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_     :     int    		=						logging.getLogger(__name__)
  def _A		(		lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      random.seed(lowercase	)
      np.random.seed(lowercase	)
      torch.manual_seed(lowercase	)
      torch.cuda.manual_seed_all(lowercase	)
  def _A		(		lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      model.eval()
      a					=next(model.parameters()	).device
      a					=1
      a					=torch.zeros((eval_batch_size, context_len) ,					dtype=torch.long ,					device=lowercase	)
      a					=DataLoader(lowercase ,					shuffle=lowercase ,					batch_size=lowercase	)
      a					=torch.zeros(1 ,					device=lowercase	)
      a					=0
      for batch in eval_dataloader:
          batch.to(lowercase	)
          # pad
          context.zero_()
          for i in range(lowercase	):
              a					=batch[i]
          a					=model(lowercase ,					labels=lowercase	)
          eval_loss += outputs[0].sum().item()
          nb_eval_examples += batch.size(0	)
      a					=eval_loss / nb_eval_examples
      a					=torch.exp(lowercase	)
      model.train()
      return perplexity
  def _A		(		lowercase="gpt2"	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=GPTaLMHeadModel.from_pretrained(lowercase ,					output_hidden_states=lowercase	)
      torch.save(model.state_dict() ,					model_name + '''local.pt'''	)
      return model
  def _A		(		lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=copy.deepcopy(lowercase	)
      model.to(lowercase	)
  
      a					=['''bias''', '''LayerNorm.weight''']
      a					=[
          {
              '''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},
      ]
      a					=AdamW(lowercase ,					lr=5E-5 ,					eps=1E-8	)
      a					=get_linear_schedule_with_warmup(lowercase ,					0 ,					lowercase	)
      torch.cuda.empty_cache()
      return model, lm_optimizer, lm_scheduler
  def _A		(		lowercase ,					lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=np.array(lowercase	).mean()
      a					=np.array(lowercase	).std()
      a					=(real_perps - avg) / std
      a					=list(zip(lowercase ,					lowercase ,					lowercase	)	)
      joblib.dump(lowercase ,					lowercase	)
  def _A		(		lowercase ,					lowercase ,					lowercase ,					lowercase ,					lowercase ,					lowercase ,					lowercase ,					lowercase="dev.jbl" ,					lowercase=recopy_gpta ,					):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=[]
      a					=[]
      a					=[]
  
      # Initialize the transformer model
      a					=copy.deepcopy(lowercase	)
      orig_model.to(device='''cpu'''	)
      torch.cuda.empty_cache()
  
      # Compute perplexity of initial transformer model for comparison
      model.train()
      a	,   a	,   a					=recopy_model(lowercase ,					lowercase ,					lowercase	)
  
      for step in tqdm(range(lowercase	)	):
          a					=torch.zeros((1, context_len) ,					dtype=torch.long ,					device=lowercase	)
          a					=random.choice(lowercase	)
          a					=random.randint(0 ,					len(story[0]	) - context_len - 1	)
          a					=story[0][start : start + context_len]
          lm_optimizer.zero_grad()
          a					=model(lowercase ,					labels=lowercase	)
          a					=outputs[0]
          a					=compute_perplexity(lowercase ,					lowercase ,					lowercase	)
          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
          a					=compute_perplexity(lowercase ,					lowercase ,					lowercase	)
  
          # Periodically save the stored (X, IG(X)) pairs
          if step % 10_00 == 0 and step > 1:
              intermittent_save(lowercase ,					lowercase ,					lowercase ,					lowercase	)
  
          # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
          a	,   a	,   a					=recopy_model(lowercase ,					lowercase ,					lowercase	)
  
          past_perps.append(past_perp.item()	)
          real_perps.append(orig_perp - real_perp.item()	)
          contexts.append(np.array(context.cpu()	)	)
  
      intermittent_save(lowercase ,					lowercase ,					lowercase ,					lowercase	)
  def _A		(		lowercase ,					lowercase="data/tokenized_stories_train_wikitext103.jbl" ,					lowercase=1_00 ,					lowercase=10_26 ,					lowercase=True	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=joblib.load(lowercase	)
      print('''data loaded'''	)
      a					=[]
      if trim:
          for i, example in enumerate(lowercase	):
              if len(example[0]	) > min_len:
                  a					=random.randint(0 ,					len(example[0]	) - context_len - 1	)
                  objective_set.append(example[0, start : start + context_len]	)
              if len(lowercase	) >= number:
                  break
          a					=[]
          for j in range(i + 1 ,					len(lowercase	)	):
              if len(data[j][0]	) > min_len:
                  train_data.append(data[j]	)
      else:
          a					=data[0:number]
          a					=data[number:]
  
      joblib.dump(lowercase ,					'''objective_set.jbl'''	)
      print('''objective set saved'''	)
      return train_data, objective_set
  def _A		(		lowercase ,					lowercase ,					lowercase ,					lowercase ,					lowercase=50 ,					lowercase="secondary_learner.pt"	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=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
      a					=train_dataset[:5_12]
      a					=train_dataset[5_12:]
      a					=DataLoader(lowercase ,					shuffle=lowercase ,					batch_size=lowercase	)
      a					=DataLoader(lowercase ,					shuffle=lowercase ,					batch_size=lowercase	)
  
