





from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput

@dataclass
class   lowerCAmelCase				(   __UpperCamelCase     ):
 UpperCAmelCase__						=						None
 UpperCAmelCase__						=						None
 UpperCAmelCase__						=						None
 UpperCAmelCase__						=						None


class   lowerCAmelCase				(   __UpperCamelCase     ):
 def __init__( self    : Dict     ,	UpperCAmelCase    : Any=1     ,	UpperCAmelCase    : Any=0     ,	UpperCAmelCase    : Optional[Any]=2     ,	UpperCAmelCase    : str=512     ,	UpperCAmelCase    : int="cls"     ,	UpperCAmelCase    : Union[str, Any]=False     ,	UpperCAmelCase    : Optional[int]=True     ,	**UpperCAmelCase    : Tuple     ,	)     ->      List[Any]:
     super().__init__(pad_token_id=UpperCAmelCase     ,	bos_token_id=UpperCAmelCase     ,	eos_token_id=UpperCAmelCase     ,	**UpperCAmelCase      )
     lowerCamelCase__       : Union[str, Any]   					=						project_dim
     lowerCamelCase__       : Union[str, Any]   					=						pooler_fn
     lowerCamelCase__       : Optional[int]   					=						learn_encoder
     lowerCamelCase__       : Any   					=						use_attention_mask


class   lowerCAmelCase				(   __UpperCamelCase     ):
 UpperCAmelCase__						=						[r"""pooler""", r"""logit_scale"""]
 UpperCAmelCase__						=						[r"""position_ids""", r"""predictions.decoder.bias"""]
 UpperCAmelCase__						=						"""roberta"""
 UpperCAmelCase__						=						RobertaSeriesConfig
 def __init__( self    : List[Any]     ,	UpperCAmelCase    : Optional[Any]      )     ->      List[str]:
     super().__init__(UpperCAmelCase      )
     lowerCamelCase__       : str   					=						XLMRobertaModel(UpperCAmelCase      )
     lowerCamelCase__       : Union[str, Any]   					=						nn.Linear(config.hidden_size     ,	config.project_dim      )
     lowerCamelCase__       : Optional[int]   					=						getattr(UpperCAmelCase     ,	'has_pre_transformation'     ,	UpperCAmelCase      )
     if self.has_pre_transformation:
         lowerCamelCase__       : List[Any]   					=						nn.Linear(config.hidden_size     ,	config.project_dim      )
         lowerCamelCase__       : Tuple   					=						nn.LayerNorm(config.hidden_size     ,	eps=config.layer_norm_eps      )
     self.post_init()







 def 	A_      ( self    : Union[str, Any]     ,	UpperCAmelCase    : Optional[torch.Tensor] = None     ,	UpperCAmelCase    : Optional[torch.Tensor] = None     ,	UpperCAmelCase    : Optional[torch.Tensor] = None     ,	UpperCAmelCase    : Optional[torch.Tensor] = None     ,	UpperCAmelCase    : Optional[torch.Tensor] = None     ,	UpperCAmelCase    : Optional[torch.Tensor] = None     ,	UpperCAmelCase    : Optional[torch.Tensor] = None     ,	UpperCAmelCase    : Optional[torch.Tensor] = None     ,	UpperCAmelCase    : Optional[bool] = None     ,	UpperCAmelCase    : Optional[bool] = None     ,	UpperCAmelCase    : Optional[bool] = None     ,	)     ->      Optional[int]:
     lowerCamelCase__       : str   					=						return_dict if return_dict is not None else self.config.use_return_dict

     lowerCamelCase__       : Tuple   					=						self.base_model(
         input_ids=UpperCAmelCase     ,	attention_mask=UpperCAmelCase     ,	token_type_ids=UpperCAmelCase     ,	position_ids=UpperCAmelCase     ,	head_mask=UpperCAmelCase     ,	inputs_embeds=UpperCAmelCase     ,	encoder_hidden_states=UpperCAmelCase     ,	encoder_attention_mask=UpperCAmelCase     ,	output_attentions=UpperCAmelCase     ,	output_hidden_states=True if self.has_pre_transformation else output_hidden_states     ,	return_dict=UpperCAmelCase     ,	)

     if self.has_pre_transformation:
         lowerCamelCase__       : int   					=						outputs['hidden_states'][-2]
         lowerCamelCase__       : str   					=						self.pre_LN(UpperCAmelCase      )
         lowerCamelCase__       : int   					=						self.transformation_pre(UpperCAmelCase      )

         return TransformationModelOutput(
             projection_state=UpperCAmelCase     ,	last_hidden_state=outputs.last_hidden_state     ,	hidden_states=outputs.hidden_states     ,	attentions=outputs.attentions     ,	)
     else:
         lowerCamelCase__       : Dict   					=						self.transformation(outputs.last_hidden_state      )
         return TransformationModelOutput(
             projection_state=UpperCAmelCase     ,	last_hidden_state=outputs.last_hidden_state     ,	hidden_states=outputs.hidden_states     ,	attentions=outputs.attentions     ,	)












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()





