
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_			(						__UpperCamelCase	,     unittest.TestCase   ):




							'''simple docstring'''

							UpperCamelCase_					   =							PaintByExamplePipeline
							UpperCamelCase_					   =							IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
							UpperCamelCase_					   =							IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							UpperCamelCase_					   =							frozenset([]   )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
							def        lowerCAmelCase__ (				self) ->	Any:
										torch.manual_seed(0)
										UpperCamelCase__    :		Optional[Any]     =				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__    :		Any     =				PNDMScheduler(skip_prk_steps=UpperCamelCase)
										torch.manual_seed(0)
										UpperCamelCase__    :		Optional[Any]     =				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__    :		Any     =				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__    :		Any     =				PaintByExampleImageEncoder(UpperCamelCase     ,						proj_size=32)
										UpperCamelCase__    :		List[str]     =				CLIPImageProcessor(crop_size=32     ,						size=32)

										UpperCamelCase__    :		Dict     =				{
										    'unet': unet,
										    'scheduler': scheduler,
										    'vae': vae,
										    'image_encoder': image_encoder,
										    'safety_checker': None,
										    'feature_extractor': feature_extractor,
										}
										return components
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	Dict:
										UpperCamelCase__    :		Union[str, Any]     =				np.array(image.convert('RGB'))
										UpperCamelCase__    :		Dict     =				image[None].transpose(0     ,						3     ,						1     ,						2)
										UpperCamelCase__    :		Tuple     =				torch.from_numpy(UpperCamelCase).to(dtype=torch.floataa) / 127.5 - 1.0
										return image
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase="cpu"     ,						UpperCamelCase=0) ->	List[str]:
										# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
										UpperCamelCase__    :		Union[str, Any]     =				floats_tensor((1, 3, 32, 32)     ,						rng=random.Random(UpperCamelCase)).to(UpperCamelCase)
										UpperCamelCase__    :		Optional[int]     =				image.cpu().permute(0     ,						2     ,						3     ,						1)[0]
										UpperCamelCase__    :		str     =				Image.fromarray(np.uinta(UpperCamelCase)).convert('RGB').resize((64, 64))
										UpperCamelCase__    :		Dict     =				Image.fromarray(np.uinta(image + 4)).convert('RGB').resize((64, 64))
										UpperCamelCase__    :		Dict     =				Image.fromarray(np.uinta(UpperCamelCase)).convert('RGB').resize((32, 32))

										if str(UpperCamelCase).startswith('mps'):
													UpperCamelCase__    :		List[Any]     =				torch.manual_seed(UpperCamelCase)
										else:
													UpperCamelCase__    :		Tuple     =				torch.Generator(device=UpperCamelCase).manual_seed(UpperCamelCase)
										UpperCamelCase__    :		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        lowerCAmelCase__ (				self) ->	int:
										UpperCamelCase__    :		Tuple     =				self.get_dummy_components()

										# make sure here that pndm scheduler skips prk
										UpperCamelCase__    :		int     =				PaintByExamplePipeline(**UpperCamelCase)
										UpperCamelCase__    :		str     =				pipe.to('cpu')
										pipe.set_progress_bar_config(disable=UpperCamelCase)

										UpperCamelCase__    :		int     =				self.get_dummy_inputs()
										UpperCamelCase__    :		List[Any]     =				pipe(**UpperCamelCase)
										UpperCamelCase__    :		Optional[Any]     =				output.images

										UpperCamelCase__    :		Any     =				image[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)
										UpperCamelCase__    :		str     =				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        lowerCAmelCase__ (				self) ->	List[Any]:
										UpperCamelCase__    :		str     =				'cpu'
										UpperCamelCase__    :		Tuple     =				self.get_dummy_inputs()
										inputs.pop('mask_image')
										UpperCamelCase__    :		Dict     =				self.convert_to_pt(inputs.pop('image'))
										UpperCamelCase__    :		Dict     =				image.clamp(0     ,						1) / 2

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

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

										UpperCamelCase__    :		Union[str, Any]     =				image.cpu().permute(0     ,						2     ,						3     ,						1)[0]
										UpperCamelCase__    :		Optional[int]     =				mask_image.cpu().permute(0     ,						2     ,						3     ,						1)[0]

										UpperCamelCase__    :		Dict     =				Image.fromarray(np.uinta(UpperCamelCase)).convert('RGB')
										UpperCamelCase__    :		List[str]     =				Image.fromarray(np.uinta(UpperCamelCase)).convert('RGB')

										UpperCamelCase__    :		Tuple     =				pipe(**self.get_dummy_inputs())
										UpperCamelCase__    :		Optional[int]     =				output.images

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






							def        lowerCAmelCase__ (				self) ->	Optional[int]:
										super().test_inference_batch_single_identical(expected_max_diff=3E-3)



