



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

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

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
a     :    List[Any]      						=					'https://storage.googleapis.com/cvdf-datasets/mnist/'



def    __lowerCamelCase						(      _lowercase	)   ->    Optional[int]:
							UpperCAmelCase  :	str							  =	numpy.dtype(numpy.uintaa	).newbyteorder(""">"""	)
							return numpy.frombuffer(bytestream.read(4	)			,       dtype=_lowerCamelCase	)[0]



@deprecated(_lowerCamelCase			,       """Please use tf.data to implement this functionality."""	)
def    __lowerCamelCase						(      _lowercase	)   ->    Any:
							print("""Extracting"""			,       f.name	)
							with gzip.GzipFile(fileobj=_lowerCamelCase	) as bytestream:
														UpperCAmelCase  :	str							  =	_readaa(_lowerCamelCase	)
														if magic != 2_0_5_1:
																					raise ValueError(
																					    """Invalid magic number %d in MNIST image file: %s""" % (magic, f.name)	)
														UpperCAmelCase  :	List[str]							  =	_readaa(_lowerCamelCase	)
														UpperCAmelCase  :	Dict							  =	_readaa(_lowerCamelCase	)
														UpperCAmelCase  :	Optional[int]							  =	_readaa(_lowerCamelCase	)
														UpperCAmelCase  :	Dict							  =	bytestream.read(rows * cols * num_images	)
														UpperCAmelCase  :	Optional[int]							  =	numpy.frombuffer(_lowerCamelCase			,       dtype=numpy.uinta	)
														UpperCAmelCase  :	Dict							  =	data.reshape(_lowerCamelCase			,       _lowerCamelCase			,       _lowerCamelCase			,       1	)
														return data



@deprecated(_lowerCamelCase			,       """Please use tf.one_hot on tensors."""	)
def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    str:
							UpperCAmelCase  :	str							  =	labels_dense.shape[0]
							UpperCAmelCase  :	str							  =	numpy.arange(_lowerCamelCase	) * num_classes
							UpperCAmelCase  :	str							  =	numpy.zeros((num_labels, num_classes)	)
							UpperCAmelCase  :	Tuple							  =	1
							return labels_one_hot



@deprecated(_lowerCamelCase			,       """Please use tf.data to implement this functionality."""	)
def    __lowerCamelCase						(      _lowercase			,       _lowercase=False			,       _lowercase=1_0	)   ->    Dict:
							print("""Extracting"""			,       f.name	)
							with gzip.GzipFile(fileobj=_lowerCamelCase	) as bytestream:
														UpperCAmelCase  :	int							  =	_readaa(_lowerCamelCase	)
														if magic != 2_0_4_9:
																					raise ValueError(
																					    """Invalid magic number %d in MNIST label file: %s""" % (magic, f.name)	)
														UpperCAmelCase  :	Any							  =	_readaa(_lowerCamelCase	)
														UpperCAmelCase  :	List[Any]							  =	bytestream.read(_lowerCamelCase	)
														UpperCAmelCase  :	Dict							  =	numpy.frombuffer(_lowerCamelCase			,       dtype=numpy.uinta	)
														if one_hot:
																					return _dense_to_one_hot(_lowerCamelCase			,       _lowerCamelCase	)
														return labels

class     UpperCamelCase_				:





	@deprecated(
	    A	,	"""Please use alternatives such as official/mnist/_DataSet.py"""
	    """ from tensorflow/models."""	,	)
	def __init__(						self	,	A	,	A	,	A=False	,	A=False	,	A=dtypes.floataa	,	A=True	,	A=None	,	)    ->    List[str]:
								UpperCAmelCase  :	Optional[int]							  =	random_seed.get_seed(A					)
								# 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  :	Optional[Any]							  =	dtypes.as_dtype(A					).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  :	str							  =	10000
															UpperCAmelCase  :	Optional[Any]							  =	one_hot
								else:
															assert (
															    images.shape[0] == labels.shape[0]
															), f'''images.shape: {images.shape} labels.shape: {labels.shape}'''
															UpperCAmelCase  :	Any							  =	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  :	Union[str, Any]							  =	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[Any]							  =	numpy.multiply(A	,	1.0 / 2_5_5.0					)
								UpperCAmelCase  :	Optional[Any]							  =	images
								UpperCAmelCase  :	List[Any]							  =	labels
								UpperCAmelCase  :	str							  =	0
								UpperCAmelCase  :	Union[str, Any]							  =	0





