




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


__a  :		Optional[int]		     =   logging.getLogger(__name__)
def  snake_case_					(		SCREAMING_SNAKE_CASE_    )      ->			Optional[Any]:
							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  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			int:
							model.eval()
							lowercase__  :	Any        =							next(model.parameters()    ).device
							lowercase__  :	int        =							1
							lowercase__  :	Optional[int]        =							torch.zeros((eval_batch_size, context_len)    ,dtype=torch.long    ,device=SCREAMING_SNAKE_CASE_    )
							lowercase__  :	Optional[Any]        =							DataLoader(SCREAMING_SNAKE_CASE_    ,shuffle=SCREAMING_SNAKE_CASE_    ,batch_size=SCREAMING_SNAKE_CASE_    )
							lowercase__  :	Tuple        =							torch.zeros(1    ,device=SCREAMING_SNAKE_CASE_    )
							lowercase__  :	Tuple        =							0
							for batch in eval_dataloader:
														batch.to(SCREAMING_SNAKE_CASE_    )
														# pad
														context.zero_()
														for i in range(SCREAMING_SNAKE_CASE_    ):
																					lowercase__  :	Any        =							batch[i]
														lowercase__  :	Union[str, Any]        =							model(SCREAMING_SNAKE_CASE_    ,labels=SCREAMING_SNAKE_CASE_    )
														eval_loss += outputs[0].sum().item()
														nb_eval_examples += batch.size(0    )
							lowercase__  :	Dict        =							eval_loss / nb_eval_examples
							lowercase__  :	Union[str, Any]        =							torch.exp(SCREAMING_SNAKE_CASE_    )
							model.train()
							return perplexity
def  snake_case_					(		SCREAMING_SNAKE_CASE_="gpt2"    )      ->			Optional[int]:
							lowercase__  :	List[Any]        =							GPTaLMHeadModel.from_pretrained(SCREAMING_SNAKE_CASE_    ,output_hidden_states=SCREAMING_SNAKE_CASE_    )
							torch.save(model.state_dict()    ,model_name + "local.pt"    )
							return model
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			Dict:
							lowercase__  :	List[Any]        =							copy.deepcopy(SCREAMING_SNAKE_CASE_    )
							model.to(SCREAMING_SNAKE_CASE_    )

							lowercase__  :	int        =							["bias", "LayerNorm.weight"]
							lowercase__  :	str        =							[
							    {
							        "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},
							]
							lowercase__  :	Any        =							AdamW(SCREAMING_SNAKE_CASE_    ,lr=5E-5    ,eps=1E-8    )
							lowercase__  :	List[str]        =							get_linear_schedule_with_warmup(SCREAMING_SNAKE_CASE_    ,0    ,SCREAMING_SNAKE_CASE_    )
							torch.cuda.empty_cache()
							return model, lm_optimizer, lm_scheduler
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			Any:
							lowercase__  :	List[str]        =							np.array(SCREAMING_SNAKE_CASE_    ).mean()
							lowercase__  :	Tuple        =							np.array(SCREAMING_SNAKE_CASE_    ).std()
							lowercase__  :	Tuple        =							(real_perps - avg) / std
							lowercase__  :	int        =							list(zip(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )    )
							joblib.dump(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_="dev.jbl"    ,SCREAMING_SNAKE_CASE_=recopy_gpta    ,)      ->			Tuple:
							lowercase__  :	Union[str, Any]        =							[]
							lowercase__  :	Optional[int]        =							[]
							lowercase__  :	List[Any]        =							[]

							# Initialize the transformer model
							lowercase__  :	List[Any]        =							copy.deepcopy(SCREAMING_SNAKE_CASE_    )
							orig_model.to(device="cpu"    )
							torch.cuda.empty_cache()

							# Compute perplexity of initial transformer model for comparison
							model.train()
							lowercase__ ,       lowercase__ ,       lowercase__  :	Optional[int]        =							recopy_model(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )

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

														# Periodically save the stored (X, IG(X)) pairs
														if step % 10_00 == 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
														lowercase__ ,       lowercase__ ,       lowercase__  :	Optional[int]        =							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  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_="data/tokenized_stories_train_wikitext103.jbl"    ,SCREAMING_SNAKE_CASE_=1_00    ,SCREAMING_SNAKE_CASE_=10_26    ,SCREAMING_SNAKE_CASE_=True    )      ->			str:
							lowercase__  :	List[Any]        =							joblib.load(SCREAMING_SNAKE_CASE_    )
							print("data loaded"    )
							lowercase__  :	str        =							[]
							if trim:
														for i, example in enumerate(SCREAMING_SNAKE_CASE_    ):
																					if len(example[0]    ) > min_len:
																												lowercase__  :	Dict        =							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
														lowercase__  :	List[Any]        =							[]
														for j in range(i + 1    ,len(SCREAMING_SNAKE_CASE_    )    ):
																					if len(data[j][0]    ) > min_len:
																												train_data.append(data[j]    )
							else:
														lowercase__  :	Dict        =							data[0:number]
														lowercase__  :	str        =							data[number:]

