


'''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					(lowerCAmelCase__	:Union[str, Any]					,    **lowerCAmelCase__	:Any		) ->	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					,    **lowerCAmelCase__					,    )






class 			a  (  _SCREAMING_SNAKE_CASE							):
 @staticmethod
 def 				__UpperCAmelCase		(			__magic_name__    )       ->				Tuple:
      _a         =				parser.add_parser('run_beam'   ,					help='Run a Beam dataset processing pipeline'    )
      run_beam_parser.add_argument('dataset'   ,					type=__magic_name__   ,					help='Name of the dataset to download'    )
      run_beam_parser.add_argument('--name'   ,					type=__magic_name__   ,					default=__magic_name__   ,					help='Dataset config name'    )
      run_beam_parser.add_argument(
          '--cache_dir'   ,					type=__magic_name__   ,					default=__magic_name__   ,					help='Cache directory where the datasets are stored'   ,					)
      run_beam_parser.add_argument(
          '--beam_pipeline_options'   ,					type=__magic_name__   ,					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=__magic_name__   ,					default=__magic_name__   ,					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=__magic_name__    )
 def __init__(			self   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					**__magic_name__   ,					)       ->				List[str]:
      _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 				__UpperCAmelCase		(			self    )       ->				Union[str, Any]:
      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(__magic_name__    )
      _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=__magic_name__   ,					cache_dir=self._cache_dir   ,					base_path=dataset_module.builder_kwargs.get('base_path'    )   ,					)    )
      else:
           builders.append(
               builder_cls(
                   config_name=__magic_name__   ,					data_dir=self._data_dir   ,					beam_options=__magic_name__   ,					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=__magic_name__   ,					)
           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(__magic_name__    ).name + '.py'

           _a         =				os.path.join(__magic_name__   ,					__magic_name__    )
           if os.path.isfile(__magic_name__    ):
                _a         =				os.path.dirname(__magic_name__    )
           elif os.path.isfile(__magic_name__    ):
                _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(__magic_name__   ,					config.DATASETDICT_INFOS_FILENAME    )
           copyfile(__magic_name__   ,					__magic_name__    )
           print(f'Dataset Infos file saved at {user_dataset_infos_path}'    )





'''simple docstring'''






from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available




@dataclass
class 			a  (  _SCREAMING_SNAKE_CASE							):
 _lowerCAmelCase			       =						{
     """sld_warmup_steps""": 1_5,
     """sld_guidance_scale""": 2_0,
     """sld_threshold""": 0.0,
     """sld_momentum_scale""": 0.0,
     """sld_mom_beta""": 0.0,
 }
 _lowerCAmelCase			       =						{
     """sld_warmup_steps""": 1_0,
     """sld_guidance_scale""": 1_0_0_0,
     """sld_threshold""": 0.01,
     """sld_momentum_scale""": 0.3,
     """sld_mom_beta""": 0.4,
 }
 _lowerCAmelCase			       =						{
     """sld_warmup_steps""": 7,
     """sld_guidance_scale""": 2_0_0_0,
     """sld_threshold""": 0.025,
     """sld_momentum_scale""": 0.5,
     """sld_mom_beta""": 0.7,
 }
 _lowerCAmelCase			       =						{
     """sld_warmup_steps""": 0,
     """sld_guidance_scale""": 5_0_0_0,
     """sld_threshold""": 1.0,
     """sld_momentum_scale""": 0.5,
     """sld_mom_beta""": 0.7,
 }





@dataclass
class 			a  (  _SCREAMING_SNAKE_CASE							):
 _lowerCAmelCase			       =						42
 _lowerCAmelCase			       =						42
 _lowerCAmelCase			       =						42
 _lowerCAmelCase			       =						42


if is_transformers_available() and is_torch_available():
 from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
 from .safety_checker import SafeStableDiffusionSafetyChecker

