
import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
   import librosa  # noqa: E402

   UpperCAmelCase__						:	List[str]	     = True
   UpperCAmelCase__						:	List[str]	     = ''
except Exception as e:
   UpperCAmelCase__						:	Optional[Any]	     = False
   UpperCAmelCase__						:	int	     = (
       F"""Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it."""
   )


from PIL import Image  # noqa: E402






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''
   __UpperCamelCase						:       Optional[Any]												=       '''mel_config.json'''


   @register_to_config
   def __init__(   self	:				Optional[int]     ,     lowerCAmelCase_	:				int = 2_5_6     ,     lowerCAmelCase_	:				int = 2_5_6     ,     lowerCAmelCase_	:				int = 2_2_0_5_0     ,     lowerCAmelCase_	:				int = 2_0_4_8     ,     lowerCAmelCase_	:				int = 5_1_2     ,     lowerCAmelCase_	:				int = 8_0     ,     lowerCAmelCase_	:				int = 3_2     ,     ):

      """simple docstring"""
      _A:    List[Any]						     =			hop_length
      _A:    Optional[Any]						     =			sample_rate
      _A:    Dict						     =			n_fft
      _A:    List[Any]						     =			top_db
      _A:    List[str]						     =			n_iter
      self.set_resolution(lowerCAmelCase_     ,     lowerCAmelCase_	)
      _A:    Tuple						     =			None

      if not _librosa_can_be_imported:
         raise ValueError(_import_error	)


   def 					__magic_name__							(   self	:				Optional[int]     ,     lowerCAmelCase_	:				int     ,     lowerCAmelCase_	:				int	):

      """simple docstring"""
      _A:    Optional[Any]						     =			x_res
      _A:    str						     =			y_res
      _A:    str						     =			self.y_res
      _A:    Tuple						     =			self.x_res * self.hop_length - 1


   def 					__magic_name__							(   self	:				str     ,     lowerCAmelCase_	:				str = None     ,     lowerCAmelCase_	:				np.ndarray = None	):

      """simple docstring"""
      if audio_file is not None:
         _A     ,    _A:    str						     =			librosa.load(lowerCAmelCase_     ,     mono=lowerCAmelCase_     ,     sr=self.sr	)
      else:
         _A:    Union[str, Any]						     =			raw_audio

      # Pad with silence if necessary.
      if len(self.audio	) < self.x_res * self.hop_length:
         _A:    Union[str, Any]						     =			np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio	),)	)]	)


   def 					__magic_name__							(   self	:				Any	):

      """simple docstring"""
      return len(self.audio	) // self.slice_size


   def 					__magic_name__							(   self	:				Any     ,     lowerCAmelCase_	:				int = 0	):

      """simple docstring"""
      return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]


   def 					__magic_name__							(   self	:				List[Any]	):

      """simple docstring"""
      return self.sr


   def 					__magic_name__							(   self	:				int     ,     lowerCAmelCase_	:				int	):

      """simple docstring"""
      _A:    Optional[Any]						     =			librosa.feature.melspectrogram(
          y=self.get_audio_slice(lowerCAmelCase_	)     ,     sr=self.sr     ,     n_fft=self.n_fft     ,     hop_length=self.hop_length     ,     n_mels=self.n_mels	)
      _A:    Tuple						     =			librosa.power_to_db(lowerCAmelCase_     ,     ref=np.max     ,     top_db=self.top_db	)
      _A:    Tuple						     =			(((log_S + self.top_db) * 2_5_5 / self.top_db).clip(0     ,     2_5_5	) + 0.5).astype(np.uinta	)
      _A:    Dict						     =			Image.fromarray(lowerCAmelCase_	)
      return image


   def 					__magic_name__							(   self	:				Optional[int]     ,     lowerCAmelCase_	:				Image.Image	):

      """simple docstring"""
      _A:    List[Any]						     =			np.frombuffer(image.tobytes()     ,     dtype='''uint8'''	).reshape((image.height, image.width)	)
      _A:    Any						     =			bytedata.astype('''float'''	) * self.top_db / 2_5_5 - self.top_db
      _A:    int						     =			librosa.db_to_power(lowerCAmelCase_	)
      _A:    int						     =			librosa.feature.inverse.mel_to_audio(
          lowerCAmelCase_     ,     sr=self.sr     ,     n_fft=self.n_fft     ,     hop_length=self.hop_length     ,     n_iter=self.n_iter	)
      return audio





import math as m






def     lowerCamelCase__				(   a						,						a						,						a=0.001 ) ->  Optional[int]:
   return (f(a + h ) - f(a - h )) / (2 * h)






def     lowerCamelCase__				(   a						,						a=0						,						a=1_00						,						a=0.0001						,						a=1E-6						,						a=False ) ->  List[Any]:
   _A:    Optional[Any]						     =			xa  # set the initial guess
   _A:    Dict						     =			[a]
   _A:    Tuple						     =			abs(f(a ) )
   _A:    Tuple						     =			lambda a : calc_derivative(a						,						a						,						h=a )  # noqa: E731  Derivative of f(x)
   for _ in range(a ):
      if fa(a ) == 0:
         raise ValueError('''No converging solution found''' )
      _A:    Any						     =			a - f(a ) / fa(a )  # Calculate the next estimate
      if logsteps:
         steps.append(a )
      if error < maxerror:
         break
   else:
      raise ValueError('''Iteration limit reached, no converging solution found''' )
   if logsteps:
      # If logstep is true, then log intermediate steps
      return a, error, steps
   return a, error


if __name__ == "__main__":
   from matplotlib import pyplot as plt

   UpperCAmelCase__						:	Optional[Any]	     = lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
   UpperCAmelCase__     ,UpperCAmelCase__     ,UpperCAmelCase__						:	str	     = newton_raphson(
       f, xa=10, maxiter=1000, step=1E-6, logsteps=True
   )
   plt.plot([abs(f(x)) for x in steps])
   plt.xlabel('step')
   plt.ylabel('error')
   plt.show()
   print(F"""solution = {{{solution:f}}}, error = {{{error:f}}}""")



