




"""simple docstring"""

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



class 			_snake_case					:

				'''simple docstring'''




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

								UpperCAmelCase_       :List[Any]         =   img
								UpperCAmelCase_       :Any         =   img.shape[1]
								UpperCAmelCase_       :str         =   img.shape[0]
								UpperCAmelCase_       :Dict         =   dst_width
								UpperCAmelCase_       :List[Any]         =   dst_height

								UpperCAmelCase_       :Optional[Any]         =   self.src_w / self.dst_w
								UpperCAmelCase_       :Dict         =   self.src_h / self.dst_h

								UpperCAmelCase_       :Dict         =   (
								    np.ones((self.dst_h, self.dst_w, 3)       ,				np.uinta  ) * 255
								)





				def 					snake_case_			(				self   :	Dict  ):
								for i in range(self.dst_h  ):
												for j in range(self.dst_w  ):
																UpperCAmelCase_       :List[Any]         =   self.img[self.get_y(snake_case  )][self.get_x(snake_case  )]





				def 					snake_case_			(				self   :	Tuple       ,				snake_case   :	int  ):
								return int(self.ratio_x * x  )





				def 					snake_case_			(				self   :	int       ,				snake_case   :	int  ):
								return int(self.ratio_y * y  )


if __name__ == "__main__":
						__lowerCamelCase										=    8_00, 6_00
						__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 typing import TYPE_CHECKING

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


UpperCamelCase_						:     int	       =							{
    """configuration_transfo_xl""": ["""TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TransfoXLConfig"""],
    """tokenization_transfo_xl""": ["""TransfoXLCorpus""", """TransfoXLTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_						:     Any	       =							[
							    """TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """AdaptiveEmbedding""",
							    """TransfoXLForSequenceClassification""",
							    """TransfoXLLMHeadModel""",
							    """TransfoXLModel""",
							    """TransfoXLPreTrainedModel""",
							    """load_tf_weights_in_transfo_xl""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_						:     Tuple	       =							[
							    """TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFAdaptiveEmbedding""",
							    """TFTransfoXLForSequenceClassification""",
							    """TFTransfoXLLMHeadModel""",
							    """TFTransfoXLMainLayer""",
							    """TFTransfoXLModel""",
							    """TFTransfoXLPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
							from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_transfo_xl import (
														    TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    AdaptiveEmbedding,
														    TransfoXLForSequenceClassification,
														    TransfoXLLMHeadModel,
														    TransfoXLModel,
														    TransfoXLPreTrainedModel,
														    load_tf_weights_in_transfo_xl,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_transfo_xl import (
														    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFAdaptiveEmbedding,
														    TFTransfoXLForSequenceClassification,
														    TFTransfoXLLMHeadModel,
														    TFTransfoXLMainLayer,
														    TFTransfoXLModel,
														    TFTransfoXLPreTrainedModel,
														)

else:
							import sys

							UpperCamelCase_						:     Optional[int]	       =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




