"""simple docstring"""






import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor


class 					UpperCAmelCase		(unittest.TestCase						):



    """simple docstring"""

    @property
    def _snake_case    (     self							):
       lowercase__:     Union[str, Any]      =   1
       lowercase__:     Union[str, Any]      =   3
       lowercase__:     Any      =   8
       lowercase__:     Optional[Any]      =   8

       lowercase__:     Optional[int]      =   torch.rand((batch_size, num_channels, height, width)							)

       return sample






    def _snake_case    (     self					,    _UpperCAmelCase							):
       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 _snake_case    (     self							):
       lowercase__:     int      =   VaeImageProcessor(do_resize=_UpperCAmelCase					,    do_normalize=_UpperCAmelCase							)

       lowercase__:     Optional[Any]      =   self.dummy_sample
       lowercase__:     List[str]      =   self.to_np(_UpperCAmelCase							)

       for output_type in ["pt", "np", "pil"]:
          lowercase__:     List[Any]      =   image_processor.postprocess(
              image_processor.preprocess(_UpperCAmelCase							)					,    output_type=_UpperCAmelCase					,    )
          lowercase__:     Optional[int]      =   self.to_np(_UpperCAmelCase							)
          lowercase__:     str      =   (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 _snake_case    (     self							):
       lowercase__:     Tuple      =   VaeImageProcessor(do_resize=_UpperCAmelCase					,    do_normalize=_UpperCAmelCase							)
       lowercase__:     List[str]      =   self.dummy_sample.cpu().numpy().transpose(0					,    2					,    3					,    1							)

       for output_type in ["pt", "np", "pil"]:
          lowercase__:     Optional[Any]      =   image_processor.postprocess(image_processor.preprocess(_UpperCAmelCase							)					,    output_type=_UpperCAmelCase							)

          lowercase__:     Optional[Any]      =   self.to_np(_UpperCAmelCase							)
          lowercase__:     Dict      =   (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 _snake_case    (     self							):
       lowercase__:     List[Any]      =   VaeImageProcessor(do_resize=_UpperCAmelCase					,    do_normalize=_UpperCAmelCase							)

       lowercase__:     Union[str, Any]      =   self.dummy_sample.cpu().numpy().transpose(0					,    2					,    3					,    1							)
       lowercase__:     List[Any]      =   image_processor.numpy_to_pil(_UpperCAmelCase							)

       for output_type in ["pt", "np", "pil"]:
          lowercase__:     Optional[int]      =   image_processor.postprocess(image_processor.preprocess(_UpperCAmelCase							)					,    output_type=_UpperCAmelCase							)
          for i, o in zip(_UpperCAmelCase					,    _UpperCAmelCase							):
             lowercase__:     List[str]      =   np.array(_UpperCAmelCase							)
             lowercase__:     str      =   self.to_np(_UpperCAmelCase							) if output_type == '''pil''' else (self.to_np(_UpperCAmelCase							) * 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 _snake_case    (     self							):
       lowercase__:     Optional[int]      =   VaeImageProcessor(do_resize=_UpperCAmelCase					,    do_normalize=_UpperCAmelCase							)

       lowercase__:     Optional[Any]      =   self.dummy_sample
       lowercase__:     int      =   input_pt_ad.squeeze(0							)

       lowercase__:     Optional[int]      =   image_processor.postprocess(
           image_processor.preprocess(_UpperCAmelCase							)					,    output_type='''np'''					,    )
       lowercase__:     Optional[int]      =   image_processor.postprocess(
           image_processor.preprocess(_UpperCAmelCase							)					,    output_type='''np'''					,    )

       lowercase__:     Dict      =   self.to_np(self.dummy_sample							)
       lowercase__:     List[Any]      =   input_np_ad.squeeze(0							)

       lowercase__:     int      =   image_processor.postprocess(
           image_processor.preprocess(_UpperCAmelCase							)					,    output_type='''np'''					,    )
       lowercase__:     Any      =   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 _snake_case    (     self							):
       lowercase__:     Dict      =   VaeImageProcessor(do_resize=_UpperCAmelCase					,    do_normalize=_UpperCAmelCase							)

       lowercase__:     Dict      =   self.dummy_sample
       lowercase__:     List[str]      =   list(_UpperCAmelCase							)

       lowercase__:     Any      =   image_processor.postprocess(
           image_processor.preprocess(_UpperCAmelCase							)					,    output_type='''np'''					,    )

       lowercase__:     Optional[Any]      =   image_processor.postprocess(
           image_processor.preprocess(_UpperCAmelCase							)					,    output_type='''np'''					,    )

       lowercase__:     Dict      =   self.to_np(self.dummy_sample							)
       list(_UpperCAmelCase							)

       lowercase__:     Dict      =   image_processor.postprocess(
           image_processor.preprocess(_UpperCAmelCase							)					,    output_type='''np'''					,    )

       lowercase__:     str      =   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

"""simple docstring"""






def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase				)						->						Optional[int]:
   lowercase__:     str      =   [7_0_1, 3_0_1, 1_3_2, 5_7, 2_3, 1_0, 4, 1]
   for gap in gaps:
      for i in range(__UpperCAmelCase ,					len(__UpperCAmelCase				)				):
         lowercase__:     Any      =   collection[i]
         lowercase__:     Tuple      =   i
         while j >= gap and collection[j - gap] > insert_value:
            lowercase__:     List[str]      =   collection[j - gap]
            j -= gap
         if j != i:
            lowercase__:     List[str]      =   insert_value
   return collection


if __name__ == "__main__":
       from doctest import testmod

       testmod()
       __A         =				input("Enter numbers separated by a comma:\n").strip()
       __A         =				[int(item) for item in user_input.split(",")]
       print(shell_sort(unsorted))
