





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 (    lowercase__				,				unittest.TestCase							):
    """simple docstring"""

    lowerCamelCase  :       List[str]   =   PaintByExamplePipeline
    lowerCamelCase  :       Optional[int]   =   IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
    lowerCamelCase  :       Union[str, Any]   =   IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    lowerCamelCase  :       Dict   =   frozenset([]							)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess



    def _a       (							self			:    List[str]			)			->       Any:
           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=_A			)
           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(_A   ,    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			:    Any   ,    __SCREAMING_SNAKE_CASE			:    Tuple			)			->       Dict:
           __UpperCAmelCase    							=np.array(image.convert("""RGB"""			)			)
           __UpperCAmelCase    							=image[None].transpose(0   ,    3   ,    1   ,    2			)
           __UpperCAmelCase    							=torch.from_numpy(_A			).to(dtype=torch.floataa			) / 127.5 - 1.0
           return image



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

           if str(_A			).startswith("""mps"""			):
                  __UpperCAmelCase    							=torch.manual_seed(_A			)
           else:
                  __UpperCAmelCase    							=torch.Generator(device=_A			).manual_seed(_A			)
           __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			:    str			)			->       Optional[Any]:
           __UpperCAmelCase    							=self.get_dummy_components()

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

           __UpperCAmelCase    							=self.get_dummy_inputs()
           __UpperCAmelCase    							=pipe(**_A			)
           __UpperCAmelCase    							=output.images

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

           assert image.shape == (1, 64, 64, 3)
           __UpperCAmelCase    							=np.array([0.4_686, 0.5_687, 0.4_007, 0.5_218, 0.5_741, 0.4_482, 0.4_940, 0.4_629, 0.4_503]			)

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



    def _a       (							self			:    List[Any]			)			->       str:
           __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(_A			)
           pipe.set_progress_bar_config(disable=_A			)

           __UpperCAmelCase    							=pipe(image=_A   ,    mask_image=mask_image[:, 0]   ,    **_A			)
           __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(_A			)			).convert("""RGB"""			)
           __UpperCAmelCase    							=Image.fromarray(np.uinta(_A			)			).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			:    Optional[int]			)			->       Tuple:
           super().test_inference_batch_single_identical(expected_max_diff=3e-3			)




@slow
@require_torch_gpu
class 							_A (    unittest.TestCase							):
    """simple docstring"""

    def _a       (							self			:    int			)			->       str:
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()



    def _a       (							self			:    Optional[int]			)			->       Dict:
           # make sure here that pndm scheduler skips prk
           __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(_A			)
           pipe.set_progress_bar_config(disable=_A			)

           __UpperCAmelCase    							=torch.manual_seed(321			)
           __UpperCAmelCase    							=pipe(
               image=_A   ,    mask_image=_A   ,    example_image=_A   ,    generator=_A   ,    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([0.4_834, 0.4_811, 0.4_874, 0.5_122, 0.5_081, 0.5_144, 0.5_291, 0.5_290, 0.5_374]			)

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












from collections import namedtuple

__A  							=  namedtuple("from_to", "from_ to")

__A  							=  {
    "millimeter": "mm",
    "centimeter": "cm",
    "meter": "m",
    "kilometer": "km",
    "inch": "in",
    "inche": "in",  # Trailing 's' has been stripped off
    "feet": "ft",
    "foot": "ft",
    "yard": "yd",
    "mile": "mi",
}

__A  							=  {
    "mm": from_to(0.001, 10_00),
    "cm": from_to(0.01, 1_00),
    "m": from_to(1, 1),
    "km": from_to(10_00, 0.001),
    "in": from_to(0.0254, 39.3701),
    "ft": from_to(0.3048, 3.2_8084),
    "yd": from_to(0.9144, 1.0_9361),
    "mi": from_to(1609.34, 0.0_0062_1371),
}







def 			lowercase__  (				A_:	float     ,			A_:	str     ,			A_:	str		)  ->							float:







       """simple docstring"""






       __UpperCAmelCase    							=from_type.lower().rstrip("""s"""		)
       __UpperCAmelCase    							=TYPE_CONVERSION.get(A_     ,			A_		)
       __UpperCAmelCase    							=to_type.lower().rstrip("""s"""		)
       __UpperCAmelCase    							=TYPE_CONVERSION.get(A_     ,			A_		)
       if new_from not in METRIC_CONVERSION:
              __UpperCAmelCase    							=(
                  F'''Invalid \'from_type\' value: {from_type!r}.\n'''
                  F'''Conversion abbreviations are: {", ".join(A_		)}'''
              )
              raise ValueError(A_		)
       if new_to not in METRIC_CONVERSION:
              __UpperCAmelCase    							=(
                  F'''Invalid \'to_type\' value: {to_type!r}.\n'''
                  F'''Conversion abbreviations are: {", ".join(A_		)}'''
              )
              raise ValueError(A_		)
       return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
   import doctest

   doctest.testmod()




