


'''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   :List[str]								=    logging.getLogger(__name__)





def lowerCamelCase    (    lowerCAmelCase       :			Tuple	):





	"""simple docstring"""







	random.seed(lowerCAmelCase	)
	np.random.seed(lowerCAmelCase	)
	torch.manual_seed(lowerCAmelCase	)
	torch.cuda.manual_seed_all(lowerCAmelCase	)





def lowerCamelCase    (    lowerCAmelCase       :			List[Any]	,	lowerCAmelCase       :			List[Any]	,	lowerCAmelCase       :			List[str]	):





	"""simple docstring"""







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





def lowerCamelCase    (    lowerCAmelCase       :			List[Any]="gpt2"	):





	"""simple docstring"""







	__magic_name__ :					Optional[Any]  			=  GPTaLMHeadModel.from_pretrained(lowerCAmelCase	,	output_hidden_states=lowerCAmelCase	)
	torch.save(model.state_dict()	,	model_name + 'local.pt'	)
	return model





def lowerCamelCase    (    lowerCAmelCase       :			List[str]	,	lowerCAmelCase       :			Optional[Any]	,	lowerCAmelCase       :			str	):





	"""simple docstring"""







	__magic_name__ :					List[str]  			=  copy.deepcopy(lowerCAmelCase	)
	model.to(lowerCAmelCase	)

	__magic_name__ :					List[Any]  			=  ['bias', 'LayerNorm.weight']
	__magic_name__ :					List[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},
	]
	__magic_name__ :					Tuple  			=  AdamW(lowerCAmelCase	,	lr=5e-5	,	eps=1e-8	)
	__magic_name__ :					int  			=  get_linear_schedule_with_warmup(lowerCAmelCase	,	0	,	lowerCAmelCase	)
	torch.cuda.empty_cache()
	return model, lm_optimizer, lm_scheduler





def lowerCamelCase    (    lowerCAmelCase       :			Union[str, Any]	,	lowerCAmelCase       :			str	,	lowerCAmelCase       :			Dict	,	lowerCAmelCase       :			List[Any]	):





	"""simple docstring"""







	__magic_name__ :					int  			=  np.array(lowerCAmelCase	).mean()
	__magic_name__ :					Tuple  			=  np.array(lowerCAmelCase	).std()
	__magic_name__ :					str  			=  (real_perps - avg) / std
	__magic_name__ :					List[str]  			=  list(zip(lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	)	)
	joblib.dump(lowerCAmelCase	,	lowerCAmelCase	)





def lowerCamelCase    (    lowerCAmelCase       :			Optional[Any]	,	lowerCAmelCase       :			int	,	lowerCAmelCase       :			Tuple	,	lowerCAmelCase       :			Any	,	lowerCAmelCase       :			Optional[int]	,	lowerCAmelCase       :			Union[str, Any]	,	lowerCAmelCase       :			Union[str, Any]	,	lowerCAmelCase       :			Union[str, Any]="dev.jbl"	,	lowerCAmelCase       :			Dict=recopy_gpta	,	):





	"""simple docstring"""







	__magic_name__ :					str  			=  []
	__magic_name__ :					Tuple  			=  []
	__magic_name__ :					List[str]  			=  []

	# Initialize the transformer model
	__magic_name__ :					List[Any]  			=  copy.deepcopy(lowerCAmelCase	)
	orig_model.to(device='cpu'	)
	torch.cuda.empty_cache()

	# Compute perplexity of initial transformer model for comparison
	model.train()
	__magic_name__			,       __magic_name__			,       __magic_name__ :					int  			=  recopy_model(lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	)

	for step in tqdm(range(lowerCAmelCase	)	):
		__magic_name__ :					Any  			=  torch.zeros((1, context_len)	,	dtype=torch.long	,	device=lowerCAmelCase	)
		__magic_name__ :					Optional[Any]  			=  random.choice(lowerCAmelCase	)
		__magic_name__ :					Tuple  			=  random.randint(0	,	len(story[0]	) - context_len - 1	)
		__magic_name__ :					int  			=  story[0][start : start + context_len]
		lm_optimizer.zero_grad()
		__magic_name__ :					Dict  			=  model(lowerCAmelCase	,	labels=lowerCAmelCase	)
		__magic_name__ :					str  			=  outputs[0]
		__magic_name__ :					Tuple  			=  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
		__magic_name__ :					Dict  			=  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
		__magic_name__			,       __magic_name__			,       __magic_name__ :					Union[str, Any]  			=  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       :			int	,	lowerCAmelCase       :			Any="data/tokenized_stories_train_wikitext103.jbl"	,	lowerCAmelCase       :			str=100	,	lowerCAmelCase       :			Any=1026	,	lowerCAmelCase       :			List[str]=True	):





	"""simple docstring"""







	__magic_name__ :					Dict  			=  joblib.load(lowerCAmelCase	)
	print('data loaded'	)
	__magic_name__ :					List[str]  			=  []
	if trim:
		for i, example in enumerate(lowerCAmelCase	):
			if len(example[0]	) > min_len:
				__magic_name__ :					Optional[Any]  			=  random.randint(0	,	len(example[0]	) - context_len - 1	)
				objective_set.append(example[0, start : start + context_len]	)
			if len(lowerCAmelCase	) >= number:
				break
		__magic_name__ :					Optional[int]  			=  []
		for j in range(i + 1	,	len(lowerCAmelCase	)	):
			if len(data[j][0]	) > min_len:
				train_data.append(data[j]	)
	else:
		__magic_name__ :					int  			=  data[0:number]
		__magic_name__ :					Any  			=  data[number:]

