from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available





@dataclass
class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : Union[List[PIL.Image.Image], np.ndarray]
   __UpperCAmelCase : Optional[List[bool]]


if is_transformers_available() and is_torch_available():
      from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline




def    lowerCAmelCase_ (     __A,  __A							)     ->				None:







      '''simple docstring'''


      UpperCAmelCase__								=  len(__A							)
      print("The following activities are selected:"							)

      # The first activity is always selected
      UpperCAmelCase__								=  0
      print(__A,  end=","							)

      # Consider rest of the activities
      for j in range(__A							):
            # If this activity has start time greater than
            # or equal to the finish time of previously
            # selected activity, then select it
            if start[j] >= finish[i]:
                  print(__A,  end=","							)
                  UpperCAmelCase__								=  j


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      UpperCamelCase__											=					[1, 3, 0, 5, 8, 5]
      UpperCamelCase__											=					[2, 4, 6, 7, 9, 9]
      print_max_activities(start, finish)



