





from ...processing_utils import ProcessorMixin



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      "SpeechT5FeatureExtractor"
 lowercase_		=      "SpeechT5Tokenizer"

 def __init__(self    :       Dict	,			UpperCAmelCase_    :       Any	,			UpperCAmelCase_    :       str)	->Optional[Any]:



   '''simple docstring'''



   super().__init__(UpperCAmelCase_	,			UpperCAmelCase_)

 def __call__(self    :       str	,			*UpperCAmelCase_    :       Dict	,			**UpperCAmelCase_    :       Optional[int])	->Tuple:



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        =kwargs.pop("audio"	,			UpperCAmelCase_)
   lowerCamelCase__:				Dict        =kwargs.pop("text"	,			UpperCAmelCase_)
   lowerCamelCase__:				Optional[int]        =kwargs.pop("text_target"	,			UpperCAmelCase_)
   lowerCamelCase__:				Union[str, Any]        =kwargs.pop("audio_target"	,			UpperCAmelCase_)
   lowerCamelCase__:				List[str]        =kwargs.pop("sampling_rate"	,			UpperCAmelCase_)

   if audio is not None and text is not None:
     raise ValueError(
         "Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?")
   if audio_target is not None and text_target is not None:
     raise ValueError(
         "Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?")
   if audio is None and audio_target is None and text is None and text_target is None:
     raise ValueError(
         "You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.")

   if audio is not None:
     lowerCamelCase__:				Dict        =self.feature_extractor(UpperCAmelCase_	,			*UpperCAmelCase_	,			sampling_rate=UpperCAmelCase_	,			**UpperCAmelCase_)
   elif text is not None:
     lowerCamelCase__:				Dict        =self.tokenizer(UpperCAmelCase_	,			**UpperCAmelCase_)
   else:
     lowerCamelCase__:				Any        =None

   if audio_target is not None:
     lowerCamelCase__:				List[Any]        =self.feature_extractor(audio_target=UpperCAmelCase_	,			*UpperCAmelCase_	,			sampling_rate=UpperCAmelCase_	,			**UpperCAmelCase_)
     lowerCamelCase__:				str        =targets["input_values"]
   elif text_target is not None:
     lowerCamelCase__:				str        =self.tokenizer(UpperCAmelCase_	,			**UpperCAmelCase_)
     lowerCamelCase__:				str        =targets["input_ids"]
   else:
     lowerCamelCase__:				Optional[Any]        =None

   if inputs is None:
     return targets

   if targets is not None:
     lowerCamelCase__:				Any        =labels

     lowerCamelCase__:				Optional[Any]        =targets.get("attention_mask")
     if decoder_attention_mask is not None:
       lowerCamelCase__:				int        =decoder_attention_mask

   return inputs

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str]	,			*UpperCAmelCase_    :       Union[str, Any]	,			**UpperCAmelCase_    :       Optional[int])	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        =kwargs.pop("input_values"	,			UpperCAmelCase_)
   lowerCamelCase__:				Tuple        =kwargs.pop("input_ids"	,			UpperCAmelCase_)
   lowerCamelCase__:				Dict        =kwargs.pop("labels"	,			UpperCAmelCase_)

   if input_values is not None and input_ids is not None:
     raise ValueError("Cannot process both `input_values` and `input_ids` inputs.")
   if input_values is None and input_ids is None and labels is None:
     raise ValueError(
         "You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.")

   if input_values is not None:
     lowerCamelCase__:				Tuple        =self.feature_extractor.pad(UpperCAmelCase_	,			*UpperCAmelCase_	,			**UpperCAmelCase_)
   elif input_ids is not None:
     lowerCamelCase__:				Optional[Any]        =self.tokenizer.pad(UpperCAmelCase_	,			**UpperCAmelCase_)
   else:
     lowerCamelCase__:				List[str]        =None

   if labels is not None:
     if "input_ids" in labels or (isinstance(UpperCAmelCase_	,			UpperCAmelCase_) and "input_ids" in labels[0]):
       lowerCamelCase__:				Tuple        =self.tokenizer.pad(UpperCAmelCase_	,			**UpperCAmelCase_)
       lowerCamelCase__:				Any        =targets["input_ids"]
     else:
       lowerCamelCase__:				Any        =self.feature_extractor.feature_size
       lowerCamelCase__:				Optional[int]        =self.feature_extractor.num_mel_bins
       lowerCamelCase__:				Optional[Any]        =self.feature_extractor.pad(UpperCAmelCase_	,			*UpperCAmelCase_	,			**UpperCAmelCase_)
       lowerCamelCase__:				int        =feature_size_hack
       lowerCamelCase__:				Tuple        =targets["input_values"]
   else:
     lowerCamelCase__:				Optional[int]        =None

   if inputs is None:
     return targets

   if targets is not None:
     lowerCamelCase__:				Optional[Any]        =labels

     lowerCamelCase__:				Dict        =targets.get("attention_mask")
     if decoder_attention_mask is not None:
       lowerCamelCase__:				List[Any]        =decoder_attention_mask

   return inputs

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any]	,			*UpperCAmelCase_    :       List[Any]	,			**UpperCAmelCase_    :       Dict)	->List[str]:



   '''simple docstring'''



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







 def 							SCREAMING_SNAKE_CASE_ (self    :       str	,			*UpperCAmelCase_    :       Optional[Any]	,			**UpperCAmelCase_    :       Tuple)	->List[str]:



   '''simple docstring'''



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








from __future__ import annotations





def 					lowerCAmelCase_			(	__a     ,   __a		)					->		List[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     ,   __a     ,   __a		)					->		Tuple:







  """simple docstring"""





  for j in range(__a		):
    lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				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 					lowerCAmelCase_			(	__a     ,   __a     ,   __a     ,   __a		)					->		list[float]:







  """simple docstring"""





  lowerCamelCase__:				List[str]        =[float("inf"		)] * vertex_count
  lowerCamelCase__:				List[str]        =0.0

  for _ in range(vertex_count - 1		):
    for j in range(__a		):
      lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				Union[str, Any]        =(graph[j][k] for k in ["src", "dst", "weight"])

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

  lowerCamelCase__:				Tuple        =check_negative_cycle(__a     ,   __a     ,   __a		)
  if negative_cycle_exists:
    raise Exception("Negative cycle found"		)

  return distance


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     __A     =    int(input("Enter number of vertices: ").strip())
     __A     =    int(input("Enter number of edges: ").strip())

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

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

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

