




from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						lowerCAmelCase_							=  '''EncodecFeatureExtractor'''
						lowerCAmelCase_							=  ('''T5Tokenizer''', '''T5TokenizerFast''')






						def __init__(		self				:	Dict			,      __lowercase				:	Union[str, Any]			,      __lowercase				:	Optional[int]		):






							"""simple docstring"""






							super().__init__(__lowercase			,      __lowercase		)
							snake_case_						       =	self.feature_extractor
							snake_case_						       =	False






						def 			snake_case__		(		self				:	Any			,      __lowercase				:	List[str]=None			,      __lowercase				:	int=None			,      __lowercase				:	Any=True		):






							"""simple docstring"""






							return self.tokenizer.get_decoder_prompt_ids(task=__lowercase			,      language=__lowercase			,      no_timestamps=__lowercase		)






						def __call__(		self				:	List[str]			,      *__lowercase				:	List[Any]			,      **__lowercase				:	List[Any]		):






							"""simple docstring"""






							if self._in_target_context_manager:
								return self.current_processor(*__lowercase			,      **__lowercase		)

							snake_case_						       =	kwargs.pop("audio"			,      __lowercase		)
							snake_case_						       =	kwargs.pop("sampling_rate"			,      __lowercase		)
							snake_case_						       =	kwargs.pop("text"			,      __lowercase		)
							if len(__lowercase		) > 0:
								snake_case_						       =	args[0]
								snake_case_						       =	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:
								snake_case_						       =	self.tokenizer(__lowercase			,      **__lowercase		)

							if audio is not None:
								snake_case_						       =	self.feature_extractor(__lowercase			,      *__lowercase			,      sampling_rate=__lowercase			,      **__lowercase		)

							if audio is None:
								return inputs

							elif text is None:
								return audio_inputs

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






						def 			snake_case__		(		self				:	List[Any]			,      *__lowercase				:	Tuple			,      **__lowercase				:	Dict		):






							"""simple docstring"""






							snake_case_						       =	kwargs.pop("audio"			,      __lowercase		)
							snake_case_						       =	kwargs.pop("padding_mask"			,      __lowercase		)

							if len(__lowercase		) > 0:
								snake_case_						       =	args[0]
								snake_case_						       =	args[1:]

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






						def 			snake_case__		(		self				:	List[str]			,      *__lowercase				:	int			,      **__lowercase				:	Any		):






							"""simple docstring"""






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






						def 			snake_case__		(		self				:	int			,      __lowercase				:	List[str]			,      __lowercase				:	Optional = None		):






							"""simple docstring"""






							snake_case_						       =	to_numpy(__lowercase		)
							snake_case_       , snake_case_       , snake_case_						       =	audio_values.shape

							if padding_mask is None:
								return list(__lowercase		)

							snake_case_						       =	to_numpy(__lowercase		)

							# 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)
							snake_case_						       =	seq_len - padding_mask.shape[-1]
							snake_case_						       =	1 - self.feature_extractor.padding_value
							snake_case_						       =	np.pad(__lowercase			,      ((0, 0), (0, difference))			,      "constant"			,      constant_values=__lowercase		)

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

							return audio_values






from abc import ABC, abstractmethod
from argparse import ArgumentParser
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						@staticmethod
						@abstractmethod
						def 			snake_case__		(		__lowercase				:	ArgumentParser		):






							"""simple docstring"""






							raise NotImplementedError()






						@abstractmethod
						def 			snake_case__		(		self				:	Optional[Any]		):






							"""simple docstring"""






							raise NotImplementedError()
