






from __future__ import annotations





def 						__UpperCAmelCase			(     a_							,     a_):
							snake_case_				=							[]
							snake_case_				=							[]
							snake_case_				=							0
							snake_case_				=							sum(a_)
							create_state_space_tree(a_							,     a_							,     a_							,     a_							,     a_							,     a_)
							return result





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




print(*result)










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_							(A : str		,					A : List[Any]		,					A : Any      ):
       # Initialise PyTorch model
       snake_case__							:	List[Any]   	=	LxmertConfig.from_json_file(A      )
       print(F'''Building PyTorch model from configuration: {config}'''      )
       snake_case__							:	List[str]   	=	LxmertForPreTraining(A      )

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

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


if __name__ == "__main__":
 a_   :Union[str, 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."
 )
 a_   :Optional[int]        =      parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






