




from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE			    ={
    """configuration_transfo_xl""": ["""TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TransfoXLConfig"""],
    """tokenization_transfo_xl""": ["""TransfoXLCorpus""", """TransfoXLTokenizer"""],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE			    =[
			    """TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """AdaptiveEmbedding""",
			    """TransfoXLForSequenceClassification""",
			    """TransfoXLLMHeadModel""",
			    """TransfoXLModel""",
			    """TransfoXLPreTrainedModel""",
			    """load_tf_weights_in_transfo_xl""",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE			    =[
			    """TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """TFAdaptiveEmbedding""",
			    """TFTransfoXLForSequenceClassification""",
			    """TFTransfoXLLMHeadModel""",
			    """TFTransfoXLMainLayer""",
			    """TFTransfoXLModel""",
			    """TFTransfoXLPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
			from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_transfo_xl import (
						    TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    AdaptiveEmbedding,
						    TransfoXLForSequenceClassification,
						    TransfoXLLMHeadModel,
						    TransfoXLModel,
						    TransfoXLPreTrainedModel,
						    load_tf_weights_in_transfo_xl,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_transfo_xl import (
						    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFAdaptiveEmbedding,
						    TFTransfoXLForSequenceClassification,
						    TFTransfoXLLMHeadModel,
						    TFTransfoXLMainLayer,
						    TFTransfoXLModel,
						    TFTransfoXLPreTrainedModel,
						)

else:
			import sys

			__SCREAMING_SNAKE_CASE			    =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)









def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	str:
    lowerCamelCase__       : Dict   					=						''
    for ch in key:
        if ch == " " or ch not in key_no_dups and ch.isalpha():
            key_no_dups += ch
    return key_no_dups


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	dict[str, str]:
    lowerCamelCase__       : List[str]   					=						[chr(i + 65					) for i in range(26					)]
    # Remove duplicate characters from key
    lowerCamelCase__       : Any   					=						remove_duplicates(key.upper()					)
    lowerCamelCase__       : List[Any]   					=						len(_UpperCAmelCase					)
    # First fill cipher with key characters
    lowerCamelCase__       : Optional[int]   					=						{alphabet[i]: char for i, char in enumerate(_UpperCAmelCase					)}
    # Then map remaining characters in alphabet to
    # the alphabet from the beginning
    for i in range(len(_UpperCAmelCase					)		,      26					):
        lowerCamelCase__       : Any   					=						alphabet[i - offset]
        # Ensure we are not mapping letters to letters previously mapped
        while char in key:
            offset -= 1
            lowerCamelCase__       : Union[str, Any]   					=						alphabet[i - offset]
        lowerCamelCase__       : int   					=						char
    return cipher_alphabet


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	str:
    return "".join(cipher_map.get(_UpperCAmelCase		,      _UpperCAmelCase					) for ch in message.upper()					)


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	str:
    lowerCamelCase__       : List[str]   					=						{v: k for k, v in cipher_map.items()}
    return "".join(rev_cipher_map.get(_UpperCAmelCase		,      _UpperCAmelCase					) for ch in message.upper()					)


def 			SCREAMING_SNAKE_CASE (			)     ->	None:
    lowerCamelCase__       : str   					=						input('Enter message to encode or decode: '					).strip()
    lowerCamelCase__       : Union[str, Any]   					=						input('Enter keyword: '					).strip()
    lowerCamelCase__       : Dict   					=						input('Encipher or decipher? E/D:'					).strip()[0].lower()
    try:
        lowerCamelCase__       : Dict   					=						{'e': encipher, 'd': decipher}[option]
    except KeyError:
        raise KeyError('invalid input option'					)
    lowerCamelCase__       : int   					=						create_cipher_map(_UpperCAmelCase					)
    print(func(_UpperCAmelCase		,      _UpperCAmelCase					)					)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()





