



"""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


lowercase__		:				List[Any]          =	logging.get_logger(__name__)  # pylint: disable=invalid-name

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Union[List, PIL.Image.Image, torch.Tensor]						)						->   Optional[Any]:

  """simple docstring"""




  warnings.warn(
      'The preprocess method is deprecated and will be removed in a future version. Please'
      ' use VaeImageProcessor.preprocess instead'    ,   lowerCAmelCase__    ,   )
  if isinstance(lowerCAmelCase__    ,   torch.Tensor						):
    return image
  elif isinstance(lowerCAmelCase__    ,   PIL.Image.Image						):
    lowerCAmelCase_   :				Any								= [image]

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

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

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Union[List, PIL.Image.Image, torch.Tensor]						)						->   Tuple:

  """simple docstring"""




  if isinstance(lowerCAmelCase__    ,   torch.Tensor						):
    return mask
  elif isinstance(lowerCAmelCase__    ,   PIL.Image.Image						):
    lowerCAmelCase_   :				List[Any]								= [mask]

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







class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     42
      _SCREAMING_SNAKE_CASE  				=     42



      def __init__(      self				:      str    ,			SCREAMING_SNAKE_CASE_				:      Any    ,			SCREAMING_SNAKE_CASE_				:      str       ):
        super().__init__()
        self.register_modules(unet=SCREAMING_SNAKE_CASE_    ,			scheduler=SCREAMING_SNAKE_CASE_       )





      @torch.no_grad()
      def __call__(      self				:      int    ,			SCREAMING_SNAKE_CASE_				:      Union[torch.Tensor, PIL.Image.Image]    ,			SCREAMING_SNAKE_CASE_				:      Union[torch.Tensor, PIL.Image.Image]    ,			SCREAMING_SNAKE_CASE_				:      int = 2_5_0    ,			SCREAMING_SNAKE_CASE_				:      float = 0.0    ,			SCREAMING_SNAKE_CASE_				:      int = 1_0    ,			SCREAMING_SNAKE_CASE_				:      int = 1_0    ,			SCREAMING_SNAKE_CASE_				:      Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[str] = "pil"    ,			SCREAMING_SNAKE_CASE_				:      bool = True    ,			):
        lowerCAmelCase_   :				Optional[int]								= image

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

        lowerCAmelCase_   :				Optional[Any]								= original_image.shape[0]

        # sample gaussian noise to begin the loop
        if isinstance(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       ) and len(SCREAMING_SNAKE_CASE_       ) != batch_size:
          raise ValueError(
              F"You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE_       )}, 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_   :				str								= randn_tensor(SCREAMING_SNAKE_CASE_    ,			generator=SCREAMING_SNAKE_CASE_    ,			device=self.device    ,			dtype=self.unet.dtype       )

        # set step values
        self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			self.device       )
        lowerCAmelCase_   :				List[str]								= eta

        lowerCAmelCase_   :				Any								= self.scheduler.timesteps[0] + 1
        lowerCAmelCase_   :				Dict								= generator[0] if isinstance(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       ) 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(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       ).sample
            # compute previous image: x_t -> x_t-1
            lowerCAmelCase_   :				List[Any]								= self.scheduler.step(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       ).prev_sample

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

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

        if not return_dict:
          return (image,)

        return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_       )










"""simple docstring"""







def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optional[Any]						)						->   Optional[int]:

  """simple docstring"""




  lowerCAmelCase_   :				Tuple								= [0] * len(lowerCAmelCase__						)
  lowerCAmelCase_   :				List[str]								= []
  lowerCAmelCase_   :				Tuple								= [1] * len(lowerCAmelCase__						)

  for values in graph.values():
    for i in values:
      indegree[i] += 1

  for i in range(len(lowerCAmelCase__						)						):
    if indegree[i] == 0:
      queue.append(lowerCAmelCase__						)

  while queue:
    lowerCAmelCase_   :				Union[str, Any]								= queue.pop(0						)
    for x in graph[vertex]:
      indegree[x] -= 1

      if long_dist[vertex] + 1 > long_dist[x]:
        lowerCAmelCase_   :				Any								= long_dist[vertex] + 1

      if indegree[x] == 0:
        queue.append(lowerCAmelCase__						)

  print(max(lowerCAmelCase__						)						)


# Adjacency list of Graph
lowercase__		:				Any          =	{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)





