





from typing import Any, Dict, List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
							from PIL import Image

							from ..image_utils import load_image

if is_torch_available():
							from ..models.auto.modeling_auto import (
							    MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
							    MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
							    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
							    MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
							)


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


__lowerCAmelCase			:Optional[Any]	 =     Dict[str, Any]
__lowerCAmelCase			:int	 =     List[Prediction]






@add_end_docstrings(__A     )
class 							_a(     __A     ):







		def __init__(     self			,    *__snake_case			,    **__snake_case					)   ->   List[Any]:


							'''simple docstring'''

							super().__init__(*__snake_case			,    **__snake_case					)

							if self.framework == "tf":
												raise ValueError(f"""The {self.__class__} is only available in PyTorch."""					)

							requires_backends(self			,    "vision"					)
							self.check_model_type(
							    dict(
							        MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
							        + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
							        + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
							        + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()					)					)







		def 							lowercase							(     self			,    **__snake_case					)   ->   Union[str, Any]:


							'''simple docstring'''

							_snake_case							:       str												= {}
							_snake_case							:       int												= {}
							if "subtask" in kwargs:
												_snake_case							:       Any												= kwargs["subtask"]
												_snake_case							:       List[Any]												= kwargs["subtask"]
							if "threshold" in kwargs:
												_snake_case							:       Optional[Any]												= kwargs["threshold"]
							if "mask_threshold" in kwargs:
												_snake_case							:       Any												= kwargs["mask_threshold"]
							if "overlap_mask_area_threshold" in kwargs:
												_snake_case							:       List[Any]												= kwargs["overlap_mask_area_threshold"]

							return preprocess_kwargs, {}, postprocess_kwargs







		def __call__(     self			,    __snake_case			,    **__snake_case					)   ->   Union[Predictions, List[Prediction]]:


							'''simple docstring'''

							return super().__call__(__snake_case			,    **__snake_case					)







		def 							lowercase							(     self			,    __snake_case			,    __snake_case=None					)   ->   List[str]:


							'''simple docstring'''

							_snake_case							:       Any												= load_image(__snake_case					)
							_snake_case							:       Tuple												= [(image.height, image.width)]
							if self.model.config.__class__.__name__ == "OneFormerConfig":
												if subtask is None:
																	_snake_case							:       List[Any]												= {}
												else:
																	_snake_case							:       int												= {"task_inputs": [subtask]}
												_snake_case							:       Tuple												= self.image_processor(images=[image]			,    return_tensors="pt"			,    **__snake_case					)
												_snake_case							:       Union[str, Any]												= self.tokenizer(
												    inputs["task_inputs"]			,    padding="max_length"			,    max_length=self.model.config.task_seq_len			,    return_tensors=self.framework			,    )["input_ids"]
							else:
												_snake_case							:       str												= self.image_processor(images=[image]			,    return_tensors="pt"					)
							_snake_case							:       Optional[int]												= target_size
							return inputs







		def 							lowercase							(     self			,    __snake_case					)   ->   Optional[Any]:


							'''simple docstring'''

							_snake_case							:       Dict												= model_inputs.pop("target_size"					)
							_snake_case							:       Union[str, Any]												= self.model(**__snake_case					)
							_snake_case							:       Optional[Any]												= target_size
							return model_outputs







		def 							lowercase							(     self			,    __snake_case			,    __snake_case=None			,    __snake_case=0.9			,    __snake_case=0.5			,    __snake_case=0.5					)   ->   Optional[int]:


							'''simple docstring'''

							_snake_case							:       List[Any]												= None
							if subtask in {"panoptic", None} and hasattr(self.image_processor			,    "post_process_panoptic_segmentation"					):
												_snake_case							:       str												= self.image_processor.post_process_panoptic_segmentation
							elif subtask in {"instance", None} and hasattr(self.image_processor			,    "post_process_instance_segmentation"					):
												_snake_case							:       str												= self.image_processor.post_process_instance_segmentation

							if fn is not None:
												_snake_case							:       Optional[Any]												= fn(
												    __snake_case			,    threshold=__snake_case			,    mask_threshold=__snake_case			,    overlap_mask_area_threshold=__snake_case			,    target_sizes=model_outputs["target_size"]			,    )[0]

												_snake_case							:       Optional[int]												= []
												_snake_case							:       int												= outputs["segmentation"]

												for segment in outputs["segments_info"]:
																	_snake_case							:       Dict												= (segmentation == segment["id"]) * 2_5_5
																	_snake_case							:       int												= Image.fromarray(mask.numpy().astype(np.uinta					)			,    mode="L"					)
																	_snake_case							:       Optional[Any]												= self.model.config.idalabel[segment["label_id"]]
																	_snake_case							:       Optional[int]												= segment["score"]
																	annotation.append({"score": score, "label": label, "mask": mask}					)

							elif subtask in {"semantic", None} and hasattr(self.image_processor			,    "post_process_semantic_segmentation"					):
												_snake_case							:       Tuple												= self.image_processor.post_process_semantic_segmentation(
												    __snake_case			,    target_sizes=model_outputs["target_size"]					)[0]

												_snake_case							:       Tuple												= []
												_snake_case							:       Any												= outputs.numpy()
												_snake_case							:       Tuple												= np.unique(__snake_case					)

												for label in labels:
																	_snake_case							:       str												= (segmentation == label) * 2_5_5
																	_snake_case							:       str												= Image.fromarray(mask.astype(np.uinta					)			,    mode="L"					)
																	_snake_case							:       List[str]												= self.model.config.idalabel[label]
																	annotation.append({"score": None, "label": label, "mask": mask}					)
							else:
												raise ValueError(f"""Subtask {subtask} is not supported for model {type(self.model					)}"""					)
							return annotation






