


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__(							_SCREAMING_SNAKE_CASE				:   List[str]   ):






			"""simple docstring"""




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






def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   int,_SCREAMING_SNAKE_CASE				:   Union[str, Any],_SCREAMING_SNAKE_CASE				:   List[str]   ):






			"""simple docstring"""




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






def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Tuple="gpt2"   ):






			"""simple docstring"""




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






def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   int,_SCREAMING_SNAKE_CASE				:   Union[str, Any],_SCREAMING_SNAKE_CASE				:   Optional[Any]   ):






			"""simple docstring"""




			__A=  copy.deepcopy(_SCREAMING_SNAKE_CASE   )
			model.to(_SCREAMING_SNAKE_CASE   )

			__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(_SCREAMING_SNAKE_CASE,lr=5e-5,eps=1e-8   )
			__A=  get_linear_schedule_with_warmup(_SCREAMING_SNAKE_CASE,0,_SCREAMING_SNAKE_CASE   )
			torch.cuda.empty_cache()
			return model, lm_optimizer, lm_scheduler






def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   int,_SCREAMING_SNAKE_CASE				:   Any,_SCREAMING_SNAKE_CASE				:   Optional[int],_SCREAMING_SNAKE_CASE				:   int   ):






			"""simple docstring"""




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






def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Any,_SCREAMING_SNAKE_CASE				:   int,_SCREAMING_SNAKE_CASE				:   Union[str, Any],_SCREAMING_SNAKE_CASE				:   Optional[int],_SCREAMING_SNAKE_CASE				:   Union[str, Any],_SCREAMING_SNAKE_CASE				:   List[str],_SCREAMING_SNAKE_CASE				:   int,_SCREAMING_SNAKE_CASE				:   Dict="dev.jbl",_SCREAMING_SNAKE_CASE				:   List[Any]=recopy_gpta,):






			"""simple docstring"""




			__A=  []
			__A=  []
			__A=  []

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

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

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

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

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

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

			intermittent_save(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   )






def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Optional[int],_SCREAMING_SNAKE_CASE				:   str="data/tokenized_stories_train_wikitext103.jbl",_SCREAMING_SNAKE_CASE				:   Optional[int]=100,_SCREAMING_SNAKE_CASE				:   str=1026,_SCREAMING_SNAKE_CASE				:   Union[str, Any]=True   ):






			"""simple docstring"""




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

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






def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Tuple,_SCREAMING_SNAKE_CASE				:   Union[str, Any],_SCREAMING_SNAKE_CASE				:   Optional[int],_SCREAMING_SNAKE_CASE				:   int,_SCREAMING_SNAKE_CASE				:   Optional[Any]=50,_SCREAMING_SNAKE_CASE				:   Optional[Any]="secondary_learner.pt"   ):






			"""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(_SCREAMING_SNAKE_CASE,shuffle=_SCREAMING_SNAKE_CASE,batch_size=_SCREAMING_SNAKE_CASE   )
			__A=  DataLoader(_SCREAMING_SNAKE_CASE,shuffle=_SCREAMING_SNAKE_CASE,batch_size=_SCREAMING_SNAKE_CASE   )

			# secondary learner model set up
			__A=  nn.MSELoss()
			__A=  nn.MSELoss(reduction='sum'   )
			secondary_learner.to(_SCREAMING_SNAKE_CASE   )
			__A=  torch.optim.Adam(secondary_learner.parameters(),lr=0.0_00_01   )
			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(_SCREAMING_SNAKE_CASE   )   ):
						__A=  0.0
						secondary_learner.train()
						for step, batch in enumerate(_SCREAMING_SNAKE_CASE   ):
									__A=  batch[0].to(_SCREAMING_SNAKE_CASE   )
									__A=  batch[1].to(_SCREAMING_SNAKE_CASE   )
									__A=  secondary_learner(_SCREAMING_SNAKE_CASE   )
									q_optimizer.zero_grad()
									__A=  loss(_SCREAMING_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(_SCREAMING_SNAKE_CASE   )):
												__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(_SCREAMING_SNAKE_CASE   ):
															__A=  batcha[0].to(_SCREAMING_SNAKE_CASE   )
															__A=  batcha[1].to(_SCREAMING_SNAKE_CASE   )
															__A=  secondary_learner(_SCREAMING_SNAKE_CASE   )
															q_lossa += test_loss(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   ).item()
															sum_qa += torch.sum(_SCREAMING_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(_SCREAMING_SNAKE_CASE   )
												print(
												    'Epoch: ',_SCREAMING_SNAKE_CASE,'step: ',_SCREAMING_SNAKE_CASE,'Avg. q:',sum_qa / len(_SCREAMING_SNAKE_CASE   ),'Train Loss: ',_SCREAMING_SNAKE_CASE,'Test Loss: ',_SCREAMING_SNAKE_CASE,)
												if q_lossa < best_test_loss:
															joblib.dump((predicted, actual),'pred_vs_actual.jbl'   )
															torch.save(secondary_learner.state_dict(),_SCREAMING_SNAKE_CASE   )
															__A=  q_lossa

									secondary_learner.train()
			return secondary_learner






class 	a__				(				nn.Module      ):





			'''simple docstring'''

			def __init__(     self       : Tuple ,						lowerCAmelCase_       : List[str]  )			->		List[Any]:
						super(__A ,						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 				lowerCAmelCase    (     self       : List[str] ,						lowerCAmelCase_       : List[Any]  )			->		List[Any]:
						__A=  torch.max(self.conv(self.embeddings(__A  ).squeeze(1  ).transpose(1 ,						2  )  ) ,						2  )[0]
						__A=  self.fc(__A  )
						return qs.squeeze(1  )

			@classmethod
			def 				lowerCAmelCase    (     cls       : Tuple ,						lowerCAmelCase_       : Optional[Any] ,						lowerCAmelCase_       : str  )			->		int:
						__A=  cls(__A  )  # this calls __init__
						__A=  torch.load(__A  )
						secondary_learner.load_state_dict(__A  )
						__A=  model.transformer.wte
						__A=  copy.deepcopy(model.transformer.wte.weight  )
						return secondary_learner







'''simple docstring'''





from typing import Any
class 	a__				:





			'''simple docstring'''

			def __init__(     self       : Any  )			->		int:
						__A=  []
						__A=  0
			def __str__(     self       : Tuple  )			->		Dict:
						__A=  '<' + str(self.stack  )[1:-1] + '>'
						return printed
			def 				lowerCAmelCase    (     self       : Tuple ,						lowerCAmelCase_       : Any  )			->		None:
						self.stack.append(lowerCAmelCase_  )
						__A=  self.length + 1
			def 				lowerCAmelCase    (     self       : int  )			->		Any:
						self.rotate(1  )
						__A=  self.stack[self.length - 1]
						__A=  self.stack[:-1]
						self.rotate(self.length - 1  )
						__A=  self.length - 1
						return dequeued
			def 				lowerCAmelCase    (     self       : Optional[int] ,						lowerCAmelCase_       : int  )			->		None:
						for _ in range(lowerCAmelCase_  ):
									__A=  self.stack[0]
									__A=  self.stack[1:]
									self.put(lowerCAmelCase_  )
									__A=  self.length - 1
			def 				lowerCAmelCase    (     self       : Tuple  )			->		Any:
						__A=  self.get()
						self.put(lowerCAmelCase_  )
						self.rotate(self.length - 1  )
						return front
			def 				lowerCAmelCase    (     self       : Dict  )			->		int:
						return self.length



