code: 
  
  
  
  """simple docstring"""
  import copy
  import logging
  import random
  
  import joblib
  import numpy as np
  import torch
  import torch.nn as nn
  from torch.utils.data import DataLoader
  from tqdm import tqdm
  
  from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup
  
  
  _lowerCAmelCase					:Tuple  =      logging.getLogger(__name__)
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	Dict							):
      random.seed(UpperCamelCase__							)
      np.random.seed(UpperCamelCase__							)
      torch.manual_seed(UpperCamelCase__							)
      torch.cuda.manual_seed_all(UpperCamelCase__							)
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	Dict  , UpperCamelCase__	:	List[Any]  , UpperCamelCase__	:	List[Any]							):
      model.eval()
      _UpperCAmelCase							:						Tuple       							=					next(model.parameters()							).device
      _UpperCAmelCase							:						List[Any]       							=					1
      _UpperCAmelCase							:						Dict       							=					torch.zeros((eval_batch_size, context_len)  , dtype=torch.long  , device=UpperCamelCase__							)
      _UpperCAmelCase							:						Dict       							=					DataLoader(UpperCamelCase__  , shuffle=UpperCamelCase__  , batch_size=UpperCamelCase__							)
      _UpperCAmelCase							:						List[Any]       							=					torch.zeros(1  , device=UpperCamelCase__							)
      _UpperCAmelCase							:						Dict       							=					0
      for batch in eval_dataloader:
          batch.to(UpperCamelCase__							)
          # pad
          context.zero_()
          for i in range(UpperCamelCase__							):
              _UpperCAmelCase							:						Union[str, Any]       							=					batch[i]
          _UpperCAmelCase							:						Tuple       							=					model(UpperCamelCase__  , labels=UpperCamelCase__							)
          eval_loss += outputs[0].sum().item()
          nb_eval_examples += batch.size(0							)
      _UpperCAmelCase							:						Tuple       							=					eval_loss / nb_eval_examples
      _UpperCAmelCase							:						List[Any]       							=					torch.exp(UpperCamelCase__							)
      model.train()
      return perplexity
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	List[Any]="gpt2"							):
      _UpperCAmelCase							:						Optional[Any]       							=					GPTaLMHeadModel.from_pretrained(UpperCamelCase__  , output_hidden_states=UpperCamelCase__							)
      torch.save(model.state_dict()  , model_name + '''local.pt'''							)
      return model
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	List[str]  , UpperCamelCase__	:	Any  , UpperCamelCase__	:	Union[str, Any]							):
      _UpperCAmelCase							:						Optional[int]       							=					copy.deepcopy(UpperCamelCase__							)
      model.to(UpperCamelCase__							)
  
      _UpperCAmelCase							:						str       							=					['''bias''', '''LayerNorm.weight''']
      _UpperCAmelCase							:						Dict       							=					[
          {
              '''params''': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay							)],
              '''weight_decay''': 0.0,
          },
          {'''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay							)], '''weight_decay''': 0.0},
      ]
      _UpperCAmelCase							:						Optional[int]       							=					AdamW(UpperCamelCase__  , lr=5E-5  , eps=1E-8							)
      _UpperCAmelCase							:						Union[str, Any]       							=					get_linear_schedule_with_warmup(UpperCamelCase__  , 0  , UpperCamelCase__							)
      torch.cuda.empty_cache()
      return model, lm_optimizer, lm_scheduler
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	int  , UpperCamelCase__	:	str  , UpperCamelCase__	:	List[Any]  , UpperCamelCase__	:	Union[str, Any]							):
      _UpperCAmelCase							:						Tuple       							=					np.array(UpperCamelCase__							).mean()
      _UpperCAmelCase							:						Tuple       							=					np.array(UpperCamelCase__							).std()
      _UpperCAmelCase							:						Dict       							=					(real_perps - avg) / std
      _UpperCAmelCase							:						List[str]       							=					list(zip(UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__							)							)
      joblib.dump(UpperCamelCase__  , UpperCamelCase__							)
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	List[Any]  , UpperCamelCase__	:	Any  , UpperCamelCase__	:	str  , UpperCamelCase__	:	int  , UpperCamelCase__	:	Union[str, Any]  , UpperCamelCase__	:	List[Any]  , UpperCamelCase__	:	Tuple  , UpperCamelCase__	:	Optional[Any]="dev.jbl"  , UpperCamelCase__	:	int=recopy_gpta  , ):
      _UpperCAmelCase							:						Union[str, Any]       							=					[]
      _UpperCAmelCase							:						Optional[int]       							=					[]
      _UpperCAmelCase							:						int       							=					[]
  
