


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()
__UpperCamelCase				:				Any  				= logging.get_logger(__name__)

def 	_a     ( SCREAMING_SNAKE_CASE		: str			,       SCREAMING_SNAKE_CASE		: int   ):







 """simple docstring"""



 UpperCamelCase__    :			int      				=      RobertaPreLayerNormConfig.from_pretrained(
     __A			,       architectures=['''RobertaPreLayerNormForMaskedLM''']   )

 # convert state_dict
 UpperCamelCase__    :			int      				=      torch.load(hf_hub_download(repo_id=__A			,       filename='''pytorch_model.bin'''   )   )
 UpperCamelCase__    :			Dict      				=      {}
 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__    :			Optional[Any]      				=      '''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__    :			int      				=      tensor_value

 UpperCamelCase__    :			List[Any]      				=      RobertaPreLayerNormForMaskedLM.from_pretrained(
     pretrained_model_name_or_path=__A			,       config=__A			,       state_dict=__A   )
 model.save_pretrained(__A   )

 # convert tokenizer
 UpperCamelCase__    :			List[str]      				=      AutoTokenizer.from_pretrained(__A   )
 tokenizer.save_pretrained(__A   )


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







'''simple docstring'''





def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     _a      =  int(__A)

     # Initialize Result
     _a      =  []

     # Traverse through all denomination
     for denomination in reversed(__A):
          # Find denominations
          while int(__A) >= int(__A):
               total_value -= int(__A)
               answer.append(__A)  # Append the "answers" array

     return answer


# Driver Code
if __name__ == "__main__":
  lowercase_		       =  []
  lowercase_		       =  "0"

  if (
      input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
      == "y"
  ):
    lowercase_		       =  int(input("Enter the number of denominations you want to add: ").strip())

    for i in range(0, n):
      denominations.append(int(input(F"""Denomination {i}: """).strip()))
    lowercase_		       =  input("Enter the change you want to make in Indian Currency: ").strip()
  else:
    # All denominations of Indian Currency if user does not enter
    lowercase_		       =  [1, 2, 5, 10, 20, 50, 100, 500, 2_000]
    lowercase_		       =  input("Enter the change you want to make: ").strip()

  if int(value) == 0 or int(value) < 0:
    print("The total value cannot be zero or negative.")

  else:
    print(F"""Following is minimal change for {value}: """)
    lowercase_		       =  find_minimum_change(denominations, value)
    # Print result
    for i in range(len(answer)):
      print(answer[i], end=" ")



