
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						(  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__):

						'''simple docstring'''



						@staticmethod
						def    A__       (  A	)  ->Optional[Any]:
									UpperCAmelCase__		:int 		=    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  , )  ->Optional[int]:
									UpperCAmelCase__		:Any 		=    dataset
									UpperCAmelCase__		:int 		=    name
									UpperCAmelCase__		:List[str] 		=    cache_dir
									UpperCAmelCase__		:Optional[int] 		=    beam_pipeline_options
									UpperCAmelCase__		:str 		=    data_dir
									UpperCAmelCase__		:Tuple 		=    all_configs
									UpperCAmelCase__		:List[str] 		=    save_infos
									UpperCAmelCase__		:List[Any] 		=    ignore_verifications
									UpperCAmelCase__		:int 		=    force_redownload
									UpperCAmelCase__		:Dict 		=    config_kwargs


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

												UpperCAmelCase__		:str 		=    Path(A	).name + '.py'

												UpperCAmelCase__		:Union[str, Any] 		=    os.path.join(A  , A	)
												if os.path.isfile(A	):
															UpperCAmelCase__		:Union[str, Any] 		=    os.path.dirname(A	)
												elif os.path.isfile(A	):
															UpperCAmelCase__		: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
												UpperCAmelCase__		:int 		=    os.path.join(A  , config.DATASETDICT_INFOS_FILENAME	)
												copyfile(A  , A	)
												print(F"""Dataset Infos file saved at {user_dataset_infos_path}"""	)







import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch




def 		A						(  ):




			"""simple docstring"""


			UpperCAmelCase__		:Tuple 		=    Dataset.from_dict({'i': range(17							)}							)
			UpperCAmelCase__		:Optional[Any] 		=    len(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:Dict 		=    3
			UpperCAmelCase__		:List[str] 		=    [
			    split_dataset_by_node(SCREAMING_SNAKE_CASE     ,						rank=SCREAMING_SNAKE_CASE     ,						world_size=SCREAMING_SNAKE_CASE							) for rank in range(SCREAMING_SNAKE_CASE							)
			]
			assert sum(len(SCREAMING_SNAKE_CASE							) for ds in datasets_per_rank							) == full_size
			assert len({tuple(x.values()							) for ds in datasets_per_rank for x in ds}							) == full_size




def 		A						(  ):




			"""simple docstring"""


			def gen():
						return ({"i": i} for i in range(17							))

			UpperCAmelCase__		:Union[str, Any] 		=    3
			UpperCAmelCase__		:Union[str, Any] 		=    IterableDataset.from_generator(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:Union[str, Any] 		=    len(list(SCREAMING_SNAKE_CASE							)							)
			UpperCAmelCase__		:Dict 		=    [
			    split_dataset_by_node(SCREAMING_SNAKE_CASE     ,						rank=SCREAMING_SNAKE_CASE     ,						world_size=SCREAMING_SNAKE_CASE							) for rank in range(SCREAMING_SNAKE_CASE							)
			]
			assert sum(len(list(SCREAMING_SNAKE_CASE							)							) for ds in datasets_per_rank							) == full_size
			assert len({tuple(x.values()							) for ds in datasets_per_rank for x in ds}							) == full_size




@pytest.mark.parametrize('shards_per_node'     ,						[1, 2, 3]							)
def 		A						(  SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			def gen(SCREAMING_SNAKE_CASE							):
						for shard in shards:
									yield from ({"i": i, "shard": shard} for i in range(17							))

