

'''simple docstring'''






from typing import List, Optional

import numpy as np

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






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						str           =    'EncodecFeatureExtractor'
  SCREAMING_SNAKE_CASE					:						List[Any]           =    ('T5Tokenizer', 'T5TokenizerFast')






  def __init__(   self     :  str      ,lowercase__     :  int      ,lowercase__     :  Optional[Any]    ):
   super().__init__(lowercase__      ,lowercase__    )
   __lowercase													=				self.feature_extractor
   __lowercase													=				False






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict      ,lowercase__     :  Any=None      ,lowercase__     :  Any=None      ,lowercase__     :  Tuple=True    ):
   return self.tokenizer.get_decoder_prompt_ids(task=lowercase__      ,language=lowercase__      ,no_timestamps=lowercase__    )






  def __call__(   self     :  Any      ,*lowercase__     :  Dict      ,**lowercase__     :  int    ):
   # For backward compatibility
   if self._in_target_context_manager:
    return self.current_processor(*lowercase__      ,**lowercase__    )

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

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

   if audio is None:
    return inputs

   elif text is None:
    return audio_inputs

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






  def       SCREAMING_SNAKE_CASE   (   self     :  Any      ,*lowercase__     :  Optional[Any]      ,**lowercase__     :  Optional[int]    ):
   __lowercase													=				kwargs.pop('''audio'''      ,lowercase__    )
   __lowercase													=				kwargs.pop('''padding_mask'''      ,lowercase__    )

   if len(lowercase__    ) > 0:
    __lowercase													=				args[0]
    __lowercase													=				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       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]      ,*lowercase__     :  int      ,**lowercase__     :  List[Any]    ):
   return self.tokenizer.decode(*lowercase__      ,**lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any      ,lowercase__     :  List[str]      ,lowercase__     :  Optional = None    ):
   __lowercase													=				to_numpy(lowercase__    )
   __lowercase						,			__lowercase						,			__lowercase													=				audio_values.shape

   if padding_mask is None:
    return list(lowercase__    )

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

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

   return audio_values




'''simple docstring'''






from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
lowerCAmelCase__      		=			numpy.array([0, 0])
lowerCAmelCase__      		=			numpy.array([0.5, 0.8_660_254])
lowerCAmelCase__      		=			numpy.array([1, 0])
lowerCAmelCase__      		=			[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				initial_vectors
 for _ in range(A__   ):
  __lowercase													=				iteration_step(A__   )
 return vectors


def 	_A  (					A__   ):






 """simple docstring"""



 __lowercase													=				[]
 for i, start_vector in enumerate(vectors[:-1]   ):
  __lowercase													=				vectors[i + 1]
  new_vectors.append(A__   )
  __lowercase													=				end_vector - start_vector
  new_vectors.append(start_vector + difference_vector / 3   )
  new_vectors.append(
      start_vector + difference_vector / 3 + rotate(difference_vector / 3  ,       60   )   )
  new_vectors.append(start_vector + difference_vector * 2 / 3   )
 new_vectors.append(vectors[-1]   )
 return new_vectors


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				numpy.radians(A__   )
 __lowercase						,			__lowercase													=				numpy.cos(A__   ), numpy.sin(A__   )
 __lowercase													=				numpy.array(((c, -s), (s, c))   )
 return numpy.dot(A__  ,       A__   )


def 	_A  (					A__   ):






 """simple docstring"""



 __lowercase													=				plt.gca()
 axes.set_aspect('''equal'''   )

 # matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
 # y-coordinates as inputs, which are constructed from the vector-list using
 # zip()
 __lowercase						,			__lowercase													=				zip(*A__   )
 plt.plot(A__  ,       A__   )
 plt.show()


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 lowerCAmelCase__      		=			iterate(INITIAL_VECTORS, 5)
 plot(processed_vectors)

