






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





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
					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):
										a__          =  [image]

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

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





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):




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

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







class        _SCREAMING_SNAKE_CASE						(  __lowerCamelCase							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =42
							_SCREAMING_SNAKE_CASE    =42




							def __init__(   self:     List[str]				,		__A:     int				,		__A:     List[Any]    ):

												'''simple docstring'''

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





							@torch.no_grad()
							def __call__(   self:     Union[str, Any]				,		__A:     Optional[Any]				,		__A:     str				,		__A:     Tuple = 250				,		__A:     Optional[Any] = 0.0				,		__A:     Tuple = 10				,		__A:     int = 10				,		__A:     str = None				,		__A:     Any = "pil"				,		__A:     Dict = True				,		):

												'''simple docstring'''

												a__          =  image

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

												a__          =  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.'    )

												a__          =  original_image.shape
												a__          =  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    )
												a__          =  eta

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

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

												if not return_dict:
																	return (image,)

												return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_    )










"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a       :	List[str]             = logging.get_logger(__name__)

__a       :	int             = {
    'facebook/vit-mae-base': 'https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='vit_mae'




							def __init__(   self:     str				,		__A:     str=768				,		__A:     Union[str, Any]=12				,		__A:     str=12				,		__A:     List[Any]=3072				,		__A:     List[Any]="gelu"				,		__A:     Union[str, Any]=0.0				,		__A:     Tuple=0.0				,		__A:     str=0.0_2				,		__A:     Tuple=1e-12				,		__A:     Dict=224				,		__A:     List[Any]=16				,		__A:     Tuple=3				,		__A:     str=True				,		__A:     Optional[Any]=16				,		__A:     List[Any]=512				,		__A:     Optional[Any]=8				,		__A:     Dict=2048				,		__A:     str=0.7_5				,		__A:     str=False				,		**__A:     Tuple				,		):

												'''simple docstring'''

												super().__init__(**__A    )

												a__          =  hidden_size
												a__          =  num_hidden_layers
												a__          =  num_attention_heads
												a__          =  intermediate_size
												a__          =  hidden_act
												a__          =  hidden_dropout_prob
												a__          =  attention_probs_dropout_prob
												a__          =  initializer_range
												a__          =  layer_norm_eps
												a__          =  image_size
												a__          =  patch_size
												a__          =  num_channels
												a__          =  qkv_bias
												a__          =  decoder_num_attention_heads
												a__          =  decoder_hidden_size
												a__          =  decoder_num_hidden_layers
												a__          =  decoder_intermediate_size
												a__          =  mask_ratio
												a__          =  norm_pix_loss