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




							'''simple docstring'''

							def        lowerCAmelCase__ (				self) ->	Union[str, Any]:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()






							def        lowerCAmelCase__ (				self) ->	Optional[int]:
										# make sure here that pndm scheduler skips prk
										UpperCamelCase__    :		List[Any]     =				load_image(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
										    '/paint_by_example/dog_in_bucket.png')
										UpperCamelCase__    :		Union[str, Any]     =				load_image(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
										    '/paint_by_example/mask.png')
										UpperCamelCase__    :		Tuple     =				load_image(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
										    '/paint_by_example/panda.jpg')

										UpperCamelCase__    :		Optional[int]     =				PaintByExamplePipeline.from_pretrained('Fantasy-Studio/Paint-by-Example')
										UpperCamelCase__    :		Optional[Any]     =				pipe.to(UpperCamelCase)
										pipe.set_progress_bar_config(disable=UpperCamelCase)

										UpperCamelCase__    :		str     =				torch.manual_seed(3_21)
										UpperCamelCase__    :		List[Any]     =				pipe(
										    image=UpperCamelCase     ,						mask_image=UpperCamelCase     ,						example_image=UpperCamelCase     ,						generator=UpperCamelCase     ,						guidance_scale=5.0     ,						num_inference_steps=50     ,						output_type='np'     ,						)

										UpperCamelCase__    :		str     =				output.images

										UpperCamelCase__    :		Optional[Any]     =				image[0, -3:, -3:, -1]

										assert image.shape == (1, 5_12, 5_12, 3)
										UpperCamelCase__    :		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





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



class 			UpperCamelCase_			(						enum.Enum   ):




							'''simple docstring'''

							UpperCamelCase_					   =							"""timm"""
							UpperCamelCase_					   =							"""transformers"""



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     Dict:
			if stage_names is None:
						raise ValueError('Stage_names must be set for transformers backbones' )

			if out_features is not None:
						if not isinstance(__SCREAMING_SNAKE_CASE				,    (list,) ):
									raise ValueError(F"""out_features must be a list {type(__SCREAMING_SNAKE_CASE )}""" )
						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(__SCREAMING_SNAKE_CASE				,    (list, tuple) ):
									raise ValueError(F"""out_indices must be a list or tuple, got {type(__SCREAMING_SNAKE_CASE )}""" )
						if any(idx >= len(__SCREAMING_SNAKE_CASE ) 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(__SCREAMING_SNAKE_CASE ) != len(__SCREAMING_SNAKE_CASE ):
									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       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    )	->     List[Any]:
			if out_indices is None and out_features is None:
						UpperCamelCase__    :		List[Any]     =				[len(__SCREAMING_SNAKE_CASE ) - 1]
						UpperCamelCase__    :		Tuple     =				[stage_names[-1]]
			elif out_indices is None and out_features is not None:
						UpperCamelCase__    :		str     =				[stage_names.index(__SCREAMING_SNAKE_CASE ) for layer in out_features]
			elif out_features is None and out_indices is not None:
						UpperCamelCase__    :		int     =				[stage_names[idx] for idx in out_indices]
			return out_features, out_indices



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    )	->     Tuple[List[str], List[int]]:
			verify_out_features_out_indices(out_features=__SCREAMING_SNAKE_CASE				,    out_indices=__SCREAMING_SNAKE_CASE				,    stage_names=__SCREAMING_SNAKE_CASE )
			UpperCamelCase__   ,			UpperCamelCase__    :		str     =				_align_output_features_output_indices(
			    out_features=__SCREAMING_SNAKE_CASE				,    out_indices=__SCREAMING_SNAKE_CASE				,    stage_names=__SCREAMING_SNAKE_CASE )
			# Verify that the aligned out_features and out_indices are valid
			verify_out_features_out_indices(out_features=__SCREAMING_SNAKE_CASE				,    out_indices=__SCREAMING_SNAKE_CASE				,    stage_names=__SCREAMING_SNAKE_CASE )
			return output_features, output_indices



class 			UpperCamelCase_			:




							'''simple docstring'''

							UpperCamelCase_					   =							None
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	None:
										if getattr(self     ,						'_backbone'     ,						UpperCamelCase) 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']
										UpperCamelCase__    :		int     =				[stage['module'] for stage in self._backbone.feature_info.info]
										UpperCamelCase__    :		str     =				[stage['num_chs'] for stage in self._backbone.feature_info.info]
										UpperCamelCase__    :		Optional[int]     =				self._backbone.feature_info.out_indices
										UpperCamelCase__    :		Optional[int]     =				self._backbone.feature_info.module_name()

