
from __future__ import annotations







def        A__					(						lowerCamelCase     ,    lowerCamelCase						)  ->	list[list[int]]:
				UpperCamelCase_:							list[list[int]]	=						[]
				UpperCamelCase_:							list[int]	=						[]
				UpperCamelCase_:							int	=						0
				UpperCamelCase_:							Any	=						sum(lowerCamelCase						)
				create_state_space_tree(lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)
				return result







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    )  ->	None:
				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]     ,    )
lowerCamelCase_						: Tuple         =  [3, 34, 4, 12, 5, 2]
lowerCamelCase_						: Optional[int]         =  9
lowerCamelCase_						: List[Any]         =  generate_sum_of_subsets_soln(nums, max_sum)





print(*result)








import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Union[str, Any]:
				# Initialise PyTorch model
				UpperCamelCase_:							List[Any]	=						TaConfig.from_json_file(lowerCamelCase						)
				print(F'''Building PyTorch model from configuration: {config}'''						)
				UpperCamelCase_:							Any	=						TaForConditionalGeneration(lowerCamelCase						)

				# Load weights from tf checkpoint
				load_tf_weights_in_ta(lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)

				# Save pytorch-model
				print(F'''Save PyTorch model to {pytorch_dump_path}'''						)
				model.save_pretrained(lowerCamelCase						)


if __name__ == "__main__":
							lowerCamelCase_						: Tuple         =  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 T5 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."""
							)
							lowerCamelCase_						: Union[str, Any]         =  parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






