






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class _lowercase     (    UpperCamelCase__    ,				UpperCamelCase__    ,				UpperCamelCase__    ,				unittest.TestCase		):
							a    				=	StableDiffusionInpaintPipeline
							a    				=	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
							a    				=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							a    				=	frozenset(
							    []		)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							a    				=	frozenset([]		)




							def 		lowerCamelCase_    (							self:    str					):
														torch.manual_seed(0					)
														lowerCamelCase__ :     int	  =	UNetaDConditionModel(
														    block_out_channels=(32, 64)      ,							layers_per_block=2      ,							sample_size=32      ,							in_channels=9      ,							out_channels=4      ,							down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")      ,							up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")      ,							cross_attention_dim=32      ,							attention_head_dim=(2, 4)      ,							use_linear_projection=__lowerCamelCase      ,							)
														lowerCamelCase__ :     Any	  =	PNDMScheduler(skip_prk_steps=__lowerCamelCase					)
														torch.manual_seed(0					)
														lowerCamelCase__ :     Optional[Any]	  =	AutoencoderKL(
														    block_out_channels=[32, 64]      ,							in_channels=3      ,							out_channels=3      ,							down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]      ,							up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]      ,							latent_channels=4      ,							sample_size=128      ,							)
														torch.manual_seed(0					)
														lowerCamelCase__ :     List[Any]	  =	CLIPTextConfig(
														    bos_token_id=0      ,							eos_token_id=2      ,							hidden_size=32      ,							intermediate_size=37      ,							layer_norm_eps=1e-05      ,							num_attention_heads=4      ,							num_hidden_layers=5      ,							pad_token_id=1      ,							vocab_size=1_000      ,							hidden_act="""gelu"""      ,							projection_dim=512      ,							)
														lowerCamelCase__ :     Tuple	  =	CLIPTextModel(__lowerCamelCase					)
														lowerCamelCase__ :     str	  =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

														lowerCamelCase__ :     Any	  =	{
														    """unet""": unet,
														    """scheduler""": scheduler,
														    """vae""": vae,
														    """text_encoder""": text_encoder,
														    """tokenizer""": tokenizer,
														    """safety_checker""": None,
														    """feature_extractor""": None,
														}
														return components




							def 		lowerCamelCase_    (							self:    List[str]      ,							UpperCamelCase__:    Optional[int]      ,							UpperCamelCase__:    Optional[Any]=0					):
														# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
														lowerCamelCase__ :     Optional[Any]	  =	floats_tensor((1, 3, 32, 32)      ,							rng=random.Random(__lowerCamelCase					)					).to(__lowerCamelCase					)
														lowerCamelCase__ :     List[Any]	  =	image.cpu().permute(0      ,							2      ,							3      ,							1					)[0]
														lowerCamelCase__ :     Any	  =	Image.fromarray(np.uinta(__lowerCamelCase					)					).convert("""RGB"""					).resize((64, 64)					)
														lowerCamelCase__ :     Any	  =	Image.fromarray(np.uinta(image + 4					)					).convert("""RGB"""					).resize((64, 64)					)
														if str(__lowerCamelCase					).startswith("""mps"""					):
																					lowerCamelCase__ :     Any	  =	torch.manual_seed(__lowerCamelCase					)
														else:
																					lowerCamelCase__ :     Dict	  =	torch.Generator(device=__lowerCamelCase					).manual_seed(__lowerCamelCase					)
														lowerCamelCase__ :     Any	  =	{
														    """prompt""": """A painting of a squirrel eating a burger""",
														    """image""": init_image,
														    """mask_image""": mask_image,
														    """generator""": generator,
														    """num_inference_steps""": 2,
														    """guidance_scale""": 6.0,
														    """output_type""": """numpy""",
														}
														return inputs




							def 		lowerCamelCase_    (							self:    Tuple					):
														lowerCamelCase__ :     List[str]	  =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
														lowerCamelCase__ :     int	  =	self.get_dummy_components()
														lowerCamelCase__ :     Optional[int]	  =	StableDiffusionInpaintPipeline(**__lowerCamelCase					)
														lowerCamelCase__ :     str	  =	sd_pipe.to(__lowerCamelCase					)
														sd_pipe.set_progress_bar_config(disable=__lowerCamelCase					)

														lowerCamelCase__ :     List[str]	  =	self.get_dummy_inputs(__lowerCamelCase					)
														lowerCamelCase__ :     Union[str, Any]	  =	sd_pipe(**__lowerCamelCase					).images
														lowerCamelCase__ :     Union[str, Any]	  =	image[0, -3:, -3:, -1]

														assert image.shape == (1, 64, 64, 3)
														lowerCamelCase__ :     Optional[int]	  =	np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476]					)

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




							def 		lowerCamelCase_    (							self:    Union[str, Any]					):
														super().test_inference_batch_single_identical(expected_max_diff=3e-3					)



