code: 
  
  
  
  
  
  
  import os
  from argparse import ArgumentParser
  from pathlib import Path
  from shutil import copyfile
  from typing import List
  
  from datasets import config
  from datasets.builder import DatasetBuilder
  from datasets.commands import BaseDatasetsCLICommand
  from datasets.download.download_config import DownloadConfig
  from datasets.download.download_manager import DownloadMode
  from datasets.load import dataset_module_factory, import_main_class
  from datasets.utils.info_utils import VerificationMode
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					**__lowerCAmelCase    )				->			Union[str, Any]:
       return RunBeamCommand(
           args.dataset      ,					args.name      ,					args.cache_dir      ,					args.beam_pipeline_options      ,					args.data_dir      ,					args.all_configs      ,					args.save_info or args.save_infos      ,					args.ignore_verifications      ,					args.force_redownload      ,					**__lowerCAmelCase      ,					)
  
  
  
  
  
  class     __a						(					A__ ):
  
  
  
  
  
  
  
   @staticmethod
   def       __lowercase      (	SCREAMING_SNAKE_CASE      :     ArgumentParser		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     str        =						parser.add_parser("run_beam"							,     help="Run a Beam dataset processing pipeline"		)
        run_beam_parser.add_argument("dataset"							,     type=SCREAMING_SNAKE_CASE							,     help="Name of the dataset to download"		)
        run_beam_parser.add_argument("--name"							,     type=SCREAMING_SNAKE_CASE							,     default=SCREAMING_SNAKE_CASE							,     help="Dataset config name"		)
        run_beam_parser.add_argument(
            "--cache_dir"							,     type=SCREAMING_SNAKE_CASE							,     default=SCREAMING_SNAKE_CASE							,     help="Cache directory where the datasets are stored"							,     )
        run_beam_parser.add_argument(
            "--beam_pipeline_options"							,     type=SCREAMING_SNAKE_CASE							,     default=""							,     help="Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`"							,     )
        run_beam_parser.add_argument(
            "--data_dir"							,     type=SCREAMING_SNAKE_CASE							,     default=SCREAMING_SNAKE_CASE							,     help="Can be used to specify a manual directory to get the files from"							,     )
        run_beam_parser.add_argument("--all_configs"							,     action="store_true"							,     help="Test all dataset configurations"		)
        run_beam_parser.add_argument("--save_info"							,     action="store_true"							,     help="Save the dataset infos file"		)
        run_beam_parser.add_argument(
            "--ignore_verifications"							,     action="store_true"							,     help="Run the test without checksums and splits checks"		)
        run_beam_parser.add_argument("--force_redownload"							,     action="store_true"							,     help="Force dataset redownload"		)
        # aliases
        run_beam_parser.add_argument("--save_infos"							,     action="store_true"							,     help="alias for save_info"		)
        run_beam_parser.set_defaults(func=SCREAMING_SNAKE_CASE		)
  
  
  
  
  
  
  
   def __init__(	self      :     Optional[Any]							,     SCREAMING_SNAKE_CASE      :     str							,     SCREAMING_SNAKE_CASE      :     str							,     SCREAMING_SNAKE_CASE      :     str							,     SCREAMING_SNAKE_CASE      :     str							,     SCREAMING_SNAKE_CASE      :     str							,     SCREAMING_SNAKE_CASE      :     bool							,     SCREAMING_SNAKE_CASE      :     bool							,     SCREAMING_SNAKE_CASE      :     bool							,     SCREAMING_SNAKE_CASE      :     bool							,     **SCREAMING_SNAKE_CASE      :     Union[str, Any]							,     ):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     int        =						dataset
        UpperCamelCase__    :     Dict        =						name
        UpperCamelCase__    :     Optional[Any]        =						cache_dir
        UpperCamelCase__    :     int        =						beam_pipeline_options
        UpperCamelCase__    :     Any        =						data_dir
        UpperCamelCase__    :     Union[str, Any]        =						all_configs
        UpperCamelCase__    :     List[Any]        =						save_infos
        UpperCamelCase__    :     Optional[Any]        =						ignore_verifications
        UpperCamelCase__    :     List[str]        =						force_redownload
        UpperCamelCase__    :     Optional[Any]        =						config_kwargs
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     int		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        import apache_beam as beam
  
