


import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor







class 				lowerCamelCase__       (	unittest.TestCase):





     @property
     def 					__A (self	)					->       Tuple:
      _lowercase											=1
      _lowercase											=3
      _lowercase											=8
      _lowercase											=8

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

      return sample





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





     def 					__A (self	)					->       Union[str, Any]:
      _lowercase											=VaeImageProcessor(do_resize=UpperCAmelCase		,					do_normalize=UpperCAmelCase	)

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

      for output_type in ["pt", "np", "pil"]:
       _lowercase											=image_processor.postprocess(
           image_processor.preprocess(UpperCAmelCase	)		,					output_type=UpperCAmelCase		,					)
       _lowercase											=self.to_np(UpperCAmelCase	)
       _lowercase											=(input_np * 2_5_5).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]:
      _lowercase											=VaeImageProcessor(do_resize=UpperCAmelCase		,					do_normalize=UpperCAmelCase	)
      _lowercase											=self.dummy_sample.cpu().numpy().transpose(0		,					2		,					3		,					1	)

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

       _lowercase											=self.to_np(UpperCAmelCase	)
       _lowercase											=(input_np * 2_5_5).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]:
      _lowercase											=VaeImageProcessor(do_resize=UpperCAmelCase		,					do_normalize=UpperCAmelCase	)

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

      for output_type in ["pt", "np", "pil"]:
       _lowercase											=image_processor.postprocess(image_processor.preprocess(UpperCAmelCase	)		,					output_type=UpperCAmelCase	)
       for i, o in zip(UpperCAmelCase		,					UpperCAmelCase	):
        _lowercase											=np.array(UpperCAmelCase	)
        _lowercase											=self.to_np(UpperCAmelCase	) if output_type == '''pil''' else (self.to_np(UpperCAmelCase	) * 2_5_5).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	)					->       List[str]:
      _lowercase											=VaeImageProcessor(do_resize=UpperCAmelCase		,					do_normalize=UpperCAmelCase	)

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

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

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

      _lowercase											=image_processor.postprocess(
          image_processor.preprocess(UpperCAmelCase	)		,					output_type='''np'''		,					)
      _lowercase											=image_processor.postprocess(
          image_processor.preprocess(UpperCAmelCase	)		,					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	)					->       str:
      _lowercase											=VaeImageProcessor(do_resize=UpperCAmelCase		,					do_normalize=UpperCAmelCase	)

      _lowercase											=self.dummy_sample
      _lowercase											=list(UpperCAmelCase	)

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

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

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

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

      _lowercase											=image_processor.postprocess(
          image_processor.preprocess(UpperCAmelCase	)		,					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





def     UpperCAmelCase_ (							__snake_case						)					->      str:







 """simple docstring"""


 return " ".join(sorted(set(sentence.split()						)						)						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()

