





'''simple docstring'''






import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM
def        UpperCamelCase__				(		lowerCAmelCase  ):





      """simple docstring"""


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

      return orig_key
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


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

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

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

      return orig_state_dict
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


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

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













'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available


A__    :       int              ={'''tokenization_herbert''': ['''HerbertTokenizer''']}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__    :       Dict              =['''HerbertTokenizerFast''']


if TYPE_CHECKING:
      from .tokenization_herbert import HerbertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_herbert_fast import HerbertTokenizerFast

else:
      import sys

      A__    :       List[Any]              =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






