
def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: Dict     )	->		Optional[Any]:
				__lowercase								=   len(SCREAMING_SNAKE_CASE     )
				for _ in range(
				    0       ,				int(((no_of_elements - 1) / 2) + 1     )     ):  # we don't need to traverse to end of list as
								for j in range(0       ,				no_of_elements - 1     ):
												if (
												    lst[j + 1] < lst[j]
												):  # applying bubble sort algorithm from left to right (or forwards)
																__lowercase								=   lst[j + 1]
																__lowercase								=   lst[j]
																__lowercase								=   temp
												if (
												    lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
												):  # applying bubble sort algorithm from right to left (or backwards)
																__lowercase								=   lst[no_of_elements - 1 - j]
																__lowercase								=   lst[no_of_elements - 2 - j]
																__lowercase								=   temp
				return lst


if __name__ == "__main__":
							print("""enter the list to be sorted""")
							SCREAMING_SNAKE_CASE__				=							[int(x) for x in input().split()]  # inputing elements of the list in one line
							SCREAMING_SNAKE_CASE__				=							double_sort(lst)
							print("""the sorted list is""")
							print(sorted_lst)







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





