


import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM







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

     return orig_key







def   A   (		A__		,		A__ ):
     for key in orig_state_dict.copy().keys():
          UpperCAmelCase						:				Optional[int]		  =			orig_state_dict.pop(A__ )

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

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

     return orig_state_dict







def   A   (		A__		,		A__		,		A__ ):
     UpperCAmelCase						:				Any		  =			torch.load(A__		,		map_location="cpu" )["model_state_dict"]
     UpperCAmelCase						:				str		  =			NystromformerConfig.from_json_file(A__ )
     UpperCAmelCase						:				Union[str, Any]		  =			NystromformerForMaskedLM(A__ )

     UpperCAmelCase						:				Optional[Any]		  =			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__":
      _snake_case   = 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."
      )
      _snake_case   = parser.parse_args()
      convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)







from collections.abc import Callable

_snake_case   = Callable[[float], float]  # type alias for a real -> real function







def   A   (		A__		,		A__		,		A__		,		):
     UpperCAmelCase						:				Any		  =			float(A__ )
     while True:
          try:
               UpperCAmelCase						:				Optional[Any]		  =			prev_guess - function(A__ ) / derivative(A__ )
          except ZeroDivisionError:
               raise ZeroDivisionError("Could not find root" ) from None
          if abs(prev_guess - next_guess ) < 1_0**-5:
               return next_guess
          UpperCAmelCase						:				List[str]		  =			next_guess







def   A   (		A__ ):
     return (x**3) - (2 * x) - 5







def   A   (		A__ ):
     return 3 * (x**2) - 2


if __name__ == "__main__":
      print(newton(f, fa, 3))



