import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM





def    __lowercase     (						lowerCamelCase					:     int	):
 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_					:						str =					orig_key.replace('norm2'  ,  'output.LayerNorm'	)
 if "norm" in orig_key:
  UpperCamelCase_					:						List[str] =					orig_key.replace('norm'  ,  'LayerNorm'	)
 if "transformer" in orig_key:
  UpperCamelCase_					:						str =					orig_key.split('.'	)[0].split('_'	)[-1]
  UpperCamelCase_					:						Dict =					orig_key.replace(F"transformer_{layer_num}"  ,  F"encoder.layer.{layer_num}"	)
 if "mha.attn" in orig_key:
  UpperCamelCase_					:						Optional[Any] =					orig_key.replace('mha.attn'  ,  'attention.self'	)
 if "mha" in orig_key:
  UpperCamelCase_					:						int =					orig_key.replace('mha'  ,  'attention'	)
 if "W_q" in orig_key:
  UpperCamelCase_					:						Tuple =					orig_key.replace('W_q'  ,  'self.query'	)
 if "W_k" in orig_key:
  UpperCamelCase_					:						Dict =					orig_key.replace('W_k'  ,  'self.key'	)
 if "W_v" in orig_key:
  UpperCamelCase_					:						str =					orig_key.replace('W_v'  ,  'self.value'	)
 if "ff1" in orig_key:
  UpperCamelCase_					:						Tuple =					orig_key.replace('ff1'  ,  'intermediate.dense'	)
 if "ff2" in orig_key:
  UpperCamelCase_					:						Optional[int] =					orig_key.replace('ff2'  ,  'output.dense'	)
 if "ff" in orig_key:
  UpperCamelCase_					:						List[Any] =					orig_key.replace('ff'  ,  'output.dense'	)
 if "mlm_class" in orig_key:
  UpperCamelCase_					:						Tuple =					orig_key.replace('mlm.mlm_class'  ,  'cls.predictions.decoder'	)
 if "mlm" in orig_key:
  UpperCamelCase_					:						str =					orig_key.replace('mlm'  ,  'cls.predictions.transform'	)
 if "cls" not in orig_key:
  UpperCamelCase_					:						str =					'yoso.' + orig_key

 return orig_key





def    __lowercase     (						lowerCamelCase					:     Tuple  ,  lowerCamelCase					:     Optional[Any]	):
 for key in orig_state_dict.copy().keys():
  UpperCamelCase_					:						Optional[int] =					orig_state_dict.pop(lowerCamelCase	)

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

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

 return orig_state_dict





def    __lowercase     (						lowerCamelCase					:     Tuple  ,  lowerCamelCase					:     Union[str, Any]  ,  lowerCamelCase					:     str	):
 UpperCamelCase_					:						Any =					torch.load(lowerCamelCase  ,  map_location='cpu'	)['model_state_dict']
 UpperCamelCase_					:						Tuple =					YosoConfig.from_json_file(lowerCamelCase	)
 UpperCamelCase_					:						Union[str, Any] =					YosoForMaskedLM(lowerCamelCase	)

 UpperCamelCase_					:						int =					convert_checkpoint_helper(config.max_position_embeddings  ,  lowerCamelCase	)

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

 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 YOSO pytorch checkpoint.'
   )
   parser.add_argument(
       '--config_file',
       default=None,
       type=str,
       required=True,
       help='The json file for YOSO 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_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)




import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer





def    __lowercase     (						lowerCamelCase					:     str  ,  lowerCamelCase					:     str  ,  **lowerCamelCase					:     List[Any]	):
 UpperCamelCase_					:						Union[str, Any] =					AutoConfig.from_pretrained(lowerCamelCase  ,  **lowerCamelCase	)
 UpperCamelCase_					:						str =					AutoModelForSeqaSeqLM.from_config(lowerCamelCase	)
 model.save_pretrained(lowerCamelCase	)
 AutoTokenizer.from_pretrained(lowerCamelCase	).save_pretrained(lowerCamelCase	)
 return model


if __name__ == "__main__":
   fire.Fire(save_randomly_initialized_version)



