





from pathlib import Path

import numpy as np
from PIL import Image





def     _UpperCamelCase     (a__   :np.ndarray       ):






							"""simple docstring"""





							UpperCamelCase__     ,     UpperCamelCase__     ,     UpperCamelCase__				     =			rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
							return 0.2989 * r + 0.5870 * g + 0.1140 * b





def     _UpperCamelCase     (a__   :np.ndarray       ):






							"""simple docstring"""





							return (gray > 127) & (gray <= 255)





def     _UpperCamelCase     (a__   :np.ndarray   ,			a__   :np.ndarray       ):






							"""simple docstring"""





							UpperCamelCase__				     =			np.zeros_like(a__       )
							UpperCamelCase__				     =			np.zeros(
							    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)       )

							# Copy image to padded image
							UpperCamelCase__				     =			image

							# Iterate over image & apply kernel
							for x in range(image.shape[1]       ):
														for y in range(image.shape[0]       ):
																					UpperCamelCase__				     =			(
																					    kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
																					).sum()
																					UpperCamelCase__				     =			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 argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
					UpperCamelCase__											= argparse.ArgumentParser()

					parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

					parser.add_argument(
					    "--txt2img_unclip",
					    default="kakaobrain/karlo-v1-alpha",
					    type=str,
					    required=False,
					    help="The pretrained txt2img unclip.",
					)

					UpperCamelCase__											= parser.parse_args()

					UpperCamelCase__											= UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

					UpperCamelCase__											= CLIPImageProcessor()
					UpperCamelCase__											= CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")

					UpperCamelCase__											= UnCLIPImageVariationPipeline(
					    decoder=txtaimg.decoder,
					    text_encoder=txtaimg.text_encoder,
					    tokenizer=txtaimg.tokenizer,
					    text_proj=txtaimg.text_proj,
					    feature_extractor=feature_extractor,
					    image_encoder=image_encoder,
					    super_res_first=txtaimg.super_res_first,
					    super_res_last=txtaimg.super_res_last,
					    decoder_scheduler=txtaimg.decoder_scheduler,
					    super_res_scheduler=txtaimg.super_res_scheduler,
					)

					imgaimg.save_pretrained(args.dump_path)




