




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


__lowerCAmelCase		:    List[Any]	      =		logging.get_logger(__name__)

__lowerCAmelCase		:    List[str]	      =		{
    'MCG-NJU/videomae-base': 'https://huggingface.co/MCG-NJU/videomae-base/resolve/main/config.json',
}




class    snake_case__							(_UpperCamelCase       ):
				"""simple docstring"""



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





				def __init__(				self    :					str ,				__lowerCamelCase    :					str=2_24 ,				__lowerCamelCase    :					List[str]=16 ,				__lowerCamelCase    :					str=3 ,				__lowerCamelCase    :					Dict=16 ,				__lowerCamelCase    :					Tuple=2 ,				__lowerCamelCase    :					Union[str, Any]=7_68 ,				__lowerCamelCase    :					List[str]=12 ,				__lowerCamelCase    :					Union[str, Any]=12 ,				__lowerCamelCase    :					Dict=30_72 ,				__lowerCamelCase    :					Optional[Any]="gelu" ,				__lowerCamelCase    :					Optional[int]=0.0 ,				__lowerCamelCase    :					int=0.0 ,				__lowerCamelCase    :					Dict=0.02 ,				__lowerCamelCase    :					List[str]=1e-12 ,				__lowerCamelCase    :					List[Any]=True ,				__lowerCamelCase    :					List[str]=True ,				__lowerCamelCase    :					Union[str, Any]=6 ,				__lowerCamelCase    :					Dict=3_84 ,				__lowerCamelCase    :					Dict=4 ,				__lowerCamelCase    :					int=15_36 ,				__lowerCamelCase    :					Union[str, Any]=True ,				**__lowerCamelCase    :					Optional[Any] ,				) ->					Dict:
							super().__init__(**__lowerCamelCase							)

							a   =      image_size
							a   =      patch_size
							a   =      num_channels
							a   =      num_frames
							a   =      tubelet_size

							a   =      hidden_size
							a   =      num_hidden_layers
							a   =      num_attention_heads
							a   =      intermediate_size
							a   =      hidden_act
							a   =      hidden_dropout_prob
							a   =      attention_probs_dropout_prob
							a   =      initializer_range
							a   =      layer_norm_eps
							a   =      qkv_bias
							a   =      use_mean_pooling

							a   =      decoder_num_attention_heads
							a   =      decoder_hidden_size
							a   =      decoder_num_hidden_layers
							a   =      decoder_intermediate_size
							a   =      norm_pix_loss












def       __magic_name__    (			A		: float       ):






			'''simple docstring'''







			return -num if num < 0 else num




def       __magic_name__    (			A		: list[int]       ):






			'''simple docstring'''







			if len(A       ) == 0:
						raise ValueError("abs_min() arg is an empty sequence"       )
			a   =      x[0]
			for i in x:
						if abs_val(A       ) < abs_val(A       ):
									a   =      i
			return j




def       __magic_name__    (			A		: list[int]       ):






			'''simple docstring'''







			if len(A       ) == 0:
						raise ValueError("abs_max() arg is an empty sequence"       )
			a   =      x[0]
			for i in x:
						if abs(A       ) > abs(A       ):
									a   =      i
			return j




def       __magic_name__    (			A		: list[int]       ):






			'''simple docstring'''







			if len(A       ) == 0:
						raise ValueError("abs_max_sort() arg is an empty sequence"       )
			return sorted(A,  key=A       )[-1]




def       __magic_name__    (			):






			'''simple docstring'''







			assert abs_val(0       ) == 0
			assert abs_val(34       ) == 34
			assert abs_val(-100000000000       ) == 100000000000

			a   =      [-3, -1, 2, -11]
			assert abs_max(A       ) == -11
			assert abs_max_sort(A       ) == -11
			assert abs_min(A       ) == -1


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		test_abs_val()
		print(abs_val(-34))  # --> 34






