





import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM





def 					lowerCAmelCase_			(	__a		)					->		Any:







  """simple docstring"""





  if "model" in orig_key:
    lowerCamelCase__:				Tuple        =orig_key.replace("model."     ,   ""		)
  if "norm1" in orig_key:
    lowerCamelCase__:				List[Any]        =orig_key.replace("norm1"     ,   "attention.output.LayerNorm"		)
  if "norm2" in orig_key:
    lowerCamelCase__:				Tuple        =orig_key.replace("norm2"     ,   "output.LayerNorm"		)
  if "norm" in orig_key:
    lowerCamelCase__:				str        =orig_key.replace("norm"     ,   "LayerNorm"		)
  if "transformer" in orig_key:
    lowerCamelCase__:				Dict        =orig_key.split("."		)[0].split("_"		)[-1]
    lowerCamelCase__:				List[Any]        =orig_key.replace(F"""transformer_{layer_num}"""     ,   F"""encoder.layer.{layer_num}"""		)
  if "mha.attn" in orig_key:
    lowerCamelCase__:				Optional[int]        =orig_key.replace("mha.attn"     ,   "attention.self"		)
  if "mha" in orig_key:
    lowerCamelCase__:				int        =orig_key.replace("mha"     ,   "attention"		)
  if "W_q" in orig_key:
    lowerCamelCase__:				str        =orig_key.replace("W_q"     ,   "self.query"		)
  if "W_k" in orig_key:
    lowerCamelCase__:				Union[str, Any]        =orig_key.replace("W_k"     ,   "self.key"		)
  if "W_v" in orig_key:
    lowerCamelCase__:				Tuple        =orig_key.replace("W_v"     ,   "self.value"		)
  if "ff1" in orig_key:
    lowerCamelCase__:				str        =orig_key.replace("ff1"     ,   "intermediate.dense"		)
  if "ff2" in orig_key:
    lowerCamelCase__:				str        =orig_key.replace("ff2"     ,   "output.dense"		)
  if "ff" in orig_key:
    lowerCamelCase__:				Dict        =orig_key.replace("ff"     ,   "output.dense"		)
  if "mlm_class" in orig_key:
    lowerCamelCase__:				List[Any]        =orig_key.replace("mlm.mlm_class"     ,   "cls.predictions.decoder"		)
  if "mlm" in orig_key:
    lowerCamelCase__:				Any        =orig_key.replace("mlm"     ,   "cls.predictions.transform"		)
  if "cls" not in orig_key:
    lowerCamelCase__:				List[str]        ="yoso." + orig_key

  return orig_key





def 					lowerCAmelCase_			(	__a     ,   __a		)					->		str:







  """simple docstring"""





  for key in orig_state_dict.copy().keys():
    lowerCamelCase__:				Dict        =orig_state_dict.pop(__a		)

    if ("pooler" in key) or ("sen_class" in key):
      continue
    else:
      lowerCamelCase__:				Dict        =val

  lowerCamelCase__:				List[Any]        =orig_state_dict["cls.predictions.decoder.bias"]
  lowerCamelCase__:				Union[str, Any]        =torch.arange(__a		).expand((1, -1)		) + 2

  return orig_state_dict





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a		)					->		int:







  """simple docstring"""





  lowerCamelCase__:				List[str]        =torch.load(__a     ,   map_location="cpu"		)["model_state_dict"]
  lowerCamelCase__:				Union[str, Any]        =YosoConfig.from_json_file(__a		)
  lowerCamelCase__:				str        =YosoForMaskedLM(__a		)

  lowerCamelCase__:				Union[str, Any]        =convert_checkpoint_helper(config.max_position_embeddings     ,   __a		)

  print(model.load_state_dict(__a		)		)
  model.eval()
  model.save_pretrained(__a		)

  print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""		)


if __name__ == "__main__":
     __A     =    argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--pytorch_model_path", default=None, type=str, required=True, help="Path to YOSO pytorch checkpoint."
     )
     parser.add_argument(
         "--config_file",
         default=None,
         type=str,
         required=True,
         help="The json file for YOSO model config.",
     )
     parser.add_argument(
         "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
     )
     __A     =    parser.parse_args()
     convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)








from math import pow





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







  """simple docstring"""





  if current_sum == needed_sum:
    # If the sum of the powers is equal to needed_sum, then we have a solution.
    solutions_count += 1
    return current_sum, solutions_count

  lowerCamelCase__:				Optional[Any]        =int(pow(__a     ,   __a		)		)
  if current_sum + i_to_n <= needed_sum:
    # If the sum of the powers is less than needed_sum, then continue adding powers.
    current_sum += i_to_n
    lowerCamelCase__  , lowerCamelCase__:				int        =backtrack(
        __a     ,   __a     ,   current_number + 1     ,   __a     ,   __a		)
    current_sum -= i_to_n
  if i_to_n < needed_sum:
    # If the power of i is less than needed_sum, then try with the next power.
    lowerCamelCase__  , lowerCamelCase__:				Dict        =backtrack(
        __a     ,   __a     ,   current_number + 1     ,   __a     ,   __a		)
  return current_sum, solutions_count





def 					lowerCAmelCase_			(	__a     ,   __a		)					->		int:







  """simple docstring"""





  if not (1 <= needed_sum <= 1000 and 2 <= power <= 10):
    raise ValueError(
        "Invalid input\n"
        "needed_sum must be between 1 and 1000, power between 2 and 10."		)

  return backtrack(__a     ,   __a     ,   1     ,   0     ,   0		)[1]  # Return the solutions_count


if __name__ == "__main__":
     import doctest

     doctest.testmod()

