


'''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

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

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


def    _snake_case  (	A			)		->       List[str]:
							lowerCAmelCase__			  =      numpy.dtype(numpy.uintaa			).newbyteorder('''>'''			)
							return numpy.frombuffer(bytestream.read(4			)					,       dtype=A			)[0]


@deprecated(A					,       '''Please use tf.data to implement this functionality.'''			)
def    _snake_case  (	A			)		->       Any:
							print('''Extracting'''					,       f.name			)
							with gzip.GzipFile(fileobj=A			) as bytestream:
														lowerCAmelCase__			  =      _readaa(A			)
														if magic != 2051:
																					raise ValueError(
																					    '''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name)			)
														lowerCAmelCase__			  =      _readaa(A			)
														lowerCAmelCase__			  =      _readaa(A			)
														lowerCAmelCase__			  =      _readaa(A			)
														lowerCAmelCase__			  =      bytestream.read(rows * cols * num_images			)
														lowerCAmelCase__			  =      numpy.frombuffer(A					,       dtype=numpy.uinta			)
														lowerCAmelCase__			  =      data.reshape(A					,       A					,       A					,       1			)
														return data


@deprecated(A					,       '''Please use tf.one_hot on tensors.'''			)
def    _snake_case  (	A					,       A			)		->       int:
							lowerCAmelCase__			  =      labels_dense.shape[0]
							lowerCAmelCase__			  =      numpy.arange(A			) * num_classes
							lowerCAmelCase__			  =      numpy.zeros((num_labels, num_classes)			)
							lowerCAmelCase__			  =      1
							return labels_one_hot


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




class 						a__							:





							'''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   ,       )			->				Optional[Any]:
														lowerCAmelCase__  ,   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_00_00
																					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     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														return self._images







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														return self._labels







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														return self._num_examples







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														return self._epochs_completed







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_=False   ,       lowerCamelCase_=True  )			->				Optional[Any]:
														if fake_data:
																					lowerCAmelCase__			  =      [1] * 7_84
																					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(A					,       '''Please write your own downloading logic.'''			)
def    _snake_case  (	A					,       A					,       A			)		->       Tuple:
							if not gfile.Exists(A			):
														gfile.MakeDirs(A			)
							lowerCAmelCase__			  =      os.path.join(A					,       A			)
							if not gfile.Exists(A			):
														urllib.request.urlretrieve(A					,       A			)  # noqa: S310
														with gfile.GFile(A			) as f:
																					lowerCAmelCase__			  =      f.size()
														print('''Successfully downloaded'''					,       A					,       A					,       '''bytes.'''			)
							return filepath






@deprecated(
    A					,       '''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')'''			)
def    _snake_case  (	A					,       A=False					,       A=False					,       A=dtypes.floataa					,       A=True					,       A=5000					,       A=None					,       A=DEFAULT_SOURCE_URL					,       )		->       Dict:
							if fake_data:

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

														lowerCAmelCase__			  =      fake()
														lowerCAmelCase__			  =      fake()
														lowerCAmelCase__			  =      fake()
														return _Datasets(train=A					,       validation=A					,       test=A			)

							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(
							    A					,       A					,       source_url + train_images_file			)
							with gfile.Open(A					,       '''rb'''			) as f:
														lowerCAmelCase__			  =      _extract_images(A			)

							lowerCAmelCase__			  =      _maybe_download(
							    A					,       A					,       source_url + train_labels_file			)
							with gfile.Open(A					,       '''rb'''			) as f:
														lowerCAmelCase__			  =      _extract_labels(A					,       one_hot=A			)

							lowerCAmelCase__			  =      _maybe_download(
							    A					,       A					,       source_url + test_images_file			)
							with gfile.Open(A					,       '''rb'''			) as f:
														lowerCAmelCase__			  =      _extract_images(A			)

							lowerCAmelCase__			  =      _maybe_download(
							    A					,       A					,       source_url + test_labels_file			)
							with gfile.Open(A					,       '''rb'''			) as f:
														lowerCAmelCase__			  =      _extract_labels(A					,       one_hot=A			)

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

							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(A					,       A					,       **A			)
							lowerCAmelCase__			  =      _DataSet(A					,       A					,       **A			)
							lowerCAmelCase__			  =      _DataSet(A					,       A					,       **A			)

