





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   : List[Any]			=				logging.get_logger(__name__)







def        __lowerCamelCase (							__a       :str   ,					__a       :str   )		->    Dict:






		"""simple docstring"""

		A__  				=       RobertaPreLayerNormConfig.from_pretrained(
		    __lowerCAmelCase   ,					architectures=["""RobertaPreLayerNormForMaskedLM"""]   )

		# convert state_dict
		A__  				=       torch.load(hf_hub_download(repo_id=__lowerCAmelCase   ,					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=__lowerCAmelCase   ,					config=__lowerCAmelCase   ,					state_dict=__lowerCAmelCase   )
		model.save_pretrained(__lowerCAmelCase   )

		# convert tokenizer
		A__  				=       AutoTokenizer.from_pretrained(__lowerCAmelCase   )
		tokenizer.save_pretrained(__lowerCAmelCase   )


if __name__ == "__main__":
					A   : Dict			=				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   : List[Any]			=				parser.parse_args()
					convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)













from string import ascii_uppercase

A   : List[str]			=				{str(ord(c) - 5_5): c for c in ascii_uppercase}







def        __lowerCamelCase (							__a       :int   ,					__a       :int   )		->    str:






		"""simple docstring"""

		if isinstance(__a   ,					__a   ):
				raise TypeError("""int() can't convert non-string with explicit base"""   )
		if num < 0:
				raise ValueError("""parameter must be positive int"""   )
		if isinstance(__a   ,					__a   ):
				raise TypeError("""'str' object cannot be interpreted as an integer"""   )
		if isinstance(__a   ,					__a   ):
				raise TypeError("""'float' object cannot be interpreted as an integer"""   )
		if base in (0, 1):
				raise ValueError("""base must be >= 2"""   )
		if base > 3_6:
				raise ValueError("""base must be <= 36"""   )
		A__  				=       """"""
		A__  				=       0
		A__  				=       0
		while div != 1:
				A__				,      A__  				=       divmod(__a   ,					__a   )
				if base >= 1_1 and 9 < mod < 3_6:
						A__  				=       ALPHABET_VALUES[str(__a   )]
				else:
						A__  				=       str(__a   )
				new_value += actual_value
				A__  				=       num // base
				A__  				=       div
				if div == 0:
						return str(new_value[::-1]   )
				elif div == 1:
						new_value += str(__a   )
						return str(new_value[::-1]   )

		return new_value[::-1]


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					for base in range(2, 3_7):
										for num in range(1_0_0_0):
															assert int(decimal_to_any(num, base), base) == num, (
															    num,
															    base,
															    decimal_to_any(num, base),
															    int(decimal_to_any(num, base), base),
															)



