





import argparse

import torch

from transformers import MraConfig, MraForMaskedLM





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

						return orig_key





def  a_       (							_lowercase ,		_lowercase     ):
						for key in orig_state_dict.copy().keys():
												_UpperCamelCase    :      Union[str, Any]						     =					orig_state_dict.pop(a_     )

												if ("pooler" in key) or ("sen_class" in key):
																		continue
												else:
																		_UpperCamelCase    :      Optional[int]						     =					val

						_UpperCamelCase    :      Any						     =					orig_state_dict['''cls.predictions.decoder.bias''']
						_UpperCamelCase    :      Tuple						     =					torch.arange(a_     ).expand((1, -1)     ) + 2

						return orig_state_dict





def  a_       (							_lowercase ,		_lowercase ,		_lowercase     ):
						_UpperCamelCase    :      Dict						     =					torch.load(a_ ,		map_location='''cpu'''     )['''model_state_dict''']
						_UpperCamelCase    :      Union[str, Any]						     =					MraConfig.from_json_file(a_     )
						_UpperCamelCase    :      Optional[int]						     =					MraForMaskedLM(a_     )

						_UpperCamelCase    :      int						     =					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__":
						UpperCamelCase_     =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_     =parser.parse_args()
						convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)










"""simple docstring"""




def  a_       (							_lowercase     ):
						_UpperCamelCase    :      str						     =					len(_lowercase     )

						# Set the gap value to be decreased by a factor of 1.3
						# after each iteration
						_UpperCamelCase    :      str						     =					1.3

						# Continue sorting until the gap is 1
						while gap > 1:
												# Decrease the gap value
												_UpperCamelCase    :      Union[str, Any]						     =					int(gap / shrink     )

												# Sort the elements using insertion sort
												for i in range(_lowercase ,		len(_lowercase     )     ):
																		_UpperCamelCase    :      Optional[Any]						     =					collection[i]
																		_UpperCamelCase    :      int						     =					i
																		while j >= gap and collection[j - gap] > temp:
																								_UpperCamelCase    :      List[str]						     =					collection[j - gap]
																								j -= gap
																		_UpperCamelCase    :      Union[str, Any]						     =					temp

						return collection


if __name__ == "__main__":
						import doctest

						doctest.testmod()



