"""simple docstring"""



import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
       __lowercase		:		List[str]														=							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.",
       )

       __lowercase		:		int														=							parser.parse_args()

       __lowercase		:		str														=							UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

       __lowercase		:		int														=							CLIPImageProcessor()
       __lowercase		:		Optional[int]														=							CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")

       __lowercase		:		int														=							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 gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()



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




    """simple docstring"""




    def  lowercase					(		self							:     Tuple    )    ->       str:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
    def  lowercase					(		self							:     int    )    ->       Tuple:
         __snake_case				=							StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''    )
         __snake_case				=							sd_pipe.to(A_    )
         sd_pipe.set_progress_bar_config(disable=A_    )

         sd_pipe.set_scheduler('''sample_euler'''    )

         __snake_case				=							'''A painting of a squirrel eating a burger'''
         __snake_case				=							torch.manual_seed(0    )
         __snake_case				=							sd_pipe([prompt]	,      generator=A_	,      guidance_scale=9.0	,      num_inference_steps=20	,      output_type='''np'''    )

         __snake_case				=							output.images

         __snake_case				=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         __snake_case				=							np.array([0.04_47, 0.04_92, 0.04_68, 0.04_08, 0.03_83, 0.04_08, 0.03_54, 0.03_80, 0.03_39]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
    def  lowercase					(		self							:     Optional[Any]    )    ->       Tuple:
         __snake_case				=							StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''    )
         __snake_case				=							sd_pipe.to(A_    )
         sd_pipe.set_progress_bar_config(disable=A_    )

         sd_pipe.set_scheduler('''sample_euler'''    )

         __snake_case				=							'''A painting of a squirrel eating a burger'''
         __snake_case				=							torch.manual_seed(0    )
         __snake_case				=							sd_pipe([prompt]	,      generator=A_	,      guidance_scale=9.0	,      num_inference_steps=20	,      output_type='''np'''    )

         __snake_case				=							output.images

         __snake_case				=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         __snake_case				=							np.array([0.12_37, 0.13_20, 0.14_38, 0.13_59, 0.13_90, 0.11_32, 0.12_77, 0.11_75, 0.11_12]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 5E-1







    def  lowercase					(		self							:     List[str]    )    ->       Optional[Any]:
         __snake_case				=							StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''    )
         __snake_case				=							sd_pipe.to(A_    )
         sd_pipe.set_progress_bar_config(disable=A_    )

         sd_pipe.set_scheduler('''sample_dpmpp_2m'''    )

         __snake_case				=							'''A painting of a squirrel eating a burger'''
         __snake_case				=							torch.manual_seed(0    )
         __snake_case				=							sd_pipe(
             [prompt]	,      generator=A_	,      guidance_scale=7.5	,      num_inference_steps=15	,      output_type='''np'''	,      use_karras_sigmas=A_	,      )

         __snake_case				=							output.images

         __snake_case				=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         __snake_case				=							np.array(
             [0.11_38_16_89, 0.12_11_29_21, 0.1_38_94_57, 0.12_54_96_06, 0.1_24_49_64, 0.10_83_15_17, 0.11_56_28_66, 0.10_86_78_16, 0.10_49_90_48]    )

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