



"""simple docstring"""







import argparse

import torch

from transformers import MraConfig, MraForMaskedLM

def 							UpperCamelCase_       (       lowerCAmelCase__ :							List[Any]		)		->  int:

  """simple docstring"""




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

  return orig_key

def 							UpperCamelCase_       (       lowerCAmelCase__ :							Tuple       ,  lowerCAmelCase__ :							Optional[int]		)		->  Optional[int]:

  """simple docstring"""




  for key in orig_state_dict.copy().keys():
    lowerCAmelCase_  :							str								=					orig_state_dict.pop(lowerCAmelCase__		)

    if ("pooler" in key) or ("sen_class" in key):
      continue
    else:
      lowerCAmelCase_  :							List[str]								=					val

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

  return orig_state_dict

def 							UpperCamelCase_       (       lowerCAmelCase__ :							List[Any]       ,  lowerCAmelCase__ :							List[str]       ,  lowerCAmelCase__ :							Dict		)		->  Dict:

  """simple docstring"""




  lowerCAmelCase_  :							Tuple								=					torch.load(lowerCAmelCase__       ,  map_location='cpu'		)['model_state_dict']
  lowerCAmelCase_  :							List[Any]								=					MraConfig.from_json_file(lowerCAmelCase__		)
  lowerCAmelCase_  :							Dict								=					MraForMaskedLM(lowerCAmelCase__		)

  lowerCAmelCase_  :							Any								=					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__":
     lowercase__						:							Optional[int]        =      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."""
     )
     lowercase__						:							Optional[int]        =      parser.parse_args()
     convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)











'''simple docstring'''

class __magic_name__:






						def __init__(   self						:						List[Any]       ):



									'''simple docstring'''






									snake_case__          =						[]
									snake_case__          =						0
									snake_case__          =						0






						def __str__(   self						:						Union[str, Any]       ):



									'''simple docstring'''






									snake_case__          =						"""<""" + str(self.entries       )[1:-1] + """>"""
									return printed






						def       __lowerCAmelCase(   self						:						List[Any]					,   __UpperCamelCase						:						List[Any]       ):



									'''simple docstring'''






									self.entries.append(__UpperCamelCase       )
									snake_case__          =						self.length + 1






						def       __lowerCAmelCase(   self						:						int       ):



									'''simple docstring'''






									snake_case__          =						self.length - 1
									snake_case__          =						self.entries[self.front]
									# self.front-=1
									# self.entries = self.entries[self.front:]
									snake_case__          =						self.entries[1:]
									return dequeued






						def       __lowerCAmelCase(   self						:						str					,   __UpperCamelCase						:						Optional[Any]       ):



									'''simple docstring'''






									for _ in range(__UpperCamelCase       ):
												self.put(self.get()       )






						def       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






									return self.entries[0]






						def       __lowerCAmelCase(   self						:						str       ):



									'''simple docstring'''






									return self.length