





'''simple docstring'''




import collections
import gzip
import os
import urllib

import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

a__					:			Union[str, Any]          =     collections.namedtuple('''_Datasets''', ['''train''', '''validation''', '''test'''])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
a__					:			str          =     '''https://storage.googleapis.com/cvdf-datasets/mnist/'''




def 						__lowerCamelCase			(	UpperCAmelCase_							)   ->str:
	snake_case__	       =   numpy.dtype(numpy.uintaa							).newbyteorder('>'							)
	return numpy.frombuffer(bytestream.read(4							)					,				dtype=UpperCAmelCase_							)[0]




@deprecated(UpperCAmelCase_					,				'Please use tf.data to implement this functionality.'							)
def 						__lowerCamelCase			(	UpperCAmelCase_							)   ->Any:
	print('Extracting'					,				f.name							)
	with gzip.GzipFile(fileobj=UpperCAmelCase_							) as bytestream:
		snake_case__	       =   _readaa(UpperCAmelCase_							)
		if magic != 20_51:
			raise ValueError(
			    'Invalid magic number %d in MNIST image file: %s' % (magic, f.name)							)
		snake_case__	       =   _readaa(UpperCAmelCase_							)
		snake_case__	       =   _readaa(UpperCAmelCase_							)
		snake_case__	       =   _readaa(UpperCAmelCase_							)
		snake_case__	       =   bytestream.read(rows * cols * num_images							)
		snake_case__	       =   numpy.frombuffer(UpperCAmelCase_					,				dtype=numpy.uinta							)
		snake_case__	       =   data.reshape(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				1							)
		return data




@deprecated(UpperCAmelCase_					,				'Please use tf.one_hot on tensors.'							)
def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_							)   ->str:
	snake_case__	       =   labels_dense.shape[0]
	snake_case__	       =   numpy.arange(UpperCAmelCase_							) * num_classes
	snake_case__	       =   numpy.zeros((num_labels, num_classes)							)
	snake_case__	       =   1
	return labels_one_hot




@deprecated(UpperCAmelCase_					,				'Please use tf.data to implement this functionality.'							)
def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_=False					,				UpperCAmelCase_=10							)   ->int:
	print('Extracting'					,				f.name							)
	with gzip.GzipFile(fileobj=UpperCAmelCase_							) as bytestream:
		snake_case__	       =   _readaa(UpperCAmelCase_							)
		if magic != 20_49:
			raise ValueError(
			    'Invalid magic number %d in MNIST label file: %s' % (magic, f.name)							)
		snake_case__	       =   _readaa(UpperCAmelCase_							)
		snake_case__	       =   bytestream.read(UpperCAmelCase_							)
		snake_case__	       =   numpy.frombuffer(UpperCAmelCase_					,				dtype=numpy.uinta							)
		if one_hot:
			return _dense_to_one_hot(UpperCAmelCase_					,				UpperCAmelCase_							)
		return labels






class 				__snake_case     :





	@deprecated(
	    UpperCamelCase_      ,       'Please use alternatives such as official/mnist/_DataSet.py'
	    ' from tensorflow/models.'      ,       )
	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_=False      ,       UpperCamelCase_=False      ,       UpperCamelCase_=dtypes.floataa      ,       UpperCamelCase_=True      ,       UpperCamelCase_=None      ,       )      ->		List[str]:
		snake_case__       ,  snake_case__	       =   random_seed.get_seed(UpperCamelCase_		)
		# If op level seed is not set, use whatever graph level seed is returned
		numpy.random.seed(seeda if seed is None else seeda		)
		snake_case__	       =   dtypes.as_dtype(UpperCamelCase_		).base_dtype
		if dtype not in (dtypes.uinta, dtypes.floataa):
			raise TypeError('Invalid image dtype %r, expected uint8 or float32' % dtype		)
		if fake_data:
			snake_case__	       =   1_0000
			snake_case__	       =   one_hot
		else:
			assert (
			    images.shape[0] == labels.shape[0]
			), F'''images.shape: {images.shape} labels.shape: {labels.shape}'''
			snake_case__	       =   images.shape[0]

