
"""simple docstring"""


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()
_lowercase							      = logging.get_logger(__name__)






def 					_snake_case (						snake_case__      :   Any		,					snake_case__      :   List[str]   ):
	A        =      RobertaPreLayerNormConfig.from_pretrained(
	    _lowercase		,					architectures=['RobertaPreLayerNormForMaskedLM']   )

	# convert state_dict
	A        =      torch.load(hf_hub_download(repo_id=_lowercase		,					filename='pytorch_model.bin'   )   )
	A        =      {}
	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.'   ):
			A        =      '''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

		A        =      tensor_value

	A        =      RobertaPreLayerNormForMaskedLM.from_pretrained(
	    pretrained_model_name_or_path=_lowercase		,					config=_lowercase		,					state_dict=_lowercase   )
	model.save_pretrained(_lowercase   )

	# convert tokenizer
	A        =      AutoTokenizer.from_pretrained(_lowercase   )
	tokenizer.save_pretrained(_lowercase   )


if __name__ == "__main__":
		_lowercase							      = 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.'''
		)
		_lowercase							      = parser.parse_args()
		convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)
from math import ceil
def       A	(    _lowercase = 1_001						):
						SCREAMING_SNAKE_CASE							:    Any							=							1

						for i in range(1		,  int(ceil(n / 2.0						)						)						):
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							2 * i + 1
												SCREAMING_SNAKE_CASE							:    int							=							2 * i
												SCREAMING_SNAKE_CASE							:    List[str]							=							total + 4 * odd**2 - 6 * even

						return total


if __name__ == "__main__":
					import sys

					if len(sys.argv) == 1:
										print(solution())
					else:
										try:
															__UpperCamelCase		:       Dict									=     int(sys.argv[1])
															print(solution(n))
										except ValueError:
															print('Invalid entry - please enter a number')





