





"""simple docstring"""


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


a__    :   Optional[Any]    						= logging.get_logger(__name__)


if is_vision_available():
   import PIL



class    lowercase_ (   __lowerCAmelCase							):
       snake_case__ :							int 							=   ["""pixel_values"""]

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE = True      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC      ,    __SCREAMING_SNAKE_CASE = True      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = True      ,    __SCREAMING_SNAKE_CASE = 1 / 255      ,    __SCREAMING_SNAKE_CASE = True      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = True      ,    **__SCREAMING_SNAKE_CASE      ,    ):






        '''simple docstring'''

        super().__init__(**__SCREAMING_SNAKE_CASE   )
        snake_case__										= size if size is not None else {"shortest_edge": 224}
        snake_case__										= get_size_dict(__SCREAMING_SNAKE_CASE      ,    default_to_square=__SCREAMING_SNAKE_CASE   )
        snake_case__										= crop_size if crop_size is not None else {"height": 224, "width": 224}
        snake_case__										= get_size_dict(__SCREAMING_SNAKE_CASE   )

        snake_case__										= do_resize
        snake_case__										= size
        snake_case__										= resample
        snake_case__										= do_center_crop
        snake_case__										= crop_size
        snake_case__										= do_rescale
        snake_case__										= rescale_factor
        snake_case__										= do_normalize
        snake_case__										= image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        snake_case__										= image_std if image_std is not None else OPENAI_CLIP_STD
        snake_case__										= do_convert_rgb

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC      ,    __SCREAMING_SNAKE_CASE = None      ,    **__SCREAMING_SNAKE_CASE      ,    ):






        '''simple docstring'''

        snake_case__										= get_size_dict(__SCREAMING_SNAKE_CASE      ,    default_to_square=__SCREAMING_SNAKE_CASE   )
        snake_case__										= get_resize_output_image_size(
            __SCREAMING_SNAKE_CASE      ,    size=(size["height"], size["width"])      ,    default_to_square=__SCREAMING_SNAKE_CASE   )
        return resize(__SCREAMING_SNAKE_CASE      ,    size=__SCREAMING_SNAKE_CASE      ,    resample=__SCREAMING_SNAKE_CASE      ,    data_format=__SCREAMING_SNAKE_CASE      ,    **__SCREAMING_SNAKE_CASE   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = None      ,    **__SCREAMING_SNAKE_CASE      ,    ):






        '''simple docstring'''

        snake_case__										= get_size_dict(__SCREAMING_SNAKE_CASE   )
        return center_crop(__SCREAMING_SNAKE_CASE      ,    size=(size["height"], size["width"])      ,    data_format=__SCREAMING_SNAKE_CASE      ,    **__SCREAMING_SNAKE_CASE   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = None      ,    **__SCREAMING_SNAKE_CASE      ,    ):






        '''simple docstring'''

        return rescale(__SCREAMING_SNAKE_CASE      ,    scale=__SCREAMING_SNAKE_CASE      ,    data_format=__SCREAMING_SNAKE_CASE      ,    **__SCREAMING_SNAKE_CASE   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = None      ,    **__SCREAMING_SNAKE_CASE      ,    ):






        '''simple docstring'''

        return normalize(__SCREAMING_SNAKE_CASE      ,    mean=__SCREAMING_SNAKE_CASE      ,    std=__SCREAMING_SNAKE_CASE      ,    data_format=__SCREAMING_SNAKE_CASE      ,    **__SCREAMING_SNAKE_CASE   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = ChannelDimension.FIRST      ,    **__SCREAMING_SNAKE_CASE      ,    ):






        '''simple docstring'''

        snake_case__										= do_resize if do_resize is not None else self.do_resize
        snake_case__										= size if size is not None else self.size
        snake_case__										= get_size_dict(__SCREAMING_SNAKE_CASE      ,    default_to_square=__SCREAMING_SNAKE_CASE   )
        snake_case__										= resample if resample is not None else self.resample
        snake_case__										= do_center_crop if do_center_crop is not None else self.do_center_crop
        snake_case__										= crop_size if crop_size is not None else self.crop_size
        snake_case__										= get_size_dict(__SCREAMING_SNAKE_CASE   )
        snake_case__										= do_rescale if do_rescale is not None else self.do_rescale
        snake_case__										= rescale_factor if rescale_factor is not None else self.rescale_factor
        snake_case__										= do_normalize if do_normalize is not None else self.do_normalize
        snake_case__										= image_mean if image_mean is not None else self.image_mean
        snake_case__										= image_std if image_std is not None else self.image_std
        snake_case__										= do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        snake_case__										= make_list_of_images(__SCREAMING_SNAKE_CASE   )

