




import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase   )->  List[str]:
		# Initialise PyTorch model
		SCREAMING_SNAKE_CASE_							=       RoFormerConfig.from_json_file(lowercase_   )
		print(F'''Building PyTorch model from configuration: {config}'''   )
		SCREAMING_SNAKE_CASE_							=       RoFormerForMaskedLM(lowercase_   )

		# Load weights from tf checkpoint
		load_tf_weights_in_roformer(lowercase_						,	lowercase_						,	lowercase_   )

		# Save pytorch-model
		print(F'''Save PyTorch model to {pytorch_dump_path}'''   )
		torch.save(model.state_dict()						,	lowercase_						,	_use_new_zipfile_serialization=lowercase_   )


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE:    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(
		    '''--bert_config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained BERT 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.'''
		)
		SCREAMING_SNAKE_CASE:    List[str]											=   parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





from __future__ import annotations

import sys


def 				_a       (				lowerCAmelCase   )->  int:
		if n < 0:
				raise ValueError('Negative arguments are not supported'   )
		return _fib(lowerCAmelCase   )[0]


def 				_a       (				lowerCAmelCase   )->  tuple[int, int]:
		if n == 0:  # (F(0), F(1))
				return (0, 1)

		# F(2n) = F(n)[2F(n+1) − F(n)]
		# F(2n+1) = F(n+1)^2+F(n)^2
		SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       _fib(n // 2   )
		SCREAMING_SNAKE_CASE_							=       a * (b * 2 - a)
		SCREAMING_SNAKE_CASE_							=       a * a + b * b
		return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE:    Optional[int]											=   int(sys.argv[1])
						print(f"""fibonacci({n}) is {fibonacci(n)}""")