


import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM






def 						_A						(						_lowercase				)			->				List[Any]:


     """simple docstring"""


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

     return orig_key






def 						_A						(						_lowercase			,       _lowercase				)			->				List[str]:


     """simple docstring"""


     for key in orig_state_dict.copy().keys():
          __UpperCamelCase				=	orig_state_dict.pop(_lowercase				)

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

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

     return orig_state_dict






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				int:


     """simple docstring"""


     __UpperCamelCase				=	torch.load(_lowercase			,       map_location='cpu'				)['model_state_dict']
     __UpperCamelCase				=	NystromformerConfig.from_json_file(_lowercase				)
     __UpperCamelCase				=	NystromformerForMaskedLM(_lowercase				)

     __UpperCamelCase				=	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__":
    __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)






import math






def 						_A						(						_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 						_A						(						_lowercase = 2_00_00_00				)			->				int:


     """simple docstring"""


     return sum(num for num in range(3			,       _lowercase			,       2				) if is_prime(_lowercase				)				) + 2 if n > 2 else 0


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


