




'''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     UpperCamelCase					(  _SCREAMING_SNAKE_CASE			,		unittest.TestCase					):
		a    :List[Any]      					=				PaintByExamplePipeline
		a    :str      					=				IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
		a    :int      					=				IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		a    :Tuple      					=				frozenset([]					)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess



		def 						a_       ( self   ):







						'''simple docstring'''


						torch.manual_seed(0   )
						_a							:       str       					=			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							:       Tuple       					=			PNDMScheduler(skip_prk_steps=UpperCamelCase__   )
						torch.manual_seed(0   )
						_a							:       str       					=			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							:       Dict       					=			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							:       List[str]       					=			PaintByExampleImageEncoder(UpperCamelCase__		,    proj_size=32   )
						_a							:       Dict       					=			CLIPImageProcessor(crop_size=32		,    size=32   )

						_a							:       List[Any]       					=			{
						    """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'''


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



		def 						a_       ( self		,    __lowerCAmelCase="cpu"		,    __lowerCAmelCase=0   ):







						'''simple docstring'''


						_a							:       Tuple       					=			floats_tensor((1, 3, 32, 32)		,    rng=random.Random(UpperCamelCase__   )   ).to(UpperCamelCase__   )
						_a							:       Tuple       					=			image.cpu().permute(0		,    2		,    3		,    1   )[0]
						_a							:       Union[str, Any]       					=			Image.fromarray(np.uinta(UpperCamelCase__   )   ).convert("""RGB"""   ).resize((64, 64)   )
						_a							:       Optional[Any]       					=			Image.fromarray(np.uinta(image + 4   )   ).convert("""RGB"""   ).resize((64, 64)   )
						_a							:       List[str]       					=			Image.fromarray(np.uinta(UpperCamelCase__   )   ).convert("""RGB"""   ).resize((32, 32)   )

						if str(UpperCamelCase__   ).startswith("""mps"""   ):
										_a							:       Tuple       					=			torch.manual_seed(UpperCamelCase__   )
						else:
										_a							:       Optional[Any]       					=			torch.Generator(device=UpperCamelCase__   ).manual_seed(UpperCamelCase__   )
						_a							:       str       					=			{
						    """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'''


						_a							:       Tuple       					=			self.get_dummy_components()

						# make sure here that pndm scheduler skips prk
						_a							:       Any       					=			PaintByExamplePipeline(**UpperCamelCase__   )
						_a							:       Union[str, Any]       					=			pipe.to("""cpu"""   )
						pipe.set_progress_bar_config(disable=UpperCamelCase__   )

						_a							:       Union[str, Any]       					=			self.get_dummy_inputs()
						_a							:       Optional[Any]       					=			pipe(**UpperCamelCase__   )
						_a							:       Any       					=			output.images

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

						assert image.shape == (1, 64, 64, 3)
						_a							:       Optional[int]       					=			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'''


						_a							:       int       					=			"""cpu"""
						_a							:       Tuple       					=			self.get_dummy_inputs()
						inputs.pop("""mask_image"""   )
						_a							:       int       					=			self.convert_to_pt(inputs.pop("""image"""   )   )
						_a							:       Union[str, Any]       					=			image.clamp(0		,    1   ) / 2

						# make sure here that pndm scheduler skips prk
						_a							:       Union[str, Any]       					=			PaintByExamplePipeline(**self.get_dummy_components()   )
						_a							:       Optional[int]       					=			pipe.to(UpperCamelCase__   )
						pipe.set_progress_bar_config(disable=UpperCamelCase__   )

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

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

						_a							:       int       					=			Image.fromarray(np.uinta(UpperCamelCase__   )   ).convert("""RGB"""   )
						_a							:       Optional[int]       					=			Image.fromarray(np.uinta(UpperCamelCase__   )   ).convert("""RGB"""   )

						_a							:       Union[str, Any]       					=			pipe(**self.get_dummy_inputs()   )
						_a							:       List[str]       					=			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     UpperCamelCase					(  unittest.TestCase					):



		def 						a_       ( self   ):







						'''simple docstring'''


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



		def 						a_       ( self   ):







						'''simple docstring'''


						_a							:       str       					=			load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/paint_by_example/dog_in_bucket.png"""   )
						_a							:       Optional[Any]       					=			load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/paint_by_example/mask.png"""   )
						_a							:       int       					=			load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/paint_by_example/panda.jpg"""   )

						_a							:       str       					=			PaintByExamplePipeline.from_pretrained("""Fantasy-Studio/Paint-by-Example"""   )
						_a							:       List[Any]       					=			pipe.to(UpperCamelCase__   )
						pipe.set_progress_bar_config(disable=UpperCamelCase__   )

						_a							:       Union[str, Any]       					=			torch.manual_seed(321   )
						_a							:       List[Any]       					=			pipe(
						    image=UpperCamelCase__		,    mask_image=UpperCamelCase__		,    example_image=UpperCamelCase__		,    generator=UpperCamelCase__		,    guidance_scale=5.0		,    num_inference_steps=50		,    output_type="""np"""		,    )

						_a							:       Optional[Any]       					=			output.images

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

						assert image.shape == (1, 512, 512, 3)
						_a							:       int       					=			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'''





def UpperCAmelCase						(					UpperCAmelCase_	):
    _a							:       List[Any]       					=			len(UpperCAmelCase_	)
    for cycle_start in range(0      ,  array_len - 1	):
        _a							:       int       					=			array[cycle_start]

        _a							:       int       					=			cycle_start
        for i in range(cycle_start + 1      ,  UpperCAmelCase_	):
            if array[i] < item:
                pos += 1

        if pos == cycle_start:
            continue

        while item == array[pos]:
            pos += 1

        _a					,   _a							:       List[str]       					=			item, array[pos]
        while pos != cycle_start:
            _a							:       Any       					=			cycle_start
            for i in range(cycle_start + 1      ,  UpperCAmelCase_	):
                if array[i] < item:
                    pos += 1

            while item == array[pos]:
                pos += 1

            _a					,   _a							:       List[Any]       					=			item, array[pos]

    return array


if __name__ == "__main__":
  assert cycle_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
  assert cycle_sort([0, 1, -1_0, 1_5, 2, -2]) == [-1_0, -2, 0, 1, 2, 1_5]


