




import sys
import turtle




def 						SCREAMING_SNAKE_CASE__					(    lowercase						,lowercase     )  ->   tuple[float, float]:
   return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2




def 						SCREAMING_SNAKE_CASE__					(    lowercase						,lowercase						,lowercase						,lowercase						,)  ->   None:
   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>'
              )
       lowerCamelCase       :       str        =    turtle.Turtle()
       my_pen.ht()
       my_pen.speed(5)
       my_pen.pencolor('red')

       lowerCamelCase       :       int        =    [(-1_7_5, -1_2_5), (0, 1_7_5), (1_7_5, -1_2_5)]  # vertices of triangle
       triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))













from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream

class     _SCREAMING_SNAKE_CASE       (			_a      ):





	def __init__(		self				:					List[Any] ,  __lowerCamelCase				:					Callable ,  __lowerCamelCase				:					Optional[Features] = None ,  __lowerCamelCase				:					str = None ,  __lowerCamelCase				:					bool = False ,  __lowerCamelCase				:					bool = False ,  __lowerCamelCase				:					Optional[dict] = None ,  __lowerCamelCase				:					Optional[int] = None ,  **__lowerCamelCase				:					List[Any] ,  ):
		super().__init__(
		    features=__lowerCamelCase ,  cache_dir=__lowerCamelCase ,  keep_in_memory=__lowerCamelCase ,  streaming=__lowerCamelCase ,  num_proc=__lowerCamelCase ,  **__lowerCamelCase ,  )
		UpperCamelCase     :Union[str, Any]          =	Generator(
		    cache_dir=__lowerCamelCase ,  features=__lowerCamelCase ,  generator=__lowerCamelCase ,  gen_kwargs=__lowerCamelCase ,  **__lowerCamelCase ,  )





	def 						_A (		self				:					List[str]	):
		# Build iterable dataset
		if self.streaming:
			UpperCamelCase     :Any          =	self.builder.as_streaming_dataset(split="""train"""	)
		# Build regular (map-style) dataset
		else:
			UpperCamelCase     :Tuple          =	None
			UpperCamelCase     :Dict          =	None
			UpperCamelCase     :Dict          =	None
			UpperCamelCase     :List[str]          =	None

			self.builder.download_and_prepare(
			    download_config=__lowerCamelCase ,  download_mode=__lowerCamelCase ,  verification_mode=__lowerCamelCase ,  base_path=__lowerCamelCase ,  num_proc=self.num_proc ,  )
			UpperCamelCase     :Tuple          =	self.builder.as_dataset(
			    split="""train""" ,  verification_mode=__lowerCamelCase ,  in_memory=self.keep_in_memory	)
		return dataset
