




import numpy as np


def    A						(__A   :    Dict   ,				__A   :    str   ,				__A   :    int   ,				__A   :    str   ,				__A   :    int       )   ->      List[str]:







     """simple docstring"""


     UpperCAmelCase_          =							int(np.ceil((x_end - xa) / h       )       )
     UpperCAmelCase_          =							np.zeros((n + 1,)       )
     UpperCAmelCase_          =							ya
     UpperCAmelCase_          =							xa

     for k in range(__A       ):
          UpperCAmelCase_          =							f(__A   ,				y[k]       )
          UpperCAmelCase_          =							f(x + 0.5 * h   ,				y[k] + 0.5 * h * ka       )
          UpperCAmelCase_          =							f(x + 0.5 * h   ,				y[k] + 0.5 * h * ka       )
          UpperCAmelCase_          =							f(x + h   ,				y[k] + h * ka       )
          UpperCAmelCase_          =							y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
          x += h

     return y


if __name__ == "__main__":
    import doctest

    doctest.testmod()







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      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  42
  lowerCamelCase__   =  42


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


