




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


UpperCAmelCase_    =logging.getLogger(__name__)



def    UpperCAmelCase							(  _snake_case				):
							random.seed(__lowerCamelCase				)
							np.random.seed(__lowerCamelCase				)
							torch.manual_seed(__lowerCamelCase				)
							torch.cuda.manual_seed_all(__lowerCamelCase				)



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



def    UpperCAmelCase							(  _snake_case="gpt2"				):
							lowerCAmelCase =		GPTaLMHeadModel.from_pretrained(__lowerCamelCase    ,			output_hidden_states=__lowerCamelCase				)
							torch.save(model.state_dict()    ,			model_name + '''local.pt'''				)
							return model



def    UpperCAmelCase							(  _snake_case    ,			_snake_case    ,			_snake_case				):
							lowerCAmelCase =		copy.deepcopy(__lowerCamelCase				)
							model.to(__lowerCamelCase				)

							lowerCAmelCase =		['''bias''', '''LayerNorm.weight''']
							lowerCAmelCase =		[
							    {
							        '''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},
							]
							lowerCAmelCase =		AdamW(__lowerCamelCase    ,			lr=5E-5    ,			eps=1E-8				)
							lowerCAmelCase =		get_linear_schedule_with_warmup(__lowerCamelCase    ,			0    ,			__lowerCamelCase				)
							torch.cuda.empty_cache()
							return model, lm_optimizer, lm_scheduler



def    UpperCAmelCase							(  _snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case				):
							lowerCAmelCase =		np.array(__lowerCamelCase				).mean()
							lowerCAmelCase =		np.array(__lowerCamelCase				).std()
							lowerCAmelCase =		(real_perps - avg) / std
							lowerCAmelCase =		list(zip(__lowerCamelCase    ,			__lowerCamelCase    ,			__lowerCamelCase				)				)
							joblib.dump(__lowerCamelCase    ,			__lowerCamelCase				)



def    UpperCAmelCase							(  _snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case="dev.jbl"    ,			_snake_case=recopy_gpta    ,			):
							lowerCAmelCase =		[]
							lowerCAmelCase =		[]
							lowerCAmelCase =		[]

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

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

							for step in tqdm(range(__lowerCamelCase				)				):
														lowerCAmelCase =		torch.zeros((1, context_len)    ,			dtype=torch.long    ,			device=__lowerCamelCase				)
														lowerCAmelCase =		random.choice(__lowerCamelCase				)
														lowerCAmelCase =		random.randint(0    ,			len(story[0]				) - context_len - 1				)
														lowerCAmelCase =		story[0][start : start + context_len]
														lm_optimizer.zero_grad()
														lowerCAmelCase =		model(__lowerCamelCase    ,			labels=__lowerCamelCase				)
														lowerCAmelCase =		outputs[0]
														lowerCAmelCase =		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
														lowerCAmelCase =		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
														lowerCAmelCase =		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    UpperCAmelCase							(  _snake_case    ,			_snake_case="data/tokenized_stories_train_wikitext103.jbl"    ,			_snake_case=100    ,			_snake_case=1026    ,			_snake_case=True				):
							lowerCAmelCase =		joblib.load(__lowerCamelCase				)
							print('''data loaded'''				)
							lowerCAmelCase =		[]
							if trim:
														for i, example in enumerate(__lowerCamelCase				):
																					if len(example[0]				) > min_len:
																												lowerCAmelCase =		random.randint(0    ,			len(example[0]				) - context_len - 1				)
																												objective_set.append(example[0, start : start + context_len]				)
																					if len(__lowerCamelCase				) >= number:
																												break
														lowerCAmelCase =		[]
														for j in range(i + 1    ,			len(__lowerCamelCase				)				):
																					if len(data[j][0]				) > min_len:
																												train_data.append(data[j]				)
							else:
														lowerCAmelCase =		data[0:number]
														lowerCAmelCase =		data[number:]

