





"""simple docstring"""




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)










"""simple docstring"""




import numpy as np
import torch
from imwatermark import WatermarkEncoder


# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
UpperCamelCase_									=0b1_0_1_1_0_0_1_1_1_1_1_0_1_1_0_0_1_0_0_1_0_0_0_0_0_1_1_1_1_0_1_1_1_0_1_1_0_0_0_1_1_0_0_1_1_1_1_0
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
UpperCamelCase_									=[int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]




class 			_a						:






				def __init__(			self		:   str							)     ->				str:




										'''simple docstring'''



										_UpperCamelCase      : str         =   WATERMARK_BITS
										_UpperCamelCase      : Optional[int]         =   WatermarkEncoder()

										self.encoder.set_watermark('''bits''',			self.watermark							)






				def     snake_case     (			self		:   Dict,			lowerCAmelCase__		:   torch.FloatTensor							)     ->				int:




										'''simple docstring'''



										if images.shape[-1] < 2_5_6:
																return images

										_UpperCamelCase      : Union[str, Any]         =   (2_5_5 * (images / 2 + 0.5)).cpu().permute(0,			2,			3,			1							).float().numpy()

										_UpperCamelCase      : List[str]         =   [self.encoder.encode(lowerCAmelCase__,			'''dwtDct'''							) for image in images]

										_UpperCamelCase      : Dict         =   torch.from_numpy(np.array(lowerCAmelCase__							)							).permute(0,			3,			1,			2							)

										_UpperCamelCase      : Optional[int]         =   torch.clamp(2 * (images / 2_5_5 - 0.5),			min=-1.0,			max=1.0							)
										return images



