






'''simple docstring'''




import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM

def       __lowercase		(    __lowercase ) ->		Optional[Any]:

      '''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       __lowercase		(    __lowercase     ,						__lowercase ) ->		int:

      '''simple docstring'''


      for key in orig_state_dict.copy().keys():
            _A      =			orig_state_dict.pop(__lowercase )

            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       __lowercase		(    __lowercase     ,						__lowercase     ,						__lowercase ) ->		Union[str, Any]:

      '''simple docstring'''


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

      _A      =			convert_checkpoint_helper(__lowercase     ,						__lowercase )

      model.load_state_dict(__lowercase )
      model.eval()
      model.save_pretrained(__lowercase )

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


if __name__ == "__main__":
    lowerCamelCase_							      =      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.'''
    )
    lowerCamelCase_							      =      parser.parse_args()
    convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)













'''simple docstring'''




import math

def       __lowercase		(    __lowercase ) ->		bool:

      '''simple docstring'''


      if 1 < number < 4:
            # 2 and 3 are primes
            return True
      elif number < 2 or number % 2 == 0 or number % 3 == 0:
            # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
            return False

      # All primes number are in format of 6k +/- 1
      for i in range(5     ,						int(math.sqrt(__lowercase ) + 1 )     ,						6 ):
            if number % i == 0 or number % (i + 2) == 0:
                  return False
      return True

def       __lowercase		(    __lowercase = 6008_5147_5143 ) ->		int:

      '''simple docstring'''


      try:
            _A      =			int(__lowercase )
      except (TypeError, ValueError):
            raise TypeError("Parameter n must be int or castable to int." )
      if n <= 0:
            raise ValueError("Parameter n must be greater than or equal to one." )
      _A      =			0
      if is_prime(__lowercase ):
            return n
      while n % 2 == 0:
            n //= 2
      if is_prime(__lowercase ):
            return n
      for i in range(3     ,						int(math.sqrt(__lowercase ) ) + 1     ,						2 ):
            if n % i == 0:
                  if is_prime(n // i ):
                        _A      =			n // i
                        break
                  elif is_prime(__lowercase ):
                        _A      =			i
      return max_number


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