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





def lowerCamelCase    (    lowerCAmelCase       :			List[str]	,	lowerCAmelCase       :			Union[str, Any]	,	lowerCAmelCase       :			Tuple	,	lowerCAmelCase       :			Union[str, Any]	,	lowerCAmelCase       :			Optional[Any]=50	,	lowerCAmelCase       :			Dict="secondary_learner.pt"	):





	"""simple docstring"""







	__magic_name__ :					int  			=  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
	__magic_name__ :					int  			=  train_dataset[:512]
	__magic_name__ :					Optional[int]  			=  train_dataset[512:]
	__magic_name__ :					Dict  			=  DataLoader(lowerCAmelCase	,	shuffle=lowerCAmelCase	,	batch_size=lowerCAmelCase	)
	__magic_name__ :					Optional[int]  			=  DataLoader(lowerCAmelCase	,	shuffle=lowerCAmelCase	,	batch_size=lowerCAmelCase	)

	# secondary learner model set up
	__magic_name__ :					List[Any]  			=  nn.MSELoss()
	__magic_name__ :					Any  			=  nn.MSELoss(reduction='sum'	)
	secondary_learner.to(lowerCAmelCase	)
	__magic_name__ :					Union[str, Any]  			=  torch.optim.Adam(secondary_learner.parameters()	,	lr=0.0_0001	)
	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
	__magic_name__ :					Any  			=  float('inf'	)
	# Iterate through batches until we've used max_steps batches
	for epoch in range(int(lowerCAmelCase	)	):
		__magic_name__ :					int  			=  0.0
		secondary_learner.train()
		for step, batch in enumerate(lowerCAmelCase	):
			__magic_name__ :					List[str]  			=  batch[0].to(lowerCAmelCase	)
			__magic_name__ :					Optional[Any]  			=  batch[1].to(lowerCAmelCase	)
			__magic_name__ :					Dict  			=  secondary_learner(lowerCAmelCase	)
			q_optimizer.zero_grad()
			__magic_name__ :					List[Any]  			=  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	)):
				__magic_name__ :					Optional[int]  			=  tr_q_loss / (step + 1)

				secondary_learner.eval()
				__magic_name__ :					Optional[Any]  			=  0.0
				__magic_name__ :					str  			=  0.0
				__magic_name__ :					int  			=  []
				__magic_name__ :					Optional[int]  			=  []
				# Compute performance of the secondary learner after this batch
				for stepa, batcha in enumerate(lowerCAmelCase	):
					__magic_name__ :					int  			=  batcha[0].to(lowerCAmelCase	)
					__magic_name__ :					Tuple  			=  batcha[1].to(lowerCAmelCase	)
					__magic_name__ :					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	)
					__magic_name__ :					Any  			=  q_lossa

			secondary_learner.train()
	return secondary_learner





class _lowerCamelCase (		nn.Module							):



		'''simple docstring'''







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







		def      __lowerCAmelCase	(      self	:		Optional[int]						,  _A	:		Union[str, Any]    )						->				Optional[int]:
			__magic_name__ :					Optional[int]  			=  torch.max(self.conv(self.embeddings(_A    ).squeeze(1    ).transpose(1						,  2    )    )						,  2    )[0]
			__magic_name__ :					Any  			=  self.fc(_A    )
			return qs.squeeze(1    )







		@classmethod
		def      __lowerCAmelCase	(      cls	:		List[Any]						,  _A	:		List[Any]						,  _A	:		str    )						->				int:
			__magic_name__ :					List[str]  			=  cls(_A    )  # this calls __init__
			__magic_name__ :					List[Any]  			=  torch.load(_A    )
			secondary_learner.load_state_dict(_A    )
			__magic_name__ :					List[Any]  			=  model.transformer.wte
			__magic_name__ :					Any  			=  copy.deepcopy(model.transformer.wte.weight    )
			return secondary_learner



'''simple docstring'''

import itertools
from pathlib import Path





def lowerCamelCase    (    lowerCAmelCase       :			list[str]	):





	"""simple docstring"""







	__magic_name__ :					str  			=  [tuple(lowerCAmelCase	) for login in logins]

	__magic_name__ :					int  			=  {char for login in split_logins for char in login}

	for permutation in itertools.permutations(lowerCAmelCase	):
		__magic_name__ :					List[Any]  			=  True
		for login in logins:
			if not (
			    permutation.index(login[0]	)
			    < permutation.index(login[1]	)
			    < permutation.index(login[2]	)
			):
				__magic_name__ :					Tuple  			=  False
				break

		if satisfied:
			return int(''.join(lowerCAmelCase	)	)

	raise Exception('Unable to find the secret passcode'	)





def lowerCamelCase    (    lowerCAmelCase       :			str = "keylog.txt"	):





	"""simple docstring"""







	__magic_name__ :					List[Any]  			=  Path(lowerCAmelCase	).parent.joinpath(lowerCAmelCase	).read_text().splitlines()

	return find_secret_passcode(lowerCAmelCase	)


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