


'''simple docstring'''

import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_UpperCAmelCase				: List[Any]							    =				logging.get_logger(__name__)  # pylint: disable=invalid-name

def 							__magic_name__(						lowerCamelCase):
						warnings.warn(
						    '''The preprocess method is deprecated and will be removed in a future version. Please'''
						    ''' use VaeImageProcessor.preprocess instead''',				lowerCamelCase,				)
						if isinstance(lowerCamelCase,				torch.Tensor):
												return image
						elif isinstance(lowerCamelCase,				PIL.Image.Image):
												__lowerCAmelCase							       =				[image]

						if isinstance(image[0],				PIL.Image.Image):
												__lowerCAmelCase       ,					__lowerCAmelCase							       =				image[0].size
												__lowerCAmelCase       ,					__lowerCAmelCase							       =				(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

												__lowerCAmelCase							       =				[np.array(i.resize((w, h),				resample=PIL_INTERPOLATION['''lanczos''']))[None, :] for i in image]
												__lowerCAmelCase							       =				np.concatenate(lowerCamelCase,				axis=0)
												__lowerCAmelCase							       =				np.array(lowerCamelCase).astype(np.floataa) / 2_55.0
												__lowerCAmelCase							       =				image.transpose(0,				3,				1,				2)
												__lowerCAmelCase							       =				2.0 * image - 1.0
												__lowerCAmelCase							       =				torch.from_numpy(lowerCamelCase)
						elif isinstance(image[0],				torch.Tensor):
												__lowerCAmelCase							       =				torch.cat(lowerCamelCase,				dim=0)
						return image

def 							__magic_name__(						lowerCamelCase):
						if isinstance(lowerCamelCase,				torch.Tensor):
												return mask
						elif isinstance(lowerCamelCase,				PIL.Image.Image):
												__lowerCAmelCase							       =				[mask]

						if isinstance(mask[0],				PIL.Image.Image):
												__lowerCAmelCase       ,					__lowerCAmelCase							       =				mask[0].size
												__lowerCAmelCase       ,					__lowerCAmelCase							       =				(x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
												__lowerCAmelCase							       =				[np.array(m.convert('''L''').resize((w, h),				resample=PIL_INTERPOLATION['''nearest''']))[None, :] for m in mask]
												__lowerCAmelCase							       =				np.concatenate(lowerCamelCase,				axis=0)
												__lowerCAmelCase							       =				mask.astype(np.floataa) / 2_55.0
												__lowerCAmelCase							       =				0
												__lowerCAmelCase							       =				1
												__lowerCAmelCase							       =				torch.from_numpy(lowerCamelCase)
						elif isinstance(mask[0],				torch.Tensor):
												__lowerCAmelCase							       =				torch.cat(lowerCamelCase,				dim=0)
						return mask




class       a__				( __A      ):







				"""simple docstring"""



				__UpperCamelCase    :		UNetaDModel
				__UpperCamelCase    :		RePaintScheduler




				def __init__(self       ,			__lowercase       ,			__lowercase		):
										super().__init__()
										self.register_modules(unet=__lowercase       ,			scheduler=__lowercase		)




				@torch.no_grad()
				def __call__(self       ,			__lowercase       ,			__lowercase       ,			__lowercase = 2_50       ,			__lowercase = 0.0       ,			__lowercase = 10       ,			__lowercase = 10       ,			__lowercase = None       ,			__lowercase = "pil"       ,			__lowercase = True       ,			):
										__lowerCAmelCase							       =				image

										__lowerCAmelCase							       =				_preprocess_image(__lowercase		)
										__lowerCAmelCase							       =				original_image.to(device=self.device       ,			dtype=self.unet.dtype		)
										__lowerCAmelCase							       =				_preprocess_mask(__lowercase		)
										__lowerCAmelCase							       =				mask_image.to(device=self.device       ,			dtype=self.unet.dtype		)

										__lowerCAmelCase							       =				original_image.shape[0]

										# sample gaussian noise to begin the loop
										if isinstance(__lowercase       ,			__lowercase		) and len(__lowercase		) != batch_size:
																raise ValueError(
																    F"""You have passed a list of generators of length {len(__lowercase		)}, but requested an effective batch"""
																    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""		)

										__lowerCAmelCase							       =				original_image.shape
										__lowerCAmelCase							       =				randn_tensor(__lowercase       ,			generator=__lowercase       ,			device=self.device       ,			dtype=self.unet.dtype		)

										# set step values
										self.scheduler.set_timesteps(__lowercase       ,			__lowercase       ,			__lowercase       ,			self.device		)
										__lowerCAmelCase							       =				eta

										__lowerCAmelCase							       =				self.scheduler.timesteps[0] + 1
										__lowerCAmelCase							       =				generator[0] if isinstance(__lowercase       ,			__lowercase		) else generator
										for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
																if t < t_last:
																						# predict the noise residual
																						__lowerCAmelCase							       =				self.unet(__lowercase       ,			__lowercase		).sample
																						# compute previous image: x_t -> x_t-1
																						__lowerCAmelCase							       =				self.scheduler.step(__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase		).prev_sample

																else:
																						# compute the reverse: x_t-1 -> x_t
																						__lowerCAmelCase							       =				self.scheduler.undo_step(__lowercase       ,			__lowercase       ,			__lowercase		)
																__lowerCAmelCase							       =				t

										__lowerCAmelCase							       =				(image / 2 + 0.5).clamp(0       ,			1		)
										__lowerCAmelCase							       =				image.cpu().permute(0       ,			2       ,			3       ,			1		).numpy()
										if output_type == "pil":
																__lowerCAmelCase							       =				self.numpy_to_pil(__lowercase		)

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=__lowercase		)





'''simple docstring'''

from PIL import Image

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):

						def brightness(lowerCamelCase) -> float:
												return 1_2_8 + level + (c - 1_2_8)

						if not -2_55.0 <= level <= 2_55.0:
												raise ValueError('''level must be between -255.0 (black) and 255.0 (white)''')
						return img.point(lowerCamelCase)


if __name__ == "__main__":
	# Load image
	with Image.open("""image_data/lena.jpg""") as img:
		# Change brightness to 100
		_UpperCAmelCase				: str							    =				change_brightness(img, 1_0_0)
		brigt_img.save("""image_data/lena_brightness.png""", format="""png""")

