

'''simple docstring'''


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



class 					lowercase						(  a_ ):
							_lowerCamelCase		:      List[Any]=   "ClapFeatureExtractor"
							_lowerCamelCase		:      int=   ("RobertaTokenizer", "RobertaTokenizerFast")


							def __init__(			self    ,			_snake_case    ,			_snake_case)							->				Optional[Any]:
								super().__init__(_snake_case    ,			_snake_case)


							def __call__(			self    ,			_snake_case=None    ,			_snake_case=None    ,			_snake_case=None    ,			**_snake_case)							->				Union[str, Any]:
								UpperCAmelCase_		:      Tuple       	=					kwargs.pop('sampling_rate'    ,			_snake_case)

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

								if text is not None:
									UpperCAmelCase_		:      str       	=					self.tokenizer(_snake_case    ,			return_tensors=_snake_case    ,			**_snake_case)

								if audios is not None:
									UpperCAmelCase_		:      List[Any]       	=					self.feature_extractor(
									    _snake_case    ,			sampling_rate=_snake_case    ,			return_tensors=_snake_case    ,			**_snake_case)

								if text is not None and audios is not None:
									UpperCAmelCase_		:      Optional[int]       	=					audio_features.input_features
									return encoding
								elif text is not None:
									return encoding
								else:
									return BatchEncoding(data=dict(**_snake_case)    ,			tensor_type=_snake_case)


							def 						_snake_case							(			self    ,			*_snake_case    ,			**_snake_case)							->				Optional[Any]:
								return self.tokenizer.batch_decode(*_snake_case    ,			**_snake_case)


							def 						_snake_case							(			self    ,			*_snake_case    ,			**_snake_case)							->				Tuple:
								return self.tokenizer.decode(*_snake_case    ,			**_snake_case)





							@property
							def 						_snake_case							(			self)							->				Optional[int]:
								UpperCAmelCase_		:      Optional[Any]       	=					self.tokenizer.model_input_names
								UpperCAmelCase_		:      Optional[int]       	=					self.feature_extractor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names))









'''simple docstring'''


import math



class 					lowercase						:


							def __init__(			self    ,			_snake_case=0)							->				Union[str, Any]:  # a graph with Node 0,1,...,N-1
								UpperCAmelCase_		:      Tuple       	=					n
								UpperCAmelCase_		:      Optional[Any]       	=					[
								    [math.inf for j in range(0    ,			_snake_case)] for i in range(0    ,			_snake_case)
								]  # adjacency matrix for weight
								UpperCAmelCase_		:      Tuple       	=					[
								    [math.inf for j in range(0    ,			_snake_case)] for i in range(0    ,			_snake_case)
								]  # dp[i][j] stores minimum distance from i to j


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				List[Any]:
								UpperCAmelCase_		:      Union[str, Any]       	=					w


							def 						_snake_case							(			self)							->				str:
								for k in range(0    ,			self.n):
									for i in range(0    ,			self.n):
										for j in range(0    ,			self.n):
											UpperCAmelCase_		:      Optional[int]       	=					min(self.dp[i][j]    ,			self.dp[i][k] + self.dp[k][j])





							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case)							->				str:
								return self.dp[u][v]


if __name__ == "__main__":
						lowerCAmelCase__										= Graph(5)
						graph.add_edge(0, 2, 9)
						graph.add_edge(0, 4, 10)
						graph.add_edge(1, 3, 5)
						graph.add_edge(2, 3, 7)
						graph.add_edge(3, 0, 10)
						graph.add_edge(3, 1, 2)
						graph.add_edge(3, 2, 1)
						graph.add_edge(3, 4, 6)
						graph.add_edge(4, 1, 3)
						graph.add_edge(4, 2, 4)
						graph.add_edge(4, 3, 9)
						graph.floyd_warshall()
						graph.show_min(1, 4)
						graph.show_min(0, 3)






