


"""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 		snake_case_(							a__				,       unittest.TestCase      ):
     __UpperCamelCase	=       PaintByExamplePipeline
     __UpperCamelCase	=       IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
     __UpperCamelCase	=       IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     __UpperCamelCase	=       frozenset([]      )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess






     def   lowerCamelCase__  (						self	:       List[str]					):
           torch.manual_seed(0					)
           lowerCAmelCase       :						Any							= UNetaDConditionModel(
               block_out_channels=(3_2, 6_4)       ,					layers_per_block=2       ,					sample_size=3_2       ,					in_channels=9       ,					out_channels=4       ,					down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,					up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,					cross_attention_dim=3_2       ,					)
           lowerCAmelCase       :						Tuple							= PNDMScheduler(skip_prk_steps=UpperCamelCase_					)
           torch.manual_seed(0					)
           lowerCAmelCase       :						Optional[int]							= AutoencoderKL(
               block_out_channels=[3_2, 6_4]       ,					in_channels=3       ,					out_channels=3       ,					down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,					up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,					latent_channels=4       ,					)
           torch.manual_seed(0					)
           lowerCAmelCase       :						Dict							= CLIPVisionConfig(
               hidden_size=3_2       ,					projection_dim=3_2       ,					intermediate_size=3_7       ,					layer_norm_eps=1E-05       ,					num_attention_heads=4       ,					num_hidden_layers=5       ,					image_size=3_2       ,					patch_size=4       ,					)
           lowerCAmelCase       :						int							= PaintByExampleImageEncoder(UpperCamelCase_       ,					proj_size=3_2					)
           lowerCAmelCase       :						int							= CLIPImageProcessor(crop_size=3_2       ,					size=3_2					)

           lowerCAmelCase       :						Union[str, Any]							= {
               '''unet''': unet,
               '''scheduler''': scheduler,
               '''vae''': vae,
               '''image_encoder''': image_encoder,
               '''safety_checker''': None,
               '''feature_extractor''': feature_extractor,
           }
           return components






     def   lowerCamelCase__  (						self	:       Union[str, Any]       ,					UpperCamelCase_	:       List[Any]					):
           lowerCAmelCase       :						Optional[int]							= np.array(image.convert('''RGB'''					)					)
           lowerCAmelCase       :						List[Any]							= image[None].transpose(0       ,					3       ,					1       ,					2					)
           lowerCAmelCase       :						List[str]							= torch.from_numpy(UpperCamelCase_					).to(dtype=torch.floataa					) / 127.5 - 1.0
           return image






     def   lowerCamelCase__  (						self	:       Dict       ,					UpperCamelCase_	:       Optional[Any]="cpu"       ,					UpperCamelCase_	:       int=0					):
           # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
           lowerCAmelCase       :						Optional[Any]							= floats_tensor((1, 3, 3_2, 3_2)       ,					rng=random.Random(UpperCamelCase_					)					).to(UpperCamelCase_					)
           lowerCAmelCase       :						Union[str, Any]							= image.cpu().permute(0       ,					2       ,					3       ,					1					)[0]
           lowerCAmelCase       :						List[Any]							= Image.fromarray(np.uinta(UpperCamelCase_					)					).convert('''RGB'''					).resize((6_4, 6_4)					)
           lowerCAmelCase       :						int							= Image.fromarray(np.uinta(image + 4					)					).convert('''RGB'''					).resize((6_4, 6_4)					)
           lowerCAmelCase       :						Optional[int]							= Image.fromarray(np.uinta(UpperCamelCase_					)					).convert('''RGB'''					).resize((3_2, 3_2)					)

           if str(UpperCamelCase_					).startswith('''mps'''					):
                 lowerCAmelCase       :						Dict							= torch.manual_seed(UpperCamelCase_					)
           else:
                 lowerCAmelCase       :						Optional[Any]							= torch.Generator(device=UpperCamelCase_					).manual_seed(UpperCamelCase_					)
           lowerCAmelCase       :						str							= {
               '''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   lowerCamelCase__  (						self	:       Dict					):
           lowerCAmelCase       :						Optional[Any]							= self.get_dummy_components()

