


import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 			_a				(     UpperCamelCase__     ):
							_lowercase      :     str     =					['''image_processor''', '''tokenizer''']
							_lowercase      :     Any     =					'''CLIPImageProcessor'''
							_lowercase      :     str     =					('''XLMRobertaTokenizer''', '''XLMRobertaTokenizerFast''')

							def __init__(				self:      str       ,		UpperCamelCase_:      Any=None       ,		UpperCamelCase_:      Optional[int]=None       ,		**UpperCamelCase_:      List[str]				)     ->  Dict:





									"""simple docstring"""







									lowercase__				= None
									if "feature_extractor" in kwargs:
											warnings.warn(
											    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
											    ''' instead.'''       ,		UpperCamelCase_       ,		)
											lowercase__				= kwargs.pop('''feature_extractor'''				)

									lowercase__				= image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
											raise ValueError('''You need to specify an `image_processor`.'''				)
									if tokenizer is None:
											raise ValueError('''You need to specify a `tokenizer`.'''				)

									super().__init__(UpperCamelCase_       ,		UpperCamelCase_				)

							def __call__(				self:      int       ,		UpperCamelCase_:      Optional[Any]=None       ,		UpperCamelCase_:      Tuple=None       ,		UpperCamelCase_:      Tuple=None       ,		**UpperCamelCase_:      Union[str, Any]				)     ->  Tuple:





									"""simple docstring"""







									if text is None and images is None:
											raise ValueError('''You have to specify either text or images. Both cannot be none.'''				)

									if text is not None:
											lowercase__				= self.tokenizer(UpperCamelCase_       ,		return_tensors=UpperCamelCase_       ,		**UpperCamelCase_				)

									if images is not None:
											lowercase__				= self.image_processor(UpperCamelCase_       ,		return_tensors=UpperCamelCase_       ,		**UpperCamelCase_				)

									if text is not None and images is not None:
											lowercase__				= image_features.pixel_values
											return encoding
									elif text is not None:
											return encoding
									else:
											return BatchEncoding(data=dict(**UpperCamelCase_				)       ,		tensor_type=UpperCamelCase_				)

							def  lowerCamelCase_					(				self:      Any       ,		*UpperCamelCase_:      Optional[Any]       ,		**UpperCamelCase_:      int				)     ->  Union[str, Any]:





									"""simple docstring"""







									return self.tokenizer.batch_decode(*UpperCamelCase_       ,		**UpperCamelCase_				)

							def  lowerCamelCase_					(				self:      int       ,		*UpperCamelCase_:      str       ,		**UpperCamelCase_:      List[Any]				)     ->  Optional[Any]:





									"""simple docstring"""







									return self.tokenizer.decode(*UpperCamelCase_       ,		**UpperCamelCase_				)


							@property
							def  lowerCamelCase_					(				self:      Optional[int]				)     ->  Optional[Any]:





									"""simple docstring"""







									lowercase__				= self.tokenizer.model_input_names
									lowercase__				= self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)





from __future__ import annotations







def 						lowerCamelCase__      ( __A    :list[float]	,__A    :Union[str, Any]		):


       """simple docstring"""




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







def 						lowerCamelCase__      ( __A    :list[dict[str, int]]	,__A    :list[float]	,__A    :int		):


       """simple docstring"""




       for j in range(__A		):
              __snake_case	,    __snake_case	,    __snake_case       	=							(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 						lowerCamelCase__      ( __A    :list[dict[str, int]]	,__A    :int	,__A    :int	,__A    :int		):


       """simple docstring"""




       __snake_case       	=							[float("""inf"""		)] * vertex_count
       __snake_case       	=							0.0

       for _ in range(vertex_count - 1		):
              for j in range(__A		):
                     __snake_case	,    __snake_case	,    __snake_case       	=							(graph[j][k] for k in ["""src""", """dst""", """weight"""])

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

       __snake_case       	=							check_negative_cycle(__A	,__A	,__A		)
       if negative_cycle_exists:
              raise Exception("""Negative cycle found"""		)

       return distance


if __name__ == "__main__":
    import doctest

    doctest.testmod()

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

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

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

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

