"""simple docstring"""







def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				List[str]  ,       _SCREAMING_SNAKE_CASE				:				Dict						)    ->     Optional[int]:







     '''simple docstring'''


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




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Union[str, Any]  ,       _SCREAMING_SNAKE_CASE				:				str  ,       _SCREAMING_SNAKE_CASE				:				str						)    ->     Dict:







     '''simple docstring'''


     __A					:					List[str]								=		float('inf'						)
     __A					:					Optional[int]								=		-1
     for i in range(_SCREAMING_SNAKE_CASE						):
          if (not vset[i]) and mdist[i] < min_val:
               __A					:					Tuple								=		i
               __A					:					str								=		mdist[i]
     return min_ind




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Tuple  ,       _SCREAMING_SNAKE_CASE				:				Optional[Any]  ,       _SCREAMING_SNAKE_CASE				:				Any						)    ->     int:







     '''simple docstring'''


     __A					:					Dict								=		[float('inf'						) for _ in range(_SCREAMING_SNAKE_CASE						)]
     __A					:					Optional[Any]								=		[False for _ in range(_SCREAMING_SNAKE_CASE						)]
     __A					:					List[str]								=		0.0

     for _ in range(v - 1						):
          __A					:					Any								=		min_dist(_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE						)
          __A					:					Union[str, Any]								=		True

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

     print_dist(_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE						)


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

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

       for i in range(V):
              lowerCamelCase      : int       =0.0

       for i in range(E):
              print('''\nEdge ''', i + 1)
              lowerCamelCase      : List[Any]       =int(input('''Enter source:''').strip())
              lowerCamelCase      : List[Any]       =int(input('''Enter destination:''').strip())
              lowerCamelCase      : Union[str, Any]       =float(input('''Enter weight:''').strip())
              lowerCamelCase      : Optional[int]       =weight

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




"""simple docstring"""







import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class 							__snake_case(					A_				,	A_				,	unittest.TestCase	):


    '''simple docstring'''





    _UpperCAmelCase       =			IFImgaImgPipeline
    _UpperCAmelCase       =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
    _UpperCAmelCase       =			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    _UpperCAmelCase       =			PipelineTesterMixin.required_optional_params - {"latents"}
    def 						_a							(  self	):




         '''simple docstring'''



         return self._get_dummy_components()
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase=0	):




         '''simple docstring'''



         if str(__lowerCamelCase	).startswith('mps'	):
              __A					:					Tuple								=		torch.manual_seed(__lowerCamelCase	)
         else:
              __A					:					int								=		torch.Generator(device=__lowerCamelCase	).manual_seed(__lowerCamelCase	)

         __A					:					str								=		floats_tensor((1, 3, 32, 32)					,       rng=random.Random(__lowerCamelCase	)	).to(__lowerCamelCase	)

         __A					:					str								=		{
             'prompt': 'A painting of a squirrel eating a burger',
             'image': image,
             'generator': generator,
             'num_inference_steps': 2,
             'output_type': 'numpy',
         }

         return inputs
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_save_load_optional_components()
    @unittest.skipIf(
        torch_device != 'cuda' or not is_xformers_available()					,       reason='XFormers attention is only available with CUDA and `xformers` installed'					,       )
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3	)
    @unittest.skipIf(torch_device != 'cuda'					,       reason='float16 requires CUDA'	)
    def 						_a							(  self	):




         '''simple docstring'''



         super().test_save_load_floataa(expected_max_diff=1e-1	)
    @unittest.skipIf(torch_device != 'cuda'					,       reason='float16 requires CUDA'	)
    def 						_a							(  self	):




         '''simple docstring'''



         super().test_floataa_inference(expected_max_diff=1e-1	)
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_attention_slicing_forward_pass(expected_max_diff=1e-2	)
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_save_load_local()

    def 						_a							(  self	):




         '''simple docstring'''



         self._test_inference_batch_single_identical(
             expected_max_diff=1e-2					,       )



