
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_  :       List[str]					      =     logging.get_logger(__name__)

lowerCamelCase_  :       Dict					      =     {
    """MCG-NJU/videomae-base""": """https://huggingface.co/MCG-NJU/videomae-base/resolve/main/config.json""",
}

class 				_UpperCamelCase (    _A     ):






			'''simple docstring'''

			__UpperCamelCase					:  List[Any]    					=       """videomae"""

			def __init__(				self       :				List[str]  ,		snake_case_       :				Optional[Any]=224  ,		snake_case_       :				Tuple=16  ,		snake_case_       :				int=3  ,		snake_case_       :				List[str]=16  ,		snake_case_       :				Optional[Any]=2  ,		snake_case_       :				str=768  ,		snake_case_       :				Tuple=12  ,		snake_case_       :				Union[str, Any]=12  ,		snake_case_       :				List[Any]=3072  ,		snake_case_       :				List[str]="gelu"  ,		snake_case_       :				Any=0.0  ,		snake_case_       :				Optional[int]=0.0  ,		snake_case_       :				int=0.02  ,		snake_case_       :				int=1e-12  ,		snake_case_       :				str=True  ,		snake_case_       :				int=True  ,		snake_case_       :				Dict=6  ,		snake_case_       :				Optional[int]=384  ,		snake_case_       :				Optional[Any]=4  ,		snake_case_       :				str=1536  ,		snake_case_       :				List[Any]=True  ,		**snake_case_       :				Optional[Any]  ,		):
							super().__init__(**snake_case_					)

							UpperCamelCase_:							List[str]		=  image_size
							UpperCamelCase_:							List[str]		=  patch_size
							UpperCamelCase_:							Optional[Any]		=  num_channels
							UpperCamelCase_:							str		=  num_frames
							UpperCamelCase_:							Dict		=  tubelet_size

							UpperCamelCase_:							Optional[Any]		=  hidden_size
							UpperCamelCase_:							Any		=  num_hidden_layers
							UpperCamelCase_:							Union[str, Any]		=  num_attention_heads
							UpperCamelCase_:							int		=  intermediate_size
							UpperCamelCase_:							List[Any]		=  hidden_act
							UpperCamelCase_:							Any		=  hidden_dropout_prob
							UpperCamelCase_:							Tuple		=  attention_probs_dropout_prob
							UpperCamelCase_:							Tuple		=  initializer_range
							UpperCamelCase_:							str		=  layer_norm_eps
							UpperCamelCase_:							List[Any]		=  qkv_bias
							UpperCamelCase_:							str		=  use_mean_pooling

							UpperCamelCase_:							Tuple		=  decoder_num_attention_heads
							UpperCamelCase_:							Dict		=  decoder_hidden_size
							UpperCamelCase_:							Optional[int]		=  decoder_num_hidden_layers
							UpperCamelCase_:							Tuple		=  decoder_intermediate_size
							UpperCamelCase_:							Any		=  norm_pix_loss








def 				A__    (  lowerCamelCase  )     ->		Optional[Any]:
				UpperCamelCase_:							Optional[int]		=  len(lowerCamelCase  )
				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)
																UpperCamelCase_:							str		=  lst[j + 1]
																UpperCamelCase_:							Tuple		=  lst[j]
																UpperCamelCase_:							str		=  temp
												if (
												    lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
												):  # applying bubble sort algorithm from right to left (or backwards)
																UpperCamelCase_:							Union[str, Any]		=  lst[no_of_elements - 1 - j]
																UpperCamelCase_:							Dict		=  lst[no_of_elements - 2 - j]
																UpperCamelCase_:							List[Any]		=  temp
				return lst


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






