

def      UpperCAmelCase__							(     _A :					List[str]      , _A :					Optional[int]				):





  '''simple docstring'''







  print('''\nVertex Distance'''				)
  for i in range(_A				):
    if dist[i] != float('''inf'''				):
      print(_A      , '''\t'''      , int(dist[i]				)      , end='''\t'''				)
    else:
      print(_A      , '''\t'''      , '''INF'''      , end='''\t'''				)
    print()




def      UpperCAmelCase__							(     _A :					Union[str, Any]      , _A :					List[str]      , _A :					Optional[Any]				):





  '''simple docstring'''







  a__    =float('''inf'''				)
  a__    =-1
  for i in range(_A				):
    if (not vset[i]) and mdist[i] < min_val:
      a__    =i
      a__    =mdist[i]
  return min_ind




def      UpperCAmelCase__							(     _A :					List[Any]      , _A :					Optional[int]      , _A :					List[str]				):





  '''simple docstring'''







  a__    =[float('''inf'''				) for _ in range(_A				)]
  a__    =[False for _ in range(_A				)]
  a__    =0.0

  for _ in range(v - 1				):
    a__    =min_dist(_A      , _A      , _A				)
    a__    =True

    for i in range(_A				):
      if (
          (not vset[i])
          and graph[u][i] != float('''inf'''				)
          and mdist[u] + graph[u][i] < mdist[i]
      ):
        a__    =mdist[u] + graph[u][i]

  print_dist(_A      , _A				)


if __name__ == "__main__":
       lowerCamelCase 	=   int(input('''Enter number of vertices: ''').strip())
       lowerCamelCase 	=   int(input('''Enter number of edges: ''').strip())

       lowerCamelCase 	=   [[float('''inf''') for i in range(V)] for j in range(V)]

       for i in range(V):
              lowerCamelCase 	=   0.0

       for i in range(E):
              print('''\nEdge ''', i + 1)
              lowerCamelCase 	=   int(input('''Enter source:''').strip())
              lowerCamelCase 	=   int(input('''Enter destination:''').strip())
              lowerCamelCase 	=   float(input('''Enter weight:''').strip())
              lowerCamelCase 	=   weight

       lowerCamelCase 	=   int(input('''\nEnter shortest path source:''').strip())
       dijkstra(graph, V, gsrc)



import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor







class 		__magic_name__     (				unittest.TestCase				):

   '''simple docstring'''




   @property
   def 					_UpperCAmelCase					(							self			)				->       Tuple:






     """simple docstring"""






     a__    =1
     a__    =3
     a__    =8
     a__    =8

     a__    =torch.rand((batch_size, num_channels, height, width)			)

     return sample



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       int:






     """simple docstring"""






     if isinstance(image[0],       PIL.Image.Image			):
       return np.stack([np.array(lowercase_			) for i in image],       axis=0			)
     elif isinstance(lowercase_,       torch.Tensor			):
       return image.cpu().numpy().transpose(0,       2,       3,       1			)
     return image



   def 					_UpperCAmelCase					(							self			)				->       int:






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)

     a__    =self.dummy_sample
     a__    =self.to_np(lowercase_			)

     for output_type in ["pt", "np", "pil"]:
       a__    =image_processor.postprocess(
           image_processor.preprocess(lowercase_			),       output_type=lowercase_,       )
       a__    =self.to_np(lowercase_			)
       a__    =(input_np * 255).round() if output_type == '''pil''' else input_np
       assert (
           np.abs(in_np - out_np			).max() < 1E-6
       ), F"""decoded output does not match input for output_type {output_type}"""



   def 					_UpperCAmelCase					(							self			)				->       List[Any]:






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)
     a__    =self.dummy_sample.cpu().numpy().transpose(0,       2,       3,       1			)

     for output_type in ["pt", "np", "pil"]:
       a__    =image_processor.postprocess(image_processor.preprocess(lowercase_			),       output_type=lowercase_			)

       a__    =self.to_np(lowercase_			)
       a__    =(input_np * 255).round() if output_type == '''pil''' else input_np
       assert (
           np.abs(in_np - out_np			).max() < 1E-6
       ), F"""decoded output does not match input for output_type {output_type}"""



   def 					_UpperCAmelCase					(							self			)				->       Optional[Any]:






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)

     a__    =self.dummy_sample.cpu().numpy().transpose(0,       2,       3,       1			)
     a__    =image_processor.numpy_to_pil(lowercase_			)

     for output_type in ["pt", "np", "pil"]:
       a__    =image_processor.postprocess(image_processor.preprocess(lowercase_			),       output_type=lowercase_			)
       for i, o in zip(lowercase_,       lowercase_			):
         a__    =np.array(lowercase_			)
         a__    =self.to_np(lowercase_			) if output_type == '''pil''' else (self.to_np(lowercase_			) * 255).round()
         assert (
             np.abs(in_np - out_np			).max() < 1E-6
         ), F"""decoded output does not match input for output_type {output_type}"""



   def 					_UpperCAmelCase					(							self			)				->       Union[str, Any]:






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)

     a__    =self.dummy_sample
     a__    =input_pt_ad.squeeze(0			)

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )
     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     a__    =self.to_np(self.dummy_sample			)
     a__    =input_np_ad.squeeze(0			)

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )
     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     assert np.abs(out_pt_ad - out_pt_ad			).max() < 1E-6
     assert np.abs(out_np_ad - out_np_ad			).max() < 1E-6



   def 					_UpperCAmelCase					(							self			)				->       List[Any]:






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)

     a__    =self.dummy_sample
     a__    =list(lowercase_			)

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     a__    =self.to_np(self.dummy_sample			)
     list(lowercase_			)

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     assert np.abs(out_pt_ad - out_pt_list			).max() < 1E-6
     assert np.abs(out_np_ad - out_np_list			).max() < 1E-6
