code: 
  
  
  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
  
  
  UpperCAmelCase__      =							logging.getLogger(__name__)
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   List[str]   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			random.seed(_SCREAMING_SNAKE_CASE   )
  			np.random.seed(_SCREAMING_SNAKE_CASE   )
  			torch.manual_seed(_SCREAMING_SNAKE_CASE   )
  			torch.cuda.manual_seed_all(_SCREAMING_SNAKE_CASE   )
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   int,_SCREAMING_SNAKE_CASE				:   Union[str, Any],_SCREAMING_SNAKE_CASE				:   List[str]   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			model.eval()
  			__A=  next(model.parameters()   ).device
  			__A=  1
  			__A=  torch.zeros((eval_batch_size, context_len),dtype=torch.long,device=_SCREAMING_SNAKE_CASE   )
  			__A=  DataLoader(_SCREAMING_SNAKE_CASE,shuffle=_SCREAMING_SNAKE_CASE,batch_size=_SCREAMING_SNAKE_CASE   )
  			__A=  torch.zeros(1,device=_SCREAMING_SNAKE_CASE   )
  			__A=  0
  			for batch in eval_dataloader:
  						batch.to(_SCREAMING_SNAKE_CASE   )
  						# pad
  						context.zero_()
  						for i in range(_SCREAMING_SNAKE_CASE   ):
  									__A=  batch[i]
  						__A=  model(_SCREAMING_SNAKE_CASE,labels=_SCREAMING_SNAKE_CASE   )
  						eval_loss += outputs[0].sum().item()
  						nb_eval_examples += batch.size(0   )
  			__A=  eval_loss / nb_eval_examples
  			__A=  torch.exp(_SCREAMING_SNAKE_CASE   )
  			model.train()
  			return perplexity
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Tuple="gpt2"   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  GPTaLMHeadModel.from_pretrained(_SCREAMING_SNAKE_CASE,output_hidden_states=_SCREAMING_SNAKE_CASE   )
  			torch.save(model.state_dict(),model_name + 'local.pt'   )
  			return model
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   int,_SCREAMING_SNAKE_CASE				:   Union[str, Any],_SCREAMING_SNAKE_CASE				:   Optional[Any]   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  copy.deepcopy(_SCREAMING_SNAKE_CASE   )
  			model.to(_SCREAMING_SNAKE_CASE   )
  
  			__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(_SCREAMING_SNAKE_CASE,lr=5e-5,eps=1e-8   )
  			__A=  get_linear_schedule_with_warmup(_SCREAMING_SNAKE_CASE,0,_SCREAMING_SNAKE_CASE   )
  			torch.cuda.empty_cache()
  			return model, lm_optimizer, lm_scheduler
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   int,_SCREAMING_SNAKE_CASE				:   Any,_SCREAMING_SNAKE_CASE				:   Optional[int],_SCREAMING_SNAKE_CASE				:   int   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  np.array(_SCREAMING_SNAKE_CASE   ).mean()
  			__A=  np.array(_SCREAMING_SNAKE_CASE   ).std()
  			__A=  (real_perps - avg) / std
  			__A=  list(zip(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   )   )
  			joblib.dump(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   )
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Any,_SCREAMING_SNAKE_CASE				:   int,_SCREAMING_SNAKE_CASE				:   Union[str, Any],_SCREAMING_SNAKE_CASE				:   Optional[int],_SCREAMING_SNAKE_CASE				:   Union[str, Any],_SCREAMING_SNAKE_CASE				:   List[str],_SCREAMING_SNAKE_CASE				:   int,_SCREAMING_SNAKE_CASE				:   Dict="dev.jbl",_SCREAMING_SNAKE_CASE				:   List[Any]=recopy_gpta,):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  []
  			__A=  []
  			__A=  []
  
  			# Initialize the transformer model
  			__A=  copy.deepcopy(_SCREAMING_SNAKE_CASE   )
  			orig_model.to(device='cpu'   )
  			torch.cuda.empty_cache()
  
  			# Compute perplexity of initial transformer model for comparison
  			model.train()
  			__A,  __A,  __A=  recopy_model(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   )
  
  			for step in tqdm(range(_SCREAMING_SNAKE_CASE   )   ):
  						__A=  torch.zeros((1, context_len),dtype=torch.long,device=_SCREAMING_SNAKE_CASE   )
  						__A=  random.choice(_SCREAMING_SNAKE_CASE   )
  						__A=  random.randint(0,len(story[0]   ) - context_len - 1   )
  						__A=  story[0][start : start + context_len]
  						lm_optimizer.zero_grad()
  						__A=  model(_SCREAMING_SNAKE_CASE,labels=_SCREAMING_SNAKE_CASE   )
  						__A=  outputs[0]
  						__A=  compute_perplexity(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   )
  						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(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   )
  
  						# Periodically save the stored (X, IG(X)) pairs
  						if step % 1000 == 0 and step > 1:
  									intermittent_save(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   )
  
