


import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()






def 							__lowercase       (			__lowerCAmelCase							:    Optional[Any] ,   __lowerCAmelCase							:    Optional[Any] ,   __lowerCAmelCase							:    Optional[int]       ):
							# Initialise PyTorch model
							a__					=					LxmertConfig.from_json_file(__lowerCAmelCase       )
							print(F'Building PyTorch model from configuration: {config}'       )
							a__					=					LxmertForPreTraining(__lowerCAmelCase       )

							# Load weights from tf checkpoint
							load_tf_weights_in_lxmert(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )

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


if __name__ == "__main__":
						snake_case							:	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(
						    '''--config_file''',
						    default=None,
						    type=str,
						    required=True,
						    help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
						)
						parser.add_argument(
						    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
						)
						snake_case							:	Optional[Any]					      =     parser.parse_args()
						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)








def 							__lowercase       (			__lowerCAmelCase							:    int       ):

							if num <= 0:
														raise ValueError('Input must be a positive integer'       )

							a__					=					[True] * (num + 1)

							a__					=					2
							while p * p <= num:
														if primes[p]:
																					for i in range(p * p ,   num + 1 ,   __lowerCAmelCase       ):
																												a__					=					False
														p += 1

							return [prime for prime in range(2 ,   num + 1       ) if primes[prime]]


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						snake_case							:	Optional[Any]					      =				int(input('''Enter a positive integer: ''').strip())
						print(prime_sieve_eratosthenes(user_num))




