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,
  )
  
  
  _UpperCAmelCase   :      Optional[int] 						=  logging.getLogger(__name__)
  
  class 			UpperCAmelCase				(					a_ ):
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		def _lowercase  (    self  ,					_snake_case  ,					_snake_case  ,					_snake_case=None  ,					_snake_case=None     )				->    List[str]:
  				if current_layer == 0:
  						_UpperCamelCase       : List[Any]    =      self.embedding_hidden_mapping_in(_snake_case     )
  				else:
  						_UpperCamelCase       : Tuple    =      hidden_states[0]
  
  				_UpperCamelCase       : int    =      int(self.config.num_hidden_layers / self.config.num_hidden_groups     )
  
  				# Index of the hidden group
  				_UpperCamelCase       : Union[str, Any]    =      int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)     )
  
  				_UpperCamelCase       : Dict    =      self.albert_layer_groups[group_idx](
  				    _snake_case  ,					_snake_case  ,					head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]  ,					)
  				_UpperCamelCase       : Dict    =      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 			UpperCAmelCase				(					a_ ):
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		def __init__(    self  ,					_snake_case     )				->    Optional[int]:
  				super().__init__(_snake_case     )
  
  				_UpperCamelCase       : int    =      AlbertTransformerWithPabee(_snake_case     )
  
  				self.init_weights()
  				_UpperCamelCase       : Optional[Any]    =      0
  				_UpperCamelCase       : List[str]    =      0
  				_UpperCamelCase       : Optional[int]    =      0
  
  				_UpperCamelCase       : Optional[int]    =      0
  		def _lowercase  (    self  ,					_snake_case     )				->    List[str]:
  				_UpperCamelCase       : Optional[int]    =      threshold
  		def _lowercase  (    self  ,					_snake_case     )				->    Any:
  				_UpperCamelCase       : Dict    =      patience
  		def _lowercase  (    self     )				->    int:
  				_UpperCamelCase       : List[Any]    =      0
  				_UpperCamelCase       : Dict    =      0
  		def _lowercase  (    self     )				->    int:
  				_UpperCamelCase       : Tuple    =      self.inference_layers_num / self.inference_instances_num
  				_UpperCamelCase       : 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(_snake_case     )
  
  
  
  
  		@add_start_docstrings_to_model_forward(_snake_case     )
  		def _lowercase  (    self  ,					_snake_case=None  ,					_snake_case=None  ,					_snake_case=None  ,					_snake_case=None  ,					_snake_case=None  ,					_snake_case=None  ,					_snake_case=None  ,					_snake_case=None  ,					_snake_case=False  ,					)				->    str:
  
  				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       : str    =      input_ids.size()
  				elif inputs_embeds is not None:
  						_UpperCamelCase       : List[Any]    =      inputs_embeds.size()[:-1]
  				else:
  						raise ValueError('''You have to specify either input_ids or inputs_embeds'''     )
  
  				_UpperCamelCase       : Optional[int]    =      input_ids.device if input_ids is not None else inputs_embeds.device
  
  				if attention_mask is None:
  						_UpperCamelCase       : int    =      torch.ones(_snake_case  ,					device=_snake_case     )
  				if token_type_ids is None:
  						_UpperCamelCase       : List[Any]    =      torch.zeros(_snake_case  ,					dtype=torch.long  ,					device=_snake_case     )
  
  				_UpperCamelCase       : Dict    =      attention_mask.unsqueeze(1     ).unsqueeze(2     )
  				_UpperCamelCase       : List[str]    =      extended_attention_mask.to(dtype=self.dtype     )  # fp16 compatibility
  				_UpperCamelCase       : Optional[int]    =      (1.0 - extended_attention_mask) * -10_000.0
  				_UpperCamelCase       : List[Any]    =      self.get_head_mask(_snake_case  ,					self.config.num_hidden_layers     )
  
  				_UpperCamelCase       : Dict    =      self.embeddings(
  				    _snake_case  ,					position_ids=_snake_case  ,					token_type_ids=_snake_case  ,					inputs_embeds=_snake_case     )
  				_UpperCamelCase       : List[Any]    =      embedding_output
  
  				if self.training:
  						_UpperCamelCase       : Optional[int]    =      []
  						for i in range(self.config.num_hidden_layers     ):
  								_UpperCamelCase       : List[Any]    =      self.encoder.adaptive_forward(
  								    _snake_case  ,					current_layer=_snake_case  ,					attention_mask=_snake_case  ,					head_mask=_snake_case  ,					)
  
