






'''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


lowerCamelCase_				: Optional[int]    	=							logging.get_logger(__name__)  # pylint: disable=invalid-name







def 	__magic_name__( _A					):
						'''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					):
												UpperCamelCase__         = [image]

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

												UpperCamelCase__         = [np.array(i.resize((w, h)						,      resample=PIL_INTERPOLATION["""lanczos"""]					)					)[None, :] for i in image]
												UpperCamelCase__         = np.concatenate(__a						,      axis=0					)
												UpperCamelCase__         = np.array(__a					).astype(np.floataa					) / 2_5_5.0
												UpperCamelCase__         = image.transpose(0						,      3						,      1						,      2					)
												UpperCamelCase__         = 2.0 * image - 1.0
												UpperCamelCase__         = torch.from_numpy(__a					)
						elif isinstance(image[0]						,      torch.Tensor					):
												UpperCamelCase__         = torch.cat(__a						,      dim=0					)
						return image







def 	__magic_name__( _A					):
						'''simple docstring'''





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

						if isinstance(mask[0]						,      PIL.Image.Image					):
												UpperCamelCase__    ,   UpperCamelCase__         = mask[0].size
												UpperCamelCase__    ,   UpperCamelCase__         = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
												UpperCamelCase__         = [np.array(m.convert("""L"""					).resize((w, h)						,      resample=PIL_INTERPOLATION["""nearest"""]					)					)[None, :] for m in mask]
												UpperCamelCase__         = np.concatenate(__a						,      axis=0					)
												UpperCamelCase__         = mask.astype(np.floataa					) / 2_5_5.0
												UpperCamelCase__         = 0
												UpperCamelCase__         = 1
												UpperCamelCase__         = torch.from_numpy(__a					)
						elif isinstance(mask[0]						,      torch.Tensor					):
												UpperCamelCase__         = torch.cat(__a						,      dim=0					)
						return mask







class    _SCREAMING_SNAKE_CASE							(		__lowerCamelCase	):


			'''simple docstring'''





			__a							:							Optional[int]					     =					42
			__a							:							Dict					     =					42






			def __init__(   self							:						Optional[Any] ,      lowercase							:						List[str] ,      lowercase							:						List[Any]			)							->    List[Any]:

									'''simple docstring'''







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







			@torch.no_grad()
			def __call__(   self							:						Optional[int] ,      lowercase							:						List[str] ,      lowercase							:						Optional[Any] ,      lowercase							:						Tuple = 2_5_0 ,      lowercase							:						Optional[Any] = 0.0 ,      lowercase							:						Optional[int] = 1_0 ,      lowercase							:						Tuple = 1_0 ,      lowercase							:						List[str] = None ,      lowercase							:						Union[str, Any] = "pil" ,      lowercase							:						Tuple = True ,      )							->    Dict:

									'''simple docstring'''







									UpperCamelCase__         = image

									UpperCamelCase__         = _preprocess_image(SCREAMING_SNAKE_CASE_			)
									UpperCamelCase__         = original_image.to(device=self.device ,      dtype=self.unet.dtype			)
									UpperCamelCase__         = _preprocess_mask(SCREAMING_SNAKE_CASE_			)
									UpperCamelCase__         = mask_image.to(device=self.device ,      dtype=self.unet.dtype			)

									UpperCamelCase__         = original_image.shape[0]

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

									UpperCamelCase__         = original_image.shape
									UpperCamelCase__         = randn_tensor(SCREAMING_SNAKE_CASE_ ,      generator=SCREAMING_SNAKE_CASE_ ,      device=self.device ,      dtype=self.unet.dtype			)

									# set step values
									self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ ,      SCREAMING_SNAKE_CASE_ ,      SCREAMING_SNAKE_CASE_ ,      self.device			)
									UpperCamelCase__         = eta

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

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

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

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_			)








'''simple docstring'''


import inspect
import unittest
class    _SCREAMING_SNAKE_CASE							(		unittest.TestCase	):


			'''simple docstring'''





			def 				A      (   self							:						Tuple			)							->    Any:

									'''simple docstring'''







									try:
															import diffusers  # noqa: F401
									except ImportError:
															assert False







			def 				A      (   self							:						Optional[int]			)							->    Union[str, Any]:

									'''simple docstring'''







									import diffusers
									from diffusers.dependency_versions_table import deps

									UpperCamelCase__         = inspect.getmembers(lowercase ,      inspect.isclass			)

									for cls_name, cls_module in all_classes:
															if "dummy_" in cls_module.__module__:
																					for backend in cls_module._backends:
																											if backend == "k_diffusion":
																																	UpperCamelCase__         = """k-diffusion"""
																											elif backend == "invisible_watermark":
																																	UpperCamelCase__         = """invisible-watermark"""
																											assert backend in deps, f"{backend} is not in the deps table!"
