"""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


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





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   List[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							):
												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(__a  ,       axis=0							)
												lowerCamelCase__   =np.array(__a							).astype(np.floataa							) / 255.0
												lowerCamelCase__   =image.transpose(0  ,       3  ,       1  ,       2							)
												lowerCamelCase__   =2.0 * image - 1.0
												lowerCamelCase__   =torch.from_numpy(__a							)
						elif isinstance(image[0]  ,       torch.Tensor							):
												lowerCamelCase__   =torch.cat(__a  ,       dim=0							)
						return image





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   Any:




						'''simple docstring'''



						if isinstance(__a  ,       torch.Tensor							):
												return mask
						elif isinstance(__a  ,       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(__a  ,       axis=0							)
												lowerCamelCase__   =mask.astype(np.floataa							) / 255.0
												lowerCamelCase__   =0
												lowerCamelCase__   =1
												lowerCamelCase__   =torch.from_numpy(__a							)
						elif isinstance(mask[0]  ,       torch.Tensor							):
												lowerCamelCase__   =torch.cat(__a  ,       dim=0							)
						return mask






class   __UpperCAmelCase		(      __lowercase      ):
				A__ :  Union[str, Any]    				=			4_2
				A__ :  Optional[Any]    				=			4_2

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






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

										lowerCamelCase__   =_preprocess_image(SCREAMING_SNAKE_CASE_ )
										lowerCamelCase__   =original_image.to(device=self.device      ,					dtype=self.unet.dtype )
										lowerCamelCase__   =_preprocess_mask(SCREAMING_SNAKE_CASE_ )
										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(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.''' )

										lowerCamelCase__   =original_image.shape
										lowerCamelCase__   =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 )
										lowerCamelCase__   =eta

										lowerCamelCase__   =self.scheduler.timesteps[0] + 1
										lowerCamelCase__   =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
																						lowerCamelCase__   =self.unet(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ ).sample
																						# compute previous image: x_t -> x_t-1
																						lowerCamelCase__   =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
																						lowerCamelCase__   =self.scheduler.undo_step(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ )
																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(SCREAMING_SNAKE_CASE_ )

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_ )




"""simple docstring"""




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest





class 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   (UnCLIPScheduler,)

							def _lowercase       (self							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			{
									    '''num_train_timesteps''': 10_00,
									    '''variance_type''': '''fixed_small_log''',
									    '''clip_sample''': True,
									    '''clip_sample_range''': 1.0,
									    '''prediction_type''': '''epsilon''',
									}

									config.update(**SCREAMING_SNAKE_CASE_     )
									return config

							def _lowercase       (self     ):

									"""simple docstring"""







									for timesteps in [1, 5, 1_00, 10_00]:
											self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for variance in ["fixed_small_log", "learned_range"]:
											self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for clip_sample_range in [1, 5, 10, 20]:
											self.check_over_configs(clip_sample_range=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for prediction_type in ["epsilon", "sample"]:
											self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for time_step in [0, 5_00, 9_99]:
											for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
													if prev_timestep is not None and prev_timestep >= time_step:
															continue

													self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_							,							prev_timestep=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config(variance_type='''fixed_small_log'''     )
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 1.0_0_0_0e-1_0     )     ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(4_87     ) - 0.0_54_96_25     )     ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_99     ) - 0.9_99_49_87     )     ) < 1e-5

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config(variance_type='''learned_range'''     )
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			0.5

									assert scheduler._get_variance(1							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -10.1_71_27_90 < 1e-5
									assert scheduler._get_variance(4_87							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -5.7_99_80_52 < 1e-5
									assert scheduler._get_variance(9_99							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -0.0_01_00_11 < 1e-5

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			scheduler.timesteps

									SCREAMING_SNAKE_CASE_    			=			self.dummy_model()
									SCREAMING_SNAKE_CASE_    			=			self.dummy_sample_deter
									SCREAMING_SNAKE_CASE_    			=			torch.manual_seed(0     )

									for i, t in enumerate(SCREAMING_SNAKE_CASE_     ):
											# 1. predict noise residual
											SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

											# 2. predict previous mean of sample x_t-1
											SCREAMING_SNAKE_CASE_    			=			scheduler.step(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							generator=SCREAMING_SNAKE_CASE_     ).prev_sample

											SCREAMING_SNAKE_CASE_    			=			pred_prev_sample

									SCREAMING_SNAKE_CASE_    			=			torch.sum(torch.abs(SCREAMING_SNAKE_CASE_     )     )
									SCREAMING_SNAKE_CASE_    			=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE_     )     )

									assert abs(result_sum.item() - 2_52.2_68_24_95     ) < 1e-2
									assert abs(result_mean.item() - 0.3_28_47_43     ) < 1e-3

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									scheduler.set_timesteps(25     )

									SCREAMING_SNAKE_CASE_    			=			scheduler.timesteps

									SCREAMING_SNAKE_CASE_    			=			self.dummy_model()
									SCREAMING_SNAKE_CASE_    			=			self.dummy_sample_deter
									SCREAMING_SNAKE_CASE_    			=			torch.manual_seed(0     )

									for i, t in enumerate(SCREAMING_SNAKE_CASE_     ):
											# 1. predict noise residual
											SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

											if i + 1 == timesteps.shape[0]:
													SCREAMING_SNAKE_CASE_    			=			None
											else:
													SCREAMING_SNAKE_CASE_    			=			timesteps[i + 1]

											# 2. predict previous mean of sample x_t-1
											SCREAMING_SNAKE_CASE_    			=			scheduler.step(
											    SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							prev_timestep=SCREAMING_SNAKE_CASE_							,							generator=SCREAMING_SNAKE_CASE_     ).prev_sample

											SCREAMING_SNAKE_CASE_    			=			pred_prev_sample

									SCREAMING_SNAKE_CASE_    			=			torch.sum(torch.abs(SCREAMING_SNAKE_CASE_     )     )
									SCREAMING_SNAKE_CASE_    			=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE_     )     )

									assert abs(result_sum.item() - 2_58.2_04_49_83     ) < 1e-2
									assert abs(result_mean.item() - 0.3_36_20_38     ) < 1e-3

							def _lowercase       (self     ):

									"""simple docstring"""







									pass







							def _lowercase       (self     ):

									"""simple docstring"""







									pass