





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   :	str      , **_lowerCamelCase   :	Optional[Any]  ):





						'''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  _a  (					lowercase__				):






							"""simple docstring"""

							@staticmethod
							def   A_	(   a					:  ArgumentParser     )						->int:
													SCREAMING_SNAKE_CASE__  :					Any =       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					:  Optional[Any]				,						)						->Optional[int]:
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       dataset
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       name
													SCREAMING_SNAKE_CASE__  :					int =       cache_dir
													SCREAMING_SNAKE_CASE__  :					Dict =       beam_pipeline_options
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       data_dir
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       all_configs
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       save_infos
													SCREAMING_SNAKE_CASE__  :					int =       ignore_verifications
													SCREAMING_SNAKE_CASE__  :					List[Any] =       force_redownload
													SCREAMING_SNAKE_CASE__  :					Tuple =       config_kwargs






							def   A_	(   self					:  Union[str, Any]     )						->str:
													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     )
													SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       self._dataset, self._name
													SCREAMING_SNAKE_CASE__  :					int =       dataset_module_factory(a     )
													SCREAMING_SNAKE_CASE__  :					str =       import_main_class(dataset_module.module_path     )
													SCREAMING_SNAKE_CASE__  :					List[DatasetBuilder] =       []
													if self._beam_pipeline_options:
																			SCREAMING_SNAKE_CASE__  :					Tuple =       beam.options.pipeline_options.PipelineOptions(
																			    flags=[f"""--{opt.strip()}""" for opt in self._beam_pipeline_options.split(","     ) if opt]     )
													else:
																			SCREAMING_SNAKE_CASE__  :					Tuple =       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:
																			SCREAMING_SNAKE_CASE__  :					List[str] =       os.path.join(builder_cls.get_imported_module_dir()				,						config.DATASETDICT_INFOS_FILENAME     )

																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       Path(a     ).name + ".py"

																			SCREAMING_SNAKE_CASE__  :					List[Any] =       os.path.join(a				,						a     )
																			if os.path.isfile(a     ):
																									SCREAMING_SNAKE_CASE__  :					Optional[int] =       os.path.dirname(a     )
																			elif os.path.isfile(a     ):
																									SCREAMING_SNAKE_CASE__  :					List[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
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       os.path.join(a				,						config.DATASETDICT_INFOS_FILENAME     )
																			copyfile(a				,						a     )
																			print(f"""Dataset Infos file saved at {user_dataset_infos_path}"""     )






from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
__lowercase							:List[Any]							=	TypeVar("T", bound="Enum")



@unique
class  _a  (					lowercase__				):






							"""simple docstring"""

							snake_case_							=    80
							snake_case_							=    70
							snake_case_							=    60
							snake_case_							=    50
							snake_case_							=    40
							snake_case_							=    30
							snake_case_							=    20
							snake_case_							=    10







@unique
class  _a  (					lowercase__				):






							"""simple docstring"""

							snake_case_							=    24
							snake_case_							=    21
							snake_case_							=    18
							snake_case_							=    15
							snake_case_							=    12
							snake_case_							=    9
							snake_case_							=    6
							snake_case_							=    3
							snake_case_							=    2
							snake_case_							=    1
							snake_case_							=    -1
							snake_case_							=    -2
							snake_case_							=    -3
							snake_case_							=    -6
							snake_case_							=    -9
							snake_case_							=    -12
							snake_case_							=    -15
							snake_case_							=    -18
							snake_case_							=    -21
							snake_case_							=    -24

							@classmethod
							def   A_	(   cls					:  type[T]     )						->dict:
													return {unit.name: unit.value for unit in cls if unit.value > 0}






							@classmethod
							def   A_	(   cls					:  type[T]     )						->dict:
													return {unit.name: unit.value for unit in cls if unit.value < 0}







def    UpperCAmelCase				(  _lowerCamelCase   :	float  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					str =       SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
						for name_prefix, value_prefix in prefixes.items():
												SCREAMING_SNAKE_CASE__  :					str =       value / (10**value_prefix)
												if numerical_part > 1:
																		return f"""{numerical_part!s} {name_prefix}"""
						return str(_lowerCamelCase  )







def    UpperCAmelCase				(  _lowerCamelCase   :	float  ):





						'''simple docstring'''


						for prefix in BinaryUnit:
												SCREAMING_SNAKE_CASE__  :					List[Any] =       value / (2**prefix.value)
												if numerical_part > 1:
																		return f"""{numerical_part!s} {prefix.name}"""
						return str(_lowerCamelCase  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()