





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_												=     collections.namedtuple("_Datasets", ["train", "validation", "test"])

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





def    __UpperCamelCase      (    a) ->List[str]:
					lowerCamelCase__       =       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      (    a) ->str:
					print("Extracting",     f.name)
					with gzip.GzipFile(fileobj=lowerCamelCase_) as bytestream:
										lowerCamelCase__       =       _readaa(lowerCamelCase_)
										if magic != 2051:
															raise ValueError(
															    "Invalid magic number %d in MNIST image file: %s" % (magic, f.name))
										lowerCamelCase__       =       _readaa(lowerCamelCase_)
										lowerCamelCase__       =       _readaa(lowerCamelCase_)
										lowerCamelCase__       =       _readaa(lowerCamelCase_)
										lowerCamelCase__       =       bytestream.read(rows * cols * num_images)
										lowerCamelCase__       =       numpy.frombuffer(lowerCamelCase_,     dtype=numpy.uinta)
										lowerCamelCase__       =       data.reshape(lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_,     1)
										return data





@deprecated(lowerCamelCase_,     "Please use tf.one_hot on tensors.")
def    __UpperCamelCase      (    a,     a) ->str:
					lowerCamelCase__       =       labels_dense.shape[0]
					lowerCamelCase__       =       numpy.arange(lowerCamelCase_) * num_classes
					lowerCamelCase__       =       numpy.zeros((num_labels, num_classes))
					lowerCamelCase__       =       1
					return labels_one_hot





@deprecated(lowerCamelCase_,     "Please use tf.data to implement this functionality.")
def    __UpperCamelCase      (    a,     a=False,     a=10) ->Optional[Any]:
					print("Extracting",     f.name)
					with gzip.GzipFile(fileobj=lowerCamelCase_) as bytestream:
										lowerCamelCase__       =       _readaa(lowerCamelCase_)
										if magic != 2049:
															raise ValueError(
															    "Invalid magic number %d in MNIST label file: %s" % (magic, f.name))
										lowerCamelCase__       =       _readaa(lowerCamelCase_)
										lowerCamelCase__       =       bytestream.read(lowerCamelCase_)
										lowerCamelCase__       =       numpy.frombuffer(lowerCamelCase_,     dtype=numpy.uinta)
										if one_hot:
															return _dense_to_one_hot(lowerCamelCase_,     lowerCamelCase_)
										return labels

class SCREAMING_SNAKE_CASE_ :





					"""simple docstring"""


					@deprecated(
					    __lowerCamelCase	,  "Please use alternatives such as official/mnist/_DataSet.py"
					    " from tensorflow/models."	,  )
					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase=False	,  _lowerCAmelCase=False	,  _lowerCAmelCase=dtypes.floataa	,  _lowerCAmelCase=True	,  _lowerCAmelCase=None	,  ):
										lowerCamelCase__       =       random_seed.get_seed(__lowerCamelCase	)
										# If op level seed is not set, use whatever graph level seed is returned
										numpy.random.seed(seeda if seed is None else seeda	)
										lowerCamelCase__       =       dtypes.as_dtype(__lowerCamelCase	).base_dtype
										if dtype not in (dtypes.uinta, dtypes.floataa):
															raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype	)
										if fake_data:
															lowerCamelCase__       =       1_0000
															lowerCamelCase__       =       one_hot
										else:
															assert (
															    images.shape[0] == labels.shape[0]
															), F"images.shape: {images.shape} labels.shape: {labels.shape}"
															lowerCamelCase__       =       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
																				lowerCamelCase__       =       images.reshape(
																				    images.shape[0]	,  images.shape[1] * images.shape[2]	)
															if dtype == dtypes.floataa:
																				# Convert from [0, 255] -> [0.0, 1.0].
																				lowerCamelCase__       =       images.astype(numpy.floataa	)
																				lowerCamelCase__       =       numpy.multiply(__lowerCamelCase	,  1.0 / 255.0	)
										lowerCamelCase__       =       images
										lowerCamelCase__       =       labels
										lowerCamelCase__       =       0
										lowerCamelCase__       =       0





					@property
					def 					__magic_name__ ( self	):
										return self._images





					@property
					def 					__magic_name__ ( self	):
										return self._labels





					@property
					def 					__magic_name__ ( self	):
										return self._num_examples





					@property
					def 					__magic_name__ ( self	):
										return self._epochs_completed






