



def     __a					(		__UpperCAmelCase       ):
 if not head:
  return True
 # split the list to two parts
 a__							,			a__												= head.next, head
 while fast and fast.next:
  a__												= fast.next.next
  a__												= slow.next
 a__												= slow.next
 a__												= None  # Don't forget here! But forget still works!
 # reverse the second part
 a__												= None
 while second:
  a__												= second.next
  a__												= node
  a__												= second
  a__												= nxt
 # compare two parts
 # second part has the same or one less node
 while node:
  if node.val != head.val:
   return False
  a__												= node.next
  a__												= head.next
 return True






def     __a					(		__UpperCAmelCase       ):
 if not head or not head.next:
  return True

 # 1. Get the midpoint (slow)
 a__												= a__												= a__												= head
 while fast and fast.next:
  a__							,			a__												= fast.next.next, slow.next

 # 2. Push the second half into the stack
 a__												= [slow.val]
 while slow.next:
  a__												= slow.next
  stack.append(slow.val       )

 # 3. Comparison
 while stack:
  if stack.pop() != cur.val:
   return False
  a__												= cur.next

 return True






def     __a					(		__UpperCAmelCase       ):
 if not head or not head.next:
  return True
 a__												= {}
 a__												= 0
 while head:
  if head.val in d:
   d[head.val].append(__UpperCAmelCase       )
  else:
   a__												= [pos]
  a__												= head.next
  pos += 1
 a__												= pos - 1
 a__												= 0
 for v in d.values():
  if len(__UpperCAmelCase       ) % 2 != 0:
   middle += 1
  else:
   a__												= 0
   for i in range(0							,  len(__UpperCAmelCase       )       ):
    if v[i] + v[len(__UpperCAmelCase       ) - 1 - step] != checksum:
     return False
    step += 1
  if middle > 1:
   return False
 return True










import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






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

 return orig_key






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase       ):
 for key in orig_state_dict.copy().keys():
  a__												= orig_state_dict.pop(__UpperCAmelCase       )

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

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

 return orig_state_dict






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= torch.load(__UpperCAmelCase							,  map_location='''cpu'''       )['''model_state_dict''']
 a__												= YosoConfig.from_json_file(__UpperCAmelCase       )
 a__												= YosoForMaskedLM(__UpperCAmelCase       )

 a__												= convert_checkpoint_helper(config.max_position_embeddings							,  __UpperCAmelCase       )

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

 print(f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"       )


if __name__ == "__main__":
     a_      :      int    =     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_      :      Any    =     parser.parse_args()
     convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





