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 			__lowercase	(						_UpperCamelCase,    **_UpperCamelCase     )    ->Dict:
  
  
  
  
  
  
  
        """simple docstring"""
  
        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,    **_UpperCamelCase,    )
  
  
  
  
  
  
  
  class 				__SCREAMING_SNAKE_CASE   (			A__   ):
  
  
        @staticmethod
        def        __lowerCamelCase			(							SCREAMING_SNAKE_CASE__		):
              lowercase			:   List[Any]					   =		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							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						**SCREAMING_SNAKE_CASE__							,						):
              lowercase			:   Optional[int]					   =		dataset
              lowercase			:   Optional[Any]					   =		name
              lowercase			:   str					   =		cache_dir
              lowercase			:   Tuple					   =		beam_pipeline_options
              lowercase			:   str					   =		data_dir
              lowercase			:   Dict					   =		all_configs
              lowercase			:   Dict					   =		save_infos
              lowercase			:   int					   =		ignore_verifications
              lowercase			:   List[Any]					   =		force_redownload
              lowercase			:   int					   =		config_kwargs
  
  
  
  
  
  
        def        __lowerCamelCase			(							self		):
              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		)
              lowercase   ,			lowercase			:   List[Any]					   =		self._dataset, self._name
              lowercase			:   List[str]					   =		dataset_module_factory(SCREAMING_SNAKE_CASE__		)
              lowercase			:   str					   =		import_main_class(dataset_module.module_path		)
              lowercase			:   List[DatasetBuilder]					   =		[]
              if self._beam_pipeline_options:
                    lowercase			:   str					   =		beam.options.pipeline_options.PipelineOptions(
                        flags=[f"""--{opt.strip()}""" for opt in self._beam_pipeline_options.split(''','''		) if opt]		)
              else:
                    lowercase			:   Dict					   =		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:
                    lowercase			:   Optional[int]					   =		os.path.join(builder_cls.get_imported_module_dir()							,						config.DATASETDICT_INFOS_FILENAME		)
  
                    lowercase			:   Any					   =		Path(SCREAMING_SNAKE_CASE__		).name + '''.py'''
  
                    lowercase			:   int					   =		os.path.join(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		)
                    if os.path.isfile(SCREAMING_SNAKE_CASE__		):
                          lowercase			:   Any					   =		os.path.dirname(SCREAMING_SNAKE_CASE__		)
                    elif os.path.isfile(SCREAMING_SNAKE_CASE__		):
                          lowercase			:   str					   =		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
                    lowercase			:   Union[str, Any]					   =		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: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  __a   				=      {
      '''configuration_decision_transformer''': [
          '''DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
          '''DecisionTransformerConfig''',
      ],
  }
  
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __a   				=      [
        '''DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''DecisionTransformerGPT2Model''',
        '''DecisionTransformerGPT2PreTrainedModel''',
        '''DecisionTransformerModel''',
        '''DecisionTransformerPreTrainedModel''',
    ]
  
  
  if TYPE_CHECKING:
    from .configuration_decision_transformer import (
        DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
        DecisionTransformerConfig,
    )
  
    try:
      if not is_torch_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .modeling_decision_transformer import (
          DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          DecisionTransformerGPTaModel,
          DecisionTransformerGPTaPreTrainedModel,
          DecisionTransformerModel,
          DecisionTransformerPreTrainedModel,
      )
  
  
  else:
    import sys
  
    __a   				=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  