code: 
  
  
  
  
  
  
  """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	(							__snake_case:   Any   ,    **__snake_case:   int     )						->		Union[str, Any]:
  
         """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 					UpperCamelCase__   (							lowerCamelCase__):
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      @staticmethod
      def 							a__ (		UpperCamelCase_   :  ArgumentParser   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						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   :  Optional[Any]							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  bool							,						UpperCamelCase_   :  bool							,						UpperCamelCase_   :  bool							,						UpperCamelCase_   :  bool							,						**UpperCamelCase_   :  Optional[int]							,						):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						dataset
             __magic_name__										=						name
             __magic_name__										=						cache_dir
             __magic_name__										=						beam_pipeline_options
             __magic_name__										=						data_dir
             __magic_name__										=						all_configs
             __magic_name__										=						save_infos
             __magic_name__										=						ignore_verifications
             __magic_name__										=						force_redownload
             __magic_name__										=						config_kwargs
  
  
  
  
  
  
  
      def 							a__ (		self   :  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   )
             __magic_name__							,     __magic_name__										=						self._dataset, self._name
             __magic_name__										=						dataset_module_factory(SCREAMING_SNAKE_CASE_   )
             __magic_name__										=						import_main_class(dataset_module.module_path   )
             __magic_name__										=						[]
             if self._beam_pipeline_options:
                    __magic_name__										=						beam.options.pipeline_options.PipelineOptions(
                        flags=[f"""--{opt.strip()}""" for opt in self._beam_pipeline_options.split(','   ) if opt]   )
             else:
                    __magic_name__										=						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:
                    __magic_name__										=						os.path.join(builder_cls.get_imported_module_dir()							,						config.DATASETDICT_INFOS_FILENAME   )
  
                    __magic_name__										=						Path(SCREAMING_SNAKE_CASE_   ).name + '.py'
  
                    __magic_name__										=						os.path.join(SCREAMING_SNAKE_CASE_							,						SCREAMING_SNAKE_CASE_   )
                    if os.path.isfile(SCREAMING_SNAKE_CASE_   ):
                           __magic_name__										=						os.path.dirname(SCREAMING_SNAKE_CASE_   )
                    elif os.path.isfile(SCREAMING_SNAKE_CASE_   ):
                           __magic_name__										=						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
                    __magic_name__										=						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}"""   )
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  import numpy as np
  
  
  def        A	(							__snake_case:   Any   ,    __snake_case:   List[str]   ,    __snake_case:   Tuple     )						->		Dict:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						n_draws * class_counts / class_counts.sum()
         # floored means we don't overshoot n_samples, but probably undershoot
         __magic_name__										=						np.floor(__snake_case     )
         # we add samples according to how much "left over" probability
         # they had, until we arrive at n_samples
         __magic_name__										=						int(n_draws - floored.sum()     )
         if need_to_add > 0:
                __magic_name__										=						continuous - floored
                __magic_name__										=						np.sort(np.unique(__snake_case     )     )[::-1]
                # add according to remainder, but break ties
                # randomly to avoid biases
                for value in values:
                       ((__magic_name__)							,     )										=						np.where(remainder == value     )
                       # if we need_to_add less than what's in inds
                       # we draw randomly from them.
                       # if we need to add more, we add them all and
                       # go to the next value
                       __magic_name__										=						min(len(__snake_case     )   ,    __snake_case     )
                       __magic_name__										=						rng.choice(__snake_case   ,    size=__snake_case   ,    replace=__snake_case     )
                       floored[inds] += 1
                       need_to_add -= add_now
                       if need_to_add == 0:
                              break
         return floored.astype(np.intaa     )
  
  
  
  
  
  
  def        A	(							__snake_case:   Any   ,    __snake_case:   str   ,    __snake_case:   int   ,    __snake_case:   Optional[Any]   ,    __snake_case:   Any=1_0     )						->		Any:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__							,     __magic_name__										=						np.unique(__snake_case   ,    return_inverse=__snake_case     )
         __magic_name__										=						classes.shape[0]
         __magic_name__										=						np.bincount(__snake_case     )
         if np.min(__snake_case     ) < 2:
                raise ValueError('Minimum class count error'     )
         if n_train < n_classes:
                raise ValueError(
                    'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)     )
         if n_test < n_classes:
                raise ValueError(
                    'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)     )
         __magic_name__										=						np.split(np.argsort(__snake_case   ,    kind='mergesort'     )   ,    np.cumsum(__snake_case     )[:-1]     )
         for _ in range(__snake_case     ):
                __magic_name__										=						approximate_mode(__snake_case   ,    __snake_case   ,    __snake_case     )
                __magic_name__										=						class_counts - n_i
                __magic_name__										=						approximate_mode(__snake_case   ,    __snake_case   ,    __snake_case     )
  
                __magic_name__										=						[]
                __magic_name__										=						[]
  
                for i in range(__snake_case     ):
                       __magic_name__										=						rng.permutation(class_counts[i]     )
                       __magic_name__										=						class_indices[i].take(__snake_case   ,    mode='clip'     )
                       train.extend(perm_indices_class_i[: n_i[i]]     )
                       test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]     )
                __magic_name__										=						rng.permutation(__snake_case     )
                __magic_name__										=						rng.permutation(__snake_case     )
  
                yield train, test