code: 
  
  
  
  
  
  from collections import deque
  
  
  
  
  def 						A_			(  lowercase_  )			->						str:
         return input(lowercase_  ).strip().split(''' '''  )
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_  )			->						dict[int, list[int]]:
         _snake_case      :							dict[int, list[int]]     				=						{}
         for i in range(lowercase_  ):
                _snake_case      :							Union[str, Any]     				=						[]
  
         for e in range(lowercase_  ):
                _snake_case				,    _snake_case      :							int     				=						(int(lowercase_  ) for i in _input(f'''Edge {e + 1}: <node1> <node2> '''  ))
                graph[x].append(lowercase_  )
         return graph
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_  )			->						dict[int, list[int]]:
         _snake_case      :							dict[int, list[int]]     				=						{}
         for i in range(lowercase_  ):
                _snake_case      :							Union[str, Any]     				=						[]
  
         for e in range(lowercase_  ):
                _snake_case				,    _snake_case      :							Tuple     				=						(int(lowercase_  ) for i in _input(f'''Edge {e + 1}: <node1> <node2> '''  ))
                graph[x].append(lowercase_  )
                graph[y].append(lowercase_  )
         return graph
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_  )			->						dict[int, list[tuple[int, int]]]:
         _snake_case      :							dict[int, list[tuple[int, int]]]     				=						{}
         for i in range(lowercase_  ):
                _snake_case      :							Dict     				=						[]
  
         for e in range(lowercase_  ):
                _snake_case				,    _snake_case				,    _snake_case      :							str     				=						(int(lowercase_  ) for i in _input(f'''Edge {e + 1}: <node1> <node2> <weight> '''  ))
                graph[x].append((y, w)  )
                graph[y].append((x, w)  )
         return graph
  
  
  if __name__ == "__main__":
     lowerCAmelCase_ ,    lowerCAmelCase_   =   (int(i) for i in _input("Number of nodes and edges: "))
  
     lowerCAmelCase_   =   int(
         _input(
             "Press 1 or 2 or 3 \n"
             "1. Unweighted directed \n"
             "2. Unweighted undirected \n"
             "3. Weighted undirected \n"
         )[0]
     )
  
     lowerCAmelCase_   =   {
         1: initialize_unweighted_directed_graph,
         2: initialize_unweighted_undirected_graph,
         3: initialize_weighted_undirected_graph,
     }[graph_choice](n, m)
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_  )			->						Dict:
         _snake_case				,    _snake_case      :							Optional[Any]     				=						{s}, [s]
         print(lowercase_  )
         while _s:
                _snake_case      :							Optional[int]     				=						0
                for i in g[_s[-1]]:
                       if i not in vis:
                              _s.append(lowercase_  )
                              vis.add(lowercase_  )
                              _snake_case      :							Union[str, Any]     				=						1
                              print(lowercase_  )
                              break
                if not flag:
                       _s.pop()
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_  )			->						str:
         _snake_case				,    _snake_case      :							List[str]     				=						{s}, deque([s]  )
         print(lowercase_  )
         while q:
                _snake_case      :							Any     				=						q.popleft()
                for v in g[u]:
                       if v not in vis:
                              vis.add(lowercase_  )
                              q.append(lowercase_  )
                              print(lowercase_  )
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_  )			->						List[Any]:
         _snake_case				,    _snake_case				,    _snake_case      :							Optional[int]     				=						{s: 0}, set(), {s: 0}
         while True:
                if len(lowercase_  ) == len(lowercase_  ) - 1:
                       break
                _snake_case      :							Optional[Any]     				=						100000
                for i in dist:
                       if i not in known and dist[i] < mini:
                              _snake_case      :							Optional[Any]     				=						dist[i]
                              _snake_case      :							Dict     				=						i
                known.add(lowercase_  )
                for v in g[u]:
                       if v[0] not in known and dist[u] + v[1] < dist.get(v[0]      ,		100000  ):
                              _snake_case      :							List[str]     				=						dist[u] + v[1]
                              _snake_case      :							List[Any]     				=						u
         for i in dist:
                if i != s:
                       print(dist[i]  )
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_=None      ,		lowercase_=None  )			->						Dict:
         if q is None:
                _snake_case      :							Optional[Any]     				=						[1]
         if ind is None:
                _snake_case      :							int     				=						[0] * (len(lowercase_  ) + 1)  # SInce oth Index is ignored
                for u in g:
                       for v in g[u]:
                              ind[v] += 1
                _snake_case      :							Optional[Any]     				=						deque()
                for i in g:
                       if ind[i] == 0:
                              q.append(lowercase_  )
         if len(lowercase_  ) == 0:
                return
         _snake_case      :							str     				=						q.popleft()
         print(lowercase_  )
         for w in g[v]:
                ind[w] -= 1
                if ind[w] == 0:
                       q.append(lowercase_  )
         topo(lowercase_      ,		lowercase_      ,		lowercase_  )
  
