




import argparse

import torch

from transformers import MraConfig, MraForMaskedLM





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

					return orig_key





def 							A__   (snake_case      :		Tuple		,  snake_case      :		List[str]							)    ->	Dict:
					for key in orig_state_dict.copy().keys():
										__UpperCamelCase				:  Optional[int]								=			orig_state_dict.pop(snake_case							)

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

					__UpperCamelCase				:  Optional[Any]								=			orig_state_dict["""cls.predictions.decoder.bias"""]
					__UpperCamelCase				:  Optional[Any]								=			torch.arange(snake_case							).expand((1, -1)							) + 2

					return orig_state_dict





def 							A__   (snake_case      :		Dict		,  snake_case      :		str		,  snake_case      :		Optional[Any]							)    ->	Optional[Any]:
					__UpperCamelCase				:  int								=			torch.load(snake_case		,  map_location="""cpu"""							)["""model_state_dict"""]
					__UpperCamelCase				:  List[str]								=			MraConfig.from_json_file(snake_case							)
					__UpperCamelCase				:  Dict								=			MraForMaskedLM(snake_case							)

					__UpperCamelCase				:  List[str]								=			convert_checkpoint_helper(config.max_position_embeddings		,  snake_case							)

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

					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)












a__		    =    {
    "kilogram": 1,
    "gram": pow(10, 3),
    "milligram": pow(10, 6),
    "metric-ton": pow(10, -3),
    "long-ton": 0.00_0984_2073,
    "short-ton": 0.00_1102_3122,
    "pound": 2.20_4624_4202,
    "stone": 0.15_7473_1728,
    "ounce": 35.2_7399_0723,
    "carrat": 5000,
    "atomic-mass-unit": 6.0_2213_6652e26,
}

a__		    =    {
    "kilogram": 1,
    "gram": pow(10, -3),
    "milligram": pow(10, -6),
    "metric-ton": pow(10, 3),
    "long-ton": 1016.0_4608,
    "short-ton": 907.184,
    "pound": 0.45_3592,
    "stone": 6.3_5029,
    "ounce": 0.028_3495,
    "carrat": 0.0002,
    "atomic-mass-unit": 1.6_6054_0199e-27,
}





def 							A__   (snake_case      :		str		,  snake_case      :		str		,  snake_case      :		float							)    ->	float:
					if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
										__UpperCamelCase				:  Any								=			(
										    F'''Invalid \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
										    F'''Supported values are: {", ".join(snake_case							)}'''
										)
										raise ValueError(snake_case							)
					return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
	import doctest

	doctest.testmod()






