



from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy






class      UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   Union[str, Any]				=	"""EncodecFeatureExtractor"""
	lowerCAmelCase	:   Tuple				=	("""T5Tokenizer""", """T5TokenizerFast""")



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							super().__init__(UpperCAmelCase__			,	UpperCAmelCase__		)
							A__							=       self.feature_extractor
							A__							=       False



	def 			__A							(       self			,	UpperCAmelCase__=None			,	UpperCAmelCase__=None			,	UpperCAmelCase__=True		):
							return self.tokenizer.get_decoder_prompt_ids(task=UpperCAmelCase__			,	language=UpperCAmelCase__			,	no_timestamps=UpperCAmelCase__		)



	def __call__(       self			,	*UpperCAmelCase__			,	**UpperCAmelCase__		):
							# For backward compatibility
							if self._in_target_context_manager:
													return self.current_processor(*UpperCAmelCase__			,	**UpperCAmelCase__		)

							A__							=       kwargs.pop("audio"			,	UpperCAmelCase__		)
							A__							=       kwargs.pop("sampling_rate"			,	UpperCAmelCase__		)
							A__							=       kwargs.pop("text"			,	UpperCAmelCase__		)
							if len(UpperCAmelCase__		) > 0:
													A__							=       args[0]
													A__							=       args[1:]

							if audio is None and text is None:
													raise ValueError("You need to specify either an `audio` or `text` input to process."		)

							if text is not None:
													A__							=       self.tokenizer(UpperCAmelCase__			,	**UpperCAmelCase__		)

							if audio is not None:
													A__							=       self.feature_extractor(UpperCAmelCase__			,	*UpperCAmelCase__			,	sampling_rate=UpperCAmelCase__			,	**UpperCAmelCase__		)

							if audio is None:
													return inputs

							elif text is None:
													return audio_inputs

							else:
													A__							=       audio_inputs["input_values"]
													if "padding_mask" in audio_inputs:
																			A__							=       audio_inputs["padding_mask"]
													return inputs



	def 			__A							(       self			,	*UpperCAmelCase__			,	**UpperCAmelCase__		):
							A__							=       kwargs.pop("audio"			,	UpperCAmelCase__		)
							A__							=       kwargs.pop("padding_mask"			,	UpperCAmelCase__		)

							if len(UpperCAmelCase__		) > 0:
													A__							=       args[0]
													A__							=       args[1:]

							if audio_values is not None:
													return self._decode_audio(UpperCAmelCase__			,	padding_mask=UpperCAmelCase__		)
							else:
													return self.tokenizer.batch_decode(*UpperCAmelCase__			,	**UpperCAmelCase__		)



	def 			__A							(       self			,	*UpperCAmelCase__			,	**UpperCAmelCase__		):
							return self.tokenizer.decode(*UpperCAmelCase__			,	**UpperCAmelCase__		)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = None		):
							A__							=       to_numpy(UpperCAmelCase__		)
							A__						,       A__						,       A__							=       audio_values.shape

							if padding_mask is None:
													return list(UpperCAmelCase__		)

							A__							=       to_numpy(UpperCAmelCase__		)

							# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
							# token (so that the generated audio values are **not** treated as padded tokens)
							A__							=       seq_len - padding_mask.shape[-1]
							A__							=       1 - self.feature_extractor.padding_value
							A__							=       np.pad(UpperCAmelCase__			,	((0, 0), (0, difference))			,	"constant"			,	constant_values=UpperCAmelCase__		)

							A__							=       audio_values.tolist()
							for i in range(UpperCAmelCase__		):
													A__							=       np.asarray(audio_values[i]		)[
													    padding_mask[i][None, :] != self.feature_extractor.padding_value
													]
													A__							=       sliced_audio.reshape(UpperCAmelCase__			,	-1		)

							return audio_values










import math
def  UpperCamelCase						(       _A			:				float			,							_A			:				float    )->       float:





						"""simple docstring"""





						if (
						    not isinstance(_A			,							(int, float)    )
						    or power_factor < -1
						    or power_factor > 1
						):
												raise ValueError("power_factor must be a valid float value between -1 and 1."    )
						return apparent_power * power_factor
def  UpperCamelCase						(       _A			:				float			,							_A			:				float    )->       float:





						"""simple docstring"""





						if (
						    not isinstance(_A			,							(int, float)    )
						    or power_factor < -1
						    or power_factor > 1
						):
												raise ValueError("power_factor must be a valid float value between -1 and 1."    )
						return apparent_power * math.sqrt(1 - power_factor**2    )


if __name__ == "__main__":
						import doctest

						doctest.testmod()





