"""simple docstring"""






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 				snake_case       ( __UpperCAmelCase      ,    __UpperCAmelCase      ,    __UpperCAmelCase      ,    unittest.TestCase				):




					"""simple docstring"""

					snake_case__				    = StableDiffusionInpaintPipeline
					snake_case__				    = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
					snake_case__				    = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					snake_case__				    = frozenset(
					    []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
					snake_case__				    = frozenset([]				)







					def 		__lowerCAmelCase						(				self   :	str			):
									torch.manual_seed(0			)
									UpperCAmelCase__             =							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__    ,)
									UpperCAmelCase__             =							PNDMScheduler(skip_prk_steps=lowerCamelCase__			)
									torch.manual_seed(0			)
									UpperCAmelCase__             =							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			)
									UpperCAmelCase__             =							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    ,)
									UpperCAmelCase__             =							CLIPTextModel(lowerCamelCase__			)
									UpperCAmelCase__             =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

									UpperCAmelCase__             =							{
									    'unet': unet,
									    'scheduler': scheduler,
									    'vae': vae,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'safety_checker': None,
									    'feature_extractor': None,
									}
									return components







					def 		__lowerCAmelCase						(				self   :	Optional[Any]    ,lowerCamelCase__   :	Tuple    ,lowerCamelCase__   :	int=0			):
									# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
									UpperCAmelCase__             =							floats_tensor((1, 3, 32, 32)    ,rng=random.Random(lowerCamelCase__			)			).to(lowerCamelCase__			)
									UpperCAmelCase__             =							image.cpu().permute(0    ,2    ,3    ,1			)[0]
									UpperCAmelCase__             =							Image.fromarray(np.uinta(lowerCamelCase__			)			).convert('RGB'			).resize((64, 64)			)
									UpperCAmelCase__             =							Image.fromarray(np.uinta(image + 4			)			).convert('RGB'			).resize((64, 64)			)
									if str(lowerCamelCase__			).startswith('mps'			):
													UpperCAmelCase__             =							torch.manual_seed(lowerCamelCase__			)
									else:
													UpperCAmelCase__             =							torch.Generator(device=lowerCamelCase__			).manual_seed(lowerCamelCase__			)
									UpperCAmelCase__             =							{
									    '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			):
									UpperCAmelCase__             =							'cpu'  # ensure determinism for the device-dependent torch.Generator
									UpperCAmelCase__             =							self.get_dummy_components()
									UpperCAmelCase__             =							StableDiffusionInpaintPipeline(**lowerCamelCase__			)
									UpperCAmelCase__             =							sd_pipe.to(lowerCamelCase__			)
									sd_pipe.set_progress_bar_config(disable=lowerCamelCase__			)

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

									assert image.shape == (1, 64, 64, 3)
									UpperCAmelCase__             =							np.array([0.4_7_2_7, 0.5_7_3_5, 0.3_9_4_1, 0.5_4_4_6, 0.5_9_2_6, 0.4_3_9_4, 0.5_0_6_2, 0.4_6_5_4, 0.4_4_7_6]			)

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







					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									super().test_inference_batch_single_identical(expected_max_diff=3e-3			)






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




					"""simple docstring"""

					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									UpperCAmelCase__             =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'			)
									UpperCAmelCase__             =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'			)
									UpperCAmelCase__             =							load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
									    '/yellow_cat_sitting_on_a_park_bench.npy'			)

									UpperCAmelCase__             =							'stabilityai/stable-diffusion-2-inpainting'
									UpperCAmelCase__             =							StableDiffusionInpaintPipeline.from_pretrained(lowerCamelCase__    ,safety_checker=lowerCamelCase__			)
									pipe.to(lowerCamelCase__			)
									pipe.set_progress_bar_config(disable=lowerCamelCase__			)
									pipe.enable_attention_slicing()

									UpperCAmelCase__             =							'Face of a yellow cat, high resolution, sitting on a park bench'

									UpperCAmelCase__             =							torch.manual_seed(0			)
									UpperCAmelCase__             =							pipe(
									    prompt=lowerCamelCase__    ,image=lowerCamelCase__    ,mask_image=lowerCamelCase__    ,generator=lowerCamelCase__    ,output_type='np'    ,)
									UpperCAmelCase__             =							output.images[0]

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







					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									UpperCAmelCase__             =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'			)
									UpperCAmelCase__             =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'			)
									UpperCAmelCase__             =							load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
									    '/yellow_cat_sitting_on_a_park_bench_fp16.npy'			)

									UpperCAmelCase__             =							'stabilityai/stable-diffusion-2-inpainting'
									UpperCAmelCase__             =							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()

									UpperCAmelCase__             =							'Face of a yellow cat, high resolution, sitting on a park bench'

									UpperCAmelCase__             =							torch.manual_seed(0			)
									UpperCAmelCase__             =							pipe(
									    prompt=lowerCamelCase__    ,image=lowerCamelCase__    ,mask_image=lowerCamelCase__    ,generator=lowerCamelCase__    ,output_type='np'    ,)
									UpperCAmelCase__             =							output.images[0]

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







					def 		__lowerCAmelCase						(				self   :	List[str]			):
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

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

									UpperCAmelCase__             =							'stabilityai/stable-diffusion-2-inpainting'
									UpperCAmelCase__             =							PNDMScheduler.from_pretrained(lowerCamelCase__    ,subfolder='scheduler'			)
									UpperCAmelCase__             =							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()

									UpperCAmelCase__             =							'Face of a yellow cat, high resolution, sitting on a park bench'

									UpperCAmelCase__             =							torch.manual_seed(0			)
									UpperCAmelCase__             =							pipe(
									    prompt=lowerCamelCase__    ,image=lowerCamelCase__    ,mask_image=lowerCamelCase__    ,generator=lowerCamelCase__    ,num_inference_steps=2    ,output_type='np'    ,)

									UpperCAmelCase__             =							torch.cuda.max_memory_allocated()
									# make sure that less than 2.65 GB is allocated
									assert mem_bytes < 2.6_5 * 10**9




"""simple docstring"""






def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				if a < 0 or b < 0:
								raise ValueError('the value of both inputs must be positive'       )

				UpperCAmelCase__             =							str(bin(lowerCamelCase       )       )[2:]  # remove the leading "0b"
				UpperCAmelCase__             =							str(bin(lowerCamelCase       )       )[2:]  # remove the leading "0b"

				UpperCAmelCase__             =							max(len(lowerCamelCase       )					,			len(lowerCamelCase       )       )

				return "0b" + "".join(
				    str(int(char_a == '1' and char_b == '1'       )       )
				    for char_a, char_b in zip(a_binary.zfill(lowerCamelCase       )					,			b_binary.zfill(lowerCamelCase       )       )       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



