





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

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

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


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


@deprecated(_UpperCAmelCase		,      'Please use tf.one_hot on tensors.'					)
def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	Tuple:
    lowerCamelCase__       : str   					=						labels_dense.shape[0]
    lowerCamelCase__       : List[Any]   					=						numpy.arange(_UpperCAmelCase					) * num_classes
    lowerCamelCase__       : List[Any]   					=						numpy.zeros((num_labels, num_classes)					)
    lowerCamelCase__       : Dict   					=						1
    return labels_one_hot


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

class   lowerCAmelCase				:
 @deprecated(
     UpperCAmelCase     ,	'Please use alternatives such as official/mnist/_DataSet.py'
     ' from tensorflow/models.'     ,	)
 def __init__( self    : Union[str, Any]     ,	UpperCAmelCase    : Optional[int]     ,	UpperCAmelCase    : List[str]     ,	UpperCAmelCase    : Any=False     ,	UpperCAmelCase    : List[str]=False     ,	UpperCAmelCase    : List[str]=dtypes.floataa     ,	UpperCAmelCase    : List[Any]=True     ,	UpperCAmelCase    : int=None     ,	)     ->      List[str]:
     lowerCamelCase__					,				lowerCamelCase__       : Any   					=						random_seed.get_seed(UpperCAmelCase      )
     # 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__       : List[str]   					=						dtypes.as_dtype(UpperCAmelCase      ).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__       : List[Any]   					=						10000
         lowerCamelCase__       : Optional[Any]   					=						one_hot
     else:
         assert (
             images.shape[0] == labels.shape[0]
         ), F"""images.shape: {images.shape} labels.shape: {labels.shape}"""
         lowerCamelCase__       : Union[str, 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
             lowerCamelCase__       : str   					=						images.reshape(
                 images.shape[0]     ,	images.shape[1] * images.shape[2]      )
         if dtype == dtypes.floataa:
             # Convert from [0, 255] -> [0.0, 1.0].
             lowerCamelCase__       : Optional[int]   					=						images.astype(numpy.floataa      )
             lowerCamelCase__       : int   					=						numpy.multiply(UpperCAmelCase     ,	1.0 / 2_5_5.0      )
     lowerCamelCase__       : Dict   					=						images
     lowerCamelCase__       : Optional[Any]   					=						labels
     lowerCamelCase__       : Union[str, Any]   					=						0
     lowerCamelCase__       : str   					=						0
 @property
 def 	A_      ( self    : List[Any]      )     ->      Dict:
     return self._images
 @property
 def 	A_      ( self    : str      )     ->      Union[str, Any]:
     return self._labels
 @property
 def 	A_      ( self    : Any      )     ->      Tuple:
     return self._num_examples
 @property
 def 	A_      ( self    : int      )     ->      Any:
     return self._epochs_completed







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


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


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

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

        lowerCamelCase__       : Dict   					=						fake()
        lowerCamelCase__       : List[Any]   					=						fake()
        lowerCamelCase__       : str   					=						fake()
        return _Datasets(train=_UpperCAmelCase		,      validation=_UpperCAmelCase		,      test=_UpperCAmelCase					)

    if not source_url:  # empty string check
        lowerCamelCase__       : Union[str, Any]   					=						DEFAULT_SOURCE_URL

    lowerCamelCase__       : int   					=						'train-images-idx3-ubyte.gz'
    lowerCamelCase__       : Union[str, Any]   					=						'train-labels-idx1-ubyte.gz'
    lowerCamelCase__       : Optional[Any]   					=						't10k-images-idx3-ubyte.gz'
    lowerCamelCase__       : Union[str, Any]   					=						't10k-labels-idx1-ubyte.gz'

    lowerCamelCase__       : List[Any]   					=						_maybe_download(
        _UpperCAmelCase		,      _UpperCAmelCase		,      source_url + train_images_file					)
    with gfile.Open(_UpperCAmelCase		,      'rb'					) as f:
        lowerCamelCase__       : str   					=						_extract_images(_UpperCAmelCase					)

    lowerCamelCase__       : Optional[Any]   					=						_maybe_download(
        _UpperCAmelCase		,      _UpperCAmelCase		,      source_url + train_labels_file					)
    with gfile.Open(_UpperCAmelCase		,      'rb'					) as f:
        lowerCamelCase__       : Any   					=						_extract_labels(_UpperCAmelCase		,      one_hot=_UpperCAmelCase					)

    lowerCamelCase__       : Tuple   					=						_maybe_download(
        _UpperCAmelCase		,      _UpperCAmelCase		,      source_url + test_images_file					)
    with gfile.Open(_UpperCAmelCase		,      'rb'					) as f:
        lowerCamelCase__       : Any   					=						_extract_images(_UpperCAmelCase					)

