



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:					Tuple      =						logging.getLogger(__name__)






def        a( A			:       Optional[Any]			)						->			str:
					"""simple docstring"""





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






def        a( A			:       int					,	A			:       List[str]					,	A			:       Optional[Any]			)						->			Dict:
					"""simple docstring"""





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






def        a( A			:       Any="gpt2"			)						->			List[str]:
					"""simple docstring"""





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






def        a( A			:       Dict					,	A			:       Any					,	A			:       Any			)						->			int:
					"""simple docstring"""





					a   	=  copy.deepcopy(A			)
					model.to(A			)

					a   	=  ["bias", "LayerNorm.weight"]
					a   	=  [
					    {
					        "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},
					]
					a   	=  AdamW(A					,	lr=5e-5					,	eps=1e-8			)
					a   	=  get_linear_schedule_with_warmup(A					,	0					,	A			)
					torch.cuda.empty_cache()
					return model, lm_optimizer, lm_scheduler






def        a( A			:       List[Any]					,	A			:       List[Any]					,	A			:       Tuple					,	A			:       List[Any]			)						->			Union[str, Any]:
					"""simple docstring"""





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






def        a( A			:       Union[str, Any]					,	A			:       Tuple					,	A			:       Optional[int]					,	A			:       str					,	A			:       Dict					,	A			:       Optional[Any]					,	A			:       str					,	A			:       str="dev.jbl"					,	A			:       Optional[Any]=recopy_gpta					,	)						->			str:
					"""simple docstring"""





					a   	=  []
					a   	=  []
					a   	=  []

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

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

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

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

										# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
										a	,							a	,							a   	=  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        a( A			:       Union[str, Any]					,	A			:       Tuple="data/tokenized_stories_train_wikitext103.jbl"					,	A			:       Tuple=100					,	A			:       Tuple=1026					,	A			:       Optional[int]=True			)						->			Any:
					"""simple docstring"""





					a   	=  joblib.load(A			)
					print("data loaded"			)
					a   	=  []
					if trim:
										for i, example in enumerate(A			):
															if len(example[0]			) > min_len:
																				a   	=  random.randint(0					,	len(example[0]			) - context_len - 1			)
																				objective_set.append(example[0, start : start + context_len]			)
															if len(A			) >= number:
																				break
										a   	=  []
										for j in range(i + 1					,	len(A			)			):
															if len(data[j][0]			) > min_len:
																				train_data.append(data[j]			)
					else:
										a   	=  data[0:number]
										a   	=  data[number:]

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






def        a( A			:       Optional[Any]					,	A			:       List[Any]					,	A			:       Union[str, Any]					,	A			:       Dict					,	A			:       Optional[Any]=50					,	A			:       Dict="secondary_learner.pt"			)						->			Any:
					"""simple docstring"""





					a   	=  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
					a   	=  train_dataset[:512]
					a   	=  train_dataset[512:]
					a   	=  DataLoader(A					,	shuffle=A					,	batch_size=A			)
					a   	=  DataLoader(A					,	shuffle=A					,	batch_size=A			)

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

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

															secondary_learner.train()
					return secondary_learner







class       _lowercase				(  nn.Module      ):





	"""simple docstring"""




	def __init__(self						,			lowerCamelCase_						):
						"""simple docstring"""

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




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

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




	@classmethod
	def 	UpperCamelCase_			(cls						,			lowerCamelCase_						,			lowerCamelCase_						):
						"""simple docstring"""

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





from __future__ import annotations

from collections import defaultdict
from dataclasses import dataclass



@dataclass
class       _lowercase				:





	"""simple docstring"""
	__A		      =				42
	__A		      =				None
	__A		      =				None






def        a( )						->			TreeNode:
					"""simple docstring"""





					return TreeNode(3					,	TreeNode(9			)					,	TreeNode(20					,	TreeNode(15			)					,	TreeNode(7			)			)			)






def        a( A			:       TreeNode			)						->			list[int]:
					"""simple docstring"""





					def depth_first_search(
					    A			:       TreeNode | None					,	A			:       int					,	A			:       list[int]			) -> None:
										if not root:
															return

										if depth == len(A			):
															right_view.append(root.val			)

										depth_first_search(root.right					,	depth + 1					,	A			)
										depth_first_search(root.left					,	depth + 1					,	A			)

					a   	=  []
					if not root:
										return right_view

					depth_first_search(A					,	0					,	A			)
					return right_view






def        a( A			:       TreeNode			)						->			list[int]:
					"""simple docstring"""





					def depth_first_search(
					    A			:       TreeNode | None					,	A			:       int					,	A			:       list[int]			) -> None:
										if not root:
															return

										if depth == len(A			):
															left_view.append(root.val			)

										depth_first_search(root.left					,	depth + 1					,	A			)
										depth_first_search(root.right					,	depth + 1					,	A			)

					a   	=  []
					if not root:
										return left_view

					depth_first_search(A					,	0					,	A			)
					return left_view






def        a( A			:       TreeNode			)						->			list[int]:
					"""simple docstring"""





					def breadth_first_search(A			:       TreeNode					,	A			:       list[int]			) -> None:
										a   	=  [(root, 0)]
										a   	=  defaultdict(A			)

										while queue:
															a   	=  queue.pop(0			)
															a	,							a   	=  first

															lookup[hd].append(node.val			)

															if node.left:
																				queue.append((node.left, hd - 1)			)
															if node.right:
																				queue.append((node.right, hd + 1)			)

										for pair in sorted(lookup.items()					,	key=lambda A			: each[0]			):
															top_view.append(pair[1][0]			)

					a   	=  []
					if not root:
										return top_view

					breadth_first_search(A					,	A			)
					return top_view






def        a( A			:       TreeNode			)						->			list[int]:
					"""simple docstring"""





					from collections import defaultdict

					def breadth_first_search(A			:       TreeNode					,	A			:       list[int]			) -> None:
										a   	=  [(root, 0)]
										a   	=  defaultdict(A			)

										while queue:
															a   	=  queue.pop(0			)
															a	,							a   	=  first
															lookup[hd].append(node.val			)

															if node.left:
																				queue.append((node.left, hd - 1)			)
															if node.right:
																				queue.append((node.right, hd + 1)			)

										for pair in sorted(lookup.items()					,	key=lambda A			: each[0]			):
															bottom_view.append(pair[1][-1]			)

					a   	=  []
					if not root:
										return bottom_view

					breadth_first_search(A					,	A			)
					return bottom_view


if __name__ == "__main__":
				import doctest

				doctest.testmod()
