






'''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	(   _UpperCAmelCase					,  **_UpperCAmelCase )				->				List[str]:






					"""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					,  **_UpperCAmelCase					,  )





class    _snake_case   (				snake_case					):

					"""simple docstring"""





					@staticmethod
					def  __SCREAMING_SNAKE_CASE (     UpperCAmelCase__     )      ->	str:
										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__   , )      ->	int:
										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  __SCREAMING_SNAKE_CASE (     self     )      ->	List[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     )
										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'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCAmelCase							={"configuration_regnet": ["REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "RegNetConfig"]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCAmelCase							=[
						    "REGNET_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "RegNetForImageClassification",
						    "RegNetModel",
						    "RegNetPreTrainedModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCAmelCase							=[
						    "TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFRegNetForImageClassification",
						    "TFRegNetModel",
						    "TFRegNetPreTrainedModel",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCAmelCase							=[
						    "FlaxRegNetForImageClassification",
						    "FlaxRegNetModel",
						    "FlaxRegNetPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_regnet import (
												    REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RegNetForImageClassification,
												    RegNetModel,
												    RegNetPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_regnet import (
												    TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRegNetForImageClassification,
												    TFRegNetModel,
												    TFRegNetPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_regnet import (
												    FlaxRegNetForImageClassification,
												    FlaxRegNetModel,
												    FlaxRegNetPreTrainedModel,
												)


else:
						import sys

						__lowerCAmelCase							=_LazyModule(__name__, globals()["__file__"], _import_structure)

