'''simple docstring'''




from __future__ import annotations

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

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






def 				snake_case__							(			_A:					list[numpy.ndarray]      ,   _A:					int )    ->	list[numpy.ndarray]:

		'''simple docstring'''


		lowerCAmelCase   					=  initial_vectors
		for _ in range(_A ):
				lowerCAmelCase   					=  iteration_step(_A )
		return vectors






def 				snake_case__							(			_A:					list[numpy.ndarray] )    ->	list[numpy.ndarray]:

		'''simple docstring'''


		lowerCAmelCase   					=  []
		for i, start_vector in enumerate(vectors[:-1] ):
				lowerCAmelCase   					=  vectors[i + 1]
				new_vectors.append(_A )
				lowerCAmelCase   					=  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 				snake_case__							(			_A:					numpy.ndarray      ,   _A:					float )    ->	numpy.ndarray:

		'''simple docstring'''


		lowerCAmelCase   					=  numpy.radians(_A )
		lowerCAmelCase					,     lowerCAmelCase   					=  numpy.cos(_A ), numpy.sin(_A )
		lowerCAmelCase   					=  numpy.array(((c, -s), (s, c)) )
		return numpy.dot(_A      ,   _A )






def 				snake_case__							(			_A:					list[numpy.ndarray] )    ->	None:

		'''simple docstring'''


		lowerCAmelCase   					=  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()
		lowerCAmelCase					,     lowerCAmelCase   					=  zip(*_A )
		plt.plot(_A      ,   _A )
		plt.show()


if __name__ == "__main__":
						import doctest

						doctest.testmod()

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





'''simple docstring'''




from typing import List, Optional

import numpy as np

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






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     str      =       '''EncodecFeatureExtractor'''
			UpperCAmelCase_			:     Dict      =       ('''T5Tokenizer''', '''T5TokenizerFast''')



			def __init__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


					super().__init__(__lowerCAmelCase       ,    __lowerCAmelCase)
					lowerCAmelCase   					=  self.feature_extractor
					lowerCAmelCase   					=  False



			def 							a_       (   self       ,    __lowerCAmelCase=None       ,    __lowerCAmelCase=None       ,    __lowerCAmelCase=True):




					"""simple docstring"""


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



			def __call__(   self       ,    *__lowerCAmelCase       ,    **__lowerCAmelCase):




					"""simple docstring"""


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

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

					if audio is not None:
							lowerCAmelCase   					=  self.feature_extractor(__lowerCAmelCase       ,    *__lowerCAmelCase       ,    sampling_rate=__lowerCAmelCase       ,    **__lowerCAmelCase)

					if audio is None:
							return inputs

					elif text is None:
							return audio_inputs

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



			def 							a_       (   self       ,    *__lowerCAmelCase       ,    **__lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  kwargs.pop("""audio"""       ,    __lowerCAmelCase)
					lowerCAmelCase   					=  kwargs.pop("""padding_mask"""       ,    __lowerCAmelCase)

					if len(__lowerCAmelCase) > 0:
							lowerCAmelCase   					=  args[0]
							lowerCAmelCase   					=  args[1:]

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



			def 							a_       (   self       ,    *__lowerCAmelCase       ,    **__lowerCAmelCase):




					"""simple docstring"""


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







			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase = None):




					"""simple docstring"""


					lowerCAmelCase   					=  to_numpy(__lowerCAmelCase)
					lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase   					=  audio_values.shape

					if padding_mask is None:
							return list(__lowerCAmelCase)

					lowerCAmelCase   					=  to_numpy(__lowerCAmelCase)

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

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

					return audio_values




