





import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class 					__lowerCAmelCase       (			unittest.TestCase     ):






			@property
			def 			lowerCamelCase  (self    )     ->   Optional[int]:


							'''simple docstring'''



							torch.manual_seed(0    )
							snake_case_						:    Optional[int]				=  UNetaDModel(
							    block_out_channels=(32, 64)					,							layers_per_block=2					,							sample_size=32					,							in_channels=3					,							out_channels=3					,							down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')					,							up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')					,							)
							return model






			def 			lowerCamelCase  (self    )     ->   Tuple:


							'''simple docstring'''



							snake_case_						:    List[Any]				=  self.dummy_uncond_unet
							snake_case_						:    int				=  PNDMScheduler()

							snake_case_						:    Tuple				=  PNDMPipeline(unet=__magic_name__					,							scheduler=__magic_name__    )
							pndm.to(__magic_name__    )
							pndm.set_progress_bar_config(disable=__magic_name__    )

							snake_case_						:    Dict				=  torch.manual_seed(0    )
							snake_case_						:    int				=  pndm(generator=__magic_name__					,							num_inference_steps=20					,							output_type='''numpy'''    ).images

							snake_case_						:    Union[str, Any]				=  torch.manual_seed(0    )
							snake_case_						:    Tuple				=  pndm(generator=__magic_name__					,							num_inference_steps=20					,							output_type='''numpy'''					,							return_dict=__magic_name__    )[0]

							snake_case_						:    Any				=  image[0, -3:, -3:, -1]
							snake_case_						:    List[str]				=  image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							snake_case_						:    Optional[int]				=  np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]    )

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




@slow
@require_torch
class 					__lowerCAmelCase       (			unittest.TestCase     ):






			def 			lowerCamelCase  (self    )     ->   Optional[int]:


							'''simple docstring'''



							snake_case_						:    int				=  '''google/ddpm-cifar10-32'''

							snake_case_						:    Optional[int]				=  UNetaDModel.from_pretrained(__magic_name__    )
							snake_case_						:    str				=  PNDMScheduler()

							snake_case_						:    List[Any]				=  PNDMPipeline(unet=__magic_name__					,							scheduler=__magic_name__    )
							pndm.to(__magic_name__    )
							pndm.set_progress_bar_config(disable=__magic_name__    )
							snake_case_						:    Union[str, Any]				=  torch.manual_seed(0    )
							snake_case_						:    str				=  pndm(generator=__magic_name__					,							output_type='''numpy'''    ).images

							snake_case_						:    Optional[Any]				=  image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							snake_case_						:    str				=  np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]    )

							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__":
	lowerCAmelCase_    =				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.''',
	)

	lowerCAmelCase_    =				parser.parse_args()

	lowerCAmelCase_    =				UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

	lowerCAmelCase_    =				CLIPImageProcessor()
	lowerCAmelCase_    =				CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

	lowerCAmelCase_    =				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)
