code: 
  '''simple docstring'''
  
  import argparse
  
  import torch
  
  from transformers import NystromformerConfig, NystromformerForMaskedLM
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase	) ->      str:
  		if "model" in orig_key:
  				_UpperCamelCase       : Optional[int]    =      orig_key.replace('''model.'''			,''''''	)
  		if "norm1" in orig_key:
  				_UpperCamelCase       : Optional[int]    =      orig_key.replace('''norm1'''			,'''attention.output.LayerNorm'''	)
  		if "norm2" in orig_key:
  				_UpperCamelCase       : Tuple    =      orig_key.replace('''norm2'''			,'''output.LayerNorm'''	)
  		if "norm" in orig_key:
  				_UpperCamelCase       : List[str]    =      orig_key.replace('''norm'''			,'''LayerNorm'''	)
  		if "transformer" in orig_key:
  				_UpperCamelCase       : List[str]    =      orig_key.split('''.'''	)[0].split('''_'''	)[-1]
  				_UpperCamelCase       : Optional[int]    =      orig_key.replace(f'''transformer_{layer_num}'''			,f'''encoder.layer.{layer_num}'''	)
  		if "mha.attn" in orig_key:
  				_UpperCamelCase       : Union[str, Any]    =      orig_key.replace('''mha.attn'''			,'''attention.self'''	)
  		if "mha" in orig_key:
  				_UpperCamelCase       : List[str]    =      orig_key.replace('''mha'''			,'''attention'''	)
  		if "W_q" in orig_key:
  				_UpperCamelCase       : Optional[Any]    =      orig_key.replace('''W_q'''			,'''self.query'''	)
  		if "W_k" in orig_key:
  				_UpperCamelCase       : Any    =      orig_key.replace('''W_k'''			,'''self.key'''	)
  		if "W_v" in orig_key:
  				_UpperCamelCase       : str    =      orig_key.replace('''W_v'''			,'''self.value'''	)
  		if "ff1" in orig_key:
  				_UpperCamelCase       : Union[str, Any]    =      orig_key.replace('''ff1'''			,'''intermediate.dense'''	)
  		if "ff2" in orig_key:
  				_UpperCamelCase       : Dict    =      orig_key.replace('''ff2'''			,'''output.dense'''	)
  		if "ff" in orig_key:
  				_UpperCamelCase       : Optional[Any]    =      orig_key.replace('''ff'''			,'''output.dense'''	)
  		if "mlm_class" in orig_key:
  				_UpperCamelCase       : Optional[Any]    =      orig_key.replace('''mlm.mlm_class'''			,'''cls.predictions.decoder'''	)
  		if "mlm" in orig_key:
  				_UpperCamelCase       : Optional[int]    =      orig_key.replace('''mlm'''			,'''cls.predictions.transform'''	)
  		if "cls" not in orig_key:
  				_UpperCamelCase       : int    =      '''nystromformer.''' + orig_key
  
  		return orig_key
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase			,UpperCamelCase	) ->      int:
  		for key in orig_state_dict.copy().keys():
  				_UpperCamelCase       : List[Any]    =      orig_state_dict.pop(UpperCamelCase	)
  
  				if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
  						continue
  				else:
  						_UpperCamelCase       : List[str]    =      val
  
  		_UpperCamelCase       : List[str]    =      orig_state_dict['''cls.predictions.decoder.bias''']
  		_UpperCamelCase       : int    =      (
  		    torch.arange(config.max_position_embeddings	).expand((1, -1)	) + 2
  		)
  
  		return orig_state_dict
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase			,UpperCamelCase			,UpperCamelCase	) ->      Dict:
  		_UpperCamelCase       : Tuple    =      torch.load(UpperCamelCase			,map_location='''cpu'''	)['''model_state_dict''']
  		_UpperCamelCase       : List[Any]    =      NystromformerConfig.from_json_file(UpperCamelCase	)
  		_UpperCamelCase       : Optional[int]    =      NystromformerForMaskedLM(UpperCamelCase	)
  
