



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


def 	_A	(   _UpperCamelCase			):
				random.seed(_UpperCamelCase			)
				np.random.seed(_UpperCamelCase			)
				torch.manual_seed(_UpperCamelCase			)
				torch.cuda.manual_seed_all(_UpperCamelCase			)


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


def 	_A	(   _UpperCamelCase="gpt2"			):
				_UpperCAmelCase						:  Union[str, Any]     					=	GPTaLMHeadModel.from_pretrained(_UpperCamelCase      ,					output_hidden_states=_UpperCamelCase			)
				torch.save(model.state_dict()      ,					model_name + '''local.pt'''			)
				return model


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase			):
				_UpperCAmelCase						:  List[Any]     					=	copy.deepcopy(_UpperCamelCase			)
				model.to(_UpperCamelCase			)

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


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase			):
				_UpperCAmelCase						:  int     					=	np.array(_UpperCamelCase			).mean()
				_UpperCAmelCase						:  Dict     					=	np.array(_UpperCamelCase			).std()
				_UpperCAmelCase						:  str     					=	(real_perps - avg) / std
				_UpperCAmelCase						:  Optional[Any]     					=	list(zip(_UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase			)			)
				joblib.dump(_UpperCamelCase      ,					_UpperCamelCase			)


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase="dev.jbl"      ,					_UpperCamelCase=recopy_gpta      ,					):
				_UpperCAmelCase						:  Any     					=	[]
				_UpperCAmelCase						:  List[str]     					=	[]
				_UpperCAmelCase						:  Optional[int]     					=	[]

				# Initialize the transformer model
				_UpperCAmelCase						:  Optional[Any]     					=	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						:  Dict     					=	recopy_model(_UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase			)

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

								# Periodically save the stored (X, IG(X)) pairs
								if step % 1_000 == 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						:  Optional[Any]     					=	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 	_A	(   _UpperCamelCase      ,					_UpperCamelCase="data/tokenized_stories_train_wikitext103.jbl"      ,					_UpperCamelCase=100      ,					_UpperCamelCase=1_026      ,					_UpperCamelCase=True			):
				_UpperCAmelCase						:  Tuple     					=	joblib.load(_UpperCamelCase			)
				print('''data loaded'''			)
				_UpperCAmelCase						:  int     					=	[]
				if trim:
								for i, example in enumerate(_UpperCamelCase			):
												if len(example[0]			) > min_len:
																_UpperCAmelCase						:  Dict     					=	random.randint(0      ,					len(example[0]			) - context_len - 1			)
																objective_set.append(example[0, start : start + context_len]			)
												if len(_UpperCamelCase			) >= number:
																break
								_UpperCAmelCase						:  Union[str, Any]     					=	[]
								for j in range(i + 1      ,					len(_UpperCamelCase			)			):
												if len(data[j][0]			) > min_len:
																train_data.append(data[j]			)
				else:
								_UpperCAmelCase						:  Optional[Any]     					=	data[0:number]
								_UpperCAmelCase						:  List[Any]     					=	data[number:]

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


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase=50      ,					_UpperCamelCase="secondary_learner.pt"			):
				_UpperCAmelCase						:  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
				_UpperCAmelCase						:  Tuple     					=	train_dataset[:512]
				_UpperCAmelCase						:  List[Any]     					=	train_dataset[512:]
				_UpperCAmelCase						:  Any     					=	DataLoader(_UpperCamelCase      ,					shuffle=_UpperCamelCase      ,					batch_size=_UpperCamelCase			)
				_UpperCAmelCase						:  str     					=	DataLoader(_UpperCamelCase      ,					shuffle=_UpperCamelCase      ,					batch_size=_UpperCamelCase			)

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

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

												secondary_learner.train()
				return secondary_learner






class 							lowerCAmelCase_		(		nn.Module     ):







		def __init__(    self  :   Any       ,		UpperCAmelCase_  :   Optional[int]					)       ->		int:


						'''simple docstring'''







						super(UpperCAmelCase_       ,		self					).__init__()
						_UpperCAmelCase						:  Optional[Any]     					=	model.transformer.wte
						_UpperCAmelCase						:  Optional[int]     					=	copy.deepcopy(model.transformer.wte.weight					)
						_UpperCAmelCase						:  List[str]     					=	nn.Convad(self.embeddings.weight.size(1					)       ,		256       ,		3       ,		padding=1					)
						_UpperCAmelCase						:  int     					=	nn.Sequential(nn.Linear(256       ,		32					)       ,		nn.Dropout(p=0.1					)       ,		nn.Linear(32       ,		32					)       ,		nn.Linear(32       ,		1					)					)







		def 					a_  (    self  :   str       ,		UpperCAmelCase_  :   List[str]					)       ->		str:


						'''simple docstring'''







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







		@classmethod
		def 					a_  (    cls  :   List[Any]       ,		UpperCAmelCase_  :   Optional[Any]       ,		UpperCAmelCase_  :   Union[str, Any]					)       ->		Dict:


						'''simple docstring'''







						_UpperCAmelCase						:  Dict     					=	cls(UpperCAmelCase_					)  # this calls __init__
						_UpperCAmelCase						:  List[Any]     					=	torch.load(UpperCAmelCase_					)
						secondary_learner.load_state_dict(UpperCAmelCase_					)
						_UpperCAmelCase						:  List[str]     					=	model.transformer.wte
						_UpperCAmelCase						:  int     					=	copy.deepcopy(model.transformer.wte.weight					)
						return secondary_learner





class 							lowerCAmelCase_		:







		def __init__(    self  :   int       ,		UpperCAmelCase_  :   int					)       ->		None:


						'''simple docstring'''







						_UpperCAmelCase						:  Tuple     					=	data
						_UpperCAmelCase						:  BinaryTreeNode | None     					=	None
						_UpperCAmelCase						:  BinaryTreeNode | None     					=	None


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase			):
				if node is None:
								_UpperCAmelCase						:  Union[str, Any]     					=	BinaryTreeNode(_UpperCamelCase			)
								return node

				# binary search tree is not empty,
				# so we will insert it into the tree
				# if new_value is less than value of data in node,
				#  add it to left subtree and proceed recursively
				if new_value < node.data:
								_UpperCAmelCase						:  Any     					=	insert(node.left_child      ,					_UpperCamelCase			)
				else:
								# if new_value is greater than value of data in node,
								#  add it to right subtree and proceed recursively
								_UpperCAmelCase						:  List[Any]     					=	insert(node.right_child      ,					_UpperCamelCase			)
				return node


def 	_A	(   _UpperCamelCase			):  # if node is None,return
				if node:
								_UpperCAmelCase						:  str     					=	inorder(node.left_child			)
								_UpperCAmelCase						:  Union[str, Any]     					=	[*inorder_array, node.data]
								_UpperCAmelCase						:  Optional[int]     					=	inorder_array + inorder(node.right_child			)
				else:
								_UpperCAmelCase						:  Tuple     					=	[]
				return inorder_array


def 	_A	(   ):
				_UpperCAmelCase						:  Dict     					=	insert(_UpperCamelCase      ,					15			)
				insert(_UpperCamelCase      ,					10			)
				insert(_UpperCamelCase      ,					25			)
				insert(_UpperCamelCase      ,					6			)
				insert(_UpperCamelCase      ,					14			)
				insert(_UpperCamelCase      ,					20			)
				insert(_UpperCamelCase      ,					60			)
				return root


def 	_A	(   ):
				# main function
				_UpperCAmelCase						:  Tuple     					=	make_tree()
				print('''Printing values of binary search tree in Inorder Traversal.'''			)
				inorder(_UpperCamelCase			)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()
