



'''simple docstring'''





import numpy as np
import torch
from imwatermark import WatermarkEncoder


# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
__a          =		0b1_0_1_1_0_0_1_1_1_1_1_0_1_1_0_0_1_0_0_1_0_0_0_0_0_1_1_1_1_0_1_1_1_0_1_1_0_0_0_1_1_0_0_1_1_1_1_0
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
__a          =		[int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]







class     UpperCAmelCase_       :
    """simple docstring"""







    def __init__(    self					:     Optional[Any]						):
          snake_case__  :    Optional[Any]    						=      WATERMARK_BITS
          snake_case__  :    List[str]    						=      WatermarkEncoder()

          self.encoder.set_watermark("""bits""" ,     self.watermark						)




    def lowerCamelCase       (    self					:     str ,     snake_case_					:     torch.FloatTensor						):
          # can't encode images that are smaller than 256
          if images.shape[-1] < 256:
                return images

          snake_case__  :    Union[str, Any]    						=      (255 * (images / 2 + 0.5)).cpu().permute(0 ,     2 ,     3 ,     1						).float().numpy()

          snake_case__  :    Optional[Any]    						=      [self.encoder.encode(SCREAMING_SNAKE_CASE_ ,     """dwtDct"""						) for image in images]

          snake_case__  :    List[Any]    						=      torch.from_numpy(np.array(SCREAMING_SNAKE_CASE_						)						).permute(0 ,     3 ,     1 ,     2						)

          snake_case__  :    Any    						=      torch.clamp(2 * (images / 255 - 0.5) ,     min=-1.0 ,     max=1.0						)
          return images





"""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)





