




'''simple docstring'''







def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_	):
    sequence.append(__a	)

    UpperCamelCase__      =			0
    while sequence[index] != target:
        index += 1

    sequence.pop()

    if index == len(__a	):
        return None

    return index


if __name__ == "__main__":
      lowercase__     :  int    					=			input('Enter numbers separated by comma:\n').strip()
      lowercase__     :  Optional[int]    					=			[int(item) for item in user_input.split(',')]

      lowercase__     :  List[Any]    					=			input('Enter a single number to be found in the list:\n')
      lowercase__     :  str    					=			int(target_input)
      lowercase__     :  str    					=			sentinel_linear_search(sequence, target)
      if result is not None:
            print(F'''{target} found at positions: {result}''')
      else:
            print('Not found')









'''simple docstring'''







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

      lowercase__     :  Optional[Any]    					=			True
      lowercase__     :  int    					=			''
except Exception as e:
      lowercase__     :  Union[str, Any]    					=			False
      lowercase__     :  List[Any]    					=			(
          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       lowerCamelCase_							(	lowerCAmelCase_					,    lowerCAmelCase_  ):






   '''simple docstring'''

   A :     int				=	"mel_config.json"


   @register_to_config
   def __init__(  self     ,						__UpperCamelCase = 2_56     ,						__UpperCamelCase = 2_56     ,						__UpperCamelCase = 2_20_50     ,						__UpperCamelCase = 20_48     ,						__UpperCamelCase = 5_12     ,						__UpperCamelCase = 80     ,						__UpperCamelCase = 32     ,						):
       UpperCamelCase__      =			hop_length
       UpperCamelCase__      =			sample_rate
       UpperCamelCase__      =			n_fft
       UpperCamelCase__      =			top_db
       UpperCamelCase__      =			n_iter
       self.set_resolution(__UpperCamelCase     ,						__UpperCamelCase )
       UpperCamelCase__      =			None

       if not _librosa_can_be_imported:
           raise ValueError(_import_error )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase ):
       UpperCamelCase__      =			x_res
       UpperCamelCase__      =			y_res
       UpperCamelCase__      =			self.y_res
       UpperCamelCase__      =			self.x_res * self.hop_length - 1


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase = None     ,						__UpperCamelCase = None ):
       if audio_file is not None:
           UpperCamelCase__    ,  UpperCamelCase__      =			librosa.load(__UpperCamelCase     ,						mono=__UpperCamelCase     ,						sr=self.sr )
       else:
           UpperCamelCase__      =			raw_audio

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


   def 	_UpperCamelCase       (  self ):
       return len(self.audio ) // self.slice_size


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase = 0 ):
       return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]


   def 	_UpperCamelCase       (  self ):
       return self.sr


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       UpperCamelCase__      =			librosa.feature.melspectrogram(
           y=self.get_audio_slice(__UpperCamelCase )     ,						sr=self.sr     ,						n_fft=self.n_fft     ,						hop_length=self.hop_length     ,						n_mels=self.n_mels )
       UpperCamelCase__      =			librosa.power_to_db(__UpperCamelCase     ,						ref=np.max     ,						top_db=self.top_db )
       UpperCamelCase__      =			(((log_S + self.top_db) * 2_55 / self.top_db).clip(0     ,						2_55 ) + 0.5).astype(np.uinta )
       UpperCamelCase__      =			Image.fromarray(__UpperCamelCase )
       return image







   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       UpperCamelCase__      =			np.frombuffer(image.tobytes()     ,						dtype="uint8" ).reshape((image.height, image.width) )
       UpperCamelCase__      =			bytedata.astype("float" ) * self.top_db / 2_55 - self.top_db
       UpperCamelCase__      =			librosa.db_to_power(__UpperCamelCase )
       UpperCamelCase__      =			librosa.feature.inverse.mel_to_audio(
           __UpperCamelCase     ,						sr=self.sr     ,						n_fft=self.n_fft     ,						hop_length=self.hop_length     ,						n_iter=self.n_iter )
       return audio




