
from __future__ import annotations

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						Union[str, Any]:


  '''simple docstring'''






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

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_ )		->						Tuple:


  '''simple docstring'''






  for j in range(lowercase_ ):
    lowercase__		,     lowercase__		,     lowercase__	: str     		=   (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						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						list[float]:


  '''simple docstring'''






  lowercase__	: Tuple     		=   [float("""inf""" )] * vertex_count
  lowercase__	: Optional[int]     		=   0.0

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

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

  lowercase__	: Union[str, Any]     		=   check_negative_cycle(lowercase_			,			lowercase_			,			lowercase_ )
  if negative_cycle_exists:
    raise Exception("""Negative cycle found""" )

  return distance


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     lowerCamelCase__     :		Dict        = int(input("""Enter number of vertices: """).strip())
     lowerCamelCase__     :		Tuple        = int(input("""Enter number of edges: """).strip())

     lowerCamelCase__     :		list[dict[str, int]]        = [{} for _ in range(E)]

     for i in range(E):
          print("""Edge """, i + 1)
          lowerCamelCase__   ,    lowerCamelCase__   ,    lowerCamelCase__     :		Optional[Any]        = (
              int(x)
              for x in input("""Enter source, destination, weight: """).strip().split(""" """)
          )
          lowerCamelCase__     :		Union[str, Any]        = {"""src""": src, """dst""": dest, """weight""": weight}

     lowerCamelCase__     :		Union[str, Any]        = int(input("""\nEnter shortest path source:""").strip())
     lowerCamelCase__     :		Any        = bellman_ford(graph, V, E, source)
     print_distance(shortest_distance, 0)










'''simple docstring'''
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							['image_processor', 'tokenizer']
		_a       					=							'ChineseCLIPImageProcessor'
		_a       					=							('BertTokenizer', 'BertTokenizerFast')




		def __init__(						self       ,  UpperCamelCase_=None       ,  UpperCamelCase_=None       ,  **UpperCamelCase_				):

							'''simple docstring'''


							UpperCamelCase__  :List[Any]				   =					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_       ,  )
												UpperCamelCase__  :List[Any]				   =					kwargs.pop('''feature_extractor'''				)

							UpperCamelCase__  :List[str]				   =					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_				)
							UpperCamelCase__  :Any				   =					self.image_processor




		def __call__(						self       ,  UpperCamelCase_=None       ,  UpperCamelCase_=None       ,  UpperCamelCase_=None       ,  **UpperCamelCase_				):

							'''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:
												UpperCamelCase__  :Tuple				   =					self.tokenizer(UpperCamelCase_       ,  return_tensors=UpperCamelCase_       ,  **UpperCamelCase_				)

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

							if text is not None and images is not None:
												UpperCamelCase__  :Optional[int]				   =					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       ,  *UpperCamelCase_       ,  **UpperCamelCase_				):

							'''simple docstring'''


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




		def        lowerCAmelCase__							(						self       ,  *UpperCamelCase_       ,  **UpperCamelCase_				):

							'''simple docstring'''


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




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :int				   =					self.tokenizer.model_input_names
							UpperCamelCase__  :Any				   =					self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							warnings.warn(
							    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''       ,  UpperCamelCase_       ,  )
							return self.image_processor_class