





import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_SCREAMING_SNAKE_CASE   						=		"""\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_SCREAMING_SNAKE_CASE   						=		"""\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
_SCREAMING_SNAKE_CASE   						=		""""""

# TODO: Add the licence for the dataset here if you can find it
_SCREAMING_SNAKE_CASE   						=		""""""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_SCREAMING_SNAKE_CASE   						=		{
    """first_domain""": """https://huggingface.co/great-new-dataset-first_domain.zip""",
    """second_domain""": """https://huggingface.co/great-new-dataset-second_domain.zip""",
}
class SCREAMING_SNAKE_CASE_    (       datasets.GeneratorBasedBuilder       ):
						__magic_name__:      Any			   =							datasets.Version("1.1.0"       )

						# This is an example of a dataset with multiple configurations.
						# If you don't want/need to define several sub-sets in your dataset,
						# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

						# If you need to make complex sub-parts in the datasets with configurable options
						# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
						# BUILDER_CONFIG_CLASS = MyBuilderConfig

						# You will be able to load one or the other configurations in the following list with
						# data = datasets.load_dataset('my_dataset', 'first_domain')
						# data = datasets.load_dataset('my_dataset', 'second_domain')
						__magic_name__:      List[Any]			   =							[
						    datasets.BuilderConfig(name="first_domain"							,					version=snake_case_							,					description="This part of my dataset covers a first domain"       ),
						    datasets.BuilderConfig(name="second_domain"							,					version=snake_case_							,					description="This part of my dataset covers a second domain"       ),
						]

						__magic_name__:      Optional[Any]			   =							"first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.






						def UpperCAmelCase_					(   self   :   Tuple						)				->     str:







							"""simple docstring"""


							if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
								snake_case_		:      Tuple           =						datasets.Features(
								    {
								        'sentence': datasets.Value('string'						),
								        'option1': datasets.Value('string'						),
								        'answer': datasets.Value('string'						)
								        # These are the features of your dataset like images, labels ...
								    }						)
							else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
								snake_case_		:      List[Any]           =						datasets.Features(
								    {
								        'sentence': datasets.Value('string'						),
								        'option2': datasets.Value('string'						),
								        'second_domain_answer': datasets.Value('string'						)
								        # These are the features of your dataset like images, labels ...
								    }						)
							return datasets.DatasetInfo(
							    # This is the description that will appear on the datasets page.
							    description=_DESCRIPTION		, features=_A		, homepage=_HOMEPAGE		, license=_LICENSE		, citation=_CITATION		, )






						def UpperCAmelCase_					(   self   :   Tuple		, _A   :   Union[str, Any]						)				->     Optional[int]:







							"""simple docstring"""


							snake_case_		:      Tuple           =						_URLS[self.config.name]
							snake_case_		:      str           =						dl_manager.download_and_extract(_A						)
							return [
							    datasets.SplitGenerator(
							        name=datasets.Split.TRAIN		, gen_kwargs={
							            'filepath': os.path.join(_A		, 'train.jsonl'						),
							            'split': 'train',
							        }		, ),
							    datasets.SplitGenerator(
							        name=datasets.Split.VALIDATION		, gen_kwargs={
							            'filepath': os.path.join(_A		, 'dev.jsonl'						),
							            'split': 'dev',
							        }		, ),
							    datasets.SplitGenerator(
							        name=datasets.Split.TEST		, gen_kwargs={
							            'filepath': os.path.join(_A		, 'test.jsonl'						),
							            'split': 'test'
							        }		, ),
							]






						def UpperCAmelCase_					(   self   :   int		, _A   :   Optional[int]		, _A   :   List[Any]						)				->     Optional[int]:







							"""simple docstring"""


							with open(_A		, encoding='utf-8'						) as f:
								for key, row in enumerate(_A						):
									snake_case_		:      str           =						json.loads(_A						)
									if self.config.name == "first_domain":
										# Yields examples as (key, example) tuples
										yield key, {
										    "sentence": data["sentence"],
										    "option1": data["option1"],
										    "answer": "" if split == "test" else data["answer"],
										}
									else:
										yield key, {
										    "sentence": data["sentence"],
										    "option2": data["option2"],
										    "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
										}





from __future__ import annotations

from random import randrange




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		list  ):







	'''simple docstring'''



	if len(__lowerCamelCase  ) < 2:
		return collection
	lowercase_			 = randrange(len(__lowerCamelCase  )  )  # Use random element as pivot
	lowercase_			 = collection[pivot_index]
	lowercase_			 = []  # All elements greater than pivot
	lowercase_			 = []  # All elements less than or equal to pivot

	for element in collection[:pivot_index]:
		(greater if element > pivot else lesser).append(__lowerCamelCase  )

	for element in collection[pivot_index + 1 :]:
		(greater if element > pivot else lesser).append(__lowerCamelCase  )

	return [*quick_sort(__lowerCamelCase  ), pivot, *quick_sort(__lowerCamelCase  )]


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__       			=				input("""Enter numbers separated by a comma:\n""").strip()
							SCREAMING_SNAKE_CASE__       			=				[int(item) for item in user_input.split(""",""")]
							print(quick_sort(unsorted))

