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    lowerCAmelCase_  (		lowercase:				Optional[Any]		)     ->					int:







 '''simple docstring'''
 _UpperCamelCase:  Optional[Any]    						=			numpy.dtype(numpy.uintaa		).newbyteorder('''>'''		)
 return numpy.frombuffer(bytestream.read(4		)	,       dtype=_SCREAMING_SNAKE_CASE		)[0]




@deprecated(_SCREAMING_SNAKE_CASE	,       '''Please use tf.data to implement this functionality.'''		)
def    lowerCAmelCase_  (		lowercase:				Optional[int]		)     ->					Optional[int]:







 '''simple docstring'''
 print('''Extracting'''	,       f.name		)
 with gzip.GzipFile(fileobj=_SCREAMING_SNAKE_CASE		) as bytestream:
  _UpperCamelCase:  List[Any]    						=			_readaa(_SCREAMING_SNAKE_CASE		)
  if magic != 2_051:
   raise ValueError(
       '''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name)		)
  _UpperCamelCase:  List[Any]    						=			_readaa(_SCREAMING_SNAKE_CASE		)
  _UpperCamelCase:  Optional[int]    						=			_readaa(_SCREAMING_SNAKE_CASE		)
  _UpperCamelCase:  Optional[Any]    						=			_readaa(_SCREAMING_SNAKE_CASE		)
  _UpperCamelCase:  Optional[int]    						=			bytestream.read(rows * cols * num_images		)
  _UpperCamelCase:  List[str]    						=			numpy.frombuffer(_SCREAMING_SNAKE_CASE	,       dtype=numpy.uinta		)
  _UpperCamelCase:  int    						=			data.reshape(_SCREAMING_SNAKE_CASE	,       _SCREAMING_SNAKE_CASE	,       _SCREAMING_SNAKE_CASE	,       1		)
  return data




@deprecated(_SCREAMING_SNAKE_CASE	,       '''Please use tf.one_hot on tensors.'''		)
def    lowerCAmelCase_  (		lowercase:				List[Any]	,       lowercase:				Optional[Any]		)     ->					int:







 '''simple docstring'''
 _UpperCamelCase:  Dict    						=			labels_dense.shape[0]
 _UpperCamelCase:  List[str]    						=			numpy.arange(_SCREAMING_SNAKE_CASE		) * num_classes
 _UpperCamelCase:  Optional[Any]    						=			numpy.zeros((num_labels, num_classes)		)
 _UpperCamelCase:  Union[str, Any]    						=			1
 return labels_one_hot




@deprecated(_SCREAMING_SNAKE_CASE	,       '''Please use tf.data to implement this functionality.'''		)
def    lowerCAmelCase_  (		lowercase:				str	,       lowercase:				Optional[int]=False	,       lowercase:				str=10		)     ->					Tuple:







 '''simple docstring'''
 print('''Extracting'''	,       f.name		)
 with gzip.GzipFile(fileobj=_SCREAMING_SNAKE_CASE		) as bytestream:
  _UpperCamelCase:  Union[str, Any]    						=			_readaa(_SCREAMING_SNAKE_CASE		)
  if magic != 2_049:
   raise ValueError(
       '''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name)		)
  _UpperCamelCase:  Union[str, Any]    						=			_readaa(_SCREAMING_SNAKE_CASE		)
  _UpperCamelCase:  Optional[Any]    						=			bytestream.read(_SCREAMING_SNAKE_CASE		)
  _UpperCamelCase:  Optional[Any]    						=			numpy.frombuffer(_SCREAMING_SNAKE_CASE	,       dtype=numpy.uinta		)
  if one_hot:
   return _dense_to_one_hot(_SCREAMING_SNAKE_CASE	,       _SCREAMING_SNAKE_CASE		)
  return labels




class 			__magic_name__	:





   """simple docstring"""




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



    """simple docstring"""



    _UpperCamelCase:  List[Any]    						=			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:  List[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    						=			10_000
     _UpperCamelCase:  Union[str, Any]    						=			one_hot
    else:
     assert (
         images.shape[0] == labels.shape[0]
     ), f"""images.shape: {images.shape} labels.shape: {labels.shape}"""
     _UpperCamelCase:  Dict    						=			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:  Dict    						=			images.reshape(
          images.shape[0]		,	images.shape[1] * images.shape[2]				)
     if dtype == dtypes.floataa:
      # Convert from [0, 255] -> [0.0, 1.0].
      _UpperCamelCase:  List[Any]    						=			images.astype(numpy.floataa				)
      _UpperCamelCase:  Optional[int]    						=			numpy.multiply(__UpperCAmelCase		,	1.0 / 255.0				)
    _UpperCamelCase:  Union[str, Any]    						=			images
    _UpperCamelCase:  Any    						=			labels
    _UpperCamelCase:  Any    						=			0
    _UpperCamelCase:  Union[str, Any]    						=			0



   @property
   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    return self._images



   @property
   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    return self._labels



   @property
   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    return self._num_examples



   @property
   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    return self._epochs_completed



   def 	lowerCAmelCase    (						self       :	Union[str, Any]		,	_lowercase       :	int		,	_lowercase       :	List[str]=False		,	_lowercase       :	Any=True				):



    """simple docstring"""



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




@deprecated(_SCREAMING_SNAKE_CASE	,       '''Please write your own downloading logic.'''		)
def    lowerCAmelCase_  (		lowercase:				Optional[int]	,       lowercase:				str	,       lowercase:				int		)     ->					Union[str, Any]:







