


'''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_					:    List[str]          =    logging.get_logger(__name__)  # pylint: disable=invalid-name






def 	_A				(lowerCAmelCase__  :Optional[int]		)   ->  int:




     '''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		):
          _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		) / 2_5_5.0
          _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 	_A				(lowerCAmelCase__  :str		)   ->  Tuple:




     '''simple docstring'''



     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		) / 2_5_5.0
          _a			      =							0
          _a			      =							1
          _a			      =							torch.from_numpy(_A		)
     elif isinstance(mask[0]				,						torch.Tensor		):
          _a			      =							torch.cat(_A				,						dim=0		)
     return mask






class 			a     (     _UpperCAmelCase       ):
 _lowerCAmelCase 					=	42
 _lowerCAmelCase 					=	42
 def __init__( self      ,				__magic_name__      ,				__magic_name__						)					->							Tuple:
      super().__init__()
      self.register_modules(unet=__UpperCamelCase      ,				scheduler=__UpperCamelCase						)




 @torch.no_grad()
 def __call__( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = 2_50      ,				__magic_name__ = 0.0      ,				__magic_name__ = 10      ,				__magic_name__ = 10      ,				__magic_name__ = None      ,				__magic_name__ = "pil"      ,				__magic_name__ = True      ,				)					->							Union[ImagePipelineOutput, Tuple]:
      _a			      =							image

      _a			      =							_preprocess_image(__UpperCamelCase						)
      _a			      =							original_image.to(device=self.device      ,				dtype=self.unet.dtype						)
      _a			      =							_preprocess_mask(__UpperCamelCase						)
      _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(__UpperCamelCase      ,				__UpperCamelCase						) and len(__UpperCamelCase						) != batch_size:
           raise ValueError(
               f'You have passed a list of generators of length {len(__UpperCamelCase						)}, 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(__UpperCamelCase      ,				generator=__UpperCamelCase      ,				device=self.device      ,				dtype=self.unet.dtype						)

      # set step values
      self.scheduler.set_timesteps(__UpperCamelCase      ,				__UpperCamelCase      ,				__UpperCamelCase      ,				self.device						)
      _a			      =							eta

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

           else:
                # compute the reverse: x_t-1 -> x_t
                _a			      =							self.scheduler.undo_step(__UpperCamelCase      ,				__UpperCamelCase      ,				__UpperCamelCase						)
           _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(__UpperCamelCase						)

      if not return_dict:
           return (image,)

      return ImagePipelineOutput(images=__UpperCamelCase						)





'''simple docstring'''






from functools import reduce

a_					:    Any          =    (
    "73167176531330624919225119674426574742355349194934"
    "96983520312774506326239578318016984801869478851843"
    "85861560789112949495459501737958331952853208805511"
    "12540698747158523863050715693290963295227443043557"
    "66896648950445244523161731856403098711121722383113"
    "62229893423380308135336276614282806444486645238749"
    "30358907296290491560440772390713810515859307960866"
    "70172427121883998797908792274921901699720888093776"
    "65727333001053367881220235421809751254540594752243"
    "52584907711670556013604839586446706324415722155397"
    "53697817977846174064955149290862569321978468622482"
    "83972241375657056057490261407972968652414535100474"
    "82166370484403199890008895243450658541227588666881"
    "16427171479924442928230863465674813919123162824586"
    "17866458359124566529476545682848912883142607690042"
    "24219022671055626321111109370544217506941658960408"
    "07198403850962455444362981230987879927244284909188"
    "84580156166097919133875499200524063689912560717606"
    "05886116467109405077541002256983155200055935729725"
    "71636269561882670428252483600823257530420752963450"
)






def 	_A				(lowerCAmelCase__  :str = N		)   ->  int:




     '''simple docstring'''



     return max(
         # mypy cannot properly interpret reduce
         int(reduce(lambda lowerCAmelCase__				,						lowerCAmelCase__		: str(int(lowerCAmelCase__		) * int(lowerCAmelCase__		)		)				,						n[i : i + 13]		)		)
         for i in range(len(lowerCAmelCase__		) - 12		)		)


if __name__ == "__main__":
 print(f'''{solution() = }''')

