code: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import argparse
  
  import torch
  
  from transformers import NystromformerConfig, NystromformerForMaskedLM
  def _A		(		lowercase	):
  
  
  
  
  
  
  
      """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 _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """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 _A		(		lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """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_     :     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."""
         )
         lowerCamelCase_     :     Optional[Any]    		=						parser.parse_args()
         convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import copy
  
  from transformers.configuration_utils import PretrainedConfig
  from transformers.utils import logging
  
  
  lowerCamelCase_     :     int    		=						logging.get_logger(__name__)
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						"hybrid-clip"
   __lowerCAmelCase				      =						True
  
  
   def __init__(	self				,					__A=512				,					**__A		)      ->  Any:
       super().__init__(**__A		)
  
       if "text_config" not in kwargs:
           raise ValueError('''`text_config` can not be `None`.'''		)
  
       if "vision_config" not in kwargs:
           raise ValueError('''`vision_config` can not be `None`.'''		)
  
       a					=kwargs.pop('''text_config'''		)
       a					=kwargs.pop('''vision_config'''		)
  
       a					=text_config.pop('''model_type'''		)
       a					=vision_config.pop('''model_type'''		)
  
       from transformers import AutoConfig
  
       a					=AutoConfig.for_model(__A				,					**__A		)
  
       if vision_model_type == "clip":
           a					=AutoConfig.for_model(__A				,					**__A		).vision_config
       elif vision_model_type == "clip_vision_model":
           from transformers import CLIPVisionConfig
  
           a					=CLIPVisionConfig(**__A		)
       else:
           a					=AutoConfig.for_model(__A				,					**__A		)
  
       a					=projection_dim
       a					=1.0
  
  
   @classmethod
   def    SCREAMING_SNAKE_CASE							(	cls				,					__A				,					__A				,					**__A		)      ->  Tuple:
       return cls(text_config=text_config.to_dict()				,					vision_config=vision_config.to_dict()				,					**__A		)
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[int]:
       a					=copy.deepcopy(self.__dict__		)
       a					=self.text_config.to_dict()
       a					=self.vision_config.to_dict()
       a					=self.__class__.model_type
       return output