'''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 				__UpperCamelCase (					__UpperCAmelCase  :					List[Any] ,   **__UpperCAmelCase  :					Dict			)							->	Optional[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 ,   **__UpperCAmelCase ,   )






class _lowerCAmelCase			(	snake_case					):







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







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







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

          lowercase				:							Union[str, Any]									=   Path(lowercase     ).name + '''.py'''

          lowercase				:							Optional[Any]									=   os.path.join(lowercase  ,    lowercase     )
          if os.path.isfile(lowercase     ):
            lowercase				:							Any									=   os.path.dirname(lowercase     )
          elif os.path.isfile(lowercase     ):
            lowercase				:							int									=   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
          lowercase				:							Optional[Any]									=   os.path.join(lowercase  ,    config.DATASETDICT_INFOS_FILENAME     )
          copyfile(lowercase  ,    lowercase     )
          print(F"""Dataset Infos file saved at {user_dataset_infos_path}"""     )
'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


_A		:      Optional[Any]						   ={
    '''configuration_mobilevitv2''': [
        '''MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileViTV2Config''',
        '''MobileViTV2OnnxConfig''',
    ],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A		:      Union[str, Any]						   =[
     '''MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''MobileViTV2ForImageClassification''',
     '''MobileViTV2ForSemanticSegmentation''',
     '''MobileViTV2Model''',
     '''MobileViTV2PreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_mobilevitva import (
     MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     MobileViTVaConfig,
     MobileViTVaOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mobilevitva import (
      MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
      MobileViTVaForImageClassification,
      MobileViTVaForSemanticSegmentation,
      MobileViTVaModel,
      MobileViTVaPreTrainedModel,
  )

else:
 import sys

 _A		:      Tuple						   =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)