

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			):
 _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      UpperCamelCase__  (							self ) ->Union[str, Any]:



       '''simple docstring'''





       torch.manual_seed(0 )
       __lowerCAmelCase :						Dict = 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				,							)
       __lowerCAmelCase :						Optional[Any] = PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE__ )
       torch.manual_seed(0 )
       __lowerCAmelCase :						List[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 )
       __lowerCAmelCase :						Optional[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				,							)
       __lowerCAmelCase :						Any = PaintByExampleImageEncoder(SCREAMING_SNAKE_CASE__				,							proj_size=32 )
       __lowerCAmelCase :						int = CLIPImageProcessor(crop_size=32				,							size=32 )

       __lowerCAmelCase :						List[str] = {
           "unet": unet,
           "scheduler": scheduler,
           "vae": vae,
           "image_encoder": image_encoder,
           "safety_checker": None,
           "feature_extractor": feature_extractor,
       }
       return components
 def      UpperCamelCase__  (							self				,							A_ ) ->int:



       '''simple docstring'''





       __lowerCAmelCase :						Union[str, Any] = np.array(image.convert('''RGB''' ) )
       __lowerCAmelCase :						Dict = image[None].transpose(0				,							3				,							1				,							2 )
       __lowerCAmelCase :						str = torch.from_numpy(SCREAMING_SNAKE_CASE__ ).to(dtype=torch.floataa ) / 127.5 - 1.0
       return image
 def      UpperCamelCase__  (							self				,							A_="cpu"				,							A_=0 ) ->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase :						str = floats_tensor((1, 3, 32, 32)				,							rng=random.Random(SCREAMING_SNAKE_CASE__ ) ).to(SCREAMING_SNAKE_CASE__ )
       __lowerCAmelCase :						List[str] = image.cpu().permute(0				,							2				,							3				,							1 )[0]
       __lowerCAmelCase :						Dict = Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__ ) ).convert('''RGB''' ).resize((64, 64) )
       __lowerCAmelCase :						Dict = Image.fromarray(np.uinta(image + 4 ) ).convert('''RGB''' ).resize((64, 64) )
       __lowerCAmelCase :						int = Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__ ) ).convert('''RGB''' ).resize((32, 32) )

       if str(SCREAMING_SNAKE_CASE__ ).startswith('''mps''' ):
             __lowerCAmelCase :						Any = torch.manual_seed(SCREAMING_SNAKE_CASE__ )
       else:
             __lowerCAmelCase :						Optional[Any] = torch.Generator(device=SCREAMING_SNAKE_CASE__ ).manual_seed(SCREAMING_SNAKE_CASE__ )
       __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      UpperCamelCase__  (							self ) ->Dict:



       '''simple docstring'''





       __lowerCAmelCase :						Optional[int] = self.get_dummy_components()

       # make sure here that pndm scheduler skips prk
       __lowerCAmelCase :						Optional[int] = PaintByExamplePipeline(**SCREAMING_SNAKE_CASE__ )
       __lowerCAmelCase :						Optional[int] = pipe.to('''cpu''' )
       pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )

       __lowerCAmelCase :						Optional[Any] = self.get_dummy_inputs()
       __lowerCAmelCase :						Dict = pipe(**SCREAMING_SNAKE_CASE__ )
       __lowerCAmelCase :						Union[str, Any] = output.images

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

       assert image.shape == (1, 64, 64, 3)
       __lowerCAmelCase :						Dict = 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      UpperCamelCase__  (							self ) ->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase :						Optional[Any] = "cpu"
       __lowerCAmelCase :						Tuple = self.get_dummy_inputs()
       inputs.pop('''mask_image''' )
       __lowerCAmelCase :						List[str] = self.convert_to_pt(inputs.pop('''image''' ) )
       __lowerCAmelCase :						List[Any] = image.clamp(0				,							1 ) / 2

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

       __lowerCAmelCase :						Union[str, Any] = pipe(image=SCREAMING_SNAKE_CASE__				,							mask_image=mask_image[:, 0]				,							**SCREAMING_SNAKE_CASE__ )
       __lowerCAmelCase :						Optional[int] = output.images

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

       __lowerCAmelCase :						Any = Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__ ) ).convert('''RGB''' )
       __lowerCAmelCase :						Optional[int] = Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__ ) ).convert('''RGB''' )

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

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





 def      UpperCamelCase__  (							self ) ->Dict:



       '''simple docstring'''





       super().test_inference_batch_single_identical(expected_max_diff=3e-3 )







