

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 							UpperCAmelCase_						(     __lowercase							,			__lowercase							,			__lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		List[str]             =       StableDiffusionInpaintPipeline
							UpperCAmelCase__			:		Optional[Any]             =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
							UpperCAmelCase__			:		int             =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							UpperCAmelCase__			:		Dict             =       frozenset(
							    []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							UpperCAmelCase__			:		Optional[int]             =       frozenset([]  )


							def 							__lowercase							(     self			)       ->       Tuple:
												torch.manual_seed(0			)
												_a      :       Tuple   =						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			,				attention_head_dim=(2, 4)			,				use_linear_projection=_a			,				)
												_a      :       List[Any]   =						PNDMScheduler(skip_prk_steps=_a			)
												torch.manual_seed(0			)
												_a      :       Tuple   =						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			,				sample_size=1_2_8			,				)
												torch.manual_seed(0			)
												_a      :       List[str]   =						CLIPTextConfig(
												    bos_token_id=0			,				eos_token_id=2			,				hidden_size=3_2			,				intermediate_size=3_7			,				layer_norm_eps=1e-0_5			,				num_attention_heads=4			,				num_hidden_layers=5			,				pad_token_id=1			,				vocab_size=1_0_0_0			,				hidden_act='''gelu'''			,				projection_dim=5_1_2			,				)
												_a      :       str   =						CLIPTextModel(_a			)
												_a      :       str   =						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

												_a      :       Dict   =						{
												    '''unet''': unet,
												    '''scheduler''': scheduler,
												    '''vae''': vae,
												    '''text_encoder''': text_encoder,
												    '''tokenizer''': tokenizer,
												    '''safety_checker''': None,
												    '''feature_extractor''': None,
												}
												return components


							def 							__lowercase							(     self			,				_a			,				_a=0			)       ->       List[Any]:
												# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
												_a      :       Union[str, Any]   =						floats_tensor((1, 3, 3_2, 3_2)			,				rng=random.Random(_a			)			).to(_a			)
												_a      :       str   =						image.cpu().permute(0			,				2			,				3			,				1			)[0]
												_a      :       Tuple   =						Image.fromarray(np.uinta(_a			)			).convert('''RGB'''			).resize((6_4, 6_4)			)
												_a      :       Dict   =						Image.fromarray(np.uinta(image + 4			)			).convert('''RGB'''			).resize((6_4, 6_4)			)
												if str(_a			).startswith('''mps'''			):
																	_a      :       Optional[int]   =						torch.manual_seed(_a			)
												else:
																	_a      :       Dict   =						torch.Generator(device=_a			).manual_seed(_a			)
												_a      :       Optional[int]   =						{
												    '''prompt''': '''A painting of a squirrel eating a burger''',
												    '''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			)       ->       List[str]:
												_a      :       int   =						'''cpu'''  # ensure determinism for the device-dependent torch.Generator
												_a      :       List[str]   =						self.get_dummy_components()
												_a      :       List[str]   =						StableDiffusionInpaintPipeline(**_a			)
												_a      :       Tuple   =						sd_pipe.to(_a			)
												sd_pipe.set_progress_bar_config(disable=_a			)

												_a      :       Union[str, Any]   =						self.get_dummy_inputs(_a			)
												_a      :       List[str]   =						sd_pipe(**_a			).images
												_a      :       Union[str, Any]   =						image[0, -3:, -3:, -1]

												assert image.shape == (1, 6_4, 6_4, 3)
												_a      :       str   =						np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]			)

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




							def 							__lowercase							(     self			)       ->       Any:
												super().test_inference_batch_single_identical(expected_max_diff=3e-3			)



@slow
@require_torch_gpu
class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







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


							def 							__lowercase							(     self			)       ->       List[Any]:
												_a      :       int   =						load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/sd2-inpaint/init_image.png'''			)
												_a      :       Dict   =						load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''			)
												_a      :       Any   =						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
												    '''/yellow_cat_sitting_on_a_park_bench.npy'''			)

												_a      :       Optional[int]   =						'''stabilityai/stable-diffusion-2-inpainting'''
												_a      :       Union[str, Any]   =						StableDiffusionInpaintPipeline.from_pretrained(_a			,				safety_checker=_a			)
												pipe.to(_a			)
												pipe.set_progress_bar_config(disable=_a			)
												pipe.enable_attention_slicing()

												_a      :       int   =						'''Face of a yellow cat, high resolution, sitting on a park bench'''

