



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

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

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






def 			lowerCAmelCase__	(					lowerCamelCase_	:	str):





				'''simple docstring'''

				lowerCAmelCase__  :       Tuple				=  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__	(					lowerCamelCase_	:	Optional[Any]):





				'''simple docstring'''

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






@deprecated(lowerCamelCase_ ,'''Please use tf.one_hot on tensors.''')
def 			lowerCAmelCase__	(					lowerCamelCase_	:	Tuple ,lowerCamelCase_	:	Union[str, Any]):





				'''simple docstring'''

				lowerCAmelCase__  :       List[Any]				=  labels_dense.shape[0]
				lowerCAmelCase__  :       int				=  numpy.arange(lowerCamelCase_) * num_classes
				lowerCAmelCase__  :       List[str]				=  numpy.zeros((num_labels, num_classes))
				lowerCAmelCase__  :       Optional[int]				=  1
				return labels_one_hot






@deprecated(lowerCamelCase_ ,'''Please use tf.data to implement this functionality.''')
def 			lowerCAmelCase__	(					lowerCamelCase_	:	int ,lowerCamelCase_	:	str=False ,lowerCamelCase_	:	str=10):





				'''simple docstring'''

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



class  lowerCamelCase__    :




		'''simple docstring'''



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

						"""simple docstring"""






						lowerCAmelCase__		,	lowerCAmelCase__  :       Union[str, Any]				=  random_seed.get_seed(__lowerCamelCase				)
						# If op level seed is not set, use whatever graph level seed is returned
						numpy.random.seed(seeda if seed is None else seeda				)
						lowerCAmelCase__  :       int				=  dtypes.as_dtype(__lowerCamelCase				).base_dtype
						if dtype not in (dtypes.uinta, dtypes.floataa):
										raise TypeError('''Invalid image dtype %r, expected uint8 or float32''' % dtype				)
						if fake_data:
										lowerCAmelCase__  :       Any				=  1_00_00
										lowerCAmelCase__  :       Optional[int]				=  one_hot
						else:
										assert (
										    images.shape[0] == labels.shape[0]
										), f"""images.shape: {images.shape} labels.shape: {labels.shape}"""
										lowerCAmelCase__  :       int				=  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
														lowerCAmelCase__  :       Any				=  images.reshape(
														    images.shape[0]	,images.shape[1] * images.shape[2]				)
										if dtype == dtypes.floataa:
														# Convert from [0, 255] -> [0.0, 1.0].
														lowerCAmelCase__  :       Union[str, Any]				=  images.astype(numpy.floataa				)
														lowerCAmelCase__  :       Dict				=  numpy.multiply(__lowerCamelCase	,1.0 / 255.0				)
						lowerCAmelCase__  :       Optional[int]				=  images
						lowerCAmelCase__  :       int				=  labels
						lowerCAmelCase__  :       Optional[Any]				=  0
						lowerCAmelCase__  :       Tuple				=  0



		@property
		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






						return self._images



		@property
		def 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

						"""simple docstring"""






						return self._labels



		@property
		def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						return self._num_examples



		@property
		def 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

						"""simple docstring"""






						return self._epochs_completed



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

						"""simple docstring"""






						if fake_data:
										lowerCAmelCase__  :       str				=  [1] * 7_84
										lowerCAmelCase__  :       Dict				=  [1] + [0] * 9 if self.one_hot else 0
										return (
										    [fake_image for _ in range(__lowerCamelCase				)],
										    [fake_label for _ in range(__lowerCamelCase				)],
										)
						lowerCAmelCase__  :       Dict				=  self._index_in_epoch
						# Shuffle for the first epoch
						if self._epochs_completed == 0 and start == 0 and shuffle:
										lowerCAmelCase__  :       Optional[int]				=  numpy.arange(self._num_examples				)
										numpy.random.shuffle(__lowerCamelCase				)
										lowerCAmelCase__  :       List[Any]				=  self.images[perma]
										lowerCAmelCase__  :       List[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
										lowerCAmelCase__  :       Optional[Any]				=  self._num_examples - start
										lowerCAmelCase__  :       List[str]				=  self._images[start : self._num_examples]
										lowerCAmelCase__  :       Any				=  self._labels[start : self._num_examples]
										# Shuffle the data
										if shuffle:
														lowerCAmelCase__  :       Tuple				=  numpy.arange(self._num_examples				)
														numpy.random.shuffle(__lowerCamelCase				)
														lowerCAmelCase__  :       int				=  self.images[perm]
														lowerCAmelCase__  :       str				=  self.labels[perm]
										# Start next epoch
										lowerCAmelCase__  :       Dict				=  0
										lowerCAmelCase__  :       Union[str, Any]				=  batch_size - rest_num_examples
										lowerCAmelCase__  :       Any				=  self._index_in_epoch
										lowerCAmelCase__  :       Optional[Any]				=  self._images[start:end]
										lowerCAmelCase__  :       List[Any]				=  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
										lowerCAmelCase__  :       str				=  self._index_in_epoch
										return self._images[start:end], self._labels[start:end]






