





"""simple docstring"""






from ..utils import is_flax_available, is_torch_available


if is_torch_available():
		from .autoencoder_kl import AutoencoderKL
		from .controlnet import ControlNetModel
		from .dual_transformer_ad import DualTransformeraDModel
		from .modeling_utils import ModelMixin
		from .prior_transformer import PriorTransformer
		from .ta_film_transformer import TaFilmDecoder
		from .transformer_ad import TransformeraDModel
		from .unet_ad import UNetaDModel
		from .unet_ad import UNetaDModel
		from .unet_ad_condition import UNetaDConditionModel
		from .unet_ad_condition import UNetaDConditionModel
		from .vq_model import VQModel

if is_flax_available():
		from .controlnet_flax import FlaxControlNetModel
		from .unet_ad_condition_flax import FlaxUNetaDConditionModel
		from .vae_flax import FlaxAutoencoderKL






"""simple docstring"""






import functools






def      lowercase__ (					lowerCAmelCase__   :			str							,			lowerCAmelCase__   :			str			)    ->			int:



			'''simple docstring'''
			a__  :	Any		 =			len(lowerCAmelCase__			)
			a__  :	Optional[int]		 =			len(lowerCAmelCase__			)

			@functools.cache
			def min_distance(lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			int			) -> int:
						# if first word index is overflow - delete all from the second word
						if indexa >= len_worda:
									return len_worda - indexa
						# if second word index is overflow - delete all from the first word
						if indexa >= len_worda:
									return len_worda - indexa
						a__  :	List[Any]		 =			int(worda[indexa] != worda[indexa]			)  # current letters not identical
						return min(
						    1 + min_distance(indexa + 1							,			lowerCAmelCase__			)							,			1 + min_distance(lowerCAmelCase__							,			indexa + 1			)							,			diff + min_distance(indexa + 1							,			indexa + 1			)							,			)

			return min_distance(0							,			0			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()