
'''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


lowercase_    =		logging.get_logger(__name__)  # pylint: disable=invalid-name


def A				(__lowerCamelCase				:Union[List, PIL.Image.Image, torch.Tensor]		):
						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		) / 255.0
												_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 A				(__lowerCamelCase				:Union[List, PIL.Image.Image, torch.Tensor]		):
						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 % 32 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		) / 255.0
												_lowerCAmelCase  						=			0
												_lowerCAmelCase  						=			1
												_lowerCAmelCase  						=			torch.from_numpy(__lowerCamelCase		)
						elif isinstance(mask[0]			,     torch.Tensor		):
												_lowerCAmelCase  						=			torch.cat(__lowerCamelCase			,     dim=0		)
						return mask





class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						_lowercase		:				UNetaDModel
						_lowercase		:				RePaintScheduler




						def __init__( self   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												super().__init__()
												self.register_modules(unet=_lowercase   ,       scheduler=_lowercase				)




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

												"""simple docstring"""



												_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'''



def A				(__lowerCamelCase				:list[int]			,     __lowerCamelCase				:list[int]		):

						# Check if the input is valid
						if not len(__lowerCamelCase		) == len(__lowerCamelCase		) == 3:
												raise ValueError("""Please enter a valid equation."""		)
						if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
												raise ValueError("""Both a & b of two equations can't be zero."""		)

						# Extract the coefficients
						_lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase  						=			equationa
						_lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase  						=			equationa

						# Calculate the determinants of the matrices
						_lowerCAmelCase  						=			aa * ba - aa * ba
						_lowerCAmelCase  						=			ca * ba - ca * ba
						_lowerCAmelCase  						=			aa * ca - aa * ca

						# Check if the system of linear equations has a solution (using Cramer's rule)
						if determinant == 0:
												if determinant_x == determinant_y == 0:
																		raise ValueError("""Infinite solutions. (Consistent system)"""		)
												else:
																		raise ValueError("""No solution. (Inconsistent system)"""		)
						else:
												if determinant_x == determinant_y == 0:
																		# Trivial solution (Inconsistent system)
																		return (0.0, 0.0)
												else:
																		_lowerCAmelCase  						=			determinant_x / determinant
																		_lowerCAmelCase  						=			determinant_y / determinant
																		# Non-Trivial Solution (Consistent system)
																		return (x, y)
