




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						:							Tuple							=  collections.namedtuple("""_Datasets""", ["""train""", """validation""", """test"""])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
__A						:							Union[str, Any]							=  """https://storage.googleapis.com/cvdf-datasets/mnist/"""
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					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_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			print("""Extracting"""					,	f.name   )
			with gzip.GzipFile(fileobj=SCREAMING_SNAKE_CASE   ) as bytestream:
						SCREAMING_SNAKE_CASE           =					_readaa(SCREAMING_SNAKE_CASE   )
						if magic != 20_51:
									raise ValueError(
									    """Invalid magic number %d in MNIST image file: %s""" % (magic, f.name)   )
						SCREAMING_SNAKE_CASE           =					_readaa(SCREAMING_SNAKE_CASE   )
						SCREAMING_SNAKE_CASE           =					_readaa(SCREAMING_SNAKE_CASE   )
						SCREAMING_SNAKE_CASE           =					_readaa(SCREAMING_SNAKE_CASE   )
						SCREAMING_SNAKE_CASE           =					bytestream.read(rows * cols * num_images   )
						SCREAMING_SNAKE_CASE           =					numpy.frombuffer(SCREAMING_SNAKE_CASE					,	dtype=numpy.uinta   )
						SCREAMING_SNAKE_CASE           =					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_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					labels_dense.shape[0]
			SCREAMING_SNAKE_CASE           =					numpy.arange(SCREAMING_SNAKE_CASE   ) * num_classes
			SCREAMING_SNAKE_CASE           =					numpy.zeros((num_labels, num_classes)   )
			SCREAMING_SNAKE_CASE           =					1
			return labels_one_hot
@deprecated(SCREAMING_SNAKE_CASE					,	"""Please use tf.data to implement this functionality."""   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=False					,	SCREAMING_SNAKE_CASE=10   ):
			'''simple docstring'''
			print("""Extracting"""					,	f.name   )
			with gzip.GzipFile(fileobj=SCREAMING_SNAKE_CASE   ) as bytestream:
						SCREAMING_SNAKE_CASE           =					_readaa(SCREAMING_SNAKE_CASE   )
						if magic != 20_49:
									raise ValueError(
									    """Invalid magic number %d in MNIST label file: %s""" % (magic, f.name)   )
						SCREAMING_SNAKE_CASE           =					_readaa(SCREAMING_SNAKE_CASE   )
						SCREAMING_SNAKE_CASE           =					bytestream.read(SCREAMING_SNAKE_CASE   )
						SCREAMING_SNAKE_CASE           =					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 			UpperCAmelCase_      :




							'''simple docstring'''



							@deprecated(
							    a       ,     """Please use alternatives such as official/mnist/_DataSet.py"""
							    """ from tensorflow/models."""       ,     )
							def __init__(					self       :	Optional[Any]       ,     a       :	List[str]       ,     a       :	str       ,     a       :	Union[str, Any]=False       ,     a       :	int=False       ,     a       :	Optional[int]=dtypes.floataa       ,     a       :	Tuple=True       ,     a       :	int=None       ,     )			->					Optional[int]:
										SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					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			)
										SCREAMING_SNAKE_CASE           =					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:
													SCREAMING_SNAKE_CASE           =					10_000
													SCREAMING_SNAKE_CASE           =					one_hot
										else:
													assert (
													    images.shape[0] == labels.shape[0]
													), f"""images.shape: {images.shape} labels.shape: {labels.shape}"""
													SCREAMING_SNAKE_CASE           =					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
																SCREAMING_SNAKE_CASE           =					images.reshape(
																    images.shape[0]       ,     images.shape[1] * images.shape[2]			)
													if dtype == dtypes.floataa:
																# Convert from [0, 255] -> [0.0, 1.0].
																SCREAMING_SNAKE_CASE           =					images.astype(numpy.floataa			)
																SCREAMING_SNAKE_CASE           =					numpy.multiply(a       ,     1.0 / 255.0			)
										SCREAMING_SNAKE_CASE           =					images
										SCREAMING_SNAKE_CASE           =					labels
										SCREAMING_SNAKE_CASE           =					0
										SCREAMING_SNAKE_CASE           =					0





							@property
							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					str:
										return self._images





							@property
							def    _UpperCAmelCase     (					self       :	str			)			->					List[str]:
										return self._labels





							@property
							def    _UpperCAmelCase     (					self       :	str			)			->					Union[str, Any]:
										return self._num_examples





