


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







def 							UpperCamelCase_     (       A__   :  Union[List, PIL.Image.Image, torch.Tensor]   ):
  '''simple docstring'''





  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_     : Optional[Any]     =  [image]

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

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







def 							UpperCamelCase_     (       A__   :  Union[List, PIL.Image.Image, torch.Tensor]   ):
  '''simple docstring'''





  if isinstance(A__				,       torch.Tensor   ):
    return mask
  elif isinstance(A__				,       PIL.Image.Image   ):
    lowerCAmelCase_     : Optional[int]     =  [mask]

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






class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase  					=   42
    lowercase  					=   42

    def __init__(  self       :    str       ,		lowerCamelCase       :    List[str]       ,		lowerCamelCase       :    Optional[Any]						)  ->    Union[str, Any]:
      super().__init__()
      self.register_modules(unet=lowerCamelCase       ,		scheduler=lowerCamelCase						)





    @torch.no_grad()
    def __call__(  self       :    int       ,		lowerCamelCase       :    Union[torch.Tensor, PIL.Image.Image]       ,		lowerCamelCase       :    Union[torch.Tensor, PIL.Image.Image]       ,		lowerCamelCase       :    int = 2_50       ,		lowerCamelCase       :    float = 0.0       ,		lowerCamelCase       :    int = 10       ,		lowerCamelCase       :    int = 10       ,		lowerCamelCase       :    Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,		lowerCamelCase       :    Optional[str] = "pil"       ,		lowerCamelCase       :    bool = True       ,		)  ->    Union[ImagePipelineOutput, Tuple]:
      lowerCAmelCase_     : Dict     =  image

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

      lowerCAmelCase_     : Dict     =  original_image.shape[0]

      # sample gaussian noise to begin the loop
      if isinstance(lowerCamelCase       ,		lowerCamelCase						) and len(lowerCamelCase						) != batch_size:
        raise ValueError(
            F'You have passed a list of generators of length {len(lowerCamelCase						)}, but requested an effective batch'
            F' size of {batch_size}. Make sure the batch size matches the length of the generators.'						)

      lowerCAmelCase_     : Tuple     =  original_image.shape
      lowerCAmelCase_     : Optional[Any]     =  randn_tensor(lowerCamelCase       ,		generator=lowerCamelCase       ,		device=self.device       ,		dtype=self.unet.dtype						)

      # set step values
      self.scheduler.set_timesteps(lowerCamelCase       ,		lowerCamelCase       ,		lowerCamelCase       ,		self.device						)
      lowerCAmelCase_     : Tuple     =  eta

      lowerCAmelCase_     : str     =  self.scheduler.timesteps[0] + 1
      lowerCAmelCase_     : Tuple     =  generator[0] if isinstance(lowerCamelCase       ,		lowerCamelCase						) else generator
      for i, t in enumerate(self.progress_bar(self.scheduler.timesteps						)						):
        if t < t_last:
          # predict the noise residual
          lowerCAmelCase_     : Any     =  self.unet(lowerCamelCase       ,		lowerCamelCase						).sample
          # compute previous image: x_t -> x_t-1
          lowerCAmelCase_     : Any     =  self.scheduler.step(lowerCamelCase       ,		lowerCamelCase       ,		lowerCamelCase       ,		lowerCamelCase       ,		lowerCamelCase       ,		lowerCamelCase						).prev_sample

        else:
          # compute the reverse: x_t-1 -> x_t
          lowerCAmelCase_     : Optional[int]     =  self.scheduler.undo_step(lowerCamelCase       ,		lowerCamelCase       ,		lowerCamelCase						)
        lowerCAmelCase_     : Any     =  t

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

      if not return_dict:
        return (image,)

      return ImagePipelineOutput(images=lowerCamelCase						)







'''simple docstring'''






from math import factorial

__A   :    dict[str, int]											= {str(digit): factorial(digit) for digit in range(10)}







def 							UpperCamelCase_     (       A__   :  int   ):
  '''simple docstring'''





  if not isinstance(A__				,       A__   ):
    raise TypeError("""Parameter number must be int"""   )

  if number < 0:
    raise ValueError("""Parameter number must be greater than or equal to 0"""   )

  # Converts number in string to iterate on its digits and adds its factorial.
  return sum(DIGIT_FACTORIAL[digit] for digit in str(A__   )   )







def 							UpperCamelCase_     (       A__   :  int = 60				,       A__   :  int = 1_00_00_00   ):
  '''simple docstring'''





  if not isinstance(A__				,       A__   ) or not isinstance(A__				,       A__   ):
    raise TypeError("""Parameters chain_length and number_limit must be int"""   )

  if chain_length <= 0 or number_limit <= 0:
    raise ValueError(
        """Parameters chain_length and number_limit must be greater than 0"""   )

  # the counter for the chains with the exact desired length
  lowerCAmelCase_     : Optional[int]     =  0
  # the cached sizes of the previous chains
  lowerCAmelCase_     : dict[int, int]     =  {}

  for start_chain_element in range(1				,       A__   ):
    # The temporary set will contain the elements of the chain
    lowerCAmelCase_     : List[str]     =  set()
    lowerCAmelCase_     : Optional[int]     =  0

    # Stop computing the chain when you find a cached size, a repeating item or the
    # length is greater then the desired one.
    lowerCAmelCase_     : int     =  start_chain_element
    while (
        chain_element not in chain_sets_lengths
        and chain_element not in chain_set
        and chain_set_length <= chain_length
    ):
      chain_set.add(A__   )
      chain_set_length += 1
      lowerCAmelCase_     : Union[str, Any]     =  digit_factorial_sum(A__   )

    if chain_element in chain_sets_lengths:
      chain_set_length += chain_sets_lengths[chain_element]

    lowerCAmelCase_     : int     =  chain_set_length

    # If chain contains the exact amount of elements increase the counter
    if chain_set_length == chain_length:
      chains_counter += 1

  return chains_counter


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(F'''{solution()}''')



