


"""simple docstring"""
class   __A  :





							'''simple docstring'''

							def __init__(					self				:   Optional[int]							)       ->int:



														"""simple docstring"""



														snake_case_     							=		[]
														snake_case_     							=		0
														snake_case_     							=		0



							def __str__(					self				:   str							)       ->Optional[Any]:



														"""simple docstring"""



														snake_case_     							=		'''<''' + str(self.entries							)[1:-1] + '''>'''
														return printed



							def 		lowerCAmelCase							(					self				:   Dict		,							UpperCAmelCase_				:   Optional[Any]							)       ->Dict:



														"""simple docstring"""



														self.entries.append(UpperCamelCase_							)
														snake_case_     							=		self.length + 1



							def 		lowerCAmelCase							(					self				:   Dict							)       ->List[str]:



														"""simple docstring"""



														snake_case_     							=		self.length - 1
														snake_case_     							=		self.entries[self.front]
														# self.front-=1
														# self.entries = self.entries[self.front:]
														snake_case_     							=		self.entries[1:]
														return dequeued



							def 		lowerCAmelCase							(					self				:   int		,							UpperCAmelCase_				:   Dict							)       ->List[str]:



														"""simple docstring"""



														for _ in range(UpperCamelCase_							):
																					self.put(self.get()							)



							def 		lowerCAmelCase							(					self				:   List[str]							)       ->str:



														"""simple docstring"""



														return self.entries[0]



							def 		lowerCAmelCase							(					self				:   Any							)       ->List[Any]:



														"""simple docstring"""



														return self.length





'''simple docstring'''






import argparse

import torch

from transformers import MraConfig, MraForMaskedLM







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ):
		if "model" in orig_key:
				lowerCAmelCase_   :       Union[str, Any]    							=orig_key.replace('''model.'''  ,					''''''  )
		if "norm1" in orig_key:
				lowerCAmelCase_   :       List[Any]    							=orig_key.replace('''norm1'''  ,					'''attention.output.LayerNorm'''  )
		if "norm2" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''norm2'''  ,					'''output.LayerNorm'''  )
		if "norm" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''norm'''  ,					'''LayerNorm'''  )
		if "transformer" in orig_key:
				lowerCAmelCase_   :       str    							=orig_key.split('''.'''  )[0].split('''_'''  )[-1]
				lowerCAmelCase_   :       Optional[Any]    							=orig_key.replace(f'transformer_{layer_num}'  ,					f'encoder.layer.{layer_num}'  )
		if "mha.attn" in orig_key:
				lowerCAmelCase_   :       Optional[Any]    							=orig_key.replace('''mha.attn'''  ,					'''attention.self'''  )
		if "mha" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''mha'''  ,					'''attention'''  )
		if "W_q" in orig_key:
				lowerCAmelCase_   :       int    							=orig_key.replace('''W_q'''  ,					'''self.query'''  )
		if "W_k" in orig_key:
				lowerCAmelCase_   :       Dict    							=orig_key.replace('''W_k'''  ,					'''self.key'''  )
		if "W_v" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''W_v'''  ,					'''self.value'''  )
		if "ff.0" in orig_key:
				lowerCAmelCase_   :       Dict    							=orig_key.replace('''ff.0'''  ,					'''intermediate.dense'''  )
		if "ff.2" in orig_key:
				lowerCAmelCase_   :       str    							=orig_key.replace('''ff.2'''  ,					'''output.dense'''  )
		if "ff" in orig_key:
				lowerCAmelCase_   :       int    							=orig_key.replace('''ff'''  ,					'''output.dense'''  )
		if "mlm_class" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''mlm.mlm_class'''  ,					'''cls.predictions.decoder'''  )
		if "mlm" in orig_key:
				lowerCAmelCase_   :       int    							=orig_key.replace('''mlm'''  ,					'''cls.predictions.transform'''  )
		if "backbone.backbone.encoders" in orig_key:
				lowerCAmelCase_   :       Any    							=orig_key.replace('''backbone.backbone.encoders'''  ,					'''encoder.layer'''  )
		if "cls" not in orig_key:
				lowerCAmelCase_   :       List[Any]    							='''mra.''' + orig_key

		return orig_key







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		for key in orig_state_dict.copy().keys():
				lowerCAmelCase_   :       Tuple    							=orig_state_dict.pop(_SCREAMING_SNAKE_CASE  )

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

		lowerCAmelCase_   :       Union[str, Any]    							=orig_state_dict['''cls.predictions.decoder.bias''']
		lowerCAmelCase_   :       int    							=torch.arange(_SCREAMING_SNAKE_CASE  ).expand((1, -1)  ) + 2

		return orig_state_dict







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		lowerCAmelCase_   :       Tuple    							=torch.load(_SCREAMING_SNAKE_CASE  ,					map_location='''cpu'''  )['''model_state_dict''']
		lowerCAmelCase_   :       Optional[int]    							=MraConfig.from_json_file(_SCREAMING_SNAKE_CASE  )
		lowerCAmelCase_   :       Optional[Any]    							=MraForMaskedLM(_SCREAMING_SNAKE_CASE  )

		lowerCAmelCase_   :       str    							=convert_checkpoint_helper(config.max_position_embeddings  ,					_SCREAMING_SNAKE_CASE  )

		print(model.load_state_dict(_SCREAMING_SNAKE_CASE  )  )
		model.eval()
		model.save_pretrained(_SCREAMING_SNAKE_CASE  )

		print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'  )


if __name__ == "__main__":
							__lowercase	=    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.'''
							)
							__lowercase	=    parser.parse_args()
							convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






