





import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM

def   a_    ( lowerCAmelCase_      :			int	):
   if "model" in orig_key:
      __lowerCAmelCase								=	orig_key.replace('model.',	''	)
   if "norm1" in orig_key:
      __lowerCAmelCase								=	orig_key.replace('norm1',	'attention.output.LayerNorm'	)
   if "norm2" in orig_key:
      __lowerCAmelCase								=	orig_key.replace('norm2',	'output.LayerNorm'	)
   if "norm" in orig_key:
      __lowerCAmelCase								=	orig_key.replace('norm',	'LayerNorm'	)
   if "transformer" in orig_key:
      __lowerCAmelCase								=	orig_key.split('.'	)[0].split('_'	)[-1]
      __lowerCAmelCase								=	orig_key.replace(F"""transformer_{layer_num}""",	F"""encoder.layer.{layer_num}"""	)
   if "mha.attn" in orig_key:
      __lowerCAmelCase								=	orig_key.replace('mha.attn',	'attention.self'	)
   if "mha" in orig_key:
      __lowerCAmelCase								=	orig_key.replace('mha',	'attention'	)
   if "W_q" in orig_key:
      __lowerCAmelCase								=	orig_key.replace('W_q',	'self.query'	)
   if "W_k" in orig_key:
      __lowerCAmelCase								=	orig_key.replace('W_k',	'self.key'	)
   if "W_v" in orig_key:
      __lowerCAmelCase								=	orig_key.replace('W_v',	'self.value'	)
   if "ff1" in orig_key:
      __lowerCAmelCase								=	orig_key.replace('ff1',	'intermediate.dense'	)
   if "ff2" in orig_key:
      __lowerCAmelCase								=	orig_key.replace('ff2',	'output.dense'	)
   if "ff" in orig_key:
      __lowerCAmelCase								=	orig_key.replace('ff',	'output.dense'	)
   if "mlm_class" in orig_key:
      __lowerCAmelCase								=	orig_key.replace('mlm.mlm_class',	'cls.predictions.decoder'	)
   if "mlm" in orig_key:
      __lowerCAmelCase								=	orig_key.replace('mlm',	'cls.predictions.transform'	)
   if "cls" not in orig_key:
      __lowerCAmelCase								=	'yoso.' + orig_key

   return orig_key

def   a_    ( lowerCAmelCase_      :			Tuple,	lowerCAmelCase_      :			List[str]	):
   for key in orig_state_dict.copy().keys():
      __lowerCAmelCase								=	orig_state_dict.pop(lowerCAmelCase_	)

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

   __lowerCAmelCase								=	orig_state_dict['cls.predictions.decoder.bias']
   __lowerCAmelCase								=	torch.arange(lowerCAmelCase_	).expand((1, -1)	) + 2

   return orig_state_dict

def   a_    ( lowerCAmelCase_      :			Optional[Any],	lowerCAmelCase_      :			Any,	lowerCAmelCase_      :			str	):
   __lowerCAmelCase								=	torch.load(lowerCAmelCase_,	map_location='cpu'	)['model_state_dict']
   __lowerCAmelCase								=	YosoConfig.from_json_file(lowerCAmelCase_	)
   __lowerCAmelCase								=	YosoForMaskedLM(lowerCAmelCase_	)

   __lowerCAmelCase								=	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__":
     _snake_case  :    str						    =  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.'
     )
     _snake_case  :    Any						    =  parser.parse_args()
     convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)








def   a_    ( lowerCAmelCase_      :			str,	lowerCAmelCase_      :			int	):
   return [sentence[i : i + ngram_size] for i in range(len(lowerCAmelCase_	) - ngram_size + 1	)]


if __name__ == "__main__":
     from doctest import testmod

     testmod()

