code: 
  
  """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.albert.modeling_albert import (
      ALBERT_INPUTS_DOCSTRING,
      ALBERT_START_DOCSTRING,
      AlbertModel,
      AlbertPreTrainedModel,
      AlbertTransformer,
  )
  
  
  _A						=     logging.getLogger(__name__)
  
  class 			lowerCamelCase  (					A__      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase=None       ,					_lowerCamelCase=None  ):
  
  
          """simple docstring"""
  
          if current_layer == 0:
                UpperCAmelCase__ :       Any						=						self.embedding_hidden_mapping_in(_lowerCAmelCase  )
          else:
                UpperCAmelCase__ :       Optional[int]						=						hidden_states[0]
  
          UpperCAmelCase__ :       int						=						int(self.config.num_hidden_layers / self.config.num_hidden_groups  )
  
          # Index of the hidden group
          UpperCAmelCase__ :       Any						=						int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)  )
  
          UpperCAmelCase__ :       Optional[Any]						=						self.albert_layer_groups[group_idx](
              _lowerCAmelCase       ,					_lowerCAmelCase       ,					head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]       ,					)
          UpperCAmelCase__ :       List[str]						=						layer_group_output[0]
  
          return (hidden_states,)
  
  
  
  
  @add_start_docstrings(
      'The bare ALBERT Model transformer with PABEE outputting raw hidden-states without any specific head on top.'				,				A__				,				)
  class 			lowerCamelCase  (					A__      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          super().__init__(_lowerCAmelCase  )
  
          UpperCAmelCase__ :       Tuple						=						AlbertTransformerWithPabee(_lowerCAmelCase  )
  
          self.init_weights()
          UpperCAmelCase__ :       List[Any]						=						0
          UpperCAmelCase__ :       int						=						0
          UpperCAmelCase__ :       Optional[Any]						=						0
  
          UpperCAmelCase__ :       Optional[int]						=						0
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[str]						=						threshold
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[str]						=						patience
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[str]						=						0
          UpperCAmelCase__ :       Tuple						=						0
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Dict						=						self.inference_layers_num / self.inference_instances_num
          UpperCAmelCase__ :       Dict						=						(
              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    _a			(self       ,					_lowerCamelCase=None       ,					_lowerCamelCase=None       ,					_lowerCamelCase=None       ,					_lowerCamelCase=None       ,					_lowerCamelCase=None       ,					_lowerCamelCase=None       ,					_lowerCamelCase=None       ,					_lowerCamelCase=None       ,					_lowerCamelCase=False       ,					):
  
  
          """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:
                UpperCAmelCase__ :       Any						=						input_ids.size()
          elif inputs_embeds is not None:
                UpperCAmelCase__ :       Tuple						=						inputs_embeds.size()[:-1]
          else:
                raise ValueError("""You have to specify either input_ids or inputs_embeds"""  )
  
          UpperCAmelCase__ :       int						=						input_ids.device if input_ids is not None else inputs_embeds.device
  
          if attention_mask is None:
                UpperCAmelCase__ :       Union[str, Any]						=						torch.ones(_lowerCAmelCase       ,					device=_lowerCAmelCase  )
          if token_type_ids is None:
                UpperCAmelCase__ :       List[str]						=						torch.zeros(_lowerCAmelCase       ,					dtype=torch.long       ,					device=_lowerCAmelCase  )
  
          UpperCAmelCase__ :       Union[str, Any]						=						attention_mask.unsqueeze(1  ).unsqueeze(2  )
          UpperCAmelCase__ :       List[Any]						=						extended_attention_mask.to(dtype=self.dtype  )  # fp16 compatibility
          UpperCAmelCase__ :       Union[str, Any]						=						(1.0 - extended_attention_mask) * -10000.0
          UpperCAmelCase__ :       Tuple						=						self.get_head_mask(_lowerCAmelCase       ,					self.config.num_hidden_layers  )
  
          UpperCAmelCase__ :       str						=						self.embeddings(
              _lowerCAmelCase       ,					position_ids=_lowerCAmelCase       ,					token_type_ids=_lowerCAmelCase       ,					inputs_embeds=_lowerCAmelCase  )
          UpperCAmelCase__ :       List[str]						=						embedding_output
  
          if self.training:
                UpperCAmelCase__ :       List[Any]						=						[]
                for i in range(self.config.num_hidden_layers  ):
                      UpperCAmelCase__ :       int						=						self.encoder.adaptive_forward(
                          _lowerCAmelCase       ,					current_layer=_lowerCAmelCase       ,					attention_mask=_lowerCAmelCase       ,					head_mask=_lowerCAmelCase       ,					)
  
                      UpperCAmelCase__ :       Dict						=						self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]  )  )
                      UpperCAmelCase__ :       Tuple						=						output_layers[i](output_dropout(_lowerCAmelCase  )  )
                      res.append(_lowerCAmelCase  )
          elif self.patience == 0:  # Use all layers for inference
                UpperCAmelCase__ :       List[Any]						=						self.encoder(_lowerCAmelCase       ,					_lowerCAmelCase       ,					head_mask=_lowerCAmelCase  )
                UpperCAmelCase__ :       int						=						self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]  )  )
                UpperCAmelCase__ :       str						=						[output_layers[self.config.num_hidden_layers - 1](_lowerCAmelCase  )]
          else:
                UpperCAmelCase__ :       Optional[Any]						=						0
                UpperCAmelCase__ :       List[Any]						=						None
                UpperCAmelCase__ :       int						=						0
                for i in range(self.config.num_hidden_layers  ):
                      calculated_layer_num += 1
                      UpperCAmelCase__ :       Dict						=						self.encoder.adaptive_forward(
                          _lowerCAmelCase       ,					current_layer=_lowerCAmelCase       ,					attention_mask=_lowerCAmelCase       ,					head_mask=_lowerCAmelCase       ,					)
  
