



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

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

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

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			List[Any]    ):
				"""simple docstring"""







				__a       =		numpy.dtype(numpy.uintaa    ).newbyteorder(""">"""    )
				return numpy.frombuffer(bytestream.read(4    )				,     dtype=UpperCamelCase__    )[0]

@deprecated(UpperCamelCase__				,     """Please use tf.data to implement this functionality."""    )
def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str    ):
				"""simple docstring"""







				print("""Extracting"""				,     f.name    )
				with gzip.GzipFile(fileobj=UpperCamelCase__    ) as bytestream:
								__a       =		_readaa(UpperCamelCase__    )
								if magic != 2051:
												raise ValueError(
												    """Invalid magic number %d in MNIST image file: %s""" % (magic, f.name)    )
								__a       =		_readaa(UpperCamelCase__    )
								__a       =		_readaa(UpperCamelCase__    )
								__a       =		_readaa(UpperCamelCase__    )
								__a       =		bytestream.read(rows * cols * num_images    )
								__a       =		numpy.frombuffer(UpperCamelCase__				,     dtype=numpy.uinta    )
								__a       =		data.reshape(UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__				,     1    )
								return data

@deprecated(UpperCamelCase__				,     """Please use tf.one_hot on tensors."""    )
def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Optional[int]				,     _SCREAMING_SNAKE_CASE       :			int    ):
				"""simple docstring"""







				__a       =		labels_dense.shape[0]
				__a       =		numpy.arange(UpperCamelCase__    ) * num_classes
				__a       =		numpy.zeros((num_labels, num_classes)    )
				__a       =		1
				return labels_one_hot

@deprecated(UpperCamelCase__				,     """Please use tf.data to implement this functionality."""    )
def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			List[str]				,     _SCREAMING_SNAKE_CASE       :			List[str]=False				,     _SCREAMING_SNAKE_CASE       :			Any=10    ):
				"""simple docstring"""







				print("""Extracting"""				,     f.name    )
				with gzip.GzipFile(fileobj=UpperCamelCase__    ) as bytestream:
								__a       =		_readaa(UpperCamelCase__    )
								if magic != 2049:
												raise ValueError(
												    """Invalid magic number %d in MNIST label file: %s""" % (magic, f.name)    )
								__a       =		_readaa(UpperCamelCase__    )
								__a       =		bytestream.read(UpperCamelCase__    )
								__a       =		numpy.frombuffer(UpperCamelCase__				,     dtype=numpy.uinta    )
								if one_hot:
												return _dense_to_one_hot(UpperCamelCase__				,     UpperCamelCase__    )
								return labels






class    SCREAMING_SNAKE_CASE			:

							'''simple docstring'''





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

											'''simple docstring'''
											__a     ,					__a       =		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       =		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       =		10000
															__a       =		one_hot
											else:
															assert (
															    images.shape[0] == labels.shape[0]
															), F"images.shape: {images.shape} labels.shape: {labels.shape}"
															__a       =		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       =		images.reshape(
																			    images.shape[0]      ,				images.shape[1] * images.shape[2]	)
															if dtype == dtypes.floataa:
																			# Convert from [0, 255] -> [0.0, 1.0].
																			__a       =		images.astype(numpy.floataa	)
																			__a       =		numpy.multiply(_lowercase      ,				1.0 / 255.0	)
											__a       =		images
											__a       =		labels
											__a       =		0
											__a       =		0





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

											'''simple docstring'''
											return self._images





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

											'''simple docstring'''
											return self._labels





							@property
							def 						UpperCamelCase_					(   self		:		List[Any]	):

											'''simple docstring'''
											return self._num_examples





							@property
							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											return self._epochs_completed







							def 						UpperCamelCase_					(   self		:		List[Any]      ,				__lowercase		:		Dict      ,				__lowercase		:		Optional[Any]=False      ,				__lowercase		:		int=True	):

											'''simple docstring'''
											if fake_data:
															__a       =		[1] * 784
															__a       =		[1] + [0] * 9 if self.one_hot else 0
															return (
															    [fake_image for _ in range(_lowercase	)],
															    [fake_label for _ in range(_lowercase	)],
															)
											__a       =		self._index_in_epoch
											# Shuffle for the first epoch
											if self._epochs_completed == 0 and start == 0 and shuffle:
															__a       =		numpy.arange(self._num_examples	)
															numpy.random.shuffle(_lowercase	)
															__a       =		self.images[perma]
															__a       =		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       =		self._num_examples - start
															__a       =		self._images[start : self._num_examples]
															__a       =		self._labels[start : self._num_examples]
															# Shuffle the data
															if shuffle:
																			__a       =		numpy.arange(self._num_examples	)
																			numpy.random.shuffle(_lowercase	)
																			__a       =		self.images[perm]
																			__a       =		self.labels[perm]
															# Start next epoch
															__a       =		0
															__a       =		batch_size - rest_num_examples
															__a       =		self._index_in_epoch
															__a       =		self._images[start:end]
															__a       =		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       =		self._index_in_epoch
															return self._images[start:end], self._labels[start:end]



@deprecated(UpperCamelCase__				,     """Please write your own downloading logic."""    )
def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Tuple				,     _SCREAMING_SNAKE_CASE       :			Any				,     _SCREAMING_SNAKE_CASE       :			Union[str, Any]    ):
				"""simple docstring"""







