

'''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__     (   UpperCAmelCase__					)      ->      Dict:
       A_						       =   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    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      Tuple:
       print("""Extracting""",							f.name					)
       with gzip.GzipFile(fileobj=UpperCAmelCase__					) as bytestream:
              A_						       =   _readaa(UpperCAmelCase__					)
              if magic != 20_51:
                     raise ValueError(
                         """Invalid magic number %d in MNIST image file: %s""" % (magic, f.name)					)
              A_						       =   _readaa(UpperCAmelCase__					)
              A_						       =   _readaa(UpperCAmelCase__					)
              A_						       =   _readaa(UpperCAmelCase__					)
              A_						       =   bytestream.read(rows * cols * num_images					)
              A_						       =   numpy.frombuffer(UpperCAmelCase__,							dtype=numpy.uinta					)
              A_						       =   data.reshape(UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							1					)
              return data
@deprecated(UpperCAmelCase__,							"""Please use tf.one_hot on tensors."""					)
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__					)      ->      Optional[Any]:
       A_						       =   labels_dense.shape[0]
       A_						       =   numpy.arange(UpperCAmelCase__					) * num_classes
       A_						       =   numpy.zeros((num_labels, num_classes)					)
       A_						       =   1
       return labels_one_hot
@deprecated(UpperCAmelCase__,							"""Please use tf.data to implement this functionality."""					)
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__=False,							UpperCAmelCase__=10					)      ->      List[Any]:
       print("""Extracting""",							f.name					)
       with gzip.GzipFile(fileobj=UpperCAmelCase__					) as bytestream:
              A_						       =   _readaa(UpperCAmelCase__					)
              if magic != 20_49:
                     raise ValueError(
                         """Invalid magic number %d in MNIST label file: %s""" % (magic, f.name)					)
              A_						       =   _readaa(UpperCAmelCase__					)
              A_						       =   bytestream.read(UpperCAmelCase__					)
              A_						       =   numpy.frombuffer(UpperCAmelCase__,							dtype=numpy.uinta					)
              if one_hot:
                     return _dense_to_one_hot(UpperCAmelCase__,							UpperCAmelCase__					)
              return labels


class        A__   :



    @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		,					)    ->   Tuple:





           '''simple docstring'''
           A_       ,      A_						       =   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							)
           A_						       =   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:
                  A_						       =   10000
                  A_						       =   one_hot
           else:
                  assert (
                      images.shape[0] == labels.shape[0]
                  ), f'''images.shape: {images.shape} labels.shape: {labels.shape}'''
                  A_						       =   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
                         A_						       =   images.reshape(
                             images.shape[0]		,					images.shape[1] * images.shape[2]							)
                  if dtype == dtypes.floataa:
                         # Convert from [0, 255] -> [0.0, 1.0].
                         A_						       =   images.astype(numpy.floataa							)
                         A_						       =   numpy.multiply(UpperCamelCase__		,					1.0 / 255.0							)
           A_						       =   images
           A_						       =   labels
           A_						       =   0
           A_						       =   0



    @property
    def        snake_case_      (       self							)    ->   Any:





           '''simple docstring'''
           return self._images



    @property
    def        snake_case_      (       self							)    ->   Optional[int]:





           '''simple docstring'''
           return self._labels



    @property
    def        snake_case_      (       self							)    ->   str:





           '''simple docstring'''
           return self._num_examples



    @property
    def        snake_case_      (       self							)    ->   Optional[Any]:





           '''simple docstring'''
           return self._epochs_completed







    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__=False		,					UpperCamelCase__=True							)    ->   List[Any]:





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

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







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

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

              A_						       =   fake()
              A_						       =   fake()
              A_						       =   fake()
              return _Datasets(train=UpperCAmelCase__,							validation=UpperCAmelCase__,							test=UpperCAmelCase__					)

       if not source_url:  # empty string check
              A_						       =   DEFAULT_SOURCE_URL

       A_						       =   """train-images-idx3-ubyte.gz"""
       A_						       =   """train-labels-idx1-ubyte.gz"""
       A_						       =   """t10k-images-idx3-ubyte.gz"""
       A_						       =   """t10k-labels-idx1-ubyte.gz"""

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

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

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

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

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

       A_						       =   train_images[:validation_size]
       A_						       =   train_labels[:validation_size]
       A_						       =   train_images[validation_size:]
       A_						       =   train_labels[validation_size:]

       A_						       =   {"""dtype""": dtype, """reshape""": reshape, """seed""": seed}

       A_						       =   _DataSet(UpperCAmelCase__,							UpperCAmelCase__,							**UpperCAmelCase__					)
       A_						       =   _DataSet(UpperCAmelCase__,							UpperCAmelCase__,							**UpperCAmelCase__					)
       A_						       =   _DataSet(UpperCAmelCase__,							UpperCAmelCase__,							**UpperCAmelCase__					)

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



'''simple docstring'''







import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("""dataset_size""",							[None, 4_00 * 2**20, 6_00 * 2**20]					)
@pytest.mark.parametrize("""input_in_memory_max_size""",							["""default""", 0, 1_00 * 2**20, 9_00 * 2**20]					)
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      Dict:
       if input_in_memory_max_size != "default":
              monkeypatch.setattr(datasets.config,							"""IN_MEMORY_MAX_SIZE""",							UpperCAmelCase__					)
       A_						       =   datasets.config.IN_MEMORY_MAX_SIZE
       if input_in_memory_max_size == "default":
              assert in_memory_max_size == 0
       else:
              assert in_memory_max_size == input_in_memory_max_size
       if dataset_size and in_memory_max_size:
              A_						       =   dataset_size < in_memory_max_size
       else:
              A_						       =   False
       A_						       =   is_small_dataset(UpperCAmelCase__					)
       assert result == expected
