





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_					:   Optional[Any] 							=						logging.get_logger(__name__)  # pylint: disable=invalid-name






def 							_lowerCAmelCase (  _a   :					Union[List, PIL.Image.Image, torch.Tensor]			)	->			Tuple:
     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_					:       Any    =		[image]

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

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






def 							_lowerCAmelCase (  _a   :					Union[List, PIL.Image.Image, torch.Tensor]			)	->			List[str]:
     if isinstance(_a					,   torch.Tensor			):
          return mask
     elif isinstance(_a					,   PIL.Image.Image			):
          lowerCAmelCase_					:       Any    =		[mask]

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







class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  42
  __UpperCamelCase	     =  42







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







  @torch.no_grad()
  def __call__(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase = 250    ,						_lowercase = 0.0    ,						_lowercase = 10    ,						_lowercase = 10    ,						_lowercase = None    ,						_lowercase = "pil"    ,						_lowercase = True    ,						):
       lowerCAmelCase_					:       List[Any]    =		image

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

       lowerCAmelCase_					:       Union[str, 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.'						)

       lowerCAmelCase_					:       Dict    =		original_image.shape
       lowerCAmelCase_					:       Tuple    =		randn_tensor(_lowercase    ,						generator=_lowercase    ,						device=self.device    ,						dtype=self.unet.dtype						)

       # set step values
       self.scheduler.set_timesteps(_lowercase    ,						_lowercase    ,						_lowercase    ,						self.device						)
       lowerCAmelCase_					:       List[str]    =		eta

       lowerCAmelCase_					:       str    =		self.scheduler.timesteps[0] + 1
       lowerCAmelCase_					:       Optional[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
                 lowerCAmelCase_					:       int    =		self.unet(_lowercase    ,						_lowercase						).sample
                 # compute previous image: x_t -> x_t-1
                 lowerCAmelCase_					:       Tuple    =		self.scheduler.step(_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase						).prev_sample

            else:
                 # compute the reverse: x_t-1 -> x_t
                 lowerCAmelCase_					:       str    =		self.scheduler.undo_step(_lowercase    ,						_lowercase    ,						_lowercase						)
            lowerCAmelCase_					:       Any    =		t

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

       if not return_dict:
            return (image,)

       return ImagePipelineOutput(images=_lowercase						)







from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin





@dataclass
class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  42
  __UpperCamelCase	     =  42
  __UpperCamelCase	     =  None





class      lowercase__    (							__A     ,					__A  ):
  __UpperCamelCase	     =  2







  @register_to_config
  def __init__(	self    ,						_lowercase = 0.02    ,						_lowercase = 100    ,						_lowercase = 1.007    ,						_lowercase = 80    ,						_lowercase = 0.05    ,						_lowercase = 50    ,						):
       # standard deviation of the initial noise distribution
       lowerCAmelCase_					:       int    =		sigma_max

       # setable values
       lowerCAmelCase_					:       int    =		None
       lowerCAmelCase_					:       np.IntTensor    =		None
       lowerCAmelCase_					:       torch.FloatTensor    =		None  # sigma(t_i)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = None						):
       return sample







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = None						):
       lowerCAmelCase_					:       int    =		num_inference_steps
       lowerCAmelCase_					:       int    =		np.arange(0    ,						self.num_inference_steps						)[::-1].copy()
       lowerCAmelCase_					:       List[Any]    =		torch.from_numpy(_lowercase						).to(_lowercase						)
       lowerCAmelCase_					:       List[str]    =		[
           (
               self.config.sigma_max**2
               * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
           )
           for i in self.timesteps
       ]
       lowerCAmelCase_					:       Optional[int]    =		torch.tensor(_lowercase    ,						dtype=torch.floataa    ,						device=_lowercase						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase = None						):
       if self.config.s_min <= sigma <= self.config.s_max:
            lowerCAmelCase_					:       List[Any]    =		min(self.config.s_churn / self.num_inference_steps    ,						2**0.5 - 1						)
       else:
            lowerCAmelCase_					:       List[str]    =		0

       # sample eps ~ N(0, S_noise^2 * I)
       lowerCAmelCase_					:       List[Any]    =		self.config.s_noise * randn_tensor(sample.shape    ,						generator=_lowercase						).to(sample.device						)
       lowerCAmelCase_					:       Optional[int]    =		sigma + gamma * sigma
       lowerCAmelCase_					:       int    =		sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

       return sample_hat, sigma_hat







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase = True    ,						):
       lowerCAmelCase_					:       Union[str, Any]    =		sample_hat + sigma_hat * model_output
       lowerCAmelCase_					:       Dict    =		(sample_hat - pred_original_sample) / sigma_hat
       lowerCAmelCase_					:       List[Any]    =		sample_hat + (sigma_prev - sigma_hat) * derivative

       if not return_dict:
            return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=_lowercase    ,						derivative=_lowercase    ,						pred_original_sample=_lowercase						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase = True    ,						):
       lowerCAmelCase_					:       str    =		sample_prev + sigma_prev * model_output
       lowerCAmelCase_					:       List[Any]    =		(sample_prev - pred_original_sample) / sigma_prev
       lowerCAmelCase_					:       Tuple    =		sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

       if not return_dict:
            return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=_lowercase    ,						derivative=_lowercase    ,						pred_original_sample=_lowercase						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase						):
       raise NotImplementedError()