  
  
  
  def 						A_			(  )			->						Any:
         _snake_case      :							List[Any]     				=						input().strip()
         _snake_case      :							Dict     				=						[]
         for _ in range(lowercase_  ):
                a.append(map(lowercase_      ,		input().strip().split()  )  )
         return a, n
  
  
  
  
  def 						A_			(  lowercase_  )			->						int:
         ((_snake_case)				,    (_snake_case))      :							Any     				=						a_and_n
         _snake_case      :							List[Any]     				=						list(lowercase_  )
         _snake_case      :							str     				=						[[0] * n for i in range(lowercase_  )]
         for k in range(lowercase_  ):
                for i in range(lowercase_  ):
                       for j in range(lowercase_  ):
                              if dist[i][j] > dist[i][k] + dist[k][j]:
                                     _snake_case      :							Tuple     				=						dist[i][k] + dist[k][j]
                                     _snake_case      :							str     				=						k
         print(lowercase_  )
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_  )			->						int:
         _snake_case				,    _snake_case				,    _snake_case      :							Union[str, Any]     				=						{s: 0}, set(), {s: 0}
         while True:
                if len(lowercase_  ) == len(lowercase_  ) - 1:
                       break
                _snake_case      :							Union[str, Any]     				=						100000
                for i in dist:
                       if i not in known and dist[i] < mini:
                              _snake_case      :							Optional[Any]     				=						dist[i]
                              _snake_case      :							List[str]     				=						i
                known.add(lowercase_  )
                for v in g[u]:
                       if v[0] not in known and v[1] < dist.get(v[0]      ,		100000  ):
                              _snake_case      :							Dict     				=						v[1]
                              _snake_case      :							Optional[Any]     				=						u
         return dist
  
  
  
  
  def 						A_			(  )			->						Tuple:
         _snake_case				,    _snake_case      :							List[Any]     				=						map(lowercase_      ,		input().split(''' '''  )  )
         _snake_case      :							List[Any]     				=						[]
         for _ in range(lowercase_  ):
                edges.append(map(lowercase_      ,		input().split(''' '''  )  )  )
         return edges, n
  
  
  
  
  def 						A_			(  lowercase_  )			->						str:
         # Sort edges on the basis of distance
         ((_snake_case)				,    (_snake_case))      :							List[str]     				=						e_and_n
         e.sort(reverse=lowercase_      ,		key=lambda lowercase_  : x[2]  )
         _snake_case      :							List[Any]     				=						[{i} for i in range(1      ,		n + 1  )]
         while True:
                if len(lowercase_  ) == 1:
                       break
                print(lowercase_  )
                _snake_case      :							Any     				=						e.pop()
                for i in range(len(lowercase_  )  ):
                       if x[0] in s[i]:
                              break
                for j in range(len(lowercase_  )  ):
                       if x[1] in s[j]:
                              if i == j:
                                     break
                              s[j].update(s[i]  )
                              s.pop(lowercase_  )
                              break
  