                      UpperCAmelCase__ :       str						=						self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]  )  )
                      UpperCAmelCase__ :       int						=						output_layers[i](_lowerCAmelCase  )
                      if regression:
                            UpperCAmelCase__ :       Optional[int]						=						logits.detach()
                            if patient_result is not None:
                                  UpperCAmelCase__ :       Union[str, Any]						=						patient_result.detach()
                            if (patient_result is not None) and torch.abs(patient_result - labels  ) < self.regression_threshold:
                                  patient_counter += 1
                            else:
                                  UpperCAmelCase__ :       Dict						=						0
                      else:
                            UpperCAmelCase__ :       List[Any]						=						logits.detach().argmax(dim=1  )
                            if patient_result is not None:
                                  UpperCAmelCase__ :       List[Any]						=						patient_result.detach().argmax(dim=1  )
                            if (patient_result is not None) and torch.all(labels.eq(_lowerCAmelCase  )  ):
                                  patient_counter += 1
                            else:
                                  UpperCAmelCase__ :       str						=						0
  
                      UpperCAmelCase__ :       Tuple						=						logits
                      if patient_counter == self.patience:
                            break
                UpperCAmelCase__ :       Optional[Any]						=						[patient_result]
                self.inference_layers_num += calculated_layer_num
                self.inference_instances_num += 1
  
          return res
  
  
  
  
  @add_start_docstrings(
      'Albert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n    the pooled output) e.g. for GLUE tasks. '				,				A__				,				)
  class 			lowerCamelCase  (					A__      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          super().__init__(_lowerCAmelCase  )
          UpperCAmelCase__ :       str						=						config.num_labels
  
          UpperCAmelCase__ :       Union[str, Any]						=						AlbertModelWithPabee(_lowerCAmelCase  )
          UpperCAmelCase__ :       Union[str, Any]						=						nn.Dropout(config.classifier_dropout_prob  )
          UpperCAmelCase__ :       Dict						=						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    _a			(self       ,					_lowerCamelCase=None       ,					_lowerCamelCase=None       ,					_lowerCamelCase=None       ,					_lowerCamelCase=None       ,					_lowerCamelCase=None       ,					_lowerCamelCase=None       ,					_lowerCamelCase=None       ,					):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[int]						=						self.albert(
              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       ,					)
  
          UpperCAmelCase__ :       Union[str, Any]						=						(logits[-1],)
  
          if labels is not None:
                UpperCAmelCase__ :       List[str]						=						None
                UpperCAmelCase__ :       Optional[int]						=						0
                for ix, logits_item in enumerate(_lowerCAmelCase  ):
                      if self.num_labels == 1:
                            #  We are doing regression
                            UpperCAmelCase__ :       Optional[int]						=						MSELoss()
                            UpperCAmelCase__ :       List[str]						=						loss_fct(logits_item.view(-1  )       ,					labels.view(-1  )  )
                      else:
                            UpperCAmelCase__ :       Optional[int]						=						CrossEntropyLoss()
                            UpperCAmelCase__ :       int						=						loss_fct(logits_item.view(-1       ,					self.num_labels  )       ,					labels.view(-1  )  )
                      if total_loss is None:
                            UpperCAmelCase__ :       List[Any]						=						loss
                      else:
                            total_loss += loss * (ix + 1)
                      total_weights += ix + 1
                UpperCAmelCase__ :       Any						=						(total_loss / total_weights,) + outputs
  
          return outputs
  
  
style_context: 
  
  """simple docstring"""
  import requests
  
  try:
      from django.contrib.auth import authenticate, login
      from django.shortcuts import redirect, render
  except ImportError:
      _A						=     _A						=     _A						=     _A						=     print
  
  
  
  
  def 				a__	(	lowerCAmelCase						)  ->   str:
        # Enter your recaptcha secret key here
        UpperCAmelCase__ :       int						=						"""secretKey"""
        UpperCAmelCase__ :       Any						=						"""https://www.google.com/recaptcha/api/siteverify"""
  
        # when method is not POST, direct user to login page
        if request.method != "POST":
              return render(lowerCAmelCase ,  """login.html"""						)
  
        # from the frontend, get username, password, and client_key
        UpperCAmelCase__ :       List[Any]						=						request.POST.get("""username"""						)
        UpperCAmelCase__ :       Optional[Any]						=						request.POST.get("""password"""						)
        UpperCAmelCase__ :       List[str]						=						request.POST.get("""g-recaptcha-response"""						)
  
        # post recaptcha response to Google's recaptcha api
        UpperCAmelCase__ :       Any						=						requests.post(lowerCAmelCase ,  data={"""secret""": secret_key, """response""": client_key}						)
        # if the recaptcha api verified our keys
        if response.json().get("""success""" ,  lowerCAmelCase						):
              # authenticate the user
              UpperCAmelCase__ :       List[str]						=						authenticate(lowerCAmelCase ,  username=lowerCAmelCase ,  password=lowerCAmelCase						)
              if user_in_database:
                    login(lowerCAmelCase ,  lowerCAmelCase						)
                    return redirect("""/your-webpage"""						)
        return render(lowerCAmelCase ,  """login.html"""						)
  
  