

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







class 	lowercase    :


		"""simple docstring"""




		def __init__(				self	,       __snake_case	,       __snake_case	,       __snake_case):
			if dst_width < 0 or dst_height < 0:
				raise ValueError('Destination width/height should be > 0')

			_UpperCamelCase   :		Dict			=						img
			_UpperCamelCase   :		Optional[int]			=						img.shape[1]
			_UpperCamelCase   :		str			=						img.shape[0]
			_UpperCamelCase   :		Any			=						dst_width
			_UpperCamelCase   :		Dict			=						dst_height

			_UpperCamelCase   :		Any			=						self.src_w / self.dst_w
			_UpperCamelCase   :		Optional[int]			=						self.src_h / self.dst_h

			_UpperCamelCase   :		str			=						(
			    np.ones((self.dst_h, self.dst_w, 3)	,       np.uinta) * 2_55
			)


		def 		A__					(				self):
			for i in range(self.dst_h):
				for j in range(self.dst_w):
					_UpperCamelCase   :		Optional[Any]			=						self.img[self.get_y(__snake_case)][self.get_x(__snake_case)]


		def 		A__					(				self	,       __snake_case):
			return int(self.ratio_x * x)






		def 		A__					(				self	,       __snake_case):
			return int(self.ratio_y * y)


if __name__ == "__main__":
							lowerCAmelCase__			, lowerCAmelCase__			=		8_0_0, 6_0_0
							lowerCAmelCase__			=		imread("""image_data/lena.jpg""", 1)
							lowerCAmelCase__			=		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


lowerCAmelCase__								=		logging.get_logger(__name__)

lowerCAmelCase__								=		{
    """facebook/vit-mae-base""": """https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json""",
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  "vit_mae"


		def __init__(				self	,       __snake_case=7_68	,       __snake_case=12	,       __snake_case=12	,       __snake_case=30_72	,       __snake_case="gelu"	,       __snake_case=0.0	,       __snake_case=0.0	,       __snake_case=0.0_2	,       __snake_case=1e-12	,       __snake_case=2_24	,       __snake_case=16	,       __snake_case=3	,       __snake_case=True	,       __snake_case=16	,       __snake_case=5_12	,       __snake_case=8	,       __snake_case=20_48	,       __snake_case=0.7_5	,       __snake_case=False	,       **__snake_case	,       ):
			super().__init__(**__snake_case)

			_UpperCamelCase   :		Optional[int]			=						hidden_size
			_UpperCamelCase   :		Optional[int]			=						num_hidden_layers
			_UpperCamelCase   :		Tuple			=						num_attention_heads
			_UpperCamelCase   :		List[str]			=						intermediate_size
			_UpperCamelCase   :		str			=						hidden_act
			_UpperCamelCase   :		List[str]			=						hidden_dropout_prob
			_UpperCamelCase   :		List[Any]			=						attention_probs_dropout_prob
			_UpperCamelCase   :		str			=						initializer_range
			_UpperCamelCase   :		Any			=						layer_norm_eps
			_UpperCamelCase   :		int			=						image_size
			_UpperCamelCase   :		Any			=						patch_size
			_UpperCamelCase   :		List[Any]			=						num_channels
			_UpperCamelCase   :		Union[str, Any]			=						qkv_bias
			_UpperCamelCase   :		str			=						decoder_num_attention_heads
			_UpperCamelCase   :		Union[str, Any]			=						decoder_hidden_size
			_UpperCamelCase   :		Union[str, Any]			=						decoder_num_hidden_layers
			_UpperCamelCase   :		Any			=						decoder_intermediate_size
			_UpperCamelCase   :		int			=						mask_ratio
			_UpperCamelCase   :		List[Any]			=						norm_pix_loss




