





"""simple docstring"""


import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union



class        lowercase_			(       enum.Enum							):
       snake_case__			:							List[str]										=       """timm"""
       snake_case__			:							Dict										=       """transformers"""





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		)      ->					List[str]:

 '''simple docstring'''






 if stage_names is None:
  raise ValueError("Stage_names must be set for transformers backbones"		)

 if out_features is not None:
  if not isinstance(_UpperCAmelCase		,       (list,)		):
   raise ValueError(f"out_features must be a list {type(_UpperCAmelCase		)}"		)
  if any(feat not in stage_names for feat in out_features		):
   raise ValueError(f"out_features must be a subset of stage_names: {stage_names} got {out_features}"		)

 if out_indices is not None:
  if not isinstance(_UpperCAmelCase		,       (list, tuple)		):
   raise ValueError(f"out_indices must be a list or tuple, got {type(_UpperCAmelCase		)}"		)
  if any(idx >= len(_UpperCAmelCase		) for idx in out_indices		):
   raise ValueError("out_indices must be valid indices for stage_names {stage_names}, got {out_indices}"		)

 if out_features is not None and out_indices is not None:
  if len(_UpperCAmelCase		) != len(_UpperCAmelCase		):
   raise ValueError("out_features and out_indices should have the same length if both are set"		)
  if out_features != [stage_names[idx] for idx in out_indices]:
   raise ValueError("out_features and out_indices should correspond to the same stages if both are set"		)





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		,       )      ->					Optional[int]:

 '''simple docstring'''






 if out_indices is None and out_features is None:
  snake_case__      							=			[len(_UpperCAmelCase		) - 1]
  snake_case__      							=			[stage_names[-1]]
 elif out_indices is None and out_features is not None:
  snake_case__      							=			[stage_names.index(_UpperCAmelCase		) for layer in out_features]
 elif out_features is None and out_indices is not None:
  snake_case__      							=			[stage_names[idx] for idx in out_indices]
 return out_features, out_indices





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		,       )      ->					Tuple[List[str], List[int]]:

 '''simple docstring'''






 verify_out_features_out_indices(out_features=_UpperCAmelCase		,       out_indices=_UpperCAmelCase		,       stage_names=_UpperCAmelCase		)
 snake_case__							,				snake_case__      							=			_align_output_features_output_indices(
     out_features=_UpperCAmelCase		,       out_indices=_UpperCAmelCase		,       stage_names=_UpperCAmelCase		)
 # Verify that the aligned out_features and out_indices are valid
 verify_out_features_out_indices(out_features=_UpperCAmelCase		,       out_indices=_UpperCAmelCase		,       stage_names=_UpperCAmelCase		)
 return output_features, output_indices



class        lowercase_			:
       snake_case__			:							Optional[BackboneType]										=       None

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        if getattr(self				,   "_backbone"				,   __SCREAMING_SNAKE_CASE       ) is None:
         raise ValueError("self._backbone must be set before calling _init_timm_backbone"       )

        # These will diagree with the defaults for the transformers models e.g. for resnet50
        # the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
        # the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
        snake_case__      							=			[stage["module"] for stage in self._backbone.feature_info.info]
        snake_case__      							=			[stage["num_chs"] for stage in self._backbone.feature_info.info]
        snake_case__      							=			self._backbone.feature_info.out_indices
        snake_case__      							=			self._backbone.feature_info.module_name()

        # We verify the out indices and out features are valid
        verify_out_features_out_indices(
            out_features=__SCREAMING_SNAKE_CASE				,   out_indices=__SCREAMING_SNAKE_CASE				,   stage_names=self.stage_names       )
        snake_case__							,				snake_case__      							=			out_features, out_indices

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			getattr(__SCREAMING_SNAKE_CASE				,   "stage_names"       )
        snake_case__      							=			getattr(__SCREAMING_SNAKE_CASE				,   "out_features"				,   __SCREAMING_SNAKE_CASE       )
        snake_case__      							=			getattr(__SCREAMING_SNAKE_CASE				,   "out_indices"				,   __SCREAMING_SNAKE_CASE       )

        snake_case__      							=			stage_names
        snake_case__							,				snake_case__      							=			get_aligned_output_features_output_indices(
            out_features=__SCREAMING_SNAKE_CASE				,   out_indices=__SCREAMING_SNAKE_CASE				,   stage_names=__SCREAMING_SNAKE_CASE       )
        # Number of channels for each stage. This is set in the transformer backbone model init
        snake_case__      							=			None

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			config

        snake_case__      							=			getattr(__SCREAMING_SNAKE_CASE				,   "use_timm_backbone"				,   __SCREAMING_SNAKE_CASE       )
        snake_case__      							=			BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

        if self.backbone_type == BackboneType.TIMM:
         self._init_timm_backbone(__SCREAMING_SNAKE_CASE       )
        elif self.backbone_type == BackboneType.TRANSFORMERS:
         self._init_transformers_backbone(__SCREAMING_SNAKE_CASE       )
        else:
         raise ValueError(F"backbone_type {self.backbone_type} not supported."       )

       @property
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        return self._out_features

       @out_features.setter
       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__							,				snake_case__      							=			get_aligned_output_features_output_indices(
            out_features=__SCREAMING_SNAKE_CASE				,   out_indices=__SCREAMING_SNAKE_CASE				,   stage_names=self.stage_names       )

       @property
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        return self._out_indices

       @out_indices.setter
       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__							,				snake_case__      							=			get_aligned_output_features_output_indices(
            out_features=__SCREAMING_SNAKE_CASE				,   out_indices=__SCREAMING_SNAKE_CASE				,   stage_names=self.stage_names       )

       @property
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names       )}

       @property
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        return [self.out_feature_channels[name] for name in self.out_features]

       def 						_SCREAMING_SNAKE_CASE(     self				,   *__SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			dict(inspect.signature(self.forward       ).parameters       )
        snake_case__      							=			{k: v for k, v in kwargs.items() if k in signature}
        return self(*__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				,   ):






        '''simple docstring'''

        raise NotImplementedError("This method should be implemented by the derived class."       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			super().to_dict()
        snake_case__      							=			output.pop("_out_features"       )
        snake_case__      							=			output.pop("_out_indices"       )
        return output



class        lowercase_			:

       @property
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        return self._out_features

       @out_features.setter
       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__							,				snake_case__      							=			get_aligned_output_features_output_indices(
            out_features=__SCREAMING_SNAKE_CASE				,   out_indices=__SCREAMING_SNAKE_CASE				,   stage_names=self.stage_names       )

       @property
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        return self._out_indices

       @out_indices.setter
       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__							,				snake_case__      							=			get_aligned_output_features_output_indices(
            out_features=__SCREAMING_SNAKE_CASE				,   out_indices=__SCREAMING_SNAKE_CASE				,   stage_names=self.stage_names       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			super().to_dict()
        snake_case__      							=			output.pop("_out_features"       )
        snake_case__      							=			output.pop("_out_indices"       )
        return output








"""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__			:							Tuple										=       PaintByExamplePipeline
       snake_case__			:							List[str]										=       IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
       snake_case__			:							List[str]										=       IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       snake_case__			:							List[str]										=       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

