


"""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   a__			(					__lowercase  ,      **__lowercase )						->						Optional[int]:
				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  ,      **__lowercase  ,      )




class snake_case   (	_UpperCamelCase):







		@staticmethod
		def 			a_			(       a__   :							ArgumentParser		)							->    Any:





						'''simple docstring'''







						_A       	=      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   :							Dict     , a__   :							str     , a__   :							str     , a__   :							str     , a__   :							str     , a__   :							str     , a__   :							bool     , a__   :							bool     , a__   :							bool     , a__   :							bool     , **a__   :							int     , )							->    Any:





						'''simple docstring'''







						_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   :							List[str]		)							->    Optional[int]:





						'''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		)
						_A	,		_A       	=      self._dataset, self._name
						_A       	=      dataset_module_factory(a__		)
						_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=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:
										_A       	=      os.path.join(builder_cls.get_imported_module_dir()     , config.DATASETDICT_INFOS_FILENAME		)

										_A       	=      Path(a__		).name + ".py"

										_A       	=      os.path.join(a__     , a__		)
										if os.path.isfile(a__		):
														_A       	=      os.path.dirname(a__		)
										elif os.path.isfile(a__		):
														_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(a__     , config.DATASETDICT_INFOS_FILENAME		)
										copyfile(a__     , a__		)
										print(F"""Dataset Infos file saved at {user_dataset_infos_path}"""		)



"""simple docstring"""


import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


a_											=     2 << 20  # 2mb
a_											=     get_logger(__name__)
class snake_case   (	_UpperCamelCase):







		def 			a_			(       self   :							Tuple		)							->    Optional[int]:





						'''simple docstring'''







						_A       	=      self._options.get_all_options().get("runner"		)
						return runner in [None, "DirectRunner", "PortableRunner"]






def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase=False )						->						Any:
				_A       	=      FileSystems
				if fs.exists(__lowercase ):
								if force_upload:
												logger.info(f"""Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True.""" )
								else:
												logger.info(f"""Remote path already exist: {remote_file_path}. Skipping it as force_upload=False.""" )
												return
				with fs.create(__lowercase ) as remote_file:
								with open(__lowercase  ,      "rb" ) as local_file:
												_A       	=      local_file.read(__lowercase )
												while chunk:
																remote_file.write(__lowercase )
																_A       	=      local_file.read(__lowercase )






def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase=False )						->						Union[str, Any]:
				_A       	=      FileSystems
				if os.path.exists(__lowercase ):
								if force_download:
												logger.info(f"""Local path already exist: {remote_file_path}. Overwriting it as force_upload=True.""" )
								else:
												logger.info(f"""Local path already exist: {remote_file_path}. Skipping it as force_upload=False.""" )
												return
				with fs.open(__lowercase ) as remote_file:
								with open(__lowercase  ,      "wb" ) as local_file:
												_A       	=      remote_file.read(__lowercase )
												while chunk:
																local_file.write(__lowercase )
																_A       	=      remote_file.read(__lowercase )