




from typing import List, Optional

import numpy as np

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


class 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






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


    def __init__(     self :      Union[str, Any]   ,					UpperCAmelCase__ :      str   ,					UpperCAmelCase__ :      int)		->Dict:





           '''simple docstring'''

           super().__init__(UpperCAmelCase__   ,					UpperCAmelCase__)
           A__ 					=       self.feature_extractor
           A__ 					=       False


    def 			SCREAMING_SNAKE_CASE				(     self :      Tuple   ,					UpperCAmelCase__ :      List[Any]=None   ,					UpperCAmelCase__ :      List[str]=None   ,					UpperCAmelCase__ :      Tuple=True)		->str:





           '''simple docstring'''

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


    def __call__(     self :      Any   ,					*UpperCAmelCase__ :      Tuple   ,					**UpperCAmelCase__ :      Union[str, Any])		->int:





           '''simple docstring'''

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

           A__ 					=       kwargs.pop('''audio'''   ,					UpperCAmelCase__)
           A__ 					=       kwargs.pop('''sampling_rate'''   ,					UpperCAmelCase__)
           A__ 					=       kwargs.pop('''text'''   ,					UpperCAmelCase__)
           if len(UpperCAmelCase__) > 0:
                  A__ 					=       args[0]
                  A__ 					=       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__ 					=       self.tokenizer(UpperCAmelCase__   ,					**UpperCAmelCase__)

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

           if audio is None:
                  return inputs

           elif text is None:
                  return audio_inputs

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


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any]   ,					*UpperCAmelCase__ :      List[str]   ,					**UpperCAmelCase__ :      Any)		->List[str]:





           '''simple docstring'''

           A__ 					=       kwargs.pop('''audio'''   ,					UpperCAmelCase__)
           A__ 					=       kwargs.pop('''padding_mask'''   ,					UpperCAmelCase__)

           if len(UpperCAmelCase__) > 0:
                  A__ 					=       args[0]
                  A__ 					=       args[1:]

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


    def 			SCREAMING_SNAKE_CASE				(     self :      Dict   ,					*UpperCAmelCase__ :      Tuple   ,					**UpperCAmelCase__ :      int)		->str:





           '''simple docstring'''

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





    def 			SCREAMING_SNAKE_CASE				(     self :      List[str]   ,					UpperCAmelCase__ :      Tuple   ,					UpperCAmelCase__ :      Optional = None)		->List[np.ndarray]:





           '''simple docstring'''

           A__ 					=       to_numpy(UpperCAmelCase__)
           A__					,				A__					,				A__ 					=       audio_values.shape

           if padding_mask is None:
                  return list(UpperCAmelCase__)

           A__ 					=       to_numpy(UpperCAmelCase__)

           # 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__ 					=       seq_len - padding_mask.shape[-1]
           A__ 					=       1 - self.feature_extractor.padding_value
           A__ 					=       np.pad(UpperCAmelCase__   ,					((0, 0), (0, difference))   ,					'''constant'''   ,					constant_values=UpperCAmelCase__)

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

           return audio_values







"""simple docstring"""







def     UpperCAmelCase_					(				__a   :			int						):




			'''simple docstring'''



			_lowerCamelCase					:					Optional[Any]	       =    int(__a						)
			if decimal in (0, 1):  # Exit cases for the recursion
						return str(__a						)
			_lowerCamelCase       ,							_lowerCamelCase					:					Union[str, Any]	       =    divmod(__a      ,							2						)
			return binary_recursive(__a						) + str(__a						)

def     UpperCAmelCase_					(				__a   :			str						):




			'''simple docstring'''



			_lowerCamelCase					:					int	       =    str(__a						).strip()
			if not number:
						raise ValueError('No input value was provided'						)
			_lowerCamelCase					:					Tuple	       =    '-' if number.startswith('-'						) else ''
			_lowerCamelCase					:					List[Any]	       =    number.lstrip('-'						)
			if not number.isnumeric():
						raise ValueError('Input value is not an integer'						)
			return f"{negative}0b{binary_recursive(int(__a						)						)}"


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

	testmod()





