





import sys
import turtle




def     a__ (					lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2




def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ,    ):






   '''simple docstring'''






   my_pen.up()
   my_pen.goto(vertexa[0]  ,    vertexa[1]  )
   my_pen.down()
   my_pen.goto(vertexa[0]  ,    vertexa[1]  )
   my_pen.goto(vertexa[0]  ,    vertexa[1]  )
   my_pen.goto(vertexa[0]  ,    vertexa[1]  )

   if depth == 0:
      return

   triangle(lowercase__  ,    get_mid(lowercase__  ,    lowercase__  )  ,    get_mid(lowercase__  ,    lowercase__  )  ,    depth - 1  )
   triangle(lowercase__  ,    get_mid(lowercase__  ,    lowercase__  )  ,    get_mid(lowercase__  ,    lowercase__  )  ,    depth - 1  )
   triangle(lowercase__  ,    get_mid(lowercase__  ,    lowercase__  )  ,    get_mid(lowercase__  ,    lowercase__  )  ,    depth - 1  )


if __name__ == "__main__":
     if len(sys.argv) != 2:
          raise ValueError(
              """Correct format for using this script: """
              """python fractals.py <int:depth_for_fractal>"""
          )
     __lowercase  :   Any            =turtle.Turtle()
     my_pen.ht()
     my_pen.speed(5)
     my_pen.pencolor("""red""")

     __lowercase  :   str            =[(-175, -125), (0, 175), (175, -125)]  # vertices of triangle
     triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase  :   Any						      ={
    """configuration_upernet""": ["""UperNetConfig"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __lowercase  :   Tuple						      =[
         """UperNetForSemanticSegmentation""",
         """UperNetPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_upernet import UperNetConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
     import sys

     __lowercase  :   Any						      =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