  
  
  
  def 						A_			(  lowercase_  )			->						int:
         _snake_case      :							List[Any]     				=						[]
         for node in graph:
                if not graph[node]:
                       isolated.append(lowercase_  )
         return isolated
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  import unittest
  
  import numpy as np
  
  from transformers.testing_utils import require_torch, require_vision
  from transformers.utils import is_torch_available, is_vision_available
  
  from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
  
  
  if is_torch_available():
     import torch
  
  if is_vision_available():
     from PIL import Image
  
     from transformers import ConvNextImageProcessor
  
  class 				A    (unittest.TestCase				):
  
  
  
        def __init__(					self     ,  lowercase_     ,  lowercase_=7     ,  lowercase_=3     ,  lowercase_=18     ,  lowercase_=30     ,  lowercase_=400     ,  lowercase_=True     ,  lowercase_=None     ,  lowercase_=0.875     ,  lowercase_=True     ,  lowercase_=[0.5, 0.5, 0.5]     ,  lowercase_=[0.5, 0.5, 0.5]     ,  )      ->   List[str]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Tuple     				=						size if size is not None else {'''shortest_edge''': 20}
               _snake_case      :							List[Any]     				=						parent
               _snake_case      :							Optional[int]     				=						batch_size
               _snake_case      :							str     				=						num_channels
               _snake_case      :							Union[str, Any]     				=						image_size
               _snake_case      :							Optional[Any]     				=						min_resolution
               _snake_case      :							Union[str, Any]     				=						max_resolution
               _snake_case      :							Tuple     				=						do_resize
               _snake_case      :							List[Any]     				=						size
               _snake_case      :							Optional[int]     				=						crop_pct
               _snake_case      :							Any     				=						do_normalize
               _snake_case      :							Optional[int]     				=						image_mean
               _snake_case      :							Union[str, Any]     				=						image_std
  
  
  
        def    __a (					self					)      ->   str:
  
  
  
               '''simple docstring'''
  
               return {
                   "image_mean": self.image_mean,
                   "image_std": self.image_std,
                   "do_normalize": self.do_normalize,
                   "do_resize": self.do_resize,
                   "size": self.size,
                   "crop_pct": self.crop_pct,
               }
  
  
  @require_torch
  @require_vision
  class 				A    (__UpperCAmelCase       ,unittest.TestCase				):
        _SCREAMING_SNAKE_CASE					=							ConvNextImageProcessor if is_vision_available() else None
  
  
  
        def    __a (					self					)      ->   Dict:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[Any]     				=						ConvNextImageProcessingTester(self					)
  
  
  
        @property
        def    __a (					self					)      ->   Dict:
  
  
  
               '''simple docstring'''
  
               return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
        def    __a (					self					)      ->   str:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[Any]     				=						self.image_processing_class(**self.image_processor_dict					)
               self.assertTrue(hasattr(lowercase_     ,  '''do_resize'''					)					)
               self.assertTrue(hasattr(lowercase_     ,  '''size'''					)					)
               self.assertTrue(hasattr(lowercase_     ,  '''crop_pct'''					)					)
               self.assertTrue(hasattr(lowercase_     ,  '''do_normalize'''					)					)
               self.assertTrue(hasattr(lowercase_     ,  '''image_mean'''					)					)
               self.assertTrue(hasattr(lowercase_     ,  '''image_std'''					)					)
  
  
  
        def    __a (					self					)      ->   List[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							int     				=						self.image_processing_class.from_dict(self.image_processor_dict					)
               self.assertEqual(image_processor.size     ,  {'''shortest_edge''': 20}					)
  
               _snake_case      :							Tuple     				=						self.image_processing_class.from_dict(self.image_processor_dict     ,  size=42					)
               self.assertEqual(image_processor.size     ,  {'''shortest_edge''': 42}					)
  
