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# Copyright (c) Hayeon Lee, Eunyoung Hyung [GitHub MetaD2A], 2021
# Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets, ICLR 2021
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import os
from tqdm import tqdm
import requests
from all_path import PROCESSED_DATA_PATH

dir_path = PROCESSED_DATA_PATH
if not os.path.exists(dir_path):
	os.makedirs(dir_path)


def download_file(url, filename):
	"""
	Helper method handling downloading large files from `url`
	to `filename`. Returns a pointer to `filename`.
	"""
	chunkSize = 1024
	r = requests.get(url, stream=True)
	with open(filename, 'wb') as f:
		pbar = tqdm( unit="B", total=int( r.headers['Content-Length'] ) )
		for chunk in r.iter_content(chunk_size=chunkSize):
			if chunk: # filter out keep-alive new chunks
				pbar.update (len(chunk))
				f.write(chunk)
	return filename


def get_preprocessed_data(file_name, url):
		print(f"Downloading {file_name} datasets\n")
		full_name = os.path.join(dir_path, file_name)
		download_file(url, full_name)
		print("Downloading done.\n")


for file_name, url in [
	('aircraftbylabel.pt', 'https://www.dropbox.com/s/nn6mlrk1jijg108/aircraft100bylabel.pt?dl=1'),
	('cifar100bylabel.pt', 'https://www.dropbox.com/s/nn6mlrk1jijg108/aircraft100bylabel.pt?dl=1'),
	('cifar10bylabel.pt', 'https://www.dropbox.com/s/wt1pcwi991xyhwr/cifar10bylabel.pt?dl=1'),
	('imgnet32bylabel.pt', 'https://www.dropbox.com/s/7r3hpugql8qgi9d/imgnet32bylabel.pt?dl=1'),
	('petsbylabel.pt', 'https://www.dropbox.com/s/mxh6qz3grhy7wcn/petsbylabel.pt?dl=1'),
	]:

	get_preprocessed_data(file_name, url)