      # Initialize the transformer model
      _UpperCAmelCase							:						Union[str, Any]       							=					copy.deepcopy(UpperCamelCase__							)
      orig_model.to(device='''cpu'''							)
      torch.cuda.empty_cache()
  
      # Compute perplexity of initial transformer model for comparison
      model.train()
      _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							:						str       							=					recopy_model(UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__							)
  
      for step in tqdm(range(UpperCamelCase__							)							):
          _UpperCAmelCase							:						int       							=					torch.zeros((1, context_len)  , dtype=torch.long  , device=UpperCamelCase__							)
          _UpperCAmelCase							:						List[str]       							=					random.choice(UpperCamelCase__							)
          _UpperCAmelCase							:						Any       							=					random.randint(0  , len(story[0]							) - context_len - 1							)
          _UpperCAmelCase							:						List[Any]       							=					story[0][start : start + context_len]
          lm_optimizer.zero_grad()
          _UpperCAmelCase							:						Union[str, Any]       							=					model(UpperCamelCase__  , labels=UpperCamelCase__							)
          _UpperCAmelCase							:						Union[str, Any]       							=					outputs[0]
          _UpperCAmelCase							:						int       							=					compute_perplexity(UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__							)
          model.train()
          lm_loss.backward()
          # Do LM backprop
          torch.nn.utils.clip_grad_norm_(model.parameters()  , 3.0							)
          lm_optimizer.step()
          lm_scheduler.step()  # Update learning rate schedule
  
          # Compute perplexity after back-propagating on the selected context
          _UpperCAmelCase							:						List[Any]       							=					compute_perplexity(UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__							)
  
          # Periodically save the stored (X, IG(X)) pairs
          if step % 1000 == 0 and step > 1:
              intermittent_save(UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__							)
  
          # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
          _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							:						Optional[Any]       							=					recopy_model(UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__							)
  
          past_perps.append(past_perp.item()							)
          real_perps.append(orig_perp - real_perp.item()							)
          contexts.append(np.array(context.cpu()							)							)
  
      intermittent_save(UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__							)
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	List[str]  , UpperCamelCase__	:	Any="data/tokenized_stories_train_wikitext103.jbl"  , UpperCamelCase__	:	Union[str, Any]=100  , UpperCamelCase__	:	str=1026  , UpperCamelCase__	:	Union[str, Any]=True							):
      _UpperCAmelCase							:						str       							=					joblib.load(UpperCamelCase__							)
      print('''data loaded'''							)
      _UpperCAmelCase							:						Tuple       							=					[]
      if trim:
          for i, example in enumerate(UpperCamelCase__							):
              if len(example[0]							) > min_len:
                  _UpperCAmelCase							:						List[str]       							=					random.randint(0  , len(example[0]							) - context_len - 1							)
                  objective_set.append(example[0, start : start + context_len]							)
              if len(UpperCamelCase__							) >= number:
                  break
          _UpperCAmelCase							:						List[str]       							=					[]
          for j in range(i + 1  , len(UpperCamelCase__							)							):
              if len(data[j][0]							) > min_len:
                  train_data.append(data[j]							)
      else:
          _UpperCAmelCase							:						Optional[Any]       							=					data[0:number]
          _UpperCAmelCase							:						List[str]       							=					data[number:]
  
      joblib.dump(UpperCamelCase__  , '''objective_set.jbl'''							)
      print('''objective set saved'''							)
      return train_data, objective_set
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	List[str]  , UpperCamelCase__	:	Optional[int]  , UpperCamelCase__	:	List[str]  , UpperCamelCase__	:	List[str]  , UpperCamelCase__	:	List[Any]=50  , UpperCamelCase__	:	Optional[int]="secondary_learner.pt"							):
      _UpperCAmelCase							:						Any       							=					torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''							)
      # We will use the first 512 pairs from our dataset as a test set for
      # our secondary learner and the rest to train
      _UpperCAmelCase							:						int       							=					train_dataset[:512]
      _UpperCAmelCase							:						Any       							=					train_dataset[512:]
      _UpperCAmelCase							:						Dict       							=					DataLoader(UpperCamelCase__  , shuffle=UpperCamelCase__  , batch_size=UpperCamelCase__							)
      _UpperCAmelCase							:						List[str]       							=					DataLoader(UpperCamelCase__  , shuffle=UpperCamelCase__  , batch_size=UpperCamelCase__							)
  