        if not valid_images(__SCREAMING_SNAKE_CASE   ):
         raise ValueError(
             "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
             "torch.Tensor, tf.Tensor or jax.ndarray."   )

        if do_resize and size is None:
         raise ValueError("Size must be specified if do_resize is True."   )

        if do_center_crop and crop_size is None:
         raise ValueError("Crop size must be specified if do_center_crop is True."   )

        if do_rescale and rescale_factor is None:
         raise ValueError("Rescale factor must be specified if do_rescale is True."   )

        if do_normalize and (image_mean is None or image_std is None):
         raise ValueError("Image mean and std must be specified if do_normalize is True."   )

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
         snake_case__										= [convert_to_rgb(__SCREAMING_SNAKE_CASE   ) for image in images]

        # All transformations expect numpy arrays.
        snake_case__										= [to_numpy_array(__SCREAMING_SNAKE_CASE   ) for image in images]

        if do_resize:
         snake_case__										= [self.resize(image=__SCREAMING_SNAKE_CASE      ,    size=__SCREAMING_SNAKE_CASE      ,    resample=__SCREAMING_SNAKE_CASE   ) for image in images]

        if do_center_crop:
         snake_case__										= [self.center_crop(image=__SCREAMING_SNAKE_CASE      ,    size=__SCREAMING_SNAKE_CASE   ) for image in images]

        if do_rescale:
         snake_case__										= [self.rescale(image=__SCREAMING_SNAKE_CASE      ,    scale=__SCREAMING_SNAKE_CASE   ) for image in images]

        if do_normalize:
         snake_case__										= [self.normalize(image=__SCREAMING_SNAKE_CASE      ,    mean=__SCREAMING_SNAKE_CASE      ,    std=__SCREAMING_SNAKE_CASE   ) for image in images]

        snake_case__										= [to_channel_dimension_format(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ) for image in images]

        snake_case__										= {"pixel_values": images}
        return BatchFeature(data=__SCREAMING_SNAKE_CASE      ,    tensor_type=__SCREAMING_SNAKE_CASE   )








"""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    lowercase_ (   __lowerCAmelCase ,unittest.TestCase							):
       snake_case__ :							List[str] 							=   PaintByExamplePipeline
       snake_case__ :							Any 							=   IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
       snake_case__ :							Optional[int] 							=   IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       snake_case__ :							int 							=   frozenset([]							)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

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

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

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE="cpu"      ,    __SCREAMING_SNAKE_CASE=0   ):






        '''simple docstring'''

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

        if str(__SCREAMING_SNAKE_CASE   ).startswith("mps"   ):
         snake_case__										= torch.manual_seed(__SCREAMING_SNAKE_CASE   )
        else:
         snake_case__										= torch.Generator(device=__SCREAMING_SNAKE_CASE   ).manual_seed(__SCREAMING_SNAKE_CASE   )
        snake_case__										= {
            "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 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        snake_case__										= self.get_dummy_components()

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

        snake_case__										= self.get_dummy_inputs()
        snake_case__										= pipe(**__SCREAMING_SNAKE_CASE   )
        snake_case__										= output.images

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

        assert image.shape == (1, 64, 64, 3)
        snake_case__										= 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 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

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

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

        snake_case__										= pipe(image=__SCREAMING_SNAKE_CASE      ,    mask_image=mask_image[:, 0]      ,    **__SCREAMING_SNAKE_CASE   )
        snake_case__										= output.images

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

        snake_case__										= Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert("RGB"   )
        snake_case__										= Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert("RGB"   )

        snake_case__										= pipe(**self.get_dummy_inputs()   )
        snake_case__										= output.images

        assert out_a.shape == (1, 64, 64, 3)
        assert np.abs(out_a.flatten() - out_a.flatten()   ).max() < 5e-2

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

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



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

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

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

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

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

        snake_case__										= output.images

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

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

