


'''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

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

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
__a:			List[Any]	       =       "https://storage.googleapis.com/cvdf-datasets/mnist/"
def       __UpperCamelCase	(       UpperCAmelCase					):
 lowercase__    :   str						= 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       __UpperCamelCase	(       UpperCAmelCase					):
 print('''Extracting'''		,					f.name					)
 with gzip.GzipFile(fileobj=UpperCAmelCase					) as bytestream:
  lowercase__    :   int						= _readaa(UpperCAmelCase					)
  if magic != 2051:
   raise ValueError(
       '''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name)					)
  lowercase__    :   Tuple						= _readaa(UpperCAmelCase					)
  lowercase__    :   Optional[Any]						= _readaa(UpperCAmelCase					)
  lowercase__    :   int						= _readaa(UpperCAmelCase					)
  lowercase__    :   List[str]						= bytestream.read(rows * cols * num_images					)
  lowercase__    :   Optional[int]						= numpy.frombuffer(UpperCAmelCase		,					dtype=numpy.uinta					)
  lowercase__    :   Optional[Any]						= data.reshape(UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					1					)
  return data
@deprecated(UpperCAmelCase		,					'''Please use tf.one_hot on tensors.'''					)
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   int						= labels_dense.shape[0]
 lowercase__    :   Any						= numpy.arange(UpperCAmelCase					) * num_classes
 lowercase__    :   List[str]						= numpy.zeros((num_labels, num_classes)					)
 lowercase__    :   Dict						= 1
 return labels_one_hot
@deprecated(UpperCAmelCase		,					'''Please use tf.data to implement this functionality.'''					)
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase=False		,					UpperCAmelCase=10					):
 print('''Extracting'''		,					f.name					)
 with gzip.GzipFile(fileobj=UpperCAmelCase					) as bytestream:
  lowercase__    :   List[Any]						= _readaa(UpperCAmelCase					)
  if magic != 2049:
   raise ValueError(
       '''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name)					)
  lowercase__    :   List[str]						= _readaa(UpperCAmelCase					)
  lowercase__    :   Optional[int]						= bytestream.read(UpperCAmelCase					)
  lowercase__    :   Dict						= numpy.frombuffer(UpperCAmelCase		,					dtype=numpy.uinta					)
  if one_hot:
   return _dense_to_one_hot(UpperCAmelCase		,					UpperCAmelCase					)
  return labels



class       UpperCAmelCase  :





       '''simple docstring'''



       @deprecated(
           __A     ,       '''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     ,       )  ->  List[Any]:
        lowercase__    :   Optional[Any]						= random_seed.get_seed(__A			)
        # If op level seed is not set, use whatever graph level seed is returned
        numpy.random.seed(seeda if seed is None else seeda			)
        lowercase__    :   Optional[Any]						= dtypes.as_dtype(__A			).base_dtype
        if dtype not in (dtypes.uinta, dtypes.floataa):
         raise TypeError('''Invalid image dtype %r, expected uint8 or float32''' % dtype			)
        if fake_data:
         lowercase__    :   Any						= 10000
         lowercase__    :   str						= one_hot
        else:
         assert (
             images.shape[0] == labels.shape[0]
         ), F"""images.shape: {images.shape} labels.shape: {labels.shape}"""
         lowercase__    :   Tuple						= 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
          lowercase__    :   Optional[Any]						= images.reshape(
              images.shape[0]     ,       images.shape[1] * images.shape[2]			)
         if dtype == dtypes.floataa:
          # Convert from [0, 255] -> [0.0, 1.0].
          lowercase__    :   Tuple						= images.astype(numpy.floataa			)
          lowercase__    :   Tuple						= numpy.multiply(__A     ,       1.0 / 255.0			)
        lowercase__    :   str						= images
        lowercase__    :   Union[str, Any]						= labels
        lowercase__    :   int						= 0
        lowercase__    :   Any						= 0

       @property
       def _lowerCAmelCase(						self			)  ->  Tuple:
        return self._images

       @property
       def _lowerCAmelCase(						self			)  ->  List[str]:
        return self._labels

       @property
       def _lowerCAmelCase(						self			)  ->  List[str]:
        return self._num_examples

       @property
       def _lowerCAmelCase(						self			)  ->  Any:
        return self._epochs_completed


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


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





@deprecated(
    UpperCAmelCase		,					'''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')'''					)
def       __UpperCamelCase	(       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					)

  lowercase__    :   Tuple						= fake()
  lowercase__    :   int						= fake()
  lowercase__    :   Union[str, Any]						= fake()
  return _Datasets(train=UpperCAmelCase		,					validation=UpperCAmelCase		,					test=UpperCAmelCase					)

