



'''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


__lowerCAmelCase		    =     logging.getLogger(__name__)



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   str:
		random.seed(lowerCAmelCase_						)
		np.random.seed(lowerCAmelCase_						)
		torch.manual_seed(lowerCAmelCase_						)
		torch.cuda.manual_seed_all(lowerCAmelCase_						)



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_						)    ->   Optional[int]:
		model.eval()
		_a  :   List[Any]				 =							next(model.parameters()						).device
		_a  :   Tuple				 =							1
		_a  :   str				 =							torch.zeros((eval_batch_size, context_len)		,    dtype=torch.long		,    device=lowerCAmelCase_						)
		_a  :   Any				 =							DataLoader(lowerCAmelCase_		,    shuffle=lowerCAmelCase_		,    batch_size=lowerCAmelCase_						)
		_a  :   Union[str, Any]				 =							torch.zeros(1		,    device=lowerCAmelCase_						)
		_a  :   Optional[Any]				 =							0
		for batch in eval_dataloader:
				batch.to(lowerCAmelCase_						)
				# pad
				context.zero_()
				for i in range(lowerCAmelCase_						):
						_a  :   Optional[Any]				 =							batch[i]
				_a  :   Optional[Any]				 =							model(lowerCAmelCase_		,    labels=lowerCAmelCase_						)
				eval_loss += outputs[0].sum().item()
				nb_eval_examples += batch.size(0						)
		_a  :   Union[str, Any]				 =							eval_loss / nb_eval_examples
		_a  :   List[Any]				 =							torch.exp(lowerCAmelCase_						)
		model.train()
		return perplexity



def   __lowerCamelCase  (   lowerCAmelCase_="gpt2"						)    ->   str:
		_a  :   Optional[int]				 =							GPTaLMHeadModel.from_pretrained(lowerCAmelCase_		,    output_hidden_states=lowerCAmelCase_						)
		torch.save(model.state_dict()		,    model_name + 'local.pt'						)
		return model



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_						)    ->   List[Any]:
		_a  :   Dict				 =							copy.deepcopy(lowerCAmelCase_						)
		model.to(lowerCAmelCase_						)

		_a  :   List[str]				 =							['bias', 'LayerNorm.weight']
		_a  :   Union[str, Any]				 =							[
		    {
		        '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  :   List[Any]				 =							AdamW(lowerCAmelCase_		,    lr=5E-5		,    eps=1E-8						)
		_a  :   int				 =							get_linear_schedule_with_warmup(lowerCAmelCase_		,    0		,    lowerCAmelCase_						)
		torch.cuda.empty_cache()
		return model, lm_optimizer, lm_scheduler



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_						)    ->   Optional[Any]:
		_a  :   Tuple				 =							np.array(lowerCAmelCase_						).mean()
		_a  :   str				 =							np.array(lowerCAmelCase_						).std()
		_a  :   Tuple				 =							(real_perps - avg) / std
		_a  :   Any				 =							list(zip(lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_						)						)
		joblib.dump(lowerCAmelCase_		,    lowerCAmelCase_						)



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_="dev.jbl"		,    lowerCAmelCase_=recopy_gpta		,    )    ->   Dict:
		_a  :   Optional[int]				 =							[]
		_a  :   Optional[int]				 =							[]
		_a  :   List[str]				 =							[]

		# Initialize the transformer model
		_a  :   str				 =							copy.deepcopy(lowerCAmelCase_						)
		orig_model.to(device='cpu'						)
		torch.cuda.empty_cache()

		# Compute perplexity of initial transformer model for comparison
		model.train()
		_a ,      _a ,      _a  :   List[str]				 =							recopy_model(lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_						)

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

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

				# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
				_a ,      _a ,      _a  :   Tuple				 =							recopy_model(lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_						)

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

		intermittent_save(lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_						)



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

		joblib.dump(lowerCAmelCase_		,    'objective_set.jbl'						)
		print('objective set saved'						)
		return train_data, objective_set



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_=50		,    lowerCAmelCase_="secondary_learner.pt"						)    ->   List[Any]:
		_a  :   List[Any]				 =							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  :   List[Any]				 =							train_dataset[:512]
		_a  :   List[str]				 =							train_dataset[512:]
		_a  :   List[str]				 =							DataLoader(lowerCAmelCase_		,    shuffle=lowerCAmelCase_		,    batch_size=lowerCAmelCase_						)
		_a  :   List[Any]				 =							DataLoader(lowerCAmelCase_		,    shuffle=lowerCAmelCase_		,    batch_size=lowerCAmelCase_						)

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

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

						secondary_learner.train()
		return secondary_learner

class 			__magic_name__		(					nn.Module     ):







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







							def 							__lowercase    (							self      : Optional[Any]			,_UpperCAmelCase      : List[str]	):
									_a  :   Union[str, Any]				 =							torch.max(self.conv(self.embeddings(_UpperCAmelCase	).squeeze(1	).transpose(1			,2	)	)			,2	)[0]
									_a  :   List[Any]				 =							self.fc(_UpperCAmelCase	)
									return qs.squeeze(1	)







							@classmethod
							def 							__lowercase    (							cls      : str			,_UpperCAmelCase      : str			,_UpperCAmelCase      : List[Any]	):
									_a  :   List[str]				 =							cls(_UpperCAmelCase	)  # this calls __init__
									_a  :   List[Any]				 =							torch.load(_UpperCAmelCase	)
									secondary_learner.load_state_dict(_UpperCAmelCase	)
									_a  :   Optional[int]				 =							model.transformer.wte
									_a  :   List[Any]				 =							copy.deepcopy(model.transformer.wte.weight	)
									return secondary_learner









'''simple docstring'''

def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   tuple[int, int]:
		# base case
		if len(lowerCAmelCase_						) == 1:
				return a[0], a[0]
		else:
				# split A into half.
				_a  :   Union[str, Any]				 =							a[: len(lowerCAmelCase_						) // 2]
				_a  :   List[Any]				 =							a[len(lowerCAmelCase_						) // 2 :]

				# 2 sub problems, 1/2 of original size.
				_a ,      _a  :   Any				 =							max_difference(lowerCAmelCase_						)
				_a ,      _a  :   Any				 =							max_difference(lowerCAmelCase_						)

				# get min of first and max of second
				# linear time
				_a  :   Union[str, Any]				 =							min(lowerCAmelCase_						)
				_a  :   Optional[int]				 =							max(lowerCAmelCase_						)

				# 3 cases, either (small1, big1),
				# (min_first, max_second), (small2, big2)
				# constant comparisons
				if biga - smalla > max_second - min_first and biga - smalla > biga - smalla:
						return smalla, biga
				elif biga - smalla > max_second - min_first:
						return smalla, biga
				else:
						return min_first, max_second


if __name__ == "__main__":
							import doctest

							doctest.testmod()




