



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        __lowerCAmelCase		(	__lowerCamelCase						:     str					, __lowerCamelCase						:     str						)	->    Union[str, Any]:
					__lowerCAmelCase						 =RobertaPreLayerNormConfig.from_pretrained(
					    __lowerCamelCase					, architectures=["""RobertaPreLayerNormForMaskedLM"""]						)

					# convert state_dict
					__lowerCAmelCase						 =torch.load(hf_hub_download(repo_id=__lowerCamelCase					, filename="""pytorch_model.bin"""						)						)
					__lowerCAmelCase						 ={}
					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						 ="""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						 =tensor_value

					__lowerCAmelCase						 =RobertaPreLayerNormForMaskedLM.from_pretrained(
					    pretrained_model_name_or_path=__lowerCamelCase					, config=__lowerCamelCase					, state_dict=__lowerCamelCase						)
					model.save_pretrained(__lowerCamelCase						)

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


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 __future__ import annotations






class     __a			:



	def __init__(    self				:     Optional[int]					,						snake_case_				:     int = 0)->				List[str]:
						__lowerCAmelCase						 =key



	def 		UpperCamelCase				(    self				:     Any					,						snake_case_				:     str					,						snake_case_				:     int)->				list[str]:
						assert isinstance(snake_case_					,						snake_case_) and isinstance(snake_case_					,						snake_case_)

						__lowerCAmelCase						 =key or self.__key or 1

						# make sure key is an appropriate size
						key %= 2_55

						return [chr(ord(snake_case_) ^ key) for ch in content]



	def 		UpperCamelCase				(    self				:     Tuple					,						snake_case_				:     str					,						snake_case_				:     int)->				list[str]:
						assert isinstance(snake_case_					,						snake_case_) and isinstance(snake_case_					,						snake_case_)

						__lowerCAmelCase						 =key or self.__key or 1

						# make sure key is an appropriate size
						key %= 2_55

						return [chr(ord(snake_case_) ^ key) for ch in content]



	def 		UpperCamelCase				(    self				:     str					,						snake_case_				:     str					,						snake_case_				:     int = 0)->				str:
						assert isinstance(snake_case_					,						snake_case_) and isinstance(snake_case_					,						snake_case_)

						__lowerCAmelCase						 =key or self.__key or 1

						# make sure key can be any size
						while key > 2_55:
											key -= 2_55

						# This will be returned
						__lowerCAmelCase						 =""""""

						for ch in content:
											ans += chr(ord(snake_case_) ^ key)

						return ans



	def 		UpperCamelCase				(    self				:     Tuple					,						snake_case_				:     str					,						snake_case_				:     int = 0)->				str:
						assert isinstance(snake_case_					,						snake_case_) and isinstance(snake_case_					,						snake_case_)

						__lowerCAmelCase						 =key or self.__key or 1

						# make sure key can be any size
						while key > 2_55:
											key -= 2_55

						# This will be returned
						__lowerCAmelCase						 =""""""

						for ch in content:
											ans += chr(ord(snake_case_) ^ key)

						return ans



	def 		UpperCamelCase				(    self				:     int					,						snake_case_				:     str					,						snake_case_				:     int = 0)->				bool:
						assert isinstance(snake_case_					,						snake_case_) and isinstance(snake_case_					,						snake_case_)

						try:
											with open(snake_case_) as fin, open("""encrypt.out"""					,						"""w+""") as fout:
																# actual encrypt-process
																for line in fin:
																					fout.write(self.encrypt_string(snake_case_					,						snake_case_))

						except OSError:
											return False

						return True



	def 		UpperCamelCase				(    self				:     Tuple					,						snake_case_				:     str					,						snake_case_				:     int)->				bool:
						assert isinstance(snake_case_					,						snake_case_) and isinstance(snake_case_					,						snake_case_)

						try:
											with open(snake_case_) as fin, open("""decrypt.out"""					,						"""w+""") as fout:
																# actual encrypt-process
																for line in fin:
																					fout.write(self.decrypt_string(snake_case_					,						snake_case_))

						except OSError:
											return False

						return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")




