


"""simple docstring"""







def      lowercase			(				A_       )->      str:
  '''simple docstring'''







  if isinstance(A_  ,   A_       ):
    raise TypeError("'float' object cannot be interpreted as an integer"       )
  if isinstance(A_  ,   A_       ):
    raise TypeError("'str' object cannot be interpreted as an integer"       )

  if num == 0:
    return "0b0"

  a						:		Optional[Any]      							=			False

  if num < 0:
    a						:		Tuple      							=			True
    a						:		str      							=			-num

  a						:		list[int]      							=			[]
  while num > 0:
    binary.insert(0  ,   num % 2       )
    num >>= 1

  if negative:
    return "-0b" + "".join(str(A_       ) for e in binary       )

  return "0b" + "".join(str(A_       ) for e in binary       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()










"""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  ):







    """simple docstring"""




    UpperCAmelCase       :							str	=       StableDiffusionInpaintPipeline
    UpperCAmelCase       :							Optional[Any]	=       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
    UpperCAmelCase       :							Any	=       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    UpperCAmelCase       :							Union[str, Any]	=       frozenset(
        []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    UpperCAmelCase       :							int	=       frozenset([]  )






    def 	__snake_case							(					self				:    Dict):
      torch.manual_seed(0)
      a						:		List[Any]      							=			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				, )
      a						:		Tuple      							=			PNDMScheduler(skip_prk_steps=__UpperCAmelCase)
      torch.manual_seed(0)
      a						:		str      							=			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)
      a						:		List[str]      							=			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=1000				, hidden_act="gelu"				, projection_dim=512				, )
      a						:		Any      							=			CLIPTextModel(__UpperCAmelCase)
      a						:		List[str]      							=			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

      a						:		Optional[Any]      							=			{
          "unet": unet,
          "scheduler": scheduler,
          "vae": vae,
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
          "safety_checker": None,
          "feature_extractor": None,
      }
      return components






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

      a						:		Any      							=			self.get_dummy_inputs(__UpperCAmelCase)
      a						:		Optional[int]      							=			sd_pipe(**__UpperCAmelCase).images
      a						:		Optional[int]      							=			image[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)
      a						:		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 	__snake_case							(					self				:    str):
      super().test_inference_batch_single_identical(expected_max_diff=3e-3)






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







    """simple docstring"""




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






    def 	__snake_case							(					self				:    Dict):
      a						:		str      							=			load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/sd2-inpaint/init_image.png")
      a						:		str      							=			load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")
      a						:		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")

      a						:		Tuple      							=			"stabilityai/stable-diffusion-2-inpainting"
      a						:		Optional[Any]      							=			StableDiffusionInpaintPipeline.from_pretrained(__UpperCAmelCase				, safety_checker=__UpperCAmelCase)
      pipe.to(__UpperCAmelCase)
      pipe.set_progress_bar_config(disable=__UpperCAmelCase)
      pipe.enable_attention_slicing()

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

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

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






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

      a						:		Optional[Any]      							=			"stabilityai/stable-diffusion-2-inpainting"
      a						:		Any      							=			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()

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

      a						:		Dict      							=			torch.manual_seed(0)
      a						:		List[Any]      							=			pipe(
          prompt=__UpperCAmelCase				, image=__UpperCAmelCase				, mask_image=__UpperCAmelCase				, generator=__UpperCAmelCase				, output_type="np"				, )
      a						:		Optional[Any]      							=			output.images[0]

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






    def 	__snake_case							(					self				:    int):
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

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

      a						:		Optional[Any]      							=			"stabilityai/stable-diffusion-2-inpainting"
      a						:		Optional[int]      							=			PNDMScheduler.from_pretrained(__UpperCAmelCase				, subfolder="scheduler")
      a						:		int      							=			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()

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

      a						:		Optional[int]      							=			torch.manual_seed(0)
      a						:		str      							=			pipe(
          prompt=__UpperCAmelCase				, image=__UpperCAmelCase				, mask_image=__UpperCAmelCase				, generator=__UpperCAmelCase				, num_inference_steps=2				, output_type="np"				, )

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






