code: 
  
  
  
  
  
  import argparse
  
  import torch
  
  from transformers import MraConfig, MraForMaskedLM
  
  
  
  
  
  
  
  def       lowerCAmelCase__(	lowercase       :    int				) ->    Optional[int]:
    if "model" in orig_key:
      __snake_case   :				Optional[Any]        =       orig_key.replace("model."   ,     ""				)
    if "norm1" in orig_key:
      __snake_case   :				Optional[Any]        =       orig_key.replace("norm1"   ,     "attention.output.LayerNorm"				)
    if "norm2" in orig_key:
      __snake_case   :				Optional[int]        =       orig_key.replace("norm2"   ,     "output.LayerNorm"				)
    if "norm" in orig_key:
      __snake_case   :				Any        =       orig_key.replace("norm"   ,     "LayerNorm"				)
    if "transformer" in orig_key:
      __snake_case   :				Optional[int]        =       orig_key.split("."				)[0].split("_"				)[-1]
      __snake_case   :				Tuple        =       orig_key.replace(f"""transformer_{layer_num}"""   ,     f"""encoder.layer.{layer_num}"""				)
    if "mha.attn" in orig_key:
      __snake_case   :				List[Any]        =       orig_key.replace("mha.attn"   ,     "attention.self"				)
    if "mha" in orig_key:
      __snake_case   :				Union[str, Any]        =       orig_key.replace("mha"   ,     "attention"				)
    if "W_q" in orig_key:
      __snake_case   :				Tuple        =       orig_key.replace("W_q"   ,     "self.query"				)
    if "W_k" in orig_key:
      __snake_case   :				List[str]        =       orig_key.replace("W_k"   ,     "self.key"				)
    if "W_v" in orig_key:
      __snake_case   :				Dict        =       orig_key.replace("W_v"   ,     "self.value"				)
    if "ff.0" in orig_key:
      __snake_case   :				int        =       orig_key.replace("ff.0"   ,     "intermediate.dense"				)
    if "ff.2" in orig_key:
      __snake_case   :				List[str]        =       orig_key.replace("ff.2"   ,     "output.dense"				)
    if "ff" in orig_key:
      __snake_case   :				int        =       orig_key.replace("ff"   ,     "output.dense"				)
    if "mlm_class" in orig_key:
      __snake_case   :				List[str]        =       orig_key.replace("mlm.mlm_class"   ,     "cls.predictions.decoder"				)
    if "mlm" in orig_key:
      __snake_case   :				List[str]        =       orig_key.replace("mlm"   ,     "cls.predictions.transform"				)
    if "backbone.backbone.encoders" in orig_key:
      __snake_case   :				Tuple        =       orig_key.replace("backbone.backbone.encoders"   ,     "encoder.layer"				)
    if "cls" not in orig_key:
      __snake_case   :				Dict        =       "mra." + orig_key
  
    return orig_key
  
  
  
  
  
  
  
  def       lowerCAmelCase__(	lowercase       :    Optional[int]   ,     lowercase       :    Tuple				) ->    Union[str, Any]:
    for key in orig_state_dict.copy().keys():
      __snake_case   :				List[str]        =       orig_state_dict.pop(lowercase				)
  
      if ("pooler" in key) or ("sen_class" in key):
        continue
      else:
        __snake_case   :				int        =       val
  
    __snake_case   :				int        =       orig_state_dict["cls.predictions.decoder.bias"]
    __snake_case   :				Tuple        =       torch.arange(lowercase				).expand((1, -1)				) + 2
  
    return orig_state_dict
  
  
  
  
  
  
  
  def       lowerCAmelCase__(	lowercase       :    List[str]   ,     lowercase       :    Dict   ,     lowercase       :    Optional[Any]				) ->    Optional[Any]:
    __snake_case   :				Optional[Any]        =       torch.load(lowercase   ,     map_location="cpu"				)["model_state_dict"]
    __snake_case   :				Union[str, Any]        =       MraConfig.from_json_file(lowercase				)
    __snake_case   :				int        =       MraForMaskedLM(lowercase				)
  
    __snake_case   :				Optional[Any]        =       convert_checkpoint_helper(config.max_position_embeddings   ,     lowercase				)
  
    print(model.load_state_dict(lowercase				)				)
    model.eval()
    model.save_pretrained(lowercase				)
  
    print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""				)
  
  
  if __name__ == "__main__":
       _UpperCamelCase       = argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to Mra pytorch checkpoint.'''
       )
       parser.add_argument(
           '''--config_file''',
           default=None,
           type=str,
           required=True,
           help='''The json file for Mra model config.''',
       )
       parser.add_argument(
           '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
       )
       _UpperCamelCase       = parser.parse_args()
       convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
  
  
style_context: 
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  _UpperCamelCase       = {'''configuration_nat''': ['''NAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NatConfig''']}
  
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       _UpperCamelCase       = [
           '''NAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''NatForImageClassification''',
           '''NatModel''',
           '''NatPreTrainedModel''',
           '''NatBackbone''',
       ]
  
  if TYPE_CHECKING:
       from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig
  
       try:
            if not is_torch_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_nat import (
                NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                NatBackbone,
                NatForImageClassification,
                NatModel,
                NatPreTrainedModel,
            )
  
  else:
       import sys
  
       _UpperCamelCase       = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  