



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_							:      Union[str, Any]      =  logging.get_logger(__name__)  # pylint: disable=invalid-name
def  UpperCamelCase						(       _A			:				Union[List, PIL.Image.Image, torch.Tensor]    )->       Union[str, Any]:





      """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    ) / 255.0
            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  UpperCamelCase						(       _A			:				Union[List, PIL.Image.Image, torch.Tensor]    )->       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    ) / 255.0
            A__							=       0
            A__							=       1
            A__							=       torch.from_numpy(_A    )
      elif isinstance(mask[0]			,							torch.Tensor    ):
            A__							=       torch.cat(_A			,							dim=0    )
      return mask






class      UpperCamelCase    (						_UpperCAmelCase     ):
 lowerCAmelCase	:   UNetaDModel
 lowerCAmelCase	:   RePaintScheduler



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



 @torch.no_grad()
 def __call__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__ = 250			,	UpperCAmelCase__ = 0.0			,	UpperCAmelCase__ = 10			,	UpperCAmelCase__ = 10			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = "pil"			,	UpperCAmelCase__ = True			,	):
       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__		)










from __future__ import annotations






class      UpperCamelCase    :



	def __init__(       self			,	UpperCAmelCase__=None		):
							A__							=       data
							A__							=       None



	def __repr__(       self		):
							A__							=       []
							A__							=       self
							while temp:
													string_rep.append(F"""{temp.data}"""		)
													A__							=       temp.next
							return "->".join(UpperCAmelCase__		)





def  UpperCamelCase						(       _A			:				list    )->       Dict:





						"""simple docstring"""





						if not elements_list:
												raise Exception("The Elements List is empty"    )

						A__							=       A__							=       Node(elements_list[0]    )
						for i in range(1			,							len(_A    )    ):
												A__							=       Node(elements_list[i]    )
												A__							=       current.next
						return head
def  UpperCamelCase						(       _A			:				Node    )->       None:





						"""simple docstring"""





						if head_node is not None and isinstance(_A			,							_A    ):
												print_reverse(head_node.next    )
												print(head_node.data    )
def  UpperCamelCase						(       )->       Tuple:





						"""simple docstring"""





						from doctest import testmod

						testmod()

						A__							=       make_linked_list([14, 52, 14, 12, 43]    )
						print("Linked List:"    )
						print(_A    )
						print("Elements in Reverse:"    )
						print_reverse(_A    )


if __name__ == "__main__":
						main()