			UpperCAmelCase__		:int 		=    3
			UpperCAmelCase__		:Any 		=    shards_per_node * world_size
			UpperCAmelCase__		:int 		=    {'shards': [f"""shard_{shard_idx}.txt""" for shard_idx in range(SCREAMING_SNAKE_CASE							)]}
			UpperCAmelCase__		:Optional[Any] 		=    IterableDataset.from_generator(SCREAMING_SNAKE_CASE     ,						gen_kwargs=SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:Tuple 		=    len(list(SCREAMING_SNAKE_CASE							)							)
			assert full_ds.n_shards == world_size * shards_per_node
			UpperCAmelCase__		:Optional[Any] 		=    [
			    split_dataset_by_node(SCREAMING_SNAKE_CASE     ,						rank=SCREAMING_SNAKE_CASE     ,						world_size=SCREAMING_SNAKE_CASE							) for rank in range(SCREAMING_SNAKE_CASE							)
			]
			assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
			assert sum(len(list(SCREAMING_SNAKE_CASE							)							) for ds in datasets_per_rank							) == full_size
			assert len({tuple(x.values()							) for ds in datasets_per_rank for x in ds}							) == full_size




def 		A						(  ):




			"""simple docstring"""


			def gen():
						return ({"i": i} for i in range(17							))

			UpperCAmelCase__		:Tuple 		=    2
			UpperCAmelCase__		:List[str] 		=    IterableDataset.from_generator(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:str 		=    len(list(SCREAMING_SNAKE_CASE							)							)

			UpperCAmelCase__		:List[str] 		=    split_dataset_by_node(SCREAMING_SNAKE_CASE     ,						rank=0     ,						world_size=SCREAMING_SNAKE_CASE							).shuffle(seed=42							)
			assert len(list(SCREAMING_SNAKE_CASE							)							) == 1 + full_size // world_size
			with pytest.raises(SCREAMING_SNAKE_CASE							):
						split_dataset_by_node(SCREAMING_SNAKE_CASE     ,						rank=0     ,						world_size=SCREAMING_SNAKE_CASE							).shuffle()

			UpperCAmelCase__		:Dict 		=    split_dataset_by_node(full_ds.shuffle(seed=42							)     ,						rank=0     ,						world_size=SCREAMING_SNAKE_CASE							)
			assert len(list(SCREAMING_SNAKE_CASE							)							) == 1 + full_size // world_size
			with pytest.raises(SCREAMING_SNAKE_CASE							):
						split_dataset_by_node(full_ds.shuffle()     ,						rank=0     ,						world_size=SCREAMING_SNAKE_CASE							)




@pytest.mark.parametrize('streaming'     ,						[False, True]							)
@require_torch
@pytest.mark.skipif(os.name == 'nt'     ,						reason='execute_subprocess_async doesn\'t support windows'							)
@pytest.mark.integration
def 		A						(  SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:Dict 		=    2
			UpperCAmelCase__		:str 		=    get_torch_dist_unique_port()
			UpperCAmelCase__		:Optional[int] 		=    Path(SCREAMING_SNAKE_CASE							).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
			UpperCAmelCase__		:Any 		=    f"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
			UpperCAmelCase__		:Optional[int] 		=    f"""
        --streaming={streaming}
    """.split()
			UpperCAmelCase__		:Optional[Any] 		=    [sys.executable] + distributed_args + args
			execute_subprocess_async(SCREAMING_SNAKE_CASE     ,						env=os.environ.copy()							)




@pytest.mark.parametrize(
    'nproc_per_node, num_workers'     ,						[
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]     ,						)
@require_torch
@pytest.mark.skipif(os.name == 'nt'     ,						reason='execute_subprocess_async doesn\'t support windows'							)
@pytest.mark.integration
def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:List[str] 		=    True
			UpperCAmelCase__		:Optional[Any] 		=    get_torch_dist_unique_port()
			UpperCAmelCase__		:Union[str, Any] 		=    Path(SCREAMING_SNAKE_CASE							).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
			UpperCAmelCase__		:Union[str, Any] 		=    f"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
			UpperCAmelCase__		:Union[str, Any] 		=    f"""
        --streaming={streaming}
        --num_workers={num_workers}
    """.split()
			UpperCAmelCase__		:List[Any] 		=    [sys.executable] + distributed_args + args
			execute_subprocess_async(SCREAMING_SNAKE_CASE     ,						env=os.environ.copy()							)





