






'''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

lowerCAmelCase__          =						collections.namedtuple('_Datasets', ['train', 'validation', 'test'])

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







def       __UpperCAmelCase						(  lowerCamelCase_)							->     str:
		UpperCamelCase__				:					Optional[int]					=					numpy.dtype(numpy.uintaa).newbyteorder('>')
		return numpy.frombuffer(bytestream.read(4)     ,      dtype=lowerCamelCase_)[0]







@deprecated(lowerCamelCase_     ,      'Please use tf.data to implement this functionality.')
def       __UpperCAmelCase						(  lowerCamelCase_)							->     Optional[int]:
		print('Extracting'     ,      f.name)
		with gzip.GzipFile(fileobj=lowerCamelCase_) as bytestream:
				UpperCamelCase__				:					Any					=					_readaa(lowerCamelCase_)
				if magic != 2_051:
						raise ValueError(
						    'Invalid magic number %d in MNIST image file: %s' % (magic, f.name))
				UpperCamelCase__				:					str					=					_readaa(lowerCamelCase_)
				UpperCamelCase__				:					str					=					_readaa(lowerCamelCase_)
				UpperCamelCase__				:					Union[str, Any]					=					_readaa(lowerCamelCase_)
				UpperCamelCase__				:					str					=					bytestream.read(rows * cols * num_images)
				UpperCamelCase__				:					List[Any]					=					numpy.frombuffer(lowerCamelCase_     ,      dtype=numpy.uinta)
				UpperCamelCase__				:					Tuple					=					data.reshape(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      1)
				return data







@deprecated(lowerCamelCase_     ,      'Please use tf.one_hot on tensors.')
def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_)							->     str:
		UpperCamelCase__				:					Dict					=					labels_dense.shape[0]
		UpperCamelCase__				:					Optional[int]					=					numpy.arange(lowerCamelCase_) * num_classes
		UpperCamelCase__				:					Optional[int]					=					numpy.zeros((num_labels, num_classes))
		UpperCamelCase__				:					Union[str, Any]					=					1
		return labels_one_hot







@deprecated(lowerCamelCase_     ,      'Please use tf.data to implement this functionality.')
def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_=False     ,      lowerCamelCase_=10)							->     Any:
		print('Extracting'     ,      f.name)
		with gzip.GzipFile(fileobj=lowerCamelCase_) as bytestream:
				UpperCamelCase__				:					int					=					_readaa(lowerCamelCase_)
				if magic != 2_049:
						raise ValueError(
						    'Invalid magic number %d in MNIST label file: %s' % (magic, f.name))
				UpperCamelCase__				:					Tuple					=					_readaa(lowerCamelCase_)
				UpperCamelCase__				:					Optional[int]					=					bytestream.read(lowerCamelCase_)
				UpperCamelCase__				:					Any					=					numpy.frombuffer(lowerCamelCase_     ,      dtype=numpy.uinta)
				if one_hot:
						return _dense_to_one_hot(lowerCamelCase_     ,      lowerCamelCase_)
				return labels







class       __lowercase					:

	@deprecated(
	    UpperCAmelCase_  ,					'Please use alternatives such as official/mnist/_DataSet.py'
	    ' from tensorflow/models.'  ,					)
	def __init__(      self   :						Dict  ,					UpperCAmelCase_   :						Optional[int]  ,					UpperCAmelCase_   :						Optional[int]  ,					UpperCAmelCase_   :						List[str]=False  ,					UpperCAmelCase_   :						Optional[int]=False  ,					UpperCAmelCase_   :						Tuple=dtypes.floataa  ,					UpperCAmelCase_   :						int=True  ,					UpperCAmelCase_   :						Union[str, Any]=None  ,					):
			UpperCamelCase__,	UpperCamelCase__				:					Dict					=					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)
			UpperCamelCase__				:					Optional[Any]					=					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:
					UpperCamelCase__				:					Any					=					10_000
					UpperCamelCase__				:					int					=					one_hot
			else:
					assert (
					    images.shape[0] == labels.shape[0]
					), F'images.shape: {images.shape} labels.shape: {labels.shape}'
					UpperCamelCase__				:					int					=					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
							UpperCamelCase__				:					Any					=					images.reshape(
							    images.shape[0]  ,					images.shape[1] * images.shape[2])
					if dtype == dtypes.floataa:
							# Convert from [0, 255] -> [0.0, 1.0].
							UpperCamelCase__				:					Union[str, Any]					=					images.astype(numpy.floataa)
							UpperCamelCase__				:					Dict					=					numpy.multiply(UpperCAmelCase_  ,					1.0 / 2_55.0)
			UpperCamelCase__				:					Union[str, Any]					=					images
			UpperCamelCase__				:					List[str]					=					labels
			UpperCamelCase__				:					int					=					0
			UpperCamelCase__				:					Any					=					0

