




import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  if "model" in orig_key:
    a_		    =  orig_key.replace('''model.'''      ,      ''''''			)
  if "norm1" in orig_key:
    a_		    =  orig_key.replace('''norm1'''      ,      '''attention.output.LayerNorm'''			)
  if "norm2" in orig_key:
    a_		    =  orig_key.replace('''norm2'''      ,      '''output.LayerNorm'''			)
  if "norm" in orig_key:
    a_		    =  orig_key.replace('''norm'''      ,      '''LayerNorm'''			)
  if "transformer" in orig_key:
    a_		    =  orig_key.split('''.'''			)[0].split('''_'''			)[-1]
    a_		    =  orig_key.replace(f"transformer_{layer_num}"      ,      f"encoder.layer.{layer_num}"			)
  if "mha.attn" in orig_key:
    a_		    =  orig_key.replace('''mha.attn'''      ,      '''attention.self'''			)
  if "mha" in orig_key:
    a_		    =  orig_key.replace('''mha'''      ,      '''attention'''			)
  if "W_q" in orig_key:
    a_		    =  orig_key.replace('''W_q'''      ,      '''self.query'''			)
  if "W_k" in orig_key:
    a_		    =  orig_key.replace('''W_k'''      ,      '''self.key'''			)
  if "W_v" in orig_key:
    a_		    =  orig_key.replace('''W_v'''      ,      '''self.value'''			)
  if "ff1" in orig_key:
    a_		    =  orig_key.replace('''ff1'''      ,      '''intermediate.dense'''			)
  if "ff2" in orig_key:
    a_		    =  orig_key.replace('''ff2'''      ,      '''output.dense'''			)
  if "ff" in orig_key:
    a_		    =  orig_key.replace('''ff'''      ,      '''output.dense'''			)
  if "mlm_class" in orig_key:
    a_		    =  orig_key.replace('''mlm.mlm_class'''      ,      '''cls.predictions.decoder'''			)
  if "mlm" in orig_key:
    a_		    =  orig_key.replace('''mlm'''      ,      '''cls.predictions.transform'''			)
  if "cls" not in orig_key:
    a_		    =  '''nystromformer.''' + orig_key

  return orig_key






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  for key in orig_state_dict.copy().keys():
    a_		    =  orig_state_dict.pop(__A			)

    if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
      continue
    else:
      a_		    =  val

  a_		    =  orig_state_dict['''cls.predictions.decoder.bias''']
  a_		    =  (
      torch.arange(config.max_position_embeddings			).expand((1, -1)			) + 2
  )

  return orig_state_dict






def 				UpperCAmelCase__  (							_A      ,      _A      ,      _A			):






  """simple docstring"""


  a_		    =  torch.load(__A      ,      map_location='''cpu'''			)['''model_state_dict''']
  a_		    =  NystromformerConfig.from_json_file(__A			)
  a_		    =  NystromformerForMaskedLM(__A			)

  a_		    =  convert_checkpoint_helper(__A      ,      __A			)

  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__":
  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)



def   _lowerCAmelCase(						):

  for a in range(300      ):
    for b in range(a + 1 ,							400      ):
      for c in range(b + 1 ,							500      ):
        if (a + b + c) == 1000 and (a**2) + (b**2) == (c**2):
          return a * b * c

  return -1
def   _lowerCAmelCase(						):

  for a in range(300      ):
    for b in range(400      ):
      UpperCAmelCase          =	1000 - a - b
      if a < b < c and (a**2) + (b**2) == (c**2):
        return a * b * c

  return -1
def   _lowerCAmelCase(						):
  import timeit

  print(
      timeit.timeit("solution()" ,							setup="from __main__ import solution" ,							number=1000      )      )
  print(
      timeit.timeit(
          "solution_fast()" ,							setup="from __main__ import solution_fast" ,							number=1000      )      )


if __name__ == "__main__":
      print(f"{solution() = }")



