





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



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Union[List, PIL.Image.Image, torch.Tensor]   ):


							'''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      :      List[str]         =[image]

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

														__lowerCamelCase      :      str         =[np.array(i.resize((w, h)    ,						resample=PIL_INTERPOLATION['''lanczos''']   )   )[None, :] for i in image]
														__lowerCamelCase      :      Optional[Any]         =np.concatenate(__a    ,						axis=0   )
														__lowerCamelCase      :      Union[str, Any]         =np.array(__a   ).astype(np.floataa   ) / 255.0
														__lowerCamelCase      :      str         =image.transpose(0    ,						3    ,						1    ,						2   )
														__lowerCamelCase      :      Tuple         =2.0 * image - 1.0
														__lowerCamelCase      :      Union[str, Any]         =torch.from_numpy(__a   )
							elif isinstance(image[0]    ,						torch.Tensor   ):
														__lowerCamelCase      :      str         =torch.cat(__a    ,						dim=0   )
							return image



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Union[List, PIL.Image.Image, torch.Tensor]   ):


							'''simple docstring'''







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

							if isinstance(mask[0]    ,						PIL.Image.Image   ):
														__lowerCamelCase    ,						__lowerCamelCase      :      Optional[Any]         =mask[0].size
														__lowerCamelCase    ,						__lowerCamelCase      :      Tuple         =(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
														__lowerCamelCase      :      int         =[np.array(m.convert('''L'''   ).resize((w, h)    ,						resample=PIL_INTERPOLATION['''nearest''']   )   )[None, :] for m in mask]
														__lowerCamelCase      :      Union[str, Any]         =np.concatenate(__a    ,						axis=0   )
														__lowerCamelCase      :      str         =mask.astype(np.floataa   ) / 255.0
														__lowerCamelCase      :      str         =0
														__lowerCamelCase      :      Optional[int]         =1
														__lowerCamelCase      :      Optional[Any]         =torch.from_numpy(__a   )
							elif isinstance(mask[0]    ,						torch.Tensor   ):
														__lowerCamelCase      :      List[Any]         =torch.cat(__a    ,						dim=0   )
							return mask
class 		SCREAMING_SNAKE_CASE_							(    __lowerCamelCase   ):
		"""simple docstring"""
		__snake_case   :       int	 =		42
		__snake_case   :       int	 =		42




		def __init__(     self  :Any						,    __lowercase  :int						,    __lowercase  :Optional[Any]			):
									super().__init__()
									self.register_modules(unet=SCREAMING_SNAKE_CASE_						,    scheduler=SCREAMING_SNAKE_CASE_			)




		@torch.no_grad()
		def __call__(     self  :int						,    __lowercase  :int						,    __lowercase  :Any						,    __lowercase  :Any = 250						,    __lowercase  :List[Any] = 0.0						,    __lowercase  :int = 10						,    __lowercase  :str = 10						,    __lowercase  :Optional[int] = None						,    __lowercase  :Optional[Any] = "pil"						,    __lowercase  :int = True						,    ):
									__lowerCamelCase      :      List[str]         =image

									__lowerCamelCase      :      List[Any]         =_preprocess_image(SCREAMING_SNAKE_CASE_			)
									__lowerCamelCase      :      Dict         =original_image.to(device=self.device						,    dtype=self.unet.dtype			)
									__lowerCamelCase      :      Tuple         =_preprocess_mask(SCREAMING_SNAKE_CASE_			)
									__lowerCamelCase      :      List[Any]         =mask_image.to(device=self.device						,    dtype=self.unet.dtype			)

									__lowerCamelCase      :      List[Any]         =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      :      List[str]         =original_image.shape
									__lowerCamelCase      :      List[Any]         =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      :      str         =eta

									__lowerCamelCase      :      Tuple         =self.scheduler.timesteps[0] + 1
									__lowerCamelCase      :      List[str]         =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      :      str         =self.unet(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_			).sample
																							# compute previous image: x_t -> x_t-1
																							__lowerCamelCase      :      List[str]         =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      :      List[str]         =self.scheduler.undo_step(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_			)
																__lowerCamelCase      :      Union[str, Any]         =t

									__lowerCamelCase      :      int         =(image / 2 + 0.5).clamp(0						,    1			)
									__lowerCamelCase      :      Optional[int]         =image.cpu().permute(0						,    2						,    3						,    1			).numpy()
									if output_type == "pil":
																__lowerCamelCase      :      List[Any]         =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 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""
				__snake_case   :       Optional[Any]	 =		(UnCLIPScheduler,)




				def 					__lowercase  (     self  :Any						,    **__lowercase  :str			):
											__lowerCamelCase      :      Optional[int]         ={
											    '''num_train_timesteps''': 1000,
											    '''variance_type''': '''fixed_small_log''',
											    '''clip_sample''': True,
											    '''clip_sample_range''': 1.0,
											    '''prediction_type''': '''epsilon''',
											}

											config.update(**__lowercase			)
											return config




				def 					__lowercase  (     self  :Tuple			):
											for timesteps in [1, 5, 100, 1000]:
																		self.check_over_configs(num_train_timesteps=__lowercase			)




