




from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class 		UpperCAmelCase   (				__lowercase			):







						'''simple docstring'''





						def __init__(						self							:     Tuple       ,	__lowercase							:     NestedDataStructureLike[PathLike]       ,	__lowercase							:     Optional[NamedSplit] = None       ,	__lowercase							:     Optional[Features] = None       ,	__lowercase							:     str = None       ,	__lowercase							:     bool = False       ,	__lowercase							:     bool = False       ,	__lowercase							:     Optional[int] = None       ,	**__lowercase							:     str       ,	):






							"""simple docstring"""






							super().__init__(
							    SCREAMING_SNAKE_CASE__       ,	split=SCREAMING_SNAKE_CASE__       ,	features=SCREAMING_SNAKE_CASE__       ,	cache_dir=SCREAMING_SNAKE_CASE__       ,	keep_in_memory=SCREAMING_SNAKE_CASE__       ,	streaming=SCREAMING_SNAKE_CASE__       ,	num_proc=SCREAMING_SNAKE_CASE__       ,	**SCREAMING_SNAKE_CASE__       ,	)
							snake_case_		    =     path_or_paths if isinstance(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__						) else {self.split: path_or_paths}
							snake_case_		    =     Text(
							    cache_dir=SCREAMING_SNAKE_CASE__       ,	data_files=SCREAMING_SNAKE_CASE__       ,	features=SCREAMING_SNAKE_CASE__       ,	**SCREAMING_SNAKE_CASE__       ,	)






						def        snake_case__						(						self							:     int						):






							"""simple docstring"""






							if self.streaming:
								snake_case_		    =     self.builder.as_streaming_dataset(split=self.split						)
							# Build regular (map-style) dataset
							else:
								snake_case_		    =     None
								snake_case_		    =     None
								snake_case_		    =     None
								snake_case_		    =     None

								self.builder.download_and_prepare(
								    download_config=SCREAMING_SNAKE_CASE__       ,	download_mode=SCREAMING_SNAKE_CASE__       ,	verification_mode=SCREAMING_SNAKE_CASE__       ,	base_path=SCREAMING_SNAKE_CASE__       ,	num_proc=self.num_proc       ,	)
								snake_case_		    =     self.builder.as_dataset(
								    split=self.split       ,	verification_mode=SCREAMING_SNAKE_CASE__       ,	in_memory=self.keep_in_memory						)
							return dataset


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

class    lowerCAmelCase__     :






		def __init__(		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							Dict:
						if dst_width < 0 or dst_height < 0:
										raise ValueError('''Destination width/height should be > 0'''  )

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

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

						__lowerCamelCase    					=	__lowerCamelCase    					=	(
						    np.ones((self.dst_h, self.dst_w, 3)							,			np.uinta  ) * 2_55
						)






		def  __A       (		self		:							List[Any]  )     ->							str:
						for i in range(self.dst_h  ):
										for j in range(self.dst_w  ):
														__lowerCamelCase    					=	self.img[self.get_y(SCREAMING_SNAKE_CASE__  )][self.get_x(SCREAMING_SNAKE_CASE__  )]






		def  __A       (		self		:							str							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							int:
						return int(self.ratio_x * x  )







		def  __A       (		self		:							str							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							int:
						return int(self.ratio_y * y  )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__   :			List[str]												=    800, 600
					SCREAMING_SNAKE_CASE__   :			int												=    imread("image_data/lena.jpg", 1)
					SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    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()



