


'''simple docstring'''

from __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

a_						:       Any      			=				TypeVar("""T""")





class   snake_case					(					Generic[T]    ):





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						data
   lowerCamelCase_  				=						None







  def __str__(					self  ):







   """simple docstring"""
   return f'''{self.data}'''





class   snake_case					(					Generic[T]    ):





  """simple docstring"""




  def __init__(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						None






  def __iter__(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.top
   while node:
    yield node.data
    lowerCamelCase_  				=						node.next






  def __str__(					self  ):







   """simple docstring"""
   return "->".join([str(UpperCamelCase  ) for item in self]  )






  def __len__(					self  ):







   """simple docstring"""
   return len(tuple(iter(self  )  )  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   return self.top is None






  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						Node(UpperCamelCase  )
   if not self.is_empty():
    lowerCamelCase_  				=						self.top
   lowerCamelCase_  				=						node






  def 						snake_case	(					self  ):







   """simple docstring"""
   if self.is_empty():
    raise IndexError("pop from empty stack"  )
   assert isinstance(self.top    ,						UpperCamelCase  )
   lowerCamelCase_  				=						self.top
   lowerCamelCase_  				=						self.top.next
   return pop_node.data






  def 						snake_case	(					self  ):







   """simple docstring"""
   if self.is_empty():
    raise IndexError("peek from empty stack"  )

   assert self.top is not None
   return self.top.data







  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						None


if __name__ == "__main__":
     from doctest import testmod

     testmod()







'''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   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						None
  _lowerCamelCase						=						None
  _lowerCamelCase						=						None
  _lowerCamelCase						=						None





class   snake_case					(					lowercase    ):





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase=1    ,						UpperCamelCase=0    ,						UpperCamelCase=2    ,						UpperCamelCase=512    ,						UpperCamelCase="cls"    ,						UpperCamelCase=False    ,						UpperCamelCase=True    ,						**UpperCamelCase    ,						):







   """simple docstring"""
   super().__init__(pad_token_id=UpperCamelCase    ,						bos_token_id=UpperCamelCase    ,						eos_token_id=UpperCamelCase    ,						**UpperCamelCase  )
   lowerCamelCase_  				=						project_dim
   lowerCamelCase_  				=						pooler_fn
   lowerCamelCase_  				=						learn_encoder
   lowerCamelCase_  				=						use_attention_mask





class   snake_case					(					lowercase    ):





  """simple docstring"""




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






  def __init__(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   super().__init__(UpperCamelCase  )
   lowerCamelCase_  				=						XLMRobertaModel(UpperCamelCase  )
   lowerCamelCase_  				=						nn.Linear(config.hidden_size    ,						config.project_dim  )
   lowerCamelCase_  				=						getattr(UpperCamelCase    ,						"has_pre_transformation"    ,						UpperCamelCase  )
   if self.has_pre_transformation:
    lowerCamelCase_  				=						nn.Linear(config.hidden_size    ,						config.project_dim  )
    lowerCamelCase_  				=						nn.LayerNorm(config.hidden_size    ,						eps=config.layer_norm_eps  )
   self.post_init()







  def 						snake_case	(					self    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						return_dict if return_dict is not None else self.config.use_return_dict

   lowerCamelCase_  				=						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_  				=						outputs["hidden_states"][-2]
    lowerCamelCase_  				=						self.pre_LN(UpperCamelCase  )
    lowerCamelCase_  				=						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_  				=						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    ,						)



