercase )
                    __UpperCamelCase =			num - key
                    if num < 0:
                         __UpperCamelCase =			num + len(string.ascii_uppercase )
                    __UpperCamelCase =			translated + string.ascii_uppercase[num]
               else:
                    __UpperCamelCase =			translated + symbol
          print(f'''Decryption using Key #{key}: {translated}''' )






def 		_A		(		)					-> None:


     """simple docstring"""


     __UpperCamelCase =			input('Encrypted message: ' )
     __UpperCamelCase =			message.upper()
     decrypt(_lowercase )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()