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







							def    _UpperCAmelCase     (					self       :	int       ,     a       :	Tuple       ,     a       :	List[Any]=False       ,     a       :	List[Any]=True			)			->					Dict:
										if fake_data:
													SCREAMING_SNAKE_CASE           =					[1] * 784
													SCREAMING_SNAKE_CASE           =					[1] + [0] * 9 if self.one_hot else 0
													return (
													    [fake_image for _ in range(a			)],
													    [fake_label for _ in range(a			)],
													)
										SCREAMING_SNAKE_CASE           =					self._index_in_epoch
										# Shuffle for the first epoch
										if self._epochs_completed == 0 and start == 0 and shuffle:
													SCREAMING_SNAKE_CASE           =					numpy.arange(self._num_examples			)
													numpy.random.shuffle(a			)
													SCREAMING_SNAKE_CASE           =					self.images[perma]
													SCREAMING_SNAKE_CASE           =					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
													SCREAMING_SNAKE_CASE           =					self._num_examples - start
													SCREAMING_SNAKE_CASE           =					self._images[start : self._num_examples]
													SCREAMING_SNAKE_CASE           =					self._labels[start : self._num_examples]
													# Shuffle the data
													if shuffle:
																SCREAMING_SNAKE_CASE           =					numpy.arange(self._num_examples			)
																numpy.random.shuffle(a			)
																SCREAMING_SNAKE_CASE           =					self.images[perm]
																SCREAMING_SNAKE_CASE           =					self.labels[perm]
													# Start next epoch
													SCREAMING_SNAKE_CASE           =					0
													SCREAMING_SNAKE_CASE           =					batch_size - rest_num_examples
													SCREAMING_SNAKE_CASE           =					self._index_in_epoch
													SCREAMING_SNAKE_CASE           =					self._images[start:end]
													SCREAMING_SNAKE_CASE           =					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
													SCREAMING_SNAKE_CASE           =					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_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			if not gfile.Exists(SCREAMING_SNAKE_CASE   ):
						gfile.MakeDirs(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					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:
									SCREAMING_SNAKE_CASE           =					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_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=False					,	SCREAMING_SNAKE_CASE=False					,	SCREAMING_SNAKE_CASE=dtypes.floataa					,	SCREAMING_SNAKE_CASE=True					,	SCREAMING_SNAKE_CASE=50_00					,	SCREAMING_SNAKE_CASE=None					,	SCREAMING_SNAKE_CASE=DEFAULT_SOURCE_URL					,	):
			'''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   )

						SCREAMING_SNAKE_CASE           =					fake()
						SCREAMING_SNAKE_CASE           =					fake()
						SCREAMING_SNAKE_CASE           =					fake()
						return _Datasets(train=SCREAMING_SNAKE_CASE					,	validation=SCREAMING_SNAKE_CASE					,	test=SCREAMING_SNAKE_CASE   )

			if not source_url:  # empty string check
						SCREAMING_SNAKE_CASE           =					DEFAULT_SOURCE_URL

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

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

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

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

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

			if not 0 <= validation_size <= len(SCREAMING_SNAKE_CASE   ):
						SCREAMING_SNAKE_CASE           =					(
						    """Validation size should be between 0 and """
						    f"""{len(SCREAMING_SNAKE_CASE   )}. Received: {validation_size}."""
						)
						raise ValueError(SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE           =					train_images[:validation_size]
			SCREAMING_SNAKE_CASE           =					train_labels[:validation_size]
			SCREAMING_SNAKE_CASE           =					train_images[validation_size:]
			SCREAMING_SNAKE_CASE           =					train_labels[validation_size:]

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

			SCREAMING_SNAKE_CASE           =					_DataSet(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	**SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					_DataSet(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	**SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					_DataSet(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	**SCREAMING_SNAKE_CASE   )

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












from __future__ import annotations
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			create_state_space_tree(SCREAMING_SNAKE_CASE					,	[]					,	0					,	[0 for i in range(len(SCREAMING_SNAKE_CASE   )   )]   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	):
			'''simple docstring'''

			if index == len(SCREAMING_SNAKE_CASE   ):
						print(SCREAMING_SNAKE_CASE   )
						return

			for i in range(len(SCREAMING_SNAKE_CASE   )   ):
						if not index_used[i]:
									current_sequence.append(sequence[i]   )
									SCREAMING_SNAKE_CASE           =					True
									create_state_space_tree(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	index + 1					,	SCREAMING_SNAKE_CASE   )
									current_sequence.pop()
									SCREAMING_SNAKE_CASE           =					False
__A						:							list[int | str]							=  [3, 1, 2, 4]
generate_all_permutations(sequence)

__A						:							list[int | str]							=  ["A", "B", "C"]




generate_all_permutations(sequence_a)






