
'''simple docstring'''



import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_  )   ->   Union[str, Any]:




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

   return orig_key
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   Optional[Any]:




   """simple docstring"""
   for key in orig_state_dict.copy().keys():
      _SCREAMING_SNAKE_CASE          = orig_state_dict.pop(SCREAMING_SNAKE_CASE_  )

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

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

   return orig_state_dict
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   int:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = torch.load(SCREAMING_SNAKE_CASE_			,					map_location="""cpu"""  )["""model_state_dict"""]
   _SCREAMING_SNAKE_CASE          = NystromformerConfig.from_json_file(SCREAMING_SNAKE_CASE_  )
   _SCREAMING_SNAKE_CASE          = NystromformerForMaskedLM(SCREAMING_SNAKE_CASE_  )

   _SCREAMING_SNAKE_CASE          = 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__					:						List[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__					:						int											=     parser.parse_args()
     convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)













"""simple docstring"""





import numpy as np




def 		lowerCAmelCase_	(  lowercase_   :							str		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Dict												=       np.exp(lowercase_		)

 # Add up the all the exponentials
 __SCREAMING_SNAKE_CASE :			Tuple												=       np.sum(lowercase_		)

 # Divide every exponent by the sum of all exponents
 __SCREAMING_SNAKE_CASE :			List[Any]												=       exponent_vector / sum_of_exponents

 return softmax_vector


if __name__ == "__main__":
    print(softmax((0,)))