        if self._name is not None and self._all_configs:
             print("Both parameters `name` and `all_configs` can't be used at once."		)
             exit(1		)
        UpperCamelCase__       ,  UpperCamelCase__    :     Optional[int]        =						self._dataset, self._name
        UpperCamelCase__    :     Dict        =						dataset_module_factory(SCREAMING_SNAKE_CASE		)
        UpperCamelCase__    :     List[str]        =						import_main_class(dataset_module.module_path		)
        UpperCamelCase__    :     List[DatasetBuilder]        =						[]
        if self._beam_pipeline_options:
             UpperCamelCase__    :     Optional[int]        =						beam.options.pipeline_options.PipelineOptions(
                 flags=[F'--{opt.strip()}' for opt in self._beam_pipeline_options.split(","		) if opt]		)
        else:
             UpperCamelCase__    :     str        =						None
        if self._all_configs and len(builder_cls.BUILDER_CONFIGS		) > 0:
             for builder_config in builder_cls.BUILDER_CONFIGS:
                  builders.append(
                      builder_cls(
                          config_name=builder_config.name							,     data_dir=self._data_dir							,     hash=dataset_module.hash							,     beam_options=SCREAMING_SNAKE_CASE							,     cache_dir=self._cache_dir							,     base_path=dataset_module.builder_kwargs.get("base_path"		)							,     )		)
        else:
             builders.append(
                 builder_cls(
                     config_name=SCREAMING_SNAKE_CASE							,     data_dir=self._data_dir							,     beam_options=SCREAMING_SNAKE_CASE							,     cache_dir=self._cache_dir							,     base_path=dataset_module.builder_kwargs.get("base_path"		)							,     **self._config_kwargs							,     )		)
  
        for builder in builders:
             builder.download_and_prepare(
                 download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
                 if not self._force_redownload
                 else DownloadMode.FORCE_REDOWNLOAD							,     download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH		)							,     verification_mode=VerificationMode.NO_CHECKS
                 if self._ignore_verifications
                 else VerificationMode.ALL_CHECKS							,     try_from_hf_gcs=SCREAMING_SNAKE_CASE							,     )
             if self._save_infos:
                  builder._save_infos()
  
        print("Apache beam run successful."		)
  
        # If save_infos=True, the dataset infos file is created next to the loaded module file.
        # Let's move it to the original directory of the dataset script, to allow the user to
        # upload them on S3 at the same time afterwards.
        if self._save_infos:
             UpperCamelCase__    :     str        =						os.path.join(builder_cls.get_imported_module_dir()							,     config.DATASETDICT_INFOS_FILENAME		)
  
             UpperCamelCase__    :     int        =						Path(SCREAMING_SNAKE_CASE		).name + ".py"
  
             UpperCamelCase__    :     int        =						os.path.join(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		)
             if os.path.isfile(SCREAMING_SNAKE_CASE		):
                  UpperCamelCase__    :     List[str]        =						os.path.dirname(SCREAMING_SNAKE_CASE		)
             elif os.path.isfile(SCREAMING_SNAKE_CASE		):
                  UpperCamelCase__    :     Optional[int]        =						path
             else:  # in case of a remote dataset
                  print(F'Dataset Infos file saved at {dataset_infos_path}'		)
                  exit(1		)
  
             # Move datasetinfo back to the user
             UpperCamelCase__    :     int        =						os.path.join(SCREAMING_SNAKE_CASE							,     config.DATASETDICT_INFOS_FILENAME		)
             copyfile(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		)
             print(F'Dataset Infos file saved at {user_dataset_infos_path}'		)
style_context: 
  
  
  
  
  
