


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

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
__snake_case		:Optional[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							:  int  ,	__SCREAMING_SNAKE_CASE							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  Any=False  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]=False  ,	__SCREAMING_SNAKE_CASE							:  Any=dtypes.floataa  ,	__SCREAMING_SNAKE_CASE							:  List[str]=True  ,	__SCREAMING_SNAKE_CASE							:  Any=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							:  Optional[Any]):


           '''simple docstring'''

           return self._images



       @property
       def 	_lowerCamelCase    (	self							:  Union[str, Any]):


           '''simple docstring'''

           return self._labels



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


           '''simple docstring'''

           return self._num_examples



       @property
       def 	_lowerCamelCase    (	self							:  str):


           '''simple docstring'''

           return self._epochs_completed





       def 	_lowerCamelCase    (	self							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  Tuple=False  ,	__SCREAMING_SNAKE_CASE							:  Optional[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





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    print(f'Vertex\tShortest Distance from vertex {src}'       )
    for i, d in enumerate(_UpperCAmelCase       ):
        print(f'{i}\t\t{d}'       )





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    for j in range(_UpperCAmelCase       ):
        __a ,     __a ,     __a		 =       (graph[j][k] for k in ['''src''', '''dst''', '''weight'''])
        if distance[u] != float('''inf'''       ) and distance[u] + w < distance[v]:
            return True
    return False





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       [float('''inf'''       )] * vertex_count
    __a		 =       0.0

    for _ in range(vertex_count - 1       ):
        for j in range(_UpperCAmelCase       ):
            __a ,     __a ,     __a		 =       (graph[j][k] for k in ['''src''', '''dst''', '''weight'''])

            if distance[u] != float('''inf'''       ) and distance[u] + w < distance[v]:
                __a		 =       distance[u] + w

    __a		 =       check_negative_cycle(_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       )
    if negative_cycle_exists:
        raise Exception('''Negative cycle found'''       )

    return distance


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 __snake_case		:Dict   	=  int(input('''Enter number of vertices: ''').strip())
 __snake_case		:Any   	=  int(input('''Enter number of edges: ''').strip())

 __snake_case		:list[dict[str, int]]   	=  [{} for _ in range(E)]

 for i in range(E):
  print('''Edge ''', i + 1)
  __snake_case	,__snake_case	,__snake_case		:int   	=  (
      int(x)
      for x in input('''Enter source, destination, weight: ''').strip().split(''' ''')
  )
  __snake_case		:Any   	=  {'''src''': src, '''dst''': dest, '''weight''': weight}

 __snake_case		:List[str]   	=  int(input('''\nEnter shortest path source:''').strip())
 __snake_case		:Optional[Any]   	=  bellman_ford(graph, V, E, source)
 print_distance(shortest_distance, 0)





