



'''simple docstring'''


from ...processing_utils import ProcessorMixin







class 	UpperCAmelCase	(		__a):


	'''simple docstring'''




	__magic_name__					: Any  =      """SpeechT5FeatureExtractor"""
	__magic_name__					: str  =      """SpeechT5Tokenizer"""




	def __init__(   self							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      Dict:



						"""simple docstring"""







						super().__init__(lowerCAmelCase_							,  lowerCAmelCase_)




	def __call__(   self							,  *lowerCAmelCase_							,  **lowerCAmelCase_) ->      Optional[int]:



						"""simple docstring"""







						a_       		=kwargs.pop("audio"							,  lowerCAmelCase_)
						a_       		=kwargs.pop("text"							,  lowerCAmelCase_)
						a_       		=kwargs.pop("text_target"							,  lowerCAmelCase_)
						a_       		=kwargs.pop("audio_target"							,  lowerCAmelCase_)
						a_       		=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:
											a_       		=self.feature_extractor(lowerCAmelCase_							,  *lowerCAmelCase_							,  sampling_rate=lowerCAmelCase_							,  **lowerCAmelCase_)
						elif text is not None:
											a_       		=self.tokenizer(lowerCAmelCase_							,  **lowerCAmelCase_)
						else:
											a_       		=None

						if audio_target is not None:
											a_       		=self.feature_extractor(audio_target=lowerCAmelCase_							,  *lowerCAmelCase_							,  sampling_rate=lowerCAmelCase_							,  **lowerCAmelCase_)
											a_       		=targets["input_values"]
						elif text_target is not None:
											a_       		=self.tokenizer(lowerCAmelCase_							,  **lowerCAmelCase_)
											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       lowercase_  (   self							,  *lowerCAmelCase_							,  **lowerCAmelCase_) ->      Any:



						"""simple docstring"""







						a_       		=kwargs.pop("input_values"							,  lowerCAmelCase_)
						a_       		=kwargs.pop("input_ids"							,  lowerCAmelCase_)
						a_       		=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:
											a_       		=self.feature_extractor.pad(lowerCAmelCase_							,  *lowerCAmelCase_							,  **lowerCAmelCase_)
						elif input_ids is not None:
											a_       		=self.tokenizer.pad(lowerCAmelCase_							,  **lowerCAmelCase_)
						else:
											a_       		=None

						if labels is not None:
											if "input_ids" in labels or (isinstance(lowerCAmelCase_							,  lowerCAmelCase_) and "input_ids" in labels[0]):
																a_       		=self.tokenizer.pad(lowerCAmelCase_							,  **lowerCAmelCase_)
																a_       		=targets["input_ids"]
											else:
																a_       		=self.feature_extractor.feature_size
																a_       		=self.feature_extractor.num_mel_bins
																a_       		=self.feature_extractor.pad(lowerCAmelCase_							,  *lowerCAmelCase_							,  **lowerCAmelCase_)
																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       lowercase_  (   self							,  *lowerCAmelCase_							,  **lowerCAmelCase_) ->      List[Any]:



						"""simple docstring"""







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




	def       lowercase_  (   self							,  *lowerCAmelCase_							,  **lowerCAmelCase_) ->      Optional[int]:



						"""simple docstring"""







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







'''simple docstring'''


from typing import Any

import numpy as np






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







     return np.array_equal(lowercase__       ,	matrix.conjugate().T       )






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     a_       		=v.conjugate().T
     a_       		=v_star.dot(lowercase__       )
     assert isinstance(lowercase__       ,	np.ndarray       )
     return (v_star_dot.dot(lowercase__       )) / (v_star.dot(lowercase__       ))






def  UpperCAmelCase_       (		):


     '''simple docstring'''







     a_       		=np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]       )
     a_       		=np.array([[1], [2], [3]]       )
     assert is_hermitian(lowercase__       ), F"""{a} is not hermitian."""
     print(rayleigh_quotient(lowercase__       ,	lowercase__       )       )

     a_       		=np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]       )
     assert is_hermitian(lowercase__       ), F"""{a} is not hermitian."""
     assert rayleigh_quotient(lowercase__       ,	lowercase__       ) == float(3       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  tests()


