
'''simple docstring'''





import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase    					=   logging.get_logger(__name__)

def      A (       A_							:					str    ,							A_							:					str       ):
      snake_case       :     Union[str, Any] =    RobertaPreLayerNormConfig.from_pretrained(
          A_    ,							architectures=['''RobertaPreLayerNormForMaskedLM''']       )

      # convert state_dict
      snake_case       :     Tuple =    torch.load(hf_hub_download(repo_id=A_    ,							filename='''pytorch_model.bin'''       )       )
      snake_case       :     List[Any] =    {}
      for tensor_key, tensor_value in original_state_dict.items():
            # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
            if tensor_key.startswith('''roberta.'''       ):
                  snake_case       :     List[str] =    '''roberta_prelayernorm.''' + tensor_key[len('''roberta.'''       ) :]

            # The original implementation contains weights which are not used, remove them from the state_dict
            if tensor_key.endswith('''.self.LayerNorm.weight'''       ) or tensor_key.endswith('''.self.LayerNorm.bias'''       ):
                  continue

            snake_case       :     str =    tensor_value

      snake_case       :     List[str] =    RobertaPreLayerNormForMaskedLM.from_pretrained(
          pretrained_model_name_or_path=A_    ,							config=A_    ,							state_dict=A_       )
      model.save_pretrained(A_       )

      # convert tokenizer
      snake_case       :     Tuple =    AutoTokenizer.from_pretrained(A_       )
      tokenizer.save_pretrained(A_       )


if __name__ == "__main__":
      UpperCAmelCase    					=   argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--checkpoint-repo",
          default=None,
          type=str,
          required=True,
          help="Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.",
      )
      parser.add_argument(
          "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      UpperCAmelCase    					=   parser.parse_args()
      convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)



'''simple docstring'''





from __future__ import annotations

def      A (       A_							:					list[int]    ,							A_							:					int       ):
						snake_case       :     list[list[int]] =    []
						snake_case       :     list[int] =    []
						snake_case       :     int =    0
						snake_case       :     int =    sum(A_       )
						create_state_space_tree(A_    ,							A_    ,							A_    ,							A_    ,							A_    ,							A_       )
						return result

def      A (       A_							:					list[int]    ,							A_							:					int    ,							A_							:					int    ,							A_							:					list[int]    ,							A_							:					list[list[int]]    ,							A_							:					int    ,							):
						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]    ,							)
UpperCAmelCase    					=   [3, 34, 4, 12, 5, 2]
UpperCAmelCase    					=   9
UpperCAmelCase    					=   generate_sum_of_subsets_soln(nums, max_sum)





print(*result)

