




'''simple docstring'''







import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor


class       _a  (     unittest.TestCase			):
       @property
       def    A				(     self						:     Optional[int]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		1
           UpperCAmelCase      	=		3
           UpperCAmelCase      	=		8
           UpperCAmelCase      	=		8

           UpperCAmelCase      	=		torch.rand((batch_size, num_channels, height, width)    )

           return sample
       def    A				(     self						:     Optional[int]       ,    lowercase						:     Any    ):



           '''simple docstring'''

           if isinstance(image[0]       ,    PIL.Image.Image    ):
               return np.stack([np.array(lowercase    ) for i in image]       ,    axis=0    )
           elif isinstance(lowercase       ,    torch.Tensor    ):
               return image.cpu().numpy().transpose(0       ,    2       ,    3       ,    1    )
           return image
       def    A				(     self						:     List[Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		VaeImageProcessor(do_resize=lowercase       ,    do_normalize=lowercase    )

           UpperCAmelCase      	=		self.dummy_sample
           UpperCAmelCase      	=		self.to_np(lowercase    )

           for output_type in ["pt", "np", "pil"]:
               UpperCAmelCase      	=		image_processor.postprocess(
                   image_processor.preprocess(lowercase    )       ,    output_type=lowercase       ,    )
               UpperCAmelCase      	=		self.to_np(lowercase    )
               UpperCAmelCase      	=		(input_np * 255).round() if output_type == '''pil''' else input_np
               assert (
                   np.abs(in_np - out_np    ).max() < 1E-6
               ), f"decoded output does not match input for output_type {output_type}"
       def    A				(     self						:     Any    ):



           '''simple docstring'''

           UpperCAmelCase      	=		VaeImageProcessor(do_resize=lowercase       ,    do_normalize=lowercase    )
           UpperCAmelCase      	=		self.dummy_sample.cpu().numpy().transpose(0       ,    2       ,    3       ,    1    )

           for output_type in ["pt", "np", "pil"]:
               UpperCAmelCase      	=		image_processor.postprocess(image_processor.preprocess(lowercase    )       ,    output_type=lowercase    )

               UpperCAmelCase      	=		self.to_np(lowercase    )
               UpperCAmelCase      	=		(input_np * 255).round() if output_type == '''pil''' else input_np
               assert (
                   np.abs(in_np - out_np    ).max() < 1E-6
               ), f"decoded output does not match input for output_type {output_type}"
       def    A				(     self						:     List[str]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		VaeImageProcessor(do_resize=lowercase       ,    do_normalize=lowercase    )

           UpperCAmelCase      	=		self.dummy_sample.cpu().numpy().transpose(0       ,    2       ,    3       ,    1    )
           UpperCAmelCase      	=		image_processor.numpy_to_pil(lowercase    )

           for output_type in ["pt", "np", "pil"]:
               UpperCAmelCase      	=		image_processor.postprocess(image_processor.preprocess(lowercase    )       ,    output_type=lowercase    )
               for i, o in zip(lowercase       ,    lowercase    ):
                   UpperCAmelCase      	=		np.array(lowercase    )
                   UpperCAmelCase      	=		self.to_np(lowercase    ) if output_type == '''pil''' else (self.to_np(lowercase    ) * 255).round()
                   assert (
                       np.abs(in_np - out_np    ).max() < 1E-6
                   ), f"decoded output does not match input for output_type {output_type}"
       def    A				(     self						:     int    ):



           '''simple docstring'''

           UpperCAmelCase      	=		VaeImageProcessor(do_resize=lowercase       ,    do_normalize=lowercase    )

           UpperCAmelCase      	=		self.dummy_sample
           UpperCAmelCase      	=		input_pt_ad.squeeze(0    )

           UpperCAmelCase      	=		image_processor.postprocess(
               image_processor.preprocess(lowercase    )       ,    output_type='''np'''       ,    )
           UpperCAmelCase      	=		image_processor.postprocess(
               image_processor.preprocess(lowercase    )       ,    output_type='''np'''       ,    )

           UpperCAmelCase      	=		self.to_np(self.dummy_sample    )
           UpperCAmelCase      	=		input_np_ad.squeeze(0    )

           UpperCAmelCase      	=		image_processor.postprocess(
               image_processor.preprocess(lowercase    )       ,    output_type='''np'''       ,    )
           UpperCAmelCase      	=		image_processor.postprocess(
               image_processor.preprocess(lowercase    )       ,    output_type='''np'''       ,    )

           assert np.abs(out_pt_ad - out_pt_ad    ).max() < 1E-6
           assert np.abs(out_np_ad - out_np_ad    ).max() < 1E-6







       def    A				(     self						:     Union[str, Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		VaeImageProcessor(do_resize=lowercase       ,    do_normalize=lowercase    )

           UpperCAmelCase      	=		self.dummy_sample
           UpperCAmelCase      	=		list(lowercase    )

           UpperCAmelCase      	=		image_processor.postprocess(
               image_processor.preprocess(lowercase    )       ,    output_type='''np'''       ,    )

           UpperCAmelCase      	=		image_processor.postprocess(
               image_processor.preprocess(lowercase    )       ,    output_type='''np'''       ,    )

           UpperCAmelCase      	=		self.to_np(self.dummy_sample    )
           list(lowercase    )

           UpperCAmelCase      	=		image_processor.postprocess(
               image_processor.preprocess(lowercase    )       ,    output_type='''np'''       ,    )

           UpperCAmelCase      	=		image_processor.postprocess(
               image_processor.preprocess(lowercase    )       ,    output_type='''np'''       ,    )

           assert np.abs(out_pt_ad - out_pt_list    ).max() < 1E-6
           assert np.abs(out_np_ad - out_np_list    ).max() < 1E-6










'''simple docstring'''







def 						snake_case_							(_a     :							int = 9_9_8_0_0_1					):
    UpperCAmelCase      	=		0
    for i in range(9_9_9   ,       9_9   ,       -1					):  # 3 digit numbers range from 999 down to 100
        for j in range(9_9_9   ,       9_9   ,       -1					):
            UpperCAmelCase      	=		str(i * j					)
            if product_string == product_string[::-1] and i * j < n:
                UpperCAmelCase      	=		max(_a   ,       i * j					)
    return answer


if __name__ == "__main__":
   print(f"""{solution() = }""")




