



import argparse

import torch

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


logging.set_verbosity_info()



def 	__a					(   lowerCAmelCase_       : str      ,lowerCAmelCase_       : Optional[int]      ,lowerCAmelCase_       : Optional[Any]    )				->			Optional[int]:






						'''simple docstring'''



						UpperCAmelCase_=       LxmertConfig.from_json_file(lowerCAmelCase_    )
						print(F"""Building PyTorch model from configuration: {config}"""    )
						UpperCAmelCase_=       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__":
	__A       =						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.'''
	)
	__A       =						parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)




from __future__ import annotations



def 	__a					(   lowerCAmelCase_       : list[int]      ,lowerCAmelCase_       : int    )				->			list[list[int]]:






						'''simple docstring'''



						UpperCAmelCase_=       []
						UpperCAmelCase_=       []
						UpperCAmelCase_=       0
						UpperCAmelCase_=       sum(lowerCAmelCase_    )
						create_state_space_tree(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )
						return result



def 	__a					(   lowerCAmelCase_       : list[int]      ,lowerCAmelCase_       : int      ,lowerCAmelCase_       : int      ,lowerCAmelCase_       : list[int]      ,lowerCAmelCase_       : list[list[int]]      ,lowerCAmelCase_       : int      ,)				->			None:






						'''simple docstring'''



						if sum(lowerCAmelCase_    ) > max_sum or (remaining_nums_sum + sum(lowerCAmelCase_    )) < max_sum:
												return
						if sum(lowerCAmelCase_    ) == max_sum:
												result.append(lowerCAmelCase_    )
												return
						for index in range(lowerCAmelCase_      ,len(lowerCAmelCase_    )    ):
												create_state_space_tree(
												    lowerCAmelCase_      ,lowerCAmelCase_      ,index + 1      ,[*path, nums[index]]      ,lowerCAmelCase_      ,remaining_nums_sum - nums[index]      ,)
__A       =						[3, 34, 4, 12, 5, 2]
__A       =						9
__A       =						generate_sum_of_subsets_soln(nums, max_sum)



print(*result)




