
"""simple docstring"""

from __future__ import annotations

class __lowerCAmelCase	:





     def __init__(			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= data
      __UpperCamelCase       						= None
      __UpperCamelCase       						= None






def 							A		(       snake_case   :Node | None	)   ->			None:  # In Order traversal of the tree
 if tree:
  display(tree.left	)
  print(tree.data	)
  display(tree.right	)






def 							A		(       snake_case   :Node | None	)   ->			int:
 return 1 + max(depth_of_tree(tree.left	)       ,      depth_of_tree(tree.right	)	) if tree else 0






def 							A		(       snake_case   :Node	)   ->			bool:
 if not tree:
  return True
 if tree.left and tree.right:
  return is_full_binary_tree(tree.left	) and is_full_binary_tree(tree.right	)
 else:
  return not tree.left and not tree.right






def 							A		(       )   ->			None:  # Main function for testing.
 __UpperCamelCase       						= Node(1	)
 __UpperCamelCase       						= Node(2	)
 __UpperCamelCase       						= Node(3	)
 __UpperCamelCase       						= Node(4	)
 __UpperCamelCase       						= Node(5	)
 __UpperCamelCase       						= Node(6	)
 __UpperCamelCase       						= Node(7	)
 __UpperCamelCase       						= Node(8	)
 __UpperCamelCase       						= Node(9	)

 print(is_full_binary_tree(snake_case	)	)
 print(depth_of_tree(snake_case	)	)
 print('Tree is: '	)
 display(snake_case	)


if __name__ == "__main__":
      main()





"""simple docstring"""

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

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
UpperCamelCase			:       Optional[int]									=				"https://storage.googleapis.com/cvdf-datasets/mnist/"






def 							A		(       snake_case   :Union[str, Any]	)   ->			int:
 __UpperCamelCase       						= numpy.dtype(numpy.uintaa	).newbyteorder('>'	)
 return numpy.frombuffer(bytestream.read(4	)       ,      dtype=snake_case	)[0]






@deprecated(snake_case       ,      'Please use tf.data to implement this functionality.'	)
def 							A		(       snake_case   :Union[str, Any]	)   ->			Dict:
 print('Extracting'       ,      f.name	)
 with gzip.GzipFile(fileobj=snake_case	) as bytestream:
  __UpperCamelCase       						= _readaa(snake_case	)
  if magic != 2_0_5_1:
   raise ValueError(
       'Invalid magic number %d in MNIST image file: %s' % (magic, f.name)	)
  __UpperCamelCase       						= _readaa(snake_case	)
  __UpperCamelCase       						= _readaa(snake_case	)
  __UpperCamelCase       						= _readaa(snake_case	)
  __UpperCamelCase       						= bytestream.read(rows * cols * num_images	)
  __UpperCamelCase       						= numpy.frombuffer(snake_case       ,      dtype=numpy.uinta	)
  __UpperCamelCase       						= data.reshape(snake_case       ,      snake_case       ,      snake_case       ,      1	)
  return data






@deprecated(snake_case       ,      'Please use tf.one_hot on tensors.'	)
def 							A		(       snake_case   :int       ,      snake_case   :Optional[Any]	)   ->			List[Any]:
 __UpperCamelCase       						= labels_dense.shape[0]
 __UpperCamelCase       						= numpy.arange(snake_case	) * num_classes
 __UpperCamelCase       						= numpy.zeros((num_labels, num_classes)	)
 __UpperCamelCase       						= 1
 return labels_one_hot






@deprecated(snake_case       ,      'Please use tf.data to implement this functionality.'	)
def 							A		(       snake_case   :int       ,      snake_case   :Optional[int]=False       ,      snake_case   :str=1_0	)   ->			Any:
 print('Extracting'       ,      f.name	)
 with gzip.GzipFile(fileobj=snake_case	) as bytestream:
  __UpperCamelCase       						= _readaa(snake_case	)
  if magic != 2_0_4_9:
   raise ValueError(
       'Invalid magic number %d in MNIST label file: %s' % (magic, f.name)	)
  __UpperCamelCase       						= _readaa(snake_case	)
  __UpperCamelCase       						= bytestream.read(snake_case	)
  __UpperCamelCase       						= numpy.frombuffer(snake_case       ,      dtype=numpy.uinta	)
  if one_hot:
   return _dense_to_one_hot(snake_case       ,      snake_case	)
  return labels

class __lowerCAmelCase	:





