
'''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   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE,						**__SCREAMING_SNAKE_CASE    ):
    """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,						**SCREAMING_SNAKE_CASE_,						)






class 				UpperCAmelCase_			(		_UpperCAmelCase   ):





 @staticmethod
 def     snake_case__						(      lowercase_):
     snake_case_	:      str            =  parser.add_parser("run_beam"	,    help="Run a Beam dataset processing pipeline")
     run_beam_parser.add_argument("dataset"	,    type=lowercase__	,    help="Name of the dataset to download")
     run_beam_parser.add_argument("--name"	,    type=lowercase__	,    default=lowercase__	,    help="Dataset config name")
     run_beam_parser.add_argument(
         "--cache_dir"	,    type=lowercase__	,    default=lowercase__	,    help="Cache directory where the datasets are stored"	,    )
     run_beam_parser.add_argument(
         "--beam_pipeline_options"	,    type=lowercase__	,    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=lowercase__	,    default=lowercase__	,    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=lowercase__)





 def __init__(      self	,    lowercase_	,    lowercase_	,    lowercase_	,    lowercase_	,    lowercase_	,    lowercase_	,    lowercase_	,    lowercase_	,    lowercase_	,    **lowercase_	,    ):
     snake_case_	:      Tuple            =  dataset
     snake_case_	:      Dict            =  name
     snake_case_	:      str            =  cache_dir
     snake_case_	:      Optional[Any]            =  beam_pipeline_options
     snake_case_	:      List[Any]            =  data_dir
     snake_case_	:      Dict            =  all_configs
     snake_case_	:      List[str]            =  save_infos
     snake_case_	:      List[Any]            =  ignore_verifications
     snake_case_	:      int            =  force_redownload
     snake_case_	:      Tuple            =  config_kwargs





 def     snake_case__						(      self):
     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)
     snake_case_	:      Any            =  self._dataset, self._name
     snake_case_	:      List[str]            =  dataset_module_factory(lowercase__)
     snake_case_	:      Optional[int]            =  import_main_class(dataset_module.module_path)
     snake_case_	:      List[DatasetBuilder]            =  []
     if self._beam_pipeline_options:
         snake_case_	:      List[str]            =  beam.options.pipeline_options.PipelineOptions(
             flags=[F'--{opt.strip()}' for opt in self._beam_pipeline_options.split(",") if opt])
     else:
         snake_case_	:      Dict            =  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=lowercase__	,    cache_dir=self._cache_dir	,    base_path=dataset_module.builder_kwargs.get("base_path")	,    ))
     else:
         builders.append(
             builder_cls(
                 config_name=lowercase__	,    data_dir=self._data_dir	,    beam_options=lowercase__	,    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=lowercase__	,    )
         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:
         snake_case_	:      Dict            =  os.path.join(builder_cls.get_imported_module_dir()	,    config.DATASETDICT_INFOS_FILENAME)

         snake_case_	:      List[Any]            =  Path(lowercase__).name + ".py"

         snake_case_	:      Optional[int]            =  os.path.join(lowercase__	,    lowercase__)
         if os.path.isfile(lowercase__):
             snake_case_	:      Any            =  os.path.dirname(lowercase__)
         elif os.path.isfile(lowercase__):
             snake_case_	:      List[str]            =  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
         snake_case_	:      Optional[int]            =  os.path.join(lowercase__	,    config.DATASETDICT_INFOS_FILENAME)
         copyfile(lowercase__	,    lowercase__)
         print(F'Dataset Infos file saved at {user_dataset_infos_path}')






'''simple docstring'''
from __future__ import annotations

from collections.abc import Callable

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE = 1_0_0,						):
    """simple docstring"""






    snake_case_	:      Optional[Any]            =  x_start
    snake_case_	:      str            =  fnc(__SCREAMING_SNAKE_CASE    )
    snake_case_	:      List[str]            =  0.0

    for _ in range(__SCREAMING_SNAKE_CASE    ):
        # Approximates small segments of curve as linear and solve
        # for trapezoidal area
        snake_case_	:      List[str]            =  (x_end - x_start) / steps + xa
        snake_case_	:      Optional[int]            =  fnc(__SCREAMING_SNAKE_CASE    )
        area += abs(fxa + fxa    ) * (xa - xa) / 2

        # Increment step
        snake_case_	:      List[Any]            =  xa
        snake_case_	:      Optional[Any]            =  fxa
    return area


if __name__ == "__main__":

  def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE    ):
      """simple docstring"""






      return x**3

  print("f(x) = x^3")
  print("The area between the curve, x = -10, x = 10 and the x axis is:")
  a_     =  10
  while i <= 10_0000:
    a_     =  trapezoidal_area(f, -5, 5, i)
    print(f"""with {i} steps: {area}""")
    i *= 10




