





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_(      A					,   **A ):
	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 	_UpperCamelCase	(							__lowerCamelCase	):







					'''simple docstring'''

					@staticmethod
					def 							SCREAMING_SNAKE_CASE				(    __lowercase     :	ArgumentParser		):






						'''simple docstring'''







						UpperCAmelCase_		    =  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     :	str			,       __lowercase     :	str			,       __lowercase     :	str			,       __lowercase     :	str			,       __lowercase     :	str			,       __lowercase     :	str			,       __lowercase     :	bool			,       __lowercase     :	bool			,       __lowercase     :	bool			,       __lowercase     :	bool			,       **__lowercase     :	List[str]			,       ):






						'''simple docstring'''







						UpperCAmelCase_		    =  dataset
						UpperCAmelCase_		    =  name
						UpperCAmelCase_		    =  cache_dir
						UpperCAmelCase_		    =  beam_pipeline_options
						UpperCAmelCase_		    =  data_dir
						UpperCAmelCase_		    =  all_configs
						UpperCAmelCase_		    =  save_infos
						UpperCAmelCase_		    =  ignore_verifications
						UpperCAmelCase_		    =  force_redownload
						UpperCAmelCase_		    =  config_kwargs





					def 							SCREAMING_SNAKE_CASE				(    self     :	Union[str, Any]		):






						'''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_		    =  self._dataset, self._name
						UpperCAmelCase_		    =  dataset_module_factory(UpperCamelCase_		)
						UpperCAmelCase_		    =  import_main_class(dataset_module.module_path		)
						UpperCAmelCase_		    =  []
						if self._beam_pipeline_options:
							UpperCAmelCase_		    =  beam.options.pipeline_options.PipelineOptions(
							    flags=[F"""--{opt.strip()}""" for opt in self._beam_pipeline_options.split(""","""		) if opt]		)
						else:
							UpperCAmelCase_		    =  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:
							UpperCAmelCase_		    =  os.path.join(builder_cls.get_imported_module_dir()			,       config.DATASETDICT_INFOS_FILENAME		)

							UpperCAmelCase_		    =  Path(UpperCamelCase_		).name + """.py"""

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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_   							=      {'configuration_dinat': ['DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DinatConfig']}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase_   							=      [
           'DINAT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'DinatForImageClassification',
           'DinatModel',
           'DinatPreTrainedModel',
           'DinatBackbone',
       ]

if TYPE_CHECKING:
       from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_dinat import (
                  DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DinatBackbone,
                  DinatForImageClassification,
                  DinatModel,
                  DinatPreTrainedModel,
              )

else:
       import sys

       UpperCAmelCase_   							=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

