





'''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      A (       A_							:					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'''    ,							A_    ,							)
						if isinstance(A_    ,							torch.Tensor       ):
												return image
						elif isinstance(A_    ,							PIL.Image.Image       ):
												snake_case       :     str =    [image]

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

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

def      A (       A_							:					Union[List, PIL.Image.Image, torch.Tensor]       ):
						if isinstance(A_    ,							torch.Tensor       ):
												return mask
						elif isinstance(A_    ,							PIL.Image.Image       ):
												snake_case       :     str =    [mask]

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





class 						a   (				__magic_name__    ):
				_snake_case		    = 42
				_snake_case		    = 42




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




				@torch.no_grad()
				def __call__(							self	:				int,    SCREAMING_SNAKE_CASE_	:				Union[torch.Tensor, PIL.Image.Image],    SCREAMING_SNAKE_CASE_	:				Union[torch.Tensor, PIL.Image.Image],    SCREAMING_SNAKE_CASE_	:				int = 2_50,    SCREAMING_SNAKE_CASE_	:				float = 0.0,    SCREAMING_SNAKE_CASE_	:				int = 10,    SCREAMING_SNAKE_CASE_	:				int = 10,    SCREAMING_SNAKE_CASE_	:				Optional[Union[torch.Generator, List[torch.Generator]]] = None,    SCREAMING_SNAKE_CASE_	:				Optional[str] = "pil",    SCREAMING_SNAKE_CASE_	:				bool = True,    ):
										snake_case       :     Union[str, Any] =    image

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

										snake_case       :     Optional[int] =    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."""      )

										snake_case       :     Tuple =    original_image.shape
										snake_case       :     Optional[int] =    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      )
										snake_case       :     int =    eta

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

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

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_      )



'''simple docstring'''





import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


UpperCAmelCase    					=   logging.get_logger(__name__)

class 						a   (				__magic_name__    ):




				def __init__(							self	:				Union[str, Any],    *SCREAMING_SNAKE_CASE_	:				Tuple,    **SCREAMING_SNAKE_CASE_	:				Tuple      ):
										warnings.warn(
										    '''The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
										    ''' Please use LayoutLMv2ImageProcessor instead.''',    SCREAMING_SNAKE_CASE_,    )
										super().__init__(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )

