





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  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							Dict    ,      **UpperCamelCase__:							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    ,      **UpperCamelCase__    ,      )




class      UpperCamelCase_				(   UpperCamelCase__			):


							@staticmethod
							def    _snake_case     (							__A			:ArgumentParser						)      ->      List[str]:



									"""simple docstring"""



									SCREAMING_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			:str							, __A			:str							, __A			:str							, __A			:str							, __A			:str							, __A			:str							, __A			:bool							, __A			:bool							, __A			:bool							, __A			:bool							, **__A			:int							, )      ->      Optional[Any]:



									"""simple docstring"""



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


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

											SCREAMING_SNAKE_CASE__								=					Path(__A						).name + """.py"""

											SCREAMING_SNAKE_CASE__								=					os.path.join(__A							, __A						)
											if os.path.isfile(__A						):
													SCREAMING_SNAKE_CASE__								=					os.path.dirname(__A						)
											elif os.path.isfile(__A						):
													SCREAMING_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
											SCREAMING_SNAKE_CASE__								=					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

from collections import deque
from queue import Queue
from timeit import timeit

_lowerCamelCase       =	{
    'A': ['B', 'C'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B'],
    'E': ['B', 'F'],
    'F': ['C', 'E'],
}






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							dict    ,      UpperCamelCase__:							str			):
		SCREAMING_SNAKE_CASE__								=					{start}
		SCREAMING_SNAKE_CASE__								=					[start]
		SCREAMING_SNAKE_CASE__								=					Queue()
		queue.put(UpperCamelCase__			)
		while not queue.empty():
				SCREAMING_SNAKE_CASE__								=					queue.get()
				for w in graph[v]:
						if w not in explored:
								explored.add(UpperCamelCase__			)
								result.append(UpperCamelCase__			)
								queue.put(UpperCamelCase__			)
		return result






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							dict    ,      UpperCamelCase__:							str			):
		SCREAMING_SNAKE_CASE__								=					{start}
		SCREAMING_SNAKE_CASE__								=					[start]
		SCREAMING_SNAKE_CASE__								=					deque([start]			)
		while queue:
				SCREAMING_SNAKE_CASE__								=					queue.popleft()
				for child in graph[v]:
						if child not in visited:
								visited.add(UpperCamelCase__			)
								result.append(UpperCamelCase__			)
								queue.append(UpperCamelCase__			)
		return result






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							str			):
		SCREAMING_SNAKE_CASE__								=					f'''from __main__ import G, {name}'''
		SCREAMING_SNAKE_CASE__								=					10_000
		SCREAMING_SNAKE_CASE__								=					timeit(f'''{name}(G, \'A\')'''    ,      setup=UpperCamelCase__    ,      number=UpperCamelCase__			)
		print(f'''{name:<35} finished {number} runs in {res:.5f} seconds'''			)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	benchmark_function('breadth_first_search')
	benchmark_function('breadth_first_search_with_deque')
	# breadth_first_search                finished 10000 runs in 0.20999 seconds
	# breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds