
"""simple docstring"""







import argparse

import torch

from transformers import MraConfig, MraForMaskedLM




def 			A_			(					lowercase      )     ->							Any:


					"""simple docstring"""







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

					return orig_key




def 			A_			(					lowercase						, lowercase      )     ->							int:


					"""simple docstring"""







					for key in orig_state_dict.copy().keys():
										UpperCAmelCase_						:    Any    					=  orig_state_dict.pop(_A      )

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

					UpperCAmelCase_						:    Any    					=  orig_state_dict["""cls.predictions.decoder.bias"""]
					UpperCAmelCase_						:    int    					=  torch.arange(_A      ).expand((1, -1)      ) + 2

					return orig_state_dict




def 			A_			(					lowercase						, lowercase						, lowercase      )     ->							str:


					"""simple docstring"""







					UpperCAmelCase_						:    Union[str, Any]    					=  torch.load(_A						, map_location="""cpu"""      )["""model_state_dict"""]
					UpperCAmelCase_						:    List[Any]    					=  MraConfig.from_json_file(_A      )
					UpperCAmelCase_						:    Dict    					=  MraForMaskedLM(_A      )

					UpperCAmelCase_						:    str    					=  convert_checkpoint_helper(config.max_position_embeddings						, _A      )

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

					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)



"""simple docstring"""







import warnings

from .generation import GenerationMixin



class 		UpperCAmelCase_	(lowerCamelCase_						):
 """simple docstring"""




 warnings.warn(
     """Importing `GenerationMixin` from `src/transformers/generation_utils.py` is deprecated and will """
     """be removed in Transformers v5. Import as `from transformers import GenerationMixin` instead."""  , lowerCamelCase_  , )