  						# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
  						__A,  __A,  __A=  recopy_model(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   )
  
  						past_perps.append(past_perp.item()   )
  						real_perps.append(orig_perp - real_perp.item()   )
  						contexts.append(np.array(context.cpu()   )   )
  
  			intermittent_save(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   )
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Optional[int],_SCREAMING_SNAKE_CASE				:   str="data/tokenized_stories_train_wikitext103.jbl",_SCREAMING_SNAKE_CASE				:   Optional[int]=100,_SCREAMING_SNAKE_CASE				:   str=1026,_SCREAMING_SNAKE_CASE				:   Union[str, Any]=True   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  joblib.load(_SCREAMING_SNAKE_CASE   )
  			print('data loaded'   )
  			__A=  []
  			if trim:
  						for i, example in enumerate(_SCREAMING_SNAKE_CASE   ):
  									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(_SCREAMING_SNAKE_CASE   ) >= number:
  												break
  						__A=  []
  						for j in range(i + 1,len(_SCREAMING_SNAKE_CASE   )   ):
  									if len(data[j][0]   ) > min_len:
  												train_data.append(data[j]   )
  			else:
  						__A=  data[0:number]
  						__A=  data[number:]
  
  			joblib.dump(_SCREAMING_SNAKE_CASE,'objective_set.jbl'   )
  			print('objective set saved'   )
  			return train_data, objective_set
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Tuple,_SCREAMING_SNAKE_CASE				:   Union[str, Any],_SCREAMING_SNAKE_CASE				:   Optional[int],_SCREAMING_SNAKE_CASE				:   int,_SCREAMING_SNAKE_CASE				:   Optional[Any]=50,_SCREAMING_SNAKE_CASE				:   Optional[Any]="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[:512]
  			__A=  train_dataset[512:]
  			__A=  DataLoader(_SCREAMING_SNAKE_CASE,shuffle=_SCREAMING_SNAKE_CASE,batch_size=_SCREAMING_SNAKE_CASE   )
  			__A=  DataLoader(_SCREAMING_SNAKE_CASE,shuffle=_SCREAMING_SNAKE_CASE,batch_size=_SCREAMING_SNAKE_CASE   )
  
  			# secondary learner model set up
  			__A=  nn.MSELoss()
  			__A=  nn.MSELoss(reduction='sum'   )
  			secondary_learner.to(_SCREAMING_SNAKE_CASE   )
  			__A=  torch.optim.Adam(secondary_learner.parameters(),lr=0.0_00_01   )
  			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(_SCREAMING_SNAKE_CASE   )   ):
  						__A=  0.0
  						secondary_learner.train()
  						for step, batch in enumerate(_SCREAMING_SNAKE_CASE   ):
  									__A=  batch[0].to(_SCREAMING_SNAKE_CASE   )
  									__A=  batch[1].to(_SCREAMING_SNAKE_CASE   )
  									__A=  secondary_learner(_SCREAMING_SNAKE_CASE   )
  									q_optimizer.zero_grad()
  									__A=  loss(_SCREAMING_SNAKE_CASE,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(_SCREAMING_SNAKE_CASE   )):
  												__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(_SCREAMING_SNAKE_CASE   ):
  															__A=  batcha[0].to(_SCREAMING_SNAKE_CASE   )
  															__A=  batcha[1].to(_SCREAMING_SNAKE_CASE   )
  															__A=  secondary_learner(_SCREAMING_SNAKE_CASE   )
  															q_lossa += test_loss(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   ).item()
  															sum_qa += torch.sum(_SCREAMING_SNAKE_CASE   ).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(_SCREAMING_SNAKE_CASE   )
  												print(
  												    'Epoch: ',_SCREAMING_SNAKE_CASE,'step: ',_SCREAMING_SNAKE_CASE,'Avg. q:',sum_qa / len(_SCREAMING_SNAKE_CASE   ),'Train Loss: ',_SCREAMING_SNAKE_CASE,'Test Loss: ',_SCREAMING_SNAKE_CASE,)
  												if q_lossa < best_test_loss:
  															joblib.dump((predicted, actual),'pred_vs_actual.jbl'   )
  															torch.save(secondary_learner.state_dict(),_SCREAMING_SNAKE_CASE   )
  															__A=  q_lossa
  
  									secondary_learner.train()
  			return secondary_learner
  
  
  
  
  
  
  class 	a__				(				nn.Module      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			def __init__(     self       : Tuple ,						lowerCAmelCase_       : List[str]  )			->		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 				lowerCAmelCase    (     self       : List[str] ,						lowerCAmelCase_       : List[Any]  )			->		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 				lowerCAmelCase    (     cls       : Tuple ,						lowerCAmelCase_       : Optional[Any] ,						lowerCAmelCase_       : str  )			->		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 typing import Any
  class 	a__				:
  
  
  
  
  
  			'''simple docstring'''
  
  			def __init__(     self       : Any  )			->		int:
  						__A=  []
  						__A=  0
  			def __str__(     self       : Tuple  )			->		Dict:
  						__A=  '<' + str(self.stack  )[1:-1] + '>'
  						return printed
  			def 				lowerCAmelCase    (     self       : Tuple ,						lowerCAmelCase_       : Any  )			->		None:
  						self.stack.append(lowerCAmelCase_  )
  						__A=  self.length + 1
  			def 				lowerCAmelCase    (     self       : int  )			->		Any:
  						self.rotate(1  )
  						__A=  self.stack[self.length - 1]
  						__A=  self.stack[:-1]
  						self.rotate(self.length - 1  )
  						__A=  self.length - 1
  						return dequeued
  			def 				lowerCAmelCase    (     self       : Optional[int] ,						lowerCAmelCase_       : int  )			->		None:
  						for _ in range(lowerCAmelCase_  ):
  									__A=  self.stack[0]
  									__A=  self.stack[1:]
  									self.put(lowerCAmelCase_  )
  									__A=  self.length - 1
  			def 				lowerCAmelCase    (     self       : Tuple  )			->		Any:
  						__A=  self.get()
  						self.put(lowerCAmelCase_  )
  						self.rotate(self.length - 1  )
  						return front
  			def 				lowerCAmelCase    (     self       : Dict  )			->		int:
  						return self.length
  
  
  
  