      # secondary learner model set up
      _UpperCAmelCase							:						Tuple       							=					nn.MSELoss()
      _UpperCAmelCase							:						Any       							=					nn.MSELoss(reduction='''sum'''							)
      secondary_learner.to(UpperCamelCase__							)
      _UpperCAmelCase							:						List[Any]       							=					torch.optim.Adam(secondary_learner.parameters()  , lr=0.0_0001							)
      secondary_learner.train()
  
      # TODO in original code this is written as number of actual batches seen
      # not number of items seen but other places it is number of items instead.
      # improve consistency! changed this to epochs for clarity
      _UpperCAmelCase							:						Dict       							=					float('''inf'''							)
      # Iterate through batches until we've used max_steps batches
      for epoch in range(int(UpperCamelCase__							)							):
          _UpperCAmelCase							:						Union[str, Any]       							=					0.0
          secondary_learner.train()
          for step, batch in enumerate(UpperCamelCase__							):
              _UpperCAmelCase							:						Optional[Any]       							=					batch[0].to(UpperCamelCase__							)
              _UpperCAmelCase							:						Dict       							=					batch[1].to(UpperCamelCase__							)
              _UpperCAmelCase							:						int       							=					secondary_learner(UpperCamelCase__							)
              q_optimizer.zero_grad()
              _UpperCAmelCase							:						List[Any]       							=					loss(UpperCamelCase__  , real_q.float()							)
              q_loss.backward()
              q_optimizer.step()
              tr_q_loss += q_loss.item()
  
              # model trains fairly quickly so we won't wait for a full epoch
              # eval is triggered at eval_freq and end of epochs
              if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(UpperCamelCase__							)):
                  _UpperCAmelCase							:						int       							=					tr_q_loss / (step + 1)
  
                  secondary_learner.eval()
                  _UpperCAmelCase							:						Optional[Any]       							=					0.0
                  _UpperCAmelCase							:						Dict       							=					0.0
                  _UpperCAmelCase							:						Union[str, Any]       							=					[]
                  _UpperCAmelCase							:						int       							=					[]
                  # Compute performance of the secondary learner after this batch
                  for stepa, batcha in enumerate(UpperCamelCase__							):
                      _UpperCAmelCase							:						List[str]       							=					batcha[0].to(UpperCamelCase__							)
                      _UpperCAmelCase							:						str       							=					batcha[1].to(UpperCamelCase__							)
                      _UpperCAmelCase							:						Optional[int]       							=					secondary_learner(UpperCamelCase__							)
                      q_lossa += test_loss(UpperCamelCase__  , UpperCamelCase__							).item()
                      sum_qa += torch.sum(UpperCamelCase__							).item()
                      for ei, i in enumerate(predicted_qa.cpu().detach().numpy()							):
                          predicted.append(i.item()							)
                      for ei, i in enumerate(real_qa.cpu().detach().numpy()							):
                          actual.append(i.item()							)
  
                  q_lossa /= len(UpperCamelCase__							)
                  print(
                      '''Epoch: '''  , UpperCamelCase__  , '''step: '''  , UpperCamelCase__  , '''Avg. q:'''  , sum_qa / len(UpperCamelCase__							)  , '''Train Loss: '''  , UpperCamelCase__  , '''Test Loss: '''  , UpperCamelCase__  , )
                  if q_lossa < best_test_loss:
                      joblib.dump((predicted, actual)  , '''pred_vs_actual.jbl'''							)
                      torch.save(secondary_learner.state_dict()  , UpperCamelCase__							)
                      _UpperCAmelCase							:						Tuple       							=					q_lossa
  
              secondary_learner.train()
      return secondary_learner
  
  
  class 							_UpperCAmelCase							(				nn.Module   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def __init__(      self ,			A	)							->      Tuple:
           super(UpperCamelCase__ ,			self	).__init__()
           _UpperCAmelCase							:						Tuple       							=					model.transformer.wte
           _UpperCAmelCase							:						Union[str, Any]       							=					copy.deepcopy(model.transformer.wte.weight	)
           _UpperCAmelCase							:						Any       							=					nn.Convad(self.embeddings.weight.size(1	) ,			2_5_6 ,			3 ,			padding=1	)
           _UpperCAmelCase							:						List[str]       							=					nn.Sequential(nn.Linear(2_5_6 ,			3_2	) ,			nn.Dropout(p=0.1	) ,			nn.Linear(3_2 ,			3_2	) ,			nn.Linear(3_2 ,			1	)	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Optional[Any]:
           _UpperCAmelCase							:						Optional[int]       							=					torch.max(self.conv(self.embeddings(UpperCamelCase__	).squeeze(1	).transpose(1 ,			2	)	) ,			2	)[0]
           _UpperCAmelCase							:						Tuple       							=					self.fc(UpperCamelCase__	)
           return qs.squeeze(1	)
  
