


'''simple docstring'''







from typing import List, Optional

import numpy as np

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


class 			__lowerCamelCase			(	_lowerCAmelCase	):
				"""simple docstring"""
				a													=   "EncodecFeatureExtractor"
				a													=   ("T5Tokenizer", "T5TokenizerFast")


				def __init__(  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							Tuple):
											super().__init__(_lowercase				,							_lowercase)
											_A					:     Dict     = self.feature_extractor
											_A					:     int     = False


				def        A   (  self      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							int=None				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]=None				,							SCREAMING_SNAKE_CASE      :							int=True):
											return self.tokenizer.get_decoder_prompt_ids(task=_lowercase				,							language=_lowercase				,							no_timestamps=_lowercase)


				def __call__(  self      :							Any				,							*SCREAMING_SNAKE_CASE      :							Optional[int]				,							**SCREAMING_SNAKE_CASE      :							int):
											# For backward compatibility
											if self._in_target_context_manager:
																		return self.current_processor(*_lowercase				,							**_lowercase)

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

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

											if audio is None:
																		return inputs

											elif text is None:
																		return audio_inputs

											else:
																		_A					:     Tuple     = audio_inputs['input_values']
																		if "padding_mask" in audio_inputs:
																									_A					:     Optional[int]     = audio_inputs['padding_mask']
																		return inputs


				def        A   (  self      :							str				,							*SCREAMING_SNAKE_CASE      :							Optional[int]				,							**SCREAMING_SNAKE_CASE      :							Union[str, Any]):
											_A					:     Union[str, Any]     = kwargs.pop('audio'				,							_lowercase)
											_A					:     str     = kwargs.pop('padding_mask'				,							_lowercase)

											if len(_lowercase) > 0:
																		_A					:     Union[str, Any]     = args[0]
																		_A					:     Tuple     = 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        A   (  self      :							Dict				,							*SCREAMING_SNAKE_CASE      :							Dict				,							**SCREAMING_SNAKE_CASE      :							Union[str, Any]):
											return self.tokenizer.decode(*_lowercase				,							**_lowercase)


				def        A   (  self      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Optional = None):
											_A					:     int     = to_numpy(_lowercase)
											_A  ,			_A  ,			_A					:     Any     = audio_values.shape

											if padding_mask is None:
																		return list(_lowercase)

											_A					:     Any     = 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)
											_A					:     List[str]     = seq_len - padding_mask.shape[-1]
											_A					:     List[str]     = 1 - self.feature_extractor.padding_value
											_A					:     List[str]     = np.pad(_lowercase				,							((0, 0), (0, difference))				,							'constant'				,							constant_values=_lowercase)

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

											return audio_values










'''simple docstring'''







def 				lowerCAmelCase__	(    lowerCamelCase					:      int = 10       ):
       if not isinstance(lowerCamelCase  ,lowerCamelCase       ) or n < 0:
              raise ValueError('Invalid input'       )
       _A					:     Optional[Any]     = 10**n
       _A					:     List[str]     = 28433 * (pow(2  ,7830457  ,lowerCamelCase       )) + 1
       return str(number % modulus       )


if __name__ == "__main__":
   from doctest import testmod

   testmod()
   print(f"""{solution(10) = }""")






