

"""simple docstring"""




__A	:       dict[str, float]        =						{
    "kilogram": 1,
    "gram": pow(10, 3),
    "milligram": pow(10, 6),
    "metric-ton": pow(10, -3),
    "long-ton": 0.00_09_84_20_73,
    "short-ton": 0.00_11_02_31_22,
    "pound": 2.20_46_24_42_02,
    "stone": 0.15_74_73_17_28,
    "ounce": 35.2_73_99_07_23,
    "carrat": 50_00,
    "atomic-mass-unit": 6.0_2213_6652E26,
}

__A	:       dict[str, float]        =						{
    "kilogram": 1,
    "gram": pow(10, -3),
    "milligram": pow(10, -6),
    "metric-ton": pow(10, 3),
    "long-ton": 10_16.0_46_08,
    "short-ton": 9_07.1_84,
    "pound": 0.45_35_92,
    "stone": 6.3_50_29,
    "ounce": 0.0_28_34_95,
    "carrat": 0.00_02,
    "atomic-mass-unit": 1.6_6054_0199E-27,
}
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
										A     =   (
										    F"""Invalid 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
										    F"""Supported values are: {", ".join(lowercase__ )}"""
										)
										raise ValueError(lowercase__ )
					return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
	import doctest

	doctest.testmod()












import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM







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

  return orig_key







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       Optional[int]:
  for key in orig_state_dict.copy().keys():
    __snake_case:		str					    =		orig_state_dict.pop(SCREAMING_SNAKE_CASE__)

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

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

  return orig_state_dict







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       Optional[Any]:
  __snake_case:		int					    =		torch.load(SCREAMING_SNAKE_CASE__   ,   map_location="""cpu""")["""model_state_dict"""]
  __snake_case:		List[Any]					    =		NystromformerConfig.from_json_file(SCREAMING_SNAKE_CASE__)
  __snake_case:		Optional[int]					    =		NystromformerForMaskedLM(SCREAMING_SNAKE_CASE__)

  __snake_case:		Dict					    =		convert_checkpoint_helper(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)

  model.load_state_dict(SCREAMING_SNAKE_CASE__)
  model.eval()
  model.save_pretrained(SCREAMING_SNAKE_CASE__)

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


if __name__ == "__main__":
      __UpperCAmelCase			:  str										=				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			:  Optional[Any]										=				parser.parse_args()
      convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






