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

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[Any]     				=					RobertaPreLayerNormConfig.from_pretrained(
         SCREAMING_SNAKE_CASE_  ,       architectures=['RobertaPreLayerNormForMaskedLM']							)

     # convert state_dict
     lowercase_					:       List[Any]     				=					torch.load(hf_hub_download(repo_id=SCREAMING_SNAKE_CASE_  ,       filename='pytorch_model.bin'							)							)
     lowercase_					:       str     				=					{}
     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.'							):
               lowercase_					:       Tuple     				=					'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

          lowercase_					:       List[Any]     				=					tensor_value

     lowercase_					:       str     				=					RobertaPreLayerNormForMaskedLM.from_pretrained(
         pretrained_model_name_or_path=SCREAMING_SNAKE_CASE_  ,       config=SCREAMING_SNAKE_CASE_  ,       state_dict=SCREAMING_SNAKE_CASE_							)
     model.save_pretrained(SCREAMING_SNAKE_CASE_							)

     # convert tokenizer
     lowercase_					:       Optional[int]     				=					AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_							)
     tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_							)


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





'''simple docstring'''




def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[Any]     				=					len(SCREAMING_SNAKE_CASE_							)
     for _ in range(SCREAMING_SNAKE_CASE_							):
          for i in range(_ % 2  ,       arr_size - 1  ,       2							):
               if arr[i + 1] < arr[i]:
                    lowercase_				,lowercase_					:       Optional[Any]     				=					arr[i + 1], arr[i]

     return arr


if __name__ == "__main__":
 _A								=   list(range(1_0, 0, -1))
 print(F"""Original: {arr}. Sorted: {odd_even_transposition(arr)}""")




