





import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase :Tuple     =    logging.get_logger(__name__)







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					Tuple				       =   RobertaPreLayerNormConfig.from_pretrained(
					    UpperCAmelCase  ,  architectures=["RobertaPreLayerNormForMaskedLM"]				)

					# convert state_dict
					_snake_case       :					Optional[Any]				       =   torch.load(hf_hub_download(repo_id=UpperCAmelCase  ,  filename="pytorch_model.bin"				)				)
					_snake_case       :					Optional[Any]				       =   {}
					for tensor_key, tensor_value in original_state_dict.items():
										# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
										if tensor_key.startswith("roberta."				):
															_snake_case       :					str				       =   "roberta_prelayernorm." + tensor_key[len("roberta."				) :]

										# The original implementation contains weights which are not used, remove them from the state_dict
										if tensor_key.endswith(".self.LayerNorm.weight"				) or tensor_key.endswith(".self.LayerNorm.bias"				):
															continue

										_snake_case       :					Union[str, Any]				       =   tensor_value

					_snake_case       :					Optional[Any]				       =   RobertaPreLayerNormForMaskedLM.from_pretrained(
					    pretrained_model_name_or_path=UpperCAmelCase  ,  config=UpperCAmelCase  ,  state_dict=UpperCAmelCase				)
					model.save_pretrained(UpperCAmelCase				)

					# convert tokenizer
					_snake_case       :					List[Any]				       =   AutoTokenizer.from_pretrained(UpperCAmelCase				)
					tokenizer.save_pretrained(UpperCAmelCase				)


if __name__ == "__main__":
							__lowerCAmelCase :List[str]     =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--checkpoint-repo',
							    default=None,
							    type=str,
							    required=True,
							    help='Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							__lowerCAmelCase :Optional[Any]     =    parser.parse_args()
							convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)






import sys
import turtle







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  ):
					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(UpperCAmelCase  ,  get_mid(UpperCAmelCase  ,  UpperCAmelCase				)  ,  get_mid(UpperCAmelCase  ,  UpperCAmelCase				)  ,  depth - 1				)
					triangle(UpperCAmelCase  ,  get_mid(UpperCAmelCase  ,  UpperCAmelCase				)  ,  get_mid(UpperCAmelCase  ,  UpperCAmelCase				)  ,  depth - 1				)
					triangle(UpperCAmelCase  ,  get_mid(UpperCAmelCase  ,  UpperCAmelCase				)  ,  get_mid(UpperCAmelCase  ,  UpperCAmelCase				)  ,  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 :Any     =    turtle.Turtle()
							my_pen.ht()
							my_pen.speed(5)
							my_pen.pencolor('red')

							__lowerCAmelCase :Optional[int]     =    [(-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]))