"""simple docstring"""


import sys
import turtle







def 					__a   (		A    ,				A      ):






     '''simple docstring'''



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







def 					__a   (		A    ,				A    ,				A    ,				A    ,				):






     '''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(_lowerCAmelCase    ,				get_mid(_lowerCAmelCase    ,				_lowerCAmelCase      )    ,				get_mid(_lowerCAmelCase    ,				_lowerCAmelCase      )    ,				depth - 1      )
     triangle(_lowerCAmelCase    ,				get_mid(_lowerCAmelCase    ,				_lowerCAmelCase      )    ,				get_mid(_lowerCAmelCase    ,				_lowerCAmelCase      )    ,				depth - 1      )
     triangle(_lowerCAmelCase    ,				get_mid(_lowerCAmelCase    ,				_lowerCAmelCase      )    ,				get_mid(_lowerCAmelCase    ,				_lowerCAmelCase      )    ,				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_:					int        =					turtle.Turtle()
 my_pen.ht()
 my_pen.speed(5)
 my_pen.pencolor("red")

 lowerCAmelCase_:					Tuple        =					[(-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]))





"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase_:					str     =					logging.get_logger(__name__)

lowerCAmelCase_:					List[Any]     =					{
    "facebook/data2vec-vision-base-ft": (
        "https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"
    ),
}






class    a__    (   _a	):
   snake_case_							=      "data2vec-vision"


   def __init__(     self,				_UpperCAmelCase=768,				_UpperCAmelCase=12,				_UpperCAmelCase=12,				_UpperCAmelCase=3072,				_UpperCAmelCase="gelu",				_UpperCAmelCase=0.0,				_UpperCAmelCase=0.0,				_UpperCAmelCase=0.02,				_UpperCAmelCase=1E-12,				_UpperCAmelCase=224,				_UpperCAmelCase=16,				_UpperCAmelCase=3,				_UpperCAmelCase=False,				_UpperCAmelCase=False,				_UpperCAmelCase=False,				_UpperCAmelCase=False,				_UpperCAmelCase=0.1,				_UpperCAmelCase=0.1,				_UpperCAmelCase=True,				_UpperCAmelCase=[3, 5, 7, 11],				_UpperCAmelCase=[1, 2, 3, 6],				_UpperCAmelCase=True,				_UpperCAmelCase=0.4,				_UpperCAmelCase=256,				_UpperCAmelCase=1,				_UpperCAmelCase=False,				_UpperCAmelCase=255,				**_UpperCAmelCase,				):
        '''simple docstring'''



        super().__init__(**_UpperCAmelCase  )

        lowercase__				 =							hidden_size
        lowercase__				 =							num_hidden_layers
        lowercase__				 =							num_attention_heads
        lowercase__				 =							intermediate_size
        lowercase__				 =							hidden_act
        lowercase__				 =							hidden_dropout_prob
        lowercase__				 =							attention_probs_dropout_prob
        lowercase__				 =							initializer_range
        lowercase__				 =							layer_norm_eps

        lowercase__				 =							image_size
        lowercase__				 =							patch_size
        lowercase__				 =							num_channels
        lowercase__				 =							use_mask_token
        lowercase__				 =							use_absolute_position_embeddings
        lowercase__				 =							use_relative_position_bias
        lowercase__				 =							use_shared_relative_position_bias
        lowercase__				 =							layer_scale_init_value
        lowercase__				 =							drop_path_rate
        lowercase__				 =							use_mean_pooling
        # decode head attributes (semantic segmentation)
        lowercase__				 =							out_indices
        lowercase__				 =							pool_scales
        # auxiliary head attributes (semantic segmentation)
        lowercase__				 =							use_auxiliary_head
        lowercase__				 =							auxiliary_loss_weight
        lowercase__				 =							auxiliary_channels
        lowercase__				 =							auxiliary_num_convs
        lowercase__				 =							auxiliary_concat_input
        lowercase__				 =							semantic_loss_ignore_index






class    a__    (   _a	):
   snake_case_							=      version.parse("1.11"	)


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return OrderedDict(
            [
                ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
            ]  )


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return 1E-4