  
  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			:							List[str]       =True
   lowerCamelCase			:							Dict       =''''''
  except Exception as e:
   lowerCamelCase			:							str       =False
   lowerCamelCase			:							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     __a						(					A__ ,     A__ ):
   _lowerCAmelCase : Optional[Any] 				=				'''mel_config.json'''
  
  
  
  
  
  
  
   @register_to_config
   def __init__(	self      :     Any							,     SCREAMING_SNAKE_CASE      :     int = 2_56							,     SCREAMING_SNAKE_CASE      :     int = 2_56							,     SCREAMING_SNAKE_CASE      :     int = 2_20_50							,     SCREAMING_SNAKE_CASE      :     int = 20_48							,     SCREAMING_SNAKE_CASE      :     int = 5_12							,     SCREAMING_SNAKE_CASE      :     int = 80							,     SCREAMING_SNAKE_CASE      :     int = 32							,     ):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     str        =						hop_length
        UpperCamelCase__    :     Optional[Any]        =						sample_rate
        UpperCamelCase__    :     Dict        =						n_fft
        UpperCamelCase__    :     str        =						top_db
        UpperCamelCase__    :     Optional[Any]        =						n_iter
        self.set_resolution(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		)
        UpperCamelCase__    :     str        =						None
  
        if not _librosa_can_be_imported:
             raise ValueError(_import_error		)
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Tuple							,     SCREAMING_SNAKE_CASE      :     int							,     SCREAMING_SNAKE_CASE      :     int		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Dict        =						x_res
        UpperCamelCase__    :     Dict        =						y_res
        UpperCamelCase__    :     Dict        =						self.y_res
        UpperCamelCase__    :     Any        =						self.x_res * self.hop_length - 1
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     str							,     SCREAMING_SNAKE_CASE      :     str = None							,     SCREAMING_SNAKE_CASE      :     np.ndarray = None		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        if audio_file is not None:
             UpperCamelCase__       ,  UpperCamelCase__    :     int        =						librosa.load(SCREAMING_SNAKE_CASE							,     mono=SCREAMING_SNAKE_CASE							,     sr=self.sr		)
        else:
             UpperCamelCase__    :     List[Any]        =						raw_audio
  
        # Pad with silence if necessary.
        if len(self.audio		) < self.x_res * self.hop_length:
             UpperCamelCase__    :     List[Any]        =						np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio		),)		)]		)
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     List[Any]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        return len(self.audio		) // self.slice_size
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     int							,     SCREAMING_SNAKE_CASE      :     int = 0		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Tuple		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        return self.sr
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Union[str, Any]							,     SCREAMING_SNAKE_CASE      :     int		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Optional[int]        =						librosa.feature.melspectrogram(
            y=self.get_audio_slice(SCREAMING_SNAKE_CASE		)							,     sr=self.sr							,     n_fft=self.n_fft							,     hop_length=self.hop_length							,     n_mels=self.n_mels		)
        UpperCamelCase__    :     Tuple        =						librosa.power_to_db(SCREAMING_SNAKE_CASE							,     ref=np.max							,     top_db=self.top_db		)
        UpperCamelCase__    :     Optional[Any]        =						(((log_S + self.top_db) * 2_55 / self.top_db).clip(0							,     2_55		) + 0.5).astype(np.uinta		)
        UpperCamelCase__    :     Optional[Any]        =						Image.fromarray(SCREAMING_SNAKE_CASE		)
        return image
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     List[Any]							,     SCREAMING_SNAKE_CASE      :     Image.Image		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Optional[Any]        =						np.frombuffer(image.tobytes()							,     dtype="uint8"		).reshape((image.height, image.width)		)
        UpperCamelCase__    :     str        =						bytedata.astype("float"		) * self.top_db / 2_55 - self.top_db
        UpperCamelCase__    :     Any        =						librosa.db_to_power(SCREAMING_SNAKE_CASE		)
        UpperCamelCase__    :     Tuple        =						librosa.feature.inverse.mel_to_audio(
            SCREAMING_SNAKE_CASE							,     sr=self.sr							,     n_fft=self.n_fft							,     hop_length=self.hop_length							,     n_iter=self.n_iter		)
        return audio