							joblib.dump(SCREAMING_SNAKE_CASE_    ,"objective_set.jbl"    )
							print("objective set saved"    )
							return train_data, objective_set
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_=50    ,SCREAMING_SNAKE_CASE_="secondary_learner.pt"    )      ->			Union[str, Any]:
							lowercase__  :	int        =							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
							lowercase__  :	Any        =							train_dataset[:5_12]
							lowercase__  :	int        =							train_dataset[5_12:]
							lowercase__  :	Optional[Any]        =							DataLoader(SCREAMING_SNAKE_CASE_    ,shuffle=SCREAMING_SNAKE_CASE_    ,batch_size=SCREAMING_SNAKE_CASE_    )
							lowercase__  :	Any        =							DataLoader(SCREAMING_SNAKE_CASE_    ,shuffle=SCREAMING_SNAKE_CASE_    ,batch_size=SCREAMING_SNAKE_CASE_    )

							# secondary learner model set up
							lowercase__  :	int        =							nn.MSELoss()
							lowercase__  :	str        =							nn.MSELoss(reduction="sum"    )
							secondary_learner.to(SCREAMING_SNAKE_CASE_    )
							lowercase__  :	Optional[int]        =							torch.optim.Adam(secondary_learner.parameters()    ,lr=0.0_0001    )
							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
							lowercase__  :	Tuple        =							float("inf"    )
							# Iterate through batches until we've used max_steps batches
							for epoch in range(int(SCREAMING_SNAKE_CASE_    )    ):
														lowercase__  :	Union[str, Any]        =							0.0
														secondary_learner.train()
														for step, batch in enumerate(SCREAMING_SNAKE_CASE_    ):
																					lowercase__  :	str        =							batch[0].to(SCREAMING_SNAKE_CASE_    )
																					lowercase__  :	int        =							batch[1].to(SCREAMING_SNAKE_CASE_    )
																					lowercase__  :	Any        =							secondary_learner(SCREAMING_SNAKE_CASE_    )
																					q_optimizer.zero_grad()
																					lowercase__  :	Dict        =							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_    )):
																												lowercase__  :	Tuple        =							tr_q_loss / (step + 1)

																												secondary_learner.eval()
																												lowercase__  :	Tuple        =							0.0
																												lowercase__  :	str        =							0.0
																												lowercase__  :	str        =							[]
																												lowercase__  :	str        =							[]
																												# Compute performance of the secondary learner after this batch
																												for stepa, batcha in enumerate(SCREAMING_SNAKE_CASE_    ):
																																			lowercase__  :	str        =							batcha[0].to(SCREAMING_SNAKE_CASE_    )
																																			lowercase__  :	Any        =							batcha[1].to(SCREAMING_SNAKE_CASE_    )
																																			lowercase__  :	str        =							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_    )
																																			lowercase__  :	Tuple        =							q_lossa

																					secondary_learner.train()
							return secondary_learner







class 				UpperCAmelCase(					nn.Module					):



	"""simple docstring"""






	def __init__(					self	,				lowerCamelCase    ) ->      List[str]:





								"""simple docstring"""


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

	def    __a     (					self	,				lowerCamelCase    ) ->      str:





								"""simple docstring"""


								lowercase__  :	Optional[Any]        =							torch.max(self.conv(self.embeddings(lowerCamelCase    ).squeeze(1    ).transpose(1	,				2    )    )	,				2    )[0]
								lowercase__  :	Dict        =							self.fc(lowerCamelCase    )
								return qs.squeeze(1    )







	@classmethod
	def    __a     (					cls	,				lowerCamelCase	,				lowerCamelCase    ) ->      List[Any]:





								"""simple docstring"""


								lowercase__  :	List[Any]        =							cls(lowerCamelCase    )  # this calls __init__
								lowercase__  :	int        =							torch.load(lowerCamelCase    )
								secondary_learner.load_state_dict(lowerCamelCase    )
								lowercase__  :	List[str]        =							model.transformer.wte
								lowercase__  :	Union[str, Any]        =							copy.deepcopy(model.transformer.wte.weight    )
								return secondary_learner





def  snake_case_					(		SCREAMING_SNAKE_CASE_    )      ->			list:
							lowercase__  :	Optional[Any]        =							len(SCREAMING_SNAKE_CASE_    )

							# Set the gap value to be decreased by a factor of 1.3
							# after each iteration
							lowercase__  :	Tuple        =							1.3

							# Continue sorting until the gap is 1
							while gap > 1:
														# Decrease the gap value
														lowercase__  :	Tuple        =							int(gap / shrink    )

														# Sort the elements using insertion sort
														for i in range(SCREAMING_SNAKE_CASE_    ,len(SCREAMING_SNAKE_CASE_    )    ):
																					lowercase__  :	int        =							collection[i]
																					lowercase__  :	Union[str, Any]        =							i
																					while j >= gap and collection[j - gap] > temp:
																												lowercase__  :	str        =							collection[j - gap]
																												j -= gap
																					lowercase__  :	List[Any]        =							temp

							return collection


if __name__ == "__main__":
						import doctest

						doctest.testmod()