





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


__A    :   Any          =       logging.getLogger(__name__)





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    Union[str, Any]:







			'''simple docstring'''

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





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    Optional[int]:







			'''simple docstring'''

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





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__="gpt2"			)  ->    Optional[int]:







			'''simple docstring'''

			UpperCAmelCase       		=    GPTaLMHeadModel.from_pretrained(UpperCamelCase__      ,      output_hidden_states=UpperCamelCase__			)
			torch.save(model.state_dict()      ,      model_name + '''local.pt'''			)
			return model





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    Any:







			'''simple docstring'''

			UpperCAmelCase       		=    copy.deepcopy(UpperCamelCase__			)
			model.to(UpperCamelCase__			)

			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(UpperCamelCase__      ,      lr=5E-5      ,      eps=1E-8			)
			UpperCAmelCase       		=    get_linear_schedule_with_warmup(UpperCamelCase__      ,      0      ,      UpperCamelCase__			)
			torch.cuda.empty_cache()
			return model, lm_optimizer, lm_scheduler





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    Union[str, Any]:







			'''simple docstring'''

			UpperCAmelCase       		=    np.array(UpperCamelCase__			).mean()
			UpperCAmelCase       		=    np.array(UpperCamelCase__			).std()
			UpperCAmelCase       		=    (real_perps - avg) / std
			UpperCAmelCase       		=    list(zip(UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)			)
			joblib.dump(UpperCamelCase__      ,      UpperCamelCase__			)





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__="dev.jbl"      ,      UpperCamelCase__=recopy_gpta      ,      )  ->    Dict:







			'''simple docstring'''

			UpperCAmelCase       		=    []
			UpperCAmelCase       		=    []
			UpperCAmelCase       		=    []

			# Initialize the transformer model
			UpperCAmelCase       		=    copy.deepcopy(UpperCamelCase__			)
			orig_model.to(device='''cpu'''			)
			torch.cuda.empty_cache()

			# Compute perplexity of initial transformer model for comparison
			model.train()
			UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase       		=    recopy_model(UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)

			for step in tqdm(range(UpperCamelCase__			)			):
						UpperCAmelCase       		=    torch.zeros((1, context_len)      ,      dtype=torch.long      ,      device=UpperCamelCase__			)
						UpperCAmelCase       		=    random.choice(UpperCamelCase__			)
						UpperCAmelCase       		=    random.randint(0      ,      len(story[0]			) - context_len - 1			)
						UpperCAmelCase       		=    story[0][start : start + context_len]
						lm_optimizer.zero_grad()
						UpperCAmelCase       		=    model(UpperCamelCase__      ,      labels=UpperCamelCase__			)
						UpperCAmelCase       		=    outputs[0]
						UpperCAmelCase       		=    compute_perplexity(UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)
						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(UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)

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

						# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
						UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase       		=    recopy_model(UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)

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

			intermittent_save(UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__="data/tokenized_stories_train_wikitext103.jbl"      ,      UpperCamelCase__=100      ,      UpperCamelCase__=1026      ,      UpperCamelCase__=True			)  ->    Union[str, Any]:







			'''simple docstring'''

			UpperCAmelCase       		=    joblib.load(UpperCamelCase__			)
			print('''data loaded'''			)
			UpperCAmelCase       		=    []
			if trim:
						for i, example in enumerate(UpperCamelCase__			):
									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(UpperCamelCase__			) >= number:
												break
						UpperCAmelCase       		=    []
						for j in range(i + 1      ,      len(UpperCamelCase__			)			):
									if len(data[j][0]			) > min_len:
												train_data.append(data[j]			)
			else:
						UpperCAmelCase       		=    data[0:number]
						UpperCAmelCase       		=    data[number:]

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





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__=50      ,      UpperCamelCase__="secondary_learner.pt"			)  ->    Optional[Any]:







			'''simple docstring'''

			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[:512]
			UpperCAmelCase       		=    train_dataset[512:]
			UpperCAmelCase       		=    DataLoader(UpperCamelCase__      ,      shuffle=UpperCamelCase__      ,      batch_size=UpperCamelCase__			)
			UpperCAmelCase       		=    DataLoader(UpperCamelCase__      ,      shuffle=UpperCamelCase__      ,      batch_size=UpperCamelCase__			)

