

import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


__lowercase     : str       =						False



class 						_A     (				unittest.TestCase				):





  '''simple docstring'''



  pass







@slow
@require_torch_gpu
class 						_A     (				unittest.TestCase				):





  '''simple docstring'''



  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		List[Any]        =		VersatileDiffusionImageVariationPipeline.from_pretrained("""shi-labs/versatile-diffusion"""    )
      pipe.to(SCREAMING_SNAKE_CASE_    )
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

      snake_case							:		List[str]        =		load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""    )
      snake_case							:		int        =		torch.manual_seed(0    )
      snake_case							:		List[Any]        =		pipe(
          image=SCREAMING_SNAKE_CASE_   ,generator=SCREAMING_SNAKE_CASE_   ,guidance_scale=7.5   ,num_inference_steps=50   ,output_type="""numpy"""   ,).images

      snake_case							:		Optional[Any]        =		image[0, 253:256, 253:256, -1]

      assert image.shape == (1, 512, 512, 3)
      snake_case							:		List[str]        =		np.array([0.04_41, 0.04_69, 0.05_07, 0.05_75, 0.06_32, 0.06_50, 0.08_65, 0.09_09, 0.09_45]    )

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







import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
      snake_case__				:	Optional[int]					       =					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.',
      )

      snake_case__				:	Tuple					       =					parser.parse_args()

      snake_case__				:	Dict					       =					UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

      snake_case__				:	Optional[int]					       =					CLIPImageProcessor()
      snake_case__				:	Optional[Any]					       =					CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

      snake_case__				:	Dict					       =					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)





