



"""simple docstring"""





from __future__ import annotations




def 	UpperCAmelCase     (		A	:						list[float]     ,				A	:						str       )     ->   Optional[int]:

		'''simple docstring'''


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




def 	UpperCAmelCase     (		A	:						list[dict[str, int]]     ,				A	:						list[float]     ,				A	:						int       )     ->   List[Any]:

		'''simple docstring'''


		for j in range(A       ):
				_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase							 =		(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 	UpperCAmelCase     (		A	:						list[dict[str, int]]     ,				A	:						int     ,				A	:						int     ,				A	:						int       )     ->   Union[str, Any]:

		'''simple docstring'''


		_UpperCAmelCase							 =		[float('inf'       )] * vertex_count
		_UpperCAmelCase							 =		0.0

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

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

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

		return distance


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					lowercase  							=					int(input('''Enter number of vertices: ''').strip())
					lowercase  							=					int(input('''Enter number of edges: ''').strip())

					lowercase  							=					[{} for _ in range(E)]

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

					lowercase  							=					int(input('''\nEnter shortest path source:''').strip())
					lowercase  							=					bellman_ford(graph, V, E, source)
					print_distance(shortest_distance, 0)







"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase  							=					{
    '''configuration_clipseg''': [
        '''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPSegConfig''',
        '''CLIPSegTextConfig''',
        '''CLIPSegVisionConfig''',
    ],
    '''processing_clipseg''': ['''CLIPSegProcessor'''],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					[
					    '''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''CLIPSegModel''',
					    '''CLIPSegPreTrainedModel''',
					    '''CLIPSegTextModel''',
					    '''CLIPSegVisionModel''',
					    '''CLIPSegForImageSegmentation''',
					]

if TYPE_CHECKING:
					from .configuration_clipseg import (
					    CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    CLIPSegConfig,
					    CLIPSegTextConfig,
					    CLIPSegVisionConfig,
					)
					from .processing_clipseg import CLIPSegProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_clipseg import (
										    CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CLIPSegForImageSegmentation,
										    CLIPSegModel,
										    CLIPSegPreTrainedModel,
										    CLIPSegTextModel,
										    CLIPSegVisionModel,
										)

else:
					import sys

					lowercase  							=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


