




'''simple docstring'''


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

class     A					:







							def __init__(						self :						List[Any]							,    lowerCAmelCase_ :						Dict							,    lowerCAmelCase_ :						int							,    lowerCAmelCase_ :						int      )    ->							Optional[Any]:


														"""simple docstring"""


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

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

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

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







							def      __lowerCAmelCase    (						self :						List[Any]      )    ->							Dict:


														"""simple docstring"""


														for i in range(self.dst_h      ):
																					for j in range(self.dst_w      ):
																												_a			  =       self.img[self.get_y(lowerCAmelCase_      )][self.get_x(lowerCAmelCase_      )]







							def      __lowerCAmelCase    (						self :						Union[str, Any]							,    lowerCAmelCase_ :						int      )    ->							int:


														"""simple docstring"""


														return int(self.ratio_x * x      )







							def      __lowerCAmelCase    (						self :						List[Any]							,    lowerCAmelCase_ :						int      )    ->							int:


														"""simple docstring"""


														return int(self.ratio_y * y      )


if __name__ == "__main__":
				_snake_case							,       _snake_case   :   List[Any]     							=	800, 600
				_snake_case   :   List[Any]     							=	imread('image_data/lena.jpg', 1)
				_snake_case   :   Dict     							=	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()







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_torch_available,
    is_vision_available,
)


_snake_case   :   List[Any]     							=	{'configuration_beit': ['BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BeitConfig', 'BeitOnnxConfig']}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case   :   Any     							=	['BeitFeatureExtractor']
				_snake_case   :   Any     							=	['BeitImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case   :   List[str]     							=	[
				    'BEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'BeitForImageClassification',
				    'BeitForMaskedImageModeling',
				    'BeitForSemanticSegmentation',
				    'BeitModel',
				    'BeitPreTrainedModel',
				]


try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case   :   List[str]     							=	[
				    'FlaxBeitForImageClassification',
				    'FlaxBeitForMaskedImageModeling',
				    'FlaxBeitModel',
				    'FlaxBeitPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_beit import BeitFeatureExtractor
								from .image_processing_beit import BeitImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_beit import (
								    BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BeitForImageClassification,
								    BeitForMaskedImageModeling,
								    BeitForSemanticSegmentation,
								    BeitModel,
								    BeitPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_beit import (
								    FlaxBeitForImageClassification,
								    FlaxBeitForMaskedImageModeling,
								    FlaxBeitModel,
								    FlaxBeitPreTrainedModel,
								)


else:
				import sys

				_snake_case   :   Union[str, Any]     							=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

