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






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE="gpt2"							):
	lowercase					  =   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			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   copy.deepcopy(SCREAMING_SNAKE_CASE_							)
	model.to(SCREAMING_SNAKE_CASE_							)

	lowercase					  =   ["bias", "LayerNorm.weight"]
	lowercase					  =   [
	    {
	        "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					  =   AdamW(SCREAMING_SNAKE_CASE_			,							lr=5e-5			,							eps=1e-8							)
	lowercase					  =   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			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   np.array(SCREAMING_SNAKE_CASE_							).mean()
	lowercase					  =   np.array(SCREAMING_SNAKE_CASE_							).std()
	lowercase					  =   (real_perps - avg) / std
	lowercase					  =   list(zip(SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							)							)
	joblib.dump(SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							)






def 							UpperCAmelCase_		(							__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			,							):
	lowercase					  =   []
	lowercase					  =   []
	lowercase					  =   []

	# Initialize the transformer model
	lowercase					  =   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					  =   recopy_model(SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							)

	for step in tqdm(range(SCREAMING_SNAKE_CASE_							)							):
		lowercase					  =   torch.zeros((1, context_len)			,							dtype=torch.long			,							device=SCREAMING_SNAKE_CASE_							)
		lowercase					  =   random.choice(SCREAMING_SNAKE_CASE_							)
		lowercase					  =   random.randint(0			,							len(story[0]							) - context_len - 1							)
		lowercase					  =   story[0][start : start + context_len]
		lm_optimizer.zero_grad()
		lowercase					  =   model(SCREAMING_SNAKE_CASE_			,							labels=SCREAMING_SNAKE_CASE_							)
		lowercase					  =   outputs[0]
		lowercase					  =   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					  =   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
		lowercase					  =   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			,							__SCREAMING_SNAKE_CASE="data/tokenized_stories_train_wikitext103.jbl"			,							__SCREAMING_SNAKE_CASE=100			,							__SCREAMING_SNAKE_CASE=1026			,							__SCREAMING_SNAKE_CASE=True							):
	lowercase					  =   joblib.load(SCREAMING_SNAKE_CASE_							)
	print('data loaded'							)
	lowercase					  =   []
	if trim:
		for i, example in enumerate(SCREAMING_SNAKE_CASE_							):
			if len(example[0]							) > min_len:
				lowercase					  =   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					  =   []
		for j in range(i + 1			,							len(SCREAMING_SNAKE_CASE_							)							):
			if len(data[j][0]							) > min_len:
				train_data.append(data[j]							)
	else:
		lowercase					  =   data[0:number]
		lowercase					  =   data[number:]

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






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE=50			,							__SCREAMING_SNAKE_CASE="secondary_learner.pt"							):
	lowercase					  =   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					  =   train_dataset[:512]
	lowercase					  =   train_dataset[512:]
	lowercase					  =   DataLoader(SCREAMING_SNAKE_CASE_			,							shuffle=SCREAMING_SNAKE_CASE_			,							batch_size=SCREAMING_SNAKE_CASE_							)
	lowercase					  =   DataLoader(SCREAMING_SNAKE_CASE_			,							shuffle=SCREAMING_SNAKE_CASE_			,							batch_size=SCREAMING_SNAKE_CASE_							)

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

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

			secondary_learner.train()
	return secondary_learner







class 	A_       (      nn.Module       ):




	'''simple docstring'''


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





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case	):
		lowercase					  =   torch.max(self.conv(self.embeddings(snake_case	).squeeze(1	).transpose(1		,   2	)	)		,   2	)[0]
		lowercase					  =   self.fc(snake_case	)
		return qs.squeeze(1	)





	@classmethod
	def      SCREAMING_SNAKE_CASE__						(	cls		,   snake_case		,   snake_case	):
		lowercase					  =   cls(snake_case	)  # this calls __init__
		lowercase					  =   torch.load(snake_case	)
		secondary_learner.load_state_dict(snake_case	)
		lowercase					  =   model.transformer.wte
		lowercase					  =   copy.deepcopy(model.transformer.wte.weight	)
		return secondary_learner



def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   [701, 301, 132, 57, 23, 10, 4, 1]
	for gap in gaps:
		for i in range(__SCREAMING_SNAKE_CASE			,							len(__SCREAMING_SNAKE_CASE							)							):
			lowercase					  =   collection[i]
			lowercase					  =   i
			while j >= gap and collection[j - gap] > insert_value:
				lowercase					  =   collection[j - gap]
				j -= gap
			if j != i:
				lowercase					  =   insert_value
	return collection


if __name__ == "__main__":
	from doctest import testmod

	testmod()
	UpperCAmelCase     				=			input('''Enter numbers separated by a comma:\n''').strip()
	UpperCAmelCase     				=			[int(item) for item in user_input.split(''',''')]
	print(shell_sort(unsorted))