           # make sure here that pndm scheduler skips prk
           lowerCAmelCase       :						List[Any]							= PaintByExamplePipeline(**UpperCamelCase_					)
           lowerCAmelCase       :						Tuple							= pipe.to('''cpu'''					)
           pipe.set_progress_bar_config(disable=UpperCamelCase_					)

           lowerCAmelCase       :						Optional[Any]							= self.get_dummy_inputs()
           lowerCAmelCase       :						Dict							= pipe(**UpperCamelCase_					)
           lowerCAmelCase       :						int							= output.images

           lowerCAmelCase       :						int							= image[0, -3:, -3:, -1]

           assert image.shape == (1, 6_4, 6_4, 3)
           lowerCAmelCase       :						Optional[Any]							= np.array([0.4_686, 0.5_687, 0.4_007, 0.5_218, 0.5_741, 0.4_482, 0.4_940, 0.4_629, 0.4_503]					)

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






     def   lowerCamelCase__  (						self	:       List[Any]					):
           lowerCAmelCase       :						Optional[int]							= '''cpu'''
           lowerCAmelCase       :						Dict							= self.get_dummy_inputs()
           inputs.pop('''mask_image'''					)
           lowerCAmelCase       :						List[Any]							= self.convert_to_pt(inputs.pop('''image'''					)					)
           lowerCAmelCase       :						Dict							= image.clamp(0       ,					1					) / 2

           # make sure here that pndm scheduler skips prk
           lowerCAmelCase       :						List[Any]							= PaintByExamplePipeline(**self.get_dummy_components()					)
           lowerCAmelCase       :						Optional[Any]							= pipe.to(UpperCamelCase_					)
           pipe.set_progress_bar_config(disable=UpperCamelCase_					)

           lowerCAmelCase       :						Dict							= pipe(image=UpperCamelCase_       ,					mask_image=mask_image[:, 0]       ,					**UpperCamelCase_					)
           lowerCAmelCase       :						Dict							= output.images

           lowerCAmelCase       :						str							= image.cpu().permute(0       ,					2       ,					3       ,					1					)[0]
           lowerCAmelCase       :						Optional[int]							= mask_image.cpu().permute(0       ,					2       ,					3       ,					1					)[0]

           lowerCAmelCase       :						Dict							= Image.fromarray(np.uinta(UpperCamelCase_					)					).convert('''RGB'''					)
           lowerCAmelCase       :						Optional[int]							= Image.fromarray(np.uinta(UpperCamelCase_					)					).convert('''RGB'''					)

           lowerCAmelCase       :						Tuple							= pipe(**self.get_dummy_inputs()					)
           lowerCAmelCase       :						int							= output.images

           assert out_a.shape == (1, 6_4, 6_4, 3)
           assert np.abs(out_a.flatten() - out_a.flatten()					).max() < 5E-2






     def   lowerCamelCase__  (						self	:       List[str]					):
           super().test_inference_batch_single_identical(expected_max_diff=3E-3					)



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






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






     def   lowerCamelCase__  (						self	:       Union[str, Any]					):
           # make sure here that pndm scheduler skips prk
           lowerCAmelCase       :						str							= load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/paint_by_example/dog_in_bucket.png'''					)
           lowerCAmelCase       :						Dict							= load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/paint_by_example/mask.png'''					)
           lowerCAmelCase       :						Any							= load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/paint_by_example/panda.jpg'''					)

           lowerCAmelCase       :						Tuple							= PaintByExamplePipeline.from_pretrained('''Fantasy-Studio/Paint-by-Example'''					)
           lowerCAmelCase       :						List[Any]							= pipe.to(UpperCamelCase_					)
           pipe.set_progress_bar_config(disable=UpperCamelCase_					)

           lowerCAmelCase       :						str							= torch.manual_seed(3_2_1					)
           lowerCAmelCase       :						str							= pipe(
               image=UpperCamelCase_       ,					mask_image=UpperCamelCase_       ,					example_image=UpperCamelCase_       ,					generator=UpperCamelCase_       ,					guidance_scale=5.0       ,					num_inference_steps=5_0       ,					output_type='''np'''       ,					)

           lowerCAmelCase       :						List[str]							= output.images

           lowerCAmelCase       :						List[str]							= image[0, -3:, -3:, -1]

           assert image.shape == (1, 5_1_2, 5_1_2, 3)
           lowerCAmelCase       :						List[Any]							= np.array([0.4_834, 0.4_811, 0.4_874, 0.5_122, 0.5_081, 0.5_144, 0.5_291, 0.5_290, 0.5_374]					)

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






"""simple docstring"""





def   _snake_case    (    _snake_case					:      int      ):
      if not isinstance(_snake_case			, _snake_case      ):
            lowerCAmelCase       :						int							= f'''Input value of [number={number}] must be an integer'''
            raise TypeError(_snake_case      )
      if number < 0 or number % 2 == 1:
            return False
      lowerCAmelCase       :						int							= int(number**0.5      )
      return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
 import doctest

 doctest.testmod()