										# We verify the out indices and out features are valid
										verify_out_features_out_indices(
										    out_features=UpperCamelCase     ,						out_indices=UpperCamelCase     ,						stage_names=self.stage_names)
										UpperCamelCase__   ,			UpperCamelCase__    :		Optional[int]     =				out_features, out_indices
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	None:
										UpperCamelCase__    :		List[str]     =				getattr(UpperCamelCase     ,						'stage_names')
										UpperCamelCase__    :		List[str]     =				getattr(UpperCamelCase     ,						'out_features'     ,						UpperCamelCase)
										UpperCamelCase__    :		Optional[Any]     =				getattr(UpperCamelCase     ,						'out_indices'     ,						UpperCamelCase)

										UpperCamelCase__    :		Tuple     =				stage_names
										UpperCamelCase__   ,			UpperCamelCase__    :		Optional[Any]     =				get_aligned_output_features_output_indices(
										    out_features=UpperCamelCase     ,						out_indices=UpperCamelCase     ,						stage_names=UpperCamelCase)
										# Number of channels for each stage. This is set in the transformer backbone model init
										UpperCamelCase__    :		Dict     =				None
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	None:
										UpperCamelCase__    :		List[Any]     =				config

										UpperCamelCase__    :		Dict     =				getattr(UpperCamelCase     ,						'use_timm_backbone'     ,						UpperCamelCase)
										UpperCamelCase__    :		List[str]     =				BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

										if self.backbone_type == BackboneType.TIMM:
													self._init_timm_backbone(UpperCamelCase)
										elif self.backbone_type == BackboneType.TRANSFORMERS:
													self._init_transformers_backbone(UpperCamelCase)
										else:
													raise ValueError(F"""backbone_type {self.backbone_type} not supported.""")
							@property
							def        lowerCAmelCase__ (				self) ->	List[str]:
										return self._out_features
							@out_features.setter
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	int:
										UpperCamelCase__   ,			UpperCamelCase__    :		str     =				get_aligned_output_features_output_indices(
										    out_features=UpperCamelCase     ,						out_indices=UpperCamelCase     ,						stage_names=self.stage_names)
							@property
							def        lowerCAmelCase__ (				self) ->	List[Any]:
										return self._out_indices
							@out_indices.setter
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	int:
										UpperCamelCase__   ,			UpperCamelCase__    :		Optional[int]     =				get_aligned_output_features_output_indices(
										    out_features=UpperCamelCase     ,						out_indices=UpperCamelCase     ,						stage_names=self.stage_names)
							@property
							def        lowerCAmelCase__ (				self) ->	List[str]:
										# the current backbones will output the number of channels for each stage
										# even if that stage is not in the out_features list.
										return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names)}
							@property
							def        lowerCAmelCase__ (				self) ->	Union[str, Any]:
										return [self.out_feature_channels[name] for name in self.out_features]
							def        lowerCAmelCase__ (				self     ,						*UpperCamelCase     ,						**UpperCamelCase) ->	str:
										UpperCamelCase__    :		List[Any]     =				dict(inspect.signature(self.forward).parameters)
										UpperCamelCase__    :		Any     =				{k: v for k, v in kwargs.items() if k in signature}
										return self(*UpperCamelCase     ,						**UpperCamelCase)
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase = None     ,						UpperCamelCase = None     ,						UpperCamelCase = None     ,						) ->	Union[str, Any]:
										raise NotImplementedError('This method should be implemented by the derived class.')






							def        lowerCAmelCase__ (				self) ->	int:
										UpperCamelCase__    :		Any     =				super().to_dict()
										UpperCamelCase__    :		Optional[Any]     =				output.pop('_out_features')
										UpperCamelCase__    :		Any     =				output.pop('_out_indices')
										return output



class 			UpperCamelCase_			:




							'''simple docstring'''

							@property
							def        lowerCAmelCase__ (				self) ->	Tuple:
										return self._out_features
							@out_features.setter
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	Tuple:
										UpperCamelCase__   ,			UpperCamelCase__    :		Union[str, Any]     =				get_aligned_output_features_output_indices(
										    out_features=UpperCamelCase     ,						out_indices=UpperCamelCase     ,						stage_names=self.stage_names)
							@property
							def        lowerCAmelCase__ (				self) ->	Optional[int]:
										return self._out_indices
							@out_indices.setter
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	int:
										UpperCamelCase__   ,			UpperCamelCase__    :		str     =				get_aligned_output_features_output_indices(
										    out_features=UpperCamelCase     ,						out_indices=UpperCamelCase     ,						stage_names=self.stage_names)






							def        lowerCAmelCase__ (				self) ->	Tuple:
										UpperCamelCase__    :		List[Any]     =				super().to_dict()
										UpperCamelCase__    :		Optional[int]     =				output.pop('_out_features')
										UpperCamelCase__    :		Tuple     =				output.pop('_out_indices')
										return output



