from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase_     =	{
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_     =	[
			    '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''CanineForMultipleChoice''',
			    '''CanineForQuestionAnswering''',
			    '''CanineForSequenceClassification''',
			    '''CanineForTokenClassification''',
			    '''CanineLayer''',
			    '''CanineModel''',
			    '''CaninePreTrainedModel''',
			    '''load_tf_weights_in_canine''',
			]


if TYPE_CHECKING:
			from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
			from .tokenization_canine import CanineTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_canine import (
						    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CanineForMultipleChoice,
						    CanineForQuestionAnswering,
						    CanineForSequenceClassification,
						    CanineForTokenClassification,
						    CanineLayer,
						    CanineModel,
						    CaninePreTrainedModel,
						    load_tf_weights_in_canine,
						)


else:
			import sys

			lowerCAmelCase_     =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



"""simple docstring"""





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




class  _snake_case		:







				"""simple docstring"""






				def __init__(      self							:							int      ,    _A							:							List[Any]      ,    _A							:							int      ,    _A							:							int):



											"""simple docstring"""







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

											_SCREAMING_SNAKE_CASE		:						str			  =    img
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    img.shape[1]
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    img.shape[0]
											_SCREAMING_SNAKE_CASE		:						Any			  =    dst_width
											_SCREAMING_SNAKE_CASE		:						Any			  =    dst_height

											_SCREAMING_SNAKE_CASE		:						Any			  =    self.src_w / self.dst_w
											_SCREAMING_SNAKE_CASE		:						Dict			  =    self.src_h / self.dst_h

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    (
											    np.ones((self.dst_h, self.dst_w, 3)      ,    np.uinta) * 2_5_5
											)





				def 		_lowerCAmelCase		(      self							:							Tuple):



											"""simple docstring"""







											for i in range(self.dst_h):
																		for j in range(self.dst_w):
																									_SCREAMING_SNAKE_CASE		:						Any			  =    self.img[self.get_y(_A)][self.get_x(_A)]





				def 		_lowerCAmelCase		(      self							:							int      ,    _A							:							int):



											"""simple docstring"""







											return int(self.ratio_x * x)





				def 		_lowerCAmelCase		(      self							:							str      ,    _A							:							int):



											"""simple docstring"""







											return int(self.ratio_y * y)


if __name__ == "__main__":
			lowerCAmelCase_ ,					lowerCAmelCase_     =	800, 600
			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()


