


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

      _snake_case				=			True
      _snake_case				=			''
except Exception as e:
      _snake_case				=			False
      _snake_case				=			(
          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  A_ (			__A   ,    __A  ):







 """simple docstring"""





 _UpperCamelCase					:			str     	=       'mel_config.json'
 @register_to_config
 def __init__(      self       :			List[str]							,     _a       :			List[Any] = 2_5_6							,     _a       :			Tuple = 2_5_6							,     _a       :			int = 2_2_0_5_0							,     _a       :			str = 2_0_4_8							,     _a       :			Optional[int] = 5_1_2							,     _a       :			List[str] = 8_0							,     _a       :			str = 3_2							,     )     ->      Optional[int]:
      UpperCAmelCase    :					List[Any]			=      hop_length
      UpperCAmelCase    :					Dict			=      sample_rate
      UpperCAmelCase    :					Optional[int]			=      n_fft
      UpperCAmelCase    :					Optional[int]			=      top_db
      UpperCAmelCase    :					Optional[int]			=      n_iter
      self.set_resolution(__snake_case							,     __snake_case					)
      UpperCAmelCase    :					List[Any]			=      None

      if not _librosa_can_be_imported:
           raise ValueError(_import_error					)
 def 	_UpperCamelCase     (      self       :			int							,     _a       :			List[str]							,     _a       :			int					)     ->      List[Any]:
      UpperCAmelCase    :					Union[str, Any]			=      x_res
      UpperCAmelCase    :					str			=      y_res
      UpperCAmelCase    :					List[Any]			=      self.y_res
      UpperCAmelCase    :					int			=      self.x_res * self.hop_length - 1
 def 	_UpperCamelCase     (      self       :			str							,     _a       :			Union[str, Any] = None							,     _a       :			Optional[int] = None					)     ->      Optional[int]:

      if audio_file is not None:
           UpperCAmelCase    :					List[str]			=      librosa.load(__snake_case							,     mono=__snake_case							,     sr=self.sr					)
      else:
           UpperCAmelCase    :					List[str]			=      raw_audio

      # Pad with silence if necessary.
      if len(self.audio					) < self.x_res * self.hop_length:
           UpperCAmelCase    :					List[str]			=      np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio					),)					)]					)
 def 	_UpperCamelCase     (      self       :			Tuple					)     ->      int:
      return len(self.audio					) // self.slice_size
 def 	_UpperCamelCase     (      self       :			Optional[Any]							,     _a       :			Union[str, Any] = 0					)     ->      np.ndarray:
      return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]
 def 	_UpperCamelCase     (      self       :			Optional[Any]					)     ->      int:
      return self.sr
 def 	_UpperCamelCase     (      self       :			int							,     _a       :			Union[str, Any]					)     ->      Image.Image:
      UpperCAmelCase    :					Dict			=      librosa.feature.melspectrogram(
          y=self.get_audio_slice(__snake_case					)							,     sr=self.sr							,     n_fft=self.n_fft							,     hop_length=self.hop_length							,     n_mels=self.n_mels					)
      UpperCAmelCase    :					int			=      librosa.power_to_db(__snake_case							,     ref=np.max							,     top_db=self.top_db					)
      UpperCAmelCase    :					List[str]			=      (((log_S + self.top_db) * 2_5_5 / self.top_db).clip(0							,     2_5_5					) + 0.5).astype(np.uinta					)
      UpperCAmelCase    :					Tuple			=      Image.fromarray(__snake_case					)
      return image

 def 	_UpperCamelCase     (      self       :			List[Any]							,     _a       :			Dict					)     ->      np.ndarray:
      UpperCAmelCase    :					Any			=      np.frombuffer(image.tobytes()							,     dtype="uint8"					).reshape((image.height, image.width)					)
      UpperCAmelCase    :					List[str]			=      bytedata.astype("float"					) * self.top_db / 2_5_5 - self.top_db
      UpperCAmelCase    :					Tuple			=      librosa.db_to_power(__snake_case					)
      UpperCAmelCase    :					List[str]			=      librosa.feature.inverse.mel_to_audio(
          __snake_case							,     sr=self.sr							,     n_fft=self.n_fft							,     hop_length=self.hop_length							,     n_iter=self.n_iter					)
      return audio










import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp







def  A					(				):
					_snake_case							:       Optional[Any]												= ArgumentParser(
					    description=(
					        "PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"
					    )					)

					# Optional arguments for the launch helper
					parser.add_argument("--num_cores"	,	type=UpperCAmelCase	,	default=1	,	help="Number of TPU cores to use (1 or 8)."					)

					# positional
					parser.add_argument(
					    "training_script"	,	type=UpperCAmelCase	,	help=(
					        "The full path to the single TPU training "
					        "program/script to be launched in parallel, "
					        "followed by all the arguments for the "
					        "training script"
					    )	,	)

					# rest from the training program
					parser.add_argument("training_script_args"	,	nargs=UpperCAmelCase					)

					return parser.parse_args()







def  A					(				):
					_snake_case							:       Any												= parse_args()

					# Import training_script as a module.
					_snake_case							:       Any												= Path(args.training_script					)
					sys.path.append(str(script_fpath.parent.resolve()					)					)
					_snake_case							:       Tuple												= script_fpath.stem
					_snake_case							:       str												= importlib.import_module(UpperCAmelCase					)

					# Patch sys.argv
					_snake_case							:       str												= [args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores					)]

					xmp.spawn(mod._mp_fn	,	args=()	,	nprocs=args.num_cores					)


if __name__ == "__main__":
							main()