





from string import ascii_uppercase

_UpperCAmelCase				:       Tuple	   =				{char: i for i, char in enumerate(ascii_uppercase)}
_UpperCAmelCase				:       Optional[Any]	   =				dict(enumerate(ascii_uppercase))


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     str:
    lowerCamelCase__    :      Optional[Any]					 =		len(_UpperCAmelCase )
    lowerCamelCase__    :      List[Any]					 =		0
    while True:
        if x == i:
            lowerCamelCase__    :      Any					 =		0
        if len(_UpperCAmelCase ) == len(_UpperCAmelCase ):
            break
        key += key[i]
        i += 1
    return key


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     str:
    lowerCamelCase__    :      Union[str, Any]					 =		''
    lowerCamelCase__    :      Union[str, Any]					 =		0
    for letter in message:
        if letter == " ":
            cipher_text += " "
        else:
            lowerCamelCase__    :      Any					 =		(dicta[letter] - dicta[key_new[i]]) % 26
            i += 1
            cipher_text += dicta[x]
    return cipher_text


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     str:
    lowerCamelCase__    :      Dict					 =		''
    lowerCamelCase__    :      Tuple					 =		0
    for letter in cipher_text:
        if letter == " ":
            or_txt += " "
        else:
            lowerCamelCase__    :      Tuple					 =		(dicta[letter] + dicta[key_new[i]] + 26) % 26
            i += 1
            or_txt += dicta[x]
    return or_txt


def        SCREAMING_SNAKE_CASE      (       )			->     None:
    lowerCamelCase__    :      Tuple					 =		'THE GERMAN ATTACK'
    lowerCamelCase__    :      Optional[Any]					 =		'SECRET'
    lowerCamelCase__    :      int					 =		generate_key(_UpperCAmelCase						,	_UpperCAmelCase )
    lowerCamelCase__    :      int					 =		cipher_text(_UpperCAmelCase						,	_UpperCAmelCase )
    print(F"""Encrypted Text = {s}""" )
    print(F"""Original Text = {original_text(_UpperCAmelCase						,	_UpperCAmelCase )}""" )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()












import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel

class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		"""M-CLIP"""
 def __init__(      self  :      Optional[Any]			,     UpperCAmelCase  :      Union[str, Any]=1024			,     UpperCAmelCase  :      Tuple=768			,     **UpperCAmelCase  :      Optional[int]	)			->	Dict:
     lowerCamelCase__    :      Optional[int]					 =		transformerDimSize
     lowerCamelCase__    :      Optional[Any]					 =		imageDimSize
     super().__init__(**UpperCAmelCase	)


class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		MCLIPConfig
 def __init__(      self  :      List[Any]			,     UpperCAmelCase  :      Dict			,     *UpperCAmelCase  :      Any			,     **UpperCAmelCase  :      Dict	)			->	Dict:
     super().__init__(UpperCAmelCase			,     *UpperCAmelCase			,     **UpperCAmelCase	)
     lowerCamelCase__    :      Tuple					 =		XLMRobertaModel(UpperCAmelCase	)
     lowerCamelCase__    :      Union[str, Any]					 =		torch.nn.Linear(
         in_features=config.transformerDimensions			,     out_features=config.numDims	)







 def      A_	(      self  :      Optional[int]			,     UpperCAmelCase  :      Optional[Any]			,     UpperCAmelCase  :      Dict	)			->	Tuple:
     lowerCamelCase__    :      Any					 =		self.transformer(input_ids=UpperCAmelCase			,     attention_mask=UpperCAmelCase	)[0]
     lowerCamelCase__    :      int					 =		(embs * attention_mask.unsqueeze(2	)).sum(dim=1	) / attention_mask.sum(dim=1	)[:, None]
     return self.LinearTransformation(UpperCAmelCase	), embs





