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__	:  str						=  logging.getLogger(__name__)
def 					UpperCamelCase__		(							A__       )  ->   Union[str, Any]:
				random.seed(A__       )
				np.random.seed(A__       )
				torch.manual_seed(A__       )
				torch.cuda.manual_seed_all(A__       )
def 					UpperCamelCase__		(							A__     ,					A__     ,					A__       )  ->   Any:
				model.eval()
				snake_case__   :      List[Any]   				= next(model.parameters()       ).device
				snake_case__   :      Tuple   				= 1
				snake_case__   :      Union[str, Any]   				= torch.zeros((eval_batch_size, context_len)     ,					dtype=torch.long     ,					device=A__       )
				snake_case__   :      int   				= DataLoader(A__     ,					shuffle=A__     ,					batch_size=A__       )
				snake_case__   :      Tuple   				= torch.zeros(1     ,					device=A__       )
				snake_case__   :      Union[str, Any]   				= 0
				for batch in eval_dataloader:
								batch.to(A__       )
								# pad
								context.zero_()
								for i in range(A__       ):
												snake_case__   :      List[str]   				= batch[i]
								snake_case__   :      Optional[Any]   				= model(A__     ,					labels=A__       )
								eval_loss += outputs[0].sum().item()
								nb_eval_examples += batch.size(0       )
				snake_case__   :      List[Any]   				= eval_loss / nb_eval_examples
				snake_case__   :      List[str]   				= torch.exp(A__       )
				model.train()
				return perplexity
def 					UpperCamelCase__		(							A__="gpt2"       )  ->   int:
				snake_case__   :      str   				= GPTaLMHeadModel.from_pretrained(A__     ,					output_hidden_states=A__       )
				torch.save(model.state_dict()     ,					model_name + 'local.pt'       )
				return model
def 					UpperCamelCase__		(							A__     ,					A__     ,					A__       )  ->   Dict:
				snake_case__   :      List[Any]   				= copy.deepcopy(A__       )
				model.to(A__       )

				snake_case__   :      List[Any]   				= ['bias', 'LayerNorm.weight']
				snake_case__   :      Tuple   				= [
				    {
				        '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},
				]
				snake_case__   :      List[str]   				= AdamW(A__     ,					lr=5e-5     ,					eps=1e-8       )
				snake_case__   :      Tuple   				= get_linear_schedule_with_warmup(A__     ,					0     ,					A__       )
				torch.cuda.empty_cache()
				return model, lm_optimizer, lm_scheduler
def 					UpperCamelCase__		(							A__     ,					A__     ,					A__     ,					A__       )  ->   List[Any]:
				snake_case__   :      Dict   				= np.array(A__       ).mean()
				snake_case__   :      Any   				= np.array(A__       ).std()
				snake_case__   :      int   				= (real_perps - avg) / std
				snake_case__   :      Tuple   				= list(zip(A__     ,					A__     ,					A__       )       )
				joblib.dump(A__     ,					A__       )
def 					UpperCamelCase__		(							A__     ,					A__     ,					A__     ,					A__     ,					A__     ,					A__     ,					A__     ,					A__="dev.jbl"     ,					A__=recopy_gpta     ,					)  ->   Optional[int]:
				snake_case__   :      str   				= []
				snake_case__   :      Optional[int]   				= []
				snake_case__   :      Dict   				= []

				# Initialize the transformer model
				snake_case__   :      Optional[Any]   				= copy.deepcopy(A__       )
				orig_model.to(device='cpu'       )
				torch.cuda.empty_cache()

				# Compute perplexity of initial transformer model for comparison
				model.train()
				snake_case__				,    snake_case__				,    snake_case__   :      Optional[Any]   				= recopy_model(A__     ,					A__     ,					A__       )

				for step in tqdm(range(A__       )       ):
								snake_case__   :      List[str]   				= torch.zeros((1, context_len)     ,					dtype=torch.long     ,					device=A__       )
								snake_case__   :      Optional[Any]   				= random.choice(A__       )
								snake_case__   :      Union[str, Any]   				= random.randint(0     ,					len(story[0]       ) - context_len - 1       )
								snake_case__   :      str   				= story[0][start : start + context_len]
								lm_optimizer.zero_grad()
								snake_case__   :      int   				= model(A__     ,					labels=A__       )
								snake_case__   :      List[Any]   				= outputs[0]
								snake_case__   :      Optional[Any]   				= compute_perplexity(A__     ,					A__     ,					A__       )
								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
								snake_case__   :      Any   				= compute_perplexity(A__     ,					A__     ,					A__       )

								# Periodically save the stored (X, IG(X)) pairs
								if step % 1000 == 0 and step > 1:
												intermittent_save(A__     ,					A__     ,					A__     ,					A__       )

								# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
								snake_case__				,    snake_case__				,    snake_case__   :      int   				= recopy_model(A__     ,					A__     ,					A__       )

								past_perps.append(past_perp.item()       )
								real_perps.append(orig_perp - real_perp.item()       )
								contexts.append(np.array(context.cpu()       )       )

				intermittent_save(A__     ,					A__     ,					A__     ,					A__       )
def 					UpperCamelCase__		(							A__     ,					A__="data/tokenized_stories_train_wikitext103.jbl"     ,					A__=100     ,					A__=1026     ,					A__=True       )  ->   Dict:
				snake_case__   :      Optional[int]   				= joblib.load(A__       )
				print('data loaded'       )
				snake_case__   :      Union[str, Any]   				= []
				if trim:
								for i, example in enumerate(A__       ):
												if len(example[0]       ) > min_len:
																snake_case__   :      Tuple   				= random.randint(0     ,					len(example[0]       ) - context_len - 1       )
																objective_set.append(example[0, start : start + context_len]       )
												if len(A__       ) >= number:
																break
								snake_case__   :      List[Any]   				= []
								for j in range(i + 1     ,					len(A__       )       ):
												if len(data[j][0]       ) > min_len:
																train_data.append(data[j]       )
				else:
								snake_case__   :      Dict   				= data[0:number]
								snake_case__   :      Any   				= data[number:]

