

'''simple docstring'''
import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
     lowerCamelCase :      Optional[Any]					 =     argparse.ArgumentParser(
         description=(
             "Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"
             " Distillation"
         )
     )
     parser.add_argument("--model_type", default="bert", choices=["bert"])
     parser.add_argument("--model_name", default="bert-base-uncased", type=str)
     parser.add_argument("--dump_checkpoint", default="serialization_dir/tf_bert-base-uncased_0247911.pth", type=str)
     parser.add_argument("--vocab_transform", action="store_true")
     lowerCamelCase :      Optional[int]					 =     parser.parse_args()

     if args.model_type == "bert":
          lowerCamelCase :      Optional[int]					 =     BertForMaskedLM.from_pretrained(args.model_name)
          lowerCamelCase :      Optional[Any]					 =     "bert"
     else:
          raise ValueError("args.model_type should be \"bert\".")

     lowerCamelCase :      Optional[Any]					 =     model.state_dict()
     lowerCamelCase :      Any					 =     {}

     for w in ["word_embeddings", "position_embeddings"]:
          lowerCamelCase :      Union[str, Any]					 =     state_dict[f'''{prefix}.embeddings.{w}.weight''']
     for w in ["weight", "bias"]:
          lowerCamelCase :      Union[str, Any]					 =     state_dict[f'''{prefix}.embeddings.LayerNorm.{w}''']

     lowerCamelCase :      Optional[int]					 =     0
     for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
          for w in ["weight", "bias"]:
               lowerCamelCase :      Any					 =     state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
               ]
               lowerCamelCase :      Union[str, Any]					 =     state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
               ]
               lowerCamelCase :      Any					 =     state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
               ]

               lowerCamelCase :      Tuple					 =     state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
               ]
               lowerCamelCase :      List[str]					 =     state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
               ]

               lowerCamelCase :      List[Any]					 =     state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
               ]
               lowerCamelCase :      Union[str, Any]					 =     state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
               ]
               lowerCamelCase :      int					 =     state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
               ]
          std_idx += 1

     lowerCamelCase :      Optional[int]					 =     state_dict["cls.predictions.decoder.weight"]
     lowerCamelCase :      Optional[Any]					 =     state_dict["cls.predictions.bias"]
     if args.vocab_transform:
          for w in ["weight", "bias"]:
               lowerCamelCase :      List[str]					 =     state_dict[f'''cls.predictions.transform.dense.{w}''']
               lowerCamelCase :      List[str]					 =     state_dict[f'''cls.predictions.transform.LayerNorm.{w}''']

     print(f'''N layers selected for distillation: {std_idx}''')
     print(f'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

     print(f'''Save transferred checkpoint to {args.dump_checkpoint}.''')
     torch.save(compressed_sd, args.dump_checkpoint)


"""simple docstring"""



def 					_UpperCAmelCase      ( __lowerCamelCase   :       Tuple		)     ->					Any:
  stooge(__lowerCamelCase ,					0 ,					len(__lowerCamelCase		) - 1		)
  return arr
def 					_UpperCAmelCase      ( __lowerCamelCase   :       str ,					__lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       str		)     ->					int:
  if i >= h:
    return

  # If first element is smaller than the last then swap them
  if arr[i] > arr[h]:
    _snake_case     ,					_snake_case					     =       arr[h], arr[i]

  # If there are more than 2 elements in the array
  if h - i + 1 > 2:
    _snake_case					     =       (int)((h - i + 1) / 3		)

    # Recursively sort first 2/3 elements
    stooge(__lowerCamelCase ,					__lowerCamelCase ,					(h - t)		)

    # Recursively sort last 2/3 elements
    stooge(__lowerCamelCase ,					i + t ,					(__lowerCamelCase)		)

    # Recursively sort first 2/3 elements
    stooge(__lowerCamelCase ,					__lowerCamelCase ,					(h - t)		)


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



