

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_					(						snake_case__				,      unittest.TestCase   ):
     _lowercase     :	List[Any]        =      PaintByExamplePipeline
     _lowercase     :	Dict        =      IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
     _lowercase     :	List[Any]        =      IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     _lowercase     :	Optional[int]        =      frozenset([]   )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess


     def     UpperCAmelCase    (				self     :						Tuple   )				->      Union[str, Any]:
      torch.manual_seed(0   )
      __lowerCAmelCase:  int        = 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:  Optional[int]        = PNDMScheduler(skip_prk_steps=UpperCAmelCase   )
      torch.manual_seed(0   )
      __lowerCAmelCase:  List[str]        = 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:  Optional[int]        = 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:  List[Any]        = CLIPImageProcessor(crop_size=3_2     ,    size=3_2   )

      __lowerCAmelCase:  Optional[int]        = {
          'unet': unet,
          'scheduler': scheduler,
          'vae': vae,
          'image_encoder': image_encoder,
          'safety_checker': None,
          'feature_extractor': feature_extractor,
      }
      return components


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


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

      if str(UpperCAmelCase   ).startswith('mps'   ):
       __lowerCAmelCase:  Tuple        = torch.manual_seed(UpperCAmelCase   )
      else:
       __lowerCAmelCase:  List[str]        = torch.Generator(device=UpperCAmelCase   ).manual_seed(UpperCAmelCase   )
      __lowerCAmelCase:  Optional[Any]        = {
          '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     :						Union[str, Any]   )				->      Optional[int]:
      __lowerCAmelCase:  Union[str, Any]        = self.get_dummy_components()

      # make sure here that pndm scheduler skips prk
      __lowerCAmelCase:  List[str]        = PaintByExamplePipeline(**UpperCAmelCase   )
      __lowerCAmelCase:  Union[str, Any]        = pipe.to('cpu'   )
      pipe.set_progress_bar_config(disable=UpperCAmelCase   )

      __lowerCAmelCase:  Optional[int]        = self.get_dummy_inputs()
      __lowerCAmelCase:  Dict        = pipe(**UpperCAmelCase   )
      __lowerCAmelCase:  int        = output.images

      __lowerCAmelCase:  Union[str, Any]        = image[0, -3:, -3:, -1]

      assert image.shape == (1, 6_4, 6_4, 3)
      __lowerCAmelCase:  Optional[Any]        = np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503]   )

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


     def     UpperCAmelCase    (				self     :						List[Any]   )				->      Dict:
      __lowerCAmelCase:  Tuple        = 'cpu'
      __lowerCAmelCase:  Dict        = self.get_dummy_inputs()
      inputs.pop('mask_image'   )
      __lowerCAmelCase:  Tuple        = self.convert_to_pt(inputs.pop('image'   )   )
      __lowerCAmelCase:  Optional[Any]        = image.clamp(0     ,    1   ) / 2

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

      __lowerCAmelCase:  Optional[Any]        = pipe(image=UpperCAmelCase     ,    mask_image=mask_image[:, 0]     ,    **UpperCAmelCase   )
      __lowerCAmelCase:  List[str]        = output.images

      __lowerCAmelCase:  List[Any]        = image.cpu().permute(0     ,    2     ,    3     ,    1   )[0]
      __lowerCAmelCase:  int        = mask_image.cpu().permute(0     ,    2     ,    3     ,    1   )[0]

      __lowerCAmelCase:  List[str]        = Image.fromarray(np.uinta(UpperCAmelCase   )   ).convert('RGB'   )
      __lowerCAmelCase:  Tuple        = Image.fromarray(np.uinta(UpperCAmelCase   )   ).convert('RGB'   )

      __lowerCAmelCase:  Optional[int]        = 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     UpperCAmelCase    (				self     :						List[Any]   )				->      int:
      super().test_inference_batch_single_identical(expected_max_diff=3E-3   )







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


     def     UpperCAmelCase    (				self     :						Any   )				->      str:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()


     def     UpperCAmelCase    (				self     :						Tuple   )				->      Optional[Any]:
      # make sure here that pndm scheduler skips prk
      __lowerCAmelCase:  List[Any]        = load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/paint_by_example/dog_in_bucket.png'   )
      __lowerCAmelCase:  Optional[int]        = 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:  Union[str, Any]        = PaintByExamplePipeline.from_pretrained('Fantasy-Studio/Paint-by-Example'   )
      __lowerCAmelCase:  Any        = pipe.to(UpperCAmelCase   )
      pipe.set_progress_bar_config(disable=UpperCAmelCase   )

      __lowerCAmelCase:  Optional[Any]        = torch.manual_seed(3_2_1   )
      __lowerCAmelCase:  Any        = pipe(
          image=UpperCAmelCase     ,    mask_image=UpperCAmelCase     ,    example_image=UpperCAmelCase     ,    generator=UpperCAmelCase     ,    guidance_scale=5.0     ,    num_inference_steps=5_0     ,    output_type='np'     ,    )

      __lowerCAmelCase:  Any        = output.images

      __lowerCAmelCase:  Optional[Any]        = image[0, -3:, -3:, -1]

      assert image.shape == (1, 5_1_2, 5_1_2, 3)
      __lowerCAmelCase:  Optional[int]        = np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374]   )

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








def _a					(			SCREAMING_SNAKE_CASE						:     int						)       ->       int:

 """simple docstring"""






 return n * (2 * n - 1)







def _a					(			SCREAMING_SNAKE_CASE						:     int						)       ->       bool:

 """simple docstring"""






 __lowerCAmelCase:  List[Any]        = (1 + 24 * n) ** 0.5
 return ((1 + root) / 6) % 1 == 0







def _a					(			SCREAMING_SNAKE_CASE						:     int = 1_44						)       ->       int:

 """simple docstring"""






 __lowerCAmelCase:  Dict        = start
 __lowerCAmelCase:  List[str]        = hexagonal_num(SCREAMING_SNAKE_CASE						)
 while not is_pentagonal(SCREAMING_SNAKE_CASE						):
  n += 1
  __lowerCAmelCase:  Any        = hexagonal_num(SCREAMING_SNAKE_CASE						)
 return num


if __name__ == "__main__":
  print(f"{solution()} = ")





