



"""simple docstring"""






from typing import List, Optional

import numpy as np

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

class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_     ):


					'''simple docstring'''





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



					def __init__( self							:						str,						lowerCamelCase							:						str,						lowerCamelCase							:						List[Any]  )->      List[str]:
								super().__init__(lowerCamelCase,						lowerCamelCase  )
								lowerCamelCase__	:	int				=self.feature_extractor
								lowerCamelCase__	:	str				=False



					def 	snake_case ( self							:						Any,						lowerCamelCase							:						Tuple=None,						lowerCamelCase							:						str=None,						lowerCamelCase							:						int=True  )->      Optional[int]:
								return self.tokenizer.get_decoder_prompt_ids(task=lowerCamelCase,						language=lowerCamelCase,						no_timestamps=lowerCamelCase  )



					def __call__( self							:						Dict,						*lowerCamelCase							:						int,						**lowerCamelCase							:						Any  )->      Any:
								# For backward compatibility
								if self._in_target_context_manager:
											return self.current_processor(*lowerCamelCase,						**lowerCamelCase  )

								lowerCamelCase__	:	str				=kwargs.pop('''audio''',						lowerCamelCase  )
								lowerCamelCase__	:	str				=kwargs.pop('''sampling_rate''',						lowerCamelCase  )
								lowerCamelCase__	:	Any				=kwargs.pop('''text''',						lowerCamelCase  )
								if len(lowerCamelCase  ) > 0:
											lowerCamelCase__	:	str				=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__	:	Union[str, Any]				=self.tokenizer(lowerCamelCase,						**lowerCamelCase  )

								if audio is not None:
											lowerCamelCase__	:	str				=self.feature_extractor(lowerCamelCase,						*lowerCamelCase,						sampling_rate=lowerCamelCase,						**lowerCamelCase  )

								if audio is None:
											return inputs

								elif text is None:
											return audio_inputs

								else:
											lowerCamelCase__	:	Union[str, Any]				=audio_inputs['''input_values''']
											if "padding_mask" in audio_inputs:
														lowerCamelCase__	:	str				=audio_inputs['''padding_mask''']
											return inputs



					def 	snake_case ( self							:						Union[str, Any],						*lowerCamelCase							:						Dict,						**lowerCamelCase							:						Union[str, Any]  )->      List[str]:
								lowerCamelCase__	:	int				=kwargs.pop('''audio''',						lowerCamelCase  )
								lowerCamelCase__	:	Optional[int]				=kwargs.pop('''padding_mask''',						lowerCamelCase  )

								if len(lowerCamelCase  ) > 0:
											lowerCamelCase__	:	Tuple				=args[0]
											lowerCamelCase__	:	List[Any]				=args[1:]

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



					def 	snake_case ( self							:						Tuple,						*lowerCamelCase							:						Optional[int],						**lowerCamelCase							:						Optional[int]  )->      List[str]:
								return self.tokenizer.decode(*lowerCamelCase,						**lowerCamelCase  )




					def 	snake_case ( self							:						str,						lowerCamelCase							:						Optional[Any],						lowerCamelCase							:						Optional = None  )->      List[np.ndarray]:
								lowerCamelCase__	:	Optional[int]				=to_numpy(lowerCamelCase  )
								lowerCamelCase__			,     lowerCamelCase__			,     lowerCamelCase__	:	Tuple				=audio_values.shape

								if padding_mask is None:
											return list(lowerCamelCase  )

								lowerCamelCase__	:	Tuple				=to_numpy(lowerCamelCase  )

								# 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__	:	Optional[Any]				=seq_len - padding_mask.shape[-1]
								lowerCamelCase__	:	Any				=1 - self.feature_extractor.padding_value
								lowerCamelCase__	:	Optional[Any]				=np.pad(lowerCamelCase,						((0, 0), (0, difference)),						'''constant''',						constant_values=lowerCamelCase  )

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

								return audio_values









"""simple docstring"""






def   snake_case__     (	__lowerCamelCase       :					list							,	__lowerCamelCase       :					list							,	__lowerCamelCase       :					int							,	__lowerCamelCase       :					int							,	__lowerCamelCase       :					int			):
			"""simple docstring"""
			if index == number_of_items:
						return 0
			lowerCamelCase__	:	Optional[int]				=0
			lowerCamelCase__	:	Union[str, Any]				=0
			lowerCamelCase__	:	List[str]				=knapsack(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	index + 1			)
			if weights[index] <= max_weight:
						lowerCamelCase__	:	Dict				=values[index] + knapsack(
						    __lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	max_weight - weights[index]							,	index + 1			)
			return max(__lowerCamelCase							,	__lowerCamelCase			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()




