code: 
  '''simple docstring'''
  
  import argparse
  
  import torch
  
  from transformers import NystromformerConfig, NystromformerForMaskedLM
  
  
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       Optional[Any]		)    ->						Optional[Any]:
    if "model" in orig_key:
      _lowerCAmelCase       :  Union[str, Any]   				=					orig_key.replace("""model."""  ,      """"""		)
    if "norm1" in orig_key:
      _lowerCAmelCase       :  Tuple   				=					orig_key.replace("""norm1"""  ,      """attention.output.LayerNorm"""		)
    if "norm2" in orig_key:
      _lowerCAmelCase       :  Dict   				=					orig_key.replace("""norm2"""  ,      """output.LayerNorm"""		)
    if "norm" in orig_key:
      _lowerCAmelCase       :  Union[str, Any]   				=					orig_key.replace("""norm"""  ,      """LayerNorm"""		)
    if "transformer" in orig_key:
      _lowerCAmelCase       :  List[str]   				=					orig_key.split("""."""		)[0].split("""_"""		)[-1]
      _lowerCAmelCase       :  Dict   				=					orig_key.replace(f'transformer_{layer_num}'  ,      f'encoder.layer.{layer_num}'		)
    if "mha.attn" in orig_key:
      _lowerCAmelCase       :  int   				=					orig_key.replace("""mha.attn"""  ,      """attention.self"""		)
    if "mha" in orig_key:
      _lowerCAmelCase       :  int   				=					orig_key.replace("""mha"""  ,      """attention"""		)
    if "W_q" in orig_key:
      _lowerCAmelCase       :  Union[str, Any]   				=					orig_key.replace("""W_q"""  ,      """self.query"""		)
    if "W_k" in orig_key:
      _lowerCAmelCase       :  int   				=					orig_key.replace("""W_k"""  ,      """self.key"""		)
    if "W_v" in orig_key:
      _lowerCAmelCase       :  Tuple   				=					orig_key.replace("""W_v"""  ,      """self.value"""		)
    if "ff1" in orig_key:
      _lowerCAmelCase       :  Optional[Any]   				=					orig_key.replace("""ff1"""  ,      """intermediate.dense"""		)
    if "ff2" in orig_key:
      _lowerCAmelCase       :  int   				=					orig_key.replace("""ff2"""  ,      """output.dense"""		)
    if "ff" in orig_key:
      _lowerCAmelCase       :  Any   				=					orig_key.replace("""ff"""  ,      """output.dense"""		)
    if "mlm_class" in orig_key:
      _lowerCAmelCase       :  Optional[int]   				=					orig_key.replace("""mlm.mlm_class"""  ,      """cls.predictions.decoder"""		)
    if "mlm" in orig_key:
      _lowerCAmelCase       :  Optional[Any]   				=					orig_key.replace("""mlm"""  ,      """cls.predictions.transform"""		)
    if "cls" not in orig_key:
      _lowerCAmelCase       :  Optional[int]   				=					"""nystromformer.""" + orig_key
  
    return orig_key
  
  
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       Any  ,      _lowerCamelCase						:       Optional[int]		)    ->						Union[str, Any]:
    for key in orig_state_dict.copy().keys():
      _lowerCAmelCase       :  List[str]   				=					orig_state_dict.pop(_lowerCamelCase		)
  
      if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
        continue
      else:
        _lowerCAmelCase       :  Union[str, Any]   				=					val
  
    _lowerCAmelCase       :  Tuple   				=					orig_state_dict["""cls.predictions.decoder.bias"""]
    _lowerCAmelCase       :  List[str]   				=					(
        torch.arange(config.max_position_embeddings		).expand((1, -1)		) + 2
    )
  
    return orig_state_dict
  
  
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       Dict  ,      _lowerCamelCase						:       List[str]  ,      _lowerCamelCase						:       Optional[Any]		)    ->						int:
    _lowerCAmelCase       :  int   				=					torch.load(_lowerCamelCase  ,      map_location="""cpu"""		)["""model_state_dict"""]
    _lowerCAmelCase       :  Union[str, Any]   				=					NystromformerConfig.from_json_file(_lowerCamelCase		)
    _lowerCAmelCase       :  List[str]   				=					NystromformerForMaskedLM(_lowerCamelCase		)
  
    _lowerCAmelCase       :  int   				=					convert_checkpoint_helper(_lowerCamelCase  ,      _lowerCamelCase		)
  
    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__":
        UpperCamelCase_     			=       argparse.ArgumentParser()
        # Required parameters
        parser.add_argument(
            """--pytorch_model_path""", default=None, type=str, required=True, help="""Path to Nystromformer pytorch checkpoint."""
        )
        parser.add_argument(
            """--config_file""",
            default=None,
            type=str,
            required=True,
            help="""The json file for Nystromformer 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_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
  
  
  
style_context: 
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  UpperCamelCase_     			=       {
      """configuration_gptsan_japanese""": ["""GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTSanJapaneseConfig"""],
      """tokenization_gptsan_japanese""": ["""GPTSanJapaneseTokenizer"""],
  }
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       UpperCamelCase_     			=       [
           """GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """GPTSanJapaneseForConditionalGeneration""",
           """GPTSanJapaneseModel""",
           """GPTSanJapanesePreTrainedModel""",
       ]
       UpperCamelCase_     			=       [
           """GPTSanJapaneseTokenizer""",
       ]
  
  
  if TYPE_CHECKING:
       from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
       from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer
  
       try:
            if not is_torch_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_gptsan_japanese import (
                GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
                GPTSanJapaneseForConditionalGeneration,
                GPTSanJapaneseModel,
                GPTSanJapanesePreTrainedModel,
            )
            from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer
  
  
  else:
       import sys
  
       UpperCamelCase_     			=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  