			# Convert shape from [num examples, rows, columns, depth]
			# to [num examples, rows*columns] (assuming depth == 1)
			if reshape:
				assert images.shape[3] == 1
				snake_case__	       =   images.reshape(
				    images.shape[0]      ,       images.shape[1] * images.shape[2]		)
			if dtype == dtypes.floataa:
				# Convert from [0, 255] -> [0.0, 1.0].
				snake_case__	       =   images.astype(numpy.floataa		)
				snake_case__	       =   numpy.multiply(UpperCamelCase_      ,       1.0 / 2_5_5.0		)
		snake_case__	       =   images
		snake_case__	       =   labels
		snake_case__	       =   0
		snake_case__	       =   0





	@property
	def   _snake_case   (		self		)      ->		List[Any]:
		return self._images





	@property
	def   _snake_case   (		self		)      ->		List[Any]:
		return self._labels





	@property
	def   _snake_case   (		self		)      ->		Union[str, Any]:
		return self._num_examples





	@property
	def   _snake_case   (		self		)      ->		Dict:
		return self._epochs_completed





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_=False      ,       UpperCamelCase_=True		)      ->		Optional[int]:
		if fake_data:
			snake_case__	       =   [1] * 784
			snake_case__	       =   [1] + [0] * 9 if self.one_hot else 0
			return (
			    [fake_image for _ in range(UpperCamelCase_		)],
			    [fake_label for _ in range(UpperCamelCase_		)],
			)
		snake_case__	       =   self._index_in_epoch
		# Shuffle for the first epoch
		if self._epochs_completed == 0 and start == 0 and shuffle:
			snake_case__	       =   numpy.arange(self._num_examples		)
			numpy.random.shuffle(UpperCamelCase_		)
			snake_case__	       =   self.images[perma]
			snake_case__	       =   self.labels[perma]
		# Go to the next epoch
		if start + batch_size > self._num_examples:
			# Finished epoch
			self._epochs_completed += 1
			# Get the rest examples in this epoch
			snake_case__	       =   self._num_examples - start
			snake_case__	       =   self._images[start : self._num_examples]
			snake_case__	       =   self._labels[start : self._num_examples]
			# Shuffle the data
			if shuffle:
				snake_case__	       =   numpy.arange(self._num_examples		)
				numpy.random.shuffle(UpperCamelCase_		)
				snake_case__	       =   self.images[perm]
				snake_case__	       =   self.labels[perm]
			# Start next epoch
			snake_case__	       =   0
			snake_case__	       =   batch_size - rest_num_examples
			snake_case__	       =   self._index_in_epoch
			snake_case__	       =   self._images[start:end]
			snake_case__	       =   self._labels[start:end]
			return (
			    numpy.concatenate((images_rest_part, images_new_part)      ,       axis=0		),
			    numpy.concatenate((labels_rest_part, labels_new_part)      ,       axis=0		),
			)
		else:
			self._index_in_epoch += batch_size
			snake_case__	       =   self._index_in_epoch
			return self._images[start:end], self._labels[start:end]





@deprecated(UpperCAmelCase_					,				'Please write your own downloading logic.'							)
def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->List[str]:
	if not gfile.Exists(UpperCAmelCase_							):
		gfile.MakeDirs(UpperCAmelCase_							)
	snake_case__	       =   os.path.join(UpperCAmelCase_					,				UpperCAmelCase_							)
	if not gfile.Exists(UpperCAmelCase_							):
		urllib.request.urlretrieve(UpperCAmelCase_					,				UpperCAmelCase_							)  # noqa: S310
		with gfile.GFile(UpperCAmelCase_							) as f:
			snake_case__	       =   f.size()
		print('Successfully downloaded'					,				UpperCAmelCase_					,				UpperCAmelCase_					,				'bytes.'							)
	return filepath







