


'''simple docstring'''



from typing import List, Optional

import numpy as np

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





class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    lowerCamelCase    :   Optional[int]					=     'EncodecFeatureExtractor'
    lowerCamelCase    :   Any					=     ('T5Tokenizer', 'T5TokenizerFast')







    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	Any:
         super().__init__(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      List[Any]														=						self.feature_extractor
         __lowerCamelCase  :      List[str]														=						False







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=True      )							->	List[Any]:
         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_      )							->	Tuple:
         # For backward compatibility
         if self._in_target_context_manager:
              return self.current_processor(*SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      str														=						kwargs.pop('audio'     ,	SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      int														=						kwargs.pop('sampling_rate'     ,	SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Tuple														=						kwargs.pop('text'     ,	SCREAMING_SNAKE_CASE_      )
         if len(SCREAMING_SNAKE_CASE_      ) > 0:
              __lowerCamelCase  :      Union[str, Any]														=						args[0]
              __lowerCamelCase  :      List[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[Any]														=						self.tokenizer(SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )

         if audio is not None:
              __lowerCamelCase  :      Any														=						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  :      Any														=						audio_inputs['input_values']
              if "padding_mask" in audio_inputs:
                   __lowerCamelCase  :      List[Any]														=						audio_inputs['padding_mask']
              return inputs







    def  lowercase_							(				self     ,	*SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )							->	Tuple:
         __lowerCamelCase  :      int														=						kwargs.pop('audio'     ,	SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Any														=						kwargs.pop('padding_mask'     ,	SCREAMING_SNAKE_CASE_      )

         if len(SCREAMING_SNAKE_CASE_      ) > 0:
              __lowerCamelCase  :      Union[str, Any]														=						args[0]
              __lowerCamelCase  :      Any														=						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  lowercase_							(				self     ,	*SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )							->	int:
         return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None      )							->	List[np.ndarray]:
         __lowerCamelCase  :      Optional[Any]														=						to_numpy(SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      str														=						audio_values.shape

         if padding_mask is None:
              return list(SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      int														=						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  :      int														=						seq_len - padding_mask.shape[-1]
         __lowerCamelCase  :      Union[str, Any]														=						1 - self.feature_extractor.padding_value
         __lowerCamelCase  :      str														=						np.pad(SCREAMING_SNAKE_CASE_     ,	((0, 0), (0, difference))     ,	'constant'     ,	constant_values=SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Dict														=						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  :      int														=						sliced_audio.reshape(SCREAMING_SNAKE_CASE_     ,	-1      )

         return audio_values










'''simple docstring'''






from collections.abc import Callable

import numpy as np


def    _snake_case  (	A					,       A					,       A					,       A					,       A			)		->       np.array:
							lowerCAmelCase__			  =      int(np.ceil((x_end - xa) / step_size			)			)
							lowerCAmelCase__			  =      np.zeros((n + 1,)			)
							lowerCAmelCase__			  =      ya
							lowerCAmelCase__			  =      xa

							for k in range(A			):
														lowerCAmelCase__			  =      y[k] + step_size * ode_func(A					,       y[k]			)
														lowerCAmelCase__			  =      y[k] + (
														    (step_size / 2) * (ode_func(A					,       y[k]			) + ode_func(x + step_size					,       A			))
														)
														x += step_size

							return y


if __name__ == "__main__":
		import doctest

		doctest.testmod()