



"""simple docstring"""
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, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import DeiTImageProcessor
class    a		(					unittest.TestCase    ):



  def __init__(  self	:   List[str]    ,						lowerCAmelCase	:   Optional[Any]    ,						lowerCAmelCase	:   Union[str, Any]=7    ,						lowerCAmelCase	:   int=3    ,						lowerCAmelCase	:   List[Any]=18    ,						lowerCAmelCase	:   List[Any]=30    ,						lowerCAmelCase	:   str=400    ,						lowerCAmelCase	:   List[str]=True    ,						lowerCAmelCase	:   Any=None    ,						lowerCAmelCase	:   Tuple=True    ,						lowerCAmelCase	:   str=None    ,						lowerCAmelCase	:   str=True    ,						lowerCAmelCase	:   Optional[int]=[0.5, 0.5, 0.5]    ,						lowerCAmelCase	:   Dict=[0.5, 0.5, 0.5]    ,						)       ->   Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Dict            =size if size is not None else {"""height""": 20, """width""": 20}
     SCREAMING_SNAKE_CASE_:	Optional[int]            =crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

     SCREAMING_SNAKE_CASE_:	Optional[int]            =parent
     SCREAMING_SNAKE_CASE_:	Dict            =batch_size
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =num_channels
     SCREAMING_SNAKE_CASE_:	Tuple            =image_size
     SCREAMING_SNAKE_CASE_:	Any            =min_resolution
     SCREAMING_SNAKE_CASE_:	int            =max_resolution
     SCREAMING_SNAKE_CASE_:	Dict            =do_resize
     SCREAMING_SNAKE_CASE_:	int            =size
     SCREAMING_SNAKE_CASE_:	Dict            =do_center_crop
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =crop_size
     SCREAMING_SNAKE_CASE_:	Tuple            =do_normalize
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =image_mean
     SCREAMING_SNAKE_CASE_:	Dict            =image_std





  def 		lowerCamelCase__    (  self	:   Union[str, Any]						)       ->   Union[str, Any]:


     '''simple docstring'''




     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_center_crop": self.do_center_crop,
         "crop_size": self.crop_size,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
     }







@require_torch
@require_vision
class    a		(					UpperCAmelCase__		, unittest.TestCase    ):
  UpperCamelCase		:			Dict         =   DeiTImageProcessor if is_vision_available() else None
  UpperCamelCase		:			Dict         =   True



  def 		lowerCamelCase__    (  self	:   int						)       ->   Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	int            =DeiTImageProcessingTester(self						)



  @property
  def 		lowerCamelCase__    (  self	:   str						)       ->   int:


     '''simple docstring'''




     return self.image_processor_tester.prepare_image_processor_dict()



  def 		lowerCamelCase__    (  self	:   Tuple						)       ->   str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[int]            =self.image_processing_class(**self.image_processor_dict						)
     self.assertTrue(hasattr(lowerCAmelCase    ,						"""do_resize"""						)						)
     self.assertTrue(hasattr(lowerCAmelCase    ,						"""size"""						)						)
     self.assertTrue(hasattr(lowerCAmelCase    ,						"""do_center_crop"""						)						)
     self.assertTrue(hasattr(lowerCAmelCase    ,						"""center_crop"""						)						)
     self.assertTrue(hasattr(lowerCAmelCase    ,						"""do_normalize"""						)						)
     self.assertTrue(hasattr(lowerCAmelCase    ,						"""image_mean"""						)						)
     self.assertTrue(hasattr(lowerCAmelCase    ,						"""image_std"""						)						)



  def 		lowerCamelCase__    (  self	:   Any						)       ->   Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[int]            =self.image_processing_class.from_dict(self.image_processor_dict						)
     self.assertEqual(image_processor.size    ,						{"""height""": 20, """width""": 20}						)
     self.assertEqual(image_processor.crop_size    ,						{"""height""": 18, """width""": 18}						)

