


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

   SCREAMING_SNAKE_CASE				:						List[Any]			   = True
   SCREAMING_SNAKE_CASE				:						Any			   = ""
except Exception as e:
   SCREAMING_SNAKE_CASE				:						List[Any]			   = False
   SCREAMING_SNAKE_CASE				:						Optional[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    UpperCamelCase    (						a_  ,					a_   ):


   '''simple docstring'''







   lowercase    :		List[str]	  ='''mel_config.json'''
   @register_to_config
   def __init__(   self      ,							UpperCamelCase_ = 256      ,							UpperCamelCase_ = 256      ,							UpperCamelCase_ = 2_2050      ,							UpperCamelCase_ = 2048      ,							UpperCamelCase_ = 512      ,							UpperCamelCase_ = 80      ,							UpperCamelCase_ = 32      ,							):
        lowercase_				:Dict       			=				hop_length
        lowercase_				:str       			=				sample_rate
        lowercase_				:List[str]       			=				n_fft
        lowercase_				:Any       			=				top_db
        lowercase_				:int       			=				n_iter
        self.set_resolution(lowercase_      ,							lowercase_						)
        lowercase_				:Tuple       			=				None

        if not _librosa_can_be_imported:
             raise ValueError(_import_error						)
   def 						UpperCamelCase     (   self      ,							UpperCamelCase_      ,							UpperCamelCase_						):
        lowercase_				:Union[str, Any]       			=				x_res
        lowercase_				:Union[str, Any]       			=				y_res
        lowercase_				:int       			=				self.y_res
        lowercase_				:List[Any]       			=				self.x_res * self.hop_length - 1
   def 						UpperCamelCase     (   self      ,							UpperCamelCase_ = None      ,							UpperCamelCase_ = None						):

        if audio_file is not None:
             lowercase_			,					lowercase_				:Dict       			=				librosa.load(lowercase_      ,							mono=lowercase_      ,							sr=self.sr						)
        else:
             lowercase_				:Union[str, Any]       			=				raw_audio

        # Pad with silence if necessary.
        if len(self.audio						) < self.x_res * self.hop_length:
             lowercase_				:Optional[int]       			=				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_						):
        lowercase_				:List[Any]       			=				librosa.feature.melspectrogram(
            y=self.get_audio_slice(lowercase_						)      ,							sr=self.sr      ,							n_fft=self.n_fft      ,							hop_length=self.hop_length      ,							n_mels=self.n_mels						)
        lowercase_				:Optional[Any]       			=				librosa.power_to_db(lowercase_      ,							ref=np.max      ,							top_db=self.top_db						)
        lowercase_				:str       			=				(((log_S + self.top_db) * 255 / self.top_db).clip(0      ,							255						) + 0.5).astype(np.uinta						)
        lowercase_				:Dict       			=				Image.fromarray(lowercase_						)
        return image




   def 						UpperCamelCase     (   self      ,							UpperCamelCase_						):
        lowercase_				:List[Any]       			=				np.frombuffer(image.tobytes()      ,							dtype='''uint8'''						).reshape((image.height, image.width)						)
        lowercase_				:Dict       			=				bytedata.astype('''float'''						) * self.top_db / 255 - self.top_db
        lowercase_				:Tuple       			=				librosa.db_to_power(lowercase_						)
        lowercase_				:Dict       			=				librosa.feature.inverse.mel_to_audio(
            lowercase_      ,							sr=self.sr      ,							n_fft=self.n_fft      ,							hop_length=self.hop_length      ,							n_iter=self.n_iter						)
        return audio









from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments
def      lowerCamelCase					(						)	->			Union[str, Any]:
							lowerCAmelCase_										=     HfArgumentParser(a_	)
							try:
														lowerCAmelCase_										=     parser.parse_args_into_dataclasses()[0]
							except ValueError as e:
														lowerCAmelCase_										=     'Arg --no_{0} is no longer used, please use --no-{0} instead.'
														lowerCAmelCase_										=     ' '.join(str(a_	).split(' '	)[:-1]	)
														lowerCAmelCase_										=     ''
														lowerCAmelCase_										=     eval(str(a_	).split(' '	)[-1]	)
														lowerCAmelCase_										=     []
														for arg in depreciated_args:
																					# arg[2:] removes '--'
																					if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
																												# arg[5:] removes '--no_'
																												full_error_msg += arg_error_msg.format(arg[5:]	)
																					else:
																												wrong_args.append(a_	)
														if len(a_	) > 0:
																					lowerCAmelCase_										=     full_error_msg + begin_error_msg + str(a_	)
														raise ValueError(a_	)

							lowerCAmelCase_										=     PyTorchBenchmark(args=a_	)
							benchmark.run()


if __name__ == "__main__":
		main()



