




import argparse

import torch

from transformers import MraConfig, MraForMaskedLM






def 					snake_case					(							A			)							->							Optional[int]:




	'''simple docstring'''


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

	return orig_key






def 					snake_case					(							A						,   A			)							->							List[Any]:




	'''simple docstring'''


	for key in orig_state_dict.copy().keys():
		lowerCamelCase_							:		Tuple							   =							orig_state_dict.pop(A			)

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

	lowerCamelCase_							:		Optional[int]							   =							orig_state_dict['cls.predictions.decoder.bias']
	lowerCamelCase_							:		List[Any]							   =							torch.arange(A			).expand((1, -1)			) + 2

	return orig_state_dict






def 					snake_case					(							A						,   A						,   A			)							->							List[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		str							   =							torch.load(A						,   map_location='cpu'			)['model_state_dict']
	lowerCamelCase_							:		Optional[Any]							   =							MraConfig.from_json_file(A			)
	lowerCamelCase_							:		List[str]							   =							MraForMaskedLM(A			)

	lowerCamelCase_							:		Dict							   =							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)






def  lowerCAmelCase  (	UpperCAmelCase       )  ->str:






					"""simple docstring"""






					return " ".join(sorted(set(sentence.split()       )       )       )


if __name__ == "__main__":
				import doctest

				doctest.testmod()



