


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







def     _UpperCamelCase      (		UpperCamelCase__					):
			warnings.warn(
			    """The preprocess method is deprecated and will be removed in a future version. Please"""
			    """ use VaeImageProcessor.preprocess instead""" ,      UpperCamelCase__ ,      )
			if isinstance(UpperCamelCase__ ,      torch.Tensor					):
						return image
			elif isinstance(UpperCamelCase__ ,      PIL.Image.Image					):
						UpperCAmelCase__  :					Tuple        =     [image]

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

						UpperCAmelCase__  :					List[Any]        =     [np.array(i.resize((w, h) ,      resample=PIL_INTERPOLATION["""lanczos"""]					)					)[None, :] for i in image]
						UpperCAmelCase__  :					Any        =     np.concatenate(UpperCamelCase__ ,      axis=0					)
						UpperCAmelCase__  :					Tuple        =     np.array(UpperCamelCase__					).astype(np.floataa					) / 2_55.0
						UpperCAmelCase__  :					List[Any]        =     image.transpose(0 ,      3 ,      1 ,      2					)
						UpperCAmelCase__  :					Any        =     2.0 * image - 1.0
						UpperCAmelCase__  :					str        =     torch.from_numpy(UpperCamelCase__					)
			elif isinstance(image[0] ,      torch.Tensor					):
						UpperCAmelCase__  :					Dict        =     torch.cat(UpperCamelCase__ ,      dim=0					)
			return image







def     _UpperCamelCase      (		UpperCamelCase__					):
			if isinstance(UpperCamelCase__ ,      torch.Tensor					):
						return mask
			elif isinstance(UpperCamelCase__ ,      PIL.Image.Image					):
						UpperCAmelCase__  :					Dict        =     [mask]

			if isinstance(mask[0] ,      PIL.Image.Image					):
						UpperCAmelCase__   ,       UpperCAmelCase__  :					Optional[int]        =     mask[0].size
						UpperCAmelCase__   ,       UpperCAmelCase__  :					Tuple        =     (x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
						UpperCAmelCase__  :					str        =     [np.array(m.convert("""L"""					).resize((w, h) ,      resample=PIL_INTERPOLATION["""nearest"""]					)					)[None, :] for m in mask]
						UpperCAmelCase__  :					str        =     np.concatenate(UpperCamelCase__ ,      axis=0					)
						UpperCAmelCase__  :					Dict        =     mask.astype(np.floataa					) / 2_55.0
						UpperCAmelCase__  :					str        =     0
						UpperCAmelCase__  :					Optional[Any]        =     1
						UpperCAmelCase__  :					Tuple        =     torch.from_numpy(UpperCamelCase__					)
			elif isinstance(mask[0] ,      torch.Tensor					):
						UpperCAmelCase__  :					str        =     torch.cat(UpperCamelCase__ ,      dim=0					)
			return mask


class 		_snake_case					(     a__	):
					lowerCAmelCase      :UNetaDModel
					lowerCAmelCase      :RePaintScheduler




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




					@torch.no_grad()
					def __call__(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase = 250							,							_lowerCamelCase = 0.0							,							_lowerCamelCase = 10							,							_lowerCamelCase = 10							,							_lowerCamelCase = None							,							_lowerCamelCase = "pil"							,							_lowerCamelCase = True							,							):
								UpperCAmelCase__  :					Optional[Any]        =     image

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

								UpperCAmelCase__  :					str        =     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.''')

								UpperCAmelCase__  :					Any        =     original_image.shape
								UpperCAmelCase__  :					Dict        =     randn_tensor(_lowerCamelCase							,							generator=_lowerCamelCase							,							device=self.device							,							dtype=self.unet.dtype)

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

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

											else:
														# compute the reverse: x_t-1 -> x_t
														UpperCAmelCase__  :					List[str]        =     self.scheduler.undo_step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase)
											UpperCAmelCase__  :					List[str]        =     t

								UpperCAmelCase__  :					Dict        =     (image / 2 + 0.5).clamp(0							,							1)
								UpperCAmelCase__  :					List[Any]        =     image.cpu().permute(0							,							2							,							3							,							1).numpy()
								if output_type == "pil":
											UpperCAmelCase__  :					Any        =     self.numpy_to_pil(_lowerCamelCase)

								if not return_dict:
											return (image,)

								return ImagePipelineOutput(images=_lowerCamelCase)



'''simple docstring'''



def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
			if height >= 1:
						move_tower(height - 1 ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)
						move_disk(UpperCamelCase__ ,      UpperCamelCase__					)
						move_tower(height - 1 ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			print("""moving disk from""" ,      UpperCamelCase__ ,      """to""" ,      UpperCamelCase__					)







def     _UpperCamelCase      (		):
			UpperCAmelCase__  :					List[str]        =     int(input("""Height of hanoi: """					).strip()					)
			move_tower(UpperCamelCase__ ,      """A""" ,      """B""" ,      """C"""					)


if __name__ == "__main__":
		main()