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__				: Any									=    logging.getLogger(__name__)
  
  
  
  
  
  
  class 							__snake_case    (				A_       ):
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_=None			,					UpperCamelCase_=None		)			->		Dict:
  
  
  
  		if current_layer == 0:
  			snake_case__  					=      self.embedding_hidden_mapping_in(UpperCamelCase_		)
  		else:
  			snake_case__  					=      hidden_states[0]
  
  		snake_case__  					=      int(self.config.num_hidden_layers / self.config.num_hidden_groups		)
  
  		# Index of the hidden group
  		snake_case__  					=      int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)		)
  
  		snake_case__  					=      self.albert_layer_groups[group_idx](
  		    UpperCamelCase_			,					UpperCamelCase_			,					head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]			,					)
  		snake_case__  					=      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 							__snake_case    (				A_       ):
  
  
  
  
  
  	def __init__(		self			,					UpperCamelCase_		)			->		List[str]:
  		super().__init__(UpperCamelCase_		)
  
  		snake_case__  					=      AlbertTransformerWithPabee(UpperCamelCase_		)
  
  		self.init_weights()
  		snake_case__  					=      0
  		snake_case__  					=      0
  		snake_case__  					=      0
  
  		snake_case__  					=      0
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_		)			->		Any:
  		snake_case__  					=      threshold
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_		)			->		Dict:
  		snake_case__  					=      patience
  
  
  
  
  
  	def      _snake_case      (		self		)			->		List[Any]:
  		snake_case__  					=      0
  		snake_case__  					=      0
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Dict:
  		snake_case__  					=      self.inference_layers_num / self.inference_instances_num
  		snake_case__  					=      (
  		    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      _snake_case      (		self			,					UpperCamelCase_=None			,					UpperCamelCase_=None			,					UpperCamelCase_=None			,					UpperCamelCase_=None			,					UpperCamelCase_=None			,					UpperCamelCase_=None			,					UpperCamelCase_=None			,					UpperCamelCase_=None			,					UpperCamelCase_=False			,					)			->		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:
  			snake_case__  					=      input_ids.size()
  		elif inputs_embeds is not None:
  			snake_case__  					=      inputs_embeds.size()[:-1]
  		else:
  			raise ValueError('You have to specify either input_ids or inputs_embeds'		)
  
  		snake_case__  					=      input_ids.device if input_ids is not None else inputs_embeds.device
  
  		if attention_mask is None:
  			snake_case__  					=      torch.ones(UpperCamelCase_			,					device=UpperCamelCase_		)
  		if token_type_ids is None:
  			snake_case__  					=      torch.zeros(UpperCamelCase_			,					dtype=torch.long			,					device=UpperCamelCase_		)
  
  		snake_case__  					=      attention_mask.unsqueeze(1		).unsqueeze(2		)
  		snake_case__  					=      extended_attention_mask.to(dtype=self.dtype		)  # fp16 compatibility
  		snake_case__  					=      (1.0 - extended_attention_mask) * -1_0_0_0_0.0
  		snake_case__  					=      self.get_head_mask(UpperCamelCase_			,					self.config.num_hidden_layers		)
  
  		snake_case__  					=      self.embeddings(
  		    UpperCamelCase_			,					position_ids=UpperCamelCase_			,					token_type_ids=UpperCamelCase_			,					inputs_embeds=UpperCamelCase_		)
  		snake_case__  					=      embedding_output
  
  		if self.training:
  			snake_case__  					=      []
  			for i in range(self.config.num_hidden_layers		):
  				snake_case__  					=      self.encoder.adaptive_forward(
  				    UpperCamelCase_			,					current_layer=UpperCamelCase_			,					attention_mask=UpperCamelCase_			,					head_mask=UpperCamelCase_			,					)
  
  				snake_case__  					=      self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]		)		)
  				snake_case__  					=      output_layers[i](output_dropout(UpperCamelCase_		)		)
  				res.append(UpperCamelCase_		)
  		elif self.patience == 0:  # Use all layers for inference
  			snake_case__  					=      self.encoder(UpperCamelCase_			,					UpperCamelCase_			,					head_mask=UpperCamelCase_		)
  			snake_case__  					=      self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]		)		)
  			snake_case__  					=      [output_layers[self.config.num_hidden_layers - 1](UpperCamelCase_		)]
  		else:
  			snake_case__  					=      0
  			snake_case__  					=      None
  			snake_case__  					=      0
  			for i in range(self.config.num_hidden_layers		):
  				calculated_layer_num += 1
  				snake_case__  					=      self.encoder.adaptive_forward(
  				    UpperCamelCase_			,					current_layer=UpperCamelCase_			,					attention_mask=UpperCamelCase_			,					head_mask=UpperCamelCase_			,					)
  