@deprecated(
    UpperCAmelCase_					,				'Please use alternatives such as:' ' tensorflow_datasets.load(\'mnist\')'							)
def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_=False					,				UpperCAmelCase_=False					,				UpperCAmelCase_=dtypes.floataa					,				UpperCAmelCase_=True					,				UpperCAmelCase_=50_00					,				UpperCAmelCase_=None					,				UpperCAmelCase_=DEFAULT_SOURCE_URL					,				)   ->Any:
	if fake_data:

		def fake():
			return _DataSet(
			    []					,				[]					,				fake_data=UpperCAmelCase_					,				one_hot=UpperCAmelCase_					,				dtype=UpperCAmelCase_					,				seed=UpperCAmelCase_							)

		snake_case__	       =   fake()
		snake_case__	       =   fake()
		snake_case__	       =   fake()
		return _Datasets(train=UpperCAmelCase_					,				validation=UpperCAmelCase_					,				test=UpperCAmelCase_							)

	if not source_url:  # empty string check
		snake_case__	       =   DEFAULT_SOURCE_URL

	snake_case__	       =   'train-images-idx3-ubyte.gz'
	snake_case__	       =   'train-labels-idx1-ubyte.gz'
	snake_case__	       =   't10k-images-idx3-ubyte.gz'
	snake_case__	       =   't10k-labels-idx1-ubyte.gz'

	snake_case__	       =   _maybe_download(
	    UpperCAmelCase_					,				UpperCAmelCase_					,				source_url + train_images_file							)
	with gfile.Open(UpperCAmelCase_					,				'rb'							) as f:
		snake_case__	       =   _extract_images(UpperCAmelCase_							)

	snake_case__	       =   _maybe_download(
	    UpperCAmelCase_					,				UpperCAmelCase_					,				source_url + train_labels_file							)
	with gfile.Open(UpperCAmelCase_					,				'rb'							) as f:
		snake_case__	       =   _extract_labels(UpperCAmelCase_					,				one_hot=UpperCAmelCase_							)

	snake_case__	       =   _maybe_download(
	    UpperCAmelCase_					,				UpperCAmelCase_					,				source_url + test_images_file							)
	with gfile.Open(UpperCAmelCase_					,				'rb'							) as f:
		snake_case__	       =   _extract_images(UpperCAmelCase_							)

	snake_case__	       =   _maybe_download(
	    UpperCAmelCase_					,				UpperCAmelCase_					,				source_url + test_labels_file							)
	with gfile.Open(UpperCAmelCase_					,				'rb'							) as f:
		snake_case__	       =   _extract_labels(UpperCAmelCase_					,				one_hot=UpperCAmelCase_							)

	if not 0 <= validation_size <= len(UpperCAmelCase_							):
		snake_case__	       =   (
		    'Validation size should be between 0 and '
		    f'''{len(UpperCAmelCase_							)}. Received: {validation_size}.'''
		)
		raise ValueError(UpperCAmelCase_							)

	snake_case__	       =   train_images[:validation_size]
	snake_case__	       =   train_labels[:validation_size]
	snake_case__	       =   train_images[validation_size:]
	snake_case__	       =   train_labels[validation_size:]

	snake_case__	       =   {'dtype': dtype, 'reshape': reshape, 'seed': seed}

	snake_case__	       =   _DataSet(UpperCAmelCase_					,				UpperCAmelCase_					,				**UpperCAmelCase_							)
	snake_case__	       =   _DataSet(UpperCAmelCase_					,				UpperCAmelCase_					,				**UpperCAmelCase_							)
	snake_case__	       =   _DataSet(UpperCAmelCase_					,				UpperCAmelCase_					,				**UpperCAmelCase_							)

	return _Datasets(train=UpperCAmelCase_					,				validation=UpperCAmelCase_					,				test=UpperCAmelCase_							)










'''simple docstring'''




def 						__lowerCamelCase			(	UpperCAmelCase_ = 10_00							)   ->int:
	return sum(e for e in range(3					,				UpperCAmelCase_							) if e % 3 == 0 or e % 5 == 0							)


if __name__ == "__main__":
			print(f"""{solution() = }""")



