




"""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


SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						logging.get_logger(__name__)  # pylint: disable=invalid-name





def  _snake_case     (    UpperCAmelCase_  :					Union[List, PIL.Image.Image, torch.Tensor]			):
     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			):
          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(UpperCAmelCase_					,   axis=0			)
          A__										=  np.array(UpperCAmelCase_			).astype(np.floataa			) / 255.0
          A__										=  image.transpose(0					,   3					,   1					,   2			)
          A__										=  2.0 * image - 1.0
          A__										=  torch.from_numpy(UpperCAmelCase_			)
     elif isinstance(image[0]					,   torch.Tensor			):
          A__										=  torch.cat(UpperCAmelCase_					,   dim=0			)
     return image





def  _snake_case     (    UpperCAmelCase_  :					Union[List, PIL.Image.Image, torch.Tensor]			):




     if isinstance(UpperCAmelCase_					,   torch.Tensor			):
          return mask
     elif isinstance(UpperCAmelCase_					,   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(UpperCAmelCase_					,   axis=0			)
          A__										=  mask.astype(np.floataa			) / 255.0
          A__										=  0
          A__										=  1
          A__										=  torch.from_numpy(UpperCAmelCase_			)
     elif isinstance(mask[0]					,   torch.Tensor			):
          A__										=  torch.cat(UpperCAmelCase_					,   dim=0			)
     return mask



class 					a						(       UpperCamelCase__				):



    """simple docstring"""



    UpperCAmelCase						 =					42
    UpperCAmelCase						 =					42

    def __init__(     self: str    ,						UpperCamelCase: List[Any]    ,						UpperCamelCase: Any       ):






         """simple docstring"""


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





    @torch.no_grad()
    def __call__(     self: List[Any]    ,						UpperCamelCase: Any    ,						UpperCamelCase: str    ,						UpperCamelCase: Dict = 2_50    ,						UpperCamelCase: int = 0.0    ,						UpperCamelCase: Tuple = 10    ,						UpperCamelCase: Any = 10    ,						UpperCamelCase: List[Any] = None    ,						UpperCamelCase: Optional[Any] = "pil"    ,						UpperCamelCase: Dict = True    ,						):






         """simple docstring"""


         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"""



def  _snake_case     (    UpperCAmelCase_  :					List[str]			):
     A__										=  [0] * len(UpperCAmelCase_			)
     A__										=  []
     A__										=  [1] * len(UpperCAmelCase_			)

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

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

     while queue:
          A__										=  queue.pop(0			)
          for x in graph[vertex]:
               indegree[x] -= 1

               if long_dist[vertex] + 1 > long_dist[x]:
                    A__										=  long_dist[vertex] + 1

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

     print(max(UpperCAmelCase_			)			)


# Adjacency list of Graph
SCREAMING_SNAKE_CASE_					:	Any							  =						{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}

longest_distance(graph)






