




'''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 ViltImageProcessor







class 	lowercase_					(				unittest.TestCase     ):


   """simple docstring"""

   def __init__(					self      :			Optional[Any]						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			str=7						,   __lowerCamelCase      :			Optional[Any]=3						,   __lowerCamelCase      :			List[str]=1_8						,   __lowerCamelCase      :			Optional[Any]=3_0						,   __lowerCamelCase      :			List[Any]=4_0_0						,   __lowerCamelCase      :			Optional[Any]=True						,   __lowerCamelCase      :			Union[str, Any]=None						,   __lowerCamelCase      :			Optional[Any]=2						,   __lowerCamelCase      :			Optional[int]=True						,   __lowerCamelCase      :			Tuple=[0.5, 0.5, 0.5]						,   __lowerCamelCase      :			Dict=[0.5, 0.5, 0.5]						,   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   size if size is not None else {"shortest_edge": 3_0}
      _SCREAMING_SNAKE_CASE =   parent
      _SCREAMING_SNAKE_CASE =   batch_size
      _SCREAMING_SNAKE_CASE =   num_channels
      _SCREAMING_SNAKE_CASE =   image_size
      _SCREAMING_SNAKE_CASE =   min_resolution
      _SCREAMING_SNAKE_CASE =   max_resolution
      _SCREAMING_SNAKE_CASE =   do_resize
      _SCREAMING_SNAKE_CASE =   size
      _SCREAMING_SNAKE_CASE =   size_divisor
      _SCREAMING_SNAKE_CASE =   do_normalize
      _SCREAMING_SNAKE_CASE =   image_mean
      _SCREAMING_SNAKE_CASE =   image_std



   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

      """simple docstring"""
      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "size_divisor": self.size_divisor,
      }



   def 							lowerCAmelCase_    (					self      :			Any						,   __lowerCamelCase      :			List[Any]						,   __lowerCamelCase      :			int=False					):

      """simple docstring"""
      if not batched:
         _SCREAMING_SNAKE_CASE =   self.size["shortest_edge"]
         _SCREAMING_SNAKE_CASE =   image_inputs[0]
         if isinstance(__lowerCamelCase						,   Image.Image					):
            _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   image.size
         else:
            _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   image.shape[1], image.shape[2]
         _SCREAMING_SNAKE_CASE =   size / min(__lowerCamelCase						,   __lowerCamelCase					)
         if h < w:
            _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   size, scale * w
         else:
            _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   scale * h, size

         _SCREAMING_SNAKE_CASE =   int((1_3_3_3 / 8_0_0) * size					)
         if max(__lowerCamelCase						,   __lowerCamelCase					) > max_size:
            _SCREAMING_SNAKE_CASE =   max_size / max(__lowerCamelCase						,   __lowerCamelCase					)
            _SCREAMING_SNAKE_CASE =   newh * scale
            _SCREAMING_SNAKE_CASE =   neww * scale

         _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   int(newh + 0.5					), int(neww + 0.5					)
         _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   (
             newh // self.size_divisor * self.size_divisor,
             neww // self.size_divisor * self.size_divisor,
         )

      else:
         _SCREAMING_SNAKE_CASE =   []
         for image in image_inputs:
            _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   self.get_expected_values([image]					)
            expected_values.append((expected_height, expected_width)					)
         _SCREAMING_SNAKE_CASE =   max(__lowerCamelCase						,   key=lambda __lowerCamelCase					: item[0]					)[0]
         _SCREAMING_SNAKE_CASE =   max(__lowerCamelCase						,   key=lambda __lowerCamelCase					: item[1]					)[1]

      return expected_height, expected_width







@require_torch
@require_vision
class 	lowercase_					(				A  ,						unittest.TestCase     ):


   """simple docstring"""

   lowerCamelCase_       						=		ViltImageProcessor if is_vision_available() else None



   def 							lowerCAmelCase_    (					self      :			List[Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   ViltImageProcessingTester(self					)



   @property
   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      return self.image_processor_tester.prepare_image_processor_dict()



   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.image_processing_class(**self.image_processor_dict					)
      self.assertTrue(hasattr(__lowerCamelCase						,   "image_mean"					)					)
      self.assertTrue(hasattr(__lowerCamelCase						,   "image_std"					)					)
      self.assertTrue(hasattr(__lowerCamelCase						,   "do_normalize"					)					)
      self.assertTrue(hasattr(__lowerCamelCase						,   "do_resize"					)					)
      self.assertTrue(hasattr(__lowerCamelCase						,   "size"					)					)
      self.assertTrue(hasattr(__lowerCamelCase						,   "size_divisor"					)					)



   def 							lowerCAmelCase_    (					self      :			Any					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.image_processing_class.from_dict(self.image_processor_dict					)
      self.assertEqual(image_processor.size						,   {"shortest_edge": 3_0}					)

      _SCREAMING_SNAKE_CASE =   self.image_processing_class.from_dict(self.image_processor_dict						,   size=4_2					)
      self.assertEqual(image_processor.size						,   {"shortest_edge": 4_2}					)



   def 							lowerCAmelCase_    (					self      :			Union[str, Any]					):

      """simple docstring"""
      pass



   def 							lowerCAmelCase_    (					self      :			Union[str, Any]					):

      """simple docstring"""
      # Initialize image_processing
      _SCREAMING_SNAKE_CASE =   self.image_processing_class(**self.image_processor_dict					)
      # create random PIL images
      _SCREAMING_SNAKE_CASE =   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 =   image_processing(image_inputs[0]						,   return_tensors="pt"					).pixel_values

      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   self.image_processor_tester.get_expected_values(__lowerCamelCase					)
      self.assertEqual(
          encoded_images.shape						,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,   )

      # Test batched
      _SCREAMING_SNAKE_CASE =   image_processing(__lowerCamelCase						,   return_tensors="pt"					).pixel_values

      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   self.image_processor_tester.get_expected_values(__lowerCamelCase						,   batched=__lowerCamelCase					)
      self.assertEqual(
          encoded_images.shape						,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )						,   )



   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      # Initialize image_processing
      _SCREAMING_SNAKE_CASE =   self.image_processing_class(**self.image_processor_dict					)
      # create random numpy tensors
      _SCREAMING_SNAKE_CASE =   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 =   image_processing(image_inputs[0]						,   return_tensors="pt"					).pixel_values

      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   self.image_processor_tester.get_expected_values(__lowerCamelCase					)
      self.assertEqual(
          encoded_images.shape						,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,   )

      # Test batched
      _SCREAMING_SNAKE_CASE =   image_processing(__lowerCamelCase						,   return_tensors="pt"					).pixel_values

      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   self.image_processor_tester.get_expected_values(__lowerCamelCase						,   batched=__lowerCamelCase					)
      self.assertEqual(
          encoded_images.shape						,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )						,   )



   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      # Initialize image_processing
      _SCREAMING_SNAKE_CASE =   self.image_processing_class(**self.image_processor_dict					)
      # create random PyTorch tensors
      _SCREAMING_SNAKE_CASE =   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 =   image_processing(image_inputs[0]						,   return_tensors="pt"					).pixel_values

      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   self.image_processor_tester.get_expected_values(__lowerCamelCase					)
      self.assertEqual(
          encoded_images.shape						,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,   )

      # Test batched
      _SCREAMING_SNAKE_CASE =   image_processing(__lowerCamelCase						,   return_tensors="pt"					).pixel_values

      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   self.image_processor_tester.get_expected_values(__lowerCamelCase						,   batched=__lowerCamelCase					)
      self.assertEqual(
          encoded_images.shape						,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_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 	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

