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






class 		a__						:

					def __init__(    self      :       Union[str, Any],_A      :       Tuple,_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_  :		Optional[Any]				      =    img
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    img.shape[1]
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    img.shape[0]
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    dst_width
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    dst_height

							SCREAMING_SNAKE_CASE_  :		Dict				      =    self.src_w / self.dst_w
							SCREAMING_SNAKE_CASE_  :		str				      =    self.src_h / self.dst_h

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

					def 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""simple docstring"""






							for i in range(self.dst_h    ):
									for j in range(self.dst_w    ):
											SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.img[self.get_y(_A    )][self.get_x(_A    )]

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







							"""simple docstring"""






							return int(self.ratio_x * x    )






					def 				__UpperCamelCase   (    self      :       Tuple,_A      :       int    ):







							"""simple docstring"""






							return int(self.ratio_y * y    )


if __name__ == "__main__":
		__lowerCamelCase   ,						__lowerCamelCase					:	Dict       =  8_00, 6_00
		__lowerCamelCase					:	Dict       =  imread('''image_data/lena.jpg''', 1)
		__lowerCamelCase					:	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()








"""simple docstring"""



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput

class        a     (   a_	):







    def __init__(			self							,		_lowerCamelCase							,		_lowerCamelCase       ):
      super().__init__()
      self.register_modules(unet=_lowerCamelCase							,		scheduler=_lowerCamelCase       )







    @torch.no_grad()
    def __call__(			self							,		_lowerCamelCase = 1							,		_lowerCamelCase = None							,		_lowerCamelCase = 5_0							,		_lowerCamelCase = "pil"							,		_lowerCamelCase = True							,		**_lowerCamelCase							,		):
      lowercase							    =       torch.randn(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)							,		generator=_lowerCamelCase							,		)
      lowercase							    =       image.to(self.device       )

      # set step values
      self.scheduler.set_timesteps(_lowerCamelCase       )

      for t in self.progress_bar(self.scheduler.timesteps       ):
        # 1. predict noise model_output
        lowercase							    =       self.unet(_lowerCamelCase							,		_lowerCamelCase       ).sample

        # 2. predict previous mean of image x_t-1 and add variance depending on eta
        # eta corresponds to η in paper and should be between [0, 1]
        # do x_t -> x_t-1
        lowercase							    =       self.scheduler.step(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       ).prev_sample

      lowercase							    =       (image / 2 + 0.5).clamp(0							,		1       )
      lowercase							    =       image.cpu().permute(0							,		2							,		3							,		1       ).numpy()
      if output_type == "pil":
        lowercase							    =       self.numpy_to_pil(_lowerCamelCase       )

      if not return_dict:
        return (image,), "This is a local test"

      return ImagePipelineOutput(images=_lowerCamelCase       ), "This is a local test"


