
"""simple docstring"""







from typing import List, Optional

import numpy as np

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




class  A__       (	_lowerCamelCase):
		A_						:       str				   =	'EncodecFeatureExtractor'
		A_						:       Any				   =	('T5Tokenizer', 'T5TokenizerFast')

		def __init__(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
			super().__init__(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						List[str]									=      self.feature_extractor
			__lowerCAmelCase			:						List[str]									=      False

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

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

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

			if audio is not None:
				__lowerCAmelCase			:						List[str]									=      self.feature_extractor(_SCREAMING_SNAKE_CASE       ,		*_SCREAMING_SNAKE_CASE       ,		sampling_rate=_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE )

			if audio is None:
				return inputs

			elif text is None:
				return audio_inputs

			else:
				__lowerCAmelCase			:						List[str]									=      audio_inputs['input_values']
				if "padding_mask" in audio_inputs:
					__lowerCAmelCase			:						int									=      audio_inputs['padding_mask']
				return inputs

		def 							__lowerCamelCase							(      self       ,		*_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						Optional[int]									=      kwargs.pop('audio'       ,		_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Union[str, Any]									=      kwargs.pop('padding_mask'       ,		_SCREAMING_SNAKE_CASE )

			if len(_SCREAMING_SNAKE_CASE ) > 0:
				__lowerCAmelCase			:						Optional[Any]									=      args[0]
				__lowerCAmelCase			:						Optional[int]									=      args[1:]

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

		def 							__lowerCamelCase							(      self       ,		*_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE ):
			return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE )







		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE = None ):
			__lowerCAmelCase			:						int									=      to_numpy(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			:						int									=      audio_values.shape

			if padding_mask is None:
				return list(_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						Union[str, Any]									=      to_numpy(_SCREAMING_SNAKE_CASE )

			# 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			:						str									=      seq_len - padding_mask.shape[-1]
			__lowerCAmelCase			:						Union[str, Any]									=      1 - self.feature_extractor.padding_value
			__lowerCAmelCase			:						Dict									=      np.pad(_SCREAMING_SNAKE_CASE       ,		((0, 0), (0, difference))       ,		'constant'       ,		constant_values=_SCREAMING_SNAKE_CASE )

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

			return audio_values



"""simple docstring"""




from __future__ import annotations


def     lowercase			(SCREAMING_SNAKE_CASE_		:				str  )   ->						list[int]:
						return [ord(SCREAMING_SNAKE_CASE_  ) - 96 for elem in plain]


def     lowercase			(SCREAMING_SNAKE_CASE_		:				list[int]  )   ->						str:
						return "".join(chr(elem + 96  ) for elem in encoded  )


def     lowercase			()   ->						None:
						SCREAMING_SNAKE_CASE													=					encode(input('-> '  ).strip().lower()  )
						print('Encoded: ' ,			SCREAMING_SNAKE_CASE_  )
						print('Decoded:' ,			decode(SCREAMING_SNAKE_CASE_  )  )


if __name__ == "__main__":
	main()


