






from __future__ import annotations



class 							SCREAMING_SNAKE_CASE__						:





					"""simple docstring"""






					def __init__( self					:  Union[str, Any]							,		__A					:  int = 0	):
						snake_case__			:				List[Any]       =  key



					def 	_lowercase					( self					:  Any							,		__A					:  str							,		__A					:  int	):
						assert isinstance(__A							,		__A	) and isinstance(__A							,		__A	)

						snake_case__			:				List[Any]       =  key or self.__key or 1

						# make sure key is an appropriate size
						key %= 2_5_5

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



					def 	_lowercase					( self					:  str							,		__A					:  str							,		__A					:  int	):
						assert isinstance(__A							,		__A	) and isinstance(__A							,		__A	)

						snake_case__			:				Tuple       =  key or self.__key or 1

						# make sure key is an appropriate size
						key %= 2_5_5

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



					def 	_lowercase					( self					:  Optional[int]							,		__A					:  str							,		__A					:  int = 0	):
						assert isinstance(__A							,		__A	) and isinstance(__A							,		__A	)

						snake_case__			:				int       =  key or self.__key or 1

						# make sure key can be any size
						while key > 2_5_5:
							key -= 2_5_5

						# This will be returned
						snake_case__			:				List[str]       =  ""

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

						return ans



					def 	_lowercase					( self					:  Any							,		__A					:  str							,		__A					:  int = 0	):
						assert isinstance(__A							,		__A	) and isinstance(__A							,		__A	)

						snake_case__			:				Optional[Any]       =  key or self.__key or 1

						# make sure key can be any size
						while key > 2_5_5:
							key -= 2_5_5

						# This will be returned
						snake_case__			:				Dict       =  ""

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

						return ans



					def 	_lowercase					( self					:  Dict							,		__A					:  str							,		__A					:  int = 0	):
						assert isinstance(__A							,		__A	) and isinstance(__A							,		__A	)

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

						except OSError:
							return False

						return True






					def 	_lowercase					( self					:  int							,		__A					:  str							,		__A					:  int	):
						assert isinstance(__A							,		__A	) and isinstance(__A							,		__A	)

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

						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")











import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Tuple					,  snake_case_ :							str					,  snake_case_ :							Union[str, Any]       ):
	# Initialise PyTorch model
	snake_case__			:				List[str]       =  MobileBertConfig.from_json_file(snake_case_       )
	print(F'''Building PyTorch model from configuration: {config}'''       )
	snake_case__			:				Dict       =  MobileBertForPreTraining(snake_case_       )
	# Load weights from tf checkpoint
	snake_case__			:				Any       =  load_tf_weights_in_mobilebert(snake_case_					,  snake_case_					,  snake_case_       )
	# Save pytorch-model
	print(F'''Save PyTorch model to {pytorch_dump_path}'''       )
	torch.save(model.state_dict()					,  snake_case_       )


if __name__ == "__main__":
	__lowerCamelCase :      List[Any]										=				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
	)
	parser.add_argument(
	    """--mobilebert_config_file""",
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        """The config json file corresponding to the pre-trained MobileBERT model. \n"""
	        """This specifies the model architecture."""
	    ),
	)
	parser.add_argument(
	    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
	)
	__lowerCamelCase :      int										=				parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)



