



"""simple docstring"""





# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput



class    _lowerCAmelCase    (				__a		):





  """simple docstring"""






  def __init__(					self							,    _lowercase							,    _lowercase   ) ->						Union[str, Any]:







        '''simple docstring'''

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





  @torch.no_grad()
  def __call__(					self							,    _lowercase = 1							,    _lowercase = None							,    _lowercase = 5_0							,    _lowercase = "pil"							,    _lowercase = True							,    **_lowercase							,    ) ->						int:







        '''simple docstring'''

        snake_case_   : List[Any]						=			torch.randn(
            (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)							,    generator=snake_case__							,    )
        snake_case_   : Tuple						=			image.to(self.device   )

        # set step values
        self.scheduler.set_timesteps(snake_case__   )

        for t in self.progress_bar(self.scheduler.timesteps   ):
              # 1. predict noise model_output
              snake_case_   : List[str]						=			self.unet(snake_case__							,    snake_case__   ).sample

              # 2. predict previous mean of image x_t-1 and add variance depending on eta
              # eta corresponds to η in paper and should be between [0, 1]
              # do x_t -> x_t-1
              snake_case_   : int						=			self.scheduler.step(snake_case__							,    snake_case__							,    snake_case__   ).prev_sample

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

        if not return_dict:
              return (image,), "This is a local test"

        return ImagePipelineOutput(images=snake_case__   ), "This is a local test"






"""simple docstring"""





def 	__lowerCAmelCase						(		__UpperCamelCase      :			Union[str, Any]    ):


						'''simple docstring'''






						snake_case_   : int						=			[0] * len(__UpperCamelCase    )
						snake_case_   : List[str]						=			[]
						snake_case_   : Any						=			[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:
												snake_case_   : Optional[int]						=			queue.pop(0    )
												for x in graph[vertex]:
																		indegree[x] -= 1

																		if long_dist[vertex] + 1 > long_dist[x]:
																								snake_case_   : Union[str, Any]						=			long_dist[vertex] + 1

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

						print(max(__UpperCamelCase    )    )


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






longest_distance(graph)