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

@deprecated(
    UpperCamelCase__				,     """Please use alternatives such as:""" """ tensorflow_datasets.load(\'mnist\')"""    )
def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Dict				,     _SCREAMING_SNAKE_CASE       :			List[Any]=False				,     _SCREAMING_SNAKE_CASE       :			str=False				,     _SCREAMING_SNAKE_CASE       :			Tuple=dtypes.floataa				,     _SCREAMING_SNAKE_CASE       :			Dict=True				,     _SCREAMING_SNAKE_CASE       :			List[Any]=5000				,     _SCREAMING_SNAKE_CASE       :			Optional[Any]=None				,     _SCREAMING_SNAKE_CASE       :			List[Any]=DEFAULT_SOURCE_URL				,     ):
				"""simple docstring"""







				if fake_data:

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

								__a       =		fake()
								__a       =		fake()
								__a       =		fake()
								return _Datasets(train=UpperCamelCase__				,     validation=UpperCamelCase__				,     test=UpperCamelCase__    )

				if not source_url:  # empty string check
								__a       =		DEFAULT_SOURCE_URL

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

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

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

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

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

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

				__a       =		train_images[:validation_size]
				__a       =		train_labels[:validation_size]
				__a       =		train_images[validation_size:]
				__a       =		train_labels[validation_size:]

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

				__a       =		_DataSet(UpperCamelCase__				,     UpperCamelCase__				,     **UpperCamelCase__    )
				__a       =		_DataSet(UpperCamelCase__				,     UpperCamelCase__				,     **UpperCamelCase__    )
				__a       =		_DataSet(UpperCamelCase__				,     UpperCamelCase__				,     **UpperCamelCase__    )

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





import itertools
import string
from collections.abc import Generator, Iterable

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Iterable[str]				,     _SCREAMING_SNAKE_CASE       :			int    ):
				"""simple docstring"""







				__a       =		iter(_SCREAMING_SNAKE_CASE    )
				while True:
								__a       =		tuple(itertools.islice(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )    )
								if not chunk:
												return
								yield chunk

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str    ):
				"""simple docstring"""







				__a       =		"""""".join([c.upper() for c in dirty if c in string.ascii_letters]    )
				__a       =		""""""

				if len(_SCREAMING_SNAKE_CASE    ) < 2:
								return dirty

				for i in range(len(_SCREAMING_SNAKE_CASE    ) - 1    ):
								clean += dirty[i]

								if dirty[i] == dirty[i + 1]:
												clean += "X"

				clean += dirty[-1]

				if len(_SCREAMING_SNAKE_CASE    ) & 1:
								clean += "X"

				return clean

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str    ):
				"""simple docstring"""







				__a       =		"""ABCDEFGHIKLMNOPQRSTUVWXYZ"""
				# we're using a list instead of a '2d' array because it makes the math
				# for setting up the table and doing the actual encoding/decoding simpler
				__a       =		[]

				# copy key chars into the table if they are in `alphabet` ignoring duplicates
				for char in key.upper():
								if char not in table and char in alphabet:
												table.append(_SCREAMING_SNAKE_CASE    )

    # fill the rest of the table in with the remaining alphabet chars
				for char in alphabet:
								if char not in table:
												table.append(_SCREAMING_SNAKE_CASE    )

				return table

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str				,     _SCREAMING_SNAKE_CASE       :			str    ):
				"""simple docstring"""







				__a       =		generate_table(_SCREAMING_SNAKE_CASE    )
				__a       =		prepare_input(_SCREAMING_SNAKE_CASE    )
				__a       =		""""""

				# https://en.wikipedia.org/wiki/Playfair_cipher#Description
				for chara, chara in chunker(_SCREAMING_SNAKE_CASE				,     2    ):
								__a     ,					__a       =		divmod(table.index(_SCREAMING_SNAKE_CASE    )				,     5    )
								__a     ,					__a       =		divmod(table.index(_SCREAMING_SNAKE_CASE    )				,     5    )

								if rowa == rowa:
												ciphertext += table[rowa * 5 + (cola + 1) % 5]
												ciphertext += table[rowa * 5 + (cola + 1) % 5]
								elif cola == cola:
												ciphertext += table[((rowa + 1) % 5) * 5 + cola]
												ciphertext += table[((rowa + 1) % 5) * 5 + cola]
								else:  # rectangle
												ciphertext += table[rowa * 5 + cola]
												ciphertext += table[rowa * 5 + cola]

				return ciphertext

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str				,     _SCREAMING_SNAKE_CASE       :			str    ):
				"""simple docstring"""







				__a       =		generate_table(_SCREAMING_SNAKE_CASE    )
				__a       =		""""""

				# https://en.wikipedia.org/wiki/Playfair_cipher#Description
				for chara, chara in chunker(_SCREAMING_SNAKE_CASE				,     2    ):
								__a     ,					__a       =		divmod(table.index(_SCREAMING_SNAKE_CASE    )				,     5    )
								__a     ,					__a       =		divmod(table.index(_SCREAMING_SNAKE_CASE    )				,     5    )

								if rowa == rowa:
												plaintext += table[rowa * 5 + (cola - 1) % 5]
												plaintext += table[rowa * 5 + (cola - 1) % 5]
								elif cola == cola:
												plaintext += table[((rowa - 1) % 5) * 5 + cola]
												plaintext += table[((rowa - 1) % 5) * 5 + cola]
								else:  # rectangle
												plaintext += table[rowa * 5 + cola]
												plaintext += table[rowa * 5 + cola]

				return plaintext
