



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_							:      Union[str, Any]      =  logging.get_logger(__name__)  # pylint: disable=invalid-name
def  UpperCamelCase						(       _A			:				Union[List, PIL.Image.Image, torch.Tensor]    )->       Union[str, Any]:





						"""simple docstring"""





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

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

												A__							=       [np.array(i.resize((w, h)			,							resample=PIL_INTERPOLATION["lanczos"]    )    )[None, :] for i in image]
												A__							=       np.concatenate(_A			,							axis=0    )
												A__							=       np.array(_A    ).astype(np.floataa    ) / 255.0
												A__							=       image.transpose(0			,							3			,							1			,							2    )
												A__							=       2.0 * image - 1.0
												A__							=       torch.from_numpy(_A    )
						elif isinstance(image[0]			,							torch.Tensor    ):
												A__							=       torch.cat(_A			,							dim=0    )
						return image
def  UpperCamelCase						(       _A			:				Union[List, PIL.Image.Image, torch.Tensor]    )->       Tuple:





						"""simple docstring"""





						if isinstance(_A			,							torch.Tensor    ):
												return mask
						elif isinstance(_A			,							PIL.Image.Image    ):
												A__							=       [mask]

						if isinstance(mask[0]			,							PIL.Image.Image    ):
												A__						,       A__							=       mask[0].size
												A__						,       A__							=       (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
												A__							=       [np.array(m.convert("L"    ).resize((w, h)			,							resample=PIL_INTERPOLATION["nearest"]    )    )[None, :] for m in mask]
												A__							=       np.concatenate(_A			,							axis=0    )
												A__							=       mask.astype(np.floataa    ) / 255.0
												A__							=       0
												A__							=       1
												A__							=       torch.from_numpy(_A    )
						elif isinstance(mask[0]			,							torch.Tensor    ):
												A__							=       torch.cat(_A			,							dim=0    )
						return mask






class      UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   UNetaDModel
	lowerCAmelCase	:   RePaintScheduler



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



	@torch.no_grad()
	def __call__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__ = 250			,	UpperCAmelCase__ = 0.0			,	UpperCAmelCase__ = 10			,	UpperCAmelCase__ = 10			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = "pil"			,	UpperCAmelCase__ = True			,	):
							A__							=       image

							A__							=       _preprocess_image(UpperCAmelCase__		)
							A__							=       original_image.to(device=self.device			,	dtype=self.unet.dtype		)
							A__							=       _preprocess_mask(UpperCAmelCase__		)
							A__							=       mask_image.to(device=self.device			,	dtype=self.unet.dtype		)

							A__							=       original_image.shape[0]

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

							A__							=       original_image.shape
							A__							=       randn_tensor(UpperCAmelCase__			,	generator=UpperCAmelCase__			,	device=self.device			,	dtype=self.unet.dtype		)

							# set step values
							self.scheduler.set_timesteps(UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	self.device		)
							A__							=       eta

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

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

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

							if not return_dict:
													return (image,)

							return ImagePipelineOutput(images=UpperCAmelCase__		)










import math
def  UpperCamelCase						(       _A			:				float			,							_A			:				float    )->       float:





						"""simple docstring"""





						if (
						    not isinstance(_A			,							(int, float)    )
						    or power_factor < -1
						    or power_factor > 1
						):
												raise ValueError("power_factor must be a valid float value between -1 and 1."    )
						return apparent_power * power_factor
def  UpperCamelCase						(       _A			:				float			,							_A			:				float    )->       float:





						"""simple docstring"""





						if (
						    not isinstance(_A			,							(int, float)    )
						    or power_factor < -1
						    or power_factor > 1
						):
												raise ValueError("power_factor must be a valid float value between -1 and 1."    )
						return apparent_power * math.sqrt(1 - power_factor**2    )


if __name__ == "__main__":
						import doctest

						doctest.testmod()





