code: 
  
  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
  
  		lowerCamelCase__           =				True
  		lowerCamelCase__           =				''''''
  except Exception as e:
  		lowerCamelCase__           =				False
  		lowerCamelCase__           =				(
  		    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	(     A__			,      A__      ):
  					__UpperCamelCase						=   '''mel_config.json'''
  
  
  
  
  					@register_to_config
  					def __init__( self :					Any      ,_lowerCAmelCase :					int = 256      ,_lowerCAmelCase :					int = 256      ,_lowerCAmelCase :					int = 22_050      ,_lowerCAmelCase :					int = 2_048      ,_lowerCAmelCase :					int = 512      ,_lowerCAmelCase :					int = 80      ,_lowerCAmelCase :					int = 32      ,):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     hop_length
  							__snake_case		       =     sample_rate
  							__snake_case		       =     n_fft
  							__snake_case		       =     top_db
  							__snake_case		       =     n_iter
  							self.set_resolution(_lowerCAmelCase      ,_lowerCAmelCase						)
  							__snake_case		       =     None
  
  							if not _librosa_can_be_imported:
  									raise ValueError(_import_error						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Tuple      ,_lowerCAmelCase :					int      ,_lowerCAmelCase :					int						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     x_res
  							__snake_case		       =     y_res
  							__snake_case		       =     self.y_res
  							__snake_case		       =     self.x_res * self.hop_length - 1
  
  
  
  
  					def 							UpperCamelCase_						( self :					str      ,_lowerCAmelCase :					str = None      ,_lowerCAmelCase :					np.ndarray = None						):
  
  
  							"""simple docstring"""
  
  
  
  
  							if audio_file is not None:
  									__snake_case		       =     librosa.load(_lowerCAmelCase      ,mono=_lowerCAmelCase      ,sr=self.sr						)
  							else:
  									__snake_case		       =     raw_audio
  
  							# Pad with silence if necessary.
  							if len(self.audio						) < self.x_res * self.hop_length:
  									__snake_case		       =     np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio						),)						)]						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					List[Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return len(self.audio						) // self.slice_size
  
  
  
  
  					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					int = 0						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]
  
  
  
  
  					def 							UpperCamelCase_						( self :					Tuple						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return self.sr
  
  
  
  
  					def 							UpperCamelCase_						( self :					Union[str, Any]      ,_lowerCAmelCase :					int						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     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						)
  							__snake_case		       =     librosa.power_to_db(_lowerCAmelCase      ,ref=np.max      ,top_db=self.top_db						)
  							__snake_case		       =     (((log_S + self.top_db) * 255 / self.top_db).clip(0      ,255						) + 0.5).astype(np.uinta						)
  							__snake_case		       =     Image.fromarray(_lowerCAmelCase						)
  							return image
  
  
  
  
  
  
  
  					def 							UpperCamelCase_						( self :					List[Any]      ,_lowerCAmelCase :					Image.Image						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     np.frombuffer(image.tobytes()      ,dtype="uint8"						).reshape((image.height, image.width)						)
  							__snake_case		       =     bytedata.astype("float"						) * self.top_db / 255 - self.top_db
  							__snake_case		       =     librosa.db_to_power(_lowerCAmelCase						)
  							__snake_case		       =     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
  
  
  
  
  
  
style_context: import os
  from string import ascii_letters
  
  lowerCamelCase__           =				ascii_letters + ' \t\n'
  
  
  
  def 							_lowerCamelCase(				)  ->   dict[str, None]:
  		__snake_case		       =     os.path.split(os.path.realpath(__snake_case	)	)
  		__snake_case		       =     {}
  		with open(path[0] + "/dictionary.txt"	) as dictionary_file:
  				for word in dictionary_file.read().split("\n"	):
  						__snake_case		       =     None
  		return english_words
  
  
  lowerCamelCase__           =				load_dictionary()
  
  
  
  def 							_lowerCamelCase(				__snake_case	)  ->   float:
  		__snake_case		       =     message.upper()
  		__snake_case		       =     remove_non_letters(__snake_case	)
  		__snake_case		       =     message.split()
  		__snake_case		       =     len([word for word in possible_words if word in ENGLISH_WORDS]	)
  		return float(__snake_case	) / len(__snake_case	)
  
  
  
  def 							_lowerCamelCase(				__snake_case	)  ->   str:
  		return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE	)
  
  
  
  def 							_lowerCamelCase(				__snake_case						,						__snake_case = 20						,						__snake_case = 85	)  ->   bool:
  		__snake_case		       =     get_english_count(__snake_case	) * 100 >= word_percentage
  		__snake_case		       =     len(remove_non_letters(__snake_case	)	)
  		__snake_case		       =     (float(__snake_case	) / len(__snake_case	)) * 100
  		__snake_case		       =     message_letters_percentage >= letter_percentage
  		return words_match and letters_match
  
  
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  
  
  
  
  
  