

from __future__ import annotations

def    A_  (							a					,   a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  list[list[int]]           =     []
      SCREAMING_SNAKE_CASE_				:  list[int]           =     []
      SCREAMING_SNAKE_CASE_				:  int           =     0
      SCREAMING_SNAKE_CASE_				:  Dict           =     sum(a  )
      create_state_space_tree(a					,   a					,   a					,   a					,   a					,   a  )
      return result

def    A_  (							a					,   a					,   a					,   a					,   a					,   a					,   ):

      """simple docstring"""



      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]					,   )
lowerCAmelCase     :   Dict						     =  [3, 34, 4, 12, 5, 2]
lowerCAmelCase     :   Dict						     =  9
lowerCAmelCase     :   Optional[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_  (							a					,   a					,   a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  int           =     TaConfig.from_json_file(a  )
      print(f"Building PyTorch model from configuration: {config}"  )
      SCREAMING_SNAKE_CASE_				:  Tuple           =     TaForConditionalGeneration(a  )

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

      # Save pytorch-model
      print(f"Save PyTorch model to {pytorch_dump_path}"  )
      model.save_pretrained(a  )


if __name__ == "__main__":
    lowerCAmelCase     :   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(
        '--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     :   List[str]						     =  parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






