


import argparse

import torch

from transformers import MraConfig, MraForMaskedLM



def       lowerCAmelCase			(		lowerCAmelCase_			)->			int:







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

				return orig_key



def       lowerCAmelCase			(		lowerCAmelCase_			, lowerCAmelCase_			)->			Any:







				for key in orig_state_dict.copy().keys():
								lowerCAmelCase_			:   Optional[int]     					=					orig_state_dict.pop(lowerCAmelCase_			)

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

				lowerCAmelCase_			:   Dict     					=					orig_state_dict['''cls.predictions.decoder.bias''']
				lowerCAmelCase_			:   List[Any]     					=					torch.arange(lowerCAmelCase_			).expand((1, -1)			) + 2

				return orig_state_dict



def       lowerCAmelCase			(		lowerCAmelCase_			, lowerCAmelCase_			, lowerCAmelCase_			)->			Optional[int]:
				lowerCAmelCase_			:   Any     					=					torch.load(lowerCAmelCase_			, map_location='''cpu'''			)['''model_state_dict''']
				lowerCAmelCase_			:   Tuple     					=					MraConfig.from_json_file(lowerCAmelCase_			)
				lowerCAmelCase_			:   List[Any]     					=					MraForMaskedLM(lowerCAmelCase_			)

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

				print(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      :	List[Any]  =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      :	Tuple  =parser.parse_args()
						convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)


"""simple docstring"""
def       UpperCamelCase_					(     lowerCamelCase							:					str   )						->     str:



       """simple docstring"""







       return " ".join([word[::-1] for word in input_str.split()]   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
