




'''simple docstring'''



from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 	lowercase_					(				A     ):


   """simple docstring"""

   lowerCamelCase_       						=		['''image_processor''', '''tokenizer''']
   lowerCamelCase_       						=		'''EfficientNetImageProcessor'''
   lowerCamelCase_       						=		('''BertTokenizer''', '''BertTokenizerFast''')



   def __init__(					self      :			Optional[Any]						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			Any					):

      """simple docstring"""
      super().__init__(__lowerCamelCase						,   __lowerCamelCase					)



   def __call__(					self      :			int						,   __lowerCamelCase      :			List[str]=None						,   __lowerCamelCase      :			List[str]=None						,   __lowerCamelCase      :			List[str]="max_length"						,   __lowerCamelCase      :			Tuple=6_4						,   __lowerCamelCase      :			List[str]=None						,   **__lowerCamelCase      :			List[str]					):

      """simple docstring"""
      if text is None and images is None:
         raise ValueError("You have to specify either text or images. Both cannot be none."					)

      if text is not None:
         _SCREAMING_SNAKE_CASE =   self.tokenizer(
             __lowerCamelCase						,   padding=__lowerCamelCase						,   max_length=__lowerCamelCase						,   return_tensors=__lowerCamelCase						,   **__lowerCamelCase					)

      if images is not None:
         _SCREAMING_SNAKE_CASE =   self.image_processor(__lowerCamelCase						,   return_tensors=__lowerCamelCase						,   **__lowerCamelCase					)

      if text is not None and images is not None:
         _SCREAMING_SNAKE_CASE =   image_features.pixel_values
         return encoding
      elif text is not None:
         return encoding
      else:
         return BatchEncoding(data=dict(**__lowerCamelCase					)						,   tensor_type=__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Dict						,   *__lowerCamelCase      :			Dict						,   **__lowerCamelCase      :			List[str]					):

      """simple docstring"""
      return self.tokenizer.batch_decode(*__lowerCamelCase						,   **__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			int						,   *__lowerCamelCase      :			int						,   **__lowerCamelCase      :			List[str]					):

      """simple docstring"""
      return self.tokenizer.decode(*__lowerCamelCase						,   **__lowerCamelCase					)



   @property
   def 							lowerCAmelCase_    (					self      :			int					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.tokenizer.model_input_names
      _SCREAMING_SNAKE_CASE =   self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)







'''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

