





import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM



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

    return orig_key



def 							lowerCAmelCase_      (				A_  ,A_):
    for key in orig_state_dict.copy().keys():
        UpperCamelCase__:     str     							=			orig_state_dict.pop(_snake_case)

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

    UpperCamelCase__:     Optional[int]     							=			orig_state_dict["""cls.predictions.decoder.bias"""]
    UpperCamelCase__:     Tuple     							=			torch.arange(_snake_case).expand((1, -1)) + 2

    return orig_state_dict



def 							lowerCAmelCase_      (				A_  ,A_  ,A_):
    UpperCamelCase__:     Optional[int]     							=			torch.load(_snake_case  ,map_location="cpu")["""model_state_dict"""]
    UpperCamelCase__:     Dict     							=			YosoConfig.from_json_file(_snake_case)
    UpperCamelCase__:     Optional[Any]     							=			YosoForMaskedLM(_snake_case)

    UpperCamelCase__:     str     							=			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__":
   A__:  Union[str, Any]        =     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.'''
   )
   A__:  List[Any]        =     parser.parse_args()
   convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)



"""simple docstring"""




def lowercase_  (   _snake_case						):
							if a < 0:
														raise ValueError("""Input value must be a positive integer"""						)
							elif isinstance(_snake_case   ,_snake_case						):
														raise TypeError("""Input value must be a 'int' type"""						)
							return bin(_snake_case						).count("""1"""						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()




