





'''simple docstring'''

lowercase__						:			Optional[Any]       =				tuple[float, float, float]
lowercase__						:			int       =				tuple[float, float, float]







def 					a__				(				lowercase					:     Dict,					lowercase					:     Any      )		->		Vectorad:





     """simple docstring"""

     _UpperCamelCase				    =       end_pointa[0] - end_pointa[0]
     _UpperCamelCase				    =       end_pointa[1] - end_pointa[1]
     _UpperCamelCase				    =       end_pointa[2] - end_pointa[2]
     return (x, y, z)







def 					a__				(				lowercase					:     Any,					lowercase					:     Dict      )		->		Vectorad:





     """simple docstring"""

     _UpperCamelCase				    =       ab[1] * ac[2] - ab[2] * ac[1]  # *i
     _UpperCamelCase				    =       (ab[0] * ac[2] - ab[2] * ac[0]) * -1  # *j
     _UpperCamelCase				    =       ab[0] * ac[1] - ab[1] * ac[0]  # *k
     return (x, y, z)







def 					a__				(				lowercase					:     Union[str, Any],					lowercase					:     Tuple      )		->		bool:





     """simple docstring"""

     return tuple(round(_lowerCAmelCase,					_lowerCAmelCase      ) for x in vector      ) == (0, 0, 0)







def 					a__				(				lowercase					:     int,					lowercase					:     Optional[Any],					lowercase					:     str,					lowercase					:     Optional[Any] = 10      )		->		bool:





     """simple docstring"""

     _UpperCamelCase				    =       create_vector(_lowerCAmelCase,					_lowerCAmelCase      )
     _UpperCamelCase				    =       create_vector(_lowerCAmelCase,					_lowerCAmelCase      )
     return is_zero_vector(get_ad_vectors_cross(_lowerCAmelCase,					_lowerCAmelCase      ),					_lowerCAmelCase      )










from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__lowerCamelCase       :     str   =     logging.get_logger(__name__)

__lowerCamelCase       :     str   =     {
    """facebook/convnextv2-tiny-1k-224""": """https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json""",
}



class 					A__   (       __snake_case       ,   __snake_case			):
      _UpperCAmelCase	:Optional[int]						    =   'convnextv2'





      def __init__( self       ,							A_=3       ,							A_=4       ,							A_=4       ,							A_=None       ,							A_=None       ,							A_="gelu"       ,							A_=0.02       ,							A_=1e-12       ,							A_=0.0       ,							A_=224       ,							A_=None       ,							A_=None       ,							**A_       ,							):
       '''simple docstring'''




       super().__init__(**A_   )

       UpperCamelCase	:					Dict       	=   num_channels
       UpperCamelCase	:					Union[str, Any]       	=   patch_size
       UpperCamelCase	:					Union[str, Any]       	=   num_stages
       UpperCamelCase	:					List[Any]       	=   [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
       UpperCamelCase	:					List[str]       	=   [3, 3, 9, 3] if depths is None else depths
       UpperCamelCase	:					Dict       	=   hidden_act
       UpperCamelCase	:					Union[str, Any]       	=   initializer_range
       UpperCamelCase	:					Tuple       	=   layer_norm_eps
       UpperCamelCase	:					str       	=   drop_path_rate
       UpperCamelCase	:					List[str]       	=   image_size
       UpperCamelCase	:					List[str]       	=   ["stem"] + [F"""stage{idx}""" for idx in range(1       ,							len(self.depths   ) + 1   )]
       UpperCamelCase	,    UpperCamelCase	:					str       	=   get_aligned_output_features_output_indices(
           out_features=A_       ,							out_indices=A_       ,							stage_names=self.stage_names   )
