"""simple docstring"""




import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
     _a :						str			       =  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')
     _a :						int			       =  parser.parse_args()

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

     _a :						List[str]			       =  model.state_dict()
     _a :						Tuple			       =  {}

     for w in ["word_embeddings", "position_embeddings"]:
          _a :						Optional[int]			       =  state_dict[F"""{prefix}.embeddings.{w}.weight"""]
     for w in ["weight", "bias"]:
          _a :						Optional[Any]			       =  state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""]

     _a :						int			       =  0
     for teacher_idx in [0, 2, 4, 7, 9, 11]:
          for w in ["weight", "bias"]:
               _a :						List[Any]			       =  state_dict[
                   F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
               ]
               _a :						Dict			       =  state_dict[
                   F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
               ]
               _a :						List[Any]			       =  state_dict[
                   F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
               ]

               _a :						int			       =  state_dict[
                   F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
               ]
               _a :						Union[str, Any]			       =  state_dict[
                   F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
               ]

               _a :						List[Any]			       =  state_dict[
                   F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
               ]
               _a :						Union[str, Any]			       =  state_dict[
                   F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
               ]
               _a :						List[str]			       =  state_dict[
                   F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
               ]
          std_idx += 1

     _a :						Dict			       =  state_dict['''cls.predictions.decoder.weight''']
     _a :						Tuple			       =  state_dict['''cls.predictions.bias''']
     if args.vocab_transform:
          for w in ["weight", "bias"]:
               _a :						Dict			       =  state_dict[F"""cls.predictions.transform.dense.{w}"""]
               _a :						Tuple			       =  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)








import logging
import os

from .state import PartialState


class      __lowerCAmelCase  (			logging.LoggerAdapter       ):






  """simple docstring"""





  @staticmethod
  def        lowerCAmelCase__	(  _lowerCAmelCase							:							int	) ->			Tuple:




     """simple docstring"""
     snake_case_            =       PartialState()
     return not main_process_only or (main_process_only and state.is_main_process)



  def        lowerCAmelCase__	(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							List[Any]     ,					_lowerCAmelCase							:							Tuple     ,					*_lowerCAmelCase							:							List[Any]     ,					**_lowerCAmelCase							:							Any	) ->			Optional[int]:




     """simple docstring"""
     if PartialState._shared_state == {}:
        raise RuntimeError(
            "You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility."	)
     snake_case_            =       kwargs.pop("main_process_only"     ,					_lowerCAmelCase	)
     snake_case_            =       kwargs.pop("in_order"     ,					_lowerCAmelCase	)

     if self.isEnabledFor(_lowerCAmelCase	):
        if self._should_log(_lowerCAmelCase	):
           snake_case_     ,     snake_case_            =       self.process(_lowerCAmelCase     ,					_lowerCAmelCase	)
           self.logger.log(_lowerCAmelCase     ,					_lowerCAmelCase     ,					*_lowerCAmelCase     ,					**_lowerCAmelCase	)

        elif in_order:
           snake_case_            =       PartialState()
           for i in range(state.num_processes	):
              if i == state.process_index:
                 snake_case_     ,     snake_case_            =       self.process(_lowerCAmelCase     ,					_lowerCAmelCase	)
                 self.logger.log(_lowerCAmelCase     ,					_lowerCAmelCase     ,					*_lowerCAmelCase     ,					**_lowerCAmelCase	)
              state.wait_for_everyone()






def       _lowerCAmelCase    (				lowerCAmelCase_				:str		,    lowerCAmelCase_				:str = None				)->Any:
   '''simple docstring'''






   if log_level is None:
      snake_case_            =       os.environ.get("ACCELERATE_LOG_LEVEL"		,    lowerCAmelCase_				)
   snake_case_            =       logging.getLogger(lowerCAmelCase_				)
   if log_level is not None:
      logger.setLevel(log_level.upper()				)
      logger.root.setLevel(log_level.upper()				)
   return MultiProcessAdapter(lowerCAmelCase_		,    {}				)




