




from itertools import permutations
def 				SCREAMING_SNAKE_CASE_				(							__A       :				Union[str, Any] = 5						)				->				int:






   if gon_side < 3 or gon_side > 5:
      raise ValueError("gon_side must be in the range [3, 5]"						)

   # Since it's 16, we know 10 is on the outer ring
   # Put the big numbers at the end so that they are never the first number
   _SCREAMING_SNAKE_CASE =   list(range(gon_side + 1 ,  0 ,  -1						)						)
   _SCREAMING_SNAKE_CASE =   list(range(gon_side + 2 ,  gon_side * 2 + 1						)						)

   for perm in permutations(small_numbers + big_numbers						):
      _SCREAMING_SNAKE_CASE =   generate_gon_ring(_UpperCAmelCase ,  list(_UpperCAmelCase						)						)
      if is_magic_gon(_UpperCAmelCase						):
         return int("".join(str(_UpperCAmelCase						) for n in numbers						)						)

   _SCREAMING_SNAKE_CASE =   f"""Magic {gon_side}-gon ring is impossible"""
   raise ValueError(_UpperCAmelCase						)
def 				SCREAMING_SNAKE_CASE_				(							__A       :				Optional[Any] ,  __A       :				List[Any]						)				->				list[int]:
   _SCREAMING_SNAKE_CASE =   [0] * (gon_side * 3)
   _SCREAMING_SNAKE_CASE =   perm[0:3]
   perm.append(perm[1]						)

   _SCREAMING_SNAKE_CASE =   1 if gon_side < 5 else 2

   for i in range(1 ,  len(_UpperCAmelCase						) // 3 + magic_number						):
      _SCREAMING_SNAKE_CASE =   perm[2 * i + 1]
      _SCREAMING_SNAKE_CASE =   result[3 * i - 1]
      _SCREAMING_SNAKE_CASE =   perm[2 * i + 2]

   return result
def 				SCREAMING_SNAKE_CASE_				(							__A       :				int						)				->				bool:






   if len(_UpperCAmelCase						) % 3 != 0:
      raise ValueError("a gon ring should have a length that is a multiple of 3"						)

   if min(numbers[::3]						) != numbers[0]:
      return False

   _SCREAMING_SNAKE_CASE =   sum(numbers[:3]						)

   return all(sum(numbers[i : i + 3]						) == total for i in range(3 ,  len(_UpperCAmelCase						) ,  3						)						)


if __name__ == "__main__":
 print(solution())







'''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 	lowercase_					(				A  ,						unittest.TestCase     ):


   """simple docstring"""

   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 							lowerCAmelCase_    (					self      :			Optional[Any]					):

      """simple docstring"""
      torch.manual_seed(0					)
      _SCREAMING_SNAKE_CASE =   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						,   )
      _SCREAMING_SNAKE_CASE =   PNDMScheduler(skip_prk_steps=__lowerCamelCase					)
      torch.manual_seed(0					)
      _SCREAMING_SNAKE_CASE =   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					)
      _SCREAMING_SNAKE_CASE =   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						,   )
      _SCREAMING_SNAKE_CASE =   PaintByExampleImageEncoder(__lowerCamelCase						,   proj_size=3_2					)
      _SCREAMING_SNAKE_CASE =   CLIPImageProcessor(crop_size=3_2						,   size=3_2					)

      _SCREAMING_SNAKE_CASE =   {
          "unet": unet,
          "scheduler": scheduler,
          "vae": vae,
          "image_encoder": image_encoder,
          "safety_checker": None,
          "feature_extractor": feature_extractor,
      }
      return components



   def 							lowerCAmelCase_    (					self      :			Any						,   __lowerCamelCase      :			str					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   np.array(image.convert("RGB"					)					)
      _SCREAMING_SNAKE_CASE =   image[None].transpose(0						,   3						,   1						,   2					)
      _SCREAMING_SNAKE_CASE =   torch.from_numpy(__lowerCamelCase					).to(dtype=torch.floataa					) / 1_2_7.5 - 1.0
      return image



   def 							lowerCAmelCase_    (					self      :			List[str]						,   __lowerCamelCase      :			Optional[int]="cpu"						,   __lowerCamelCase      :			Union[str, Any]=0					):