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



'''simple docstring'''






from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

__UpperCAmelCase       							=				TypeVar('''KEY''')
__UpperCAmelCase       							=				TypeVar('''VAL''')




@dataclass(frozen=a__							,							slots=a__    )
class 						a__							(				Generic[KEY, VAL]    ):





							'''simple docstring'''






							lowercase__     :  KEY
							lowercase__     :  VAL




class 						a__							(				_Item    ):





							'''simple docstring'''






							def __init__(						self  )			->				None:
														super().__init__(lowerCamelCase_   ,       lowerCamelCase_  )







							def __bool__(						self  )			->				bool:
														return False

__UpperCAmelCase       							=				_DeletedItem()




class 						a__							(				MutableMapping[KEY, VAL]    ):





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_ = 8   ,       lowerCamelCase_ = 0.75  )			->				None:
														lowerCAmelCase__			  =      initial_block_size
														lowerCAmelCase__			  =      [None] * initial_block_size
														assert 0.0 < capacity_factor < 1.0
														lowerCAmelCase__			  =      capacity_factor
														lowerCAmelCase__			  =      0







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				int:
														return hash(lowerCamelCase_  ) % len(self._buckets  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				int:
														return (ind + 1) % len(self._buckets  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				bool:
														lowerCAmelCase__			  =      self._buckets[ind]
														if not stored:
																					lowerCAmelCase__			  =      _Item(lowerCamelCase_   ,       lowerCamelCase_  )
																					self._len += 1
																					return True
														elif stored.key == key:
																					lowerCAmelCase__			  =      _Item(lowerCamelCase_   ,       lowerCamelCase_  )
																					return True
														else:
																					return False







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				bool:
														lowerCAmelCase__			  =      len(self._buckets  ) * self._capacity_factor
														return len(self  ) >= int(lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				bool:
														if len(self._buckets  ) <= self._initial_block_size:
																					return False
														lowerCAmelCase__			  =      len(self._buckets  ) * self._capacity_factor / 2
														return len(self  ) < limit







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				None:
														lowerCAmelCase__			  =      self._buckets
														lowerCAmelCase__			  =      [None] * new_size
														lowerCAmelCase__			  =      0
														for item in old_buckets:
																					if item:
																												self._add_item(item.key   ,       item.val  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				None:
														self._resize(len(self._buckets  ) * 2  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				None:
														self._resize(len(self._buckets  ) // 2  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				Iterator[int]:
														lowerCAmelCase__			  =      self._get_bucket_index(lowerCamelCase_  )
														for _ in range(len(self._buckets  )  ):
																					yield ind
																					lowerCAmelCase__			  =      self._get_next_ind(lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				None:
														for ind in self._iterate_buckets(lowerCamelCase_  ):
																					if self._try_set(lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  ):
																												break







							def __setitem__(						self   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				None:
														if self._is_full():
																					self._size_up()

														self._add_item(lowerCamelCase_   ,       lowerCamelCase_  )







							def __delitem__(						self   ,       lowerCamelCase_  )			->				None:
														for ind in self._iterate_buckets(lowerCamelCase_  ):
																					lowerCAmelCase__			  =      self._buckets[ind]
																					if item is None:
																												raise KeyError(lowerCamelCase_  )
																					if item is _deleted:
																												continue
																					if item.key == key:
																												lowerCAmelCase__			  =      _deleted
																												self._len -= 1
																												break
														if self._is_sparse():
																					self._size_down()







							def __getitem__(						self   ,       lowerCamelCase_  )			->				VAL:
														for ind in self._iterate_buckets(lowerCamelCase_  ):
																					lowerCAmelCase__			  =      self._buckets[ind]
																					if item is None:
																												break
																					if item is _deleted:
																												continue
																					if item.key == key:
																												return item.val
														raise KeyError(lowerCamelCase_  )







							def __len__(						self  )			->				int:
														return self._len







							def __iter__(						self  )			->				Iterator[KEY]:
														yield from (item.key for item in self._buckets if item)







							def __repr__(						self  )			->				str:
														lowerCAmelCase__			  =      ''' ,'''.join(
														    F"""{item.key}: {item.val}""" for item in self._buckets if item  )
														return F"""HashMap({val_string})"""