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




				def 					__lowercase  (     self  :List[str]			):
											for clip_sample in [True, False]:
																		self.check_over_configs(clip_sample=__lowercase			)




				def 					__lowercase  (     self  :List[Any]			):
											for clip_sample_range in [1, 5, 10, 20]:
																		self.check_over_configs(clip_sample_range=__lowercase			)




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




				def 					__lowercase  (     self  :Union[str, Any]			):
											for time_step in [0, 500, 999]:
																		for prev_timestep in [None, 5, 100, 250, 500, 750]:
																									if prev_timestep is not None and prev_timestep >= time_step:
																																continue

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




				def 					__lowercase  (     self  :Union[str, Any]			):
											__lowerCamelCase      :      List[Any]         =self.scheduler_classes[0]
											__lowerCamelCase      :      str         =self.get_scheduler_config(variance_type='''fixed_small_log'''			)
											__lowerCamelCase      :      Tuple         =scheduler_class(**__lowercase			)

											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(487			) - 0.0549625			)			) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(999			) - 0.9994987			)			) < 1e-5




				def 					__lowercase  (     self  :Optional[Any]			):
											__lowerCamelCase      :      Tuple         =self.scheduler_classes[0]
											__lowerCamelCase      :      Optional[Any]         =self.get_scheduler_config(variance_type='''learned_range'''			)
											__lowerCamelCase      :      Optional[Any]         =scheduler_class(**__lowercase			)

											__lowerCamelCase      :      Optional[int]         =0.5

											assert scheduler._get_variance(1						,    predicted_variance=__lowercase			) - -10.1712790 < 1e-5
											assert scheduler._get_variance(487						,    predicted_variance=__lowercase			) - -5.7998052 < 1e-5
											assert scheduler._get_variance(999						,    predicted_variance=__lowercase			) - -0.0010011 < 1e-5




				def 					__lowercase  (     self  :Optional[Any]			):
											__lowerCamelCase      :      Any         =self.scheduler_classes[0]
											__lowerCamelCase      :      Optional[int]         =self.get_scheduler_config()
											__lowerCamelCase      :      str         =scheduler_class(**__lowercase			)

											__lowerCamelCase      :      Tuple         =scheduler.timesteps

											__lowerCamelCase      :      Any         =self.dummy_model()
											__lowerCamelCase      :      Any         =self.dummy_sample_deter
											__lowerCamelCase      :      List[Any]         =torch.manual_seed(0			)

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

																		# 2. predict previous mean of sample x_t-1
																		__lowerCamelCase      :      List[Any]         =scheduler.step(__lowercase						,    __lowercase						,    __lowercase						,    generator=__lowercase			).prev_sample

																		__lowerCamelCase      :      List[Any]         =pred_prev_sample

											__lowerCamelCase      :      Optional[Any]         =torch.sum(torch.abs(__lowercase			)			)
											__lowerCamelCase      :      Optional[int]         =torch.mean(torch.abs(__lowercase			)			)

											assert abs(result_sum.item() - 252.2682495			) < 1e-2
											assert abs(result_mean.item() - 0.3284743			) < 1e-3




				def 					__lowercase  (     self  :int			):
											__lowerCamelCase      :      Optional[Any]         =self.scheduler_classes[0]
											__lowerCamelCase      :      Any         =self.get_scheduler_config()
											__lowerCamelCase      :      Optional[Any]         =scheduler_class(**__lowercase			)

											scheduler.set_timesteps(25			)

											__lowerCamelCase      :      Dict         =scheduler.timesteps

											__lowerCamelCase      :      List[str]         =self.dummy_model()
											__lowerCamelCase      :      List[str]         =self.dummy_sample_deter
											__lowerCamelCase      :      Any         =torch.manual_seed(0			)

											for i, t in enumerate(__lowercase			):
																		# 1. predict noise residual
																		__lowerCamelCase      :      Optional[Any]         =model(__lowercase						,    __lowercase			)

																		if i + 1 == timesteps.shape[0]:
																									__lowerCamelCase      :      Any         =None
																		else:
																									__lowerCamelCase      :      Union[str, Any]         =timesteps[i + 1]

																		# 2. predict previous mean of sample x_t-1
																		__lowerCamelCase      :      List[str]         =scheduler.step(
																		    __lowercase						,    __lowercase						,    __lowercase						,    prev_timestep=__lowercase						,    generator=__lowercase			).prev_sample

																		__lowerCamelCase      :      int         =pred_prev_sample

											__lowerCamelCase      :      List[Any]         =torch.sum(torch.abs(__lowercase			)			)
											__lowerCamelCase      :      List[str]         =torch.mean(torch.abs(__lowercase			)			)

											assert abs(result_sum.item() - 258.2044983			) < 1e-2
											assert abs(result_mean.item() - 0.3362038			) < 1e-3




				def 					__lowercase  (     self  :List[str]			):
											pass




				def 					__lowercase  (     self  :Tuple			):
											pass




