




"""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()
__lowerCamelCase										=    logging.get_logger(__name__)



def 					a			(    __snake_case  :					str,     __snake_case  :					str	):



				'''simple docstring'''





				UpperCAmelCase_       :Tuple         =   RobertaPreLayerNormConfig.from_pretrained(
				    UpperCamelCase__,     architectures=['''RobertaPreLayerNormForMaskedLM''']	)

				# convert state_dict
				UpperCAmelCase_       :Dict         =   torch.load(hf_hub_download(repo_id=UpperCamelCase__,     filename='''pytorch_model.bin'''	)	)
				UpperCAmelCase_       :List[str]         =   {}
				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.'''	):
												UpperCAmelCase_       :List[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

								UpperCAmelCase_       :Optional[int]         =   tensor_value

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

				# convert tokenizer
				UpperCAmelCase_       :str         =   AutoTokenizer.from_pretrained(UpperCamelCase__	)
				tokenizer.save_pretrained(UpperCamelCase__	)


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)









def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[str]     ):
		SCREAMING_SNAKE_CASE__								=   len(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   sum(UpperCamelCase__     )

		SCREAMING_SNAKE_CASE__								=   [[False for x in range(s + 1     )] for y in range(n + 1     )]

		for i in range(1       , n + 1     ):
				SCREAMING_SNAKE_CASE__								=   True

		for i in range(1       , s + 1     ):
				SCREAMING_SNAKE_CASE__								=   False

		for i in range(1       , n + 1     ):
				for j in range(1       , s + 1     ):
						SCREAMING_SNAKE_CASE__								=   dp[i][j - 1]

						if arr[i - 1] <= j:
								SCREAMING_SNAKE_CASE__								=   dp[i][j] or dp[i - 1][j - arr[i - 1]]

		for j in range(int(s / 2     )       , -1       , -1     ):
				if dp[n][j] is True:
						SCREAMING_SNAKE_CASE__								=   s - 2 * j
						break

		return diff