	@property
	def 				_lowercase(						self					)    ->    Any:
								return self._images





	@property
	def 				_lowercase(						self					)    ->    Dict:
								return self._labels





	@property
	def 				_lowercase(						self					)    ->    str:
								return self._num_examples





	@property
	def 				_lowercase(						self					)    ->    Dict:
								return self._epochs_completed






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




@deprecated(_lowerCamelCase			,       """Please write your own downloading logic."""	)
def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    Tuple:





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



@deprecated(
    _lowerCamelCase			,       """Please use alternatives such as:""" """ tensorflow_datasets.load('mnist')"""	)
def    __lowerCamelCase						(      _lowercase			,       _lowercase=False			,       _lowercase=False			,       _lowercase=dtypes.floataa			,       _lowercase=True			,       _lowercase=5_0_0_0			,       _lowercase=None			,       _lowercase=DEFAULT_SOURCE_URL			,       )   ->    List[Any]:





							if fake_data:

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

														UpperCAmelCase  :	Optional[int]							  =	fake()
														UpperCAmelCase  :	Tuple							  =	fake()
														UpperCAmelCase  :	List[str]							  =	fake()
														return _Datasets(train=_lowerCamelCase			,       validation=_lowerCamelCase			,       test=_lowerCamelCase	)

							if not source_url:  # empty string check
														UpperCAmelCase  :	str							  =	DEFAULT_SOURCE_URL

							UpperCAmelCase  :	Optional[int]							  =	"train-images-idx3-ubyte.gz"
							UpperCAmelCase  :	Dict							  =	"train-labels-idx1-ubyte.gz"
							UpperCAmelCase  :	List[str]							  =	"t10k-images-idx3-ubyte.gz"
							UpperCAmelCase  :	List[str]							  =	"t10k-labels-idx1-ubyte.gz"

							UpperCAmelCase  :	Optional[int]							  =	_maybe_download(
							    _lowerCamelCase			,       _lowerCamelCase			,       source_url + train_images_file	)
							with gfile.Open(_lowerCamelCase			,       """rb"""	) as f:
														UpperCAmelCase  :	int							  =	_extract_images(_lowerCamelCase	)

							UpperCAmelCase  :	Optional[Any]							  =	_maybe_download(
							    _lowerCamelCase			,       _lowerCamelCase			,       source_url + train_labels_file	)
							with gfile.Open(_lowerCamelCase			,       """rb"""	) as f:
														UpperCAmelCase  :	int							  =	_extract_labels(_lowerCamelCase			,       one_hot=_lowerCamelCase	)

							UpperCAmelCase  :	int							  =	_maybe_download(
							    _lowerCamelCase			,       _lowerCamelCase			,       source_url + test_images_file	)
							with gfile.Open(_lowerCamelCase			,       """rb"""	) as f:
														UpperCAmelCase  :	Optional[int]							  =	_extract_images(_lowerCamelCase	)

							UpperCAmelCase  :	str							  =	_maybe_download(
							    _lowerCamelCase			,       _lowerCamelCase			,       source_url + test_labels_file	)
							with gfile.Open(_lowerCamelCase			,       """rb"""	) as f:
														UpperCAmelCase  :	List[str]							  =	_extract_labels(_lowerCamelCase			,       one_hot=_lowerCamelCase	)

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

							UpperCAmelCase  :	Any							  =	train_images[:validation_size]
							UpperCAmelCase  :	Optional[Any]							  =	train_labels[:validation_size]
							UpperCAmelCase  :	Optional[int]							  =	train_images[validation_size:]
							UpperCAmelCase  :	Tuple							  =	train_labels[validation_size:]

							UpperCAmelCase  :	List[str]							  =	{"dtype": dtype, "reshape": reshape, "seed": seed}

							UpperCAmelCase  :	Union[str, Any]							  =	_DataSet(_lowerCamelCase			,       _lowerCamelCase			,       **_lowerCamelCase	)
							UpperCAmelCase  :	str							  =	_DataSet(_lowerCamelCase			,       _lowerCamelCase			,       **_lowerCamelCase	)
							UpperCAmelCase  :	Optional[Any]							  =	_DataSet(_lowerCamelCase			,       _lowerCamelCase			,       **_lowerCamelCase	)

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






'''simple docstring'''




from ...utils import is_torch_available, is_transformers_available


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

