




'''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,
)


UpperCamelCase       			= logging.getLogger(__name__)






class 		lowerCAmelCase_     (    UpperCamelCase__				):







  '''simple docstring'''

  def  _snake_case			(      self							:				Union[str, Any] ,   SCREAMING_SNAKE_CASE_							:				Dict ,   SCREAMING_SNAKE_CASE_							:				Tuple ,   SCREAMING_SNAKE_CASE_							:				str=None ,   SCREAMING_SNAKE_CASE_							:				str=None     )       ->						Dict:







       '''simple docstring'''



       A:						Union[str, Any]						=						self.layer[current_layer](__lowerCamelCase ,   __lowerCamelCase ,   head_mask[current_layer]     )

       A:						Tuple						=						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."""    , UpperCamelCase__    , )
class 		lowerCAmelCase_     (    UpperCamelCase__				):







  '''simple docstring'''

  def __init__(      self							:				str ,   SCREAMING_SNAKE_CASE_							:				Tuple     )       ->						List[Any]:







       '''simple docstring'''



       super().__init__(__lowerCamelCase     )

       A:						Tuple						=						BertEncoderWithPabee(__lowerCamelCase     )

       self.init_weights()
       A:						Tuple						=						0
       A:						str						=						0
       A:						Optional[Any]						=						0

       A:						Any						=						0



  def  _snake_case			(      self							:				Tuple ,   SCREAMING_SNAKE_CASE_							:				Tuple     )       ->						List[str]:







       '''simple docstring'''



       A:						str						=						threshold



  def  _snake_case			(      self							:				str ,   SCREAMING_SNAKE_CASE_							:				Union[str, Any]     )       ->						int:







       '''simple docstring'''



       A:						Dict						=						patience



  def  _snake_case			(      self							:				Optional[int]     )       ->						Any:







       '''simple docstring'''



       A:						int						=						0
       A:						Optional[Any]						=						0



  def  _snake_case			(      self							:				Tuple     )       ->						Dict:







       '''simple docstring'''



       A:						int						=						self.inference_layers_num / self.inference_instances_num
       A:						Optional[Any]						=						(
           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(__lowerCamelCase     )




  @add_start_docstrings_to_model_forward(__lowerCamelCase     )
  def  _snake_case			(      self							:				List[Any] ,   SCREAMING_SNAKE_CASE_							:				int=None ,   SCREAMING_SNAKE_CASE_							:				int=None ,   SCREAMING_SNAKE_CASE_							:				int=None ,   SCREAMING_SNAKE_CASE_							:				int=None ,   SCREAMING_SNAKE_CASE_							:				List[Any]=None ,   SCREAMING_SNAKE_CASE_							:				Any=None ,   SCREAMING_SNAKE_CASE_							:				Union[str, Any]=None ,   SCREAMING_SNAKE_CASE_							:				List[Any]=None ,   SCREAMING_SNAKE_CASE_							:				List[Any]=None ,   SCREAMING_SNAKE_CASE_							:				Dict=None ,   SCREAMING_SNAKE_CASE_							:				Union[str, Any]=False ,   )       ->						List[Any]:







       '''simple docstring'''



       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:
            A:						int						=						input_ids.size()
       elif inputs_embeds is not None:
            A:						Union[str, Any]						=						inputs_embeds.size()[:-1]
       else:
            raise ValueError('''You have to specify either input_ids or inputs_embeds'''     )

       A:						Tuple						=						input_ids.device if input_ids is not None else inputs_embeds.device

       if attention_mask is None:
            A:						List[Any]						=						torch.ones(__lowerCamelCase ,   device=__lowerCamelCase     )
       if token_type_ids is None:
            A:						List[str]						=						torch.zeros(__lowerCamelCase ,   dtype=torch.long ,   device=__lowerCamelCase     )

       # 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.
       A:						List[Any]						=						self.get_extended_attention_mask(__lowerCamelCase ,   __lowerCamelCase ,   __lowerCamelCase     )

       # 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:
            A			,  A			,  A:						str						=						encoder_hidden_states.size()
            A:						Union[str, Any]						=						(encoder_batch_size, encoder_sequence_length)
            if encoder_attention_mask is None:
                 A:						Tuple						=						torch.ones(__lowerCamelCase ,   device=__lowerCamelCase     )
            A:						Union[str, Any]						=						self.invert_attention_mask(__lowerCamelCase     )
       else:
            A:						Optional[Any]						=						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]
       A:						Union[str, Any]						=						self.get_head_mask(__lowerCamelCase ,   self.config.num_hidden_layers     )

       A:						Optional[Any]						=						self.embeddings(
           input_ids=__lowerCamelCase ,   position_ids=__lowerCamelCase ,   token_type_ids=__lowerCamelCase ,   inputs_embeds=__lowerCamelCase     )
       A:						Tuple						=						embedding_output

       if self.training:
            A:						Any						=						[]
            for i in range(self.config.num_hidden_layers     ):
                 A:						int						=						self.encoder.adaptive_forward(
                     __lowerCamelCase ,   current_layer=__lowerCamelCase ,   attention_mask=__lowerCamelCase ,   head_mask=__lowerCamelCase     )

