

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


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Any    ):
  warnings.warn(
      'The preprocess method is deprecated and will be removed in a future version. Please'
      ' use VaeImageProcessor.preprocess instead'							, lowercase_							, )
  if isinstance(lowercase_							, torch.Tensor    ):
    return image
  elif isinstance(lowercase_							, PIL.Image.Image    ):
    UpperCAmelCase    				=						[image]

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

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


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : int    ):






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

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



class 							lowercase_						(		UpperCAmelCase_			):

  '''simple docstring'''





  __lowerCAmelCase       :			UNetaDModel
  __lowerCAmelCase       :			RePaintScheduler
  def __init__(  self ,			a_ ,			a_  )				->					Optional[int]:


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





  @torch.no_grad()
  def __call__(  self ,			a_ ,			a_ ,			a_ = 2_5_0 ,			a_ = 0.0 ,			a_ = 1_0 ,			a_ = 1_0 ,			a_ = None ,			a_ = "pil" ,			a_ = True ,			)				->					Any:


    """simple docstring"""
    UpperCAmelCase    				=						image

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

    UpperCAmelCase    				=						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.'''  )

    UpperCAmelCase    				=						original_image.shape
    UpperCAmelCase    				=						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  )
    UpperCAmelCase    				=						eta

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

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

    if not return_dict:
      return (image,)

    return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_  )




lowerCamelCase__     :		dict[tuple[int, int, int], int]        = {}

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_ )		->						int:


  '''simple docstring'''






  if late == 3 or absent == 2:
    return 0

  # if we have no days left, and have not failed any other rules,
  # we have a prize string
  if days == 0:
    return 1

  # No easy solution, so now we need to do the recursive calculation

  # First, check if the combination is already in the cache, and
  # if yes, return the stored value from there since we already
  # know the number of possible prize strings from this point on
  lowercase__	: Tuple     		=   (days, absent, late)
  if key in cache:
    return cache[key]

  # now we calculate the three possible ways that can unfold from
  # this point on, depending on our attendance today

  # 1) if we are late (but not absent), the "absent" counter stays as
  # it is, but the "late" counter increases by one
  lowercase__	: Union[str, Any]     		=   _calculate(days - 1			,			lowercase_			,			late + 1 )

  # 2) if we are absent, the "absent" counter increases by 1, and the
  # "late" counter resets to 0
  lowercase__	: List[str]     		=   _calculate(days - 1			,			absent + 1			,			0 )

  # 3) if we are on time, this resets the "late" counter and keeps the
  # absent counter
  lowercase__	: Dict     		=   _calculate(days - 1			,			lowercase_			,			0 )

  lowercase__	: List[str]     		=   state_late + state_absent + state_ontime

  lowercase__	: List[Any]     		=   prizestrings
  return prizestrings

def 		UpperCamelCase						(   lowercase_ = 30 )		->						int:


  '''simple docstring'''






  return _calculate(lowercase_			,			absent=0			,			late=0 )


if __name__ == "__main__":
     print(solution())






