






"""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}"""   )







"""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