
"""simple docstring"""




import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM



def 				a__			(					lowerCAmelCase						:							Optional[Any]		):






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

     return orig_key



def 				a__			(					lowerCAmelCase						:							str							,     lowerCAmelCase						:							Dict		):






     '''simple docstring'''
     for key in orig_state_dict.copy().keys():
          UpperCAmelCase__				:			Union[str, Any]							=    orig_state_dict.pop(lowerCAmelCase		)

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

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

     return orig_state_dict



def 				a__			(					lowerCAmelCase						:							Tuple							,     lowerCAmelCase						:							int							,     lowerCAmelCase						:							Optional[int]		):






     '''simple docstring'''
     UpperCAmelCase__				:			Tuple							=    torch.load(lowerCAmelCase							,     map_location="cpu"		)["model_state_dict"]
     UpperCAmelCase__				:			Union[str, Any]							=    NystromformerConfig.from_json_file(lowerCAmelCase		)
     UpperCAmelCase__				:			Any							=    NystromformerForMaskedLM(lowerCAmelCase		)

     UpperCAmelCase__				:			Optional[Any]							=    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__   :						Any 							=     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__   :						str 							=     parser.parse_args()
    convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





"""simple docstring"""




def 				a__			(					lowerCAmelCase						:							str		):






     '''simple docstring'''
     UpperCAmelCase__				:			Optional[Any]							=    str(lowerCAmelCase		).strip()
     if not oct_string:
          raise ValueError("Empty string was passed to the function"		)
     UpperCAmelCase__				:			Tuple							=    oct_string[0] == "-"
     if is_negative:
          UpperCAmelCase__				:			List[Any]							=    oct_string[1:]
     if not oct_string.isdigit() or not all(0 <= int(lowerCAmelCase		) <= 7 for char in oct_string		):
          raise ValueError("Non-octal value was passed to the function"		)
     UpperCAmelCase__				:			Optional[int]							=    0
     for char in oct_string:
          UpperCAmelCase__				:			Tuple							=    8 * decimal_number + int(lowerCAmelCase		)
     if is_negative:
          UpperCAmelCase__				:			int							=    -decimal_number
     return decimal_number


if __name__ == "__main__":
    from doctest import testmod

    testmod()



