



'''simple docstring'''





import argparse

import torch

from transformers import MraConfig, MraForMaskedLM



def     UpperCAmelCase     (    a_		)   ->       Optional[Any]:



   """simple docstring"""






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

   return orig_key



def     UpperCAmelCase     (    a_      ,		a_		)   ->       str:



   """simple docstring"""






   for key in orig_state_dict.copy().keys():
      A_  :		Tuple						=					orig_state_dict.pop(a_		)

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

   A_  :		Tuple						=					orig_state_dict["""cls.predictions.decoder.bias"""]
   A_  :		Dict						=					torch.arange(a_		).expand((1, -1)		) + 2

   return orig_state_dict



def     UpperCAmelCase     (    a_      ,		a_      ,		a_		)   ->       str:



   """simple docstring"""






   A_  :		str						=					torch.load(a_      ,		map_location="""cpu"""		)["""model_state_dict"""]
   A_  :		Optional[int]						=					MraConfig.from_json_file(a_		)
   A_  :		Optional[int]						=					MraForMaskedLM(a_		)

   A_  :		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__    :     str			  =   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__    :     Optional[Any]			  =   parser.parse_args()
  convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





'''simple docstring'''





from ..utils import DummyObject, requires_backends


class 							_lowerCAmelCase	(  metaclass=__A							):


 """simple docstring"""

 lowerCamelCase									=     ['''torch''', '''librosa''']







 def __init__(      self					,    *_lowerCamelCase					,    **_lowerCamelCase  )    ->    str:
    requires_backends(self					,    ["""torch""", """librosa"""]  )







 @classmethod
 def       UpperCAmelCase_       (      cls					,    *_lowerCamelCase					,    **_lowerCamelCase  )    ->    Dict:
    requires_backends(cls					,    ["""torch""", """librosa"""]  )







 @classmethod
 def       UpperCAmelCase_       (      cls					,    *_lowerCamelCase					,    **_lowerCamelCase  )    ->    Optional[int]:
    requires_backends(cls					,    ["""torch""", """librosa"""]  )


class 							_lowerCAmelCase	(  metaclass=__A							):


 """simple docstring"""

 lowerCamelCase									=     ['''torch''', '''librosa''']







 def __init__(      self					,    *_lowerCamelCase					,    **_lowerCamelCase  )    ->    Dict:
    requires_backends(self					,    ["""torch""", """librosa"""]  )







 @classmethod
 def       UpperCAmelCase_       (      cls					,    *_lowerCamelCase					,    **_lowerCamelCase  )    ->    str:
    requires_backends(cls					,    ["""torch""", """librosa"""]  )







 @classmethod
 def       UpperCAmelCase_       (      cls					,    *_lowerCamelCase					,    **_lowerCamelCase  )    ->    Dict:
    requires_backends(cls					,    ["""torch""", """librosa"""]  )
