



from typing import List, Optional

import numpy as np

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



class 							__snake_case		(	_lowercase		):
							_a      =					'''EncodecFeatureExtractor'''
							_a      =					('''T5Tokenizer''', '''T5TokenizerFast''')




							def __init__(			self	:			List[Any]							,							A_	:			Any							,							A_	:			Tuple):
									super().__init__(__A							,							__A)
									lowerCAmelCase_     :	Dict							   =					self.feature_extractor
									lowerCAmelCase_     :	List[str]							   =					False




							def   UpperCAmelCase__     (			self	:			Union[str, Any]							,							A_	:			str=None							,							A_	:			Tuple=None							,							A_	:			Dict=True):
									return self.tokenizer.get_decoder_prompt_ids(task=__A							,							language=__A							,							no_timestamps=__A)




							def __call__(			self	:			Optional[Any]							,							*A_	:			Tuple							,							**A_	:			Tuple):
									# For backward compatibility
									if self._in_target_context_manager:
											return self.current_processor(*__A							,							**__A)

									lowerCAmelCase_     :	str							   =					kwargs.pop('''audio'''							,							__A)
									lowerCAmelCase_     :	Optional[Any]							   =					kwargs.pop('''sampling_rate'''							,							__A)
									lowerCAmelCase_     :	int							   =					kwargs.pop('''text'''							,							__A)
									if len(__A) > 0:
											lowerCAmelCase_     :	int							   =					args[0]
											lowerCAmelCase_     :	Dict							   =					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:
											lowerCAmelCase_     :	Dict							   =					self.tokenizer(__A							,							**__A)

									if audio is not None:
											lowerCAmelCase_     :	Optional[int]							   =					self.feature_extractor(__A							,							*__A							,							sampling_rate=__A							,							**__A)

									if audio is None:
											return inputs

									elif text is None:
											return audio_inputs

									else:
											lowerCAmelCase_     :	List[Any]							   =					audio_inputs["""input_values"""]
											if "padding_mask" in audio_inputs:
													lowerCAmelCase_     :	int							   =					audio_inputs["""padding_mask"""]
											return inputs




							def   UpperCAmelCase__     (			self	:			List[str]							,							*A_	:			int							,							**A_	:			Tuple):
									lowerCAmelCase_     :	Optional[int]							   =					kwargs.pop('''audio'''							,							__A)
									lowerCAmelCase_     :	List[str]							   =					kwargs.pop('''padding_mask'''							,							__A)

									if len(__A) > 0:
											lowerCAmelCase_     :	Dict							   =					args[0]
											lowerCAmelCase_     :	Optional[int]							   =					args[1:]

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




							def   UpperCAmelCase__     (			self	:			Optional[Any]							,							*A_	:			Dict							,							**A_	:			Any):
									return self.tokenizer.decode(*__A							,							**__A)







							def   UpperCAmelCase__     (			self	:			Tuple							,							A_	:			Union[str, Any]							,							A_	:			Optional = None):
									lowerCAmelCase_     :	List[str]							   =					to_numpy(__A)
									lowerCAmelCase_     :	Tuple							   =					audio_values.shape

									if padding_mask is None:
											return list(__A)

									lowerCAmelCase_     :	Union[str, Any]							   =					to_numpy(__A)

									# 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)
									lowerCAmelCase_     :	List[str]							   =					seq_len - padding_mask.shape[-1]
									lowerCAmelCase_     :	Tuple							   =					1 - self.feature_extractor.padding_value
									lowerCAmelCase_     :	Optional[int]							   =					np.pad(__A							,							((0, 0), (0, difference))							,							'''constant'''							,							constant_values=__A)

									lowerCAmelCase_     :	int							   =					audio_values.tolist()
									for i in range(__A):
											lowerCAmelCase_     :	str							   =					np.asarray(audio_values[i])[
											    padding_mask[i][None, :] != self.feature_extractor.padding_value
											]
											lowerCAmelCase_     :	List[Any]							   =					sliced_audio.reshape(__A							,							-1)

									return audio_values








def 							__SCREAMING_SNAKE_CASE     (   a__    :   int       )					->				int:
	if not isinstance(a__			,a__       ):
		raise TypeError("""Input value must be an 'int' type"""       )

	__A :			Union[str, Any]		    =			0
	while number:
		position += 1
		number >>= 1

	return position


if __name__ == "__main__":
			import doctest

			doctest.testmod()





