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     lowerCamelCase_    (  _lowercase					,	**_lowercase				)							->       List[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					,	**A__					,	)





class _a   (       __SCREAMING_SNAKE_CASE					):

	'''simple docstring'''



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

	def __init__(   self							, __UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase							, **__UpperCAmelCase							, ):
			__A							:    int         =      dataset
			__A							:    int         =      name
			__A							:    Optional[int]         =      cache_dir
			__A							:    List[Any]         =      beam_pipeline_options
			__A							:    Any         =      data_dir
			__A							:    Tuple         =      all_configs
			__A							:    Tuple         =      save_infos
			__A							:    str         =      ignore_verifications
			__A							:    Any         =      force_redownload
			__A							:    Tuple         =      config_kwargs







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

					__A							:    Optional[int]         =      Path(lowerCAmelCase__    ).name + '.py'

					__A							:    int         =      os.path.join(lowerCAmelCase__							, lowerCAmelCase__    )
					if os.path.isfile(lowerCAmelCase__    ):
							__A							:    Union[str, Any]         =      os.path.dirname(lowerCAmelCase__    )
					elif os.path.isfile(lowerCAmelCase__    ):
							__A							:    Union[str, Any]         =      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							:    Dict         =      os.path.join(lowerCAmelCase__							, config.DATASETDICT_INFOS_FILENAME    )
					copyfile(lowerCAmelCase__							, lowerCAmelCase__    )
					print(F"Dataset Infos file saved at {user_dataset_infos_path}"    )









from __future__ import annotations






def 		a__       (							A__							):
			if len(A__							) == 0:
						raise ValueError('find_max() arg is an empty sequence'							)
			SCREAMING_SNAKE_CASE_						:			str       						= nums[0]
			for x in nums:
						if x > max_num:
									SCREAMING_SNAKE_CASE_						:			Optional[int]       						= x
			return max_num


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)





