





def   __lowerCamelCase   (				__a  :int       ,	__a  :List[str]       )						->							Optional[int]:






		"""simple docstring"""

		print("""\nThe shortest path matrix using Floyd Warshall algorithm\n"""       )
		for i in range(__a       ):
				for j in range(__a       ):
						if dist[i][j] != float("""inf"""       ):
								print(int(dist[i][j]       )       ,	end="""\t"""       )
						else:
								print("""INF"""       ,	end="""\t"""       )
				print()







def   __lowerCamelCase   (				__a  :Union[str, Any]       ,	__a  :Union[str, Any]       )						->							Any:






		"""simple docstring"""

		A__     =  [[float("""inf"""       ) for _ in range(__a       )] for _ in range(__a       )]

		for i in range(__a       ):
				for j in range(__a       ):
						A__     =  graph[i][j]

						# check vertex k against all other vertices (i, j)
		for k in range(__a       ):
				# looping through rows of graph array
				for i in range(__a       ):
						# looping through columns of graph array
						for j in range(__a       ):
								if (
								    dist[i][k] != float("""inf"""       )
								    and dist[k][j] != float("""inf"""       )
								    and dist[i][k] + dist[k][j] < dist[i][j]
								):
										A__     =  dist[i][k] + dist[k][j]

		_print_dist(__a       ,	__a       )
		return dist, v


if __name__ == "__main__":
					A       :   List[Any]					= int(input('''Enter number of vertices: '''))
					A       :   Dict					= int(input('''Enter number of edges: '''))

					A       :   int					= [[float('''inf''') for i in range(v)] for j in range(v)]

					for i in range(v):
										A       :   Any					= 0.0

										# src and dst are indices that must be within the array size graph[e][v]
										# failure to follow this will result in an error
					for i in range(e):
										print('''\nEdge ''', i + 1)
										A       :   Optional[Any]					= int(input('''Enter source:'''))
										A       :   int					= int(input('''Enter destination:'''))
										A       :   Optional[Any]					= float(input('''Enter weight:'''))
										A       :   Optional[int]					= weight

					floyd_warshall(graph, v)

					# Example Input
					# Enter number of vertices: 3
					# Enter number of edges: 2

					# # generated graph from vertex and edge inputs
					# [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
					# [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]

					# specify source, destination and weight for edge #1
					# Edge  1
					# Enter source:1
					# Enter destination:2
					# Enter weight:2

					# specify source, destination and weight for edge #2
					# Edge  2
					# Enter source:2
					# Enter destination:1
					# Enter weight:1

					# # Expected Output from the vertice, edge and src, dst, weight inputs!!
					# 0		INF	INF
					# INF	0	2
					# INF	1	0










import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer


class     A     (SCREAMING_SNAKE_CASE       ):
			'''simple docstring'''




			__lowerCamelCase      :			Dict							     =       '''Wav2Vec2FeatureExtractor'''
			__lowerCamelCase      :			str							     =       '''AutoTokenizer'''






			def __init__(     self  :       Union[str, Any]				,      __lowerCAmelCase  :       List[Any]				,      __lowerCAmelCase  :       Optional[Any]  )      ->      Tuple:







					"""simple docstring"""






					super().__init__(__lowerCAmelCase				,      __lowerCAmelCase  )
					A__     =  self.feature_extractor
					A__     =  False






			@classmethod
			def   a_			(     cls  :       Union[str, Any]				,      __lowerCAmelCase  :       Optional[Any]				,      **__lowerCAmelCase  :       Union[str, Any]  )      ->      int:







					"""simple docstring"""






					try:
							return super().from_pretrained(__lowerCAmelCase				,      **__lowerCAmelCase  )
					except OSError:
							warnings.warn(
							    f'Loading a tokenizer inside {cls.__name__} from a config that does not'
							    """ include a `tokenizer_class` attribute is deprecated and will be """
							    """removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"""
							    """ attribute to either your `config.json` or `tokenizer_config.json` """
							    """file to suppress this warning: """				,      __lowerCAmelCase				,      )

							A__     =  WavaVecaFeatureExtractor.from_pretrained(__lowerCAmelCase				,      **__lowerCAmelCase  )
							A__     =  WavaVecaCTCTokenizer.from_pretrained(__lowerCAmelCase				,      **__lowerCAmelCase  )

							return cls(feature_extractor=__lowerCAmelCase				,      tokenizer=__lowerCAmelCase  )






			def __call__(     self  :       Any				,      *__lowerCAmelCase  :       Union[str, Any]				,      **__lowerCAmelCase  :       List[Any]  )      ->      Optional[Any]:







					"""simple docstring"""






					if self._in_target_context_manager:
							return self.current_processor(*__lowerCAmelCase				,      **__lowerCAmelCase  )

					if "raw_speech" in kwargs:
							warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""  )
							A__     =  kwargs.pop("""raw_speech"""  )
					else:
							A__     =  kwargs.pop("""audio"""				,      __lowerCAmelCase  )
					A__     =  kwargs.pop("""sampling_rate"""				,      __lowerCAmelCase  )
					A__     =  kwargs.pop("""text"""				,      __lowerCAmelCase  )
					if len(__lowerCAmelCase  ) > 0:
							A__     =  args[0]
							A__     =  args[1:]

					if audio is None and text is None:
							raise ValueError("""You need to specify either an `audio` or `text` input to process."""  )

					if audio is not None:
							A__     =  self.feature_extractor(__lowerCAmelCase				,      *__lowerCAmelCase				,      sampling_rate=__lowerCAmelCase				,      **__lowerCAmelCase  )
					if text is not None:
							A__     =  self.tokenizer(__lowerCAmelCase				,      **__lowerCAmelCase  )

					if text is None:
							return inputs
					elif audio is None:
							return encodings
					else:
							A__     =  encodings["""input_ids"""]
							return inputs






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







					"""simple docstring"""






					if self._in_target_context_manager:
							return self.current_processor.pad(*__lowerCAmelCase				,      **__lowerCAmelCase  )

					A__     =  kwargs.pop("""input_features"""				,      __lowerCAmelCase  )
					A__     =  kwargs.pop("""labels"""				,      __lowerCAmelCase  )
					if len(__lowerCAmelCase  ) > 0:
							A__     =  args[0]
							A__     =  args[1:]

					if input_features is not None:
							A__     =  self.feature_extractor.pad(__lowerCAmelCase				,      *__lowerCAmelCase				,      **__lowerCAmelCase  )
					if labels is not None:
							A__     =  self.tokenizer.pad(__lowerCAmelCase				,      **__lowerCAmelCase  )

					if labels is None:
							return input_features
					elif input_features is None:
							return labels
					else:
							A__     =  labels["""input_ids"""]
							return input_features






			def   a_			(     self  :       Optional[int]				,      *__lowerCAmelCase  :       List[str]				,      **__lowerCAmelCase  :       List[Any]  )      ->      Dict:







					"""simple docstring"""






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






			def   a_			(     self  :       Any				,      *__lowerCAmelCase  :       List[str]				,      **__lowerCAmelCase  :       Union[str, Any]  )      ->      List[str]:







					"""simple docstring"""






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







			@contextmanager
			def   a_			(     self  :       Union[str, Any]  )      ->      int:







					"""simple docstring"""






					warnings.warn(
					    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
					    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
					    """your audio inputs, or in a separate call."""  )
					A__     =  True
					A__     =  self.tokenizer
					yield
					A__     =  self.feature_extractor
					A__     =  False