  								_UpperCamelCase       : List[str]    =      self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]     )     )
  								_UpperCamelCase       : Dict    =      output_layers[i](output_dropout(_snake_case     )     )
  								res.append(_snake_case     )
  				elif self.patience == 0:  # Use all layers for inference
  						_UpperCamelCase       : Dict    =      self.encoder(_snake_case  ,					_snake_case  ,					head_mask=_snake_case     )
  						_UpperCamelCase       : List[Any]    =      self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]     )     )
  						_UpperCamelCase       : List[Any]    =      [output_layers[self.config.num_hidden_layers - 1](_snake_case     )]
  				else:
  						_UpperCamelCase       : Optional[int]    =      0
  						_UpperCamelCase       : Tuple    =      None
  						_UpperCamelCase       : Optional[Any]    =      0
  						for i in range(self.config.num_hidden_layers     ):
  								calculated_layer_num += 1
  								_UpperCamelCase       : Union[str, Any]    =      self.encoder.adaptive_forward(
  								    _snake_case  ,					current_layer=_snake_case  ,					attention_mask=_snake_case  ,					head_mask=_snake_case  ,					)
  
  								_UpperCamelCase       : Any    =      self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]     )     )
  								_UpperCamelCase       : Any    =      output_layers[i](_snake_case     )
  								if regression:
  										_UpperCamelCase       : Optional[Any]    =      logits.detach()
  										if patient_result is not None:
  												_UpperCamelCase       : str    =      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       : Union[str, Any]    =      logits.detach().argmax(dim=1     )
  										if patient_result is not None:
  												_UpperCamelCase       : Any    =      patient_result.detach().argmax(dim=1     )
  										if (patient_result is not None) and torch.all(labels.eq(_snake_case     )     ):
  												patient_counter += 1
  										else:
  												_UpperCamelCase       : Any    =      0
  
  								_UpperCamelCase       : Optional[int]    =      logits
  								if patient_counter == self.patience:
  										break
  						_UpperCamelCase       : 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 			UpperCAmelCase				(					a_ ):
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		def __init__(    self  ,					_snake_case     )				->    Tuple:
  				super().__init__(_snake_case     )
  				_UpperCamelCase       : Any    =      config.num_labels
  
  				_UpperCamelCase       : Optional[int]    =      AlbertModelWithPabee(_snake_case     )
  				_UpperCamelCase       : Dict    =      nn.Dropout(config.classifier_dropout_prob     )
  				_UpperCamelCase       : 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(_snake_case     )
  		def _lowercase  (    self  ,					_snake_case=None  ,					_snake_case=None  ,					_snake_case=None  ,					_snake_case=None  ,					_snake_case=None  ,					_snake_case=None  ,					_snake_case=None  ,					)				->    Tuple:
  				_UpperCamelCase       : List[str]    =      self.albert(
  				    input_ids=_snake_case  ,					attention_mask=_snake_case  ,					token_type_ids=_snake_case  ,					position_ids=_snake_case  ,					head_mask=_snake_case  ,					inputs_embeds=_snake_case  ,					output_dropout=self.dropout  ,					output_layers=self.classifiers  ,					regression=self.num_labels == 1  ,					)
  
  				_UpperCamelCase       : int    =      (logits[-1],)
  
  				if labels is not None:
  						_UpperCamelCase       : List[str]    =      None
  						_UpperCamelCase       : Any    =      0
  						for ix, logits_item in enumerate(_snake_case     ):
  								if self.num_labels == 1:
  										#  We are doing regression
  										_UpperCamelCase       : Optional[Any]    =      MSELoss()
  										_UpperCamelCase       : Optional[int]    =      loss_fct(logits_item.view(-1     )  ,					labels.view(-1     )     )
  								else:
  										_UpperCamelCase       : Optional[Any]    =      CrossEntropyLoss()
  										_UpperCamelCase       : List[str]    =      loss_fct(logits_item.view(-1  ,					self.num_labels     )  ,					labels.view(-1     )     )
  								if total_loss is None:
  										_UpperCamelCase       : Dict    =      loss
  								else:
  										total_loss += loss * (ix + 1)
  								total_weights += ix + 1
  						_UpperCamelCase       : Optional[int]    =      (total_loss / total_weights,) + outputs
  
  				return outputs
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  _UpperCAmelCase   :      Optional[int] 						=  """
  # Transformers installation
  ! pip install transformers datasets evaluate
  # To install from source instead of the last release, comment the command above and uncomment the following one.
  # ! pip install git+https://github.com/huggingface/transformers.git
  """
  
  _UpperCAmelCase   :      Union[str, Any] 						=  [{"""type""": """code""", """content""": INSTALL_CONTENT}]
  _UpperCAmelCase   :      Union[str, Any] 						=  {
      """{processor_class}""": """FakeProcessorClass""",
      """{model_class}""": """FakeModelClass""",
      """{object_class}""": """FakeObjectClass""",
  }
  
  
  
  
  
  