


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        UpperCamelCase__   (	__magic_name__							,				__magic_name__							,				__magic_name__							,				unittest.TestCase   ):
					__SCREAMING_SNAKE_CASE       :  int						=   StableDiffusionInpaintPipeline
					__SCREAMING_SNAKE_CASE       :  int						=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
					__SCREAMING_SNAKE_CASE       :  Tuple						=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					__SCREAMING_SNAKE_CASE       :  List[str]						=   frozenset(
					    []   )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
					__SCREAMING_SNAKE_CASE       :  Optional[Any]						=   frozenset([]   )

					def   UpperCAmelCase__       (     self       :   Any       ):






												'''simple docstring'''






												torch.manual_seed(0       )
												lowercase_  							=			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=UpperCamelCase__							,					)
												lowercase_  							=			PNDMScheduler(skip_prk_steps=UpperCamelCase__       )
												torch.manual_seed(0       )
												lowercase_  							=			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       )
												lowercase_  							=			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							,					)
												lowercase_  							=			CLIPTextModel(UpperCamelCase__       )
												lowercase_  							=			CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )

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

					def   UpperCAmelCase__       (     self       :   List[Any]							,					UpperCamelCase__       :   str							,					UpperCamelCase__       :   str=0       ):






												'''simple docstring'''






												lowercase_  							=			floats_tensor((1, 3, 32, 32)							,					rng=random.Random(UpperCamelCase__       )       ).to(UpperCamelCase__       )
												lowercase_  							=			image.cpu().permute(0							,					2							,					3							,					1       )[0]
												lowercase_  							=			Image.fromarray(np.uinta(UpperCamelCase__       )       ).convert("""RGB"""       ).resize((64, 64)       )
												lowercase_  							=			Image.fromarray(np.uinta(image + 4       )       ).convert("""RGB"""       ).resize((64, 64)       )
												if str(UpperCamelCase__       ).startswith("""mps"""       ):
																			lowercase_  							=			torch.manual_seed(UpperCamelCase__       )
												else:
																			lowercase_  							=			torch.Generator(device=UpperCamelCase__       ).manual_seed(UpperCamelCase__       )
												lowercase_  							=			{
												    """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   UpperCAmelCase__       (     self       :   List[Any]       ):






												'''simple docstring'''






												lowercase_  							=			"""cpu"""  # ensure determinism for the device-dependent torch.Generator
												lowercase_  							=			self.get_dummy_components()
												lowercase_  							=			StableDiffusionInpaintPipeline(**UpperCamelCase__       )
												lowercase_  							=			sd_pipe.to(UpperCamelCase__       )
												sd_pipe.set_progress_bar_config(disable=UpperCamelCase__       )

												lowercase_  							=			self.get_dummy_inputs(UpperCamelCase__       )
												lowercase_  							=			sd_pipe(**UpperCamelCase__       ).images
												lowercase_  							=			image[0, -3:, -3:, -1]

												assert image.shape == (1, 64, 64, 3)
												lowercase_  							=			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   UpperCAmelCase__       (     self       :   Tuple       ):






												'''simple docstring'''






												super().test_inference_batch_single_identical(expected_max_diff=3e-3       )







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

					def   UpperCAmelCase__       (     self       :   Union[str, Any]       ):






												'''simple docstring'''






												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()

					def   UpperCAmelCase__       (     self       :   int       ):






												'''simple docstring'''






												lowercase_  							=			load_image(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
												    """/sd2-inpaint/init_image.png"""       )
												lowercase_  							=			load_image(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""       )
												lowercase_  							=			load_numpy(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
												    """/yellow_cat_sitting_on_a_park_bench.npy"""       )

												lowercase_  							=			"""stabilityai/stable-diffusion-2-inpainting"""
												lowercase_  							=			StableDiffusionInpaintPipeline.from_pretrained(UpperCamelCase__							,					safety_checker=UpperCamelCase__       )
												pipe.to(UpperCamelCase__       )
												pipe.set_progress_bar_config(disable=UpperCamelCase__       )
												pipe.enable_attention_slicing()

												lowercase_  							=			"""Face of a yellow cat, high resolution, sitting on a park bench"""

												lowercase_  							=			torch.manual_seed(0       )
												lowercase_  							=			pipe(
												    prompt=UpperCamelCase__							,					image=UpperCamelCase__							,					mask_image=UpperCamelCase__							,					generator=UpperCamelCase__							,					output_type="""np"""							,					)
												lowercase_  							=			output.images[0]

