

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







class 			lowercase    :




				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'	)

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

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

											snake_case_          =   snake_case_          =   (
											    np.ones((self.dst_h, self.dst_w, 3)				,       np.uinta	) * 255
											)




				def    a							( self	):
											for i in range(self.dst_h	):
																		for j in range(self.dst_w	):
																									snake_case_          =   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__":
					_UpperCAmelCase    ,						_UpperCAmelCase		:       Optional[int]						    =	800, 600
					_UpperCAmelCase		:       List[Any]						    =	imread("""image_data/lena.jpg""", 1)
					_UpperCAmelCase		:       str						    =	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		:       str						    =	logging.get_logger(__name__)

_UpperCAmelCase		:       int						    =	{
    """google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}







class 			lowercase    (	lowercase_      ):
				__SCREAMING_SNAKE_CASE :			int	      =      '''canine'''




				def __init__( self				,       snake_case=768				,       snake_case=12				,       snake_case=12				,       snake_case=3072				,       snake_case="gelu"				,       snake_case=0.1				,       snake_case=0.1				,       snake_case=1_6384				,       snake_case=16				,       snake_case=0.02				,       snake_case=1e-1_2				,       snake_case=0				,       snake_case=0xE000				,       snake_case=0xE001				,       snake_case=4				,       snake_case=4				,       snake_case=8				,       snake_case=1_6384				,       snake_case=128				,       **snake_case				,       ):
											super().__init__(pad_token_id=snake_case				,       bos_token_id=snake_case				,       eos_token_id=snake_case				,       **snake_case	)

											snake_case_          =   max_position_embeddings
											snake_case_          =   hidden_size
											snake_case_          =   num_hidden_layers
											snake_case_          =   num_attention_heads
											snake_case_          =   intermediate_size
											snake_case_          =   hidden_act
											snake_case_          =   hidden_dropout_prob
											snake_case_          =   attention_probs_dropout_prob
											snake_case_          =   initializer_range
											snake_case_          =   type_vocab_size
											snake_case_          =   layer_norm_eps

											# Character config:
											snake_case_          =   downsampling_rate
											snake_case_          =   upsampling_kernel_size
											snake_case_          =   num_hash_functions
											snake_case_          =   num_hash_buckets
											snake_case_          =   local_transformer_stride




