




"""simple docstring"""




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       a					(       _A      ):

     '''simple docstring'''





     lowerCAmelCase    :	Optional[torch.FloatTensor]       		=  None
     lowerCAmelCase    :	torch.FloatTensor       		=  None
     lowerCAmelCase    :	Optional[Tuple[torch.FloatTensor]]       		=  None
     lowerCAmelCase    :	Optional[Tuple[torch.FloatTensor]]       		=  None



class       a					(       _A      ):

     '''simple docstring'''





     def __init__(     self       :      Dict ,   __snake_case       :      List[str]=1 ,   __snake_case       :      List[str]=0 ,   __snake_case       :      Any=2 ,   __snake_case       :      int=5_12 ,   __snake_case       :      Tuple="cls" ,   __snake_case       :      List[Any]=False ,   __snake_case       :      Dict=True ,   **__snake_case       :      Optional[int] ,   ):
         super().__init__(pad_token_id=__snake_case ,   bos_token_id=__snake_case ,   eos_token_id=__snake_case ,   **__snake_case    )
         UpperCAmelCase_										=					project_dim
         UpperCAmelCase_										=					pooler_fn
         UpperCAmelCase_										=					learn_encoder
         UpperCAmelCase_										=					use_attention_mask



class       a					(       _A      ):

     '''simple docstring'''





     lowerCAmelCase    :	Optional[Any]       		=  [R'pooler', R'logit_scale']
     lowerCAmelCase    :	List[Any]       		=  [R'position_ids', R'predictions.decoder.bias']
     lowerCAmelCase    :	Optional[Any]       		=  'roberta'
     lowerCAmelCase    :	Dict       		=  RobertaSeriesConfig
     def __init__(     self       :      Optional[int] ,   __snake_case       :      Dict    ):
         super().__init__(__snake_case    )
         UpperCAmelCase_										=					XLMRobertaModel(__snake_case    )
         UpperCAmelCase_										=					nn.Linear(config.hidden_size ,   config.project_dim    )
         UpperCAmelCase_										=					getattr(__snake_case ,   '''has_pre_transformation''' ,   __snake_case    )
         if self.has_pre_transformation:
             UpperCAmelCase_										=					nn.Linear(config.hidden_size ,   config.project_dim    )
             UpperCAmelCase_										=					nn.LayerNorm(config.hidden_size ,   eps=config.layer_norm_eps    )
         self.post_init()




     def   lowerCamelCase_       (     self       :      List[str] ,   __snake_case       :      Optional[torch.Tensor] = None ,   __snake_case       :      Optional[torch.Tensor] = None ,   __snake_case       :      Optional[torch.Tensor] = None ,   __snake_case       :      Optional[torch.Tensor] = None ,   __snake_case       :      Optional[torch.Tensor] = None ,   __snake_case       :      Optional[torch.Tensor] = None ,   __snake_case       :      Optional[torch.Tensor] = None ,   __snake_case       :      Optional[torch.Tensor] = None ,   __snake_case       :      Optional[bool] = None ,   __snake_case       :      Optional[bool] = None ,   __snake_case       :      Optional[bool] = None ,   ):
         UpperCAmelCase_										=					return_dict if return_dict is not None else self.config.use_return_dict

         UpperCAmelCase_										=					self.base_model(
             input_ids=__snake_case ,   attention_mask=__snake_case ,   token_type_ids=__snake_case ,   position_ids=__snake_case ,   head_mask=__snake_case ,   inputs_embeds=__snake_case ,   encoder_hidden_states=__snake_case ,   encoder_attention_mask=__snake_case ,   output_attentions=__snake_case ,   output_hidden_states=True if self.has_pre_transformation else output_hidden_states ,   return_dict=__snake_case ,   )

         if self.has_pre_transformation:
             UpperCAmelCase_										=					outputs['''hidden_states'''][-2]
             UpperCAmelCase_										=					self.pre_LN(__snake_case    )
             UpperCAmelCase_										=					self.transformation_pre(__snake_case    )

             return TransformationModelOutput(
                 projection_state=__snake_case ,   last_hidden_state=outputs.last_hidden_state ,   hidden_states=outputs.hidden_states ,   attentions=outputs.attentions ,   )
         else:
             UpperCAmelCase_										=					self.transformation(outputs.last_hidden_state    )
             return TransformationModelOutput(
                 projection_state=__snake_case ,   last_hidden_state=outputs.last_hidden_state ,   hidden_states=outputs.hidden_states ,   attentions=outputs.attentions ,   )











import baseaa




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						str				)  ->							bytes:
    return baseaa.baaencode(string.encode('''utf-8'''				)				)




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						bytes				)  ->							str:
    return baseaa.baadecode(__UpperCamelCase				).decode('''utf-8'''				)


if __name__ == "__main__":
 _lowerCamelCase      			=						'Hello World!'
 _lowerCamelCase      			=						baseaa_encode(test)
 print(encoded)

 _lowerCamelCase      			=						baseaa_decode(encoded)
 print(decoded)





