

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

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





def 					__snake_case     (      _UpperCAmelCase    ):
    __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 					__snake_case     (      _UpperCAmelCase    ):
    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 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):
    __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 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase=False       ,			_UpperCAmelCase=10    ):
    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    _A	:



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


           '''simple docstring'''

           __a	,       __a							=    random_seed.get_seed(__SCREAMING_SNAKE_CASE)
           # 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(__SCREAMING_SNAKE_CASE).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							=    10_000
               __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(__SCREAMING_SNAKE_CASE					,		1.0 / 255.0)
           __a							=    images
           __a							=    labels
           __a							=    0
           __a							=    0



       @property
       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           return self._images



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


           '''simple docstring'''

           return self._labels



       @property
       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           return self._num_examples



       @property
       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

           return self._epochs_completed





       def 		_lowerCamelCase      (		self							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					Any					,		__SCREAMING_SNAKE_CASE							:					Tuple=False					,		__SCREAMING_SNAKE_CASE							:					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(__SCREAMING_SNAKE_CASE)],
                   [fake_label for _ in range(__SCREAMING_SNAKE_CASE)],
               )
           __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(__SCREAMING_SNAKE_CASE)
               __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(__SCREAMING_SNAKE_CASE)
                   __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 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
    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 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase=False       ,			_UpperCAmelCase=False       ,			_UpperCAmelCase=dtypes.floataa       ,			_UpperCAmelCase=True       ,			_UpperCAmelCase=5000       ,			_UpperCAmelCase=None       ,			_UpperCAmelCase=DEFAULT_SOURCE_URL       ,			):
    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    )










from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass

@dataclass
class    _A	:
       UpperCamelCase__  :   int
       UpperCamelCase__  :   TreeNode | None						 =						None
       UpperCamelCase__  :   TreeNode | None						 =						None


__snake_case						:Optional[Any]						=					namedtuple('''CoinsDistribResult''', '''moves excess''')





def 					__snake_case     (      _UpperCAmelCase    ):

    if root is None:
        return 0

    # Validation
    def count_nodes(_UpperCAmelCase    ) -> int:
        if node is None:
            return 0

        return count_nodes(node.left    ) + count_nodes(node.right    ) + 1

    def count_coins(_UpperCAmelCase    ) -> int:
        if node is None:
            return 0

        return count_coins(node.left    ) + count_coins(node.right    ) + node.data

    if count_nodes(_UpperCAmelCase    ) != count_coins(_UpperCAmelCase    ):
        raise ValueError('''The nodes number should be same as the number of coins'''    )

    # Main calculation
    def get_distrib(_UpperCAmelCase    ) -> CoinsDistribResult:

        if node is None:
            return CoinsDistribResult(0       ,			1    )

        __a	,       __a							=    get_distrib(node.left    )
        __a	,       __a							=    get_distrib(node.right    )

        __a							=    1 - left_distrib_excess
        __a							=    1 - right_distrib_excess

        __a							=    (
            left_distrib_moves
            + right_distrib_moves
            + abs(_UpperCAmelCase    )
            + abs(_UpperCAmelCase    )
        )
        __a							=    node.data - coins_to_left - coins_to_right

        return CoinsDistribResult(_UpperCAmelCase       ,			_UpperCAmelCase    )

    return get_distrib(_UpperCAmelCase    )[0]


if __name__ == "__main__":
 import doctest

 doctest.testmod()





