


'''simple docstring'''






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 			a  (  _SCREAMING_SNAKE_CASE	,					unittest.TestCase							):
 _lowerCAmelCase			       =						PaintByExamplePipeline
 _lowerCAmelCase			       =						IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
 _lowerCAmelCase			       =						IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 _lowerCAmelCase			       =						frozenset([]							)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
 def 				__UpperCAmelCase		(			self    )       ->				Any:
      torch.manual_seed(0    )
      _a         =				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   ,					)
      _a         =				PNDMScheduler(skip_prk_steps=__magic_name__    )
      torch.manual_seed(0    )
      _a         =				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   ,					)
      torch.manual_seed(0    )
      _a         =				CLIPVisionConfig(
          hidden_size=32   ,					projection_dim=32   ,					intermediate_size=37   ,					layer_norm_eps=1e-05   ,					num_attention_heads=4   ,					num_hidden_layers=5   ,					image_size=32   ,					patch_size=4   ,					)
      _a         =				PaintByExampleImageEncoder(__magic_name__   ,					proj_size=32    )
      _a         =				CLIPImageProcessor(crop_size=32   ,					size=32    )

      _a         =				{
          'unet': unet,
          'scheduler': scheduler,
          'vae': vae,
          'image_encoder': image_encoder,
          'safety_checker': None,
          'feature_extractor': feature_extractor,
      }
      return components
 def 				__UpperCAmelCase		(			self   ,					__magic_name__    )       ->				int:
      _a         =				np.array(image.convert('RGB'    )    )
      _a         =				image[None].transpose(0   ,					3   ,					1   ,					2    )
      _a         =				torch.from_numpy(__magic_name__    ).to(dtype=torch.floataa    ) / 1_2_7.5 - 1.0
      return image
 def 				__UpperCAmelCase		(			self   ,					__magic_name__="cpu"   ,					__magic_name__=0    )       ->				Optional[Any]:
      # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
      _a         =				floats_tensor((1, 3, 32, 32)   ,					rng=random.Random(__magic_name__    )    ).to(__magic_name__    )
      _a         =				image.cpu().permute(0   ,					2   ,					3   ,					1    )[0]
      _a         =				Image.fromarray(np.uinta(__magic_name__    )    ).convert('RGB'    ).resize((64, 64)    )
      _a         =				Image.fromarray(np.uinta(image + 4    )    ).convert('RGB'    ).resize((64, 64)    )
      _a         =				Image.fromarray(np.uinta(__magic_name__    )    ).convert('RGB'    ).resize((32, 32)    )

      if str(__magic_name__    ).startswith('mps'    ):
           _a         =				torch.manual_seed(__magic_name__    )
      else:
           _a         =				torch.Generator(device=__magic_name__    ).manual_seed(__magic_name__    )
      _a         =				{
          'example_image': example_image,
          '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[str]:
      _a         =				self.get_dummy_components()

      # make sure here that pndm scheduler skips prk
      _a         =				PaintByExamplePipeline(**__magic_name__    )
      _a         =				pipe.to('cpu'    )
      pipe.set_progress_bar_config(disable=__magic_name__    )

      _a         =				self.get_dummy_inputs()
      _a         =				pipe(**__magic_name__    )
      _a         =				output.images

      _a         =				image[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)
      _a         =				np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3]    )

      assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
 def 				__UpperCAmelCase		(			self    )       ->				List[Any]:
      _a         =				'cpu'
      _a         =				self.get_dummy_inputs()
      inputs.pop('mask_image'    )
      _a         =				self.convert_to_pt(inputs.pop('image'    )    )
      _a         =				image.clamp(0   ,					1    ) / 2

      # make sure here that pndm scheduler skips prk
      _a         =				PaintByExamplePipeline(**self.get_dummy_components()    )
      _a         =				pipe.to(__magic_name__    )
      pipe.set_progress_bar_config(disable=__magic_name__    )

      _a         =				pipe(image=__magic_name__   ,					mask_image=mask_image[:, 0]   ,					**__magic_name__    )
      _a         =				output.images

      _a         =				image.cpu().permute(0   ,					2   ,					3   ,					1    )[0]
      _a         =				mask_image.cpu().permute(0   ,					2   ,					3   ,					1    )[0]

      _a         =				Image.fromarray(np.uinta(__magic_name__    )    ).convert('RGB'    )
      _a         =				Image.fromarray(np.uinta(__magic_name__    )    ).convert('RGB'    )

      _a         =				pipe(**self.get_dummy_inputs()    )
      _a         =				output.images

      assert out_a.shape == (1, 64, 64, 3)
      assert np.abs(out_a.flatten() - out_a.flatten()    ).max() < 5e-2




 def 				__UpperCAmelCase		(			self    )       ->				List[Any]:
      super().test_inference_batch_single_identical(expected_max_diff=3e-3    )





@slow
@require_torch_gpu
class 			a  (  unittest.TestCase							):
 def 				__UpperCAmelCase		(			self    )       ->				Optional[Any]:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()




 def 				__UpperCAmelCase		(			self    )       ->				int:
      # make sure here that pndm scheduler skips prk
      _a         =				load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/paint_by_example/dog_in_bucket.png'    )
      _a         =				load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/paint_by_example/mask.png'    )
      _a         =				load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/paint_by_example/panda.jpg'    )

      _a         =				PaintByExamplePipeline.from_pretrained('Fantasy-Studio/Paint-by-Example'    )
      _a         =				pipe.to(__magic_name__    )
      pipe.set_progress_bar_config(disable=__magic_name__    )

      _a         =				torch.manual_seed(3_21    )
      _a         =				pipe(
          image=__magic_name__   ,					mask_image=__magic_name__   ,					example_image=__magic_name__   ,					generator=__magic_name__   ,					guidance_scale=5.0   ,					num_inference_steps=50   ,					output_type='np'   ,					)

      _a         =				output.images

      _a         =				image[0, -3:, -3:, -1]

      assert image.shape == (1, 5_12, 5_12, 3)
      _a         =				np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4]    )

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





'''simple docstring'''






from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available




@dataclass
class 			a  (  _SCREAMING_SNAKE_CASE							):
 _lowerCAmelCase			       =						{
     """sld_warmup_steps""": 1_5,
     """sld_guidance_scale""": 2_0,
     """sld_threshold""": 0.0,
     """sld_momentum_scale""": 0.0,
     """sld_mom_beta""": 0.0,
 }
 _lowerCAmelCase			       =						{
     """sld_warmup_steps""": 1_0,
     """sld_guidance_scale""": 1_0_0_0,
     """sld_threshold""": 0.01,
     """sld_momentum_scale""": 0.3,
     """sld_mom_beta""": 0.4,
 }
 _lowerCAmelCase			       =						{
     """sld_warmup_steps""": 7,
     """sld_guidance_scale""": 2_0_0_0,
     """sld_threshold""": 0.025,
     """sld_momentum_scale""": 0.5,
     """sld_mom_beta""": 0.7,
 }
 _lowerCAmelCase			       =						{
     """sld_warmup_steps""": 0,
     """sld_guidance_scale""": 5_0_0_0,
     """sld_threshold""": 1.0,
     """sld_momentum_scale""": 0.5,
     """sld_mom_beta""": 0.7,
 }





@dataclass
class 			a  (  _SCREAMING_SNAKE_CASE							):
 _lowerCAmelCase			       =						42
 _lowerCAmelCase			       =						42
 _lowerCAmelCase			       =						42
 _lowerCAmelCase			       =						42


if is_transformers_available() and is_torch_available():
 from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
 from .safety_checker import SafeStableDiffusionSafetyChecker

