
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__		(     SCREAMING_SNAKE_CASE_,      unittest.TestCase ):







	'''simple docstring'''
	_lowerCAmelCase											=					PaintByExamplePipeline
	_lowerCAmelCase											=					IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
	_lowerCAmelCase											=					IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	_lowerCAmelCase											=					frozenset([] )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess





	def 	__snake_case					(      self		):
					torch.manual_seed(0		)
					A__	:						List[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					,							)
					A__	:						Any								=				PNDMScheduler(skip_prk_steps=UpperCamelCase__		)
					torch.manual_seed(0		)
					A__	:						Tuple								=				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		)
					A__	:						List[str]								=				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					,							)
					A__	:						Union[str, Any]								=				PaintByExampleImageEncoder(UpperCamelCase__					,							proj_size=32		)
					A__	:						Optional[int]								=				CLIPImageProcessor(crop_size=32					,							size=32		)

					A__	:						Any								=				{
					    '''unet''': unet,
					    '''scheduler''': scheduler,
					    '''vae''': vae,
					    '''image_encoder''': image_encoder,
					    '''safety_checker''': None,
					    '''feature_extractor''': feature_extractor,
					}
					return components





	def 	__snake_case					(      self					,							UpperCamelCase__		):
					A__	:						Optional[Any]								=				np.array(image.convert('''RGB'''		)		)
					A__	:						Optional[int]								=				image[None].transpose(0					,							3					,							1					,							2		)
					A__	:						Optional[Any]								=				torch.from_numpy(UpperCamelCase__		).to(dtype=torch.floataa		) / 1_2_7.5 - 1.0
					return image





	def 	__snake_case					(      self					,							UpperCamelCase__="cpu"					,							UpperCamelCase__=0		):
					# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
					A__	:						Tuple								=				floats_tensor((1, 3, 32, 32)					,							rng=random.Random(UpperCamelCase__		)		).to(UpperCamelCase__		)
					A__	:						List[Any]								=				image.cpu().permute(0					,							2					,							3					,							1		)[0]
					A__	:						Optional[Any]								=				Image.fromarray(np.uinta(UpperCamelCase__		)		).convert('''RGB'''		).resize((64, 64)		)
					A__	:						Dict								=				Image.fromarray(np.uinta(image + 4		)		).convert('''RGB'''		).resize((64, 64)		)
					A__	:						int								=				Image.fromarray(np.uinta(UpperCamelCase__		)		).convert('''RGB'''		).resize((32, 32)		)

					if str(UpperCamelCase__		).startswith('''mps'''		):
									A__	:						Union[str, Any]								=				torch.manual_seed(UpperCamelCase__		)
					else:
									A__	:						Any								=				torch.Generator(device=UpperCamelCase__		).manual_seed(UpperCamelCase__		)
					A__	:						Any								=				{
					    '''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 	__snake_case					(      self		):
					A__	:						str								=				self.get_dummy_components()

					# make sure here that pndm scheduler skips prk
					A__	:						Optional[Any]								=				PaintByExamplePipeline(**UpperCamelCase__		)
					A__	:						List[str]								=				pipe.to('''cpu'''		)
					pipe.set_progress_bar_config(disable=UpperCamelCase__		)

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

					A__	:						str								=				image[0, -3:, -3:, -1]

					assert image.shape == (1, 64, 64, 3)
					A__	:						int								=				np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3]		)

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





	def 	__snake_case					(      self		):
					A__	:						Any								=				'''cpu'''
					A__	:						List[Any]								=				self.get_dummy_inputs()
					inputs.pop('''mask_image'''		)
					A__	:						Any								=				self.convert_to_pt(inputs.pop('''image'''		)		)
					A__	:						Optional[Any]								=				image.clamp(0					,							1		) / 2

					# make sure here that pndm scheduler skips prk
					A__	:						int								=				PaintByExamplePipeline(**self.get_dummy_components()		)
					A__	:						Tuple								=				pipe.to(UpperCamelCase__		)
					pipe.set_progress_bar_config(disable=UpperCamelCase__		)

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

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

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

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

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





	def 	__snake_case					(      self		):
					super().test_inference_batch_single_identical(expected_max_diff=3e-3		)







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







	'''simple docstring'''





	def 	__snake_case					(      self		):
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()





	def 	__snake_case					(      self		):
					# make sure here that pndm scheduler skips prk
					A__	:						Any								=				load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/paint_by_example/dog_in_bucket.png'''		)
					A__	:						Dict								=				load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/paint_by_example/mask.png'''		)
					A__	:						Dict								=				load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/paint_by_example/panda.jpg'''		)

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

					A__	:						Tuple								=				torch.manual_seed(321		)
					A__	:						List[str]								=				pipe(
					    image=UpperCamelCase__					,							mask_image=UpperCamelCase__					,							example_image=UpperCamelCase__					,							generator=UpperCamelCase__					,							guidance_scale=5.0					,							num_inference_steps=50					,							output_type='''np'''					,							)

					A__	:						Optional[Any]								=				output.images

					A__	:						int								=				image[0, -3:, -3:, -1]

					assert image.shape == (1, 512, 512, 3)
					A__	:						Optional[Any]								=				np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4]		)

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

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    list[int]							)			->							int:

				"""simple docstring"""







				if not isinstance(__UpperCamelCase				,    __UpperCamelCase							) or not all(
				    isinstance(__UpperCamelCase				,    __UpperCamelCase							) and item >= 0 for item in citations							):
								raise ValueError('''The citations should be a list of non negative integers.'''							)

				citations.sort()
				A__	:						str								=				len(__UpperCamelCase							)

				for i in range(__UpperCamelCase							):
								if citations[len_citations - 1 - i] <= i:
												return i

				return len_citations


if __name__ == "__main__":
					import doctest

					doctest.testmod()