"""simple docstring"""




def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	str     )	->				str:
  _lowerCAmelCase						:	Dict        =					0

  # if input_string is "aba" than new_input_string become "a|b|a"
  _lowerCAmelCase						:	Any        =					""""""
  _lowerCAmelCase						:	List[str]        =					""""""

  # append each character + "|" in new_string for range(0, length-1)
  for i in input_string[: len(_lowerCamelCase     ) - 1]:
    new_input_string += i + "|"
  # append last character
  new_input_string += input_string[-1]

  # we will store the starting and ending of previous furthest ending palindromic
  # substring
  _lowerCAmelCase      ,     _lowerCAmelCase						:	Optional[Any]        =					0, 0

  # length[i] shows the length of palindromic substring with center i
  _lowerCAmelCase						:	Optional[int]        =					[1 for i in range(len(_lowerCamelCase     )     )]

  # for each character in new_string find corresponding palindromic string
  _lowerCAmelCase						:	int        =					0
  for j in range(len(_lowerCamelCase     )     ):
    _lowerCAmelCase						:	List[Any]        =					1 if j > r else min(length[l + r - j] // 2      ,r - j + 1     )
    while (
        j - k >= 0
        and j + k < len(_lowerCamelCase     )
        and new_input_string[k + j] == new_input_string[j - k]
    ):
      k += 1

    _lowerCAmelCase						:	Any        =					2 * k - 1

    # does this string is ending after the previously explored end (that is r) ?
    # if yes the update the new r to the last index of this
    if j + k - 1 > r:
      _lowerCAmelCase						:	Dict        =					j - k + 1  # noqa: E741
      _lowerCAmelCase						:	List[Any]        =					j + k - 1

    # update max_length and start position
    if max_length < length[j]:
      _lowerCAmelCase						:	Union[str, Any]        =					length[j]
      _lowerCAmelCase						:	Union[str, Any]        =					j

    # create that string
  _lowerCAmelCase						:	Optional[Any]        =					new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
  for i in s:
    if i != "|":
      output_string += i

  return output_string


if __name__ == "__main__":
     import doctest

     doctest.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 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Optional[torch.FloatTensor]		 =		None
     _UpperCamelCase							:      torch.FloatTensor		 =		None
     _UpperCamelCase							:      Optional[Tuple[torch.FloatTensor]]		 =		None
     _UpperCamelCase							:      Optional[Tuple[torch.FloatTensor]]		 =		None







class 						__A							(			SCREAMING_SNAKE_CASE_		):






     def __init__(		self    ,					a__=1    ,					a__=0    ,					a__=2    ,					a__=512    ,					a__="cls"    ,					a__=False    ,					a__=True    ,					**a__    ,					):
       super().__init__(pad_token_id=a__    ,					bos_token_id=a__    ,					eos_token_id=a__    ,					**a__  )
       _lowerCAmelCase						:	Optional[Any]        =					project_dim
       _lowerCAmelCase						:	List[str]        =					pooler_fn
       _lowerCAmelCase						:	Any        =					learn_encoder
       _lowerCAmelCase						:	Optional[int]        =					use_attention_mask







class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Optional[int]		 =		[R"pooler", R"logit_scale"]
     _UpperCamelCase							:      List[Any]		 =		[R"position_ids", R"predictions.decoder.bias"]
     _UpperCamelCase							:      List[Any]		 =		"roberta"
     _UpperCamelCase							:      Optional[int]		 =		RobertaSeriesConfig






     def __init__(		self    ,					a__  ):
       super().__init__(a__  )
       _lowerCAmelCase						:	str        =					XLMRobertaModel(a__  )
       _lowerCAmelCase						:	Optional[Any]        =					nn.Linear(config.hidden_size    ,					config.project_dim  )
       _lowerCAmelCase						:	List[Any]        =					getattr(a__    ,					"""has_pre_transformation"""    ,					a__  )
       if self.has_pre_transformation:
         _lowerCAmelCase						:	List[str]        =					nn.Linear(config.hidden_size    ,					config.project_dim  )
         _lowerCAmelCase						:	Optional[int]        =					nn.LayerNorm(config.hidden_size    ,					eps=config.layer_norm_eps  )
       self.post_init()






     def    __A  (		self    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					):
       _lowerCAmelCase						:	List[str]        =					return_dict if return_dict is not None else self.config.use_return_dict

       _lowerCAmelCase						:	Optional[int]        =					self.base_model(
           input_ids=a__    ,					attention_mask=a__    ,					token_type_ids=a__    ,					position_ids=a__    ,					head_mask=a__    ,					inputs_embeds=a__    ,					encoder_hidden_states=a__    ,					encoder_attention_mask=a__    ,					output_attentions=a__    ,					output_hidden_states=True if self.has_pre_transformation else output_hidden_states    ,					return_dict=a__    ,					)

       if self.has_pre_transformation:
         _lowerCAmelCase						:	Optional[Any]        =					outputs["""hidden_states"""][-2]
         _lowerCAmelCase						:	Optional[Any]        =					self.pre_LN(a__  )
         _lowerCAmelCase						:	int        =					self.transformation_pre(a__  )

         return TransformationModelOutput(
             projection_state=a__    ,					last_hidden_state=outputs.last_hidden_state    ,					hidden_states=outputs.hidden_states    ,					attentions=outputs.attentions    ,					)
       else:
         _lowerCAmelCase						:	Union[str, Any]        =					self.transformation(outputs.last_hidden_state  )
         return TransformationModelOutput(
             projection_state=a__    ,					last_hidden_state=outputs.last_hidden_state    ,					hidden_states=outputs.hidden_states    ,					attentions=outputs.attentions    ,					)




