


from ...processing_utils import ProcessorMixin

class        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							Any  						=						"SpeechT5FeatureExtractor"
							__UpperCAmelCase					:							Union[str, Any]  						=						"SpeechT5Tokenizer"

							def __init__(							self :						List[str]					,			lowerCamelCase :						Optional[int]					,			lowerCamelCase :						Union[str, Any]       )			->					int:
														super().__init__(lowerCamelCase					,			lowerCamelCase       )

							def __call__(							self :						Union[str, Any]					,			*lowerCamelCase :						Optional[int]					,			**lowerCamelCase :						List[Any]       )			->					str:
														__snake_case	:    Optional[Any]											=     kwargs.pop("audio"					,			lowerCamelCase       )
														__snake_case	:    Optional[Any]											=     kwargs.pop("text"					,			lowerCamelCase       )
														__snake_case	:    str											=     kwargs.pop("text_target"					,			lowerCamelCase       )
														__snake_case	:    Any											=     kwargs.pop("audio_target"					,			lowerCamelCase       )
														__snake_case	:    Optional[Any]											=     kwargs.pop("sampling_rate"					,			lowerCamelCase       )

														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	:    Optional[int]											=     self.feature_extractor(lowerCamelCase					,			*lowerCamelCase					,			sampling_rate=lowerCamelCase					,			**lowerCamelCase       )
														elif text is not None:
																					__snake_case	:    Union[str, Any]											=     self.tokenizer(lowerCamelCase					,			**lowerCamelCase       )
														else:
																					__snake_case	:    str											=     None

														if audio_target is not None:
																					__snake_case	:    Optional[int]											=     self.feature_extractor(audio_target=lowerCamelCase					,			*lowerCamelCase					,			sampling_rate=lowerCamelCase					,			**lowerCamelCase       )
																					__snake_case	:    Any											=     targets["input_values"]
														elif text_target is not None:
																					__snake_case	:    Dict											=     self.tokenizer(lowerCamelCase					,			**lowerCamelCase       )
																					__snake_case	:    Dict											=     targets["input_ids"]
														else:
																					__snake_case	:    int											=     None

														if inputs is None:
																					return targets

														if targets is not None:
																					__snake_case	:    Union[str, Any]											=     labels

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

														return inputs

							def 					__snake_case      (							self :						int					,			*lowerCamelCase :						Optional[Any]					,			**lowerCamelCase :						Optional[int]       )			->					Dict:
														__snake_case	:    Union[str, Any]											=     kwargs.pop("input_values"					,			lowerCamelCase       )
														__snake_case	:    Optional[Any]											=     kwargs.pop("input_ids"					,			lowerCamelCase       )
														__snake_case	:    List[str]											=     kwargs.pop("labels"					,			lowerCamelCase       )

														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	:    Union[str, Any]											=     self.feature_extractor.pad(lowerCamelCase					,			*lowerCamelCase					,			**lowerCamelCase       )
														elif input_ids is not None:
																					__snake_case	:    str											=     self.tokenizer.pad(lowerCamelCase					,			**lowerCamelCase       )
														else:
																					__snake_case	:    Optional[Any]											=     None

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

														if inputs is None:
																					return targets

														if targets is not None:
																					__snake_case	:    Tuple											=     labels

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

														return inputs

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






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





import unittest

import numpy as np







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase = None							,			):
							__snake_case	:    List[str]											=     np.shape(__lowerCamelCase			)
							__snake_case	:    Optional[Any]											=     np.shape(__lowerCamelCase			)
							__snake_case	:    List[str]											=     np.shape(__lowerCamelCase			)

							if shape_a[0] != shape_b[0]:
														__snake_case	:    Any											=     (
														    "Expected the same number of rows for A and B. "
														    F'Instead found A of size {shape_a} and B of size {shape_b}'
														)
														raise ValueError(__lowerCamelCase			)

							if shape_b[1] != shape_c[1]:
														__snake_case	:    int											=     (
														    "Expected the same number of columns for B and C. "
														    F'Instead found B of size {shape_b} and C of size {shape_c}'
														)
														raise ValueError(__lowerCamelCase			)

							__snake_case	:    str											=     pseudo_inv
							if a_inv is None:
														try:
																					__snake_case	:    Optional[Any]											=     np.linalg.inv(__lowerCamelCase			)
														except np.linalg.LinAlgError:
																					raise ValueError(
																					    "Input matrix A is not invertible. Cannot compute Schur complement."			)

							return mat_c - mat_b.T @ a_inv @ mat_b

class        a	(unittest.TestCase   ):




							"""simple docstring"""


							def 					__snake_case      (							self :						Tuple       )			->					None:
														__snake_case	:    str											=     np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]       )
														__snake_case	:    str											=     np.array([[0, 3], [3, 0], [2, 3]]       )
														__snake_case	:    Dict											=     np.array([[2, 1], [6, 3]]       )

														__snake_case	:    Dict											=     schur_complement(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )

														__snake_case	:    int											=     np.block([[a, b], [b.T, c]]       )

														__snake_case	:    Optional[int]											=     np.linalg.det(lowerCamelCase       )
														__snake_case	:    Any											=     np.linalg.det(lowerCamelCase       )
														__snake_case	:    Tuple											=     np.linalg.det(lowerCamelCase       )

														self.assertAlmostEqual(lowerCamelCase					,			det_a * det_s       )

							def 					__snake_case      (							self :						int       )			->					None:
														__snake_case	:    str											=     np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]       )
														__snake_case	:    Dict											=     np.array([[0, 3], [3, 0], [2, 3]]       )
														__snake_case	:    Tuple											=     np.array([[2, 1], [6, 3]]       )

														with self.assertRaises(lowerCamelCase       ):
																					schur_complement(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )






							def 					__snake_case      (							self :						List[Any]       )			->					None:
														__snake_case	:    List[Any]											=     np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]       )
														__snake_case	:    Tuple											=     np.array([[0, 3], [3, 0], [2, 3]]       )
														__snake_case	:    List[Any]											=     np.array([[2, 1, 3], [6, 3, 5]]       )

														with self.assertRaises(lowerCamelCase       ):
																					schur_complement(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			unittest.main()

