



"""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       __lowerCamelCase	(   _SCREAMING_SNAKE_CASE   ,							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				):




      '''simple docstring'''




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

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







   def 							A__						(self       ,	lowerCamelCase				):




      '''simple docstring'''




      _lowerCAmelCase					     =		np.array(image.convert("""RGB"""				)				)
      _lowerCAmelCase					     =		image[None].transpose(0       ,	3       ,	1       ,	2				)
      _lowerCAmelCase					     =		torch.from_numpy(UpperCamelCase__				).to(dtype=torch.floataa				) / 127.5 - 1.0
      return image







   def 							A__						(self       ,	lowerCamelCase="cpu"       ,	lowerCamelCase=0				):




      '''simple docstring'''




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

      if str(UpperCamelCase__				).startswith("""mps"""				):
         _lowerCAmelCase					     =		torch.manual_seed(UpperCamelCase__				)
      else:
         _lowerCAmelCase					     =		torch.Generator(device=UpperCamelCase__				).manual_seed(UpperCamelCase__				)
      _lowerCAmelCase					     =		{
          """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				):




      '''simple docstring'''




      _lowerCAmelCase					     =		self.get_dummy_components()

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

      _lowerCAmelCase					     =		self.get_dummy_inputs()
      _lowerCAmelCase					     =		pipe(**UpperCamelCase__				)
      _lowerCAmelCase					     =		output.images

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

      assert image.shape == (1, 64, 64, 3)
      _lowerCAmelCase					     =		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				):




      '''simple docstring'''




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

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

      _lowerCAmelCase					     =		pipe(image=UpperCamelCase__       ,	mask_image=mask_image[:, 0]       ,	**UpperCamelCase__				)
      _lowerCAmelCase					     =		output.images

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

      _lowerCAmelCase					     =		Image.fromarray(np.uinta(UpperCamelCase__				)				).convert("""RGB"""				)
      _lowerCAmelCase					     =		Image.fromarray(np.uinta(UpperCamelCase__				)				).convert("""RGB"""				)

      _lowerCAmelCase					     =		pipe(**self.get_dummy_inputs()				)
      _lowerCAmelCase					     =		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				):




      '''simple docstring'''




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





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







   def 							A__						(self				):




      '''simple docstring'''




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







   def 							A__						(self				):




      '''simple docstring'''




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

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

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

      _lowerCAmelCase					     =		output.images

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

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


SCREAMING_SNAKE_CASE				:     Union[str, Any]          =	{
    '''configuration_align''': [
        '''ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''AlignConfig''',
        '''AlignTextConfig''',
        '''AlignVisionConfig''',
    ],
    '''processing_align''': ['''AlignProcessor'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE				:     Any          =	[
      '''ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''AlignModel''',
      '''AlignPreTrainedModel''',
      '''AlignTextModel''',
      '''AlignVisionModel''',
  ]

if TYPE_CHECKING:
  from .configuration_align import (
      ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP,
      AlignConfig,
      AlignTextConfig,
      AlignVisionConfig,
  )
  from .processing_align import AlignProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_align import (
        ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST,
        AlignModel,
        AlignPreTrainedModel,
        AlignTextModel,
        AlignVisionModel,
    )

else:
  import sys

  SCREAMING_SNAKE_CASE				:     Any          =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)