

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:     str     ,					**lowerCAmelCase:     List[Any]       )      ->				List[str]:
				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     (		UpperCAmelCase ):


						@staticmethod
						def 				_UpperCAmelCase (				A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						int       						=			parser.add_parser("run_beam"  ,    help="Run a Beam dataset processing pipeline"		)
										run_beam_parser.add_argument("dataset"  ,    type=A_  ,    help="Name of the dataset to download"		)
										run_beam_parser.add_argument("--name"  ,    type=A_  ,    default=A_  ,    help="Dataset config name"		)
										run_beam_parser.add_argument(
										    "--cache_dir"  ,    type=A_  ,    default=A_  ,    help="Cache directory where the datasets are stored"  ,    )
										run_beam_parser.add_argument(
										    "--beam_pipeline_options"  ,    type=A_  ,    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=A_  ,    default=A_  ,    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=A_		)


						def __init__(				self  ,    A_  ,    A_  ,    A_  ,    A_  ,    A_  ,    A_  ,    A_  ,    A_  ,    A_  ,    **A_  ,    ):







										'''simple docstring'''


										_UpperCAmelCase				:						Union[str, Any]       						=			dataset
										_UpperCAmelCase				:						List[Any]       						=			name
										_UpperCAmelCase				:						Optional[Any]       						=			cache_dir
										_UpperCAmelCase				:						Optional[int]       						=			beam_pipeline_options
										_UpperCAmelCase				:						int       						=			data_dir
										_UpperCAmelCase				:						int       						=			all_configs
										_UpperCAmelCase				:						List[str]       						=			save_infos
										_UpperCAmelCase				:						List[str]       						=			ignore_verifications
										_UpperCAmelCase				:						List[str]       						=			force_redownload
										_UpperCAmelCase				:						int       						=			config_kwargs





						def 				_UpperCAmelCase (				self		):







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

														_UpperCAmelCase				:						Dict       						=			Path(A_		).name + ".py"

														_UpperCAmelCase				:						str       						=			os.path.join(A_  ,    A_		)
														if os.path.isfile(A_		):
																		_UpperCAmelCase				:						Dict       						=			os.path.dirname(A_		)
														elif os.path.isfile(A_		):
																		_UpperCAmelCase				:						Tuple       						=			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				:						Optional[Any]       						=			os.path.join(A_  ,    config.DATASETDICT_INFOS_FILENAME		)
														copyfile(A_  ,    A_		)
														print(f'Dataset Infos file saved at {user_dataset_infos_path}'		)





import math

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     int       )      ->				list:
				if n <= 0:
								raise ValueError("Only positive integers have prime factors"       )
				_UpperCAmelCase				:						Union[str, Any]       						=			[]
				while n % 2 == 0:
								pf.append(2       )
								_UpperCAmelCase				:						Tuple       						=			int(n / 2       )
				for i in range(3     ,					int(math.sqrt(lowerCAmelCase       )       ) + 1     ,					2       ):
								while n % i == 0:
												pf.append(lowerCAmelCase       )
												_UpperCAmelCase				:						Optional[int]       						=			int(n / i       )
				if n > 2:
								pf.append(lowerCAmelCase       )
				return pf

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     int       )      ->				int:
				if n <= 0:
								raise ValueError("Only positive numbers are accepted"       )
				_UpperCAmelCase				:						Dict       						=			1
				_UpperCAmelCase				:						str       						=			1
				while n % 2 == 0:
								temp += 1
								_UpperCAmelCase				:						List[str]       						=			int(n / 2       )
				div *= temp
				for i in range(3     ,					int(math.sqrt(lowerCAmelCase       )       ) + 1     ,					2       ):
								_UpperCAmelCase				:						Any       						=			1
								while n % i == 0:
												temp += 1
												_UpperCAmelCase				:						Any       						=			int(n / i       )
								div *= temp
				if n > 1:
								div *= 2
				return div

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     int       )      ->				int:
				if n <= 0:
								raise ValueError("Only positive numbers are accepted"       )
				_UpperCAmelCase				:						Optional[Any]       						=			1
				_UpperCAmelCase				:						Union[str, Any]       						=			1
				while n % 2 == 0:
								temp += 1
								_UpperCAmelCase				:						Optional[Any]       						=			int(n / 2       )
				if temp > 1:
								s *= (2**temp - 1) / (2 - 1)
				for i in range(3     ,					int(math.sqrt(lowerCAmelCase       )       ) + 1     ,					2       ):
								_UpperCAmelCase				:						List[str]       						=			1
								while n % i == 0:
												temp += 1
												_UpperCAmelCase				:						List[Any]       						=			int(n / i       )
								if temp > 1:
												s *= (i**temp - 1) / (i - 1)
				return int(lowerCAmelCase       )

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     int       )      ->				int:
				_UpperCAmelCase				:						Any       						=			n
				for x in set(prime_factors(lowerCAmelCase       )       ):
								s *= (x - 1) / x
				return int(lowerCAmelCase       )


if __name__ == "__main__":
					import doctest

					doctest.testmod()


