
import argparse

import torch

from transformers import MraConfig, MraForMaskedLM




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

							return orig_key




def 				lowerCamelCase__				(						a__  :  Union[str, Any]	,      a__  :  List[Any]     )		->	List[str]:
							for key in orig_state_dict.copy().keys():
														UpperCamelCase_							       =       orig_state_dict.pop(a__     )

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

							UpperCamelCase_							       =       orig_state_dict["""cls.predictions.decoder.bias"""]
							UpperCamelCase_							       =       torch.arange(a__     ).expand((1, -1)     ) + 2

							return orig_state_dict




def 				lowerCamelCase__				(						a__  :  Optional[int]	,      a__  :  str	,      a__  :  Optional[int]     )		->	Optional[int]:
							UpperCamelCase_							       =       torch.load(a__	,      map_location="""cpu"""     )["""model_state_dict"""]
							UpperCamelCase_							       =       MraConfig.from_json_file(a__     )
							UpperCamelCase_							       =       MraForMaskedLM(a__     )

							UpperCamelCase_							       =       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__":
						_A	  =					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.'''
						)
						_A	  =					parser.parse_args()
						convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





def 				lowerCamelCase__				(						a__  :  dict	,      a__  :  int     )		->	Optional[Any]:
       if not root or root not in binary_tree_mirror_dictionary:
              return
       UpperCamelCase_       ,				UpperCamelCase_							       =       binary_tree_mirror_dictionary[root][:2]
       UpperCamelCase_							       =       [right_child, left_child]
       binary_tree_mirror_dict(a__	,      a__     )
       binary_tree_mirror_dict(a__	,      a__     )




def 				lowerCamelCase__				(						a__  :  dict	,      a__  :  int = 1     )		->	dict:
       if not binary_tree:
              raise ValueError("""binary tree cannot be empty"""     )
       if root not in binary_tree:
              UpperCamelCase_							       =       f'''root {root} is not present in the binary_tree'''
              raise ValueError(a__     )
       UpperCamelCase_							       =       dict(a__     )
       binary_tree_mirror_dict(a__	,      a__     )
       return binary_tree_mirror_dictionary


if __name__ == "__main__":
      _A	  =					{1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
      print(F'''Binary tree: {binary_tree}''')
      _A	  =					binary_tree_mirror(binary_tree, 5)
      print(F'''Binary tree mirror: {binary_tree_mirror_dictionary}''')



