




'''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        _a    (						a_ , a_ , a_ , 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_							:   int       =       frozenset(
         []							)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
     SCREAMING_SNAKE_CASE_							:   List[str]       =       frozenset([]							)






     def     _lowercase     (			self   )							->      Dict:
          torch.manual_seed(0   )
          _snake_case							=				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=lowercase_ ,)
          _snake_case							=				PNDMScheduler(skip_prk_steps=lowercase_   )
          torch.manual_seed(0   )
          _snake_case							=				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   )
          _snake_case							=				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 ,)
          _snake_case							=				CLIPTextModel(lowercase_   )
          _snake_case							=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"   )

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






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=0   )							->      int:
          _snake_case							=				floats_tensor((1, 3, 32, 32) ,rng=random.Random(lowercase_   )   ).to(lowercase_   )
          _snake_case							=				image.cpu().permute(0 ,2 ,3 ,1   )[0]
          _snake_case							=				Image.fromarray(np.uinta(lowercase_   )   ).convert("RGB"   ).resize((64, 64)   )
          _snake_case							=				Image.fromarray(np.uinta(image + 4   )   ).convert("RGB"   ).resize((64, 64)   )
          if str(lowercase_   ).startswith("mps"   ):
               _snake_case							=				torch.manual_seed(lowercase_   )
          else:
               _snake_case							=				torch.Generator(device=lowercase_   ).manual_seed(lowercase_   )
          _snake_case							=				{
              "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     _lowercase     (			self   )							->      str:
          _snake_case							=				"cpu"  # ensure determinism for the device-dependent torch.Generator
          _snake_case							=				self.get_dummy_components()
          _snake_case							=				StableDiffusionInpaintPipeline(**lowercase_   )
          _snake_case							=				sd_pipe.to(lowercase_   )
          sd_pipe.set_progress_bar_config(disable=lowercase_   )

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

          assert image.shape == (1, 64, 64, 3)
          _snake_case							=				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     _lowercase     (			self   )							->      Any:
          super().test_inference_batch_single_identical(expected_max_diff=3e-3   )







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






     def     _lowercase     (			self   )							->      Tuple:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






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

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

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

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

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






     def     _lowercase     (			self   )							->      Union[str, Any]:
          _snake_case							=				load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/sd2-inpaint/init_image.png"   )
          _snake_case							=				load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"   )
          _snake_case							=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
              "/yellow_cat_sitting_on_a_park_bench_fp16.npy"   )

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

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

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

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






     def     _lowercase     (			self   )							->      List[str]:
          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()
          torch.cuda.reset_peak_memory_stats()

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

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

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

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

          _snake_case							=				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     (       _UpperCamelCase:   int   )						->			str:

     """simple docstring"""




     if number > 0:
          raise ValueError("input must be a negative integer"   )
     _snake_case							=				len(bin(_UpperCamelCase   )[3:]   )
     _snake_case							=				bin(abs(_UpperCamelCase   ) - (1 << binary_number_length)   )[3:]
     _snake_case							=				(
         (
             "1"
             + "0" * (binary_number_length - len(_UpperCamelCase   ))
             + twos_complement_number
         )
         if number < 0
         else "0"
     )
     return "0b" + twos_complement_number


if __name__ == "__main__":
   import doctest

   doctest.testmod()


