





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  (_SCREAMING_SNAKE_CASE		,						**_SCREAMING_SNAKE_CASE		)				->   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		,						**UpperCamelCase_		,						)


class 				SCREAMING_SNAKE_CASE   (lowerCAmelCase_      ):


   @staticmethod
   def        SCREAMING_SNAKE_CASE_							(    a			:				ArgumentParser       )->   Any:





     """simple docstring"""







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


   def __init__(    self			:				List[Any]			,     a			:				str			,     a			:				str			,     a			:				str			,     a			:				str			,     a			:				str			,     a			:				bool			,     a			:				bool			,     a			:				bool			,     a			:				bool			,     **a			:				str			,     )->   int:





     """simple docstring"""







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


   def        SCREAMING_SNAKE_CASE_							(    self			:				Dict       )->   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       )
     lowercase__			,    lowercase__				=  self._dataset, self._name
     lowercase__				=  dataset_module_factory(_SCREAMING_SNAKE_CASE       )
     lowercase__				=  import_main_class(dataset_module.module_path       )
     lowercase__				=  []
     if self._beam_pipeline_options:
       lowercase__				=  beam.options.pipeline_options.PipelineOptions(
           flags=[f"""--{opt.strip()}""" for opt in self._beam_pipeline_options.split(','       ) if opt]       )
     else:
       lowercase__				=  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=_SCREAMING_SNAKE_CASE			,     cache_dir=self._cache_dir			,     base_path=dataset_module.builder_kwargs.get('base_path'       )			,     )       )
     else:
       builders.append(
           builder_cls(
               config_name=_SCREAMING_SNAKE_CASE			,     data_dir=self._data_dir			,     beam_options=_SCREAMING_SNAKE_CASE			,     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=_SCREAMING_SNAKE_CASE			,     )
       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__				=  os.path.join(builder_cls.get_imported_module_dir()			,     config.DATASETDICT_INFOS_FILENAME       )

       lowercase__				=  Path(_SCREAMING_SNAKE_CASE       ).name + '.py'

       lowercase__				=  os.path.join(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE       )
       if os.path.isfile(_SCREAMING_SNAKE_CASE       ):
         lowercase__				=  os.path.dirname(_SCREAMING_SNAKE_CASE       )
       elif os.path.isfile(_SCREAMING_SNAKE_CASE       ):
         lowercase__				=  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__				=  os.path.join(_SCREAMING_SNAKE_CASE			,     config.DATASETDICT_INFOS_FILENAME       )
       copyfile(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE       )
       print(f"""Dataset Infos file saved at {user_dataset_infos_path}"""       )









import warnings

from .generation import GenerationMixin



class 							_UpperCamelCase    (		lowerCAmelCase_   ):
       # warning at import time
       warnings.warn(
           '''Importing `GenerationMixin` from `src/transformers/generation_utils.py` is deprecated and will '''
           '''be removed in Transformers v5. Import as `from transformers import GenerationMixin` instead.'''     ,  lowerCAmelCase_     ,  )

