



"""simple docstring"""





import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase            =		logging.get_logger(__name__)
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							RobertaPreLayerNormConfig.from_pretrained(
          __snake_case			,    architectures=["RobertaPreLayerNormForMaskedLM"]   )

      # convert state_dict
      UpperCAmelCase_    				=							torch.load(hf_hub_download(repo_id=__snake_case			,    filename="pytorch_model.bin"   )   )
      UpperCAmelCase_    				=							{}
      for tensor_key, tensor_value in original_state_dict.items():
            # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
            if tensor_key.startswith("roberta."   ):
                  UpperCAmelCase_    				=							"roberta_prelayernorm." + tensor_key[len("roberta."   ) :]

            # The original implementation contains weights which are not used, remove them from the state_dict
            if tensor_key.endswith(".self.LayerNorm.weight"   ) or tensor_key.endswith(".self.LayerNorm.bias"   ):
                  continue

            UpperCAmelCase_    				=							tensor_value

      UpperCAmelCase_    				=							RobertaPreLayerNormForMaskedLM.from_pretrained(
          pretrained_model_name_or_path=__snake_case			,    config=__snake_case			,    state_dict=__snake_case   )
      model.save_pretrained(__snake_case   )

      # convert tokenizer
      UpperCAmelCase_    				=							AutoTokenizer.from_pretrained(__snake_case   )
      tokenizer.save_pretrained(__snake_case   )


if __name__ == "__main__":
  lowerCamelCase            =		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--checkpoint-repo""",
      default=None,
      type=str,
      required=True,
      help="""Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  lowerCamelCase            =		parser.parse_args()
  convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)









"""simple docstring"""





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

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

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

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

      return True


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




