





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












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() = }""")