    lowerCamelCase__       : Optional[Any]   					=						_maybe_download(
        _UpperCAmelCase		,      _UpperCAmelCase		,      source_url + test_labels_file					)
    with gfile.Open(_UpperCAmelCase		,      'rb'					) as f:
        lowerCamelCase__       : str   					=						_extract_labels(_UpperCAmelCase		,      one_hot=_UpperCAmelCase					)

    if not 0 <= validation_size <= len(_UpperCAmelCase					):
        lowerCamelCase__       : Union[str, Any]   					=						(
            'Validation size should be between 0 and '
            F"""{len(_UpperCAmelCase					)}. Received: {validation_size}."""
        )
        raise ValueError(_UpperCAmelCase					)

    lowerCamelCase__       : Dict   					=						train_images[:validation_size]
    lowerCamelCase__       : List[Any]   					=						train_labels[:validation_size]
    lowerCamelCase__       : int   					=						train_images[validation_size:]
    lowerCamelCase__       : Tuple   					=						train_labels[validation_size:]

    lowerCamelCase__       : Any   					=						{'dtype': dtype, 'reshape': reshape, 'seed': seed}

    lowerCamelCase__       : List[str]   					=						_DataSet(_UpperCAmelCase		,      _UpperCAmelCase		,      **_UpperCAmelCase					)
    lowerCamelCase__       : Union[str, Any]   					=						_DataSet(_UpperCAmelCase		,      _UpperCAmelCase		,      **_UpperCAmelCase					)
    lowerCamelCase__       : List[str]   					=						_DataSet(_UpperCAmelCase		,      _UpperCAmelCase		,      **_UpperCAmelCase					)

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












from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)     ->	tuple[int | None, int | None, float]:
    if not arr:
        return None, None, 0
    if low == high:
        return low, high, arr[low]

    lowerCamelCase__       : Union[str, Any]   					=						(low + high) // 2
    lowerCamelCase__					,				lowerCamelCase__					,				lowerCamelCase__       : str   					=						max_subarray(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)
    lowerCamelCase__					,				lowerCamelCase__					,				lowerCamelCase__       : Optional[Any]   					=						max_subarray(_UpperCAmelCase		,      mid + 1		,      _UpperCAmelCase					)
    lowerCamelCase__					,				lowerCamelCase__					,				lowerCamelCase__       : int   					=						max_cross_sum(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)
    if left_sum >= right_sum and left_sum >= cross_sum:
        return left_low, left_high, left_sum
    elif right_sum >= left_sum and right_sum >= cross_sum:
        return right_low, right_high, right_sum
    return cross_left, cross_right, cross_sum


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)     ->	tuple[int, int, float]:
    lowerCamelCase__					,				lowerCamelCase__       : Union[str, Any]   					=						float('-inf'					), -1
    lowerCamelCase__					,				lowerCamelCase__       : Tuple   					=						float('-inf'					), -1

    lowerCamelCase__       : int | float   					=						0
    for i in range(_UpperCAmelCase		,      low - 1		,      -1					):
        summ += arr[i]
        if summ > left_sum:
            lowerCamelCase__       : Optional[int]   					=						summ
            lowerCamelCase__       : Tuple   					=						i

    lowerCamelCase__       : int   					=						0
    for i in range(mid + 1		,      high + 1					):
        summ += arr[i]
        if summ > right_sum:
            lowerCamelCase__       : int   					=						summ
            lowerCamelCase__       : List[Any]   					=						i

    return max_left, max_right, (left_sum + right_sum)


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	float:
    lowerCamelCase__       : Any   					=						[randint(1		,      _UpperCAmelCase					) for _ in range(_UpperCAmelCase					)]
    lowerCamelCase__       : Tuple   					=						time.time()
    max_subarray(_UpperCAmelCase		,      0		,      input_size - 1					)
    lowerCamelCase__       : List[Any]   					=						time.time()
    return end - start


def 			SCREAMING_SNAKE_CASE (			)     ->	None:
    lowerCamelCase__       : Optional[Any]   					=						[10, 100, 1000, 1_0000, 5_0000, 10_0000, 20_0000, 30_0000, 40_0000, 50_0000]
    lowerCamelCase__       : Optional[Any]   					=						[time_max_subarray(_UpperCAmelCase					) for input_size in input_sizes]
    print('No of Inputs\t\tTime Taken'					)
    for input_size, runtime in zip(_UpperCAmelCase		,      _UpperCAmelCase					):
        print(_UpperCAmelCase		,      '\t\t'		,      _UpperCAmelCase					)
    plt.plot(_UpperCAmelCase		,      _UpperCAmelCase					)
    plt.xlabel('Number of Inputs'					)
    plt.ylabel('Time taken in seconds'					)
    plt.show()


if __name__ == "__main__":
    from doctest import testmod

    testmod()