  		_UpperCamelCase       : str    =      convert_checkpoint_helper(UpperCamelCase			,UpperCamelCase	)
  
  		model.load_state_dict(UpperCamelCase	)
  		model.eval()
  		model.save_pretrained(UpperCamelCase	)
  
  		print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''	)
  
  
  if __name__ == "__main__":
  	_UpperCAmelCase   :      Tuple 						=  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   :      List[Any] 						=  parser.parse_args()
  	convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  _UpperCAmelCase   :      List[Any] 						=  logging.get_logger(__name__)
  
  _UpperCAmelCase   :      List[Any] 						=  {
      """nghuyong/ernie-1.0-base-zh""": """https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json""",
      """nghuyong/ernie-2.0-base-en""": """https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json""",
      """nghuyong/ernie-2.0-large-en""": """https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json""",
      """nghuyong/ernie-3.0-base-zh""": """https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json""",
      """nghuyong/ernie-3.0-medium-zh""": """https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json""",
      """nghuyong/ernie-3.0-mini-zh""": """https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json""",
      """nghuyong/ernie-3.0-micro-zh""": """https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json""",
      """nghuyong/ernie-3.0-nano-zh""": """https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json""",
      """nghuyong/ernie-gram-zh""": """https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json""",
      """nghuyong/ernie-health-zh""": """https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json""",
  }
  
  class 			UpperCAmelCase				(					a_ ):
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		A__  :   str          =    'ernie'
  		def __init__(    self  ,					_snake_case=30522  ,					_snake_case=768  ,					_snake_case=12  ,					_snake_case=12  ,					_snake_case=3072  ,					_snake_case="gelu"  ,					_snake_case=0.1  ,					_snake_case=0.1  ,					_snake_case=512  ,					_snake_case=2  ,					_snake_case=3  ,					_snake_case=False  ,					_snake_case=0.02  ,					_snake_case=1E-12  ,					_snake_case=0  ,					_snake_case="absolute"  ,					_snake_case=True  ,					_snake_case=None  ,					**_snake_case  ,					)				->    List[Any]:
  				super().__init__(pad_token_id=_snake_case  ,					**_snake_case     )
  
  				_UpperCamelCase       : Dict    =      vocab_size
  				_UpperCamelCase       : int    =      hidden_size
  				_UpperCamelCase       : List[Any]    =      num_hidden_layers
  				_UpperCamelCase       : Tuple    =      num_attention_heads
  				_UpperCamelCase       : List[str]    =      hidden_act
  				_UpperCamelCase       : Optional[int]    =      intermediate_size
  				_UpperCamelCase       : Tuple    =      hidden_dropout_prob
  				_UpperCamelCase       : Optional[int]    =      attention_probs_dropout_prob
  				_UpperCamelCase       : List[str]    =      max_position_embeddings
  				_UpperCamelCase       : Tuple    =      type_vocab_size
  				_UpperCamelCase       : Union[str, Any]    =      task_type_vocab_size
  				_UpperCamelCase       : Union[str, Any]    =      use_task_id
  				_UpperCamelCase       : Any    =      initializer_range
  				_UpperCamelCase       : Optional[int]    =      layer_norm_eps
  				_UpperCamelCase       : str    =      position_embedding_type
  				_UpperCamelCase       : Any    =      use_cache
  				_UpperCamelCase       : str    =      classifier_dropout
  
  
  
  
  
  
  
  class 			UpperCAmelCase				(					a_ ):
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		@property
  		def _lowercase  (    self     )				->    Mapping[str, Mapping[int, str]]:
  				if self.task == "multiple-choice":
  						_UpperCamelCase       : str    =      {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
  				else:
  						_UpperCamelCase       : Dict    =      {0: '''batch''', 1: '''sequence'''}
  				return OrderedDict(
  				    [
  				        ('''input_ids''', dynamic_axis),
  				        ('''attention_mask''', dynamic_axis),
  				        ('''token_type_ids''', dynamic_axis),
  				        ('''task_type_ids''', dynamic_axis),
  				    ]     )
  
  
  
  
  
  