code: 
  
  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_		:			Optional[int]										=   logging.getLogger(__name__)
  class 					A_   (      _a						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__=None     ,      lowercase__=None  )     ->      List[Any]:
          if current_layer == 0:
              __UpperCAmelCase									=			self.embedding_hidden_mapping_in(lowercase__  )
          else:
              __UpperCAmelCase									=			hidden_states[0]
  
          __UpperCAmelCase									=			int(self.config.num_hidden_layers / self.config.num_hidden_groups  )
  
          # Index of the hidden group
          __UpperCAmelCase									=			int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)  )
  
          __UpperCAmelCase									=			self.albert_layer_groups[group_idx](
              lowercase__     ,      lowercase__     ,      head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]     ,      )
          __UpperCAmelCase									=			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 					A_   (      _a						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      def __init__(self     ,      lowercase__  )     ->      Tuple:
          super().__init__(lowercase__  )
  
          __UpperCAmelCase									=			AlbertTransformerWithPabee(lowercase__  )
  
          self.init_weights()
          __UpperCAmelCase									=			0
          __UpperCAmelCase									=			0
          __UpperCAmelCase									=			0
  
          __UpperCAmelCase									=			0
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__  )     ->      Tuple:
          __UpperCAmelCase									=			threshold
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__  )     ->      int:
          __UpperCAmelCase									=			patience
  
  
      def      lowerCAmelCase_						(self  )     ->      Dict:
          __UpperCAmelCase									=			0
          __UpperCAmelCase									=			0
  
  
      def      lowerCAmelCase_						(self  )     ->      Any:
          __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(lowercase__  )
  
  
  
  
  
  
      @add_start_docstrings_to_model_forward(lowercase__  )
      def      lowerCAmelCase_						(self     ,      lowercase__=None     ,      lowercase__=None     ,      lowercase__=None     ,      lowercase__=None     ,      lowercase__=None     ,      lowercase__=None     ,      lowercase__=None     ,      lowercase__=None     ,      lowercase__=False     ,      )     ->      Optional[int]:
  
          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(lowercase__     ,      device=lowercase__  )
          if token_type_ids is None:
              __UpperCAmelCase									=			torch.zeros(lowercase__     ,      dtype=torch.long     ,      device=lowercase__  )
  
          __UpperCAmelCase									=			attention_mask.unsqueeze(1  ).unsqueeze(2  )
          __UpperCAmelCase									=			extended_attention_mask.to(dtype=self.dtype  )  # fp16 compatibility
          __UpperCAmelCase									=			(1.0 - extended_attention_mask) * -10000.0
          __UpperCAmelCase									=			self.get_head_mask(lowercase__     ,      self.config.num_hidden_layers  )
  
          __UpperCAmelCase									=			self.embeddings(
              lowercase__     ,      position_ids=lowercase__     ,      token_type_ids=lowercase__     ,      inputs_embeds=lowercase__  )
          __UpperCAmelCase									=			embedding_output
  
          if self.training:
              __UpperCAmelCase									=			[]
              for i in range(self.config.num_hidden_layers  ):
                  __UpperCAmelCase									=			self.encoder.adaptive_forward(
                      lowercase__     ,      current_layer=lowercase__     ,      attention_mask=lowercase__     ,      head_mask=lowercase__     ,      )
  
                  __UpperCAmelCase									=			self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]  )  )
                  __UpperCAmelCase									=			output_layers[i](output_dropout(lowercase__  )  )
                  res.append(lowercase__  )
          elif self.patience == 0:  # Use all layers for inference
              __UpperCAmelCase									=			self.encoder(lowercase__     ,      lowercase__     ,      head_mask=lowercase__  )
              __UpperCAmelCase									=			self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]  )  )
              __UpperCAmelCase									=			[output_layers[self.config.num_hidden_layers - 1](lowercase__  )]
          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(
                      lowercase__     ,      current_layer=lowercase__     ,      attention_mask=lowercase__     ,      head_mask=lowercase__     ,      )
  
                  __UpperCAmelCase									=			self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]  )  )
                  __UpperCAmelCase									=			output_layers[i](lowercase__  )
                  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(lowercase__  )  ):
                          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(
      "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 					A_   (      _a						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      def __init__(self     ,      lowercase__  )     ->      Union[str, Any]:
          super().__init__(lowercase__  )
          __UpperCAmelCase									=			config.num_labels
  
          __UpperCAmelCase									=			AlbertModelWithPabee(lowercase__  )
          __UpperCAmelCase									=			nn.Dropout(config.classifier_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(lowercase__  )
      def      lowerCAmelCase_						(self     ,      lowercase__=None     ,      lowercase__=None     ,      lowercase__=None     ,      lowercase__=None     ,      lowercase__=None     ,      lowercase__=None     ,      lowercase__=None     ,      )     ->      List[str]:
          __UpperCAmelCase									=			self.albert(
              input_ids=lowercase__     ,      attention_mask=lowercase__     ,      token_type_ids=lowercase__     ,      position_ids=lowercase__     ,      head_mask=lowercase__     ,      inputs_embeds=lowercase__     ,      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(lowercase__  ):
                  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
  
