




from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class 			a__    (					a__      ):
						_a							:    int					=    """EncodecFeatureExtractor"""
						_a							:    Tuple					=    ("""T5Tokenizer""", """T5TokenizerFast""")






						def __init__(   self		,			_A		,			_A			):




											"""simple docstring"""



											super().__init__(__lowerCAmelCase		,			__lowerCAmelCase			)
											__lowerCAmelCase				   =       self.feature_extractor
											__lowerCAmelCase				   =       False






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A=None		,			_A=None		,			_A=True			):




											"""simple docstring"""



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






						def __call__(   self		,			*_A		,			**_A			):




											"""simple docstring"""



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

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

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

											if audio is None:
																return inputs

											elif text is None:
																return audio_inputs

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






						def 				__SCREAMING_SNAKE_CASE(   self		,			*_A		,			**_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       kwargs.pop("audio"		,			__lowerCAmelCase			)
											__lowerCAmelCase				   =       kwargs.pop("padding_mask"		,			__lowerCAmelCase			)

											if len(__lowerCAmelCase			) > 0:
																__lowerCAmelCase				   =       args[0]
																__lowerCAmelCase				   =       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 				__SCREAMING_SNAKE_CASE(   self		,			*_A		,			**_A			):




											"""simple docstring"""



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






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A = None			):




											"""simple docstring"""



											__lowerCAmelCase				   =       to_numpy(__lowerCAmelCase			)
											__lowerCAmelCase				   =       audio_values.shape

											if padding_mask is None:
																return list(__lowerCAmelCase			)

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

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

											return audio_values





'''simple docstring'''

def 			__UpperCamelCase       (   UpperCAmelCase     ):
 lowercase__				:    List[str]  =					0
 while num > 0:
  digit_sum += num % 10
  num //= 10
 return digit_sum
def 			__UpperCamelCase       (   UpperCAmelCase = 100     ):
 lowercase__				:    Dict  =					1
 lowercase__				:    Optional[int]  =					2

 for i in range(2		,     max_n + 1     ):
  lowercase__				:    List[str]  =					pre_numerator
  lowercase__				:    Optional[Any]  =					2 * i // 3 if i % 3 == 0 else 1
  lowercase__				:    Union[str, Any]  =					cur_numerator
  lowercase__				:    int  =					e_cont * pre_numerator + temp

 return sum_digits(UpperCAmelCase     )


if __name__ == "__main__":
  print(F'{solution() = }')

