




import argparse

import torch

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


logging.set_verbosity_info()




def     __magic_name__       (				A	:      Tuple,						A	:      Any,						A	:      Union[str, Any]     ):






			'''simple docstring'''







			a					=    MobileBertConfig.from_json_file(A     )
			print(F"""Building PyTorch model from configuration: {config}"""     )
			a					=    MobileBertForPreTraining(A     )
			# Load weights from tf checkpoint
			a					=    load_tf_weights_in_mobilebert(A,						A,						A     )
			# Save pytorch-model
			print(F"""Save PyTorch model to {pytorch_dump_path}"""     )
			torch.save(model.state_dict(),						A     )


if __name__ == "__main__":
		__lowerCAmelCase	:       Optional[int]     =	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	:       List[Any]     =	parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)












__lowerCAmelCase	:       List[Any]     =	{str(digit): digit**5 for digit in range(10)}




def     __magic_name__       (				A	:      int     ):






			'''simple docstring'''







			return sum(DIGITS_FIFTH_POWER[digit] for digit in str(A     )     )




def     __magic_name__       (				):






			'''simple docstring'''







			return sum(
			    number
			    for number in range(1000,						1000000     )
			    if number == digits_fifth_powers_sum(A     )     )


if __name__ == "__main__":
		print(solution())