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







		def 							lowercase							(     self					)   ->   Dict:


							'''simple docstring'''

							torch.manual_seed(0					)
							_snake_case							:       Union[str, Any]												= UNetaDConditionModel(
							    block_out_channels=(3_2, 6_4)			,    layers_per_block=2			,    sample_size=3_2			,    in_channels=9			,    out_channels=4			,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")			,    up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")			,    cross_attention_dim=3_2			,    )
							_snake_case							:       int												= PNDMScheduler(skip_prk_steps=__snake_case					)
							torch.manual_seed(0					)
							_snake_case							:       Any												= AutoencoderKL(
							    block_out_channels=[3_2, 6_4]			,    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							:       Dict												= CLIPVisionConfig(
							    hidden_size=3_2			,    projection_dim=3_2			,    intermediate_size=3_7			,    layer_norm_eps=1E-05			,    num_attention_heads=4			,    num_hidden_layers=5			,    image_size=3_2			,    patch_size=4			,    )
							_snake_case							:       Any												= PaintByExampleImageEncoder(__snake_case			,    proj_size=3_2					)
							_snake_case							:       Dict												= CLIPImageProcessor(crop_size=3_2			,    size=3_2					)

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







		def 							lowercase							(     self			,    __snake_case					)   ->   Tuple:


							'''simple docstring'''

							_snake_case							:       List[str]												= np.array(image.convert("RGB"					)					)
							_snake_case							:       str												= image[None].transpose(0			,    3			,    1			,    2					)
							_snake_case							:       Dict												= torch.from_numpy(__snake_case					).to(dtype=torch.floataa					) / 1_27.5 - 1.0
							return image







		def 							lowercase							(     self			,    __snake_case="cpu"			,    __snake_case=0					)   ->   Any:


							'''simple docstring'''

							_snake_case							:       Tuple												= floats_tensor((1, 3, 3_2, 3_2)			,    rng=random.Random(__snake_case					)					).to(__snake_case					)
							_snake_case							:       Union[str, Any]												= image.cpu().permute(0			,    2			,    3			,    1					)[0]
							_snake_case							:       List[Any]												= Image.fromarray(np.uinta(__snake_case					)					).convert("RGB"					).resize((6_4, 6_4)					)
							_snake_case							:       List[Any]												= Image.fromarray(np.uinta(image + 4					)					).convert("RGB"					).resize((6_4, 6_4)					)
							_snake_case							:       Any												= Image.fromarray(np.uinta(__snake_case					)					).convert("RGB"					).resize((3_2, 3_2)					)

							if str(__snake_case					).startswith("mps"					):
												_snake_case							:       Union[str, Any]												= torch.manual_seed(__snake_case					)
							else:
												_snake_case							:       List[Any]												= torch.Generator(device=__snake_case					).manual_seed(__snake_case					)
							_snake_case							:       Optional[int]												= {
							    "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 							lowercase							(     self					)   ->   int:


							'''simple docstring'''

							_snake_case							:       Optional[Any]												= self.get_dummy_components()

							# make sure here that pndm scheduler skips prk
							_snake_case							:       int												= PaintByExamplePipeline(**__snake_case					)
							_snake_case							:       List[Any]												= pipe.to("cpu"					)
							pipe.set_progress_bar_config(disable=__snake_case					)

							_snake_case							:       List[str]												= self.get_dummy_inputs()
							_snake_case							:       int												= pipe(**__snake_case					)
							_snake_case							:       Dict												= output.images

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

							assert image.shape == (1, 6_4, 6_4, 3)
							_snake_case							:       int												= np.array([0.46_86, 0.56_87, 0.40_07, 0.52_18, 0.57_41, 0.44_82, 0.49_40, 0.46_29, 0.45_03]					)

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







		def 							lowercase							(     self					)   ->   List[Any]:


							'''simple docstring'''

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

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

							_snake_case							:       int												= pipe(image=__snake_case			,    mask_image=mask_image[:, 0]			,    **__snake_case					)
							_snake_case							:       Optional[int]												= output.images

							_snake_case							:       Any												= image.cpu().permute(0			,    2			,    3			,    1					)[0]
							_snake_case							:       List[str]												= mask_image.cpu().permute(0			,    2			,    3			,    1					)[0]

							_snake_case							:       Any												= Image.fromarray(np.uinta(__snake_case					)					).convert("RGB"					)
							_snake_case							:       int												= Image.fromarray(np.uinta(__snake_case					)					).convert("RGB"					)

							_snake_case							:       Optional[int]												= pipe(**self.get_dummy_inputs()					)
							_snake_case							:       Dict												= output.images

							assert out_a.shape == (1, 6_4, 6_4, 3)
							assert np.abs(out_a.flatten() - out_a.flatten()					).max() < 5E-2







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


							'''simple docstring'''

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






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







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


							'''simple docstring'''

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







		def 							lowercase							(     self					)   ->   int:


							'''simple docstring'''

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

							_snake_case							:       Any												= PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"					)
							_snake_case							:       Any												= pipe.to(__snake_case					)
							pipe.set_progress_bar_config(disable=__snake_case					)

							_snake_case							:       Union[str, Any]												= torch.manual_seed(3_2_1					)
							_snake_case							:       int												= pipe(
							    image=__snake_case			,    mask_image=__snake_case			,    example_image=__snake_case			,    generator=__snake_case			,    guidance_scale=5.0			,    num_inference_steps=5_0			,    output_type="np"			,    )

							_snake_case							:       str												= output.images

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

							assert image.shape == (1, 5_1_2, 5_1_2, 3)
							_snake_case							:       int												= np.array([0.48_34, 0.48_11, 0.48_74, 0.51_22, 0.50_81, 0.51_44, 0.52_91, 0.52_90, 0.53_74]					)

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