                 A:						int						=						self.pooler(__lowerCamelCase     )
                 A:						Optional[int]						=						output_layers[i](output_dropout(__lowerCamelCase     )     )
                 res.append(__lowerCamelCase     )
       elif self.patience == 0:  # Use all layers for inference
            A:						Tuple						=						self.encoder(
                __lowerCamelCase ,   attention_mask=__lowerCamelCase ,   head_mask=__lowerCamelCase ,   encoder_hidden_states=__lowerCamelCase ,   encoder_attention_mask=__lowerCamelCase ,   )
            A:						str						=						self.pooler(encoder_outputs[0]     )
            A:						Union[str, Any]						=						[output_layers[self.config.num_hidden_layers - 1](__lowerCamelCase     )]
       else:
            A:						List[Any]						=						0
            A:						Dict						=						None
            A:						Any						=						0
            for i in range(self.config.num_hidden_layers     ):
                 calculated_layer_num += 1
                 A:						List[str]						=						self.encoder.adaptive_forward(
                     __lowerCamelCase ,   current_layer=__lowerCamelCase ,   attention_mask=__lowerCamelCase ,   head_mask=__lowerCamelCase     )

                 A:						Optional[Any]						=						self.pooler(__lowerCamelCase     )
                 A:						Any						=						output_layers[i](__lowerCamelCase     )
                 if regression:
                      A:						Any						=						logits.detach()
                      if patient_result is not None:
                           A:						Optional[int]						=						patient_result.detach()
                      if (patient_result is not None) and torch.abs(patient_result - labels     ) < self.regression_threshold:
                           patient_counter += 1
                      else:
                           A:						Optional[Any]						=						0
                 else:
                      A:						Tuple						=						logits.detach().argmax(dim=1     )
                      if patient_result is not None:
                           A:						List[Any]						=						patient_result.detach().argmax(dim=1     )
                      if (patient_result is not None) and torch.all(labels.eq(__lowerCamelCase     )     ):
                           patient_counter += 1
                      else:
                           A:						List[str]						=						0

                 A:						str						=						logits
                 if patient_counter == self.patience:
                      break
            A:						List[Any]						=						[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. """    , UpperCamelCase__    , )
class 		lowerCAmelCase_     (    UpperCamelCase__				):







  '''simple docstring'''

  def __init__(      self							:				List[str] ,   SCREAMING_SNAKE_CASE_							:				Optional[int]     )       ->						Optional[Any]:







       '''simple docstring'''



       super().__init__(__lowerCamelCase     )
       A:						Tuple						=						config.num_labels

       A:						Optional[Any]						=						BertModelWithPabee(__lowerCamelCase     )
       A:						Optional[int]						=						nn.Dropout(config.hidden_dropout_prob     )
       A:						Any						=						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(__lowerCamelCase     )
  def  _snake_case			(      self							:				Tuple ,   SCREAMING_SNAKE_CASE_							:				int=None ,   SCREAMING_SNAKE_CASE_							:				List[str]=None ,   SCREAMING_SNAKE_CASE_							:				int=None ,   SCREAMING_SNAKE_CASE_							:				Union[str, Any]=None ,   SCREAMING_SNAKE_CASE_							:				List[str]=None ,   SCREAMING_SNAKE_CASE_							:				Any=None ,   SCREAMING_SNAKE_CASE_							:				str=None ,   )       ->						Union[str, Any]:







       '''simple docstring'''



       A:						List[Any]						=						self.bert(
           input_ids=__lowerCamelCase ,   attention_mask=__lowerCamelCase ,   token_type_ids=__lowerCamelCase ,   position_ids=__lowerCamelCase ,   head_mask=__lowerCamelCase ,   inputs_embeds=__lowerCamelCase ,   output_dropout=self.dropout ,   output_layers=self.classifiers ,   regression=self.num_labels == 1 ,   )

       A:						Dict						=						(logits[-1],)

       if labels is not None:
            A:						Optional[Any]						=						None
            A:						Union[str, Any]						=						0
            for ix, logits_item in enumerate(__lowerCamelCase     ):
                 if self.num_labels == 1:
                      #  We are doing regression
                      A:						Union[str, Any]						=						MSELoss()
                      A:						Optional[Any]						=						loss_fct(logits_item.view(-1     ) ,   labels.view(-1     )     )
                 else:
                      A:						str						=						CrossEntropyLoss()
                      A:						str						=						loss_fct(logits_item.view(-1 ,   self.num_labels     ) ,   labels.view(-1     )     )
                 if total_loss is None:
                      A:						Dict						=						loss
                 else:
                      total_loss += loss * (ix + 1)
                 total_weights += ix + 1
            A:						Dict						=						(total_loss / total_weights,) + outputs

       return outputs













import random


def 	UpperCamelCase__(       UpperCamelCase__			:       list ,     UpperCamelCase__			:       List[Any]  )->tuple:
       A__					,	A__					,	A__											=						[], [], []
       for element in data:
              if element < pivot:
                     less.append(UpperCamelCase__  )
              elif element > pivot:
                     greater.append(UpperCamelCase__  )
              else:
                     equal.append(UpperCamelCase__  )
       return less, equal, greater


def 	UpperCamelCase__(       UpperCamelCase__			:       list ,     UpperCamelCase__			:       int  )->Optional[int]:
       # index = len(items) // 2 when trying to find the median
       #   (value of index when items is sorted)

       # invalid input
       if index >= len(UpperCamelCase__  ) or index < 0:
              return None

       A__											=						items[random.randint(0 ,     len(UpperCamelCase__  ) - 1  )]
       A__											=						0
       A__					,	A__					,	A__											=						_partition(UpperCamelCase__ ,     UpperCamelCase__  )
       A__											=						len(UpperCamelCase__  )
       A__											=						len(UpperCamelCase__  )

       # index is the pivot
       if m <= index < m + count:
              return pivot
       # must be in smaller
       elif m > index:
              return quick_select(UpperCamelCase__ ,     UpperCamelCase__  )
       # must be in larger
       else:
              return quick_select(UpperCamelCase__ ,     index - (m + count)  )





