'''simple docstring'''





import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM





def       lowerCAmelCase_							(   snake_case_			:					Tuple     )       -> 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_			       =  "yoso." + orig_key

     return orig_key





def       lowerCAmelCase_							(   snake_case_			:					Dict   ,       snake_case_			:					int     )       -> Any:


     '''simple docstring'''




     for key in orig_state_dict.copy().keys():
          UpperCAmelCase_			       =  orig_state_dict.pop(snake_case_     )

          if ("pooler" in key) or ("sen_class" in key):
               continue
          else:
               UpperCAmelCase_			       =  val

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

     return orig_state_dict





def       lowerCAmelCase_							(   snake_case_			:					int   ,       snake_case_			:					Union[str, Any]   ,       snake_case_			:					int     )       -> Any:


     '''simple docstring'''




     UpperCAmelCase_			       =  torch.load(snake_case_   ,       map_location="cpu"     )["model_state_dict"]
     UpperCAmelCase_			       =  YosoConfig.from_json_file(snake_case_     )
     UpperCAmelCase_			       =  YosoForMaskedLM(snake_case_     )

     UpperCAmelCase_			       =  convert_checkpoint_helper(config.max_position_embeddings   ,       snake_case_     )

     print(model.load_state_dict(snake_case_     )     )
     model.eval()
     model.save_pretrained(snake_case_     )

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


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:	int    =argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
    )
    parser.add_argument(
        '--config_file',
        default=None,
        type=str,
        required=True,
        help='The json file for YOSO model config.',
    )
    parser.add_argument(
        '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    SCREAMING_SNAKE_CASE_:	Optional[int]    =parser.parse_args()
    convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)











from ..utils import DummyObject, requires_backends





class 				a			(      metaclass=__lowercase			):
				SCREAMING_SNAKE_CASE__     :   List[Any]		      =     ['''torch''', '''scipy''']






				def __init__(  self			, *_lowerCAmelCase			, **_lowerCAmelCase					):

										"""simple docstring"""




										requires_backends(self			, ['''torch''', '''scipy''']					)






				@classmethod
				def 			snake_case_    (  cls			, *_lowerCAmelCase			, **_lowerCAmelCase					):

										"""simple docstring"""




										requires_backends(cls			, ['''torch''', '''scipy''']					)






				@classmethod
				def 			snake_case_    (  cls			, *_lowerCAmelCase			, **_lowerCAmelCase					):

										"""simple docstring"""




										requires_backends(cls			, ['''torch''', '''scipy''']					)



