

"""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    __SCREAMING_SNAKE_CASE						(						lowercase__			,							**lowercase__      ):
					"""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			,							**lowercase__			,							)





class 			__UpperCamelCase (   _A						):


		@staticmethod
		def 					SCREAMING_SNAKE_CASE__   (__SCREAMING_SNAKE_CASE :		ArgumentParser):
							A			=					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 :		int     ,     __SCREAMING_SNAKE_CASE :		str     ,     __SCREAMING_SNAKE_CASE :		str     ,     __SCREAMING_SNAKE_CASE :		str     ,     __SCREAMING_SNAKE_CASE :		str     ,     __SCREAMING_SNAKE_CASE :		str     ,     __SCREAMING_SNAKE_CASE :		bool     ,     __SCREAMING_SNAKE_CASE :		bool     ,     __SCREAMING_SNAKE_CASE :		bool     ,     __SCREAMING_SNAKE_CASE :		bool     ,     **__SCREAMING_SNAKE_CASE :		Optional[Any]     ,     ):
							A			=					dataset
							A			=					name
							A			=					cache_dir
							A			=					beam_pipeline_options
							A			=					data_dir
							A			=					all_configs
							A			=					save_infos
							A			=					ignore_verifications
							A			=					force_redownload
							A			=					config_kwargs


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

												A			=					Path(__SCREAMING_SNAKE_CASE).name + ".py"

												A			=					os.path.join(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)
												if os.path.isfile(__SCREAMING_SNAKE_CASE):
																	A			=					os.path.dirname(__SCREAMING_SNAKE_CASE)
												elif os.path.isfile(__SCREAMING_SNAKE_CASE):
																	A			=					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
												A			=					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}""")








"""simple docstring"""




def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					index
					A			=					2 * index + 1
					A			=					2 * index + 2
					if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
										A			=					left_index

					if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
										A			=					right_index

					if largest != index:
										A			,    A			=					unsorted[index], unsorted[largest]
										heapify(lowercase__			,							lowercase__			,							lowercase__      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					A			=					len(lowercase__      )
					for i in range(n // 2 - 1			,							-1			,							-1      ):
										heapify(lowercase__			,							lowercase__			,							lowercase__      )
					for i in range(n - 1			,							0			,							-1      ):
										A			,    A			=					unsorted[i], unsorted[0]
										heapify(lowercase__			,							0			,							lowercase__      )
					return unsorted


if __name__ == "__main__":
	__A     :    List[Any]    =		input('Enter numbers separated by a comma:\n').strip()
	__A     :    List[str]    =		[int(item) for item in user_input.split(',')]
	print(heap_sort(unsorted))