	@property
	def      __UpperCamelCase							(      self   :						List[Any]):
			return self._images

	@property
	def      __UpperCamelCase							(      self   :						List[Any]):
			return self._labels

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

	@property
	def      __UpperCamelCase							(      self   :						Dict):
			return self._epochs_completed





	def      __UpperCamelCase							(      self   :						Optional[int]  ,					UpperCAmelCase_   :						int  ,					UpperCAmelCase_   :						List[Any]=False  ,					UpperCAmelCase_   :						Dict=True):
			if fake_data:
					UpperCamelCase__				:					List[Any]					=					[1] * 784
					UpperCamelCase__				:					Optional[int]					=					[1] + [0] * 9 if self.one_hot else 0
					return (
					    [fake_image for _ in range(UpperCAmelCase_)],
					    [fake_label for _ in range(UpperCAmelCase_)],
					)
			UpperCamelCase__				:					Tuple					=					self._index_in_epoch
			# Shuffle for the first epoch
			if self._epochs_completed == 0 and start == 0 and shuffle:
					UpperCamelCase__				:					List[str]					=					numpy.arange(self._num_examples)
					numpy.random.shuffle(UpperCAmelCase_)
					UpperCamelCase__				:					Optional[int]					=					self.images[perma]
					UpperCamelCase__				:					List[Any]					=					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
					UpperCamelCase__				:					List[Any]					=					self._num_examples - start
					UpperCamelCase__				:					int					=					self._images[start : self._num_examples]
					UpperCamelCase__				:					int					=					self._labels[start : self._num_examples]
					# Shuffle the data
					if shuffle:
							UpperCamelCase__				:					Any					=					numpy.arange(self._num_examples)
							numpy.random.shuffle(UpperCAmelCase_)
							UpperCamelCase__				:					int					=					self.images[perm]
							UpperCamelCase__				:					str					=					self.labels[perm]
					# Start next epoch
					UpperCamelCase__				:					Optional[int]					=					0
					UpperCamelCase__				:					List[str]					=					batch_size - rest_num_examples
					UpperCamelCase__				:					List[Any]					=					self._index_in_epoch
					UpperCamelCase__				:					Any					=					self._images[start:end]
					UpperCamelCase__				:					Optional[int]					=					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
					UpperCamelCase__				:					List[Any]					=					self._index_in_epoch
					return self._images[start:end], self._labels[start:end]







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







@deprecated(
    lowerCamelCase_     ,      'Please use alternatives such as:' ' tensorflow_datasets.load(\'mnist\')')
def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_=False     ,      lowerCamelCase_=False     ,      lowerCamelCase_=dtypes.floataa     ,      lowerCamelCase_=True     ,      lowerCamelCase_=5_000     ,      lowerCamelCase_=None     ,      lowerCamelCase_=DEFAULT_SOURCE_URL     ,      )							->     List[str]:
		if fake_data:

