
'''simple docstring'''
import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM







def        A_     (						__SCREAMING_SNAKE_CASE						:  Optional[Any] )				->       List[str]:





  """simple docstring"""

  if "model" in orig_key:
    __A		:     Optional[int]												=		orig_key.replace("""model."""			,     """""" )
  if "norm1" in orig_key:
    __A		:     Dict												=		orig_key.replace("""norm1"""			,     """attention.output.LayerNorm""" )
  if "norm2" in orig_key:
    __A		:     Dict												=		orig_key.replace("""norm2"""			,     """output.LayerNorm""" )
  if "norm" in orig_key:
    __A		:     Tuple												=		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		:     Tuple												=		orig_key.replace("""mha.attn"""			,     """attention.self""" )
  if "mha" in orig_key:
    __A		:     Dict												=		orig_key.replace("""mha"""			,     """attention""" )
  if "W_q" in orig_key:
    __A		:     Any												=		orig_key.replace("""W_q"""			,     """self.query""" )
  if "W_k" in orig_key:
    __A		:     Optional[int]												=		orig_key.replace("""W_k"""			,     """self.key""" )
  if "W_v" in orig_key:
    __A		:     str												=		orig_key.replace("""W_v"""			,     """self.value""" )
  if "ff1" in orig_key:
    __A		:     Union[str, Any]												=		orig_key.replace("""ff1"""			,     """intermediate.dense""" )
  if "ff2" in orig_key:
    __A		:     Optional[Any]												=		orig_key.replace("""ff2"""			,     """output.dense""" )
  if "ff" in orig_key:
    __A		:     Optional[int]												=		orig_key.replace("""ff"""			,     """output.dense""" )
  if "mlm_class" in orig_key:
    __A		:     List[Any]												=		orig_key.replace("""mlm.mlm_class"""			,     """cls.predictions.decoder""" )
  if "mlm" in orig_key:
    __A		:     str												=		orig_key.replace("""mlm"""			,     """cls.predictions.transform""" )
  if "cls" not in orig_key:
    __A		:     Tuple												=		"""yoso.""" + orig_key

  return orig_key







def        A_     (						__SCREAMING_SNAKE_CASE						:  int			,     __SCREAMING_SNAKE_CASE						:  Optional[Any] )				->       List[Any]:





  """simple docstring"""

  for key in orig_state_dict.copy().keys():
    __A		:     Dict												=		orig_state_dict.pop(__SCREAMING_SNAKE_CASE )

    if ("pooler" in key) or ("sen_class" in key):
      continue
    else:
      __A		:     Optional[Any]												=		val

  __A		:     int												=		orig_state_dict["""cls.predictions.decoder.bias"""]
  __A		:     Dict												=		torch.arange(__SCREAMING_SNAKE_CASE ).expand((1, -1) ) + 2

  return orig_state_dict







def        A_     (						__SCREAMING_SNAKE_CASE						:  Optional[Any]			,     __SCREAMING_SNAKE_CASE						:  Any			,     __SCREAMING_SNAKE_CASE						:  List[str] )				->       Any:





  """simple docstring"""

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

  __A		:     Optional[Any]												=		convert_checkpoint_helper(config.max_position_embeddings			,     __SCREAMING_SNAKE_CASE )

  print(model.load_state_dict(__SCREAMING_SNAKE_CASE ) )
  model.eval()
  model.save_pretrained(__SCREAMING_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__   :		Dict 		=parser.parse_args()
     convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





'''simple docstring'''
from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
     import accelerate







def        A_     (						__SCREAMING_SNAKE_CASE						:  Dict )				->       Optional[int]:





  """simple docstring"""

  if not is_accelerate_available():
    return method
  __A		:     int												=		version.parse(accelerate.__version__ ).base_version
  if version.parse(__SCREAMING_SNAKE_CASE ) < version.parse("""0.17.0""" ):
    return method

  def wrapper(self						:  str			,     *__SCREAMING_SNAKE_CASE						:  Tuple			,     **__SCREAMING_SNAKE_CASE						:  Tuple ):
    if hasattr(self			,     """_hf_hook""" ) and hasattr(self._hf_hook			,     """pre_forward""" ):
      self._hf_hook.pre_forward(self )
    return method(self			,     *__SCREAMING_SNAKE_CASE			,     **__SCREAMING_SNAKE_CASE )

  return wrapper



