


'''simple docstring'''

from __future__ import annotations

from typing import TypedDict





class   snake_case					(					UpperCamelCase_    ):





  """simple docstring"""




  _lowerCamelCase						=						42
  _lowerCamelCase						=						42





def __snake_case      (   UpperCAmelCase_		:		Any	):
 if not isinstance(__lowerCAmelCase      ,       __lowerCAmelCase	):
  raise TypeError("The parameter s type must be str."	)

 return [s[i:] + s[:i] for i in range(len(__lowerCAmelCase	)	)]





def __snake_case      (   UpperCAmelCase_		:		Any	):
 if not isinstance(__lowerCAmelCase      ,       __lowerCAmelCase	):
  raise TypeError("The parameter s type must be str."	)
 if not s:
  raise ValueError("The parameter s must not be empty."	)

 lowerCamelCase_  				=						all_rotations(__lowerCAmelCase	)
 rotations.sort()  # sort the list of rotations in alphabetically order
 # make a string composed of the last char of each rotation
 lowerCamelCase_  				=						{
     "bwt_string": "".join([word[-1] for word in rotations]	),
     "idx_original_string": rotations.index(__lowerCAmelCase	),
 }
 return response





def __snake_case      (   UpperCAmelCase_		:		Optional[Any]      ,       UpperCAmelCase_		:		Optional[int]	):
 if not isinstance(__lowerCAmelCase      ,       __lowerCAmelCase	):
  raise TypeError("The parameter bwt_string type must be str."	)
 if not bwt_string:
  raise ValueError("The parameter bwt_string must not be empty."	)
 try:
  lowerCamelCase_  				=						int(__lowerCAmelCase	)
 except ValueError:
  raise TypeError(
      "The parameter idx_original_string type must be int or passive"
      " of cast to int."	)
 if idx_original_string < 0:
  raise ValueError("The parameter idx_original_string must not be lower than 0."	)
 if idx_original_string >= len(__lowerCAmelCase	):
  raise ValueError(
      "The parameter idx_original_string must be lower than" " len(bwt_string)."	)

 lowerCamelCase_  				=						[""""""] * len(__lowerCAmelCase	)
 for _ in range(len(__lowerCAmelCase	)	):
  for i in range(len(__lowerCAmelCase	)	):
   lowerCamelCase_  				=						bwt_string[i] + ordered_rotations[i]
  ordered_rotations.sort()
 return ordered_rotations[idx_original_string]


if __name__ == "__main__":
       a_						:       Union[str, Any]      			=				"Provide a string that I will generate its BWT transform: "
       a_						:       List[str]      			=				input(entry_msg).strip()
       a_						:       List[str]      			=				bwt_transform(s)
       print(
           f'''Burrows Wheeler transform for string \'{s}\' results '''
           f'''in \'{result['bwt_string']}\''''
       )
       a_						:       Dict      			=				reverse_bwt(result["""bwt_string"""], result["""idx_original_string"""])
       print(
           f'''Reversing Burrows Wheeler transform for entry \'{result['bwt_string']}\' '''
           f'''we get original string \'{original_string}\''''
       )







'''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    ,						)



