

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()
a_						     =    logging.get_logger(__name__)

def  lowerCamelCase__ (					_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Any				     =       RobertaPreLayerNormConfig.from_pretrained(
	    _a							,			architectures=["RobertaPreLayerNormForMaskedLM"])

	# convert state_dict
	SCREAMING_SNAKE_CASE   :      int				     =       torch.load(hf_hub_download(repo_id=_a							,			filename="pytorch_model.bin"))
	SCREAMING_SNAKE_CASE   :      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."):
			SCREAMING_SNAKE_CASE   :      List[Any]				     =       "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

		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       tensor_value

	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       RobertaPreLayerNormForMaskedLM.from_pretrained(
	    pretrained_model_name_or_path=_a							,			config=_a							,			state_dict=_a)
	model.save_pretrained(_a)

	# convert tokenizer
	SCREAMING_SNAKE_CASE   :      int				     =       AutoTokenizer.from_pretrained(_a)
	tokenizer.save_pretrained(_a)


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


import math

def  lowerCamelCase__ (					_a							,			_a):
	if (
	    not isinstance(_a							,			(int, float))
	    or power_factor < -1
	    or power_factor > 1
	):
		raise ValueError("power_factor must be a valid float value between -1 and 1.")
	return apparent_power * power_factor

def  lowerCamelCase__ (					_a							,			_a):
	if (
	    not isinstance(_a							,			(int, float))
	    or power_factor < -1
	    or power_factor > 1
	):
		raise ValueError("power_factor must be a valid float value between -1 and 1.")
	return apparent_power * math.sqrt(1 - power_factor**2)


if __name__ == "__main__":
			import doctest

			doctest.testmod()