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
  
  
  __lowercase:		str							=     logging.getLogger(__name__)
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Any   ):
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     random.seed(_UpperCamelCase   )
     np.random.seed(_UpperCamelCase   )
     torch.manual_seed(_UpperCamelCase   )
     torch.cuda.manual_seed_all(_UpperCamelCase   )
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				str    ,		_UpperCamelCase      :				List[str]    ,		_UpperCamelCase      :				Optional[Any]   ):
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     model.eval()
     UpperCamelCase__ 						=						next(model.parameters()   ).device
     UpperCamelCase__ 						=						1
     UpperCamelCase__ 						=						torch.zeros((eval_batch_size, context_len)    ,		dtype=torch.long    ,		device=_UpperCamelCase   )
     UpperCamelCase__ 						=						DataLoader(_UpperCamelCase    ,		shuffle=_UpperCamelCase    ,		batch_size=_UpperCamelCase   )
     UpperCamelCase__ 						=						torch.zeros(1    ,		device=_UpperCamelCase   )
     UpperCamelCase__ 						=						0
     for batch in eval_dataloader:
        batch.to(_UpperCamelCase   )
        # pad
        context.zero_()
        for i in range(_UpperCamelCase   ):
           UpperCamelCase__ 						=						batch[i]
        UpperCamelCase__ 						=						model(_UpperCamelCase    ,		labels=_UpperCamelCase   )
        eval_loss += outputs[0].sum().item()
        nb_eval_examples += batch.size(0   )
     UpperCamelCase__ 						=						eval_loss / nb_eval_examples
     UpperCamelCase__ 						=						torch.exp(_UpperCamelCase   )
     model.train()
     return perplexity
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Optional[int]="gpt2"   ):
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						GPTaLMHeadModel.from_pretrained(_UpperCamelCase    ,		output_hidden_states=_UpperCamelCase   )
     torch.save(model.state_dict()    ,		model_name + "local.pt"   )
     return model
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Optional[Any]    ,		_UpperCamelCase      :				Optional[int]    ,		_UpperCamelCase      :				List[Any]   ):
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						copy.deepcopy(_UpperCamelCase   )
     model.to(_UpperCamelCase   )
  
     UpperCamelCase__ 						=						["bias", "LayerNorm.weight"]
     UpperCamelCase__ 						=						[
         {
             "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__ 						=						AdamW(_UpperCamelCase    ,		lr=5e-5    ,		eps=1e-8   )
     UpperCamelCase__ 						=						get_linear_schedule_with_warmup(_UpperCamelCase    ,		0    ,		_UpperCamelCase   )
     torch.cuda.empty_cache()
     return model, lm_optimizer, lm_scheduler
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Union[str, Any]    ,		_UpperCamelCase      :				str    ,		_UpperCamelCase      :				Dict    ,		_UpperCamelCase      :				List[Any]   ):
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						np.array(_UpperCamelCase   ).mean()
     UpperCamelCase__ 						=						np.array(_UpperCamelCase   ).std()
     UpperCamelCase__ 						=						(real_perps - avg) / std
     UpperCamelCase__ 						=						list(zip(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase   )   )
     joblib.dump(_UpperCamelCase    ,		_UpperCamelCase   )
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				List[str]    ,		_UpperCamelCase      :				List[Any]    ,		_UpperCamelCase      :				Tuple    ,		_UpperCamelCase      :				Tuple    ,		_UpperCamelCase      :				Optional[int]    ,		_UpperCamelCase      :				int    ,		_UpperCamelCase      :				List[str]    ,		_UpperCamelCase      :				Union[str, Any]="dev.jbl"    ,		_UpperCamelCase      :				str=recopy_gpta    ,		):
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						[]
     UpperCamelCase__ 						=						[]
     UpperCamelCase__ 						=						[]
  
     # Initialize the transformer model
     UpperCamelCase__ 						=						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__ 						=						recopy_model(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase   )
  
     for step in tqdm(range(_UpperCamelCase   )   ):
        UpperCamelCase__ 						=						torch.zeros((1, context_len)    ,		dtype=torch.long    ,		device=_UpperCamelCase   )
        UpperCamelCase__ 						=						random.choice(_UpperCamelCase   )
        UpperCamelCase__ 						=						random.randint(0    ,		len(story[0]   ) - context_len - 1   )
        UpperCamelCase__ 						=						story[0][start : start + context_len]
        lm_optimizer.zero_grad()
        UpperCamelCase__ 						=						model(_UpperCamelCase    ,		labels=_UpperCamelCase   )
        UpperCamelCase__ 						=						outputs[0]
        UpperCamelCase__ 						=						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__ 						=						compute_perplexity(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase   )
  
        # Periodically save the stored (X, IG(X)) pairs
        if step % 10_00 == 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__ 						=						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        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Optional[int]    ,		_UpperCamelCase      :				List[str]="data/tokenized_stories_train_wikitext103.jbl"    ,		_UpperCamelCase      :				str=1_00    ,		_UpperCamelCase      :				Optional[int]=10_26    ,		_UpperCamelCase      :				Tuple=True   ):
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						joblib.load(_UpperCamelCase   )
     print("data loaded"   )
     UpperCamelCase__ 						=						[]
     if trim:
        for i, example in enumerate(_UpperCamelCase   ):
           if len(example[0]   ) > min_len:
              UpperCamelCase__ 						=						random.randint(0    ,		len(example[0]   ) - context_len - 1   )
              objective_set.append(example[0, start : start + context_len]   )
           if len(_UpperCamelCase   ) >= number:
              break
        UpperCamelCase__ 						=						[]
        for j in range(i + 1    ,		len(_UpperCamelCase   )   ):
           if len(data[j][0]   ) > min_len:
              train_data.append(data[j]   )
     else:
        UpperCamelCase__ 						=						data[0:number]
        UpperCamelCase__ 						=						data[number:]
  
