



'''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						):



 '''simple docstring'''







 __lowerCamelCase   :	Optional[Any]										=	PaintByExamplePipeline
 __lowerCamelCase   :	Optional[int]										=	IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
 __lowerCamelCase   :	str										=	IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 __lowerCamelCase   :	Tuple										=	frozenset([]						)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess



 def  a__  (self	)   ->					List[str]:


      """simple docstring"""



      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=A	)
      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(A			,       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			,       A	)   ->					Optional[Any]:


      """simple docstring"""



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



 def  a__  (self			,       A="cpu"			,       A=0	)   ->					Union[str, Any]:


      """simple docstring"""



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

      if str(A	).startswith('''mps'''	):
           _a     =  torch.manual_seed(A	)
      else:
           _a     =  torch.Generator(device=A	).manual_seed(A	)
      _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:


      """simple docstring"""



      _a     =  self.get_dummy_components()

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

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

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

      assert image.shape == (1, 64, 64, 3)
      _a     =  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	)   ->					Tuple:


      """simple docstring"""



      _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(A	)
      pipe.set_progress_bar_config(disable=A	)

      _a     =  pipe(image=A			,       mask_image=mask_image[:, 0]			,       **A	)
      _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(A	)	).convert('''RGB'''	)
      _a     =  Image.fromarray(np.uinta(A	)	).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:


      """simple docstring"""



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






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



 '''simple docstring'''







 def  a__  (self	)   ->					Union[str, Any]:


      """simple docstring"""



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



 def  a__  (self	)   ->					int:


      """simple docstring"""



      _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(A	)
      pipe.set_progress_bar_config(disable=A	)

      _a     =  torch.manual_seed(321	)
      _a     =  pipe(
          image=A			,       mask_image=A			,       example_image=A			,       generator=A			,       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([0.4834, 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_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase_			=  {
    "configuration_segformer": ["SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SegformerConfig", "SegformerOnnxConfig"]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_			=  ["SegformerFeatureExtractor"]
  lowercase_			=  ["SegformerImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_			=  [
      "SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "SegformerDecodeHead",
      "SegformerForImageClassification",
      "SegformerForSemanticSegmentation",
      "SegformerLayer",
      "SegformerModel",
      "SegformerPreTrainedModel",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_			=  [
      "TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFSegformerDecodeHead",
      "TFSegformerForImageClassification",
      "TFSegformerForSemanticSegmentation",
      "TFSegformerModel",
      "TFSegformerPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_segformer import SegformerFeatureExtractor
    from .image_processing_segformer import SegformerImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_segformer import (
        SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        SegformerDecodeHead,
        SegformerForImageClassification,
        SegformerForSemanticSegmentation,
        SegformerLayer,
        SegformerModel,
        SegformerPreTrainedModel,
    )
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_segformer import (
        TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFSegformerDecodeHead,
        TFSegformerForImageClassification,
        TFSegformerForSemanticSegmentation,
        TFSegformerModel,
        TFSegformerPreTrainedModel,
    )

else:
  import sys

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