												_a      :       List[Any]   =						torch.manual_seed(0			)
												_a      :       Any   =						pipe(
												    prompt=_a			,				image=_a			,				mask_image=_a			,				generator=_a			,				output_type='''np'''			,				)
												_a      :       Union[str, Any]   =						output.images[0]

												assert image.shape == (5_1_2, 5_1_2, 3)
												assert np.abs(expected_image - image			).max() < 9e-3


							def 							__lowercase							(     self			)       ->       Dict:
												_a      :       List[Any]   =						load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/sd2-inpaint/init_image.png'''			)
												_a      :       Optional[int]   =						load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''			)
												_a      :       List[Any]   =						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
												    '''/yellow_cat_sitting_on_a_park_bench_fp16.npy'''			)

												_a      :       Tuple   =						'''stabilityai/stable-diffusion-2-inpainting'''
												_a      :       Optional[int]   =						StableDiffusionInpaintPipeline.from_pretrained(
												    _a			,				torch_dtype=torch.floataa			,				safety_checker=_a			,				)
												pipe.to(_a			)
												pipe.set_progress_bar_config(disable=_a			)
												pipe.enable_attention_slicing()

												_a      :       List[Any]   =						'''Face of a yellow cat, high resolution, sitting on a park bench'''

												_a      :       Dict   =						torch.manual_seed(0			)
												_a      :       Any   =						pipe(
												    prompt=_a			,				image=_a			,				mask_image=_a			,				generator=_a			,				output_type='''np'''			,				)
												_a      :       List[str]   =						output.images[0]

												assert image.shape == (5_1_2, 5_1_2, 3)
												assert np.abs(expected_image - image			).max() < 5e-1




							def 							__lowercase							(     self			)       ->       List[Any]:
												torch.cuda.empty_cache()
												torch.cuda.reset_max_memory_allocated()
												torch.cuda.reset_peak_memory_stats()

												_a      :       Any   =						load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/sd2-inpaint/init_image.png'''			)
												_a      :       List[str]   =						load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''			)

												_a      :       List[Any]   =						'''stabilityai/stable-diffusion-2-inpainting'''
												_a      :       Optional[int]   =						PNDMScheduler.from_pretrained(_a			,				subfolder='''scheduler'''			)
												_a      :       Tuple   =						StableDiffusionInpaintPipeline.from_pretrained(
												    _a			,				safety_checker=_a			,				scheduler=_a			,				torch_dtype=torch.floataa			,				)
												pipe.to(_a			)
												pipe.set_progress_bar_config(disable=_a			)
												pipe.enable_attention_slicing(1			)
												pipe.enable_sequential_cpu_offload()

												_a      :       Tuple   =						'''Face of a yellow cat, high resolution, sitting on a park bench'''

												_a      :       Tuple   =						torch.manual_seed(0			)
												_a      :       Dict   =						pipe(
												    prompt=_a			,				image=_a			,				mask_image=_a			,				generator=_a			,				num_inference_steps=2			,				output_type='''np'''			,				)

												_a      :       List[str]   =						torch.cuda.max_memory_allocated()
												# make sure that less than 2.65 GB is allocated
												assert mem_bytes < 2.65 * 1_0**9






class 							UpperCAmelCase_						:
							"""simple docstring"""







							def __init__(     self			,				_a			,				_a			,				_a			)       ->       List[str]:
												_a      :       List[Any]   =						name
												_a      :       List[str]   =						value
												_a      :       List[str]   =						weight


							def __repr__(     self			)       ->       Optional[int]:
												return F"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""


							def 							__lowercase							(     self			)       ->       List[Any]:
												return self.value


							def 							__lowercase							(     self			)       ->       int:
												return self.name


							def 							__lowercase							(     self			)       ->       Optional[int]:
												return self.weight




							def 							__lowercase							(     self			)       ->       Optional[Any]:
												return self.value / self.weight



def   __UpperCAmelCase					(      __a :     Optional[int]						,__a :     Tuple						,__a :     List[str]      )     ->	List[str]:

					"""simple docstring"""

					_a      :       Optional[int]   =						[]
					for i in range(len(__a      )      ):
										menu.append(Things(name[i]						,value[i]						,weight[i]      )      )
					return menu



def   __UpperCAmelCase					(      __a :     int						,__a :     Union[str, Any]						,__a :     int      )     ->	Union[str, Any]:

					"""simple docstring"""

					_a      :       Union[str, Any]   =						sorted(__a						,key=__a						,reverse=__a      )
					_a      :       Any   =						[]
					_a ,   _a      :       Optional[int]   =						0.0, 0.0
					for i in range(len(__a      )      ):
										if (total_cost + items_copy[i].get_weight()) <= max_cost:
															result.append(items_copy[i]      )
															total_cost += items_copy[i].get_weight()
															total_value += items_copy[i].get_value()
					return (result, total_value)



def   __UpperCAmelCase					(      )     ->	int:

					"""simple docstring"""



if __name__ == "__main__":
			import doctest

			doctest.testmod()



