


from typing import List, Optional

import numpy as np

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







class lowerCAmelCase_		(				lowerCamelCase__ ):

 '''simple docstring'''

 __snake_case				=						"EncodecFeatureExtractor"
 __snake_case				=						("T5Tokenizer", "T5TokenizerFast")



 def __init__(			self	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       super().__init__(_UpperCAmelCase	,   _UpperCAmelCase      )
       snake_case_ =   self.feature_extractor
       snake_case_ =   False



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase=None	,   _UpperCAmelCase=None	,   _UpperCAmelCase=True      ):
       return self.tokenizer.get_decoder_prompt_ids(task=_UpperCAmelCase	,   language=_UpperCAmelCase	,   no_timestamps=_UpperCAmelCase      )



 def __call__(			self	,   *_UpperCAmelCase	,   **_UpperCAmelCase      ):
       # For backward compatibility
       if self._in_target_context_manager:
             return self.current_processor(*_UpperCAmelCase	,   **_UpperCAmelCase      )

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

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

       if audio is None:
             return inputs

       elif text is None:
             return audio_inputs

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



 def 		UpperCamelCase__  (			self	,   *_UpperCAmelCase	,   **_UpperCAmelCase      ):
       snake_case_ =   kwargs.pop('''audio'''	,   _UpperCAmelCase      )
       snake_case_ =   kwargs.pop('''padding_mask'''	,   _UpperCAmelCase      )

       if len(_UpperCAmelCase      ) > 0:
             snake_case_ =   args[0]
             snake_case_ =   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 		UpperCamelCase__  (			self	,   *_UpperCAmelCase	,   **_UpperCAmelCase      ):
       return self.tokenizer.decode(*_UpperCAmelCase	,   **_UpperCAmelCase      )





 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase = None      ):
       snake_case_ =   to_numpy(_UpperCAmelCase      )
       snake_case_ ,					snake_case_ ,					snake_case_ =   audio_values.shape

       if padding_mask is None:
             return list(_UpperCAmelCase      )

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

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

       return audio_values



from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
UpperCAmelCase    =					2_9979_2458

# Symbols
UpperCAmelCase,							UpperCAmelCase,							UpperCAmelCase,							UpperCAmelCase    =					symbols("""ct x y z""")






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						float:




      """simple docstring"""

      if velocity > c:
            raise ValueError('''Speed must not exceed light speed 299,792,458 [m/s]!'''      )
      elif velocity < 1:
            # Usually the speed should be much higher than 1 (c order of magnitude)
            raise ValueError('''Speed must be greater than or equal to 1!'''      )

      return velocity / c






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						float:




      """simple docstring"""

      return 1 / sqrt(1 - beta(SCREAMING_SNAKE_CASE      ) ** 2      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						np.ndarray:




      """simple docstring"""

      return np.array(
          [
              [gamma(SCREAMING_SNAKE_CASE      ), -gamma(SCREAMING_SNAKE_CASE      ) * beta(SCREAMING_SNAKE_CASE      ), 0, 0],
              [-gamma(SCREAMING_SNAKE_CASE      ) * beta(SCREAMING_SNAKE_CASE      ), gamma(SCREAMING_SNAKE_CASE      ), 0, 0],
              [0, 0, 1, 0],
              [0, 0, 0, 1],
          ]      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE = None      )->						np.ndarray:




      """simple docstring"""

      if event is None:
            snake_case_ =   np.array([ct, x, y, z]      )  # Symbolic four vector
      else:
            event[0] *= c  # x0 is ct (speed of light * time)

      return transformation_matrix(SCREAMING_SNAKE_CASE      ) @ event


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   # Example of symbolic vector:
   UpperCAmelCase    =					transform(2997_9245)
   print("""Example of four vector: """)
   print(f'''ct\' = {four_vector[0]}''')
   print(f'''x\' = {four_vector[1]}''')
   print(f'''y\' = {four_vector[2]}''')
   print(f'''z\' = {four_vector[3]}''')

   # Substitute symbols with numerical values
   UpperCAmelCase    =					{ct: c, x: 1, y: 1, z: 1}
   UpperCAmelCase    =					[four_vector[i].subs(sub_dict) for i in range(4)]

   print(f'''\n{numerical_vector}''')