				joblib.dump(A__     ,					'objective_set.jbl'       )
				print('objective set saved'       )
				return train_data, objective_set
def 					UpperCamelCase__		(							A__     ,					A__     ,					A__     ,					A__     ,					A__=50     ,					A__="secondary_learner.pt"       )  ->   List[Any]:
				snake_case__   :      Tuple   				= 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
				snake_case__   :      List[Any]   				= train_dataset[:512]
				snake_case__   :      Any   				= train_dataset[512:]
				snake_case__   :      List[str]   				= DataLoader(A__     ,					shuffle=A__     ,					batch_size=A__       )
				snake_case__   :      str   				= DataLoader(A__     ,					shuffle=A__     ,					batch_size=A__       )

				# secondary learner model set up
				snake_case__   :      Union[str, Any]   				= nn.MSELoss()
				snake_case__   :      Union[str, Any]   				= nn.MSELoss(reduction='sum'       )
				secondary_learner.to(A__       )
				snake_case__   :      str   				= 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
				snake_case__   :      List[Any]   				= float('inf'       )
				# Iterate through batches until we've used max_steps batches
				for epoch in range(int(A__       )       ):
								snake_case__   :      Union[str, Any]   				= 0.0
								secondary_learner.train()
								for step, batch in enumerate(A__       ):
												snake_case__   :      str   				= batch[0].to(A__       )
												snake_case__   :      List[str]   				= batch[1].to(A__       )
												snake_case__   :      List[str]   				= secondary_learner(A__       )
												q_optimizer.zero_grad()
												snake_case__   :      Optional[Any]   				= loss(A__     ,					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(A__       )):
																snake_case__   :      Dict   				= tr_q_loss / (step + 1)

																secondary_learner.eval()
																snake_case__   :      Any   				= 0.0
																snake_case__   :      str   				= 0.0
																snake_case__   :      List[str]   				= []
																snake_case__   :      Dict   				= []
																# Compute performance of the secondary learner after this batch
																for stepa, batcha in enumerate(A__       ):
																				snake_case__   :      Tuple   				= batcha[0].to(A__       )
																				snake_case__   :      Dict   				= batcha[1].to(A__       )
																				snake_case__   :      List[str]   				= secondary_learner(A__       )
																				q_lossa += test_loss(A__     ,					A__       ).item()
																				sum_qa += torch.sum(A__       ).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(A__       )
																print(
																    'Epoch: '     ,					A__     ,					'step: '     ,					A__     ,					'Avg. q:'     ,					sum_qa / len(A__       )     ,					'Train Loss: '     ,					A__     ,					'Test Loss: '     ,					A__     ,					)
																if q_lossa < best_test_loss:
																				joblib.dump((predicted, actual)     ,					'pred_vs_actual.jbl'       )
																				torch.save(secondary_learner.state_dict()     ,					A__       )
																				snake_case__   :      int   				= q_lossa

												secondary_learner.train()
				return secondary_learner





class 					__snake_case    (     nn.Module      ):




					def __init__(  self       ,			__UpperCamelCase						)	->    List[Any]:



									'''simple docstring'''







									super(__UpperCamelCase       ,			self						).__init__()
									snake_case__   :      int   				= model.transformer.wte
									snake_case__   :      Any   				= copy.deepcopy(model.transformer.wte.weight						)
									snake_case__   :      List[str]   				= nn.Convad(self.embeddings.weight.size(1						)       ,			256       ,			3       ,			padding=1						)
									snake_case__   :      str   				= nn.Sequential(nn.Linear(256       ,			32						)       ,			nn.Dropout(p=0.1						)       ,			nn.Linear(32       ,			32						)       ,			nn.Linear(32       ,			1						)						)




					def      __a     (  self       ,			__UpperCamelCase						)	->    Any:



									'''simple docstring'''







									snake_case__   :      Dict   				= torch.max(self.conv(self.embeddings(__UpperCamelCase						).squeeze(1						).transpose(1       ,			2						)						)       ,			2						)[0]
									snake_case__   :      Dict   				= self.fc(__UpperCamelCase						)
									return qs.squeeze(1						)




					@classmethod
					def      __a     (  cls       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    Dict:



									'''simple docstring'''







									snake_case__   :      Tuple   				= cls(__UpperCamelCase						)  # this calls __init__
									snake_case__   :      int   				= torch.load(__UpperCamelCase						)
									secondary_learner.load_state_dict(__UpperCamelCase						)
									snake_case__   :      Tuple   				= model.transformer.wte
									snake_case__   :      Union[str, Any]   				= copy.deepcopy(model.transformer.wte.weight						)
									return secondary_learner



def 					UpperCamelCase__		(							A__ = 400_0000       )  ->   int:

				if n <= 1:
								return 0
				snake_case__   :      Optional[Any]   				= 0
				snake_case__   :      Optional[int]   				= 2
				snake_case__   :      str   				= 0
				while 4 * b + a <= n:
								snake_case__				,    snake_case__   :      Optional[Any]   				= b, 4 * b + a
								count += a
				return count + b


if __name__ == "__main__":
				print(F'''{solution() = }''')