 '''simple docstring'''


 if not gfile.Exists(_SCREAMING_SNAKE_CASE		):
  gfile.MakeDirs(_SCREAMING_SNAKE_CASE		)
 _UpperCamelCase:  Dict    						=			os.path.join(_SCREAMING_SNAKE_CASE	,       _SCREAMING_SNAKE_CASE		)
 if not gfile.Exists(_SCREAMING_SNAKE_CASE		):
  urllib.request.urlretrieve(_SCREAMING_SNAKE_CASE	,       _SCREAMING_SNAKE_CASE		)  # noqa: S310
  with gfile.GFile(_SCREAMING_SNAKE_CASE		) as f:
   _UpperCamelCase:  Optional[Any]    						=			f.size()
  print('''Successfully downloaded'''	,       _SCREAMING_SNAKE_CASE	,       _SCREAMING_SNAKE_CASE	,       '''bytes.'''		)
 return filepath





@deprecated(
    _SCREAMING_SNAKE_CASE	,       '''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')'''		)
def    lowerCAmelCase_  (		lowercase:				int	,       lowercase:				Any=False	,       lowercase:				List[Any]=False	,       lowercase:				Dict=dtypes.floataa	,       lowercase:				List[str]=True	,       lowercase:				List[Any]=5_000	,       lowercase:				Union[str, Any]=None	,       lowercase:				int=DEFAULT_SOURCE_URL	,       )     ->					Tuple:







 '''simple docstring'''


 if fake_data:

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

  _UpperCamelCase:  int    						=			fake()
  _UpperCamelCase:  Optional[int]    						=			fake()
  _UpperCamelCase:  List[str]    						=			fake()
  return _Datasets(train=_SCREAMING_SNAKE_CASE	,       validation=_SCREAMING_SNAKE_CASE	,       test=_SCREAMING_SNAKE_CASE		)

 if not source_url:  # empty string check
  _UpperCamelCase:  List[str]    						=			DEFAULT_SOURCE_URL

 _UpperCamelCase:  List[str]    						=			'train-images-idx3-ubyte.gz'
 _UpperCamelCase:  Dict    						=			'train-labels-idx1-ubyte.gz'
 _UpperCamelCase:  Dict    						=			't10k-images-idx3-ubyte.gz'
 _UpperCamelCase:  Tuple    						=			't10k-labels-idx1-ubyte.gz'

 _UpperCamelCase:  Dict    						=			_maybe_download(
     _SCREAMING_SNAKE_CASE	,       _SCREAMING_SNAKE_CASE	,       source_url + train_images_file		)
 with gfile.Open(_SCREAMING_SNAKE_CASE	,       '''rb'''		) as f:
  _UpperCamelCase:  List[str]    						=			_extract_images(_SCREAMING_SNAKE_CASE		)

 _UpperCamelCase:  Any    						=			_maybe_download(
     _SCREAMING_SNAKE_CASE	,       _SCREAMING_SNAKE_CASE	,       source_url + train_labels_file		)
 with gfile.Open(_SCREAMING_SNAKE_CASE	,       '''rb'''		) as f:
  _UpperCamelCase:  Tuple    						=			_extract_labels(_SCREAMING_SNAKE_CASE	,       one_hot=_SCREAMING_SNAKE_CASE		)

 _UpperCamelCase:  str    						=			_maybe_download(
     _SCREAMING_SNAKE_CASE	,       _SCREAMING_SNAKE_CASE	,       source_url + test_images_file		)
 with gfile.Open(_SCREAMING_SNAKE_CASE	,       '''rb'''		) as f:
  _UpperCamelCase:  int    						=			_extract_images(_SCREAMING_SNAKE_CASE		)

 _UpperCamelCase:  int    						=			_maybe_download(
     _SCREAMING_SNAKE_CASE	,       _SCREAMING_SNAKE_CASE	,       source_url + test_labels_file		)
 with gfile.Open(_SCREAMING_SNAKE_CASE	,       '''rb'''		) as f:
  _UpperCamelCase:  Dict    						=			_extract_labels(_SCREAMING_SNAKE_CASE	,       one_hot=_SCREAMING_SNAKE_CASE		)

 if not 0 <= validation_size <= len(_SCREAMING_SNAKE_CASE		):
  _UpperCamelCase:  List[Any]    						=			(
      'Validation size should be between 0 and '
      F"""{len(_SCREAMING_SNAKE_CASE		)}. Received: {validation_size}."""
  )
  raise ValueError(_SCREAMING_SNAKE_CASE		)

 _UpperCamelCase:  List[Any]    						=			train_images[:validation_size]
 _UpperCamelCase:  Any    						=			train_labels[:validation_size]
 _UpperCamelCase:  Union[str, Any]    						=			train_images[validation_size:]
 _UpperCamelCase:  Union[str, Any]    						=			train_labels[validation_size:]

 _UpperCamelCase:  Any    						=			{'dtype': dtype, 'reshape': reshape, 'seed': seed}

 _UpperCamelCase:  Optional[int]    						=			_DataSet(_SCREAMING_SNAKE_CASE	,       _SCREAMING_SNAKE_CASE	,       **_SCREAMING_SNAKE_CASE		)
 _UpperCamelCase:  Optional[int]    						=			_DataSet(_SCREAMING_SNAKE_CASE	,       _SCREAMING_SNAKE_CASE	,       **_SCREAMING_SNAKE_CASE		)
 _UpperCamelCase:  Tuple    						=			_DataSet(_SCREAMING_SNAKE_CASE	,       _SCREAMING_SNAKE_CASE	,       **_SCREAMING_SNAKE_CASE		)

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






"""simple docstring"""





def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->list[str]:





  """simple docstring"""


  return [sentence[i : i + ngram_size] for i in range(len(_SCREAMING_SNAKE_CASE     ) - ngram_size + 1     )]


if __name__ == "__main__":
  from doctest import testmod

  testmod()
