


'''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  __lowerCAmelCase (__lowerCAmelCase   ):
		_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  __lowerCAmelCase (__lowerCAmelCase   ):
		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  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase   ):
		_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  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase=False      , __lowerCAmelCase=10   ):
		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 	lowerCAmelCase__			:




			@deprecated(
			    UpperCAmelCase_	,  "Please use alternatives such as official/mnist/_DataSet.py"
			    " from tensorflow/models."	,  )
			def __init__(		self  :     Dict	,  lowerCamelCase__  :     Optional[int]	,  lowerCamelCase__  :     Optional[int]	,  lowerCamelCase__  :     List[str]=False	,  lowerCamelCase__  :     Optional[int]=False	,  lowerCamelCase__  :     Tuple=dtypes.floataa	,  lowerCamelCase__  :     int=True	,  lowerCamelCase__  :     Union[str, Any]=None	,  )   ->Union[str, Any]:



					'''simple docstring'''







					_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								=      1_00_00
							_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 							lowerCAmelCase__    (		self  :     List[Any] )   ->List[str]:



					'''simple docstring'''







					return self._images




			@property
			def 							lowerCAmelCase__    (		self  :     List[Any] )   ->List[str]:



					'''simple docstring'''







					return self._labels




			@property
			def 							lowerCAmelCase__    (		self  :     Union[str, Any] )   ->Optional[int]:



					'''simple docstring'''







					return self._num_examples




			@property
			def 							lowerCAmelCase__    (		self  :     Dict )   ->Tuple:



					'''simple docstring'''







					return self._epochs_completed






			def 							lowerCAmelCase__    (		self  :     Optional[int]	,  lowerCamelCase__  :     int	,  lowerCamelCase__  :     List[Any]=False	,  lowerCamelCase__  :     Dict=True )   ->List[Any]:



					'''simple docstring'''







					if fake_data:
							_UpperCAmelCase		:       List[Any]								=      [1] * 7_84
							_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  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   ):
		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  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase=False      , __lowerCAmelCase=False      , __lowerCAmelCase=dtypes.floataa      , __lowerCAmelCase=True      , __lowerCAmelCase=5_000      , __lowerCAmelCase=None      , __lowerCAmelCase=DEFAULT_SOURCE_URL      , ):
		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 __future__ import annotations

import numpy as np






def  __lowerCAmelCase (__lowerCAmelCase   ):
		return np.maximum(0      , __lowerCAmelCase   )


if __name__ == "__main__":
						print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]



