


'''simple docstring'''

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 __UpperCamelCase (      __lowerCamelCase		:			Any   ,	**__lowerCamelCase		:			Any					)   ->  Tuple:







     '''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   ,	**__lowerCamelCase   ,	)






class     __SCREAMING_SNAKE_CASE       (   lowerCamelCase__			):






     @staticmethod
     def 			a_       (      __UpperCamelCase		)							->     Dict:
          _a            =		parser.add_parser("run_beam"  ,				help="Run a Beam dataset processing pipeline"		)
          run_beam_parser.add_argument("dataset"  ,				type=__UpperCamelCase  ,				help="Name of the dataset to download"		)
          run_beam_parser.add_argument("--name"  ,				type=__UpperCamelCase  ,				default=__UpperCamelCase  ,				help="Dataset config name"		)
          run_beam_parser.add_argument(
              "--cache_dir"  ,				type=__UpperCamelCase  ,				default=__UpperCamelCase  ,				help="Cache directory where the datasets are stored"  ,				)
          run_beam_parser.add_argument(
              "--beam_pipeline_options"  ,				type=__UpperCamelCase  ,				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=__UpperCamelCase  ,				default=__UpperCamelCase  ,				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=__UpperCamelCase		)






     def __init__(      self  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				**__UpperCamelCase  ,				)							->     Union[str, Any]:
          _a            =		dataset
          _a            =		name
          _a            =		cache_dir
          _a            =		beam_pipeline_options
          _a            =		data_dir
          _a            =		all_configs
          _a            =		save_infos
          _a            =		ignore_verifications
          _a            =		force_redownload
          _a            =		config_kwargs






     def 			a_       (      self		)							->     Optional[Any]:
          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		)
          _a      ,      _a            =		self._dataset, self._name
          _a            =		dataset_module_factory(__UpperCamelCase		)
          _a            =		import_main_class(dataset_module.module_path		)
          _a            =		[]
          if self._beam_pipeline_options:
               _a            =		beam.options.pipeline_options.PipelineOptions(
                   flags=[f"--{opt.strip()}" for opt in self._beam_pipeline_options.split(","		) if opt]		)
          else:
               _a            =		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=__UpperCamelCase  ,				cache_dir=self._cache_dir  ,				base_path=dataset_module.builder_kwargs.get("base_path"		)  ,				)		)
          else:
               builders.append(
                   builder_cls(
                       config_name=__UpperCamelCase  ,				data_dir=self._data_dir  ,				beam_options=__UpperCamelCase  ,				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=__UpperCamelCase  ,				)
               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:
               _a            =		os.path.join(builder_cls.get_imported_module_dir()  ,				config.DATASETDICT_INFOS_FILENAME		)

               _a            =		Path(__UpperCamelCase		).name + ".py"

               _a            =		os.path.join(__UpperCamelCase  ,				__UpperCamelCase		)
               if os.path.isfile(__UpperCamelCase		):
                    _a            =		os.path.dirname(__UpperCamelCase		)
               elif os.path.isfile(__UpperCamelCase		):
                    _a            =		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
               _a            =		os.path.join(__UpperCamelCase  ,				config.DATASETDICT_INFOS_FILENAME		)
               copyfile(__UpperCamelCase  ,				__UpperCamelCase		)
               print(f"Dataset Infos file saved at {user_dataset_infos_path}"		)





'''simple docstring'''

def __UpperCamelCase (      __lowerCamelCase		:			float   ,	__lowerCamelCase		:			float   ,	__lowerCamelCase		:			float					)   ->  float:







     '''simple docstring'''






     if mass < 0:
          raise ValueError("The mass of the body cannot be negative"					)
     if radius <= 0:
          raise ValueError("The radius is always a positive non zero integer"					)
     return (mass * (velocity) ** 2) / radius


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)