  
  
  
  
       @classmethod
       def 				__lowerCAmelCase			(      cls ,			A ,			A	)							->      List[str]:
           _UpperCAmelCase							:						Dict       							=					cls(UpperCamelCase__	)  # this calls __init__
           _UpperCAmelCase							:						List[str]       							=					torch.load(UpperCamelCase__	)
           secondary_learner.load_state_dict(UpperCamelCase__	)
           _UpperCAmelCase							:						Optional[int]       							=					model.transformer.wte
           _UpperCAmelCase							:						int       							=					copy.deepcopy(model.transformer.wte.weight	)
           return secondary_learner
  
  
  
style_context: 
  
  
  
  """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,
  )
  
  
  _lowerCAmelCase					:Union[str, Any]  =      logging.getLogger(__name__)
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A ,			A=None ,			A=None	)							->      Tuple:
           if current_layer == 0:
               _UpperCAmelCase							:						Optional[Any]       							=					self.embedding_hidden_mapping_in(A	)
           else:
               _UpperCAmelCase							:						Union[str, Any]       							=					hidden_states[0]
  
           _UpperCAmelCase							:						Dict       							=					int(self.config.num_hidden_layers / self.config.num_hidden_groups	)
  
           # Index of the hidden group
           _UpperCAmelCase							:						Dict       							=					int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)	)
  
           _UpperCAmelCase							:						List[Any]       							=					self.albert_layer_groups[group_idx](
               A ,			A ,			head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group] ,			)
           _UpperCAmelCase							:						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 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def __init__(      self ,			A	)							->      str:
           super().__init__(A	)
  
           _UpperCAmelCase							:						Dict       							=					AlbertTransformerWithPabee(A	)
  
           self.init_weights()
           _UpperCAmelCase							:						str       							=					0
           _UpperCAmelCase							:						Union[str, Any]       							=					0
           _UpperCAmelCase							:						Dict       							=					0
  
           _UpperCAmelCase							:						str       							=					0
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Optional[int]:
           _UpperCAmelCase							:						List[Any]       							=					threshold
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Optional[Any]:
           _UpperCAmelCase							:						int       							=					patience
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      Any:
           _UpperCAmelCase							:						Dict       							=					0
           _UpperCAmelCase							:						List[str]       							=					0
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      List[Any]:
           _UpperCAmelCase							:						Union[str, Any]       							=					self.inference_layers_num / self.inference_instances_num
           _UpperCAmelCase							:						str       							=					(
               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(A	)
  
  
  
  
  
       @add_start_docstrings_to_model_forward(A	)
       def 				__lowerCAmelCase			(      self ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=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:
               _UpperCAmelCase							:						Optional[Any]       							=					input_ids.size()
           elif inputs_embeds is not None:
               _UpperCAmelCase							:						Optional[Any]       							=					inputs_embeds.size()[:-1]
           else:
               raise ValueError('''You have to specify either input_ids or inputs_embeds'''	)
  
           _UpperCAmelCase							:						Tuple       							=					input_ids.device if input_ids is not None else inputs_embeds.device
  
           if attention_mask is None:
               _UpperCAmelCase							:						Union[str, Any]       							=					torch.ones(A ,			device=A	)
           if token_type_ids is None:
               _UpperCAmelCase							:						Tuple       							=					torch.zeros(A ,			dtype=torch.long ,			device=A	)
  
           _UpperCAmelCase							:						Optional[Any]       							=					attention_mask.unsqueeze(1	).unsqueeze(2	)
           _UpperCAmelCase							:						Union[str, Any]       							=					extended_attention_mask.to(dtype=self.dtype	)  # fp16 compatibility
           _UpperCAmelCase							:						Dict       							=					(1.0 - extended_attention_mask) * -10_000.0
           _UpperCAmelCase							:						Any       							=					self.get_head_mask(A ,			self.config.num_hidden_layers	)
  