			# secondary learner model set up
			UpperCAmelCase       		=    nn.MSELoss()
			UpperCAmelCase       		=    nn.MSELoss(reduction='''sum'''			)
			secondary_learner.to(UpperCamelCase__			)
			UpperCAmelCase       		=    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
			UpperCAmelCase       		=    float('''inf'''			)
			# Iterate through batches until we've used max_steps batches
			for epoch in range(int(UpperCamelCase__			)			):
						UpperCAmelCase       		=    0.0
						secondary_learner.train()
						for step, batch in enumerate(UpperCamelCase__			):
									UpperCAmelCase       		=    batch[0].to(UpperCamelCase__			)
									UpperCAmelCase       		=    batch[1].to(UpperCamelCase__			)
									UpperCAmelCase       		=    secondary_learner(UpperCamelCase__			)
									q_optimizer.zero_grad()
									UpperCAmelCase       		=    loss(UpperCamelCase__      ,      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(UpperCamelCase__			)):
												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(UpperCamelCase__			):
															UpperCAmelCase       		=    batcha[0].to(UpperCamelCase__			)
															UpperCAmelCase       		=    batcha[1].to(UpperCamelCase__			)
															UpperCAmelCase       		=    secondary_learner(UpperCamelCase__			)
															q_lossa += test_loss(UpperCamelCase__      ,      UpperCamelCase__			).item()
															sum_qa += torch.sum(UpperCamelCase__			).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(UpperCamelCase__			)
												print(
												    '''Epoch: '''      ,      UpperCamelCase__      ,      '''step: '''      ,      UpperCamelCase__      ,      '''Avg. q:'''      ,      sum_qa / len(UpperCamelCase__			)      ,      '''Train Loss: '''      ,      UpperCamelCase__      ,      '''Test Loss: '''      ,      UpperCamelCase__      ,      )
												if q_lossa < best_test_loss:
															joblib.dump((predicted, actual)      ,      '''pred_vs_actual.jbl'''			)
															torch.save(secondary_learner.state_dict()      ,      UpperCamelCase__			)
															UpperCAmelCase       		=    q_lossa

									secondary_learner.train()
			return secondary_learner






class      A_	(nn.Module      ):
	def __init__(   self							, _A		):






				'''simple docstring'''







				super(_A							, self		).__init__()
				UpperCAmelCase       		=    model.transformer.wte
				UpperCAmelCase       		=    copy.deepcopy(model.transformer.wte.weight		)
				UpperCAmelCase       		=    nn.Convad(self.embeddings.weight.size(1		)							, 2_5_6							, 3							, padding=1		)
				UpperCAmelCase       		=    nn.Sequential(nn.Linear(2_5_6							, 3_2		)							, nn.Dropout(p=0.1		)							, nn.Linear(3_2							, 3_2		)							, nn.Linear(3_2							, 1		)		)
	def 					_lowercase    (   self							, _A		):






				'''simple docstring'''







				UpperCAmelCase       		=    torch.max(self.conv(self.embeddings(_A		).squeeze(1		).transpose(1							, 2		)		)							, 2		)[0]
				UpperCAmelCase       		=    self.fc(_A		)
				return qs.squeeze(1		)







	@classmethod
	def 					_lowercase    (   cls							, _A							, _A		):






				'''simple docstring'''







				UpperCAmelCase       		=    cls(_A		)  # this calls __init__
				UpperCAmelCase       		=    torch.load(_A		)
				secondary_learner.load_state_dict(_A		)
				UpperCAmelCase       		=    model.transformer.wte
				UpperCAmelCase       		=    copy.deepcopy(model.transformer.wte.weight		)
				return secondary_learner










import math





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    bool:







			'''simple docstring'''

			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(UpperCamelCase__			) + 1			)      ,      6			):
						if number % i == 0 or number % (i + 2) == 0:
									return False
			return True





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__ = 6008_5147_5143			)  ->    int:







			'''simple docstring'''

			try:
						UpperCAmelCase       		=    int(UpperCamelCase__			)
			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.'''			)
			UpperCAmelCase       		=    0
			if is_prime(UpperCamelCase__			):
						return n
			while n % 2 == 0:
						n //= 2
			if is_prime(UpperCamelCase__			):
						return n
			for i in range(3      ,      int(math.sqrt(UpperCamelCase__			)			) + 1      ,      2			):
						if n % i == 0:
									if is_prime(n // i			):
												UpperCAmelCase       		=    n // i
												break
									elif is_prime(UpperCamelCase__			):
												UpperCAmelCase       		=    i
			return max_number


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



