

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







def 		_lowercase (     lowercase__						):
      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						):
            __lowerCAmelCase					:						Tuple					=					[image]

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

            __lowerCAmelCase					:						List[Any]					=					[np.array(i.resize((w, h)   ,	resample=PIL_INTERPOLATION['''lanczos''']						)						)[None, :] for i in image]
            __lowerCAmelCase					:						List[str]					=					np.concatenate(lowercase__   ,	axis=0						)
            __lowerCAmelCase					:						Optional[Any]					=					np.array(lowercase__						).astype(np.floataa						) / 2_5_5.0
            __lowerCAmelCase					:						Optional[Any]					=					image.transpose(0   ,	3   ,	1   ,	2						)
            __lowerCAmelCase					:						List[str]					=					2.0 * image - 1.0
            __lowerCAmelCase					:						List[str]					=					torch.from_numpy(lowercase__						)
      elif isinstance(image[0]   ,	torch.Tensor						):
            __lowerCAmelCase					:						int					=					torch.cat(lowercase__   ,	dim=0						)
      return image







def 		_lowercase (     lowercase__						):
      if isinstance(lowercase__   ,	torch.Tensor						):
            return mask
      elif isinstance(lowercase__   ,	PIL.Image.Image						):
            __lowerCAmelCase					:						List[Any]					=					[mask]

      if isinstance(mask[0]   ,	PIL.Image.Image						):
            __lowerCAmelCase,							__lowerCAmelCase					:						Dict					=					mask[0].size
            __lowerCAmelCase,							__lowerCAmelCase					:						Optional[Any]					=					(x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
            __lowerCAmelCase					:						Optional[int]					=					[np.array(m.convert('''L'''						).resize((w, h)   ,	resample=PIL_INTERPOLATION['''nearest''']						)						)[None, :] for m in mask]
            __lowerCAmelCase					:						Optional[Any]					=					np.concatenate(lowercase__   ,	axis=0						)
            __lowerCAmelCase					:						Tuple					=					mask.astype(np.floataa						) / 2_5_5.0
            __lowerCAmelCase					:						Any					=					0
            __lowerCAmelCase					:						Tuple					=					1
            __lowerCAmelCase					:						Dict					=					torch.from_numpy(lowercase__						)
      elif isinstance(mask[0]   ,	torch.Tensor						):
            __lowerCAmelCase					:						List[str]					=					torch.cat(lowercase__   ,	dim=0						)
      return mask







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  42
 _UpperCamelCase			  =  42
 def __init__(							self      ,							A_      ,							A_					)					->int:



       '''simple docstring'''





       super().__init__()
       self.register_modules(unet=A_      ,							scheduler=A_					)





 @torch.no_grad()
 def __call__(							self      ,							A_      ,							A_      ,							A_ = 250      ,							A_ = 0.0      ,							A_ = 10      ,							A_ = 10      ,							A_ = None      ,							A_ = "pil"      ,							A_ = True      ,							)					->Union[ImagePipelineOutput, Tuple]:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					image

       __lowerCAmelCase					:						Dict					=					_preprocess_image(A_					)
       __lowerCAmelCase					:						Optional[Any]					=					original_image.to(device=self.device      ,							dtype=self.unet.dtype					)
       __lowerCAmelCase					:						Any					=					_preprocess_mask(A_					)
       __lowerCAmelCase					:						Dict					=					mask_image.to(device=self.device      ,							dtype=self.unet.dtype					)

       __lowerCAmelCase					:						Union[str, Any]					=					original_image.shape[0]

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

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

       # set step values
       self.scheduler.set_timesteps(A_      ,							A_      ,							A_      ,							self.device					)
       __lowerCAmelCase					:						List[Any]					=					eta

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

             else:
                   # compute the reverse: x_t-1 -> x_t
                   __lowerCAmelCase					:						Any					=					self.scheduler.undo_step(A_      ,							A_      ,							A_					)
             __lowerCAmelCase					:						int					=					t

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

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=A_					)





from collections import deque

from .hash_table import HashTable







class   __lowercase      (_UpperCAmelCase ):
 def __init__(							self      ,							*A_      ,							**A_					)					->int:



       '''simple docstring'''





       super().__init__(*A_      ,							**A_					)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_					)					->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase					:						Dict					=					deque([]					) if self.values[key] is None else self.values[key]
       self.values[key].appendleft(A_					)
       __lowerCAmelCase					:						int					=					self.values[key]
 def 	UpperCamelCase__  (							self					)					->Tuple:



       '''simple docstring'''





       return (
           sum(self.charge_factor - len(A_					) for slot in self.values					)
           / self.size_table
           * self.charge_factor
       )





 def 	UpperCamelCase__  (							self      ,							A_      ,							A_=None					)					->str:



       '''simple docstring'''





       if not (
           len(self.values[key]					) == self.charge_factor and self.values.count(A_					) == 0
       ):
             return key
       return super()._collision_resolution(A_      ,							A_					)


