
import argparse

import torch

from transformers import MraConfig, MraForMaskedLM

def a__   (	A_ ):






  '''simple docstring'''





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

  return orig_key

def a__   (	A_,					A_ ):






  '''simple docstring'''





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

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

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

  return orig_state_dict

def a__   (	A_,					A_,					A_ ):






  '''simple docstring'''





  __magic_name__    					=	torch.load(A_,					map_location="""cpu""" )["""model_state_dict"""]
  __magic_name__    					=	MraConfig.from_json_file(A_ )
  __magic_name__    					=	MraForMaskedLM(A_ )

  __magic_name__    					=	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__":
    __lowerCAmelCase      :					Any   	=       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.'
    )
    __lowerCAmelCase      :					List[Any]   	=       parser.parse_args()
    convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)



class 	UpperCAmelCase_      :


  '''simple docstring'''







  def __init__(			self      :			Tuple      ,							UpperCamelCase__      :			Dict=None   )      ->     List[str]:







    """simple docstring"""






    __magic_name__    					=	data
    __magic_name__    					=	None





  def __repr__(			self      :			Optional[int]   )      ->     Optional[int]:







    """simple docstring"""






    __magic_name__    					=	""""""
    __magic_name__    					=	self
    while temp:
      string_rep += F'''<{temp.data}> ---> '''
      __magic_name__    					=	temp.next
    string_rep += "<END>"
    return string_rep







def a__   (	A_ ):






  '''simple docstring'''





  if not elements_list:
    raise Exception("""The Elements List is empty""" )

  # Set first element as Head
  __magic_name__    					=	Node(elements_list[0] )
  __magic_name__    					=	head
  # Loop through elements from position 1
  for data in elements_list[1:]:
    __magic_name__    					=	Node(A_ )
    __magic_name__    					=	current.next
  return head


__lowerCAmelCase      :					Union[str, Any]   	=       [1, 3, 5, 32, 44, 12, 43]
print(F'''List: {list_data}''')
print('Creating Linked List from List.')
__lowerCAmelCase      :					Optional[Any]   	=       make_linked_list(list_data)
print('Linked List:')





print(linked_list)

