





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

lowercase      :       List[Any]									=  collections.namedtuple("""_Datasets""", ["""train""", """validation""", """test"""])

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



def A_	(		A__				)  ->		str:
       a__						:	Optional[Any] =				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 A_	(		A__				)  ->		Optional[Any]:
       print('Extracting'		,      f.name				)
       with gzip.GzipFile(fileobj=A__				) as bytestream:
              a__						:	Dict =				_readaa(A__				)
              if magic != 2051:
                     raise ValueError(
                         'Invalid magic number %d in MNIST image file: %s' % (magic, f.name)				)
              a__						:	Optional[Any] =				_readaa(A__				)
              a__						:	Union[str, Any] =				_readaa(A__				)
              a__						:	Tuple =				_readaa(A__				)
              a__						:	Any =				bytestream.read(rows * cols * num_images				)
              a__						:	List[Any] =				numpy.frombuffer(A__		,      dtype=numpy.uinta				)
              a__						:	Any =				data.reshape(A__		,      A__		,      A__		,      1				)
              return data



@deprecated(A__		,      'Please use tf.one_hot on tensors.'				)
def A_	(		A__		,      A__				)  ->		Union[str, Any]:
       a__						:	str =				labels_dense.shape[0]
       a__						:	Union[str, Any] =				numpy.arange(A__				) * num_classes
       a__						:	Any =				numpy.zeros((num_labels, num_classes)				)
       a__						:	int =				1
       return labels_one_hot



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





class  A__    :




   """simple docstring"""



   @deprecated(
       lowercase					,  'Please use alternatives such as official/mnist/_DataSet.py'
       ' from tensorflow/models.'					,  )
   def __init__(					self					,  lowercase					,  lowercase					,  lowercase=False					,  lowercase=False					,  lowercase=dtypes.floataa					,  lowercase=True					,  lowercase=None					,  )			->						Optional[int]:







          '''simple docstring'''

          a__ ,							a__						:	int =				random_seed.get_seed(lowercase)
          # 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__						:	Union[str, Any] =				dtypes.as_dtype(lowercase).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__						:	str =				1_0000
                 a__						:	Optional[int] =				one_hot
          else:
                 assert (
                     images.shape[0] == labels.shape[0]
                 ), F'images.shape: {images.shape} labels.shape: {labels.shape}'
                 a__						:	List[str] =				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__						:	str =				images.reshape(
                            images.shape[0]					,  images.shape[1] * images.shape[2])
                 if dtype == dtypes.floataa:
                        # Convert from [0, 255] -> [0.0, 1.0].
                        a__						:	Tuple =				images.astype(numpy.floataa)
                        a__						:	List[str] =				numpy.multiply(lowercase					,  1.0 / 2_55.0)
          a__						:	Optional[int] =				images
          a__						:	Tuple =				labels
          a__						:	Any =				0
          a__						:	Dict =				0

   @property
   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          return self._images

   @property
   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          return self._labels

   @property
   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          return self._num_examples

   @property
   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          return self._epochs_completed

   def 							__lowercase		(					self					,  lowercase					,  lowercase=False					,  lowercase=True)			->						Union[str, Any]:







          '''simple docstring'''

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



@deprecated(A__		,      'Please write your own downloading logic.'				)
def A_	(		A__		,      A__		,      A__				)  ->		Dict:
       if not gfile.Exists(A__				):
              gfile.MakeDirs(A__				)
       a__						:	Union[str, Any] =				os.path.join(A__		,      A__				)
       if not gfile.Exists(A__				):
              urllib.request.urlretrieve(A__		,      A__				)  # noqa: S310
              with gfile.GFile(A__				) as f:
                     a__						:	Optional[Any] =				f.size()
              print('Successfully downloaded'		,      A__		,      A__		,      'bytes.'				)
       return filepath



@deprecated(
    A__		,      'Please use alternatives such as:' ' tensorflow_datasets.load(\'mnist\')'				)
def A_	(		A__		,      A__=False		,      A__=False		,      A__=dtypes.floataa		,      A__=True		,      A__=5000		,      A__=None		,      A__=DEFAULT_SOURCE_URL		,      )  ->		List[Any]:
       if fake_data:

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

              a__						:	List[str] =				fake()
              a__						:	int =				fake()
              a__						:	str =				fake()
              return _Datasets(train=A__		,      validation=A__		,      test=A__				)

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

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

       a__						:	Dict =				_maybe_download(
           A__		,      A__		,      source_url + train_images_file				)
       with gfile.Open(A__		,      'rb'				) as f:
              a__						:	Union[str, Any] =				_extract_images(A__				)

       a__						:	Any =				_maybe_download(
           A__		,      A__		,      source_url + train_labels_file				)
       with gfile.Open(A__		,      'rb'				) as f:
              a__						:	List[Any] =				_extract_labels(A__		,      one_hot=A__				)

       a__						:	Union[str, Any] =				_maybe_download(
           A__		,      A__		,      source_url + test_images_file				)
       with gfile.Open(A__		,      'rb'				) as f:
              a__						:	Optional[Any] =				_extract_images(A__				)

       a__						:	List[Any] =				_maybe_download(
           A__		,      A__		,      source_url + test_labels_file				)
       with gfile.Open(A__		,      'rb'				) as f:
              a__						:	List[str] =				_extract_labels(A__		,      one_hot=A__				)

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

       a__						:	Union[str, Any] =				train_images[:validation_size]
       a__						:	str =				train_labels[:validation_size]
       a__						:	Optional[int] =				train_images[validation_size:]
       a__						:	str =				train_labels[validation_size:]

       a__						:	Optional[int] =				{'dtype': dtype, 'reshape': reshape, 'seed': seed}

       a__						:	Dict =				_DataSet(A__		,      A__		,      **A__				)
       a__						:	Union[str, Any] =				_DataSet(A__		,      A__		,      **A__				)
       a__						:	Dict =				_DataSet(A__		,      A__		,      **A__				)

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









from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
    from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline


