code: import argparse
  
  import torch
  
  from transformers import MraConfig, MraForMaskedLM
  
  
  
  def 							_lowerCamelCase(				__snake_case	)  ->   Dict:
  		if "model" in orig_key:
  				__snake_case		       =     orig_key.replace("model."						,						""	)
  		if "norm1" in orig_key:
  				__snake_case		       =     orig_key.replace("norm1"						,						"attention.output.LayerNorm"	)
  		if "norm2" in orig_key:
  				__snake_case		       =     orig_key.replace("norm2"						,						"output.LayerNorm"	)
  		if "norm" in orig_key:
  				__snake_case		       =     orig_key.replace("norm"						,						"LayerNorm"	)
  		if "transformer" in orig_key:
  				__snake_case		       =     orig_key.split("."	)[0].split("_"	)[-1]
  				__snake_case		       =     orig_key.replace(f"""transformer_{layer_num}"""						,						f"""encoder.layer.{layer_num}"""	)
  		if "mha.attn" in orig_key:
  				__snake_case		       =     orig_key.replace("mha.attn"						,						"attention.self"	)
  		if "mha" in orig_key:
  				__snake_case		       =     orig_key.replace("mha"						,						"attention"	)
  		if "W_q" in orig_key:
  				__snake_case		       =     orig_key.replace("W_q"						,						"self.query"	)
  		if "W_k" in orig_key:
  				__snake_case		       =     orig_key.replace("W_k"						,						"self.key"	)
  		if "W_v" in orig_key:
  				__snake_case		       =     orig_key.replace("W_v"						,						"self.value"	)
  		if "ff.0" in orig_key:
  				__snake_case		       =     orig_key.replace("ff.0"						,						"intermediate.dense"	)
  		if "ff.2" in orig_key:
  				__snake_case		       =     orig_key.replace("ff.2"						,						"output.dense"	)
  		if "ff" in orig_key:
  				__snake_case		       =     orig_key.replace("ff"						,						"output.dense"	)
  		if "mlm_class" in orig_key:
  				__snake_case		       =     orig_key.replace("mlm.mlm_class"						,						"cls.predictions.decoder"	)
  		if "mlm" in orig_key:
  				__snake_case		       =     orig_key.replace("mlm"						,						"cls.predictions.transform"	)
  		if "backbone.backbone.encoders" in orig_key:
  				__snake_case		       =     orig_key.replace("backbone.backbone.encoders"						,						"encoder.layer"	)
  		if "cls" not in orig_key:
  				__snake_case		       =     "mra." + orig_key
  
  		return orig_key
  
  
  
  def 							_lowerCamelCase(				__snake_case						,						__snake_case	)  ->   Union[str, Any]:
  		for key in orig_state_dict.copy().keys():
  				__snake_case		       =     orig_state_dict.pop(__snake_case	)
  
  				if ("pooler" in key) or ("sen_class" in key):
  						continue
  				else:
  						__snake_case		       =     val
  
  		__snake_case		       =     orig_state_dict["cls.predictions.decoder.bias"]
  		__snake_case		       =     torch.arange(__snake_case	).expand((1, -1)	) + 2
  
  		return orig_state_dict
  
  
  
  def 							_lowerCamelCase(				__snake_case						,						__snake_case						,						__snake_case	)  ->   Any:
  		__snake_case		       =     torch.load(__snake_case						,						map_location="cpu"	)["model_state_dict"]
  		__snake_case		       =     MraConfig.from_json_file(__snake_case	)
  		__snake_case		       =     MraForMaskedLM(__snake_case	)
  
  		__snake_case		       =     convert_checkpoint_helper(config.max_position_embeddings						,						__snake_case	)
  
  		print(model.load_state_dict(__snake_case	)	)
  		model.eval()
  		model.save_pretrained(__snake_case	)
  
  		print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""	)
  
  
  if __name__ == "__main__":
  		lowerCamelCase__	    =				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.'
  		)
  		lowerCamelCase__	    =				parser.parse_args()
  		convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
  
  
  
  
  
  
style_context: from __future__ import annotations
  
  from collections.abc import Iterable
  from dataclasses import dataclass
  from typing import Any
  
  
  
  
  class 					UpperCamelCase	:
  					__UpperCamelCase						=   ("""_front""", """_back""", """_len""")
  
  
  
  
  					@dataclass
  					class 					UpperCamelCase	:
  										__UpperCamelCase						=   None
  										__UpperCamelCase						=   None
  										__UpperCamelCase						=   None
  
  
  
  
  					class 					UpperCamelCase	:
  										__UpperCamelCase						=   ("""_cur""",)
  
  
  
  
  										def __init__( self :					Dict      ,_lowerCAmelCase :					Deque._Node | None						):
  
  
  												"""simple docstring"""
  
  
  
  
  												__snake_case		       =     cur
  
  
  
  
  										def __iter__( self :					Dict						):
  
  
  												"""simple docstring"""
  
  
  
  
  												return self
  
  
  
  
  
  
  
  										def 							UpperCamelCase_						( self :					Optional[Any]						):
  
  
  												"""simple docstring"""
  
  
  
  
  												if self._cur is None:
  														# finished iterating
  														raise StopIteration
  												__snake_case		       =     self._cur.val
  												__snake_case		       =     self._cur.next_node
  
  												return val
  
  
  
  
  					def __init__( self :					Optional[int]      ,_lowerCAmelCase :					Iterable[Any] | None = None						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     None
  							__snake_case		       =     None
  							__snake_case		       =     0
  
  							if iterable is not None:
  									# append every value to the deque
  									for val in iterable:
  											self.append(_lowerCAmelCase						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					Any						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     self._Node(_lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase						)
  							if self.is_empty():
  									# front = back
  									__snake_case		       =     __snake_case		       =     node
  									__snake_case		       =     1
  							else:
  									# connect nodes
  									__snake_case		       =     node
  									__snake_case		       =     self._back
  									__snake_case		       =     node  # assign new back to the new node
  
  									self._len += 1
  
  									# make sure there were no errors
  									assert not self.is_empty(), "Error on appending value."
  
  
  
  
  					def 							UpperCamelCase_						( self :					Any      ,_lowerCAmelCase :					Any						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     self._Node(_lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase						)
  							if self.is_empty():
  									# front = back
  									__snake_case		       =     __snake_case		       =     node
  									__snake_case		       =     1
  							else:
  									# connect nodes
  									__snake_case		       =     self._front
  									__snake_case		       =     node
  									__snake_case		       =     node  # assign new front to the new node
  
  									self._len += 1
  
  									# make sure there were no errors
  									assert not self.is_empty(), "Error on appending value."
  
  
  
  
  					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					Iterable[Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							for val in iterable:
  									self.append(_lowerCAmelCase						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Optional[int]      ,_lowerCAmelCase :					Iterable[Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							for val in iterable:
  									self.appendleft(_lowerCAmelCase						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					List[Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							assert not self.is_empty(), "Deque is empty."
  
  							__snake_case		       =     self._back
  							__snake_case		       =     self._back.prev_node  # set new back
  							# drop the last node - python will deallocate memory automatically
  							__snake_case		       =     None
  
  							self._len -= 1
  
  							return topop.val
  
  
  
  
  					def 							UpperCamelCase_						( self :					List[str]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							assert not self.is_empty(), "Deque is empty."
  
  							__snake_case		       =     self._front
  							__snake_case		       =     self._front.next_node  # set new front and drop the first node
  							__snake_case		       =     None
  
  							self._len -= 1
  
  							return topop.val
  
  
  
  
  					def 							UpperCamelCase_						( self :					int						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return self._front is None
  
  
  
  
  					def __len__( self :					Tuple						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return self._len
  
  
  
  
  					def __eq__( self :					Union[str, Any]      ,_lowerCAmelCase :					object						):
  
  
  							"""simple docstring"""
  
  
  
  
  							if not isinstance(_lowerCAmelCase      ,_lowerCAmelCase						):
  									return NotImplemented
  
  							__snake_case		       =     self._front
  							__snake_case		       =     other._front
  
  							# if the length of the dequeues are not the same, they are not equal
  							if len(self						) != len(_lowerCAmelCase						):
  									return False
  
  							while me is not None and oth is not None:
  									# compare every value
  									if me.val != oth.val:
  											return False
  									__snake_case		       =     me.next_node
  									__snake_case		       =     oth.next_node
  
  							return True
  
  
  
  
  					def __iter__( self :					Tuple						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return Deque._Iterator(self._front						)
  
  
  
  
  
  
  
  					def __repr__( self :					List[Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     []
  							__snake_case		       =     self._front
  							while aux is not None:
  									# append the values in a list to display
  									values_list.append(aux.val						)
  									__snake_case		       =     aux.next_node
  
  							return F"""[{", ".join(repr(_lowerCAmelCase						) for val in values_list						)}]"""
  
  
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  
  
  
  
  
  