      """simple docstring"""
      # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
      _SCREAMING_SNAKE_CASE =   floats_tensor((1, 3, 3_2, 3_2)						,   rng=random.Random(__lowerCamelCase					)					).to(__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   image.cpu().permute(0						,   2						,   3						,   1					)[0]
      _SCREAMING_SNAKE_CASE =   Image.fromarray(np.uinta(__lowerCamelCase					)					).convert("RGB"					).resize((6_4, 6_4)					)
      _SCREAMING_SNAKE_CASE =   Image.fromarray(np.uinta(image + 4					)					).convert("RGB"					).resize((6_4, 6_4)					)
      _SCREAMING_SNAKE_CASE =   Image.fromarray(np.uinta(__lowerCamelCase					)					).convert("RGB"					).resize((3_2, 3_2)					)

      if str(__lowerCamelCase					).startswith("mps"					):
         _SCREAMING_SNAKE_CASE =   torch.manual_seed(__lowerCamelCase					)
      else:
         _SCREAMING_SNAKE_CASE =   torch.Generator(device=__lowerCamelCase					).manual_seed(__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   {
          "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      :			List[str]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.get_dummy_components()

      # make sure here that pndm scheduler skips prk
      _SCREAMING_SNAKE_CASE =   PaintByExamplePipeline(**__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   pipe.to("cpu"					)
      pipe.set_progress_bar_config(disable=__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   self.get_dummy_inputs()
      _SCREAMING_SNAKE_CASE =   pipe(**__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   output.images

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

      assert image.shape == (1, 6_4, 6_4, 3)
      _SCREAMING_SNAKE_CASE =   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 							lowerCAmelCase_    (					self      :			Tuple					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   "cpu"
      _SCREAMING_SNAKE_CASE =   self.get_dummy_inputs()
      inputs.pop("mask_image"					)
      _SCREAMING_SNAKE_CASE =   self.convert_to_pt(inputs.pop("image"					)					)
      _SCREAMING_SNAKE_CASE =   image.clamp(0						,   1					) / 2

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

      _SCREAMING_SNAKE_CASE =   pipe(image=__lowerCamelCase						,   mask_image=mask_image[:, 0]						,   **__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   output.images

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

      _SCREAMING_SNAKE_CASE =   Image.fromarray(np.uinta(__lowerCamelCase					)					).convert("RGB"					)
      _SCREAMING_SNAKE_CASE =   Image.fromarray(np.uinta(__lowerCamelCase					)					).convert("RGB"					)

      _SCREAMING_SNAKE_CASE =   pipe(**self.get_dummy_inputs()					)
      _SCREAMING_SNAKE_CASE =   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[Any]					):

      """simple docstring"""
      super().test_inference_batch_single_identical(expected_max_diff=3e-3					)







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


   """simple docstring"""

   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()



   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

      """simple docstring"""
      # make sure here that pndm scheduler skips prk
      _SCREAMING_SNAKE_CASE =   load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/paint_by_example/dog_in_bucket.png"					)
      _SCREAMING_SNAKE_CASE =   load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/paint_by_example/mask.png"					)
      _SCREAMING_SNAKE_CASE =   load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/paint_by_example/panda.jpg"					)

      _SCREAMING_SNAKE_CASE =   PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"					)
      _SCREAMING_SNAKE_CASE =   pipe.to(__lowerCamelCase					)
      pipe.set_progress_bar_config(disable=__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   torch.manual_seed(3_2_1					)
      _SCREAMING_SNAKE_CASE =   pipe(
          image=__lowerCamelCase						,   mask_image=__lowerCamelCase						,   example_image=__lowerCamelCase						,   generator=__lowerCamelCase						,   guidance_scale=5.0						,   num_inference_steps=5_0						,   output_type="np"						,   )

      _SCREAMING_SNAKE_CASE =   output.images

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

      assert image.shape == (1, 5_1_2, 5_1_2, 3)
      _SCREAMING_SNAKE_CASE =   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

