




'''simple docstring'''



from pathlib import Path

import numpy as np
from PIL import Image




def   lowercase__(			__UpperCamelCase:					Tuple    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Tuple    					=						rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
      return 0.2_9_8_9 * r + 0.5_8_7_0 * g + 0.1_1_4_0 * b




def   lowercase__(			__UpperCamelCase:					str    ):

      """simple docstring"""






      return (gray > 1_27) & (gray <= 2_55)




def   lowercase__(			__UpperCamelCase:					Dict      ,__UpperCamelCase:					Tuple    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      int    					=						np.zeros_like(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Tuple    					=						np.zeros(
          (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)    )

      # Copy image to padded image
      SCREAMING_SNAKE_CASE   :      str    					=						image

      # Iterate over image & apply kernel
      for x in range(image.shape[1]    ):
            for y in range(image.shape[0]    ):
                  SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						(
                      kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
                  ).sum()
                  SCREAMING_SNAKE_CASE   :      Dict    					=						int(summation > 0    )
      return output


if __name__ == "__main__":
      # read original image
      UpperCamelCase_    	=				Path(__file__).resolve().parent / """image_data""" / """lena.jpg"""
      UpperCamelCase_    	=				np.array(Image.open(lena_path))
      # kernel to be applied
      UpperCamelCase_    	=				np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
      UpperCamelCase_    	=				dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
      # Save the output image
      UpperCamelCase_    	=				Image.fromarray(output).convert("RGB")
      pil_img.save("result_dilation.png")



import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


a  :      List[str]            =    False

class 					_lowercase    (     unittest.TestCase			):






    '''simple docstring'''
    pass





@nightly
@require_torch_gpu
class 					_lowercase    (     unittest.TestCase			):






    '''simple docstring'''




    def      _a    (    self ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




    def      _a    (    self ):
       lowerCAmelCase_:							List[str]       =	VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion" )
       # remove text_unet
       pipe.remove_unused_weights()
       pipe.to(lowerCamelCase__ )
       pipe.set_progress_bar_config(disable=lowerCamelCase__ )

       lowerCAmelCase_:							Tuple       =	"A painting of a squirrel eating a burger "
       lowerCAmelCase_:							List[str]       =	torch.manual_seed(0 )
       lowerCAmelCase_:							Dict       =	pipe(
           prompt=lowerCamelCase__       ,						generator=lowerCamelCase__       ,						guidance_scale=7.5       ,						num_inference_steps=2       ,						output_type="numpy" ).images

       with tempfile.TemporaryDirectory() as tmpdirname:
          pipe.save_pretrained(lowerCamelCase__ )
          lowerCAmelCase_:							int       =	VersatileDiffusionTextToImagePipeline.from_pretrained(lowerCamelCase__ )
       pipe.to(lowerCamelCase__ )
       pipe.set_progress_bar_config(disable=lowerCamelCase__ )

       lowerCAmelCase_:							List[str]       =	generator.manual_seed(0 )
       lowerCAmelCase_:							Dict       =	pipe(
           prompt=lowerCamelCase__       ,						generator=lowerCamelCase__       ,						guidance_scale=7.5       ,						num_inference_steps=2       ,						output_type="numpy" ).images

       assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"




    def      _a    (    self ):
       lowerCAmelCase_:							Optional[int]       =	VersatileDiffusionTextToImagePipeline.from_pretrained(
           "shi-labs/versatile-diffusion"       ,						torch_dtype=torch.floataa )
       pipe.to(lowerCamelCase__ )
       pipe.set_progress_bar_config(disable=lowerCamelCase__ )

       lowerCAmelCase_:							str       =	"A painting of a squirrel eating a burger "
       lowerCAmelCase_:							int       =	torch.manual_seed(0 )
       lowerCAmelCase_:							List[str]       =	pipe(
           prompt=lowerCamelCase__       ,						generator=lowerCamelCase__       ,						guidance_scale=7.5       ,						num_inference_steps=50       ,						output_type="numpy" ).images

       lowerCAmelCase_:							List[Any]       =	image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       lowerCAmelCase_:							Optional[int]       =	np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2