


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		   =			logging.get_logger(__name__)




def       a_ (       SCREAMING_SNAKE_CASE__   : str						,     SCREAMING_SNAKE_CASE__   : str	):
       '''simple docstring'''





       _lowerCamelCase			:						Union[str, Any]            =RobertaPreLayerNormConfig.from_pretrained(
           SCREAMING_SNAKE_CASE__						,     architectures=['RobertaPreLayerNormForMaskedLM']	)

       # convert state_dict
       _lowerCamelCase			:						str            =torch.load(hf_hub_download(repo_id=SCREAMING_SNAKE_CASE__						,     filename='pytorch_model.bin'	)	)
       _lowerCamelCase			:						int            ={}
       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.'	):
                     _lowerCamelCase			:						Optional[int]            ='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

              _lowerCamelCase			:						Optional[int]            =tensor_value

       _lowerCamelCase			:						int            =RobertaPreLayerNormForMaskedLM.from_pretrained(
           pretrained_model_name_or_path=SCREAMING_SNAKE_CASE__						,     config=SCREAMING_SNAKE_CASE__						,     state_dict=SCREAMING_SNAKE_CASE__	)
       model.save_pretrained(SCREAMING_SNAKE_CASE__	)

       # convert tokenizer
       _lowerCamelCase			:						Union[str, Any]            =AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__	)
       tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
 lowerCamelCase		   =			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		   =			parser.parse_args()
 convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)






from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
lowerCamelCase		   =			numpy.array([0, 0])
lowerCamelCase		   =			numpy.array([0.5, 0.866_0254])
lowerCamelCase		   =			numpy.array([1, 0])
lowerCamelCase		   =			[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]




def       a_ (       SCREAMING_SNAKE_CASE__   : list[numpy.ndarray]						,     SCREAMING_SNAKE_CASE__   : int	):
       '''simple docstring'''





       _lowerCamelCase			:						List[Any]            =initial_vectors
       for _ in range(SCREAMING_SNAKE_CASE__	):
              _lowerCamelCase			:						List[str]            =iteration_step(SCREAMING_SNAKE_CASE__	)
       return vectors




def       a_ (       SCREAMING_SNAKE_CASE__   : list[numpy.ndarray]	):
       '''simple docstring'''





       _lowerCamelCase			:						Dict            =[]
       for i, start_vector in enumerate(vectors[:-1]	):
              _lowerCamelCase			:						Tuple            =vectors[i + 1]
              new_vectors.append(SCREAMING_SNAKE_CASE__	)
              _lowerCamelCase			:						Tuple            =end_vector - start_vector
              new_vectors.append(start_vector + difference_vector / 3	)
              new_vectors.append(
                  start_vector + difference_vector / 3 + rotate(difference_vector / 3						,     60	)	)
              new_vectors.append(start_vector + difference_vector * 2 / 3	)
       new_vectors.append(vectors[-1]	)
       return new_vectors




def       a_ (       SCREAMING_SNAKE_CASE__   : numpy.ndarray						,     SCREAMING_SNAKE_CASE__   : float	):
       '''simple docstring'''





       _lowerCamelCase			:						Union[str, Any]            =numpy.radians(SCREAMING_SNAKE_CASE__	)
       _lowerCamelCase      ,	_lowerCamelCase			:						List[Any]            =numpy.cos(SCREAMING_SNAKE_CASE__	), numpy.sin(SCREAMING_SNAKE_CASE__	)
       _lowerCamelCase			:						str            =numpy.array(((c, -s), (s, c))	)
       return numpy.dot(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)




def       a_ (       SCREAMING_SNAKE_CASE__   : list[numpy.ndarray]	):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[Any]            =plt.gca()
       axes.set_aspect('equal'	)

       # matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
       # y-coordinates as inputs, which are constructed from the vector-list using
       # zip()
       _lowerCamelCase      ,	_lowerCamelCase			:						Tuple            =zip(*SCREAMING_SNAKE_CASE__	)
       plt.plot(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)
       plt.show()


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 lowerCamelCase		   =			iterate(INITIAL_VECTORS, 5)
 plot(processed_vectors)


