





'''simple docstring'''




import sys
import turtle






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






def 	_lowerCAmelCase      (  __snake_case  :			tuple[float, float]			,							__snake_case  :			tuple[float, float]			,							__snake_case  :			tuple[float, float]			,							__snake_case  :			int			,							)					->					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(__snake_case			,							get_mid(__snake_case			,							__snake_case      )			,							get_mid(__snake_case			,							__snake_case      )			,							depth - 1      )
     triangle(__snake_case			,							get_mid(__snake_case			,							__snake_case      )			,							get_mid(__snake_case			,							__snake_case      )			,							depth - 1      )
     triangle(__snake_case			,							get_mid(__snake_case			,							__snake_case      )			,							get_mid(__snake_case			,							__snake_case      )			,							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__   :				Optional[int]      =   turtle.Turtle()
  my_pen.ht()
  my_pen.speed(5)
  my_pen.pencolor('''red''')

  lowercase__   :				Dict      =   [(-1_75, -1_25), (0, 1_75), (1_75, -1_25)]  # vertices of triangle
  triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))






'''simple docstring'''




from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowercase__   :				List[Any]      =   HfArgumentParser(InitializationArguments)
lowercase__   :				List[Any]      =   parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
lowercase__   :				Union[str, Any]      =   AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
lowercase__   :				List[str]      =   {
    '''vocab_size''': len(tokenizer),
    '''scale_attn_by_inverse_layer_idx''': True,
    '''reorder_and_upcast_attn''': True,
}

# Load model config (GPT-2 large in this case)
lowercase__   :				Optional[int]      =   AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
lowercase__   :				Optional[int]      =   AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)