@deprecated(lowerCamelCase_ ,'''Please write your own downloading logic.''')
def 			lowerCAmelCase__	(					lowerCamelCase_	:	int ,lowerCamelCase_	:	Union[str, Any] ,lowerCamelCase_	:	int):





				'''simple docstring'''

				if not gfile.Exists(lowerCamelCase_):
								gfile.MakeDirs(lowerCamelCase_)
				lowerCAmelCase__  :       str				=  os.path.join(lowerCamelCase_ ,lowerCamelCase_)
				if not gfile.Exists(lowerCamelCase_):
								urllib.request.urlretrieve(lowerCamelCase_ ,lowerCamelCase_)  # noqa: S310
								with gfile.GFile(lowerCamelCase_) as f:
												lowerCAmelCase__  :       Any				=  f.size()
								print('''Successfully downloaded''' ,lowerCamelCase_ ,lowerCamelCase_ ,'''bytes.''')
				return filepath






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





				'''simple docstring'''

				if fake_data:

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

								lowerCAmelCase__  :       List[str]				=  fake()
								lowerCAmelCase__  :       List[Any]				=  fake()
								lowerCAmelCase__  :       int				=  fake()
								return _Datasets(train=lowerCamelCase_ ,validation=lowerCamelCase_ ,test=lowerCamelCase_)

				if not source_url:  # empty string check
								lowerCAmelCase__  :       List[Any]				=  DEFAULT_SOURCE_URL

				lowerCAmelCase__  :       Tuple				=  '''train-images-idx3-ubyte.gz'''
				lowerCAmelCase__  :       Optional[int]				=  '''train-labels-idx1-ubyte.gz'''
				lowerCAmelCase__  :       Optional[int]				=  '''t10k-images-idx3-ubyte.gz'''
				lowerCAmelCase__  :       List[Any]				=  '''t10k-labels-idx1-ubyte.gz'''

				lowerCAmelCase__  :       Optional[Any]				=  _maybe_download(
				    lowerCamelCase_ ,lowerCamelCase_ ,source_url + train_images_file)
				with gfile.Open(lowerCamelCase_ ,'''rb''') as f:
								lowerCAmelCase__  :       Optional[int]				=  _extract_images(lowerCamelCase_)

				lowerCAmelCase__  :       Optional[int]				=  _maybe_download(
				    lowerCamelCase_ ,lowerCamelCase_ ,source_url + train_labels_file)
				with gfile.Open(lowerCamelCase_ ,'''rb''') as f:
								lowerCAmelCase__  :       Dict				=  _extract_labels(lowerCamelCase_ ,one_hot=lowerCamelCase_)

				lowerCAmelCase__  :       List[str]				=  _maybe_download(
				    lowerCamelCase_ ,lowerCamelCase_ ,source_url + test_images_file)
				with gfile.Open(lowerCamelCase_ ,'''rb''') as f:
								lowerCAmelCase__  :       int				=  _extract_images(lowerCamelCase_)

				lowerCAmelCase__  :       Tuple				=  _maybe_download(
				    lowerCamelCase_ ,lowerCamelCase_ ,source_url + test_labels_file)
				with gfile.Open(lowerCamelCase_ ,'''rb''') as f:
								lowerCAmelCase__  :       str				=  _extract_labels(lowerCamelCase_ ,one_hot=lowerCamelCase_)

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

				lowerCAmelCase__  :       Any				=  train_images[:validation_size]
				lowerCAmelCase__  :       str				=  train_labels[:validation_size]
				lowerCAmelCase__  :       Any				=  train_images[validation_size:]
				lowerCAmelCase__  :       Any				=  train_labels[validation_size:]

				lowerCAmelCase__  :       Optional[Any]				=  {'''dtype''': dtype, '''reshape''': reshape, '''seed''': seed}

				lowerCAmelCase__  :       List[str]				=  _DataSet(lowerCamelCase_ ,lowerCamelCase_ ,**lowerCamelCase_)
				lowerCAmelCase__  :       List[Any]				=  _DataSet(lowerCamelCase_ ,lowerCamelCase_ ,**lowerCamelCase_)
				lowerCAmelCase__  :       List[Any]				=  _DataSet(lowerCamelCase_ ,lowerCamelCase_ ,**lowerCamelCase_)

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






from bisect import bisect
from itertools import accumulate






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[str] ,lowerCamelCase_	:	Optional[Any] ,lowerCamelCase_	:	List[str] ,lowerCamelCase_	:	Optional[int]):





				'''simple docstring'''

				lowerCAmelCase__  :       Any				=  sorted(zip(lowerCamelCase_ ,lowerCamelCase_) ,key=lambda lowerCamelCase_: x[0] / x[1] ,reverse=lowerCamelCase_)
				lowerCAmelCase__		,	lowerCAmelCase__  :       List[str]				=  [i[0] for i in r], [i[1] for i in r]
				lowerCAmelCase__  :       Tuple				=  list(accumulate(lowerCamelCase_))
				lowerCAmelCase__  :       str				=  bisect(lowerCamelCase_ ,lowerCamelCase_)
				return (
				    0
				    if k == 0
				    else sum(vl[:k]) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
				    if k != n
				    else sum(vl[:k])
				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()

