






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			:   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}"""		)





__a   				=      [
    [0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
    [0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0],
    [0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
]




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )    ->bool:







      """simple docstring"""

      return 0 <= row < rows and 0 <= col < cols




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )    ->int:







      """simple docstring"""

      lowercase			:   Optional[int]					   =		len(_UpperCamelCase     )
      lowercase			:   str					   =		len(mat[0]     )
      if is_safe(_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     ) and (row, col) not in seen and mat[row][col] == 1:
            seen.add((row, col)     )
            return (
                1
                + depth_first_search(row + 1,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )
                + depth_first_search(row - 1,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )
                + depth_first_search(_UpperCamelCase,    col + 1,    _UpperCamelCase,    _UpperCamelCase     )
                + depth_first_search(_UpperCamelCase,    col - 1,    _UpperCamelCase,    _UpperCamelCase     )
            )
      else:
            return 0




def 			__lowercase	(						_UpperCamelCase     )    ->int:







      """simple docstring"""

      lowercase			:   set					   =		set()

      lowercase			:   Tuple					   =		0
      for row, line in enumerate(_UpperCamelCase     ):
            for col, item in enumerate(_UpperCamelCase     ):
                  if item == 1 and (row, col) not in seen:
                        # Maximizing the area
                        lowercase			:   Tuple					   =		max(_UpperCamelCase,    depth_first_search(_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )     )
      return max_area


if __name__ == "__main__":
  import doctest

  print(find_max_area(matrix))  # Output -> 6
  doctest.testmod()