     SCREAMING_SNAKE_CASE_:	str            =self.image_processing_class.from_dict(self.image_processor_dict    ,						size=42    ,						crop_size=84						)
     self.assertEqual(image_processor.size    ,						{"""height""": 42, """width""": 42}						)
     self.assertEqual(image_processor.crop_size    ,						{"""height""": 84, """width""": 84}						)



  def 		lowerCamelCase__    (  self	:   List[str]						)       ->   Optional[int]:


     '''simple docstring'''




     pass



  def 		lowerCamelCase__    (  self	:   Union[str, Any]						)       ->   str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Any            =self.image_processing_class(**self.image_processor_dict						)
     # create random PIL images
     SCREAMING_SNAKE_CASE_:	Optional[int]            =prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=lowerCAmelCase						)
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase    ,						Image.Image						)

     # Test not batched input
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =image_processing(image_inputs[0]    ,						return_tensors="""pt"""						).pixel_values
     self.assertEqual(
         encoded_images.shape    ,						(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )    ,						)

     # Test batched
     SCREAMING_SNAKE_CASE_:	List[Any]            =image_processing(lowerCAmelCase    ,						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.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )    ,						)



  def 		lowerCamelCase__    (  self	:   Dict						)       ->   List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	List[Any]            =self.image_processing_class(**self.image_processor_dict						)
     # create random numpy tensors
     SCREAMING_SNAKE_CASE_:	List[Any]            =prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=lowerCAmelCase    ,						numpify=lowerCAmelCase						)
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase    ,						np.ndarray						)

     # Test not batched input
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =image_processing(image_inputs[0]    ,						return_tensors="""pt"""						).pixel_values
     self.assertEqual(
         encoded_images.shape    ,						(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )    ,						)

     # Test batched
     SCREAMING_SNAKE_CASE_:	Optional[int]            =image_processing(lowerCAmelCase    ,						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.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )    ,						)





  def 		lowerCamelCase__    (  self	:   int						)       ->   List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Dict            =self.image_processing_class(**self.image_processor_dict						)
     # create random PyTorch tensors
     SCREAMING_SNAKE_CASE_:	List[str]            =prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=lowerCAmelCase    ,						torchify=lowerCAmelCase						)
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase    ,						torch.Tensor						)

     # Test not batched input
     SCREAMING_SNAKE_CASE_:	Any            =image_processing(image_inputs[0]    ,						return_tensors="""pt"""						).pixel_values
     self.assertEqual(
         encoded_images.shape    ,						(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )    ,						)

     # Test batched
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =image_processing(lowerCAmelCase    ,						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.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )    ,						)








"""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		(					UpperCAmelCase__		, unittest.TestCase    ):
  UpperCamelCase		:			Dict         =   PaintByExamplePipeline
  UpperCamelCase		:			str         =   IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
  UpperCamelCase		:			Tuple         =   IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  UpperCamelCase		:			List[str]         =   frozenset([]    )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess



  def 		lowerCamelCase__    (  self	:   Union[str, Any]						)       ->   Union[str, Any]:


     '''simple docstring'''




     torch.manual_seed(0						)
     SCREAMING_SNAKE_CASE_:	Any            =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    ,						)
     SCREAMING_SNAKE_CASE_:	int            =PNDMScheduler(skip_prk_steps=lowerCAmelCase						)
     torch.manual_seed(0						)
     SCREAMING_SNAKE_CASE_:	str            =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						)
     SCREAMING_SNAKE_CASE_:	Tuple            =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    ,						)
     SCREAMING_SNAKE_CASE_:	List[Any]            =PaintByExampleImageEncoder(lowerCAmelCase    ,						proj_size=32						)
     SCREAMING_SNAKE_CASE_:	Tuple            =CLIPImageProcessor(crop_size=32    ,						size=32						)