           _UpperCAmelCase							:						int       							=					self.embeddings(
               A ,			position_ids=A ,			token_type_ids=A ,			inputs_embeds=A	)
           _UpperCAmelCase							:						Any       							=					embedding_output
  
           if self.training:
               _UpperCAmelCase							:						Optional[Any]       							=					[]
               for i in range(self.config.num_hidden_layers	):
                   _UpperCAmelCase							:						Union[str, Any]       							=					self.encoder.adaptive_forward(
                       A ,			current_layer=A ,			attention_mask=A ,			head_mask=A ,			)
  
                   _UpperCAmelCase							:						List[str]       							=					self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]	)	)
                   _UpperCAmelCase							:						Dict       							=					output_layers[i](output_dropout(A	)	)
                   res.append(A	)
           elif self.patience == 0:  # Use all layers for inference
               _UpperCAmelCase							:						Dict       							=					self.encoder(A ,			A ,			head_mask=A	)
               _UpperCAmelCase							:						Tuple       							=					self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]	)	)
               _UpperCAmelCase							:						Optional[int]       							=					[output_layers[self.config.num_hidden_layers - 1](A	)]
           else:
               _UpperCAmelCase							:						List[Any]       							=					0
               _UpperCAmelCase							:						int       							=					None
               _UpperCAmelCase							:						int       							=					0
               for i in range(self.config.num_hidden_layers	):
                   calculated_layer_num += 1
                   _UpperCAmelCase							:						Tuple       							=					self.encoder.adaptive_forward(
                       A ,			current_layer=A ,			attention_mask=A ,			head_mask=A ,			)
  
                   _UpperCAmelCase							:						Any       							=					self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]	)	)
                   _UpperCAmelCase							:						Optional[Any]       							=					output_layers[i](A	)
                   if regression:
                       _UpperCAmelCase							:						str       							=					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							:						List[Any]       							=					0
                   else:
                       _UpperCAmelCase							:						List[str]       							=					logits.detach().argmax(dim=1	)
                       if patient_result is not None:
                           _UpperCAmelCase							:						Optional[Any]       							=					patient_result.detach().argmax(dim=1	)
                       if (patient_result is not None) and torch.all(labels.eq(A	)	):
                           patient_counter += 1
                       else:
                           _UpperCAmelCase							:						Tuple       							=					0
  
                   _UpperCAmelCase							:						Any       							=					logits
                   if patient_counter == self.patience:
                       break
               _UpperCAmelCase							:						List[str]       							=					[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 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def __init__(      self ,			A	)							->      Optional[int]:
           super().__init__(A	)
           _UpperCAmelCase							:						List[Any]       							=					config.num_labels
  
           _UpperCAmelCase							:						Optional[int]       							=					AlbertModelWithPabee(A	)
           _UpperCAmelCase							:						List[str]       							=					nn.Dropout(config.classifier_dropout_prob	)
           _UpperCAmelCase							:						Optional[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(A	)
       def 				__lowerCAmelCase			(      self ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			)							->      List[str]:
           _UpperCAmelCase							:						Optional[Any]       							=					self.albert(
               input_ids=A ,			attention_mask=A ,			token_type_ids=A ,			position_ids=A ,			head_mask=A ,			inputs_embeds=A ,			output_dropout=self.dropout ,			output_layers=self.classifiers ,			regression=self.num_labels == 1 ,			)
  
           _UpperCAmelCase							:						int       							=					(logits[-1],)
  
           if labels is not None:
               _UpperCAmelCase							:						int       							=					None
               _UpperCAmelCase							:						Tuple       							=					0
               for ix, logits_item in enumerate(A	):
                   if self.num_labels == 1:
                       #  We are doing regression
                       _UpperCAmelCase							:						Dict       							=					MSELoss()
                       _UpperCAmelCase							:						Optional[int]       							=					loss_fct(logits_item.view(-1	) ,			labels.view(-1	)	)
                   else:
                       _UpperCAmelCase							:						List[str]       							=					CrossEntropyLoss()
                       _UpperCAmelCase							:						Any       							=					loss_fct(logits_item.view(-1 ,			self.num_labels	) ,			labels.view(-1	)	)
                   if total_loss is None:
                       _UpperCAmelCase							:						Optional[Any]       							=					loss
                   else:
                       total_loss += loss * (ix + 1)
                   total_weights += ix + 1
               _UpperCAmelCase							:						Any       							=					(total_loss / total_weights,) + outputs
  
           return outputs
  
  
  