






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


lowercase     :							Optional[int]	     =			logging.getLogger(__name__)

def    lowerCAmelCase__		(			_a				:	Tuple			):
		random.seed(_a			)
		np.random.seed(_a			)
		torch.manual_seed(_a			)
		torch.cuda.manual_seed_all(_a			)

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

def    lowerCAmelCase__		(			_a				:	Optional[int]="gpt2"			):
		snake_case_ :							Any         =				GPTaLMHeadModel.from_pretrained(_a     ,					output_hidden_states=_a			)
		torch.save(model.state_dict()     ,					model_name + "local.pt"			)
		return model

def    lowerCAmelCase__		(			_a				:	str     ,					_a				:	int     ,					_a				:	Any			):
		snake_case_ :							Optional[Any]         =				copy.deepcopy(_a			)
		model.to(_a			)

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

def    lowerCAmelCase__		(			_a				:	Union[str, Any]     ,					_a				:	Any     ,					_a				:	Any     ,					_a				:	Tuple			):
		snake_case_ :							List[Any]         =				np.array(_a			).mean()
		snake_case_ :							str         =				np.array(_a			).std()
		snake_case_ :							int         =				(real_perps - avg) / std
		snake_case_ :							Dict         =				list(zip(_a     ,					_a     ,					_a			)			)
		joblib.dump(_a     ,					_a			)

def    lowerCAmelCase__		(			_a				:	Tuple     ,					_a				:	Tuple     ,					_a				:	str     ,					_a				:	Dict     ,					_a				:	Union[str, Any]     ,					_a				:	List[Any]     ,					_a				:	Union[str, Any]     ,					_a				:	Tuple="dev.jbl"     ,					_a				:	Dict=recopy_gpta     ,					):
		snake_case_ :							Tuple         =				[]
		snake_case_ :							Dict         =				[]
		snake_case_ :							List[str]         =				[]

		# Initialize the transformer model
		snake_case_ :							List[str]         =				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_ :							List[str]         =				recopy_model(_a     ,					_a     ,					_a			)

		for step in tqdm(range(_a			)			):
				snake_case_ :							Dict         =				torch.zeros((1, context_len)     ,					dtype=torch.long     ,					device=_a			)
				snake_case_ :							List[Any]         =				random.choice(_a			)
				snake_case_ :							Union[str, Any]         =				random.randint(0     ,					len(story[0]			) - context_len - 1			)
				snake_case_ :							int         =				story[0][start : start + context_len]
				lm_optimizer.zero_grad()
				snake_case_ :							List[Any]         =				model(_a     ,					labels=_a			)
				snake_case_ :							List[Any]         =				outputs[0]
				snake_case_ :							Tuple         =				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 % 10_00 == 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_ :							Optional[Any]         =				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    lowerCAmelCase__		(			_a				:	Tuple     ,					_a				:	Dict="data/tokenized_stories_train_wikitext103.jbl"     ,					_a				:	Optional[int]=1_00     ,					_a				:	Tuple=10_26     ,					_a				:	Any=True			):
		snake_case_ :							str         =				joblib.load(_a			)
		print("data loaded"			)
		snake_case_ :							List[str]         =				[]
		if trim:
				for i, example in enumerate(_a			):
						if len(example[0]			) > min_len:
								snake_case_ :							Dict         =				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_ :							Tuple         =				[]
				for j in range(i + 1     ,					len(_a			)			):
						if len(data[j][0]			) > min_len:
								train_data.append(data[j]			)
		else:
				snake_case_ :							Union[str, Any]         =				data[0:number]
				snake_case_ :							Optional[Any]         =				data[number:]

		joblib.dump(_a     ,					"objective_set.jbl"			)
		print("objective set saved"			)
		return train_data, objective_set

def    lowerCAmelCase__		(			_a				:	Optional[Any]     ,					_a				:	Any     ,					_a				:	Union[str, Any]     ,					_a				:	Tuple     ,					_a				:	Optional[int]=50     ,					_a				:	Optional[int]="secondary_learner.pt"			):
		snake_case_ :							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
		snake_case_ :							str         =				train_dataset[:5_12]
		snake_case_ :							Union[str, Any]         =				train_dataset[5_12:]
		snake_case_ :							Union[str, Any]         =				DataLoader(_a     ,					shuffle=_a     ,					batch_size=_a			)
		snake_case_ :							Any         =				DataLoader(_a     ,					shuffle=_a     ,					batch_size=_a			)

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

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

						secondary_learner.train()
		return secondary_learner




class 						UpperCAmelCase_  (		nn.Module    ):


						'''simple docstring'''




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






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE )       -> Optional[int]:
								snake_case_ :							List[Any]         =				torch.max(self.conv(self.embeddings(_SCREAMING_SNAKE_CASE ).squeeze(1 ).transpose(1						, 2 ) )						, 2 )[0]
								snake_case_ :							Optional[int]         =				self.fc(_SCREAMING_SNAKE_CASE )
								return qs.squeeze(1 )






						@classmethod
						def 					_lowerCAmelCase				(		cls						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE )       -> Tuple:
								snake_case_ :							Optional[int]         =				cls(_SCREAMING_SNAKE_CASE )  # this calls __init__
								snake_case_ :							int         =				torch.load(_SCREAMING_SNAKE_CASE )
								secondary_learner.load_state_dict(_SCREAMING_SNAKE_CASE )
								snake_case_ :							Optional[int]         =				model.transformer.wte
								snake_case_ :							List[Any]         =				copy.deepcopy(model.transformer.wte.weight )
								return secondary_learner








import math

def    lowerCAmelCase__		(			_a				:	int			):

		if 1 < number < 4:
				# 2 and 3 are primes
				return True
		elif number < 2 or number % 2 == 0 or number % 3 == 0:
				# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
				return False

		# All primes number are in format of 6k +/- 1
		for i in range(5     ,					int(math.sqrt(_a			) + 1			)     ,					6			):
				if number % i == 0 or number % (i + 2) == 0:
						return False
		return True

def    lowerCAmelCase__		(			_a				:	int = 60_08_51_47_51_43			):

		try:
				snake_case_ :							int         =				int(_a			)
		except (TypeError, ValueError):
				raise TypeError("Parameter n must be int or castable to int."			)
		if n <= 0:
				raise ValueError("Parameter n must be greater than or equal to one."			)
		snake_case_ :							Dict         =				0
		if is_prime(_a			):
				return n
		while n % 2 == 0:
				n //= 2
		if is_prime(_a			):
				return n
		for i in range(3     ,					int(math.sqrt(_a			)			) + 1     ,					2			):
				if n % i == 0:
						if is_prime(n // i			):
								snake_case_ :							List[Any]         =				n // i
								break
						elif is_prime(_a			):
								snake_case_ :							str         =				i
		return max_number


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