




'''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  (     __a							,							unittest.TestCase			):
       __a    :					Union[str, Any]           = PaintByExamplePipeline
       __a    :					int           = IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
       __a    :					Union[str, Any]           = IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       __a    :					Union[str, Any]           = frozenset([]			)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
       def    A				(     self						:     List[Any]    ):



           '''simple docstring'''

           torch.manual_seed(0    )
           UpperCAmelCase      	=		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       ,    )
           UpperCAmelCase      	=		PNDMScheduler(skip_prk_steps=lowercase    )
           torch.manual_seed(0    )
           UpperCAmelCase      	=		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    )
           UpperCAmelCase      	=		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       ,    )
           UpperCAmelCase      	=		PaintByExampleImageEncoder(lowercase       ,    proj_size=32    )
           UpperCAmelCase      	=		CLIPImageProcessor(crop_size=32       ,    size=32    )

           UpperCAmelCase      	=		{
               '''unet''': unet,
               '''scheduler''': scheduler,
               '''vae''': vae,
               '''image_encoder''': image_encoder,
               '''safety_checker''': None,
               '''feature_extractor''': feature_extractor,
           }
           return components
       def    A				(     self						:     List[str]       ,    lowercase						:     Union[str, Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		np.array(image.convert('''RGB'''    )    )
           UpperCAmelCase      	=		image[None].transpose(0       ,    3       ,    1       ,    2    )
           UpperCAmelCase      	=		torch.from_numpy(lowercase    ).to(dtype=torch.floataa    ) / 127.5 - 1.0
           return image
       def    A				(     self						:     List[str]       ,    lowercase						:     Dict="cpu"       ,    lowercase						:     Optional[Any]=0    ):



           '''simple docstring'''

           UpperCAmelCase      	=		floats_tensor((1, 3, 32, 32)       ,    rng=random.Random(lowercase    )    ).to(lowercase    )
           UpperCAmelCase      	=		image.cpu().permute(0       ,    2       ,    3       ,    1    )[0]
           UpperCAmelCase      	=		Image.fromarray(np.uinta(lowercase    )    ).convert('''RGB'''    ).resize((64, 64)    )
           UpperCAmelCase      	=		Image.fromarray(np.uinta(image + 4    )    ).convert('''RGB'''    ).resize((64, 64)    )
           UpperCAmelCase      	=		Image.fromarray(np.uinta(lowercase    )    ).convert('''RGB'''    ).resize((32, 32)    )

           if str(lowercase    ).startswith('''mps'''    ):
               UpperCAmelCase      	=		torch.manual_seed(lowercase    )
           else:
               UpperCAmelCase      	=		torch.Generator(device=lowercase    ).manual_seed(lowercase    )
           UpperCAmelCase      	=		{
               '''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    A				(     self						:     Tuple    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.get_dummy_components()

           # make sure here that pndm scheduler skips prk
           UpperCAmelCase      	=		PaintByExamplePipeline(**lowercase    )
           UpperCAmelCase      	=		pipe.to('''cpu'''    )
           pipe.set_progress_bar_config(disable=lowercase    )

           UpperCAmelCase      	=		self.get_dummy_inputs()
           UpperCAmelCase      	=		pipe(**lowercase    )
           UpperCAmelCase      	=		output.images

           UpperCAmelCase      	=		image[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)
           UpperCAmelCase      	=		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    A				(     self						:     int    ):



           '''simple docstring'''

           UpperCAmelCase      	=		'''cpu'''
           UpperCAmelCase      	=		self.get_dummy_inputs()
           inputs.pop('''mask_image'''    )
           UpperCAmelCase      	=		self.convert_to_pt(inputs.pop('''image'''    )    )
           UpperCAmelCase      	=		image.clamp(0       ,    1    ) / 2

           # make sure here that pndm scheduler skips prk
           UpperCAmelCase      	=		PaintByExamplePipeline(**self.get_dummy_components()    )
           UpperCAmelCase      	=		pipe.to(lowercase    )
           pipe.set_progress_bar_config(disable=lowercase    )

           UpperCAmelCase      	=		pipe(image=lowercase       ,    mask_image=mask_image[:, 0]       ,    **lowercase    )
           UpperCAmelCase      	=		output.images

           UpperCAmelCase      	=		image.cpu().permute(0       ,    2       ,    3       ,    1    )[0]
           UpperCAmelCase      	=		mask_image.cpu().permute(0       ,    2       ,    3       ,    1    )[0]

           UpperCAmelCase      	=		Image.fromarray(np.uinta(lowercase    )    ).convert('''RGB'''    )
           UpperCAmelCase      	=		Image.fromarray(np.uinta(lowercase    )    ).convert('''RGB'''    )

           UpperCAmelCase      	=		pipe(**self.get_dummy_inputs()    )
           UpperCAmelCase      	=		output.images

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







       def    A				(     self						:     str    ):



           '''simple docstring'''

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






@slow
@require_torch_gpu
class       _a  (     unittest.TestCase			):
       def    A				(     self						:     Dict    ):



           '''simple docstring'''

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







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



           '''simple docstring'''

           UpperCAmelCase      	=		load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/paint_by_example/dog_in_bucket.png'''    )
           UpperCAmelCase      	=		load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/paint_by_example/mask.png'''    )
           UpperCAmelCase      	=		load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/paint_by_example/panda.jpg'''    )

           UpperCAmelCase      	=		PaintByExamplePipeline.from_pretrained('''Fantasy-Studio/Paint-by-Example'''    )
           UpperCAmelCase      	=		pipe.to(lowercase    )
           pipe.set_progress_bar_config(disable=lowercase    )

           UpperCAmelCase      	=		torch.manual_seed(321    )
           UpperCAmelCase      	=		pipe(
               image=lowercase       ,    mask_image=lowercase       ,    example_image=lowercase       ,    generator=lowercase       ,    guidance_scale=5.0       ,    num_inference_steps=50       ,    output_type='''np'''       ,    )

           UpperCAmelCase      	=		output.images

           UpperCAmelCase      	=		image[0, -3:, -3:, -1]

           assert image.shape == (1, 512, 512, 3)
           UpperCAmelCase      	=		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










'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A			   ={'configuration_dinat': ['DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DinatConfig']}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A			   =[
       'DINAT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'DinatForImageClassification',
       'DinatModel',
       'DinatPreTrainedModel',
       'DinatBackbone',
   ]

if TYPE_CHECKING:
   from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_dinat import (
          DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
          DinatBackbone,
          DinatForImageClassification,
          DinatModel,
          DinatPreTrainedModel,
      )

else:
   import sys

   A			   =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