     joblib.dump(_UpperCamelCase    ,		"objective_set.jbl"   )
     print("objective set saved"   )
     return train_data, objective_set
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Union[str, Any]    ,		_UpperCamelCase      :				Optional[int]    ,		_UpperCamelCase      :				Dict    ,		_UpperCamelCase      :				int    ,		_UpperCamelCase      :				Tuple=50    ,		_UpperCamelCase      :				Dict="secondary_learner.pt"   ):
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						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__ 						=						train_dataset[:5_12]
     UpperCamelCase__ 						=						train_dataset[5_12:]
     UpperCamelCase__ 						=						DataLoader(_UpperCamelCase    ,		shuffle=_UpperCamelCase    ,		batch_size=_UpperCamelCase   )
     UpperCamelCase__ 						=						DataLoader(_UpperCamelCase    ,		shuffle=_UpperCamelCase    ,		batch_size=_UpperCamelCase   )
  
     # secondary learner model set up
     UpperCamelCase__ 						=						nn.MSELoss()
     UpperCamelCase__ 						=						nn.MSELoss(reduction="sum"   )
     secondary_learner.to(_UpperCamelCase   )
     UpperCamelCase__ 						=						torch.optim.Adam(secondary_learner.parameters()    ,		lr=0.0_0_0_0_1   )
     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__ 						=						float("inf"   )
     # Iterate through batches until we've used max_steps batches
     for epoch in range(int(_UpperCamelCase   )   ):
        UpperCamelCase__ 						=						0.0
        secondary_learner.train()
        for step, batch in enumerate(_UpperCamelCase   ):
           UpperCamelCase__ 						=						batch[0].to(_UpperCamelCase   )
           UpperCamelCase__ 						=						batch[1].to(_UpperCamelCase   )
           UpperCamelCase__ 						=						secondary_learner(_UpperCamelCase   )
           q_optimizer.zero_grad()
           UpperCamelCase__ 						=						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__ 						=						tr_q_loss / (step + 1)
  
              secondary_learner.eval()
              UpperCamelCase__ 						=						0.0
              UpperCamelCase__ 						=						0.0
              UpperCamelCase__ 						=						[]
              UpperCamelCase__ 						=						[]
              # Compute performance of the secondary learner after this batch
              for stepa, batcha in enumerate(_UpperCamelCase   ):
                 UpperCamelCase__ 						=						batcha[0].to(_UpperCamelCase   )
                 UpperCamelCase__ 						=						batcha[1].to(_UpperCamelCase   )
                 UpperCamelCase__ 						=						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__ 						=						q_lossa
  
           secondary_learner.train()
     return secondary_learner
  
  
  
  
  
  
  
  class  UpperCAmelCase (       nn.Module):
  
   def __init__( self :	List[Any],					a_ :	Optional[Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      super(UpperCamelCase__,					self					).__init__()
      UpperCamelCase__ 						=						model.transformer.wte
      UpperCamelCase__ 						=						copy.deepcopy(model.transformer.wte.weight					)
      UpperCamelCase__ 						=						nn.Convad(self.embeddings.weight.size(1					),					256,					3,					padding=1					)
      UpperCamelCase__ 						=						nn.Sequential(nn.Linear(256,					32					),					nn.Dropout(p=0.1					),					nn.Linear(32,					32					),					nn.Linear(32,					1					)					)
  
   def 		lowercase_						( self :	Optional[Any],					a_ :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						torch.max(self.conv(self.embeddings(UpperCamelCase__					).squeeze(1					).transpose(1,					2					)					),					2					)[0]
      UpperCamelCase__ 						=						self.fc(UpperCamelCase__					)
      return qs.squeeze(1					)
  
   @classmethod
   def 		lowercase_						( cls :	Optional[int],					a_ :	Optional[int],					a_ :	Dict					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						cls(UpperCamelCase__					)  # this calls __init__
      UpperCamelCase__ 						=						torch.load(UpperCamelCase__					)
      secondary_learner.load_state_dict(UpperCamelCase__					)
      UpperCamelCase__ 						=						model.transformer.wte
      UpperCamelCase__ 						=						copy.deepcopy(model.transformer.wte.weight					)
      return secondary_learner
style_context: 
  
  
  '''simple docstring'''
  
  from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
  from transformers.testing_utils import require_tokenizers, slow
  
  from ..bert.test_tokenization_bert import BertTokenizationTest
  
  
  
  
  
  
  
  @require_tokenizers
  class  UpperCAmelCase (       SCREAMING_SNAKE_CASE__):
   _lowerCamelCase :					Optional[int]    					=					SqueezeBertTokenizer
   _lowerCamelCase :					List[Any]    					=					SqueezeBertTokenizerFast
   _lowerCamelCase :					int    					=					True
  
   def 		lowercase_						( self :	List[str],					**a_ :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname,					**a_					)
  
   @slow
   def 		lowercase_						( self :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						SqueezeBertTokenizer.from_pretrained("squeezebert/squeezebert-mnli-headless"					)
  
      UpperCamelCase__ 						=						tokenizer.encode("sequence builders",					add_special_tokens=a_					)
      UpperCamelCase__ 						=						tokenizer.encode("multi-sequence build",					add_special_tokens=a_					)
  
      UpperCamelCase__ 						=						tokenizer.build_inputs_with_special_tokens(a_					)
      UpperCamelCase__ 						=						tokenizer.build_inputs_with_special_tokens(a_,					a_					)
  
      assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
      assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
          tokenizer.sep_token_id
      ]