






"""simple docstring"""




import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM






def 							lowerCAmelCase			(   UpperCamelCase_:				Tuple       )							->				Dict:







  '''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 							lowerCAmelCase			(   UpperCamelCase_:				Optional[int]  ,			UpperCamelCase_:				Dict       )							->				Dict:







  '''simple docstring'''







  for key in orig_state_dict.copy().keys():
    _a					      =						orig_state_dict.pop(UpperCamelCase_       )

    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 							lowerCAmelCase			(   UpperCamelCase_:				Optional[int]  ,			UpperCamelCase_:				Any  ,			UpperCamelCase_:				Optional[int]       )							->				Optional[int]:







  '''simple docstring'''







  _a					      =						torch.load(UpperCamelCase_  ,			map_location="cpu"       )["model_state_dict"]
  _a					      =						NystromformerConfig.from_json_file(UpperCamelCase_       )
  _a					      =						NystromformerForMaskedLM(UpperCamelCase_       )

  _a					      =						convert_checkpoint_helper(UpperCamelCase_  ,			UpperCamelCase_       )

  model.load_state_dict(UpperCamelCase_       )
  model.eval()
  model.save_pretrained(UpperCamelCase_       )

  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 math






def 							lowerCAmelCase			(   UpperCamelCase_:				int       )							->				list:







  '''simple docstring'''







  if n <= 0:
    raise ValueError("Only positive integers have prime factors"       )
  _a					      =						[]
  while n % 2 == 0:
    pf.append(2       )
    _a					      =						int(n / 2       )
  for i in range(3  ,			int(math.sqrt(UpperCamelCase_       )       ) + 1  ,			2       ):
    while n % i == 0:
      pf.append(UpperCamelCase_       )
      _a					      =						int(n / i       )
  if n > 2:
    pf.append(UpperCamelCase_       )
  return pf






def 							lowerCAmelCase			(   UpperCamelCase_:				int       )							->				int:







  '''simple docstring'''







  if n <= 0:
    raise ValueError("Only positive numbers are accepted"       )
  _a					      =						1
  _a					      =						1
  while n % 2 == 0:
    temp += 1
    _a					      =						int(n / 2       )
  div *= temp
  for i in range(3  ,			int(math.sqrt(UpperCamelCase_       )       ) + 1  ,			2       ):
    _a					      =						1
    while n % i == 0:
      temp += 1
      _a					      =						int(n / i       )
    div *= temp
  if n > 1:
    div *= 2
  return div






def 							lowerCAmelCase			(   UpperCamelCase_:				int       )							->				int:







  '''simple docstring'''







  if n <= 0:
    raise ValueError("Only positive numbers are accepted"       )
  _a					      =						1
  _a					      =						1
  while n % 2 == 0:
    temp += 1
    _a					      =						int(n / 2       )
  if temp > 1:
    s *= (2**temp - 1) / (2 - 1)
  for i in range(3  ,			int(math.sqrt(UpperCamelCase_       )       ) + 1  ,			2       ):
    _a					      =						1
    while n % i == 0:
      temp += 1
      _a					      =						int(n / i       )
    if temp > 1:
      s *= (i**temp - 1) / (i - 1)
  return int(UpperCamelCase_       )






def 							lowerCAmelCase			(   UpperCamelCase_:				int       )							->				int:







  '''simple docstring'''







  _a					      =						n
  for x in set(prime_factors(UpperCamelCase_       )       ):
    s *= (x - 1) / x
  return int(UpperCamelCase_       )


if __name__ == "__main__":
     import doctest

     doctest.testmod()


