from __future__ import annotations

import math

__lowerCamelCase    :				Optional[Any]							     =	'''2020.9.26'''
__lowerCamelCase    :				List[str]							     =	'''xcodz-dot, cclaus, dhruvmanila'''



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: float				,  __UpperCamelCase					: float				,  __UpperCamelCase					: float				,  __UpperCamelCase					: float				,  __UpperCamelCase					: float			)							->      tuple[float, float]:







  """simple docstring"""





  if not all(isinstance(__UpperCamelCase				,  (float, int)			) for val in locals().values()			):
    SCREAMING_SNAKE_CASE__    							=   f"""Input values must either be float or int: {list(locals().values()			)}"""
    raise TypeError(__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   ((x * distance) / (z + distance)) * scale
  SCREAMING_SNAKE_CASE__    							=   ((y * distance) / (z + distance)) * scale
  return projected_x, projected_y



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: float				,  __UpperCamelCase					: float				,  __UpperCamelCase					: float				,  __UpperCamelCase					: str				,  __UpperCamelCase					: float			)							->      tuple[float, float, float]:







  """simple docstring"""





  if not isinstance(__UpperCamelCase				,  __UpperCamelCase			):
    raise TypeError("""Axis must be a str"""			)
  SCREAMING_SNAKE_CASE__    							=   locals()
  del input_variables["axis"]
  if not all(isinstance(__UpperCamelCase				,  (float, int)			) for val in input_variables.values()			):
    SCREAMING_SNAKE_CASE__    							=   (
        """Input values except axis must either be float or int: """
        f"""{list(input_variables.values()			)}"""
    )
    raise TypeError(__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   (angle % 3_60) / 4_50 * 1_80 / math.pi
  if axis == "z":
    SCREAMING_SNAKE_CASE__    							=   x * math.cos(__UpperCamelCase			) - y * math.sin(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   y * math.cos(__UpperCamelCase			) + x * math.sin(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   z
  elif axis == "x":
    SCREAMING_SNAKE_CASE__    							=   y * math.cos(__UpperCamelCase			) - z * math.sin(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   z * math.cos(__UpperCamelCase			) + y * math.sin(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   x
  elif axis == "y":
    SCREAMING_SNAKE_CASE__    							=   x * math.cos(__UpperCamelCase			) - z * math.sin(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   z * math.cos(__UpperCamelCase			) + x * math.sin(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   y
  else:
    raise ValueError("""not a valid axis, choose one of 'x', 'y', 'z'"""			)

  return new_x, new_y, new_z


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(F"""{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }""")
   print(F"""{rotate(1.0, 2.0, 3.0, "y", 90.0) = }""")

from typing import List, Optional

import numpy as np

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







class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							"EncodecFeatureExtractor"
   lowerCAmelCase_														=							("T5Tokenizer", "T5TokenizerFast")




   def __init__(					self    :       List[Any] ,			_lowercase    :       int ,			_lowercase    :       Any   ):



     """simple docstring"""






     super().__init__(_lowercase ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   self.feature_extractor
     SCREAMING_SNAKE_CASE__    							=   False




   def 			__a		(					self    :       Tuple ,			_lowercase    :       str=None ,			_lowercase    :       int=None ,			_lowercase    :       Any=True   ):



     """simple docstring"""






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




   def __call__(					self    :       Tuple ,			*_lowercase    :       Tuple ,			**_lowercase    :       Union[str, Any]   ):



     """simple docstring"""






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

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

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

     if audio is None:
       return inputs

     elif text is None:
       return audio_inputs

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




   def 			__a		(					self    :       Dict ,			*_lowercase    :       Any ,			**_lowercase    :       Optional[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""audio""" ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""padding_mask""" ,			_lowercase   )

     if len(_lowercase   ) > 0:
       SCREAMING_SNAKE_CASE__    							=   args[0]
       SCREAMING_SNAKE_CASE__    							=   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    :       List[str] ,			*_lowercase    :       Tuple ,			**_lowercase    :       Optional[Any]   ):



     """simple docstring"""






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




   def 			__a		(					self    :       List[str] ,			_lowercase    :       Tuple ,			_lowercase    :       Optional = None   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   to_numpy(_lowercase   )
     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   audio_values.shape

     if padding_mask is None:
       return list(_lowercase   )

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

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

     return audio_values
