





from ...processing_utils import ProcessorMixin







class 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   Optional[int]   				=      '''SpeechT5FeatureExtractor'''
					UpperCAmelCase__ :   Tuple   				=      '''SpeechT5Tokenizer'''
					def __init__(				self       :Dict      ,__snake_case       :List[str]      ,__snake_case       :Dict				) ->   int:
												super().__init__(__a      ,__a				)
					def __call__(				self       :Union[str, Any]      ,*__snake_case       :List[str]      ,**__snake_case       :List[Any]				) ->   Tuple:
												a__					=					kwargs.pop('audio'      ,__a				)
												a__					=					kwargs.pop('text'      ,__a				)
												a__					=					kwargs.pop('text_target'      ,__a				)
												a__					=					kwargs.pop('audio_target'      ,__a				)
												a__					=					kwargs.pop('sampling_rate'      ,__a				)

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

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

												if inputs is None:
																			return targets

												if targets is not None:
																			a__					=					labels

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

												return inputs
					def 			lowerCamelCase__(				self       :Tuple      ,*__snake_case       :Union[str, Any]      ,**__snake_case       :Dict				) ->   Optional[int]:
												a__					=					kwargs.pop('input_values'      ,__a				)
												a__					=					kwargs.pop('input_ids'      ,__a				)
												a__					=					kwargs.pop('labels'      ,__a				)

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

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

												if inputs is None:
																			return targets

												if targets is not None:
																			a__					=					labels

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

												return inputs
					def 			lowerCamelCase__(				self       :Optional[int]      ,*__snake_case       :Optional[Any]      ,**__snake_case       :Dict				) ->   Optional[Any]:
												return self.tokenizer.batch_decode(*__a      ,**__a				)



					def 			lowerCamelCase__(				self       :Optional[int]      ,*__snake_case       :str      ,**__snake_case       :Union[str, Any]				) ->   Dict:
												return self.tokenizer.decode(*__a      ,**__a				)








from __future__ import annotations






def 							__lowercase       (			__lowerCAmelCase							:    List[Any] ,   __lowerCAmelCase							:    Optional[Any] ,   __lowerCAmelCase							:    str ,   __lowerCAmelCase							:    List[str]       ):  # noqa: E741
							while r - l > 1:
														a__					=					(l + r) // 2
														if v[m] >= key:
																					a__					=					m
														else:
																					a__					=					m  # noqa: E741
							return r






def 							__lowercase       (			__lowerCAmelCase							:    list[int]       ):
							if len(__lowerCAmelCase       ) == 0:
														return 0

							a__					=					[0] * len(__lowerCAmelCase       )
							a__					=					1

							a__					=					v[0]

							for i in range(1 ,   len(__lowerCAmelCase       )       ):
														if v[i] < tail[0]:
																					a__					=					v[i]
														elif v[i] > tail[length - 1]:
																					a__					=					v[i]
																					length += 1
														else:
																					a__					=					v[i]

							return length


if __name__ == "__main__":
						import doctest

						doctest.testmod()




