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
  
  
  __lowerCAmelCase     =			logging.getLogger(__name__)
  
  def     a						(      _lowercase   :     str       )     ->Any:
  		random.seed(_lowercase       )
  		np.random.seed(_lowercase       )
  		torch.manual_seed(_lowercase       )
  		torch.cuda.manual_seed_all(_lowercase       )
  
  def     a						(      _lowercase   :     Optional[Any] ,						_lowercase   :     Union[str, Any] ,						_lowercase   :     Optional[Any]       )     ->str:
  		model.eval()
  		UpperCamelCase__         =	next(model.parameters()       ).device
  		UpperCamelCase__         =	1
  		UpperCamelCase__         =	torch.zeros((eval_batch_size, context_len) ,						dtype=torch.long ,						device=_lowercase       )
  		UpperCamelCase__         =	DataLoader(_lowercase ,						shuffle=_lowercase ,						batch_size=_lowercase       )
  		UpperCamelCase__         =	torch.zeros(1 ,						device=_lowercase       )
  		UpperCamelCase__         =	0
  		for batch in eval_dataloader:
  				batch.to(_lowercase       )
  				# pad
  				context.zero_()
  				for i in range(_lowercase       ):
  						UpperCamelCase__         =	batch[i]
  				UpperCamelCase__         =	model(_lowercase ,						labels=_lowercase       )
  				eval_loss += outputs[0].sum().item()
  				nb_eval_examples += batch.size(0       )
  		UpperCamelCase__         =	eval_loss / nb_eval_examples
  		UpperCamelCase__         =	torch.exp(_lowercase       )
  		model.train()
  		return perplexity
  
  def     a						(      _lowercase   :     int="gpt2"       )     ->Dict:
  		UpperCamelCase__         =	GPTaLMHeadModel.from_pretrained(_lowercase ,						output_hidden_states=_lowercase       )
  		torch.save(model.state_dict() ,						model_name + """local.pt"""       )
  		return model
  
  def     a						(      _lowercase   :     Optional[Any] ,						_lowercase   :     int ,						_lowercase   :     Dict       )     ->Any:
  		UpperCamelCase__         =	copy.deepcopy(_lowercase       )
  		model.to(_lowercase       )
  
  		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(_lowercase ,						lr=5e-5 ,						eps=1e-8       )
  		UpperCamelCase__         =	get_linear_schedule_with_warmup(_lowercase ,						0 ,						_lowercase       )
  		torch.cuda.empty_cache()
  		return model, lm_optimizer, lm_scheduler
  
  def     a						(      _lowercase   :     Union[str, Any] ,						_lowercase   :     Optional[int] ,						_lowercase   :     str ,						_lowercase   :     Optional[int]       )     ->List[str]:
  		UpperCamelCase__         =	np.array(_lowercase       ).mean()
  		UpperCamelCase__         =	np.array(_lowercase       ).std()
  		UpperCamelCase__         =	(real_perps - avg) / std
  		UpperCamelCase__         =	list(zip(_lowercase ,						_lowercase ,						_lowercase       )       )
  		joblib.dump(_lowercase ,						_lowercase       )
  
  def     a						(      _lowercase   :     Dict ,						_lowercase   :     Tuple ,						_lowercase   :     List[Any] ,						_lowercase   :     List[Any] ,						_lowercase   :     Optional[int] ,						_lowercase   :     Union[str, Any] ,						_lowercase   :     Any ,						_lowercase   :     str="dev.jbl" ,						_lowercase   :     Any=recopy_gpta ,						)     ->int:
  		UpperCamelCase__         =	[]
  		UpperCamelCase__         =	[]
  		UpperCamelCase__         =	[]
  
  		# Initialize the transformer model
  		UpperCamelCase__         =	copy.deepcopy(_lowercase       )
  		orig_model.to(device="""cpu"""       )
  		torch.cuda.empty_cache()
  
  		# Compute perplexity of initial transformer model for comparison
  		model.train()
  		UpperCamelCase__  ,			UpperCamelCase__  ,			UpperCamelCase__         =	recopy_model(_lowercase ,						_lowercase ,						_lowercase       )
  
  		for step in tqdm(range(_lowercase       )       ):
  				UpperCamelCase__         =	torch.zeros((1, context_len) ,						dtype=torch.long ,						device=_lowercase       )
  				UpperCamelCase__         =	random.choice(_lowercase       )
  				UpperCamelCase__         =	random.randint(0 ,						len(story[0]       ) - context_len - 1       )
  				UpperCamelCase__         =	story[0][start : start + context_len]
  				lm_optimizer.zero_grad()
  				UpperCamelCase__         =	model(_lowercase ,						labels=_lowercase       )
  				UpperCamelCase__         =	outputs[0]
  				UpperCamelCase__         =	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
  				UpperCamelCase__         =	compute_perplexity(_lowercase ,						_lowercase ,						_lowercase       )
  
