



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



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Union[str, Any]:
		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	):
				lowercase__   :    List[str]				=			[image]

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

				lowercase__   :    Dict				=			[np.array(i.resize((w, h)						,     resample=PIL_INTERPOLATION['''lanczos''']	)	)[None, :] for i in image]
				lowercase__   :    List[Any]				=			np.concatenate(_lowerCamelCase						,     axis=0	)
				lowercase__   :    str				=			np.array(_lowerCamelCase	).astype(np.floataa	) / 2_55.0
				lowercase__   :    Union[str, Any]				=			image.transpose(0						,     3						,     1						,     2	)
				lowercase__   :    int				=			2.0 * image - 1.0
				lowercase__   :    List[Any]				=			torch.from_numpy(_lowerCamelCase	)
		elif isinstance(image[0]						,     torch.Tensor	):
				lowercase__   :    Any				=			torch.cat(_lowerCamelCase						,     dim=0	)
		return image



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Dict:
		if isinstance(_lowerCamelCase						,     torch.Tensor	):
				return mask
		elif isinstance(_lowerCamelCase						,     PIL.Image.Image	):
				lowercase__   :    str				=			[mask]

		if isinstance(mask[0]						,     PIL.Image.Image	):
				lowercase__		,			lowercase__   :    Union[str, Any]				=			mask[0].size
				lowercase__		,			lowercase__   :    Optional[Any]				=			(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
				lowercase__   :    Any				=			[np.array(m.convert('''L'''	).resize((w, h)						,     resample=PIL_INTERPOLATION['''nearest''']	)	)[None, :] for m in mask]
				lowercase__   :    int				=			np.concatenate(_lowerCamelCase						,     axis=0	)
				lowercase__   :    str				=			mask.astype(np.floataa	) / 2_55.0
				lowercase__   :    Any				=			0
				lowercase__   :    Optional[Any]				=			1
				lowercase__   :    Optional[Any]				=			torch.from_numpy(_lowerCamelCase	)
		elif isinstance(mask[0]						,     torch.Tensor	):
				lowercase__   :    Union[str, Any]				=			torch.cat(_lowerCamelCase						,     dim=0	)
		return mask





class        __A       (	UpperCamelCase__    ):







		'''simple docstring'''





		lowerCAmelCase			:					Tuple   	=				4_2
		lowerCAmelCase			:					Dict   	=				4_2







		def __init__(	self						:  str			,_snake_case						:  Union[str, Any]			,_snake_case						:  Tuple						)					->		Dict:






				"""simple docstring"""



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







		@torch.no_grad()
		def __call__(	self						:  str			,_snake_case						:  Union[torch.Tensor, PIL.Image.Image]			,_snake_case						:  Union[torch.Tensor, PIL.Image.Image]			,_snake_case						:  int = 250			,_snake_case						:  float = 0.0			,_snake_case						:  int = 10			,_snake_case						:  int = 10			,_snake_case						:  Optional[Union[torch.Generator, List[torch.Generator]]] = None			,_snake_case						:  Optional[str] = "pil"			,_snake_case						:  bool = True			,)					->		Union[ImagePipelineOutput, Tuple]:






				"""simple docstring"""



				lowercase__   :    Union[str, Any]				=			image

				lowercase__   :    Any				=			_preprocess_image(lowercase_						)
				lowercase__   :    Tuple				=			original_image.to(device=self.device			,dtype=self.unet.dtype						)
				lowercase__   :    Dict				=			_preprocess_mask(lowercase_						)
				lowercase__   :    Optional[int]				=			mask_image.to(device=self.device			,dtype=self.unet.dtype						)

				lowercase__   :    Optional[Any]				=			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."""						)

				lowercase__   :    Union[str, Any]				=			original_image.shape
				lowercase__   :    str				=			randn_tensor(lowercase_			,generator=lowercase_			,device=self.device			,dtype=self.unet.dtype						)

				# set step values
				self.scheduler.set_timesteps(lowercase_			,lowercase_			,lowercase_			,self.device						)
				lowercase__   :    str				=			eta

				lowercase__   :    Optional[Any]				=			self.scheduler.timesteps[0] + 1
				lowercase__   :    List[Any]				=			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
								lowercase__   :    int				=			self.unet(lowercase_			,lowercase_						).sample
								# compute previous image: x_t -> x_t-1
								lowercase__   :    Optional[int]				=			self.scheduler.step(lowercase_			,lowercase_			,lowercase_			,lowercase_			,lowercase_			,lowercase_						).prev_sample

						else:
								# compute the reverse: x_t-1 -> x_t
								lowercase__   :    List[Any]				=			self.scheduler.undo_step(lowercase_			,lowercase_			,lowercase_						)
						lowercase__   :    Union[str, Any]				=			t

				lowercase__   :    Union[str, Any]				=			(image / 2 + 0.5).clamp(0			,1						)
				lowercase__   :    str				=			image.cpu().permute(0			,2			,3			,1						).numpy()
				if output_type == "pil":
						lowercase__   :    List[str]				=			self.numpy_to_pil(lowercase_						)

				if not return_dict:
						return (image,)

				return ImagePipelineOutput(images=lowercase_						)







'''simple docstring'''

import warnings


warnings.warn(
    "memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: "
    "`from accelerate import find_executable_batch_size` to avoid this warning.",
    FutureWarning,
)