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



def    UpperCAmelCase							(  _snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case=50    ,			_snake_case="secondary_learner.pt"				):
							lowerCAmelCase =		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
							lowerCAmelCase =		train_dataset[:512]
							lowerCAmelCase =		train_dataset[512:]
							lowerCAmelCase =		DataLoader(__lowerCamelCase    ,			shuffle=__lowerCamelCase    ,			batch_size=__lowerCamelCase				)
							lowerCAmelCase =		DataLoader(__lowerCamelCase    ,			shuffle=__lowerCamelCase    ,			batch_size=__lowerCamelCase				)

							# secondary learner model set up
							lowerCAmelCase =		nn.MSELoss()
							lowerCAmelCase =		nn.MSELoss(reduction='''sum'''				)
							secondary_learner.to(__lowerCamelCase				)
							lowerCAmelCase =		torch.optim.Adam(secondary_learner.parameters()    ,			lr=0.00001				)
							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
							lowerCAmelCase =		float('''inf'''				)
							# Iterate through batches until we've used max_steps batches
							for epoch in range(int(__lowerCamelCase				)				):
														lowerCAmelCase =		0.0
														secondary_learner.train()
														for step, batch in enumerate(__lowerCamelCase				):
																					lowerCAmelCase =		batch[0].to(__lowerCamelCase				)
																					lowerCAmelCase =		batch[1].to(__lowerCamelCase				)
																					lowerCAmelCase =		secondary_learner(__lowerCamelCase				)
																					q_optimizer.zero_grad()
																					lowerCAmelCase =		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				)):
																												lowerCAmelCase =		tr_q_loss / (step + 1)

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

																					secondary_learner.train()
							return secondary_learner


class 				__UpperCamelCase      (			nn.Module	):





				'''simple docstring'''



				def __init__(			self							,			UpperCAmelCase_    ):
											super(UpperCAmelCase_							,			self    ).__init__()
											lowerCAmelCase =		model.transformer.wte
											lowerCAmelCase =		copy.deepcopy(model.transformer.wte.weight    )
											lowerCAmelCase =		nn.Convad(self.embeddings.weight.size(1    )							,			2_56							,			3							,			padding=1    )
											lowerCAmelCase =		nn.Sequential(nn.Linear(2_56							,			32    )							,			nn.Dropout(p=0.1    )							,			nn.Linear(32							,			32    )							,			nn.Linear(32							,			1    )    )



				def    __snake_case		(			self							,			UpperCAmelCase_    ):
											lowerCAmelCase =		torch.max(self.conv(self.embeddings(UpperCAmelCase_    ).squeeze(1    ).transpose(1							,			2    )    )							,			2    )[0]
											lowerCAmelCase =		self.fc(UpperCAmelCase_    )
											return qs.squeeze(1    )






				@classmethod
				def    __snake_case		(			cls							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											lowerCAmelCase =		cls(UpperCAmelCase_    )  # this calls __init__
											lowerCAmelCase =		torch.load(UpperCAmelCase_    )
											secondary_learner.load_state_dict(UpperCAmelCase_    )
											lowerCAmelCase =		model.transformer.wte
											lowerCAmelCase =		copy.deepcopy(model.transformer.wte.weight    )
											return secondary_learner








"""simple docstring"""






def 	snake_case__  (						__lowerCamelCase							:       int = 998001      ):
			"""simple docstring"""

			# fetches the next number
			for number in range(n - 1				,						9999				,						-1      ):
						lowerCamelCase__						:						Optional[int]						      =str(__lowerCamelCase      )

						# checks whether 'str_number' is a palindrome.
						if str_number == str_number[::-1]:
									lowerCamelCase__						:						Optional[int]						      =999

									# if 'number' is a product of two 3-digit numbers
									# then number is the answer otherwise fetch next number.
									while divisor != 99:
												if (number % divisor == 0) and (len(str(number // divisor      )      ) == 3.0):
															return number
												divisor -= 1
			raise ValueError('''That number is larger than our acceptable range.'''      )


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




