




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_			:       str						       =				logging.getLogger(__name__)


def 							__SCREAMING_SNAKE_CASE						(    snake_case_		):

						'''simple docstring'''


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


def 							__SCREAMING_SNAKE_CASE						(    snake_case_			,					snake_case_			,					snake_case_		):

						'''simple docstring'''


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


def 							__SCREAMING_SNAKE_CASE						(    snake_case_="gpt2"		):

						'''simple docstring'''


						_UpperCAmelCase									=  GPTaLMHeadModel.from_pretrained(snake_case_			,					output_hidden_states=snake_case_		)
						torch.save(model.state_dict()			,					model_name + "local.pt"		)
						return model


def 							__SCREAMING_SNAKE_CASE						(    snake_case_			,					snake_case_			,					snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase									=  copy.deepcopy(snake_case_		)
						model.to(snake_case_		)

						_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(snake_case_			,					lr=5e-5			,					eps=1e-8		)
						_UpperCAmelCase									=  get_linear_schedule_with_warmup(snake_case_			,					0			,					snake_case_		)
						torch.cuda.empty_cache()
						return model, lm_optimizer, lm_scheduler


def 							__SCREAMING_SNAKE_CASE						(    snake_case_			,					snake_case_			,					snake_case_			,					snake_case_		):

						'''simple docstring'''


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


def 							__SCREAMING_SNAKE_CASE						(    snake_case_			,					snake_case_			,					snake_case_			,					snake_case_			,					snake_case_			,					snake_case_			,					snake_case_			,					snake_case_="dev.jbl"			,					snake_case_=recopy_gpta			,					):

						'''simple docstring'''


						_UpperCAmelCase									=  []
						_UpperCAmelCase									=  []
						_UpperCAmelCase									=  []

						# Initialize the transformer model
						_UpperCAmelCase									=  copy.deepcopy(snake_case_		)
						orig_model.to(device="cpu"		)
						torch.cuda.empty_cache()

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

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

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

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

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

						intermittent_save(snake_case_			,					snake_case_			,					snake_case_			,					snake_case_		)


def 							__SCREAMING_SNAKE_CASE						(    snake_case_			,					snake_case_="data/tokenized_stories_train_wikitext103.jbl"			,					snake_case_=100			,					snake_case_=1026			,					snake_case_=True		):

						'''simple docstring'''


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

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


def 							__SCREAMING_SNAKE_CASE						(    snake_case_			,					snake_case_			,					snake_case_			,					snake_case_			,					snake_case_=50			,					snake_case_="secondary_learner.pt"		):

						'''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(snake_case_			,					shuffle=snake_case_			,					batch_size=snake_case_		)
						_UpperCAmelCase									=  DataLoader(snake_case_			,					shuffle=snake_case_			,					batch_size=snake_case_		)

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

																		secondary_learner.train()
						return secondary_learner






class 					__lowerCAmelCase				( nn.Module				):







			def __init__( self					:    List[Any]					,					snake_case__					:    Optional[Any]   ):




									"""simple docstring"""


									super(UpperCamelCase__					,					self   ).__init__()
									_UpperCAmelCase									=  model.transformer.wte
									_UpperCAmelCase									=  copy.deepcopy(model.transformer.wte.weight   )
									_UpperCAmelCase									=  nn.Convad(self.embeddings.weight.size(1   )					,					256					,					3					,					padding=1   )
									_UpperCAmelCase									=  nn.Sequential(nn.Linear(256					,					32   )					,					nn.Dropout(p=0.1   )					,					nn.Linear(32					,					32   )					,					nn.Linear(32					,					1   )   )







			def UpperCamelCase    ( self					:    Optional[Any]					,					snake_case__					:    str   ):




									"""simple docstring"""


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







			@classmethod
			def UpperCamelCase    ( cls					:    Optional[int]					,					snake_case__					:    Optional[int]					,					snake_case__					:    Dict   ):




									"""simple docstring"""


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










def 							__SCREAMING_SNAKE_CASE						(    snake_case_ = 99_8001		):

						'''simple docstring'''


						for number in range(n - 1			,					9999			,					-1		):
												_UpperCAmelCase									=  str(snake_case_		)

												# checks whether 'str_number' is a palindrome.
												if str_number == str_number[::-1]:
																		_UpperCAmelCase									=  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() = }""")




