



'''simple docstring'''



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
lowerCAmelCase						:		str						      = '''\
@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
lowerCAmelCase						:		List[str]						      = '''\
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
lowerCAmelCase						:		Union[str, Any]						      = ''''''

# TODO: Add the licence for the dataset here if you can find it
lowerCAmelCase						:		Any						      = ''''''

# 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)
lowerCAmelCase						:		Optional[Any]						      = {
    '''first_domain''': '''https://huggingface.co/great-new-dataset-first_domain.zip''',
    '''second_domain''': '''https://huggingface.co/great-new-dataset-second_domain.zip''',
}






class        UpperCAmelCase__						(		datasets.GeneratorBasedBuilder			):
					a   :  Tuple   =				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')
					a   :  Optional[int]   =				[
					    datasets.BuilderConfig(name="""first_domain"""				,							version=UpperCamelCase__				,							description="""This part of my dataset covers a first domain"""			),
					    datasets.BuilderConfig(name="""second_domain"""				,							version=UpperCamelCase__				,							description="""This part of my dataset covers a second domain"""			),
					]

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



					def 							UpperCAmelCase_			(     self  )  ->      Optional[int]:
						# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
						if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
							__lowerCAmelCase       				=	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"
							__lowerCAmelCase       				=	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=UpperCamelCase      ,					homepage=_HOMEPAGE      ,					license=_LICENSE      ,					citation=_CITATION      ,					)



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase  )  ->      str:
						# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
						# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

						# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
						# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
						# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
						__lowerCAmelCase       				=	_URLS[self.config.name]
						__lowerCAmelCase       				=	dl_manager.download_and_extract(UpperCamelCase  )
						return [
						    datasets.SplitGenerator(
						        name=datasets.Split.TRAIN      ,					gen_kwargs={
						            "filepath": os.path.join(UpperCamelCase      ,					"train.jsonl"  ),
						            "split": "train",
						        }      ,					),
						    datasets.SplitGenerator(
						        name=datasets.Split.VALIDATION      ,					gen_kwargs={
						            "filepath": os.path.join(UpperCamelCase      ,					"dev.jsonl"  ),
						            "split": "dev",
						        }      ,					),
						    datasets.SplitGenerator(
						        name=datasets.Split.TEST      ,					gen_kwargs={
						            "filepath": os.path.join(UpperCamelCase      ,					"test.jsonl"  ),
						            "split": "test"
						        }      ,					),
						]



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase  )  ->      Any:
						# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
						# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
						with open(UpperCamelCase      ,					encoding="utf-8"  ) as f:
							for key, row in enumerate(UpperCamelCase  ):
								__lowerCAmelCase       				=	json.loads(UpperCamelCase  )
								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"],
									}




'''simple docstring'''



from __future__ import annotations

from random import randrange



def 	__lowerCAmelCase       (				lowerCamelCase :    list       ):






	'''simple docstring'''




	if len(lowerCamelCase       ) < 2:
		return collection
	__lowerCAmelCase       				=	randrange(len(lowerCamelCase       )       )  # Use random element as pivot
	__lowerCAmelCase       				=	collection[pivot_index]
	__lowerCAmelCase       				=	[]  # All elements greater than pivot
	__lowerCAmelCase       				=	[]  # 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__":
	lowerCAmelCase						:		List[Any]						      = input('''Enter numbers separated by a comma:\n''').strip()
	lowerCAmelCase						:		Any						      = [int(item) for item in user_input.split(''',''')]
	print(quick_sort(unsorted))