
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 						lowercase_			(   A__ ,		**A__		)							->							List[str]:
    """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 ,		**A__ ,		)







class      lowerCamelCase		(  A_  ):





     @staticmethod
     def 		UpperCAmelCase(_A      :    ArgumentParser   )						->						List[str]:
         snake_case      =					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      :    List[str]				,    _A      :    str				,    _A      :    str				,    _A      :    str				,    _A      :    str				,    _A      :    str				,    _A      :    bool				,    _A      :    bool				,    _A      :    bool				,    _A      :    bool				,    **_A      :    List[str]				,    )						->						Optional[int]:
         snake_case      =					dataset
         snake_case      =					name
         snake_case      =					cache_dir
         snake_case      =					beam_pipeline_options
         snake_case      =					data_dir
         snake_case      =					all_configs
         snake_case      =					save_infos
         snake_case      =					ignore_verifications
         snake_case      =					force_redownload
         snake_case      =					config_kwargs





     def 		UpperCAmelCase(self      :    List[Any]   )						->						Union[str, Any]:
         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     ,							snake_case      =					self._dataset, self._name
         snake_case      =					dataset_module_factory(_A   )
         snake_case      =					import_main_class(dataset_module.module_path   )
         snake_case      =					[]
         if self._beam_pipeline_options:
             snake_case      =					beam.options.pipeline_options.PipelineOptions(
                 flags=[f'--{opt.strip()}' for opt in self._beam_pipeline_options.split(","   ) if opt]   )
         else:
             snake_case      =					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:
             snake_case      =					os.path.join(builder_cls.get_imported_module_dir()				,    config.DATASETDICT_INFOS_FILENAME   )

             snake_case      =					Path(_A   ).name + ".py"

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



import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_A  					= 50_00_00

_A					,  _A  					= os.path.split(__file__)
_A  					= os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))



@get_duration
def 						lowercase_			(   A__		)							->							str:
    """simple docstring"""

    snake_case      =					dataset.select(range(0 ,		len(A__		) ,		2		)		)



@get_duration
def 						lowercase_			(   A__		)							->							int:
    """simple docstring"""

    snake_case      =					dataset.sort("numbers"		)



@get_duration
def 						lowercase_			(   A__		)							->							Optional[int]:
    """simple docstring"""

    snake_case      =					dataset.shuffle()



@get_duration
def 						lowercase_			(   A__		)							->							int:
    """simple docstring"""

    snake_case      =					dataset.train_test_split(0.1		)



@get_duration
def 						lowercase_			(   A__ ,		A__=10		)							->							List[Any]:
    """simple docstring"""

    for shard_id in range(A__		):
        snake_case      =					dataset.shard(A__ ,		A__		)



def 						lowercase_			(   )							->							List[str]:
    """simple docstring"""

    snake_case      =					{"num examples": SPEED_TEST_N_EXAMPLES}
    snake_case      =					(select, sort, shuffle, train_test_split, shard)
    with tempfile.TemporaryDirectory() as tmp_dir:
        print("generating dataset"		)
        snake_case      =					datasets.Features({"text": datasets.Value("string"		), "numbers": datasets.Value("float32"		)}		)
        snake_case      =					generate_example_dataset(
            os.path.join(A__ ,		"dataset.arrow"		) ,		A__ ,		num_examples=A__		)
        print("Functions"		)
        for func in functions:
            print(func.__name__		)
            snake_case      =					func(A__		)

    with open(A__ ,		"wb"		) as f:
        f.write(json.dumps(A__		).encode("utf-8"		)		)


if __name__ == "__main__":  # useful to run the profiler
   benchmark_indices_mapping()