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




							def 		lowerCamelCase_    (							self:    Optional[int]					):
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()




							def 		lowerCamelCase_    (							self:    List[str]					):
														lowerCamelCase__ :     int	  =	load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/sd2-inpaint/init_image.png"""					)
														lowerCamelCase__ :     Optional[Any]	  =	load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""					)
														lowerCamelCase__ :     List[Any]	  =	load_numpy(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
														    """/yellow_cat_sitting_on_a_park_bench.npy"""					)

														lowerCamelCase__ :     List[Any]	  =	"""stabilityai/stable-diffusion-2-inpainting"""
														lowerCamelCase__ :     int	  =	StableDiffusionInpaintPipeline.from_pretrained(__lowerCamelCase      ,							safety_checker=__lowerCamelCase					)
														pipe.to(__lowerCamelCase					)
														pipe.set_progress_bar_config(disable=__lowerCamelCase					)
														pipe.enable_attention_slicing()

														lowerCamelCase__ :     Union[str, Any]	  =	"""Face of a yellow cat, high resolution, sitting on a park bench"""

														lowerCamelCase__ :     Optional[int]	  =	torch.manual_seed(0					)
														lowerCamelCase__ :     List[str]	  =	pipe(
														    prompt=__lowerCamelCase      ,							image=__lowerCamelCase      ,							mask_image=__lowerCamelCase      ,							generator=__lowerCamelCase      ,							output_type="""np"""      ,							)
														lowerCamelCase__ :     Tuple	  =	output.images[0]

														assert image.shape == (512, 512, 3)
														assert np.abs(expected_image - image					).max() < 9e-3




							def 		lowerCamelCase_    (							self:    Tuple					):
														lowerCamelCase__ :     Tuple	  =	load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/sd2-inpaint/init_image.png"""					)
														lowerCamelCase__ :     Dict	  =	load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""					)
														lowerCamelCase__ :     List[Any]	  =	load_numpy(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
														    """/yellow_cat_sitting_on_a_park_bench_fp16.npy"""					)

														lowerCamelCase__ :     List[Any]	  =	"""stabilityai/stable-diffusion-2-inpainting"""
														lowerCamelCase__ :     Any	  =	StableDiffusionInpaintPipeline.from_pretrained(
														    __lowerCamelCase      ,							torch_dtype=torch.floataa      ,							safety_checker=__lowerCamelCase      ,							)
														pipe.to(__lowerCamelCase					)
														pipe.set_progress_bar_config(disable=__lowerCamelCase					)
														pipe.enable_attention_slicing()

														lowerCamelCase__ :     Optional[int]	  =	"""Face of a yellow cat, high resolution, sitting on a park bench"""

														lowerCamelCase__ :     int	  =	torch.manual_seed(0					)
														lowerCamelCase__ :     Union[str, Any]	  =	pipe(
														    prompt=__lowerCamelCase      ,							image=__lowerCamelCase      ,							mask_image=__lowerCamelCase      ,							generator=__lowerCamelCase      ,							output_type="""np"""      ,							)
														lowerCamelCase__ :     int	  =	output.images[0]

														assert image.shape == (512, 512, 3)
														assert np.abs(expected_image - image					).max() < 5e-1




							def 		lowerCamelCase_    (							self:    Optional[Any]					):
														torch.cuda.empty_cache()
														torch.cuda.reset_max_memory_allocated()
														torch.cuda.reset_peak_memory_stats()

														lowerCamelCase__ :     Optional[Any]	  =	load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/sd2-inpaint/init_image.png"""					)
														lowerCamelCase__ :     List[str]	  =	load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""					)

														lowerCamelCase__ :     List[Any]	  =	"""stabilityai/stable-diffusion-2-inpainting"""
														lowerCamelCase__ :     List[str]	  =	PNDMScheduler.from_pretrained(__lowerCamelCase      ,							subfolder="""scheduler"""					)
														lowerCamelCase__ :     Any	  =	StableDiffusionInpaintPipeline.from_pretrained(
														    __lowerCamelCase      ,							safety_checker=__lowerCamelCase      ,							scheduler=__lowerCamelCase      ,							torch_dtype=torch.floataa      ,							)
														pipe.to(__lowerCamelCase					)
														pipe.set_progress_bar_config(disable=__lowerCamelCase					)
														pipe.enable_attention_slicing(1					)
														pipe.enable_sequential_cpu_offload()

														lowerCamelCase__ :     Optional[int]	  =	"""Face of a yellow cat, high resolution, sitting on a park bench"""

														lowerCamelCase__ :     List[Any]	  =	torch.manual_seed(0					)
														lowerCamelCase__ :     Any	  =	pipe(
														    prompt=__lowerCamelCase      ,							image=__lowerCamelCase      ,							mask_image=__lowerCamelCase      ,							generator=__lowerCamelCase      ,							num_inference_steps=2      ,							output_type="""np"""      ,							)

														lowerCamelCase__ :     str	  =	torch.cuda.max_memory_allocated()
														# make sure that less than 2.65 GB is allocated
														assert mem_bytes < 2.65 * 10**9









'''simple docstring'''







import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
			_A     :      List[Any]   					=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.''',
			)

			_A     :      Dict   					=parser.parse_args()

			_A     :      List[str]   					=UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

			_A     :      Any   					=CLIPImageProcessor()
			_A     :      Union[str, Any]   					=CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

			_A     :      Union[str, Any]   					=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)