@slow
@require_torch_gpu
class   __lowercase				(unittest.TestCase			):
 def      UpperCamelCase__  (							self ) ->Optional[Any]:



       '''simple docstring'''





       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()





 def      UpperCamelCase__  (							self ) ->str:



       '''simple docstring'''





       __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 :						Union[str, Any] = load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/paint_by_example/panda.jpg''' )

       __lowerCAmelCase :						int = PaintByExamplePipeline.from_pretrained('''Fantasy-Studio/Paint-by-Example''' )
       __lowerCAmelCase :						Optional[int] = pipe.to(SCREAMING_SNAKE_CASE__ )
       pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )

       __lowerCAmelCase :						List[str] = torch.manual_seed(321 )
       __lowerCAmelCase :						Optional[int] = pipe(
           image=SCREAMING_SNAKE_CASE__				,							mask_image=SCREAMING_SNAKE_CASE__				,							example_image=SCREAMING_SNAKE_CASE__				,							generator=SCREAMING_SNAKE_CASE__				,							guidance_scale=5.0				,							num_inference_steps=50				,							output_type='''np'''				,							)

       __lowerCAmelCase :						Optional[int] = output.images

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

       assert image.shape == (1, 512, 512, 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





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import BlipImageProcessor







class   __lowercase				(unittest.TestCase			):
 def __init__(							self				,							A_				,							A_=7				,							A_=3				,							A_=18				,							A_=30				,							A_=400				,							A_=True				,							A_=None				,							A_=True				,							A_=False				,							A_=[0.48_145_466, 0.4_578_275, 0.40_821_073]				,							A_=[0.26_862_954, 0.26_130_258, 0.27_577_711]				,							A_=True				,							) ->Dict:



       '''simple docstring'''





       __lowerCAmelCase :						Union[str, Any] = size if size is not None else {'''height''': 20, '''width''': 20}
       __lowerCAmelCase :						List[str] = parent
       __lowerCAmelCase :						List[str] = batch_size
       __lowerCAmelCase :						Optional[Any] = num_channels
       __lowerCAmelCase :						List[Any] = image_size
       __lowerCAmelCase :						Optional[int] = min_resolution
       __lowerCAmelCase :						Optional[int] = max_resolution
       __lowerCAmelCase :						List[Any] = do_resize
       __lowerCAmelCase :						Dict = size
       __lowerCAmelCase :						Dict = do_normalize
       __lowerCAmelCase :						Any = image_mean
       __lowerCAmelCase :						Optional[int] = image_std
       __lowerCAmelCase :						Any = do_pad
       __lowerCAmelCase :						Any = do_convert_rgb
 def      UpperCamelCase__  (							self ) ->List[str]:



       '''simple docstring'''





       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_convert_rgb": self.do_convert_rgb,
           "do_pad": self.do_pad,
       }





 def      UpperCamelCase__  (							self				,							A_=False				,							A_=False				,							A_=False ) ->Dict:



       '''simple docstring'''





       assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

       if equal_resolution:
             __lowerCAmelCase :						int = []
             for i in range(self.batch_size ):
                   image_inputs.append(
                       np.random.randint(
                           255				,							size=(self.num_channels, self.max_resolution, self.max_resolution)				,							dtype=np.uinta ) )
       else:
             __lowerCAmelCase :						List[Any] = []
             for i in range(self.batch_size ):
                   __lowerCAmelCase,							__lowerCAmelCase :						Any = np.random.choice(np.arange(self.min_resolution				,							self.max_resolution )				,							2 )
                   image_inputs.append(np.random.randint(255				,							size=(self.num_channels, width, height)				,							dtype=np.uinta ) )

       if not numpify and not torchify:
             # PIL expects the channel dimension as last dimension
             __lowerCAmelCase :						List[str] = [Image.fromarray(np.moveaxis(A_				,							0				,							-1 ) ) for x in image_inputs]

       if torchify:
             __lowerCAmelCase :						int = [torch.from_numpy(A_ ) for x in image_inputs]

       return image_inputs







@require_torch
@require_vision
class   __lowercase				(_UpperCAmelCase		,       unittest.TestCase			):
 _UpperCamelCase        =  BlipImageProcessor if is_vision_available() else None
 def      UpperCamelCase__  (							self ) ->Tuple:



       '''simple docstring'''





       __lowerCAmelCase :						Union[str, Any] = BlipImageProcessingTester(self )
 @property
 def      UpperCamelCase__  (							self ) ->int:



       '''simple docstring'''





       return self.image_processor_tester.prepare_image_processor_dict()
 def      UpperCamelCase__  (							self ) ->int:



       '''simple docstring'''





