


"""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		(					_SCREAMING_SNAKE_CASE				,       unittest.TestCase      ):







   """simple docstring"""




   lowerCAmelCase_				:   List[Any]     	=			PaintByExamplePipeline
   lowerCAmelCase_				:   str     	=			IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
   lowerCAmelCase_				:   int     	=			IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   lowerCAmelCase_				:   Tuple     	=			frozenset([]      )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess




   def SCREAMING_SNAKE_CASE_      (		self							:str       ):
          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=UpperCamelCase__       )
          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(UpperCamelCase__				,  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 SCREAMING_SNAKE_CASE_      (		self							:Optional[int]				,  UpperCamelCase__							:Tuple       ):
          _a          =				np.array(image.convert("RGB"       )       )
          _a          =				image[None].transpose(0				,  3				,  1				,  2       )
          _a          =				torch.from_numpy(UpperCamelCase__       ).to(dtype=torch.floataa       ) / 127.5 - 1.0
          return image




   def SCREAMING_SNAKE_CASE_      (		self							:Tuple				,  UpperCamelCase__							:int="cpu"				,  UpperCamelCase__							:Any=0       ):
          # 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(UpperCamelCase__       )       ).to(UpperCamelCase__       )
          _a          =				image.cpu().permute(0				,  2				,  3				,  1       )[0]
          _a          =				Image.fromarray(np.uinta(UpperCamelCase__       )       ).convert("RGB"       ).resize((64, 64)       )
          _a          =				Image.fromarray(np.uinta(image + 4       )       ).convert("RGB"       ).resize((64, 64)       )
          _a          =				Image.fromarray(np.uinta(UpperCamelCase__       )       ).convert("RGB"       ).resize((32, 32)       )

          if str(UpperCamelCase__       ).startswith("mps"       ):
                 _a          =				torch.manual_seed(UpperCamelCase__       )
          else:
                 _a          =				torch.Generator(device=UpperCamelCase__       ).manual_seed(UpperCamelCase__       )
          _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 SCREAMING_SNAKE_CASE_      (		self							:int       ):
          _a          =				self.get_dummy_components()

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

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

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

          assert image.shape == (1, 64, 64, 3)
          _a          =				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 SCREAMING_SNAKE_CASE_      (		self							:List[str]       ):
          _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(UpperCamelCase__       )
          pipe.set_progress_bar_config(disable=UpperCamelCase__       )

          _a          =				pipe(image=UpperCamelCase__				,  mask_image=mask_image[:, 0]				,  **UpperCamelCase__       )
          _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(UpperCamelCase__       )       ).convert("RGB"       )
          _a          =				Image.fromarray(np.uinta(UpperCamelCase__       )       ).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 SCREAMING_SNAKE_CASE_      (		self							:Dict       ):
          super().test_inference_batch_single_identical(expected_max_diff=3E-3       )






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







   """simple docstring"""




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




   def SCREAMING_SNAKE_CASE_      (		self							:List[str]       ):
          # 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(UpperCamelCase__       )
          pipe.set_progress_bar_config(disable=UpperCamelCase__       )

          _a          =				torch.manual_seed(321       )
          _a          =				pipe(
              image=UpperCamelCase__				,  mask_image=UpperCamelCase__				,  example_image=UpperCamelCase__				,  generator=UpperCamelCase__				,  guidance_scale=5.0				,  num_inference_steps=50				,  output_type="np"				,  )

          _a          =				output.images

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

          assert image.shape == (1, 512, 512, 3)
          _a          =				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




"""simple docstring"""




from __future__ import annotations

from collections.abc import Iterator
from typing import Any





class __snake_case		:







   """simple docstring"""




   def __init__(		self							:List[Any]				,  UpperCamelCase__							:Any       ):
          _a          =				data
          _a          =				None




   def __str__(		self							:Any       ):
          return f'{self.data}'






class __snake_case		:







   """simple docstring"""




   def __init__(		self							:Any       ):
          _a          =				None
          _a          =				None




   def __iter__(		self							:List[str]       ):
          _a          =				self.front
          while node:
                 yield node.data
                 _a          =				node.next




   def __len__(		self							:List[str]       ):
          return len(tuple(iter(self       )       )       )




   def __str__(		self							:Union[str, Any]       ):
          return " <- ".join(str(UpperCamelCase__       ) for item in self       )




   def SCREAMING_SNAKE_CASE_      (		self							:Any       ):
          return len(self       ) == 0




   def SCREAMING_SNAKE_CASE_      (		self							:List[str]				,  UpperCamelCase__							:Any       ):
          _a          =				Node(UpperCamelCase__       )
          if self.is_empty():
                 _a          =				_a          =				node
          else:
                 assert isinstance(self.rear				,  UpperCamelCase__       )
                 _a          =				node
                 _a          =				node




   def SCREAMING_SNAKE_CASE_      (		self							:Tuple       ):
          if self.is_empty():
                 raise IndexError("dequeue from empty queue"       )
          assert isinstance(self.front				,  UpperCamelCase__       )
          _a          =				self.front
          _a          =				self.front.next
          if self.front is None:
                 _a          =				None
          return node.data




   def SCREAMING_SNAKE_CASE_      (		self							:int       ):
          _a          =				_a          =				None


if __name__ == "__main__":
   from doctest import testmod

   testmod()
