

"""simple docstring"""





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						:   Dict							   =	logging.getLogger(__name__)

def 						__SCREAMING_SNAKE_CASE						(   A_				):
	random.seed(A_				)
	np.random.seed(A_				)
	torch.manual_seed(A_				)
	torch.cuda.manual_seed_all(A_				)

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

def 						__SCREAMING_SNAKE_CASE						(   A_="gpt2"				):
	lowerCAmelCase__			:		Any						       =			GPTaLMHeadModel.from_pretrained(A_		,  output_hidden_states=A_				)
	torch.save(model.state_dict()		,  model_name + '''local.pt'''				)
	return model

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_		,  A_				):
	lowerCAmelCase__			:		int						       =			copy.deepcopy(A_				)
	model.to(A_				)

	lowerCAmelCase__			:		Tuple						       =			['''bias''', '''LayerNorm.weight''']
	lowerCAmelCase__			:		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},
	]
	lowerCAmelCase__			:		Any						       =			AdamW(A_		,  lr=5e-5		,  eps=1e-8				)
	lowerCAmelCase__			:		Optional[int]						       =			get_linear_schedule_with_warmup(A_		,  0		,  A_				)
	torch.cuda.empty_cache()
	return model, lm_optimizer, lm_scheduler

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_		,  A_		,  A_				):
	lowerCAmelCase__			:		Any						       =			np.array(A_				).mean()
	lowerCAmelCase__			:		str						       =			np.array(A_				).std()
	lowerCAmelCase__			:		Optional[Any]						       =			(real_perps - avg) / std
	lowerCAmelCase__			:		Dict						       =			list(zip(A_		,  A_		,  A_				)				)
	joblib.dump(A_		,  A_				)

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_		,  A_		,  A_		,  A_		,  A_		,  A_		,  A_="dev.jbl"		,  A_=recopy_gpta		,  ):
	lowerCAmelCase__			:		Union[str, Any]						       =			[]
	lowerCAmelCase__			:		List[Any]						       =			[]
	lowerCAmelCase__			:		List[str]						       =			[]

	# Initialize the transformer model
	lowerCAmelCase__			:		Any						       =			copy.deepcopy(A_				)
	orig_model.to(device='''cpu'''				)
	torch.cuda.empty_cache()

	# Compute perplexity of initial transformer model for comparison
	model.train()
	lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__			:		List[str]						       =			recopy_model(A_		,  A_		,  A_				)

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

		# Periodically save the stored (X, IG(X)) pairs
		if step % 10_00 == 0 and step > 1:
			intermittent_save(A_		,  A_		,  A_		,  A_				)

		# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
		lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__			:		List[Any]						       =			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 						__SCREAMING_SNAKE_CASE						(   A_		,  A_="data/tokenized_stories_train_wikitext103.jbl"		,  A_=1_00		,  A_=10_26		,  A_=True				):
	lowerCAmelCase__			:		Dict						       =			joblib.load(A_				)
	print('''data loaded'''				)
	lowerCAmelCase__			:		int						       =			[]
	if trim:
		for i, example in enumerate(A_				):
			if len(example[0]				) > min_len:
				lowerCAmelCase__			:		str						       =			random.randint(0		,  len(example[0]				) - context_len - 1				)
				objective_set.append(example[0, start : start + context_len]				)
			if len(A_				) >= number:
				break
		lowerCAmelCase__			:		Union[str, Any]						       =			[]
		for j in range(i + 1		,  len(A_				)				):
			if len(data[j][0]				) > min_len:
				train_data.append(data[j]				)
	else:
		lowerCAmelCase__			:		Tuple						       =			data[0:number]
		lowerCAmelCase__			:		List[str]						       =			data[number:]

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

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_		,  A_		,  A_		,  A_=50		,  A_="secondary_learner.pt"				):
	lowerCAmelCase__			:		List[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
	lowerCAmelCase__			:		Optional[int]						       =			train_dataset[:5_12]
	lowerCAmelCase__			:		Union[str, Any]						       =			train_dataset[5_12:]
	lowerCAmelCase__			:		Optional[int]						       =			DataLoader(A_		,  shuffle=A_		,  batch_size=A_				)
	lowerCAmelCase__			:		Tuple						       =			DataLoader(A_		,  shuffle=A_		,  batch_size=A_				)

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

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

			secondary_learner.train()
	return secondary_learner







class        SCREAMING_SNAKE_CASE      (	nn.Module ):






					"""simple docstring"""



					def __init__( self    :       Dict     ,lowercase_    :       int	):
						super(lowercase_     ,self	).__init__()
						lowerCAmelCase__			:		Optional[int]						       =			model.transformer.wte
						lowerCAmelCase__			:		Optional[int]						       =			copy.deepcopy(model.transformer.wte.weight	)
						lowerCAmelCase__			:		Union[str, Any]						       =			nn.Convad(self.embeddings.weight.size(1	)     ,2_5_6     ,3     ,padding=1	)
						lowerCAmelCase__			:		Union[str, Any]						       =			nn.Sequential(nn.Linear(2_5_6     ,3_2	)     ,nn.Dropout(p=0.1	)     ,nn.Linear(3_2     ,3_2	)     ,nn.Linear(3_2     ,1	)	)







					def  __lowerCAmelCase				( self    :       Optional[Any]     ,lowercase_    :       Dict	):
						lowerCAmelCase__			:		Union[str, Any]						       =			torch.max(self.conv(self.embeddings(lowercase_	).squeeze(1	).transpose(1     ,2	)	)     ,2	)[0]
						lowerCAmelCase__			:		Any						       =			self.fc(lowercase_	)
						return qs.squeeze(1	)







					@classmethod
					def  __lowerCAmelCase				( cls    :       Any     ,lowercase_    :       str     ,lowercase_    :       Dict	):
						lowerCAmelCase__			:		Union[str, Any]						       =			cls(lowercase_	)  # this calls __init__
						lowerCAmelCase__			:		int						       =			torch.load(lowercase_	)
						secondary_learner.load_state_dict(lowercase_	)
						lowerCAmelCase__			:		Dict						       =			model.transformer.wte
						lowerCAmelCase__			:		Dict						       =			copy.deepcopy(model.transformer.wte.weight	)
						return secondary_learner







"""simple docstring"""





def 						__SCREAMING_SNAKE_CASE						(   A_				):
	lowerCAmelCase__			:		List[Any]						       =			53_81
	for x in s:
		lowerCAmelCase__			:		Tuple						       =			((hash_value << 5) + hash_value) + ord(A_				)
	return hash_value & 0Xf_f_f_f_f_f_f_f