  				snake_case__  					=      self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]		)		)
  				snake_case__  					=      output_layers[i](UpperCamelCase_		)
  				if regression:
  					snake_case__  					=      logits.detach()
  					if patient_result is not None:
  						snake_case__  					=      patient_result.detach()
  					if (patient_result is not None) and torch.abs(patient_result - labels		) < self.regression_threshold:
  						patient_counter += 1
  					else:
  						snake_case__  					=      0
  				else:
  					snake_case__  					=      logits.detach().argmax(dim=1		)
  					if patient_result is not None:
  						snake_case__  					=      patient_result.detach().argmax(dim=1		)
  					if (patient_result is not None) and torch.all(labels.eq(UpperCamelCase_		)		):
  						patient_counter += 1
  					else:
  						snake_case__  					=      0
  
  				snake_case__  					=      logits
  				if patient_counter == self.patience:
  					break
  			snake_case__  					=      [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
      the pooled output) e.g. for GLUE tasks. '''					,					A_					,					)
  class 							__snake_case    (				A_       ):
  
  
  
  
  
  	def __init__(		self			,					UpperCamelCase_		)			->		List[Any]:
  		super().__init__(UpperCamelCase_		)
  		snake_case__  					=      config.num_labels
  
  		snake_case__  					=      AlbertModelWithPabee(UpperCamelCase_		)
  		snake_case__  					=      nn.Dropout(config.classifier_dropout_prob		)
  		snake_case__  					=      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      _snake_case      (		self			,					UpperCamelCase_=None			,					UpperCamelCase_=None			,					UpperCamelCase_=None			,					UpperCamelCase_=None			,					UpperCamelCase_=None			,					UpperCamelCase_=None			,					UpperCamelCase_=None			,					)			->		List[str]:
  		snake_case__  					=      self.albert(
  		    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			,					)
  
  		snake_case__  					=      (logits[-1],)
  
  		if labels is not None:
  			snake_case__  					=      None
  			snake_case__  					=      0
  			for ix, logits_item in enumerate(UpperCamelCase_		):
  				if self.num_labels == 1:
  					#  We are doing regression
  					snake_case__  					=      MSELoss()
  					snake_case__  					=      loss_fct(logits_item.view(-1		)			,					labels.view(-1		)		)
  				else:
  					snake_case__  					=      CrossEntropyLoss()
  					snake_case__  					=      loss_fct(logits_item.view(-1			,					self.num_labels		)			,					labels.view(-1		)		)
  				if total_loss is None:
  					snake_case__  					=      loss
  				else:
  					total_loss += loss * (ix + 1)
  				total_weights += ix + 1
  			snake_case__  					=      (total_loss / total_weights,) + outputs
  
  		return outputs
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  a__				: Any									=    {
      0: '''underwear''',
      1: '''pants''',
      2: '''belt''',
      3: '''suit''',
      4: '''shoe''',
      5: '''socks''',
      6: '''shirt''',
      7: '''tie''',
      8: '''watch''',
  }
  
  a__				: Optional[Any]									=    [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]
  
  a__				: List[str]									=    [0 for x in range(len(graph))]
  a__				: List[Any]									=    []
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_       )      ->Dict:
  	snake_case__  					=      1
  	while stack:
  		snake_case__  					=      stack.pop()
  		print(UpperCAmelCase_				,							clothes[current_clothing]       )
  		order += 1
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_       )      ->Dict:
  	snake_case__  					=      1
  	for v in graph[u]:
  		if not visited[v]:
  			depth_first_search(UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_       )
  
  	stack.append(UpperCAmelCase_       )
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_       )      ->Union[str, Any]:
  	for v in range(len(UpperCAmelCase_       )       ):
  		if not visited[v]:
  			depth_first_search(UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_       )
  
  
  if __name__ == "__main__":
  			topological_sort(graph, visited)
  			print(stack)
  			print_stack(stack, clothes)
  
  
  
  