  
  
        def    __a (					self					)      ->   List[Any]:
  
  
  
               '''simple docstring'''
  
               pass
  
  
  
        def    __a (					self					)      ->   int:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Tuple     				=						self.image_processing_class(**self.image_processor_dict					)
               # create random PIL images
               _snake_case      :							List[str]     				=						prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_					)
               for image in image_inputs:
                      self.assertIsInstance(lowercase_     ,  Image.Image					)
  
               # Test not batched input
               _snake_case      :							List[str]     				=						image_processing(image_inputs[0]     ,  return_tensors='''pt'''					).pixel_values
               self.assertEqual(
                   encoded_images.shape     ,  (
                       1,
                       self.image_processor_tester.num_channels,
                       self.image_processor_tester.size['''shortest_edge'''],
                       self.image_processor_tester.size['''shortest_edge'''],
                   )     ,  )
  
               # Test batched
               _snake_case      :							int     				=						image_processing(lowercase_     ,  return_tensors='''pt'''					).pixel_values
               self.assertEqual(
                   encoded_images.shape     ,  (
                       self.image_processor_tester.batch_size,
                       self.image_processor_tester.num_channels,
                       self.image_processor_tester.size['''shortest_edge'''],
                       self.image_processor_tester.size['''shortest_edge'''],
                   )     ,  )
  
  
  
        def    __a (					self					)      ->   Dict:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Tuple     				=						self.image_processing_class(**self.image_processor_dict					)
               # create random numpy tensors
               _snake_case      :							Tuple     				=						prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_     ,  numpify=lowercase_					)
               for image in image_inputs:
                      self.assertIsInstance(lowercase_     ,  np.ndarray					)
  
               # Test not batched input
               _snake_case      :							List[str]     				=						image_processing(image_inputs[0]     ,  return_tensors='''pt'''					).pixel_values
               self.assertEqual(
                   encoded_images.shape     ,  (
                       1,
                       self.image_processor_tester.num_channels,
                       self.image_processor_tester.size['''shortest_edge'''],
                       self.image_processor_tester.size['''shortest_edge'''],
                   )     ,  )
  
               # Test batched
               _snake_case      :							Union[str, Any]     				=						image_processing(lowercase_     ,  return_tensors='''pt'''					).pixel_values
               self.assertEqual(
                   encoded_images.shape     ,  (
                       self.image_processor_tester.batch_size,
                       self.image_processor_tester.num_channels,
                       self.image_processor_tester.size['''shortest_edge'''],
                       self.image_processor_tester.size['''shortest_edge'''],
                   )     ,  )
  
  
  
        def    __a (					self					)      ->   Union[str, Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Union[str, Any]     				=						self.image_processing_class(**self.image_processor_dict					)
               # create random PyTorch tensors
               _snake_case      :							Dict     				=						prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_     ,  torchify=lowercase_					)
               for image in image_inputs:
                      self.assertIsInstance(lowercase_     ,  torch.Tensor					)
  
               # Test not batched input
               _snake_case      :							Union[str, Any]     				=						image_processing(image_inputs[0]     ,  return_tensors='''pt'''					).pixel_values
               self.assertEqual(
                   encoded_images.shape     ,  (
                       1,
                       self.image_processor_tester.num_channels,
                       self.image_processor_tester.size['''shortest_edge'''],
                       self.image_processor_tester.size['''shortest_edge'''],
                   )     ,  )
  
               # Test batched
               _snake_case      :							List[Any]     				=						image_processing(lowercase_     ,  return_tensors='''pt'''					).pixel_values
               self.assertEqual(
                   encoded_images.shape     ,  (
                       self.image_processor_tester.batch_size,
                       self.image_processor_tester.num_channels,
                       self.image_processor_tester.size['''shortest_edge'''],
                       self.image_processor_tester.size['''shortest_edge'''],
                   )     ,  )
  
  
  
  
  
  
  