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



  def 		lowerCamelCase__    (  self	:   str    ,						lowerCAmelCase	:   Dict						)       ->   Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =np.array(image.convert("""RGB"""						)						)
     SCREAMING_SNAKE_CASE_:	Optional[int]            =image[None].transpose(0    ,						3    ,						1    ,						2						)
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =torch.from_numpy(lowerCAmelCase						).to(dtype=torch.floataa						) / 1_2_7.5 - 1.0
     return image



  def 		lowerCamelCase__    (  self	:   Union[str, Any]    ,						lowerCAmelCase	:   int="cpu"    ,						lowerCAmelCase	:   Dict=0						)       ->   Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	str            =floats_tensor((1, 3, 32, 32)    ,						rng=random.Random(lowerCAmelCase						)						).to(lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	Tuple            =image.cpu().permute(0    ,						2    ,						3    ,						1						)[0]
     SCREAMING_SNAKE_CASE_:	Any            =Image.fromarray(np.uinta(lowerCAmelCase						)						).convert("""RGB"""						).resize((64, 64)						)
     SCREAMING_SNAKE_CASE_:	Tuple            =Image.fromarray(np.uinta(image + 4						)						).convert("""RGB"""						).resize((64, 64)						)
     SCREAMING_SNAKE_CASE_:	List[str]            =Image.fromarray(np.uinta(lowerCAmelCase						)						).convert("""RGB"""						).resize((32, 32)						)

     if str(lowerCAmelCase						).startswith("""mps"""						):
        SCREAMING_SNAKE_CASE_:	Dict            =torch.manual_seed(lowerCAmelCase						)
     else:
        SCREAMING_SNAKE_CASE_:	Optional[Any]            =torch.Generator(device=lowerCAmelCase						).manual_seed(lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	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 		lowerCamelCase__    (  self	:   List[Any]						)       ->   Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Tuple            =self.get_dummy_components()

     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =PaintByExamplePipeline(**lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	Any            =pipe.to("""cpu"""						)
     pipe.set_progress_bar_config(disable=lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:	Optional[int]            =self.get_dummy_inputs()
     SCREAMING_SNAKE_CASE_:	int            =pipe(**lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	int            =output.images

     SCREAMING_SNAKE_CASE_:	List[str]            =image[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =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	:   Any						)       ->   Union[str, Any]:


     '''simple docstring'''




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

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

     SCREAMING_SNAKE_CASE_:	int            =pipe(image=lowerCAmelCase    ,						mask_image=mask_image[:, 0]    ,						**lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	int            =output.images

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

     SCREAMING_SNAKE_CASE_:	str            =Image.fromarray(np.uinta(lowerCAmelCase						)						).convert("""RGB"""						)
     SCREAMING_SNAKE_CASE_:	Tuple            =Image.fromarray(np.uinta(lowerCAmelCase						)						).convert("""RGB"""						)

     SCREAMING_SNAKE_CASE_:	int            =pipe(**self.get_dummy_inputs()						)
     SCREAMING_SNAKE_CASE_:	Optional[int]            =output.images

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





  def 		lowerCamelCase__    (  self	:   Optional[Any]						)       ->   Dict:


     '''simple docstring'''




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







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



  def 		lowerCamelCase__    (  self	:   Dict						)       ->   Any:


     '''simple docstring'''




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





  def 		lowerCamelCase__    (  self	:   Union[str, Any]						)       ->   Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	List[str]            =load_image(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/paint_by_example/dog_in_bucket.png"""						)
     SCREAMING_SNAKE_CASE_:	Tuple            =load_image(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/paint_by_example/mask.png"""						)
     SCREAMING_SNAKE_CASE_:	List[str]            =load_image(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/paint_by_example/panda.jpg"""						)

     SCREAMING_SNAKE_CASE_:	Tuple            =PaintByExamplePipeline.from_pretrained("""Fantasy-Studio/Paint-by-Example"""						)
     SCREAMING_SNAKE_CASE_:	Optional[int]            =pipe.to(lowerCAmelCase						)
     pipe.set_progress_bar_config(disable=lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:	str            =torch.manual_seed(321						)
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =pipe(
         image=lowerCAmelCase    ,						mask_image=lowerCAmelCase    ,						example_image=lowerCAmelCase    ,						generator=lowerCAmelCase    ,						guidance_scale=5.0    ,						num_inference_steps=50    ,						output_type="""np"""    ,						)

     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =output.images

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

     assert image.shape == (1, 512, 512, 3)
     SCREAMING_SNAKE_CASE_:	Any            =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



