



"""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 							lowercase					(				self   :				str      )     ->							str:
         torch.manual_seed(0      )
         lowercase	:   Optional[int]											= 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,  )
         lowercase	:   List[str]											= PNDMScheduler(skip_prk_steps=_lowerCAmelCase      )
         torch.manual_seed(0      )
         lowercase	:   Optional[Any]											= 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      )
         lowercase	:   List[Any]											= 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,  )
         lowercase	:   List[str]											= PaintByExampleImageEncoder(_lowerCAmelCase,  proj_size=32      )
         lowercase	:   str											= CLIPImageProcessor(crop_size=32,  size=32      )

         lowercase	:   List[str]											= {
             'unet': unet,
             'scheduler': scheduler,
             'vae': vae,
             'image_encoder': image_encoder,
             'safety_checker': None,
             'feature_extractor': feature_extractor,
         }
         return components


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


   def 							lowercase					(				self   :				Any,  lowerCAmelCase   :				Tuple="cpu",  lowerCAmelCase   :				List[str]=0      )     ->							str:
         # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
         lowercase	:   Dict											= floats_tensor((1, 3, 32, 32),  rng=random.Random(_lowerCAmelCase      )      ).to(_lowerCAmelCase      )
         lowercase	:   Union[str, Any]											= image.cpu().permute(0,  2,  3,  1      )[0]
         lowercase	:   Optional[int]											= Image.fromarray(np.uinta(_lowerCAmelCase      )      ).convert('RGB'      ).resize((64, 64)      )
         lowercase	:   Dict											= Image.fromarray(np.uinta(image + 4      )      ).convert('RGB'      ).resize((64, 64)      )
         lowercase	:   List[str]											= Image.fromarray(np.uinta(_lowerCAmelCase      )      ).convert('RGB'      ).resize((32, 32)      )

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

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

         lowercase	:   Union[str, Any]											= self.get_dummy_inputs()
         lowercase	:   Optional[Any]											= pipe(**_lowerCAmelCase      )
         lowercase	:   Any											= output.images

         lowercase	:   Any											= image[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)
         lowercase	:   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 							lowercase					(				self   :				List[str]      )     ->							List[str]:
         lowercase	:   Tuple											= 'cpu'
         lowercase	:   int											= self.get_dummy_inputs()
         inputs.pop('mask_image'      )
         lowercase	:   Tuple											= self.convert_to_pt(inputs.pop('image'      )      )
         lowercase	:   Tuple											= image.clamp(0,  1      ) / 2

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

         lowercase	:   str											= pipe(image=_lowerCAmelCase,  mask_image=mask_image[:, 0],  **_lowerCAmelCase      )
         lowercase	:   List[Any]											= output.images

         lowercase	:   Tuple											= image.cpu().permute(0,  2,  3,  1      )[0]
         lowercase	:   Dict											= mask_image.cpu().permute(0,  2,  3,  1      )[0]

         lowercase	:   List[str]											= Image.fromarray(np.uinta(_lowerCAmelCase      )      ).convert('RGB'      )
         lowercase	:   int											= Image.fromarray(np.uinta(_lowerCAmelCase      )      ).convert('RGB'      )

         lowercase	:   List[str]											= pipe(**self.get_dummy_inputs()      )
         lowercase	:   Tuple											= output.images

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






   def 							lowercase					(				self   :				Union[str, Any]      )     ->							Dict:
         super().test_inference_batch_single_identical(expected_max_diff=3e-3      )




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


   def 							lowercase					(				self   :				int      )     ->							Dict:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






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

         lowercase	:   Optional[Any]											= PaintByExamplePipeline.from_pretrained('Fantasy-Studio/Paint-by-Example'      )
         lowercase	:   Any											= pipe.to(_lowerCAmelCase      )
         pipe.set_progress_bar_config(disable=_lowerCAmelCase      )

         lowercase	:   Tuple											= torch.manual_seed(321      )
         lowercase	:   Optional[int]											= pipe(
             image=_lowerCAmelCase,  mask_image=_lowerCAmelCase,  example_image=_lowerCAmelCase,  generator=_lowerCAmelCase,  guidance_scale=5.0,  num_inference_steps=50,  output_type='np',  )

         lowercase	:   Any											= output.images

         lowercase	:   Any											= image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         lowercase	:   Optional[Any]											= 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


import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


_UpperCamelCase:					List[Any]     =TypeVar('T', bound='TaskTemplate')





@dataclass(frozen=SCREAMING_SNAKE_CASE   )
class 						__lowercase(     abc.ABC   ):


       """simple docstring"""







       UpperCamelCase_				 =							42
       UpperCamelCase_				 =							42
       UpperCamelCase_				 =							42



       def SCREAMING_SNAKE_CASE_				(     self					:			T  ,      _lowerCAmelCase					:			Features       )					->   T:
         return copy.deepcopy(self       )



       @property
       def SCREAMING_SNAKE_CASE_				(     self					:			str       )					->   Features:
         return Features(**self.input_schema  ,      **self.label_schema       )



       @property
       @abc.abstractmethod
       def SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Dict[str, str]:
         raise NotImplementedError






       @classmethod
       def SCREAMING_SNAKE_CASE_				(     cls					:			Type[T]  ,      _lowerCAmelCase					:			dict       )					->   T:
         _lowerCAmelCase						=		{f.name for f in dataclasses.fields(cls       )}
         return cls(**{k: v for k, v in template_dict.items() if k in field_names}       )