  
  
  
  
style_context: 
  
  import csv
  import json
  import os
  
  import datasets
  
  
  # TODO: Add BibTeX citation
  # Find for instance the citation on arxiv or on the dataset repo/website
  A_		:			Any										=   '\\n@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n'
  
  # TODO: Add description of the dataset here
  # You can copy an official description
  A_		:			Optional[Any]										=   '\\nThis new dataset is designed to solve this great NLP task and is crafted with a lot of care.\n'
  
  # TODO: Add a link to an official homepage for the dataset here
  A_		:			Dict										=   ''
  
  # TODO: Add the licence for the dataset here if you can find it
  A_		:			Dict										=   ''
  
  # TODO: Add link to the official dataset URLs here
  # The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
  # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
  A_		:			Union[str, Any]										=   {
      'first_domain': 'https://huggingface.co/great-new-dataset-first_domain.zip',
      'second_domain': 'https://huggingface.co/great-new-dataset-second_domain.zip',
  }
  class 					A_   (      datasets.GeneratorBasedBuilder						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       datasets.Version("1.1.0"						)
  
      # This is an example of a dataset with multiple configurations.
      # If you don't want/need to define several sub-sets in your dataset,
      # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
  
      # If you need to make complex sub-parts in the datasets with configurable options
      # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
      # BUILDER_CONFIG_CLASS = MyBuilderConfig
  
      # You will be able to load one or the other configurations in the following list with
      # data = datasets.load_dataset('my_dataset', 'first_domain')
      # data = datasets.load_dataset('my_dataset', 'second_domain')
      a__     							=       [
          datasets.BuilderConfig(name="first_domain" ,     version=_a ,     description="This part of my dataset covers a first domain"						),
          datasets.BuilderConfig(name="second_domain" ,     version=_a ,     description="This part of my dataset covers a second domain"						),
      ]
  
      a__     							=       "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
  
  
      def      lowerCAmelCase_						(self  )     ->      Tuple:
          # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
          if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
              __UpperCAmelCase									=			datasets.Features(
                  {
                      '''sentence''': datasets.Value('''string'''  ),
                      '''option1''': datasets.Value('''string'''  ),
                      '''answer''': datasets.Value('''string'''  )
                      # These are the features of your dataset like images, labels ...
                  }  )
          else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
              __UpperCAmelCase									=			datasets.Features(
                  {
                      '''sentence''': datasets.Value('''string'''  ),
                      '''option2''': datasets.Value('''string'''  ),
                      '''second_domain_answer''': datasets.Value('''string'''  )
                      # These are the features of your dataset like images, labels ...
                  }  )
          return datasets.DatasetInfo(
              # This is the description that will appear on the datasets page.
              description=_DESCRIPTION     ,      features=lowercase__     ,      homepage=_HOMEPAGE     ,      license=_LICENSE     ,      citation=_CITATION     ,      )
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__  )     ->      int:
          # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
          # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
  
          # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
          # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
          # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
          __UpperCAmelCase									=			_URLS[self.config.name]
          __UpperCAmelCase									=			dl_manager.download_and_extract(lowercase__  )
          return [
              datasets.SplitGenerator(
                  name=datasets.Split.TRAIN     ,      gen_kwargs={
                      '''filepath''': os.path.join(lowercase__     ,      '''train.jsonl'''  ),
                      '''split''': '''train''',
                  }     ,      ),
              datasets.SplitGenerator(
                  name=datasets.Split.VALIDATION     ,      gen_kwargs={
                      '''filepath''': os.path.join(lowercase__     ,      '''dev.jsonl'''  ),
                      '''split''': '''dev''',
                  }     ,      ),
              datasets.SplitGenerator(
                  name=datasets.Split.TEST     ,      gen_kwargs={
                      '''filepath''': os.path.join(lowercase__     ,      '''test.jsonl'''  ),
                      '''split''': '''test'''
                  }     ,      ),
          ]
  
  
  
  
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__  )     ->      Tuple:
          # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
          # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
          with open(lowercase__     ,      encoding='''utf-8'''  ) as f:
              for key, row in enumerate(lowercase__  ):
                  __UpperCAmelCase									=			json.loads(lowercase__  )
                  if self.config.name == "first_domain":
                      # Yields examples as (key, example) tuples
                      yield key, {
                          "sentence": data["sentence"],
                          "option1": data["option1"],
                          "answer": "" if split == "test" else data["answer"],
                      }
                  else:
                      yield key, {
                          "sentence": data["sentence"],
                          "option2": data["option2"],
                          "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
                      }
  
  
  
  
  