Datasets:
mnist

	# coding=utf-8
	# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.

	# Lint as: python3
	"""MNIST Data Set"""


	import struct

	import numpy as np

	import datasets
	from datasets.tasks import ImageClassification


	_CITATION = """\
	@article{lecun2010mnist,
	title={MNIST handwritten digit database},
	author={LeCun, Yann and Cortes, Corinna and Burges, CJ},
	journal={ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist},
	volume={2},
	year={2010}
	}
	"""

	_DESCRIPTION = """\
	The MNIST dataset consists of 70,000 28x28 black-and-white images in 10 classes (one for each digits), with 7,000
	images per class. There are 60,000 training images and 10,000 test images.
	"""

	_URL = "https://storage.googleapis.com/cvdf-datasets/mnist/"
	_URLS = {
	"train_images": "train-images-idx3-ubyte.gz",
	"train_labels": "train-labels-idx1-ubyte.gz",
	"test_images": "t10k-images-idx3-ubyte.gz",
	"test_labels": "t10k-labels-idx1-ubyte.gz",
	}


	class MNIST(datasets.GeneratorBasedBuilder):
	"""MNIST Data Set"""

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="mnist",
	version=datasets.Version("1.0.0"),
	description=_DESCRIPTION,
	)
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"image": datasets.Image(),
	"label": datasets.features.ClassLabel(names=["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]),
	}
	),
	supervised_keys=("image", "label"),
	homepage="http://yann.lecun.com/exdb/mnist/",
	citation=_CITATION,
	task_templates=[
	ImageClassification(
	image_column="image",
	label_column="label",
	)
	],
	)

	def _split_generators(self, dl_manager):
	urls_to_download = {key: _URL + fname for key, fname in _URLS.items()}
	downloaded_files = dl_manager.download_and_extract(urls_to_download)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": (downloaded_files["train_images"], downloaded_files["train_labels"]),
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepath": (downloaded_files["test_images"], downloaded_files["test_labels"]),
	"split": "test",
	},
	),
	]

	def _generate_examples(self, filepath, split):
	"""This function returns the examples in the raw form."""
	# Images
	with open(filepath[0], "rb") as f:
	# First 16 bytes contain some metadata
	_ = f.read(4)
	size = struct.unpack(">I", f.read(4))[0]
	_ = f.read(8)
	images = np.frombuffer(f.read(), dtype=np.uint8).reshape(size, 28, 28)

	# Labels
	with open(filepath[1], "rb") as f:
	# First 8 bytes contain some metadata
	_ = f.read(8)
	labels = np.frombuffer(f.read(), dtype=np.uint8)

	for idx in range(size):
	yield idx, {"image": images[idx], "label": str(labels[idx])}