





'''simple docstring'''







from __future__ import annotations

from random import randrange





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      list )			->						list:
						if len(SCREAMING_SNAKE_CASE__ ) < 2:
												return collection
						UpperCAmelCase_      :   List[Any]      =	randrange(len(SCREAMING_SNAKE_CASE__ ) )  # Use random element as pivot
						UpperCAmelCase_      :   str      =	collection[pivot_index]
						UpperCAmelCase_      :   list[int]      =	[]  # All elements greater than pivot
						UpperCAmelCase_      :   list[int]      =	[]  # 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__":
					snake_case_  :							Optional[Any]            =	input("Enter numbers separated by a comma:\n").strip()
					snake_case_  :							Optional[int]            =	[int(item) for item in user_input.split(",")]
					print(quick_sort(unsorted))







from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
							from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
class       A__  (		lowerCAmelCase__							):
							lowerCAmelCase__						:		Any            =				c_float





							@property
							def      a__					(					self    :  List[str]							)		->						int:



											"""simple docstring"""


											return sizeof(self.value							)






def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: int       ,				SCREAMING_SNAKE_CASE			: int       ,				SCREAMING_SNAKE_CASE			: int = 0     )	->		int:
				# < 0 is possible if using a dynamic axis
				if dimension <= 0:
								__lowercase								=   fixed_dimension

				dimension -= num_token_to_add
				return dimension




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: int       ,				SCREAMING_SNAKE_CASE			: ParameterFormat     )	->		int:
				return num_parameters * dtype.size




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: str     )	->		Optional[Union["AutoTokenizer", "AutoFeatureExtractor", "AutoProcessor"]]:
				from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

				try:
								return AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE     )
				except (ValueError, OSError, KeyError):
								__lowercase						,   __lowercase								=   None, None
								try:
												__lowercase								=   AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE     )
								except (OSError, KeyError):
												pass
								try:
												__lowercase								=   AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE     )
								except (OSError, KeyError):
												pass

								if tokenizer is not None and feature_extractor is not None:
												raise ValueError(
												    F"""Couldn't auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor."""     )
								elif tokenizer is None and feature_extractor is None:
												return None
								elif tokenizer is not None:
												return tokenizer
								else:
												return feature_extractor