     @deprecated(
         __UpperCAmelCase			,  'Please use alternatives such as official/mnist/_DataSet.py'
         ' from tensorflow/models.'			,  )
     def __init__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase=False			,  __UpperCAmelCase=False			,  __UpperCAmelCase=dtypes.floataa			,  __UpperCAmelCase=True			,  __UpperCAmelCase=None			,  ):

      '''simple docstring'''




      __UpperCamelCase						,			__UpperCamelCase       						= 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   )
      __UpperCamelCase       						= 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:
       __UpperCamelCase       						= 1_0000
       __UpperCamelCase       						= one_hot
      else:
       assert (
           images.shape[0] == labels.shape[0]
       ), F'images.shape: {images.shape} labels.shape: {labels.shape}'
       __UpperCamelCase       						= 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
        __UpperCamelCase       						= images.reshape(
            images.shape[0]			,  images.shape[1] * images.shape[2]   )
       if dtype == dtypes.floataa:
        # Convert from [0, 255] -> [0.0, 1.0].
        __UpperCamelCase       						= images.astype(numpy.floataa   )
        __UpperCamelCase       						= numpy.multiply(__UpperCAmelCase			,  1.0 / 2_5_5.0   )
      __UpperCamelCase       						= images
      __UpperCamelCase       						= labels
      __UpperCamelCase       						= 0
      __UpperCamelCase       						= 0





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self._images





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self._labels





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self._num_examples





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self._epochs_completed







     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase=False			,  __UpperCAmelCase=True   ):

      '''simple docstring'''




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






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






@deprecated(
    snake_case       ,      'Please use alternatives such as:' ' tensorflow_datasets.load(\'mnist\')'	)
def 							A		(       snake_case   :str       ,      snake_case   :int=False       ,      snake_case   :Optional[int]=False       ,      snake_case   :int=dtypes.floataa       ,      snake_case   :List[Any]=True       ,      snake_case   :Any=5_0_0_0       ,      snake_case   :Any=None       ,      snake_case   :Tuple=DEFAULT_SOURCE_URL       ,      )   ->			Union[str, Any]:
 if fake_data:

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

  __UpperCamelCase       						= fake()
  __UpperCamelCase       						= fake()
  __UpperCamelCase       						= fake()
  return _Datasets(train=snake_case       ,      validation=snake_case       ,      test=snake_case	)

 if not source_url:  # empty string check
  __UpperCamelCase       						= DEFAULT_SOURCE_URL

 __UpperCamelCase       						= 'train-images-idx3-ubyte.gz'
 __UpperCamelCase       						= 'train-labels-idx1-ubyte.gz'
 __UpperCamelCase       						= 't10k-images-idx3-ubyte.gz'
 __UpperCamelCase       						= 't10k-labels-idx1-ubyte.gz'

 __UpperCamelCase       						= _maybe_download(
     snake_case       ,      snake_case       ,      source_url + train_images_file	)
 with gfile.Open(snake_case       ,      'rb'	) as f:
  __UpperCamelCase       						= _extract_images(snake_case	)

 __UpperCamelCase       						= _maybe_download(
     snake_case       ,      snake_case       ,      source_url + train_labels_file	)
 with gfile.Open(snake_case       ,      'rb'	) as f:
  __UpperCamelCase       						= _extract_labels(snake_case       ,      one_hot=snake_case	)

 __UpperCamelCase       						= _maybe_download(
     snake_case       ,      snake_case       ,      source_url + test_images_file	)
 with gfile.Open(snake_case       ,      'rb'	) as f:
  __UpperCamelCase       						= _extract_images(snake_case	)

 __UpperCamelCase       						= _maybe_download(
     snake_case       ,      snake_case       ,      source_url + test_labels_file	)
 with gfile.Open(snake_case       ,      'rb'	) as f:
  __UpperCamelCase       						= _extract_labels(snake_case       ,      one_hot=snake_case	)

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

 __UpperCamelCase       						= train_images[:validation_size]
 __UpperCamelCase       						= train_labels[:validation_size]
 __UpperCamelCase       						= train_images[validation_size:]
 __UpperCamelCase       						= train_labels[validation_size:]

 __UpperCamelCase       						= {'dtype': dtype, 'reshape': reshape, 'seed': seed}

 __UpperCamelCase       						= _DataSet(snake_case       ,      snake_case       ,      **snake_case	)
 __UpperCamelCase       						= _DataSet(snake_case       ,      snake_case       ,      **snake_case	)
 __UpperCamelCase       						= _DataSet(snake_case       ,      snake_case       ,      **snake_case	)

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



