'''simple docstring'''
import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


lowercase__  :      Optional[int]        =					logging.getLogger(__name__)







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  def  lowerCAmelCase__ (      self			:       Tuple  ,							UpperCAmelCase__			:       List[Any]  ,							UpperCAmelCase__			:       List[Any]  ,							UpperCAmelCase__			:       Optional[int]=None  ,							UpperCAmelCase__			:       List[str]=None  )				->List[str]:
        UpperCAmelCase_            =  self.layer[current_layer](UpperCAmelCase__  ,							UpperCAmelCase__  ,							head_mask[current_layer]  )

        UpperCAmelCase_            =  layer_outputs[0]

        return hidden_states







@add_start_docstrings(
    '''The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.'''     ,		lowerCamelCase     ,		)
class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  def __init__(      self			:       Union[str, Any]  ,							UpperCAmelCase__			:       Any  )				->str:
        super().__init__(UpperCAmelCase__  )

        UpperCAmelCase_            =  BertEncoderWithPabee(UpperCAmelCase__  )

        self.init_weights()
        UpperCAmelCase_            =  0
        UpperCAmelCase_            =  0
        UpperCAmelCase_            =  0

        UpperCAmelCase_            =  0






  def  lowerCAmelCase__ (      self			:       List[str]  ,							UpperCAmelCase__			:       Dict  )				->Optional[Any]:
        UpperCAmelCase_            =  threshold






  def  lowerCAmelCase__ (      self			:       Optional[int]  ,							UpperCAmelCase__			:       int  )				->Dict:
        UpperCAmelCase_            =  patience






  def  lowerCAmelCase__ (      self			:       Any  )				->List[Any]:
        UpperCAmelCase_            =  0
        UpperCAmelCase_            =  0






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->int:
        UpperCAmelCase_            =  self.inference_layers_num / self.inference_instances_num
        UpperCAmelCase_            =  (
            f"""*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ="""
            f""" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"""
        )
        print(UpperCAmelCase__  )






  @add_start_docstrings_to_model_forward(UpperCAmelCase__  )
  def  lowerCAmelCase__ (      self			:       str  ,							UpperCAmelCase__			:       int=None  ,							UpperCAmelCase__			:       List[str]=None  ,							UpperCAmelCase__			:       Union[str, Any]=None  ,							UpperCAmelCase__			:       str=None  ,							UpperCAmelCase__			:       Tuple=None  ,							UpperCAmelCase__			:       Any=None  ,							UpperCAmelCase__			:       int=None  ,							UpperCAmelCase__			:       Tuple=None  ,							UpperCAmelCase__			:       int=None  ,							UpperCAmelCase__			:       str=None  ,							UpperCAmelCase__			:       str=False  ,							)				->Any:

        if input_ids is not None and inputs_embeds is not None:
              raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time'''  )
        elif input_ids is not None:
              UpperCAmelCase_            =  input_ids.size()
        elif inputs_embeds is not None:
              UpperCAmelCase_            =  inputs_embeds.size()[:-1]
        else:
              raise ValueError('''You have to specify either input_ids or inputs_embeds'''  )

        UpperCAmelCase_            =  input_ids.device if input_ids is not None else inputs_embeds.device

        if attention_mask is None:
              UpperCAmelCase_            =  torch.ones(UpperCAmelCase__  ,							device=UpperCAmelCase__  )
        if token_type_ids is None:
              UpperCAmelCase_            =  torch.zeros(UpperCAmelCase__  ,							dtype=torch.long  ,							device=UpperCAmelCase__  )

        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
        # ourselves in which case we just need to make it broadcastable to all heads.
        UpperCAmelCase_            =  self.get_extended_attention_mask(UpperCAmelCase__  ,							UpperCAmelCase__  ,							UpperCAmelCase__  )

