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


__lowerCAmelCase					   =       logging.get_logger(__name__)

__lowerCAmelCase					   =       {
    "google/vivit-b-16x2-kinetics400": (
        "https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}





class     lowerCamelCase      (       __a					):
	UpperCamelCase_  :					Optional[int]    		=	'''vivit'''







	def __init__(     self      :Optional[int]   ,     lowercase      :Dict=2_2_4   ,     lowercase      :List[str]=3_2   ,     lowercase      :Dict=[2, 1_6, 1_6]   ,     lowercase      :Dict=3   ,     lowercase      :Union[str, Any]=7_6_8   ,     lowercase      :Optional[Any]=1_2   ,     lowercase      :List[Any]=1_2   ,     lowercase      :List[Any]=3_0_7_2   ,     lowercase      :Optional[Any]="gelu_fast"   ,     lowercase      :Optional[int]=0.0   ,     lowercase      :Optional[Any]=0.0   ,     lowercase      :Dict=0.02   ,     lowercase      :int=1e-06   ,     lowercase      :Dict=True   ,     **lowercase      :Optional[int]   ,     )		->      List[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= hidden_size
				SCREAMING_SNAKE_CASE     						= num_hidden_layers
				SCREAMING_SNAKE_CASE     						= num_attention_heads
				SCREAMING_SNAKE_CASE     						= intermediate_size
				SCREAMING_SNAKE_CASE     						= hidden_act
				SCREAMING_SNAKE_CASE     						= hidden_dropout_prob
				SCREAMING_SNAKE_CASE     						= attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE     						= initializer_range
				SCREAMING_SNAKE_CASE     						= layer_norm_eps

				SCREAMING_SNAKE_CASE     						= image_size
				SCREAMING_SNAKE_CASE     						= num_frames
				SCREAMING_SNAKE_CASE     						= tubelet_size
				SCREAMING_SNAKE_CASE     						= num_channels
				SCREAMING_SNAKE_CASE     						= qkv_bias

				super().__init__(**__UpperCamelCase			)
import numpy as np






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				np.array			):
						return (2 / (1 + np.exp(-2 * vector			))) - 1


if __name__ == "__main__":
	import doctest

	doctest.testmod()