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

				UpperCamelCase__				:					Any					=					fake()
				UpperCamelCase__				:					Optional[int]					=					fake()
				UpperCamelCase__				:					int					=					fake()
				return _Datasets(train=lowerCamelCase_     ,      validation=lowerCamelCase_     ,      test=lowerCamelCase_)

		if not source_url:  # empty string check
				UpperCamelCase__				:					Any					=					DEFAULT_SOURCE_URL

		UpperCamelCase__				:					int					=					'train-images-idx3-ubyte.gz'
		UpperCamelCase__				:					int					=					'train-labels-idx1-ubyte.gz'
		UpperCamelCase__				:					Union[str, Any]					=					't10k-images-idx3-ubyte.gz'
		UpperCamelCase__				:					Union[str, Any]					=					't10k-labels-idx1-ubyte.gz'

		UpperCamelCase__				:					Any					=					_maybe_download(
		    lowerCamelCase_     ,      lowerCamelCase_     ,      source_url + train_images_file)
		with gfile.Open(lowerCamelCase_     ,      'rb') as f:
				UpperCamelCase__				:					Any					=					_extract_images(lowerCamelCase_)

		UpperCamelCase__				:					List[str]					=					_maybe_download(
		    lowerCamelCase_     ,      lowerCamelCase_     ,      source_url + train_labels_file)
		with gfile.Open(lowerCamelCase_     ,      'rb') as f:
				UpperCamelCase__				:					int					=					_extract_labels(lowerCamelCase_     ,      one_hot=lowerCamelCase_)

		UpperCamelCase__				:					List[Any]					=					_maybe_download(
		    lowerCamelCase_     ,      lowerCamelCase_     ,      source_url + test_images_file)
		with gfile.Open(lowerCamelCase_     ,      'rb') as f:
				UpperCamelCase__				:					str					=					_extract_images(lowerCamelCase_)

		UpperCamelCase__				:					Tuple					=					_maybe_download(
		    lowerCamelCase_     ,      lowerCamelCase_     ,      source_url + test_labels_file)
		with gfile.Open(lowerCamelCase_     ,      'rb') as f:
				UpperCamelCase__				:					Tuple					=					_extract_labels(lowerCamelCase_     ,      one_hot=lowerCamelCase_)

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

		UpperCamelCase__				:					Dict					=					train_images[:validation_size]
		UpperCamelCase__				:					str					=					train_labels[:validation_size]
		UpperCamelCase__				:					List[str]					=					train_images[validation_size:]
		UpperCamelCase__				:					Any					=					train_labels[validation_size:]

		UpperCamelCase__				:					List[str]					=					{'dtype': dtype, 'reshape': reshape, 'seed': seed}

		UpperCamelCase__				:					Any					=					_DataSet(lowerCamelCase_     ,      lowerCamelCase_     ,      **lowerCamelCase_)
		UpperCamelCase__				:					Optional[int]					=					_DataSet(lowerCamelCase_     ,      lowerCamelCase_     ,      **lowerCamelCase_)
		UpperCamelCase__				:					Union[str, Any]					=					_DataSet(lowerCamelCase_     ,      lowerCamelCase_     ,      **lowerCamelCase_)

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












'''simple docstring'''




from ..utils import DummyObject, requires_backends







class       __lowercase					(metaclass=__lowerCamelCase    ):
	_lowerCamelCase              =  ['''torch''', '''scipy''']

	def __init__(      self   :						List[Any]  ,					*UpperCAmelCase_   :						Any  ,					**UpperCAmelCase_   :						int):
			requires_backends(self  ,					['torch', 'scipy'])

	@classmethod
	def      __UpperCamelCase							(      cls   :						Union[str, Any]  ,					*UpperCAmelCase_   :						Any  ,					**UpperCAmelCase_   :						List[Any]):
			requires_backends(cls  ,					['torch', 'scipy'])





	@classmethod
	def      __UpperCamelCase							(      cls   :						Union[str, Any]  ,					*UpperCAmelCase_   :						Optional[Any]  ,					**UpperCAmelCase_   :						Any):
			requires_backends(cls  ,					['torch', 'scipy'])




