





'''simple docstring'''






import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM
def      UpperCamelCase__   (	lowerCAmelCase						):





      """simple docstring"""


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

      return orig_key
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase						):





      """simple docstring"""


      for key in orig_state_dict.copy().keys():
            _lowerCAmelCase							  =							orig_state_dict.pop(lowerCAmelCase						)

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

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

      return orig_state_dict
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase						):





      """simple docstring"""


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

      _lowerCAmelCase							  =							convert_checkpoint_helper(lowerCAmelCase							,			lowerCAmelCase						)

      model.load_state_dict(lowerCAmelCase						)
      model.eval()
      model.save_pretrained(lowerCAmelCase						)

      print(f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"						)


if __name__ == "__main__":
      A__       :     Optional[int]          =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.'''
      )
      A__       :     int          =parser.parse_args()
      convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)













'''simple docstring'''






import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig




class 				UpperCAmelCase  (    snake_case_     ):
      _lowercase:    str		  =     CustomConfig




      def __init__(    self    :				Optional[int]    ,    __snake_case    :				Tuple						)       ->   Optional[Any]:
            super().__init__(__snake_case						)
            _lowerCAmelCase							  =							torch.nn.Linear(config.hidden_size    ,    config.hidden_size						)




      def        lowercase__					(    self    :				Dict    ,    __snake_case    :				List[Any]						)       ->   Dict:
            return self.linear(__snake_case						)




      def        lowercase__					(    self    :				List[Any]    ,    __snake_case    :				int						)       ->   Any:
            pass




class 				UpperCAmelCase  (    snake_case_     ):
      _lowercase:    List[str]		  =     NoSuperInitConfig




      def __init__(    self    :				List[str]    ,    __snake_case    :				List[str]						)       ->   int:
            super().__init__(__snake_case						)
            _lowerCAmelCase							  =							torch.nn.Linear(config.attribute    ,    config.attribute						)




      def        lowercase__					(    self    :				List[str]    ,    __snake_case    :				Dict						)       ->   Any:
            return self.linear(__snake_case						)




      def        lowercase__					(    self    :				Optional[int]    ,    __snake_case    :				List[Any]						)       ->   Dict:
            pass






