'''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       UpperCAmelCase  (						lowerCamelCase__ ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   None



class       UpperCAmelCase  (						lowerCamelCase__ ):





       '''simple docstring'''



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



class       UpperCAmelCase  (						lowerCamelCase__ ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   [r"pooler", r"logit_scale"]
       SCREAMING_SNAKE_CASE 		=   [r"position_ids", r"predictions.decoder.bias"]
       SCREAMING_SNAKE_CASE 		=   "roberta"
       SCREAMING_SNAKE_CASE 		=   RobertaSeriesConfig

       def __init__(						self     ,       __lowerCAmelCase			)  ->  Optional[Any]:
        super().__init__(__lowerCAmelCase			)
        lowercase__    :   Any						=				XLMRobertaModel(__lowerCAmelCase			)
        lowercase__    :   int						=				nn.Linear(config.hidden_size     ,       config.project_dim			)
        lowercase__    :   Union[str, Any]						=				getattr(__lowerCAmelCase     ,       '''has_pre_transformation'''     ,       __lowerCAmelCase			)
        if self.has_pre_transformation:
         lowercase__    :   Any						=				nn.Linear(config.hidden_size     ,       config.project_dim			)
         lowercase__    :   int						=				nn.LayerNorm(config.hidden_size     ,       eps=config.layer_norm_eps			)
        self.post_init()


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       )  ->  str:
        lowercase__    :   Optional[int]						=				return_dict if return_dict is not None else self.config.use_return_dict

        lowercase__    :   int						=				self.base_model(
            input_ids=__lowerCAmelCase     ,       attention_mask=__lowerCAmelCase     ,       token_type_ids=__lowerCAmelCase     ,       position_ids=__lowerCAmelCase     ,       head_mask=__lowerCAmelCase     ,       inputs_embeds=__lowerCAmelCase     ,       encoder_hidden_states=__lowerCAmelCase     ,       encoder_attention_mask=__lowerCAmelCase     ,       output_attentions=__lowerCAmelCase     ,       output_hidden_states=True if self.has_pre_transformation else output_hidden_states     ,       return_dict=__lowerCAmelCase     ,       )

        if self.has_pre_transformation:
         lowercase__    :   List[Any]						=				outputs["hidden_states"][-2]
         lowercase__    :   List[str]						=				self.pre_LN(__lowerCAmelCase			)
         lowercase__    :   List[str]						=				self.transformation_pre(__lowerCAmelCase			)

         return TransformationModelOutput(
             projection_state=__lowerCAmelCase     ,       last_hidden_state=outputs.last_hidden_state     ,       hidden_states=outputs.hidden_states     ,       attentions=outputs.attentions     ,       )
        else:
         lowercase__    :   int						=				self.transformation(outputs.last_hidden_state			)
         return TransformationModelOutput(
             projection_state=__lowerCAmelCase     ,       last_hidden_state=outputs.last_hidden_state     ,       hidden_states=outputs.hidden_states     ,       attentions=outputs.attentions     ,       )


'''simple docstring'''

import numpy as np
def       __UpperCamelCase	(       UpperCAmelCase					):
 return 1 / (1 + np.exp(-vector					))
def       __UpperCamelCase	(       UpperCAmelCase					):
 return vector * sigmoid(UpperCAmelCase					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()

