
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

lowercase		:						Any =					{
    'MCG-NJU/videomae-base': 'https://huggingface.co/MCG-NJU/videomae-base/resolve/main/config.json',
}




class    _snake_case  (						_lowercase       ):
							SCREAMING_SNAKE_CASE		     =						'''videomae'''






							def __init__(					self						:			Dict						, snake_case__						:			Optional[Any]=2_2_4						, snake_case__						:			int=1_6						, snake_case__						:			Any=3						, snake_case__						:			str=1_6						, snake_case__						:			Optional[int]=2						, snake_case__						:			Union[str, Any]=7_6_8						, snake_case__						:			Optional[int]=1_2						, snake_case__						:			Tuple=1_2						, snake_case__						:			int=3_0_7_2						, snake_case__						:			Dict="gelu"						, snake_case__						:			int=0.0						, snake_case__						:			Union[str, Any]=0.0						, snake_case__						:			str=0.02						, snake_case__						:			int=1e-12						, snake_case__						:			Optional[int]=True						, snake_case__						:			int=True						, snake_case__						:			int=6						, snake_case__						:			Dict=3_8_4						, snake_case__						:			Union[str, Any]=4						, snake_case__						:			Tuple=1_5_3_6						, snake_case__						:			Optional[Any]=True						, **snake_case__						:			List[Any]						, ):






												"""simple docstring"""



												super().__init__(**snake_case__    )

												__a:						Dict		    =					image_size
												__a:						Union[str, Any]		    =					patch_size
												__a:						List[str]		    =					num_channels
												__a:						Any		    =					num_frames
												__a:						List[str]		    =					tubelet_size

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

												__a:						Tuple		    =					decoder_num_attention_heads
												__a:						Optional[Any]		    =					decoder_hidden_size
												__a:						List[str]		    =					decoder_num_hidden_layers
												__a:						Dict		    =					decoder_intermediate_size
												__a:						Tuple		    =					norm_pix_loss


"""simple docstring"""
def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			int:


					'''simple docstring'''
					for index, item in enumerate(lowerCamelCase_ ):
										if item == target:
															return index
					return -1


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			int:


					'''simple docstring'''
					if not (0 <= high < len(lowerCamelCase_ ) and 0 <= low < len(lowerCamelCase_ )):
										raise Exception("""Invalid upper or lower bound!""" )
					if high < low:
										return -1
					if sequence[low] == target:
										return low
					if sequence[high] == target:
										return high
					return rec_linear_search(lowerCamelCase_	,				low + 1	,				high - 1	,				lowerCamelCase_ )


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

			lowercase		:						Optional[Any] =					int(input('Enter a single number to be found in the list:\n').strip())
			lowercase		:						str =					linear_search(sequence, target)
			if result != -1:
						print(F"linear_search({sequence}, {target}) = {result}")
			else:
						print(F"{target} was not found in {sequence}")
