


'''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 					lowercase_     (    a__  ,						a__  ,						a__  ,						unittest.TestCase   ):
    __UpperCAmelCase   			=	StableDiffusionInpaintPipeline
    __UpperCAmelCase   			=	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
    __UpperCAmelCase   			=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    __UpperCAmelCase   			=	frozenset(
        []   )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    __UpperCAmelCase   			=	frozenset([]   )


    def    __a							(     self    ):
          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=a  ,					)
          UpperCamelCase__										=				PNDMScheduler(skip_prk_steps=a    )
          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=1_28  ,					)
          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=10_00  ,					hidden_act="gelu"  ,					projection_dim=5_12  ,					)
          UpperCamelCase__										=				CLIPTextModel(a    )
          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    __a							(     self  ,					a  ,					a=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(a    )    ).to(a    )
          UpperCamelCase__										=				image.cpu().permute(0  ,					2  ,					3  ,					1    )[0]
          UpperCamelCase__										=				Image.fromarray(np.uinta(a    )    ).convert("RGB"    ).resize((64, 64)    )
          UpperCamelCase__										=				Image.fromarray(np.uinta(image + 4    )    ).convert("RGB"    ).resize((64, 64)    )
          if str(a    ).startswith("mps"    ):
                UpperCamelCase__										=				torch.manual_seed(a    )
          else:
                UpperCamelCase__										=				torch.Generator(device=a    ).manual_seed(a    )
          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    __a							(     self    ):
          UpperCamelCase__										=				"cpu"  # ensure determinism for the device-dependent torch.Generator
          UpperCamelCase__										=				self.get_dummy_components()
          UpperCamelCase__										=				StableDiffusionInpaintPipeline(**a    )
          UpperCamelCase__										=				sd_pipe.to(a    )
          sd_pipe.set_progress_bar_config(disable=a    )

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

          assert image.shape == (1, 64, 64, 3)
          UpperCamelCase__										=				np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]    )

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


    def    __a							(     self    ):
          super().test_inference_batch_single_identical(expected_max_diff=3e-3    )

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


    def    __a							(     self    ):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()


    def    __a							(     self    ):
          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(a  ,					safety_checker=a    )
          pipe.to(a    )
          pipe.set_progress_bar_config(disable=a    )
          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=a  ,					image=a  ,					mask_image=a  ,					generator=a  ,					output_type="np"  ,					)
          UpperCamelCase__										=				output.images[0]

          assert image.shape == (5_12, 5_12, 3)
          assert np.abs(expected_image - image    ).max() < 9e-3


    def    __a							(     self    ):
          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(
              a  ,					torch_dtype=torch.floataa  ,					safety_checker=a  ,					)
          pipe.to(a    )
          pipe.set_progress_bar_config(disable=a    )
          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=a  ,					image=a  ,					mask_image=a  ,					generator=a  ,					output_type="np"  ,					)
          UpperCamelCase__										=				output.images[0]

          assert image.shape == (5_12, 5_12, 3)
          assert np.abs(expected_image - image    ).max() < 5e-1


    def    __a							(     self    ):
          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(a  ,					subfolder="scheduler"    )
          UpperCamelCase__										=				StableDiffusionInpaintPipeline.from_pretrained(
              a  ,					safety_checker=a  ,					scheduler=a  ,					torch_dtype=torch.floataa  ,					)
          pipe.to(a    )
          pipe.set_progress_bar_config(disable=a    )
          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=a  ,					image=a  ,					mask_image=a  ,					generator=a  ,					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 < 2.65 * 10**9






from numpy import exp, pi, sqrt



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Any ,     __UpperCamelCase    :       float = 0.0 ,     __UpperCamelCase    :       float = 1.0	)							->					int:







  """simple docstring"""





  return 1 / sqrt(2 * pi * sigma**2	) * exp(-((x - mu) ** 2) / (2 * sigma**2)	)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