       __lowerCAmelCase :						str = self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(A_				,							'''do_resize''' ) )
       self.assertTrue(hasattr(A_				,							'''size''' ) )
       self.assertTrue(hasattr(A_				,							'''do_normalize''' ) )
       self.assertTrue(hasattr(A_				,							'''image_mean''' ) )
       self.assertTrue(hasattr(A_				,							'''image_std''' ) )
       self.assertTrue(hasattr(A_				,							'''do_convert_rgb''' ) )
 def      UpperCamelCase__  (							self ) ->str:



       '''simple docstring'''





       pass
 def      UpperCamelCase__  (							self ) ->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase :						Dict = self.image_processing_class(**self.image_processor_dict )
       # create random PIL images
       __lowerCAmelCase :						List[str] = self.image_processor_tester.prepare_inputs(equal_resolution=A_ )
       for image in image_inputs:
             self.assertIsInstance(A_				,							Image.Image )

       # Test not batched input
       __lowerCAmelCase :						Optional[Any] = image_processor(image_inputs[0]				,							return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape				,							(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)

       # Test batched
       __lowerCAmelCase :						str = image_processor(A_				,							return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape				,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)
 def      UpperCamelCase__  (							self ) ->Union[str, Any]:



       '''simple docstring'''





       __lowerCAmelCase :						Optional[Any] = self.image_processing_class(**self.image_processor_dict )
       # create random numpy tensors
       __lowerCAmelCase :						Union[str, Any] = self.image_processor_tester.prepare_inputs(equal_resolution=A_				,							numpify=A_ )
       for image in image_inputs:
             self.assertIsInstance(A_				,							np.ndarray )

       # Test not batched input
       __lowerCAmelCase :						Dict = image_processor(image_inputs[0]				,							return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape				,							(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)

       # Test batched
       __lowerCAmelCase :						List[Any] = image_processor(A_				,							return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape				,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)





 def      UpperCamelCase__  (							self ) ->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase :						List[Any] = self.image_processing_class(**self.image_processor_dict )
       # create random PyTorch tensors
       __lowerCAmelCase :						str = self.image_processor_tester.prepare_inputs(equal_resolution=A_				,							torchify=A_ )
       for image in image_inputs:
             self.assertIsInstance(A_				,							torch.Tensor )

       # Test not batched input
       __lowerCAmelCase :						List[Any] = image_processor(image_inputs[0]				,							return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape				,							(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)

       # Test batched
       __lowerCAmelCase :						Tuple = image_processor(A_				,							return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape				,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)







@require_torch
@require_vision
class   __lowercase				(_UpperCAmelCase		,       unittest.TestCase			):
 _UpperCamelCase        =  BlipImageProcessor if is_vision_available() else None
 def      UpperCamelCase__  (							self ) ->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase :						Any = BlipImageProcessingTester(self				,							num_channels=4 )
       __lowerCAmelCase :						str = 3
 @property
 def      UpperCamelCase__  (							self ) ->List[str]:



       '''simple docstring'''





       return self.image_processor_tester.prepare_image_processor_dict()
 def      UpperCamelCase__  (							self ) ->Tuple:



       '''simple docstring'''





       __lowerCAmelCase :						int = self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(A_				,							'''do_resize''' ) )
       self.assertTrue(hasattr(A_				,							'''size''' ) )
       self.assertTrue(hasattr(A_				,							'''do_normalize''' ) )
       self.assertTrue(hasattr(A_				,							'''image_mean''' ) )
       self.assertTrue(hasattr(A_				,							'''image_std''' ) )
       self.assertTrue(hasattr(A_				,							'''do_convert_rgb''' ) )
 def      UpperCamelCase__  (							self ) ->Union[str, Any]:



       '''simple docstring'''





       pass





 def      UpperCamelCase__  (							self ) ->Any:



       '''simple docstring'''





       __lowerCAmelCase :						Tuple = self.image_processing_class(**self.image_processor_dict )
       # create random PIL images
       __lowerCAmelCase :						Optional[int] = self.image_processor_tester.prepare_inputs(equal_resolution=A_ )
       for image in image_inputs:
             self.assertIsInstance(A_				,							Image.Image )

       # Test not batched input
       __lowerCAmelCase :						Tuple = image_processor(image_inputs[0]				,							return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape				,							(
               1,
               self.expected_encoded_image_num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)

       # Test batched
       __lowerCAmelCase :						List[str] = image_processor(A_				,							return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape				,							(
               self.image_processor_tester.batch_size,
               self.expected_encoded_image_num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)


