





from ...processing_utils import ProcessorMixin
class      UpperCamelCase_						(					a_   ):
					lowerCAmelCase_							=		'''SpeechT5FeatureExtractor'''
					lowerCAmelCase_							=		'''SpeechT5Tokenizer'''




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Optional[int]:
										super().__init__(lowercase_			,  lowercase_					)




					def __call__(						self			,  *lowerCAmelCase_			,  **lowerCAmelCase_					)						->							int:
										_snake_case													=			kwargs.pop('audio'			,  lowercase_					)
										_snake_case													=			kwargs.pop('text'			,  lowercase_					)
										_snake_case													=			kwargs.pop('text_target'			,  lowercase_					)
										_snake_case													=			kwargs.pop('audio_target'			,  lowercase_					)
										_snake_case													=			kwargs.pop('sampling_rate'			,  lowercase_					)

										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:
															_snake_case													=			self.feature_extractor(lowercase_			,  *lowercase_			,  sampling_rate=lowercase_			,  **lowercase_					)
										elif text is not None:
															_snake_case													=			self.tokenizer(lowercase_			,  **lowercase_					)
										else:
															_snake_case													=			None

										if audio_target is not None:
															_snake_case													=			self.feature_extractor(audio_target=lowercase_			,  *lowercase_			,  sampling_rate=lowercase_			,  **lowercase_					)
															_snake_case													=			targets['''input_values''']
										elif text_target is not None:
															_snake_case													=			self.tokenizer(lowercase_			,  **lowercase_					)
															_snake_case													=			targets['''input_ids''']
										else:
															_snake_case													=			None

										if inputs is None:
															return targets

										if targets is not None:
															_snake_case													=			labels

															_snake_case													=			targets.get('attention_mask'					)
															if decoder_attention_mask is not None:
																				_snake_case													=			decoder_attention_mask

										return inputs




					def 		lowerCAmelCase     (						self			,  *lowerCAmelCase_			,  **lowerCAmelCase_					)						->							List[str]:
										_snake_case													=			kwargs.pop('input_values'			,  lowercase_					)
										_snake_case													=			kwargs.pop('input_ids'			,  lowercase_					)
										_snake_case													=			kwargs.pop('labels'			,  lowercase_					)

										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:
															_snake_case													=			self.feature_extractor.pad(lowercase_			,  *lowercase_			,  **lowercase_					)
										elif input_ids is not None:
															_snake_case													=			self.tokenizer.pad(lowercase_			,  **lowercase_					)
										else:
															_snake_case													=			None

										if labels is not None:
															if "input_ids" in labels or (isinstance(lowercase_			,  lowercase_					) and "input_ids" in labels[0]):
																				_snake_case													=			self.tokenizer.pad(lowercase_			,  **lowercase_					)
																				_snake_case													=			targets['''input_ids''']
															else:
																				_snake_case													=			self.feature_extractor.feature_size
																				_snake_case													=			self.feature_extractor.num_mel_bins
																				_snake_case													=			self.feature_extractor.pad(lowercase_			,  *lowercase_			,  **lowercase_					)
																				_snake_case													=			feature_size_hack
																				_snake_case													=			targets['''input_values''']
										else:
															_snake_case													=			None

										if inputs is None:
															return targets

										if targets is not None:
															_snake_case													=			labels

															_snake_case													=			targets.get('attention_mask'					)
															if decoder_attention_mask is not None:
																				_snake_case													=			decoder_attention_mask

										return inputs




					def 		lowerCAmelCase     (						self			,  *lowerCAmelCase_			,  **lowerCAmelCase_					)						->							List[str]:
										return self.tokenizer.batch_decode(*lowercase_			,  **lowercase_					)






					def 		lowerCAmelCase     (						self			,  *lowerCAmelCase_			,  **lowerCAmelCase_					)						->							str:
										return self.tokenizer.decode(*lowercase_			,  **lowercase_					)










from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst





def   lowerCamelCase__       (       )     ->	List[str]:



					'''simple docstring'''




					_snake_case							,       _snake_case													=			9, 14  # noqa: F841
					_snake_case													=			[
					    [0, 1, 4],
					    [0, 7, 8],
					    [1, 2, 8],
					    [7, 8, 7],
					    [7, 6, 1],
					    [2, 8, 2],
					    [8, 6, 6],
					    [2, 3, 7],
					    [2, 5, 4],
					    [6, 5, 2],
					    [3, 5, 14],
					    [3, 4, 9],
					    [5, 4, 10],
					    [1, 7, 11],
					]

					_snake_case													=			defaultdict(UpperCamelCase__  )
					for nodea, nodea, cost in edges:
										adjancency[nodea].append([nodea, cost]  )
										adjancency[nodea].append([nodea, cost]  )

					_snake_case													=			mst(UpperCamelCase__  )

					_snake_case													=			[
					    [7, 6, 1],
					    [2, 8, 2],
					    [6, 5, 2],
					    [0, 1, 4],
					    [2, 5, 4],
					    [2, 3, 7],
					    [0, 7, 8],
					    [3, 4, 9],
					]

					for answer in expected:
										_snake_case													=			tuple(answer[:2]  )
										_snake_case													=			tuple(edge[::-1]  )
										assert edge in result or reverse in result