        # If a 2D ou 3D attention mask is provided for the cross-attention
        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
        if self.config.is_decoder and encoder_hidden_states is not None:
              UpperCAmelCase_      ,		UpperCAmelCase_      ,		UpperCAmelCase_            =  encoder_hidden_states.size()
              UpperCAmelCase_            =  (encoder_batch_size, encoder_sequence_length)
              if encoder_attention_mask is None:
                    UpperCAmelCase_            =  torch.ones(UpperCAmelCase__  ,							device=UpperCAmelCase__  )
              UpperCAmelCase_            =  self.invert_attention_mask(UpperCAmelCase__  )
        else:
              UpperCAmelCase_            =  None

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        UpperCAmelCase_            =  self.get_head_mask(UpperCAmelCase__  ,							self.config.num_hidden_layers  )

        UpperCAmelCase_            =  self.embeddings(
            input_ids=UpperCAmelCase__  ,							position_ids=UpperCAmelCase__  ,							token_type_ids=UpperCAmelCase__  ,							inputs_embeds=UpperCAmelCase__  )
        UpperCAmelCase_            =  embedding_output

        if self.training:
              UpperCAmelCase_            =  []
              for i in range(self.config.num_hidden_layers  ):
                    UpperCAmelCase_            =  self.encoder.adaptive_forward(
                        UpperCAmelCase__  ,							current_layer=UpperCAmelCase__  ,							attention_mask=UpperCAmelCase__  ,							head_mask=UpperCAmelCase__  )

                    UpperCAmelCase_            =  self.pooler(UpperCAmelCase__  )
                    UpperCAmelCase_            =  output_layers[i](output_dropout(UpperCAmelCase__  )  )
                    res.append(UpperCAmelCase__  )
        elif self.patience == 0:  # Use all layers for inference
              UpperCAmelCase_            =  self.encoder(
                  UpperCAmelCase__  ,							attention_mask=UpperCAmelCase__  ,							head_mask=UpperCAmelCase__  ,							encoder_hidden_states=UpperCAmelCase__  ,							encoder_attention_mask=UpperCAmelCase__  ,							)
              UpperCAmelCase_            =  self.pooler(encoder_outputs[0]  )
              UpperCAmelCase_            =  [output_layers[self.config.num_hidden_layers - 1](UpperCAmelCase__  )]
        else:
              UpperCAmelCase_            =  0
              UpperCAmelCase_            =  None
              UpperCAmelCase_            =  0
              for i in range(self.config.num_hidden_layers  ):
                    calculated_layer_num += 1
                    UpperCAmelCase_            =  self.encoder.adaptive_forward(
                        UpperCAmelCase__  ,							current_layer=UpperCAmelCase__  ,							attention_mask=UpperCAmelCase__  ,							head_mask=UpperCAmelCase__  )

                    UpperCAmelCase_            =  self.pooler(UpperCAmelCase__  )
                    UpperCAmelCase_            =  output_layers[i](UpperCAmelCase__  )
                    if regression:
                          UpperCAmelCase_            =  logits.detach()
                          if patient_result is not None:
                                UpperCAmelCase_            =  patient_result.detach()
                          if (patient_result is not None) and torch.abs(patient_result - labels  ) < self.regression_threshold:
                                patient_counter += 1
                          else:
                                UpperCAmelCase_            =  0
                    else:
                          UpperCAmelCase_            =  logits.detach().argmax(dim=1  )
                          if patient_result is not None:
                                UpperCAmelCase_            =  patient_result.detach().argmax(dim=1  )
                          if (patient_result is not None) and torch.all(labels.eq(UpperCAmelCase__  )  ):
                                patient_counter += 1
                          else:
                                UpperCAmelCase_            =  0

                    UpperCAmelCase_            =  logits
                    if patient_counter == self.patience:
                          break
              UpperCAmelCase_            =  [patient_result]
              self.inference_layers_num += calculated_layer_num
              self.inference_instances_num += 1

        return res







@add_start_docstrings(
    '''Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. '''     ,		lowerCamelCase     ,		)
class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  def __init__(      self			:       Tuple  ,							UpperCAmelCase__			:       Tuple  )				->Any:
        super().__init__(UpperCAmelCase__  )
        UpperCAmelCase_            =  config.num_labels

