


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

							def  lowerCamelCase_					(				self:      Optional[Any]				)     ->  Optional[Any]:





									"""simple docstring"""







									torch.manual_seed(0				)
									lowercase__				= 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       ,		attention_head_dim=(2, 4)       ,		use_linear_projection=UpperCamelCase_       ,		)
									lowercase__				= PNDMScheduler(skip_prk_steps=UpperCamelCase_				)
									torch.manual_seed(0				)
									lowercase__				= 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       ,		sample_size=128       ,		)
									torch.manual_seed(0				)
									lowercase__				= CLIPTextConfig(
									    bos_token_id=0       ,		eos_token_id=2       ,		hidden_size=32       ,		intermediate_size=37       ,		layer_norm_eps=1E-0_5       ,		num_attention_heads=4       ,		num_hidden_layers=5       ,		pad_token_id=1       ,		vocab_size=1_000       ,		hidden_act='''gelu'''       ,		projection_dim=512       ,		)
									lowercase__				= CLIPTextModel(UpperCamelCase_				)
									lowercase__				= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

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

							def  lowerCamelCase_					(				self:      Dict       ,		UpperCamelCase_:      Union[str, Any]       ,		UpperCamelCase_:      str=0				)     ->  int:





									"""simple docstring"""







									lowercase__				= floats_tensor((1, 3, 32, 32)       ,		rng=random.Random(UpperCamelCase_				)				).to(UpperCamelCase_				)
									lowercase__				= image.cpu().permute(0       ,		2       ,		3       ,		1				)[0]
									lowercase__				= Image.fromarray(np.uinta(UpperCamelCase_				)				).convert('''RGB'''				).resize((64, 64)				)
									lowercase__				= Image.fromarray(np.uinta(image + 4				)				).convert('''RGB'''				).resize((64, 64)				)
									if str(UpperCamelCase_				).startswith('''mps'''				):
											lowercase__				= torch.manual_seed(UpperCamelCase_				)
									else:
											lowercase__				= torch.Generator(device=UpperCamelCase_				).manual_seed(UpperCamelCase_				)
									lowercase__				= {
									    '''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  lowerCamelCase_					(				self:      Optional[int]				)     ->  Optional[int]:





									"""simple docstring"""







									lowercase__				= '''cpu'''  # ensure determinism for the device-dependent torch.Generator
									lowercase__				= self.get_dummy_components()
									lowercase__				= StableDiffusionInpaintPipeline(**UpperCamelCase_				)
									lowercase__				= sd_pipe.to(UpperCamelCase_				)
									sd_pipe.set_progress_bar_config(disable=UpperCamelCase_				)

									lowercase__				= self.get_dummy_inputs(UpperCamelCase_				)
									lowercase__				= sd_pipe(**UpperCamelCase_				).images
									lowercase__				= image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									lowercase__				= 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  lowerCamelCase_					(				self:      Any				)     ->  Dict:





									"""simple docstring"""







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


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

							def  lowerCamelCase_					(				self:      int				)     ->  int:





									"""simple docstring"""







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

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





									"""simple docstring"""







									lowercase__				= load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/sd2-inpaint/init_image.png'''				)
									lowercase__				= load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''				)
									lowercase__				= load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
									    '''/yellow_cat_sitting_on_a_park_bench.npy'''				)

									lowercase__				= '''stabilityai/stable-diffusion-2-inpainting'''
									lowercase__				= StableDiffusionInpaintPipeline.from_pretrained(UpperCamelCase_       ,		safety_checker=UpperCamelCase_				)
									pipe.to(UpperCamelCase_				)
									pipe.set_progress_bar_config(disable=UpperCamelCase_				)
									pipe.enable_attention_slicing()

									lowercase__				= '''Face of a yellow cat, high resolution, sitting on a park bench'''

									lowercase__				= torch.manual_seed(0				)
									lowercase__				= pipe(
									    prompt=UpperCamelCase_       ,		image=UpperCamelCase_       ,		mask_image=UpperCamelCase_       ,		generator=UpperCamelCase_       ,		output_type='''np'''       ,		)
									lowercase__				= output.images[0]

									assert image.shape == (512, 512, 3)
									assert np.abs(expected_image - image				).max() < 9E-3

							def  lowerCamelCase_					(				self:      Tuple				)     ->  Tuple:





									"""simple docstring"""







									lowercase__				= load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/sd2-inpaint/init_image.png'''				)
									lowercase__				= load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''				)
									lowercase__				= load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
									    '''/yellow_cat_sitting_on_a_park_bench_fp16.npy'''				)

									lowercase__				= '''stabilityai/stable-diffusion-2-inpainting'''
									lowercase__				= StableDiffusionInpaintPipeline.from_pretrained(
									    UpperCamelCase_       ,		torch_dtype=torch.floataa       ,		safety_checker=UpperCamelCase_       ,		)
									pipe.to(UpperCamelCase_				)
									pipe.set_progress_bar_config(disable=UpperCamelCase_				)
									pipe.enable_attention_slicing()

									lowercase__				= '''Face of a yellow cat, high resolution, sitting on a park bench'''

									lowercase__				= torch.manual_seed(0				)
									lowercase__				= pipe(
									    prompt=UpperCamelCase_       ,		image=UpperCamelCase_       ,		mask_image=UpperCamelCase_       ,		generator=UpperCamelCase_       ,		output_type='''np'''       ,		)
									lowercase__				= output.images[0]

									assert image.shape == (512, 512, 3)
									assert np.abs(expected_image - image				).max() < 5E-1


							def  lowerCamelCase_					(				self:      Tuple				)     ->  Dict:





									"""simple docstring"""







									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									lowercase__				= load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/sd2-inpaint/init_image.png'''				)
									lowercase__				= load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''				)

									lowercase__				= '''stabilityai/stable-diffusion-2-inpainting'''
									lowercase__				= PNDMScheduler.from_pretrained(UpperCamelCase_       ,		subfolder='''scheduler'''				)
									lowercase__				= StableDiffusionInpaintPipeline.from_pretrained(
									    UpperCamelCase_       ,		safety_checker=UpperCamelCase_       ,		scheduler=UpperCamelCase_       ,		torch_dtype=torch.floataa       ,		)
									pipe.to(UpperCamelCase_				)
									pipe.set_progress_bar_config(disable=UpperCamelCase_				)
									pipe.enable_attention_slicing(1				)
									pipe.enable_sequential_cpu_offload()

									lowercase__				= '''Face of a yellow cat, high resolution, sitting on a park bench'''

									lowercase__				= torch.manual_seed(0				)
									lowercase__				= pipe(
									    prompt=UpperCamelCase_       ,		image=UpperCamelCase_       ,		mask_image=UpperCamelCase_       ,		generator=UpperCamelCase_       ,		num_inference_steps=2       ,		output_type='''np'''       ,		)

									lowercase__				= torch.cuda.max_memory_allocated()
									# make sure that less than 2.65 GB is allocated
									assert mem_bytes < 2.65 * 10**9






from PIL import Image




def    a__    (		snake_case__					:							Image						,   snake_case__					:							int     ):
  _UpperCAmelCase		:							Optional[Any]							=     (259 * (level + 255)) / (255 * (259 - level))

  def contrast(snake_case__					:							int     ) -> int:
    return int(128 + factor * (c - 128)     )

  return img.point(snake_case__     )


if __name__ == "__main__":
 # Load image
 with Image.open('image_data/lena.jpg') as img:
  # Change contrast to 170
  SCREAMING_SNAKE_CASE__					:     int       =     change_contrast(img, 170)
  cont_img.save('image_data/lena_high_contrast.png', format='png')





