

import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						assert isinstance(_A  ,		_A	)
						assert dataset.num_rows == 4
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_1", "col_2", "col_3"]
						for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize('''keep_in_memory'''  ,		[False, True]	)
def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						snake_case_       =     tmp_path / '''cache'''
						snake_case_       =     {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												snake_case_       =     CsvDatasetReader(_A  ,		cache_dir=_A  ,		keep_in_memory=_A	).read()
						_check_csv_dataset(_A  ,		_A	)





@pytest.mark.parametrize(
    '''features'''  ,		[
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]  ,		)
def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						snake_case_       =     tmp_path / '''cache'''
						# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
						snake_case_       =     {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						snake_case_       =     features.copy() if features else default_expected_features
						snake_case_       =     (
						    Features({feature: Value(_A	) for feature, dtype in features.items()}	) if features is not None else None
						)
						snake_case_       =     CsvDatasetReader(_A  ,		features=_A  ,		cache_dir=_A	).read()
						_check_csv_dataset(_A  ,		_A	)





@pytest.mark.parametrize('''split'''  ,		[None, NamedSplit('''train'''	), '''train''', '''test''']	)
def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						snake_case_       =     tmp_path / '''cache'''
						snake_case_       =     {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						snake_case_       =     CsvDatasetReader(_A  ,		cache_dir=_A  ,		split=_A	).read()
						_check_csv_dataset(_A  ,		_A	)
						assert dataset.split == split if split else "train"





@pytest.mark.parametrize('''path_type'''  ,		[str, list]	)
def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):







						if issubclass(_A  ,		_A	):
												snake_case_       =     csv_path
						elif issubclass(_A  ,		_A	):
												snake_case_       =     [csv_path]
						snake_case_       =     tmp_path / '''cache'''
						snake_case_       =     {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						snake_case_       =     CsvDatasetReader(_A  ,		cache_dir=_A	).read()
						_check_csv_dataset(_A  ,		_A	)





def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__=("train",)	):
						assert isinstance(_A  ,		_A	)
						for split in splits:
												snake_case_       =     dataset_dict[split]
												assert dataset.num_rows == 4
												assert dataset.num_columns == 3
												assert dataset.column_names == ["col_1", "col_2", "col_3"]
												for feature, expected_dtype in expected_features.items():
																		assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize('''keep_in_memory'''  ,		[False, True]	)
def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						snake_case_       =     tmp_path / '''cache'''
						snake_case_       =     {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												snake_case_       =     CsvDatasetReader({'''train''': csv_path}  ,		cache_dir=_A  ,		keep_in_memory=_A	).read()
						_check_csv_datasetdict(_A  ,		_A	)





@pytest.mark.parametrize(
    '''features'''  ,		[
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]  ,		)
def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						snake_case_       =     tmp_path / '''cache'''
						# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
						snake_case_       =     {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						snake_case_       =     features.copy() if features else default_expected_features
						snake_case_       =     (
						    Features({feature: Value(_A	) for feature, dtype in features.items()}	) if features is not None else None
						)
						snake_case_       =     CsvDatasetReader({'''train''': csv_path}  ,		features=_A  ,		cache_dir=_A	).read()
						_check_csv_datasetdict(_A  ,		_A	)





@pytest.mark.parametrize('''split'''  ,		[None, NamedSplit('''train'''	), '''train''', '''test''']	)
def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):







						if split:
												snake_case_       =     {split: csv_path}
						else:
												snake_case_       =     {'''train''': csv_path, '''test''': csv_path}
						snake_case_       =     tmp_path / '''cache'''
						snake_case_       =     {'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
						snake_case_       =     CsvDatasetReader(_A  ,		cache_dir=_A	).read()
						_check_csv_datasetdict(_A  ,		_A  ,		splits=list(path.keys()	)	)
						assert all(dataset[split].split == split for split in path.keys()	)





def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__	):







						with open(_A  ,		encoding='''utf-8'''	) as csvfile:
												yield from csv.reader(_A	)





def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						snake_case_       =     tmp_path / '''cache'''
						snake_case_       =     os.path.join(_A  ,		'''tmp.csv'''	)
						snake_case_       =     CsvDatasetReader({'''train''': csv_path}  ,		cache_dir=_A	).read()
						CsvDatasetWriter(dataset['''train''']  ,		_A  ,		num_proc=1	).write()

						snake_case_       =     iter_csv_file(_A	)
						snake_case_       =     iter_csv_file(_A	)

						for rowa, rowa in zip(_A  ,		_A	):
												assert rowa == rowa





def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						snake_case_       =     tmp_path / '''cache'''
						snake_case_       =     os.path.join(_A  ,		'''tmp.csv'''	)
						snake_case_       =     CsvDatasetReader({'''train''': csv_path}  ,		cache_dir=_A	).read()
						CsvDatasetWriter(dataset['''train''']  ,		_A  ,		num_proc=2	).write()

						snake_case_       =     iter_csv_file(_A	)
						snake_case_       =     iter_csv_file(_A	)

						for rowa, rowa in zip(_A  ,		_A	):
												assert rowa == rowa





def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						snake_case_       =     tmp_path / '''cache'''
						snake_case_       =     os.path.join(_A  ,		'''tmp.csv'''	)
						snake_case_       =     CsvDatasetReader({'''train''': csv_path}  ,		cache_dir=_A	).read()
						with pytest.raises(_A	):
												CsvDatasetWriter(dataset['''train''']  ,		_A  ,		num_proc=0	)


from __future__ import annotations

from random import randrange


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

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

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

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


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