  				# Periodically save the stored (X, IG(X)) pairs
  				if step % 1_0_0_0 == 0 and step > 1:
  						intermittent_save(_lowercase ,						_lowercase ,						_lowercase ,						_lowercase       )
  
  				# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
  				UpperCamelCase__  ,			UpperCamelCase__  ,			UpperCamelCase__         =	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   :     Optional[int] ,						_lowercase   :     Dict="data/tokenized_stories_train_wikitext103.jbl" ,						_lowercase   :     Any=1_0_0 ,						_lowercase   :     List[Any]=1_0_2_6 ,						_lowercase   :     Union[str, Any]=True       )     ->Optional[int]:
  		UpperCamelCase__         =	joblib.load(_lowercase       )
  		print("""data loaded"""       )
  		UpperCamelCase__         =	[]
  		if trim:
  				for i, example in enumerate(_lowercase       ):
  						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(_lowercase       ) >= number:
  								break
  				UpperCamelCase__         =	[]
  				for j in range(i + 1 ,						len(_lowercase       )       ):
  						if len(data[j][0]       ) > min_len:
  								train_data.append(data[j]       )
  		else:
  				UpperCamelCase__         =	data[0:number]
  				UpperCamelCase__         =	data[number:]
  
  		joblib.dump(_lowercase ,						"""objective_set.jbl"""       )
  		print("""objective set saved"""       )
  		return train_data, objective_set
  
  def     a						(      _lowercase   :     int ,						_lowercase   :     int ,						_lowercase   :     Optional[Any] ,						_lowercase   :     Union[str, Any] ,						_lowercase   :     List[Any]=5_0 ,						_lowercase   :     Union[str, Any]="secondary_learner.pt"       )     ->Any:
  		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_1_2]
  		UpperCamelCase__         =	train_dataset[5_1_2:]
  		UpperCamelCase__         =	DataLoader(_lowercase ,						shuffle=_lowercase ,						batch_size=_lowercase       )
  		UpperCamelCase__         =	DataLoader(_lowercase ,						shuffle=_lowercase ,						batch_size=_lowercase       )
  
  		# secondary learner model set up
  		UpperCamelCase__         =	nn.MSELoss()
  		UpperCamelCase__         =	nn.MSELoss(reduction="""sum"""       )
  		secondary_learner.to(_lowercase       )
  		UpperCamelCase__         =	torch.optim.Adam(secondary_learner.parameters() ,						lr=0.00_001       )
  		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(_lowercase       )       ):
  				UpperCamelCase__         =	0.0
  				secondary_learner.train()
  				for step, batch in enumerate(_lowercase       ):
  						UpperCamelCase__         =	batch[0].to(_lowercase       )
  						UpperCamelCase__         =	batch[1].to(_lowercase       )
  						UpperCamelCase__         =	secondary_learner(_lowercase       )
  						q_optimizer.zero_grad()
  						UpperCamelCase__         =	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       )):
  								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(_lowercase       ):
  										UpperCamelCase__         =	batcha[0].to(_lowercase       )
  										UpperCamelCase__         =	batcha[1].to(_lowercase       )
  										UpperCamelCase__         =	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       )
  										UpperCamelCase__         =	q_lossa
  
  						secondary_learner.train()
  		return secondary_learner
  
  
  
  
  class   _UpperCAmelCase	(   nn.Module					):
  
  
  
  
  					def __init__(						self    ,      __lowerCAmelCase       )						->  Dict:
  							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 	snake_case_		(						self    ,      __lowerCAmelCase       )						->  List[str]:
  							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 	snake_case_		(						cls    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )						->  Dict:
  							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: 
  
  
  
  
  
  def     a						(      _lowercase   :     int ,						_lowercase   :     int       )     ->int:
  		UpperCamelCase__         =	1.0
  		for i in range(1 ,						r + 1       ):
  				ret *= (n + 1 - i) / i
  		return round(_lowercase       )
  
  def     a						(      _lowercase   :     int       )     ->int:
  		return choose(8 + n ,						_lowercase       ) + choose(9 + n ,						_lowercase       ) - 1_0
  
  def     a						(      _lowercase   :     int       )     ->int:
  		return sum(non_bouncy_exact(_lowercase       ) for i in range(1 ,						n + 1       )       )
  
  def     a						(      _lowercase   :     int = 1_0_0       )     ->int:
  		return non_bouncy_upto(_lowercase       )
  
  
  if __name__ == "__main__":
  				print(f"""{solution() = }""")
  
  
  
  
  
  