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

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

 lowercase__    :   Any						= _maybe_download(
     UpperCAmelCase		,					UpperCAmelCase		,					source_url + train_images_file					)
 with gfile.Open(UpperCAmelCase		,					'''rb'''					) as f:
  lowercase__    :   Any						= _extract_images(UpperCAmelCase					)

 lowercase__    :   List[Any]						= _maybe_download(
     UpperCAmelCase		,					UpperCAmelCase		,					source_url + train_labels_file					)
 with gfile.Open(UpperCAmelCase		,					'''rb'''					) as f:
  lowercase__    :   List[str]						= _extract_labels(UpperCAmelCase		,					one_hot=UpperCAmelCase					)

 lowercase__    :   Optional[int]						= _maybe_download(
     UpperCAmelCase		,					UpperCAmelCase		,					source_url + test_images_file					)
 with gfile.Open(UpperCAmelCase		,					'''rb'''					) as f:
  lowercase__    :   Optional[int]						= _extract_images(UpperCAmelCase					)

 lowercase__    :   Dict						= _maybe_download(
     UpperCAmelCase		,					UpperCAmelCase		,					source_url + test_labels_file					)
 with gfile.Open(UpperCAmelCase		,					'''rb'''					) as f:
  lowercase__    :   Optional[int]						= _extract_labels(UpperCAmelCase		,					one_hot=UpperCAmelCase					)

 if not 0 <= validation_size <= len(UpperCAmelCase					):
  lowercase__    :   List[str]						= (
      '''Validation size should be between 0 and '''
      F"""{len(UpperCAmelCase					)}. Received: {validation_size}."""
  )
  raise ValueError(UpperCAmelCase					)

 lowercase__    :   Dict						= train_images[:validation_size]
 lowercase__    :   Optional[Any]						= train_labels[:validation_size]
 lowercase__    :   Union[str, Any]						= train_images[validation_size:]
 lowercase__    :   Optional[int]						= train_labels[validation_size:]

 lowercase__    :   Tuple						= {'''dtype''': dtype, '''reshape''': reshape, '''seed''': seed}

 lowercase__    :   int						= _DataSet(UpperCAmelCase		,					UpperCAmelCase		,					**UpperCAmelCase					)
 lowercase__    :   List[str]						= _DataSet(UpperCAmelCase		,					UpperCAmelCase		,					**UpperCAmelCase					)
 lowercase__    :   Dict						= _DataSet(UpperCAmelCase		,					UpperCAmelCase		,					**UpperCAmelCase					)

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


'''simple docstring'''

class       UpperCAmelCase  :





       '''simple docstring'''



       def __init__(						self			)  ->  List[str]:
        lowercase__    :   Dict						= {}

       def _lowerCAmelCase(						self			)  ->  None:
        print(self.vertex			)
        for i in self.vertex:
         print(__lowerCAmelCase     ,       ''' -> '''     ,       ''' -> '''.join([str(__lowerCAmelCase			) for j in self.vertex[i]]			)			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  None:
        # check if vertex is already present,
        if from_vertex in self.vertex:
         self.vertex[from_vertex].append(__lowerCAmelCase			)
        else:
         # else make a new vertex
         lowercase__    :   Union[str, Any]						= [to_vertex]

       def _lowerCAmelCase(						self			)  ->  None:
        # visited array for storing already visited nodes
        lowercase__    :   str						= [False] * len(self.vertex			)

        # call the recursive helper function
        for i in range(len(self.vertex			)			):
         if not visited[i]:
          self.dfs_recursive(__lowerCAmelCase     ,       __lowerCAmelCase			)


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  None:
        # mark start vertex as visited
        lowercase__    :   List[str]						= True

        print(__lowerCAmelCase     ,       end=''' '''			)

        # Recur for all the vertices that are adjacent to this node
        for i in self.vertex:
         if not visited[i]:
          self.dfs_recursive(__lowerCAmelCase     ,       __lowerCAmelCase			)


if __name__ == "__main__":
  __a:			Optional[Any]	       =       Graph()
  g.add_edge(0, 1)
  g.add_edge(0, 2)
  g.add_edge(1, 2)
  g.add_edge(2, 0)
  g.add_edge(2, 3)
  g.add_edge(3, 3)

  g.print_graph()
  print("""DFS:""")
  g.dfs()

  # OUTPUT:
  # 0  ->  1 -> 2
  # 1  ->  2
  # 2  ->  0 -> 3
  # 3  ->  3
  # DFS:
  #  0 1 2 3

