
"""simple docstring"""






import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey

class 					__a     :



							'''simple docstring'''







							def __init__(     self  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase       )			->							Any:




										'''simple docstring'''
										if dst_width < 0 or dst_height < 0:
													raise ValueError("Destination width/height should be > 0"       )

										__lowercase       =       img
										__lowercase       =       img.shape[1]
										__lowercase       =       img.shape[0]
										__lowercase       =       dst_width
										__lowercase       =       dst_height

										__lowercase       =       self.src_w / self.dst_w
										__lowercase       =       self.src_h / self.dst_h

										__lowercase       =       (
										    np.ones((self.dst_h, self.dst_w, 3)  ,    np.uinta       ) * 255
										)
							def   SCREAMING_SNAKE_CASE						(     self       )			->							List[str]:




										'''simple docstring'''
										for i in range(self.dst_h       ):
													for j in range(self.dst_w       ):
																__lowercase       =       self.img[self.get_y(lowerCamelCase_       )][self.get_x(lowerCamelCase_       )]
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							str:




										'''simple docstring'''
										return int(self.ratio_x * x       )





							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Optional[Any]:




										'''simple docstring'''
										return int(self.ratio_y * y       )


if __name__ == "__main__":
					_lowercase							=				800, 600
					_lowercase							=				imread('''image_data/lena.jpg''', 1)
					_lowercase							=				NearestNeighbour(im, dst_w, dst_h)
					n.process()

					imshow(
					    F"Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}", n.output
					)
					waitKey(0)
					destroyAllWindows()












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


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

__UpperCamelCase:	Dict = {
    """studio-ousia/luke-base""": """https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json""",
    """studio-ousia/luke-large""": """https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json""",
}


class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				"luke"

	def __init__(						self:						Union[str, Any],					lowerCamelCase_:						str=50267,					lowerCamelCase_:						Dict=500000,					lowerCamelCase_:						Union[str, Any]=768,					lowerCamelCase_:						Dict=256,					lowerCamelCase_:						int=12,					lowerCamelCase_:						Union[str, Any]=12,					lowerCamelCase_:						str=3072,					lowerCamelCase_:						List[Any]="gelu",					lowerCamelCase_:						int=0.1,					lowerCamelCase_:						Optional[Any]=0.1,					lowerCamelCase_:						Any=512,					lowerCamelCase_:						List[str]=2,					lowerCamelCase_:						str=0.0_2,					lowerCamelCase_:						Union[str, Any]=1E-12,					lowerCamelCase_:						List[Any]=True,					lowerCamelCase_:						List[Any]=None,					lowerCamelCase_:						Optional[Any]=1,					lowerCamelCase_:						Dict=0,					lowerCamelCase_:						List[Any]=2,					**lowerCamelCase_:						Optional[Any],					):
			super().__init__(pad_token_id=lowerCamelCase_,					bos_token_id=lowerCamelCase_,					eos_token_id=lowerCamelCase_,					**lowerCamelCase_			)

			lowercase__					:      Tuple      =		vocab_size
			lowercase__					:      Any      =		entity_vocab_size
			lowercase__					:      Tuple      =		hidden_size
			lowercase__					:      List[str]      =		entity_emb_size
			lowercase__					:      Tuple      =		num_hidden_layers
			lowercase__					:      Optional[Any]      =		num_attention_heads
			lowercase__					:      Dict      =		hidden_act
			lowercase__					:      Optional[int]      =		intermediate_size
			lowercase__					:      List[str]      =		hidden_dropout_prob
			lowercase__					:      Dict      =		attention_probs_dropout_prob
			lowercase__					:      Union[str, Any]      =		max_position_embeddings
			lowercase__					:      int      =		type_vocab_size
			lowercase__					:      Optional[Any]      =		initializer_range
			lowercase__					:      Dict      =		layer_norm_eps
			lowercase__					:      List[Any]      =		use_entity_aware_attention
			lowercase__					:      Optional[Any]      =		classifier_dropout


