


'''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()
__snake_case    						=logging.get_logger(__name__)





def 	a_					( lowerCamelCase	:     str	, lowerCamelCase	:     str				):
   lowerCAmelCase     =			RobertaPreLayerNormConfig.from_pretrained(
       lowerCamelCase	, architectures=['RobertaPreLayerNormForMaskedLM']				)

   # convert state_dict
   lowerCAmelCase     =			torch.load(hf_hub_download(repo_id=lowerCamelCase	, filename='pytorch_model.bin'				)				)
   lowerCAmelCase     =			{}
   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.'				):
         lowerCAmelCase     =			'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

      lowerCAmelCase     =			tensor_value

   lowerCAmelCase     =			RobertaPreLayerNormForMaskedLM.from_pretrained(
       pretrained_model_name_or_path=lowerCamelCase	, config=lowerCamelCase	, state_dict=lowerCamelCase				)
   model.save_pretrained(lowerCamelCase				)

   # convert tokenizer
   lowerCAmelCase     =			AutoTokenizer.from_pretrained(lowerCamelCase				)
   tokenizer.save_pretrained(lowerCamelCase				)


if __name__ == "__main__":
       __snake_case    						=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."""
       )
       __snake_case    						=parser.parse_args()
       convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)









'''simple docstring'''




from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge

@total_ordering
class 		UpperCAmelCase_      (  __lowercase		):





     def __lt__( self       :      Optional[int]    ,      UpperCAmelCase__       :      List[str]    )				->  List[Any]:
        return self[-1] < other[-1]





     def __eq__( self       :      str    ,      UpperCAmelCase__       :      List[str]    )				->  Tuple:
        return self[-1] == other[-1]





def 	a_					( lowerCamelCase	:     list				):
   lowerCAmelCase     =			[]
   # sort into stacks
   for element in collection:
      lowerCAmelCase     =			Stack([element]				)
      lowerCAmelCase     =			bisect_left(lowerCamelCase	, lowerCamelCase				)
      if i != len(lowerCamelCase				):
         stacks[i].append(lowerCamelCase				)
      else:
         stacks.append(lowerCamelCase				)

    # use a heap-based merge to merge stack efficiently
   lowerCAmelCase     =			merge(*(reversed(lowerCamelCase				) for stack in stacks)				)
   return collection


if __name__ == "__main__":
       __snake_case    						=input("""Enter numbers separated by a comma:\n""").strip()
       __snake_case    						=[int(item) for item in user_input.split(""",""")]
       print(patience_sort(unsorted))