      # secondary learner model set up
      a					=nn.MSELoss()
      a					=nn.MSELoss(reduction='''sum'''	)
      secondary_learner.to(lowercase	)
      a					=torch.optim.Adam(secondary_learner.parameters() ,					lr=0.00001	)
      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
      a					=float('''inf'''	)
      # Iterate through batches until we've used max_steps batches
      for epoch in range(int(lowercase	)	):
          a					=0.0
          secondary_learner.train()
          for step, batch in enumerate(lowercase	):
              a					=batch[0].to(lowercase	)
              a					=batch[1].to(lowercase	)
              a					=secondary_learner(lowercase	)
              q_optimizer.zero_grad()
              a					=loss(lowercase ,					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(lowercase	)):
                  a					=tr_q_loss / (step + 1)
  
                  secondary_learner.eval()
                  a					=0.0
                  a					=0.0
                  a					=[]
                  a					=[]
                  # Compute performance of the secondary learner after this batch
                  for stepa, batcha in enumerate(lowercase	):
                      a					=batcha[0].to(lowercase	)
                      a					=batcha[1].to(lowercase	)
                      a					=secondary_learner(lowercase	)
                      q_lossa += test_loss(lowercase ,					lowercase	).item()
                      sum_qa += torch.sum(lowercase	).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(lowercase	)
                  print(
                      '''Epoch: ''' ,					lowercase ,					'''step: ''' ,					lowercase ,					'''Avg. q:''' ,					sum_qa / len(lowercase	) ,					'''Train Loss: ''' ,					lowercase ,					'''Test Loss: ''' ,					lowercase ,					)
                  if q_lossa < best_test_loss:
                      joblib.dump((predicted, actual) ,					'''pred_vs_actual.jbl'''	)
                      torch.save(secondary_learner.state_dict() ,					lowercase	)
                      a					=q_lossa
  
              secondary_learner.train()
      return secondary_learner
  
  
  
  
  
  
  class    __A       ( nn.Module    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   def __init__(	self				,					__A		)      ->  List[Any]:
       super(__A				,					self		).__init__()
       a					=model.transformer.wte
       a					=copy.deepcopy(model.transformer.wte.weight		)
       a					=nn.Convad(self.embeddings.weight.size(1		)				,					256				,					3				,					padding=1		)
       a					=nn.Sequential(nn.Linear(256				,					32		)				,					nn.Dropout(p=0.1		)				,					nn.Linear(32				,					32		)				,					nn.Linear(32				,					1		)		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  List[Any]:
       a					=torch.max(self.conv(self.embeddings(__A		).squeeze(1		).transpose(1				,					2		)		)				,					2		)[0]
       a					=self.fc(__A		)
       return qs.squeeze(1		)
  
  
  
  
  
   @classmethod
   def    SCREAMING_SNAKE_CASE							(	cls				,					__A				,					__A		)      ->  int:
       a					=cls(__A		)  # this calls __init__
       a					=torch.load(__A		)
       secondary_learner.load_state_dict(__A		)
       a					=model.transformer.wte
       a					=copy.deepcopy(model.transformer.wte.weight		)
       return secondary_learner
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  lowerCamelCase_     :     int    		=						logging.get_logger(__name__)
  
  lowerCamelCase_     :     Optional[Any]    		=						{
      """dandelin/vilt-b32-mlm""": """https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"""
  }
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						"vilt"
  
  
   def __init__(	self				,					__A=3_0522				,					__A=2				,					__A=2				,					__A=40				,					__A=768				,					__A=12				,					__A=12				,					__A=3072				,					__A="gelu"				,					__A=0.0				,					__A=0.0				,					__A=0.02				,					__A=1E-1_2				,					__A=384				,					__A=32				,					__A=3				,					__A=True				,					__A=-1				,					__A=False				,					__A=-1				,					**__A				,					)      ->  Dict:
       super().__init__(tie_word_embeddings=__A				,					**__A		)
  
       a					=vocab_size
       a					=type_vocab_size
       a					=modality_type_vocab_size
       a					=max_position_embeddings
  
       a					=hidden_size
       a					=num_hidden_layers
       a					=num_attention_heads
       a					=intermediate_size
       a					=hidden_act
       a					=hidden_dropout_prob
       a					=attention_probs_dropout_prob
       a					=initializer_range
       a					=layer_norm_eps
  
       a					=image_size
       a					=patch_size
       a					=num_channels
       a					=qkv_bias
       a					=max_image_length
       a					=num_images