


'''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     __SCREAMING_SNAKE_CASE       (   lowerCamelCase__			,      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 			a_       (      self		)							->     Union[str, Any]:
          torch.manual_seed(0		)
          _a            =		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  ,				)
          _a            =		PNDMScheduler(skip_prk_steps=__UpperCamelCase		)
          torch.manual_seed(0		)
          _a            =		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		)
          _a            =		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  ,				)
          _a            =		PaintByExampleImageEncoder(__UpperCamelCase  ,				proj_size=32		)
          _a            =		CLIPImageProcessor(crop_size=32  ,				size=32		)

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






     def 			a_       (      self  ,				__UpperCamelCase		)							->     Any:
          _a            =		np.array(image.convert("RGB"		)		)
          _a            =		image[None].transpose(0  ,				3  ,				1  ,				2		)
          _a            =		torch.from_numpy(__UpperCamelCase		).to(dtype=torch.floataa		) / 1_27.5 - 1.0
          return image






     def 			a_       (      self  ,				__UpperCamelCase="cpu"  ,				__UpperCamelCase=0		)							->     Optional[int]:
          # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
          _a            =		floats_tensor((1, 3, 32, 32)  ,				rng=random.Random(__UpperCamelCase		)		).to(__UpperCamelCase		)
          _a            =		image.cpu().permute(0  ,				2  ,				3  ,				1		)[0]
          _a            =		Image.fromarray(np.uinta(__UpperCamelCase		)		).convert("RGB"		).resize((64, 64)		)
          _a            =		Image.fromarray(np.uinta(image + 4		)		).convert("RGB"		).resize((64, 64)		)
          _a            =		Image.fromarray(np.uinta(__UpperCamelCase		)		).convert("RGB"		).resize((32, 32)		)

          if str(__UpperCamelCase		).startswith("mps"		):
               _a            =		torch.manual_seed(__UpperCamelCase		)
          else:
               _a            =		torch.Generator(device=__UpperCamelCase		).manual_seed(__UpperCamelCase		)
          _a            =		{
              "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		)							->     Dict:
          _a            =		self.get_dummy_components()

          # make sure here that pndm scheduler skips prk
          _a            =		PaintByExamplePipeline(**__UpperCamelCase		)
          _a            =		pipe.to("cpu"		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a            =		self.get_dummy_inputs()
          _a            =		pipe(**__UpperCamelCase		)
          _a            =		output.images

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

          assert image.shape == (1, 64, 64, 3)
          _a            =		np.array([0.46_86, 0.56_87, 0.40_07, 0.52_18, 0.57_41, 0.44_82, 0.49_40, 0.46_29, 0.45_03]		)

          assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2






     def 			a_       (      self		)							->     Optional[int]:
          _a            =		"cpu"
          _a            =		self.get_dummy_inputs()
          inputs.pop("mask_image"		)
          _a            =		self.convert_to_pt(inputs.pop("image"		)		)
          _a            =		image.clamp(0  ,				1		) / 2

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

          _a            =		pipe(image=__UpperCamelCase  ,				mask_image=mask_image[:, 0]  ,				**__UpperCamelCase		)
          _a            =		output.images

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

          _a            =		Image.fromarray(np.uinta(__UpperCamelCase		)		).convert("RGB"		)
          _a            =		Image.fromarray(np.uinta(__UpperCamelCase		)		).convert("RGB"		)

          _a            =		pipe(**self.get_dummy_inputs()		)
          _a            =		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:
          super().test_inference_batch_single_identical(expected_max_diff=3e-3		)







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






     def 			a_       (      self		)							->     List[Any]:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






     def 			a_       (      self		)							->     Tuple:
          # make sure here that pndm scheduler skips prk
          _a            =		load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/paint_by_example/dog_in_bucket.png"		)
          _a            =		load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/paint_by_example/mask.png"		)
          _a            =		load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/paint_by_example/panda.jpg"		)

          _a            =		PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"		)
          _a            =		pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a            =		torch.manual_seed(321		)
          _a            =		pipe(
              image=__UpperCamelCase  ,				mask_image=__UpperCamelCase  ,				example_image=__UpperCamelCase  ,				generator=__UpperCamelCase  ,				guidance_scale=5.0  ,				num_inference_steps=50  ,				output_type="np"  ,				)

          _a            =		output.images

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

          assert image.shape == (1, 512, 512, 3)
          _a            =		np.array([0.48_34, 0.48_11, 0.48_74, 0.51_22, 0.50_81, 0.51_44, 0.52_91, 0.52_90, 0.53_74]		)

          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, is_vision_available


lowercase__					  =		{
    "configuration_swin2sr": ["SWIN2SR_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swin2SRConfig"],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__					  =		[
       "SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST",
       "Swin2SRForImageSuperResolution",
       "Swin2SRModel",
       "Swin2SRPreTrainedModel",
   ]


try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__					  =		["Swin2SRImageProcessor"]


if TYPE_CHECKING:
   from .configuration_swinasr import SWIN2SR_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinaSRConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_swinasr import (
          SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST,
          SwinaSRForImageSuperResolution,
          SwinaSRModel,
          SwinaSRPreTrainedModel,
      )

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_swinasr import SwinaSRImageProcessor


else:
   import sys

   lowercase__					  =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

