"""simple docstring"""







from __future__ import annotations

from collections.abc import Callable



def 			A__						(  lowercase_    :  Callable[[int | float], int | float]			,   lowercase_    :  int | float			,   lowercase_    :  int | float			,   lowercase_    :  int = 100			,   )     ->       float:
   __lowerCamelCase								=				x_start
   __lowerCamelCase								=				fnc(lowercase_	)
   __lowerCamelCase								=				0.0

   for _ in range(lowercase_	):
      # Approximates small segments of curve as linear and solve
      # for trapezoidal area
      __lowerCamelCase								=				(x_end - x_start) / steps + xa
      __lowerCamelCase								=				fnc(lowercase_	)
      area += abs(fxa + fxa	) * (xa - xa) / 2

      # Increment step
      __lowerCamelCase								=				xa
      __lowerCamelCase								=				fxa
   return area


if __name__ == "__main__":



     def 			A__						(  lowercase_    :  Union[str, Any]	)     ->       int:
        return x**3

     print('''f(x) = x^3''')
     print('''The area between the curve, x = -10, x = 10 and the x axis is:''')
     _SCREAMING_SNAKE_CASE  :		Any        =      1_0
     while i <= 1_0_0_0_0_0:
          _SCREAMING_SNAKE_CASE  :		List[str]        =      trapezoidal_area(f, -5, 5, i)
          print(F"with {i} steps: {area}")
          i *= 1_0







"""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__						(  lowercase_    :  Dict			,   **lowercase_    :  Union[str, Any]	)     ->       Dict:
   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			,   **lowercase_			,   )






class     _SCREAMING_SNAKE_CASE       ( UpperCAmelCase							):



   '''simple docstring'''



   @staticmethod
   def       _A							(  _SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      __lowerCamelCase								=				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   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							**_SCREAMING_SNAKE_CASE   ,							):


      '''simple docstring'''

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







   def       _A							(  self  ):


      '''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  )
      __lowerCamelCase		,__lowerCamelCase								=				self._dataset, self._name
      __lowerCamelCase								=				dataset_module_factory(_SCREAMING_SNAKE_CASE  )
      __lowerCamelCase								=				import_main_class(dataset_module.module_path  )
      __lowerCamelCase								=				[]
      if self._beam_pipeline_options:
         __lowerCamelCase								=				beam.options.pipeline_options.PipelineOptions(
             flags=[F'''--{opt.strip()}''' for opt in self._beam_pipeline_options.split(','  ) if opt]  )
      else:
         __lowerCamelCase								=				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:
         __lowerCamelCase								=				os.path.join(builder_cls.get_imported_module_dir()   ,							config.DATASETDICT_INFOS_FILENAME  )

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

         __lowerCamelCase								=				os.path.join(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  )
         if os.path.isfile(_SCREAMING_SNAKE_CASE  ):
            __lowerCamelCase								=				os.path.dirname(_SCREAMING_SNAKE_CASE  )
         elif os.path.isfile(_SCREAMING_SNAKE_CASE  ):
            __lowerCamelCase								=				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
         __lowerCamelCase								=				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}'''  )