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

					def   UpperCAmelCase__       (     self       :   int       ):






												'''simple docstring'''






												lowercase_  							=			load_image(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
												    """/sd2-inpaint/init_image.png"""       )
												lowercase_  							=			load_image(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""       )
												lowercase_  							=			load_numpy(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
												    """/yellow_cat_sitting_on_a_park_bench_fp16.npy"""       )

												lowercase_  							=			"""stabilityai/stable-diffusion-2-inpainting"""
												lowercase_  							=			StableDiffusionInpaintPipeline.from_pretrained(
												    UpperCamelCase__							,					torch_dtype=torch.floataa							,					safety_checker=UpperCamelCase__							,					)
												pipe.to(UpperCamelCase__       )
												pipe.set_progress_bar_config(disable=UpperCamelCase__       )
												pipe.enable_attention_slicing()

												lowercase_  							=			"""Face of a yellow cat, high resolution, sitting on a park bench"""

												lowercase_  							=			torch.manual_seed(0       )
												lowercase_  							=			pipe(
												    prompt=UpperCamelCase__							,					image=UpperCamelCase__							,					mask_image=UpperCamelCase__							,					generator=UpperCamelCase__							,					output_type="""np"""							,					)
												lowercase_  							=			output.images[0]

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







					def   UpperCAmelCase__       (     self       :   Tuple       ):






												'''simple docstring'''






												torch.cuda.empty_cache()
												torch.cuda.reset_max_memory_allocated()
												torch.cuda.reset_peak_memory_stats()

												lowercase_  							=			load_image(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
												    """/sd2-inpaint/init_image.png"""       )
												lowercase_  							=			load_image(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""       )

												lowercase_  							=			"""stabilityai/stable-diffusion-2-inpainting"""
												lowercase_  							=			PNDMScheduler.from_pretrained(UpperCamelCase__							,					subfolder="""scheduler"""       )
												lowercase_  							=			StableDiffusionInpaintPipeline.from_pretrained(
												    UpperCamelCase__							,					safety_checker=UpperCamelCase__							,					scheduler=UpperCamelCase__							,					torch_dtype=torch.floataa							,					)
												pipe.to(UpperCamelCase__       )
												pipe.set_progress_bar_config(disable=UpperCamelCase__       )
												pipe.enable_attention_slicing(1       )
												pipe.enable_sequential_cpu_offload()

												lowercase_  							=			"""Face of a yellow cat, high resolution, sitting on a park bench"""

												lowercase_  							=			torch.manual_seed(0       )
												lowercase_  							=			pipe(
												    prompt=UpperCamelCase__							,					image=UpperCamelCase__							,					mask_image=UpperCamelCase__							,					generator=UpperCamelCase__							,					num_inference_steps=2							,					output_type="""np"""							,					)

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






def 			UpperCAmelCase_				(      UpperCAmelCase__=2_8_1_2_3  ):
					lowercase_  							=			[1] * (limit + 1)

					for i in range(2						,							int(limit**0.5  ) + 1  ):
										sum_divs[i * i] += i
										for k in range(i + 1						,							limit // i + 1  ):
															sum_divs[k * i] += k + i

					lowercase_  							=			set()
					lowercase_  							=			0

					for n in range(1						,							limit + 1  ):
										if sum_divs[n] > n:
															abundants.add(UpperCAmelCase__  )

										if not any((n - a in abundants) for a in abundants  ):
															res += n

					return res


if __name__ == "__main__":
							print(solution())