					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase=False	,  _lowerCAmelCase=True	):






										if fake_data:
															lowerCamelCase__       =       [1] * 784
															lowerCamelCase__       =       [1] + [0] * 9 if self.one_hot else 0
															return (
															    [fake_image for _ in range(__lowerCamelCase	)],
															    [fake_label for _ in range(__lowerCamelCase	)],
															)
										lowerCamelCase__       =       self._index_in_epoch
										# Shuffle for the first epoch
										if self._epochs_completed == 0 and start == 0 and shuffle:
															lowerCamelCase__       =       numpy.arange(self._num_examples	)
															numpy.random.shuffle(__lowerCamelCase	)
															lowerCamelCase__       =       self.images[perma]
															lowerCamelCase__       =       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
															lowerCamelCase__       =       self._num_examples - start
															lowerCamelCase__       =       self._images[start : self._num_examples]
															lowerCamelCase__       =       self._labels[start : self._num_examples]
															# Shuffle the data
															if shuffle:
																				lowerCamelCase__       =       numpy.arange(self._num_examples	)
																				numpy.random.shuffle(__lowerCamelCase	)
																				lowerCamelCase__       =       self.images[perm]
																				lowerCamelCase__       =       self.labels[perm]
															# Start next epoch
															lowerCamelCase__       =       0
															lowerCamelCase__       =       batch_size - rest_num_examples
															lowerCamelCase__       =       self._index_in_epoch
															lowerCamelCase__       =       self._images[start:end]
															lowerCamelCase__       =       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
															lowerCamelCase__       =       self._index_in_epoch
															return self._images[start:end], self._labels[start:end]





@deprecated(lowerCamelCase_,     "Please write your own downloading logic.")
def    __UpperCamelCase      (    a,     a,     a) ->List[str]:

					if not gfile.Exists(lowerCamelCase_):
										gfile.MakeDirs(lowerCamelCase_)
					lowerCamelCase__       =       os.path.join(lowerCamelCase_,     lowerCamelCase_)
					if not gfile.Exists(lowerCamelCase_):
										urllib.request.urlretrieve(lowerCamelCase_,     lowerCamelCase_)  # noqa: S310
										with gfile.GFile(lowerCamelCase_) as f:
															lowerCamelCase__       =       f.size()
										print("Successfully downloaded",     lowerCamelCase_,     lowerCamelCase_,     "bytes.")
					return filepath







@deprecated(
    lowerCamelCase_,     "Please use alternatives such as:" " tensorflow_datasets.load(\'mnist\')")
def    __UpperCamelCase      (    a,     a=False,     a=False,     a=dtypes.floataa,     a=True,     a=5000,     a=None,     a=DEFAULT_SOURCE_URL,     ) ->List[str]:

					if fake_data:

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

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

					if not source_url:  # empty string check
										lowerCamelCase__       =       DEFAULT_SOURCE_URL

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

					lowerCamelCase__       =       _maybe_download(
					    lowerCamelCase_,     lowerCamelCase_,     source_url + train_images_file)
					with gfile.Open(lowerCamelCase_,     "rb") as f:
										lowerCamelCase__       =       _extract_images(lowerCamelCase_)

					lowerCamelCase__       =       _maybe_download(
					    lowerCamelCase_,     lowerCamelCase_,     source_url + train_labels_file)
					with gfile.Open(lowerCamelCase_,     "rb") as f:
										lowerCamelCase__       =       _extract_labels(lowerCamelCase_,     one_hot=lowerCamelCase_)

					lowerCamelCase__       =       _maybe_download(
					    lowerCamelCase_,     lowerCamelCase_,     source_url + test_images_file)
					with gfile.Open(lowerCamelCase_,     "rb") as f:
										lowerCamelCase__       =       _extract_images(lowerCamelCase_)

					lowerCamelCase__       =       _maybe_download(
					    lowerCamelCase_,     lowerCamelCase_,     source_url + test_labels_file)
					with gfile.Open(lowerCamelCase_,     "rb") as f:
										lowerCamelCase__       =       _extract_labels(lowerCamelCase_,     one_hot=lowerCamelCase_)

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

					lowerCamelCase__       =       train_images[:validation_size]
					lowerCamelCase__       =       train_labels[:validation_size]
					lowerCamelCase__       =       train_images[validation_size:]
					lowerCamelCase__       =       train_labels[validation_size:]

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

					lowerCamelCase__       =       _DataSet(lowerCamelCase_,     lowerCamelCase_,     **lowerCamelCase_)
					lowerCamelCase__       =       _DataSet(lowerCamelCase_,     lowerCamelCase_,     **lowerCamelCase_)
					lowerCamelCase__       =       _DataSet(lowerCamelCase_,     lowerCamelCase_,     **lowerCamelCase_)

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








def    __UpperCamelCase      (    a,     a) ->Optional[int]:
					lowerCamelCase__       =       [1]
					for i in range(2,     a):
										factorials.append(factorials[-1] * i)
					assert 0 <= k < factorials[-1] * n, "k out of bounds"

					lowerCamelCase__       =       []
					lowerCamelCase__       =       list(range(a))

					# Find permutation
					while factorials:
										lowerCamelCase__       =       factorials.pop()
										lowerCamelCase__    ,		lowerCamelCase__       =       divmod(a,     a)
										permutation.append(elements[number])
										elements.remove(elements[number])
					permutation.append(elements[0])

					return permutation


if __name__ == "__main__":
						import doctest

						doctest.testmod()

