




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       __lowerCamelCase							(     __UpperCamelCase	,      **__UpperCamelCase)    ->  Union[str, 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	,      **_SCREAMING_SNAKE_CASE	,      )





class     a__ (      lowerCAmelCase_   ):







     @staticmethod
     def     lowerCAmelCase_       (			A				)						->					Optional[Any]:



          '''simple docstring'''



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







     def __init__(			self						,	A						,	A						,	A						,	A						,	A						,	A						,	A						,	A						,	A						,	**A						,	)						->					Dict:



          '''simple docstring'''



          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     lowerCAmelCase_       (			self				)						->					Dict:



          '''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				)
          a											=   self._dataset, self._name
          a											=   dataset_module_factory(__a				)
          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=__a						,	cache_dir=self._cache_dir						,	base_path=dataset_module.builder_kwargs.get("base_path"				)						,	)				)
          else:
               builders.append(
                   builder_cls(
                       config_name=__a						,	data_dir=self._data_dir						,	beam_options=__a						,	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=__a						,	)
               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(__a				).name + '.py'

               a											=   os.path.join(__a						,	__a				)
               if os.path.isfile(__a				):
                    a											=   os.path.dirname(__a				)
               elif os.path.isfile(__a				):
                    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(__a						,	config.DATASETDICT_INFOS_FILENAME				)
               copyfile(__a						,	__a				)
               print(F'''Dataset Infos file saved at {user_dataset_infos_path}'''				)







from __future__ import annotations

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase)    ->  int | None:
     for r in range(1	,      __UpperCamelCase):
          a											=   []
          for x in range(n - 1):
               a											=   pow(__UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase)
               if val in li:
                    break
               li.append(__UpperCamelCase)
          else:
               return r
     return None


if __name__ == "__main__":
   lowercase__   :    Tuple 					=      int(input("Enter a prime number q: "))
   lowercase__   :    List[Any] 					=      find_primitive(q)
   if a is None:
      print(F'Cannot find the primitive for the value: {a!r}')
   else:
      lowercase__   :    Any 					=      int(input("Enter private key of A: "))
      lowercase__   :    Optional[int] 					=      pow(a, a_private, q)
      lowercase__   :    Optional[Any] 					=      int(input("Enter private key of B: "))
      lowercase__   :    Any 					=      pow(a, b_private, q)

      lowercase__   :    Optional[Any] 					=      pow(b_public, a_private, q)
      lowercase__   :    List[str] 					=      pow(a_public, b_private, q)

      print("The key value generated by A is: ", a_secret)
      print("The key value generated by B is: ", b_secret)