        UpperCAmelCase_            =  BertModelWithPabee(UpperCAmelCase__  )
        UpperCAmelCase_            =  nn.Dropout(config.hidden_dropout_prob  )
        UpperCAmelCase_            =  nn.ModuleList(
            [nn.Linear(config.hidden_size  ,							self.config.num_labels  ) for _ in range(config.num_hidden_layers  )]  )

        self.init_weights()






  @add_start_docstrings_to_model_forward(UpperCAmelCase__  )
  def  lowerCAmelCase__ (      self			:       Optional[Any]  ,							UpperCAmelCase__			:       Dict=None  ,							UpperCAmelCase__			:       List[Any]=None  ,							UpperCAmelCase__			:       str=None  ,							UpperCAmelCase__			:       Optional[Any]=None  ,							UpperCAmelCase__			:       Optional[Any]=None  ,							UpperCAmelCase__			:       Dict=None  ,							UpperCAmelCase__			:       Tuple=None  ,							)				->List[Any]:
        UpperCAmelCase_            =  self.bert(
            input_ids=UpperCAmelCase__  ,							attention_mask=UpperCAmelCase__  ,							token_type_ids=UpperCAmelCase__  ,							position_ids=UpperCAmelCase__  ,							head_mask=UpperCAmelCase__  ,							inputs_embeds=UpperCAmelCase__  ,							output_dropout=self.dropout  ,							output_layers=self.classifiers  ,							regression=self.num_labels == 1  ,							)

        UpperCAmelCase_            =  (logits[-1],)

        if labels is not None:
              UpperCAmelCase_            =  None
              UpperCAmelCase_            =  0
              for ix, logits_item in enumerate(UpperCAmelCase__  ):
                    if self.num_labels == 1:
                          #  We are doing regression
                          UpperCAmelCase_            =  MSELoss()
                          UpperCAmelCase_            =  loss_fct(logits_item.view(-1  )  ,							labels.view(-1  )  )
                    else:
                          UpperCAmelCase_            =  CrossEntropyLoss()
                          UpperCAmelCase_            =  loss_fct(logits_item.view(-1  ,							self.num_labels  )  ,							labels.view(-1  )  )
                    if total_loss is None:
                          UpperCAmelCase_            =  loss
                    else:
                          total_loss += loss * (ix + 1)
                    total_weights += ix + 1
              UpperCAmelCase_            =  (total_loss / total_weights,) + outputs

        return outputs






'''simple docstring'''
from __future__ import annotations

from typing import TypedDict







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	42
  lowerCAmelCase__										=	42






def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


      if not isinstance(_UpperCamelCase							,    _UpperCamelCase					):
            raise TypeError('''The parameter s type must be str.'''					)

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



def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


      if not isinstance(_UpperCamelCase							,    _UpperCamelCase					):
            raise TypeError('''The parameter s type must be str.'''					)
      if not s:
            raise ValueError('''The parameter s must not be empty.'''					)

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



def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  int					):


      '''simple docstring'''


      if not isinstance(_UpperCamelCase							,    _UpperCamelCase					):
            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:
            UpperCAmelCase_            =  int(_UpperCamelCase					)
      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(_UpperCamelCase					):
            raise ValueError(
                '''The parameter idx_original_string must be lower than''' ''' len(bwt_string).'''					)

      UpperCAmelCase_            =  [''''''] * len(_UpperCamelCase					)
      for _ in range(len(_UpperCamelCase					)					):
            for i in range(len(_UpperCamelCase					)					):
                  UpperCAmelCase_            =  bwt_string[i] + ordered_rotations[i]
            ordered_rotations.sort()
      return ordered_rotations[idx_original_string]


if __name__ == "__main__":
      lowercase__  :      Optional[int]        =					"Provide a string that I will generate its BWT transform: "
      lowercase__  :      List[Any]        =					input(entry_msg).strip()
      lowercase__  :      Any        =					bwt_transform(s)
      print(
          F'''Burrows Wheeler transform for string \'{s}\' results '''
          F'''in \'{result['bwt_string']}\''''
      )
      lowercase__  :      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}\''''
      )





