'''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

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

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

def   A_      (       SCREAMING_SNAKE_CASE_						)			->Any:
  lowercase_     =       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   A_      (       SCREAMING_SNAKE_CASE_						)			->List[Any]:
  print("""Extracting""" ,	f.name						)
  with gzip.GzipFile(fileobj=UpperCAmelCase_						) as bytestream:
    lowercase_     =       _readaa(UpperCAmelCase_						)
    if magic != 20_51:
      raise ValueError(
          """Invalid magic number %d in MNIST image file: %s""" % (magic, f.name)						)
    lowercase_     =       _readaa(UpperCAmelCase_						)
    lowercase_     =       _readaa(UpperCAmelCase_						)
    lowercase_     =       _readaa(UpperCAmelCase_						)
    lowercase_     =       bytestream.read(rows * cols * num_images						)
    lowercase_     =       numpy.frombuffer(UpperCAmelCase_ ,	dtype=numpy.uinta						)
    lowercase_     =       data.reshape(UpperCAmelCase_ ,	UpperCAmelCase_ ,	UpperCAmelCase_ ,	1						)
    return data

@deprecated(UpperCAmelCase_ ,	"""Please use tf.one_hot on tensors."""						)
def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Optional[Any]:
  lowercase_     =       labels_dense.shape[0]
  lowercase_     =       numpy.arange(UpperCAmelCase_						) * num_classes
  lowercase_     =       numpy.zeros((num_labels, num_classes)						)
  lowercase_     =       1
  return labels_one_hot

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





class       _a    :


  """simple docstring"""

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







    '''simple docstring'''


    lowercase_     =       random_seed.get_seed(SCREAMING_SNAKE_CASE_      )
    # If op level seed is not set, use whatever graph level seed is returned
    numpy.random.seed(seeda if seed is None else seeda      )
    lowercase_     =       dtypes.as_dtype(SCREAMING_SNAKE_CASE_      ).base_dtype
    if dtype not in (dtypes.uinta, dtypes.floataa):
      raise TypeError("""Invalid image dtype %r, expected uint8 or float32""" % dtype      )
    if fake_data:
      lowercase_     =       10_000
      lowercase_     =       one_hot
    else:
      assert (
          images.shape[0] == labels.shape[0]
      ), F"""images.shape: {images.shape} labels.shape: {labels.shape}"""
      lowercase_     =       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
        lowercase_     =       images.reshape(
            images.shape[0]   ,				images.shape[1] * images.shape[2]      )
      if dtype == dtypes.floataa:
        # Convert from [0, 255] -> [0.0, 1.0].
        lowercase_     =       images.astype(numpy.floataa      )
        lowercase_     =       numpy.multiply(SCREAMING_SNAKE_CASE_   ,				1.0 / 255.0      )
    lowercase_     =       images
    lowercase_     =       labels
    lowercase_     =       0
    lowercase_     =       0




  @property
  def 		lowerCamelCase__  (		self      :       Any      ):







    '''simple docstring'''


    return self._images




  @property
  def 		lowerCamelCase__  (		self      :       str      ):







    '''simple docstring'''


    return self._labels




  @property
  def 		lowerCamelCase__  (		self      :       Union[str, Any]      ):







    '''simple docstring'''


    return self._num_examples




  @property
  def 		lowerCamelCase__  (		self      :       int      ):







    '''simple docstring'''


    return self._epochs_completed





  def 		lowerCamelCase__  (		self      :       Dict   ,				lowercase_      :       str   ,				lowercase_      :       Tuple=False   ,				lowercase_      :       Any=True      ):







    '''simple docstring'''


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






@deprecated(UpperCAmelCase_ ,	"""Please write your own downloading logic."""						)
def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Tuple:
  if not gfile.Exists(UpperCAmelCase_						):
    gfile.MakeDirs(UpperCAmelCase_						)
  lowercase_     =       os.path.join(UpperCAmelCase_ ,	UpperCAmelCase_						)
  if not gfile.Exists(UpperCAmelCase_						):
    urllib.request.urlretrieve(UpperCAmelCase_ ,	UpperCAmelCase_						)  # noqa: S310
    with gfile.GFile(UpperCAmelCase_						) as f:
      lowercase_     =       f.size()
    print("""Successfully downloaded""" ,	UpperCAmelCase_ ,	UpperCAmelCase_ ,	"""bytes."""						)
  return filepath




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

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

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

  if not source_url:  # empty string check
    lowercase_     =       DEFAULT_SOURCE_URL

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

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

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

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

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

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

  lowercase_     =       train_images[:validation_size]
  lowercase_     =       train_labels[:validation_size]
  lowercase_     =       train_images[validation_size:]
  lowercase_     =       train_labels[validation_size:]

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

  lowercase_     =       _DataSet(UpperCAmelCase_ ,	UpperCAmelCase_ ,	**UpperCAmelCase_						)
  lowercase_     =       _DataSet(UpperCAmelCase_ ,	UpperCAmelCase_ ,	**UpperCAmelCase_						)
  lowercase_     =       _DataSet(UpperCAmelCase_ ,	UpperCAmelCase_ ,	**UpperCAmelCase_						)

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





'''simple docstring'''



from __future__ import annotations

A__						:   int				=    10



def      UpperCAmelCase__   (      UpperCAmelCase_       :							list[int]				)	->						list[int]:
     __lowerCamelCase  :      List[Any]														=						1
     __lowerCamelCase  :      Any														=						max(UpperCAmelCase_				)
     while placement <= max_digit:
          # declare and initialize empty buckets
          __lowerCamelCase  :      list[list]														=						[[] for _ in range(UpperCAmelCase_				)]
          # split list_of_ints between the buckets
          for i in list_of_ints:
               __lowerCamelCase  :      List[Any]														=						int((i / placement) % RADIX				)
               buckets[tmp].append(UpperCAmelCase_				)
          # put each buckets' contents into list_of_ints
          __lowerCamelCase  :      Tuple														=						0
          for b in range(UpperCAmelCase_				):
               for i in buckets[b]:
                    __lowerCamelCase  :      List[Any]														=						i
                    a += 1
        # move to next
          placement *= RADIX
     return list_of_ints


if __name__ == "__main__":
      import doctest

      doctest.testmod()






