



import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM




def 	__UpperCamelCase	(  _lowerCAmelCase		)       ->  Dict:



    """simple docstring"""






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

    return orig_key




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  List[str]:



    """simple docstring"""






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

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

    A :       Any          =   orig_state_dict["""cls.predictions.decoder.bias"""]
    A :       Optional[Any]          =   torch.arange(_lowerCAmelCase		).expand((1, -1)		) + 2

    return orig_state_dict




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  Optional[Any]:



    """simple docstring"""






    A :       Tuple          =   torch.load(_lowerCAmelCase      ,     map_location="""cpu"""		)["""model_state_dict"""]
    A :       int          =   YosoConfig.from_json_file(_lowerCAmelCase		)
    A :       Optional[Any]          =   YosoForMaskedLM(_lowerCAmelCase		)

    A :       Any          =   convert_checkpoint_helper(config.max_position_embeddings      ,     _lowerCAmelCase		)

    print(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__":
     SCREAMING_SNAKE_CASE_:List[str]             =						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_:Union[str, Any]             =						parser.parse_args()
     convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)







from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE_:List[Any]             =						TypeVar("""KEY""")
SCREAMING_SNAKE_CASE_:Dict             =						TypeVar("""VAL""")






@dataclass(frozen=SCREAMING_SNAKE_CASE__ ,						slots=SCREAMING_SNAKE_CASE__    )
class SCREAMING_SNAKE_CASE__     (		Generic[KEY, VAL]    ):




    '''simple docstring'''






    __lowerCamelCase			: KEY
    __lowerCamelCase			: VAL






class SCREAMING_SNAKE_CASE__     (		_Item    ):




    '''simple docstring'''






    def __init__(       self		):
        super().__init__(lowerCamelCase__,		lowerCamelCase__		)






    def __bool__(       self		):
        return False


SCREAMING_SNAKE_CASE_:Optional[Any]             =						_DeletedItem()






class SCREAMING_SNAKE_CASE__     (		MutableMapping[KEY, VAL]    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__ = 8,		lowerCamelCase__ = 0.75		):
        A :       List[str]          =   initial_block_size
        A :       list[_Item | None]          =   [None] * initial_block_size
        assert 0.0 < capacity_factor < 1.0
        A :       Dict          =   capacity_factor
        A :       Optional[Any]          =   0






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        return hash(lowerCamelCase__		) % len(self._buckets		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        return (ind + 1) % len(self._buckets		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A :       List[Any]          =   self._buckets[ind]
        if not stored:
            A :       Optional[int]          =   _Item(lowerCamelCase__,		lowerCamelCase__		)
            self._len += 1
            return True
        elif stored.key == key:
            A :       int          =   _Item(lowerCamelCase__,		lowerCamelCase__		)
            return True
        else:
            return False






    def 			_lowerCAmelCase					(       self		):
        A :       Union[str, Any]          =   len(self._buckets		) * self._capacity_factor
        return len(self		) >= int(lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        if len(self._buckets		) <= self._initial_block_size:
            return False
        A :       Optional[Any]          =   len(self._buckets		) * self._capacity_factor / 2
        return len(self		) < limit






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A :       Dict          =   self._buckets
        A :       Optional[Any]          =   [None] * new_size
        A :       List[str]          =   0
        for item in old_buckets:
            if item:
                self._add_item(item.key,		item.val		)






    def 			_lowerCAmelCase					(       self		):
        self._resize(len(self._buckets		) * 2		)






    def 			_lowerCAmelCase					(       self		):
        self._resize(len(self._buckets		) // 2		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A :       Union[str, Any]          =   self._get_bucket_index(lowerCamelCase__		)
        for _ in range(len(self._buckets		)		):
            yield ind
            A :       Dict          =   self._get_next_ind(lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__		):
        for ind in self._iterate_buckets(lowerCamelCase__		):
            if self._try_set(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
                break






    def __setitem__(       self,		lowerCamelCase__,		lowerCamelCase__		):
        if self._is_full():
            self._size_up()

        self._add_item(lowerCamelCase__,		lowerCamelCase__		)






    def __delitem__(       self,		lowerCamelCase__		):
        for ind in self._iterate_buckets(lowerCamelCase__		):
            A :       Tuple          =   self._buckets[ind]
            if item is None:
                raise KeyError(lowerCamelCase__		)
            if item is _deleted:
                continue
            if item.key == key:
                A :       int          =   _deleted
                self._len -= 1
                break
        if self._is_sparse():
            self._size_down()






    def __getitem__(       self,		lowerCamelCase__		):
        for ind in self._iterate_buckets(lowerCamelCase__		):
            A :       int          =   self._buckets[ind]
            if item is None:
                break
            if item is _deleted:
                continue
            if item.key == key:
                return item.val
        raise KeyError(lowerCamelCase__		)






    def __len__(       self		):
        return self._len






    def __iter__(       self		):
        yield from (item.key for item in self._buckets if item)






    def __repr__(       self		):
        A :       Union[str, Any]          =   """ ,""".join(
            f'''{item.key}: {item.val}''' for item in self._buckets if item		)
        return f'''HashMap({val_string})'''


