

'''simple docstring'''
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile


def        _lowerCAmelCase					(						_UpperCamelCase						:     Any							, _UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     int							, _UpperCamelCase						:     int				)      ->			Dict:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =multiprocessing.Manager()
     _SCREAMING_SNAKE_CASE							    =manager.list()

     _SCREAMING_SNAKE_CASE							    =multiprocessing.Process(target=__lowerCamelCase							, args=(check_program, result, timeout)				)
     p.start()
     p.join(timeout=timeout + 1				)
     if p.is_alive():
          p.kill()

     if not result:
          result.append('timed out'				)

     return {
         "task_id": task_id,
         "passed": result[0] == "passed",
         "result": result[0],
         "completion_id": completion_id,
     }


def        _lowerCAmelCase					(						_UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     str							, _UpperCamelCase						:     Any				)      ->			Optional[int]:
     """simple docstring"""




     with create_tempdir():
          # These system calls are needed when cleaning up tempdir.
          import os
          import shutil

          _SCREAMING_SNAKE_CASE							    =shutil.rmtree
          _SCREAMING_SNAKE_CASE							    =os.rmdir
          _SCREAMING_SNAKE_CASE							    =os.chdir

          # Disable functionalities that can make destructive changes to the test.
          reliability_guard()

          # Run program.
          try:
               _SCREAMING_SNAKE_CASE							    ={}
               with swallow_io():
                    with time_limit(__lowerCamelCase				):
                         exec(__lowerCamelCase							, __lowerCamelCase				)
               result.append('passed'				)
          except TimeoutException:
               result.append('timed out'				)
          except BaseException as e:
               result.append(f"failed: {e}"				)

          # Needed for cleaning up.
          _SCREAMING_SNAKE_CASE							    =rmtree
          _SCREAMING_SNAKE_CASE							    =rmdir
          _SCREAMING_SNAKE_CASE							    =chdir


@contextlib.contextmanager
def        _lowerCAmelCase					(						_UpperCamelCase						:     List[str]				)      ->			Optional[Any]:
     """simple docstring"""




     def signal_handler(_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     Union[str, Any]				):
          raise TimeoutException('Timed out!'				)

     signal.setitimer(signal.ITIMER_REAL							, __lowerCamelCase				)
     signal.signal(signal.SIGALRM							, __lowerCamelCase				)
     try:
          yield
     finally:
          signal.setitimer(signal.ITIMER_REAL							, 0				)


@contextlib.contextmanager
def        _lowerCAmelCase					(						)      ->			List[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =WriteOnlyStringIO()
     with contextlib.redirect_stdout(__lowerCamelCase				):
          with contextlib.redirect_stderr(__lowerCamelCase				):
               with redirect_stdin(__lowerCamelCase				):
                    yield


@contextlib.contextmanager
def        _lowerCAmelCase					(						)      ->			Optional[int]:
     """simple docstring"""




     with tempfile.TemporaryDirectory() as dirname:
          with chdir(__lowerCamelCase				):
               yield dirname







class 							A__    (       A_  ):
  pass







class 							A__    (       io.StringIO  ):





  def 						A  (		self      :	Optional[Any]							,					*_a      :	List[Any]							,					**_a      :	Union[str, Any] )	->    Any:





       '''simple docstring'''






       raise OSError





  def 						A  (		self      :	Optional[int]							,					*_a      :	Any							,					**_a      :	List[str] )	->    Dict:





       '''simple docstring'''






       raise OSError





  def 						A  (		self      :	str							,					*_a      :	str							,					**_a      :	Optional[int] )	->    Any:





       '''simple docstring'''






       raise OSError





  def 						A  (		self      :	Tuple							,					*_a      :	Dict							,					**_a      :	str )	->    Any:





       '''simple docstring'''






       return False







class 							A__    (       contextlib._RedirectStream  ):  # type: ignore
  A__		 =				'stdin'


@contextlib.contextmanager
def        _lowerCAmelCase					(						_UpperCamelCase						:     Optional[Any]				)      ->			str:
     """simple docstring"""




     if root == ".":
          yield
          return
     _SCREAMING_SNAKE_CASE							    =os.getcwd()
     os.chdir(__lowerCamelCase				)
     try:
          yield
     except BaseException as exc:
          raise exc
     finally:
          os.chdir(__lowerCamelCase				)







def        _lowerCAmelCase					(						_UpperCamelCase						:     Any=None				)      ->			Any:
     """simple docstring"""




     if maximum_memory_bytes is not None:
          import resource

          resource.setrlimit(resource.RLIMIT_AS							, (maximum_memory_bytes, maximum_memory_bytes)				)
          resource.setrlimit(resource.RLIMIT_DATA							, (maximum_memory_bytes, maximum_memory_bytes)				)
          if not platform.uname().system == "Darwin":
               resource.setrlimit(resource.RLIMIT_STACK							, (maximum_memory_bytes, maximum_memory_bytes)				)

     faulthandler.disable()

     import builtins

     _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =None

     import os

     _SCREAMING_SNAKE_CASE							    ='1'

     _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =None

     import shutil

     _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =None

     import subprocess

     _SCREAMING_SNAKE_CASE							    =None  # type: ignore

     _SCREAMING_SNAKE_CASE							    =None

     import sys

     _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =None


"""simple docstring"""



import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
 UpperCAmelCase__          = 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')
 UpperCAmelCase__          = parser.parse_args()

 if args.model_type == "bert":
  UpperCAmelCase__          = BertForMaskedLM.from_pretrained(args.model_name)
  UpperCAmelCase__          = 'bert'
 else:
  raise ValueError('args.model_type should be "bert".')

 UpperCAmelCase__          = model.state_dict()
 UpperCAmelCase__          = {}

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

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

   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"
   ]
   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"
   ]

   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"
   ]
   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"
   ]
   UpperCAmelCase__          = state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"
   ]
  std_idx += 1

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



