






import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()





def 		__UpperCAmelCase     (	a_				,	a_				,	a_):
							# Initialise PyTorch model
							snake_case_           =				AlbertConfig.from_json_file(a_)
							print(f'''Building PyTorch model from configuration: {config}''')
							snake_case_           =				AlbertForPreTraining(a_)

							# Load weights from tf checkpoint
							load_tf_weights_in_albert(a_				,	a_				,	a_)

							# Save pytorch-model
							print(f'''Save PyTorch model to {pytorch_dump_path}''')
							torch.save(model.state_dict()				,	a_)


if __name__ == "__main__":
	lowercase    				=   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(
	    "--albert_config_file",
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        "The config json file corresponding to the pre-trained ALBERT 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."
	)
	lowercase    				=   parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)













def 		__UpperCAmelCase     (	a_ = 10**9):
							snake_case_           =				1
							snake_case_           =				2

							snake_case_           =				0
							snake_case_           =				0
							snake_case_           =				0
							while perimeter <= max_perimeter:
														perimeters_sum += perimeter

														prev_value += 2 * value
														value += prev_value

														snake_case_           =				2 * value + 2 if i % 2 == 0 else 2 * value - 2
														i += 1

							return perimeters_sum


if __name__ == "__main__":
	print(f'{solution() = }')





