
'''simple docstring'''

import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM


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

  return orig_key


def 	_UpperCAmelCase				(  _lowerCamelCase						:       Any  ,      _lowerCamelCase						:       Optional[int]		)    ->						Union[str, Any]:
  for key in orig_state_dict.copy().keys():
    _lowerCAmelCase       :  List[str]   				=					orig_state_dict.pop(_lowerCamelCase		)

    if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
      continue
    else:
      _lowerCAmelCase       :  Union[str, Any]   				=					val

  _lowerCAmelCase       :  Tuple   				=					orig_state_dict["""cls.predictions.decoder.bias"""]
  _lowerCAmelCase       :  List[str]   				=					(
      torch.arange(config.max_position_embeddings		).expand((1, -1)		) + 2
  )

  return orig_state_dict


def 	_UpperCAmelCase				(  _lowerCamelCase						:       Dict  ,      _lowerCamelCase						:       List[str]  ,      _lowerCamelCase						:       Optional[Any]		)    ->						int:
  _lowerCAmelCase       :  int   				=					torch.load(_lowerCamelCase  ,      map_location="""cpu"""		)["""model_state_dict"""]
  _lowerCAmelCase       :  Union[str, Any]   				=					NystromformerConfig.from_json_file(_lowerCamelCase		)
  _lowerCAmelCase       :  List[str]   				=					NystromformerForMaskedLM(_lowerCamelCase		)

  _lowerCAmelCase       :  int   				=					convert_checkpoint_helper(_lowerCamelCase  ,      _lowerCamelCase		)

  model.load_state_dict(_lowerCamelCase		)
  model.eval()
  model.save_pretrained(_lowerCamelCase		)

  print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'		)


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




'''simple docstring'''

import os


def 	_UpperCAmelCase				(  )    ->						int:
  _lowerCAmelCase       :  Optional[int]   				=					os.path.dirname(os.path.realpath(_lowerCamelCase		)		)
  _lowerCAmelCase       :  Optional[Any]   				=					os.path.join(_lowerCamelCase  ,      """triangle.txt"""		)

  with open(_lowerCamelCase		) as in_file:
    _lowerCAmelCase       :  List[Any]   				=					[[int(_lowerCamelCase		) for i in line.split()] for line in in_file]

  while len(_lowerCamelCase		) != 1:
    _lowerCAmelCase       :  Any   				=					triangle.pop()
    _lowerCAmelCase       :  List[str]   				=					triangle[-1]
    for j in range(len(_lowerCamelCase		) - 1		):
      curr_row[j] += max(last_row[j]  ,      last_row[j + 1]		)
  return triangle[0][0]


if __name__ == "__main__":
     print(solution())


