




"""simple docstring"""


import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
     A_						=				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_						=				parser.parse_args()

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

     A_						=				model.state_dict()
     A_						=				{}

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

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

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

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

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


from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


A_						=				logging.get_logger(__name__)






class 					lowercase:







 '''simple docstring'''





 lowercase__ 					=					42
 lowercase__ 					=					None


 @staticmethod
 def  UpperCamelCase_   (				):




   '''simple docstring'''



   raise NotImplementedError


 def  UpperCamelCase_   (				self:		Tuple,      a_:		int,      a_:		int,      a_:		str,      **a_:		Dict		):




   '''simple docstring'''



   raise NotImplementedError


 def  UpperCamelCase_   (				self:		Union[str, Any],      a_:		List[str]		):




   '''simple docstring'''



   raise NotImplementedError


 def  UpperCamelCase_   (				self:		Union[str, Any]		):




   '''simple docstring'''



   if not self.is_available():
     raise RuntimeError(
         f"You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}."		)


 @classmethod
 def  UpperCamelCase_   (				cls:		Tuple		):




   '''simple docstring'''



   return f"`pip install {cls.pip_package or cls.name}`"







class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"optuna"


 @staticmethod
 def  UpperCamelCase_   (				):




   '''simple docstring'''



   return is_optuna_available()


 def  UpperCamelCase_   (				self:		Union[str, Any],      a_:		List[Any],      a_:		int,      a_:		str,      **a_:		List[str]		):




   '''simple docstring'''



   return run_hp_search_optuna(a_,      a_,      a_,      **a_		)


 def  UpperCamelCase_   (				self:		Optional[Any],      a_:		Any		):




   '''simple docstring'''



   return default_hp_space_optuna(a_		)







class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"ray"
 lowercase__ 					=					"'ray[tune]'"


 @staticmethod
 def  UpperCamelCase_   (				):




   '''simple docstring'''



   return is_ray_available()


 def  UpperCamelCase_   (				self:		int,      a_:		Optional[Any],      a_:		int,      a_:		str,      **a_:		List[Any]		):




   '''simple docstring'''



   return run_hp_search_ray(a_,      a_,      a_,      **a_		)


 def  UpperCamelCase_   (				self:		str,      a_:		Tuple		):




   '''simple docstring'''



   return default_hp_space_ray(a_		)







class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"sigopt"


 @staticmethod
 def  UpperCamelCase_   (				):




   '''simple docstring'''



   return is_sigopt_available()


 def  UpperCamelCase_   (				self:		Dict,      a_:		str,      a_:		int,      a_:		str,      **a_:		int		):




   '''simple docstring'''



   return run_hp_search_sigopt(a_,      a_,      a_,      **a_		)


 def  UpperCamelCase_   (				self:		str,      a_:		List[str]		):




   '''simple docstring'''



   return default_hp_space_sigopt(a_		)







class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"wandb"


 @staticmethod
 def  UpperCamelCase_   (				):




   '''simple docstring'''



   return is_wandb_available()


 def  UpperCamelCase_   (				self:		Optional[Any],      a_:		str,      a_:		int,      a_:		str,      **a_:		Union[str, Any]		):




   '''simple docstring'''



   return run_hp_search_wandb(a_,      a_,      a_,      **a_		)


 def  UpperCamelCase_   (				self:		str,      a_:		Any		):




   '''simple docstring'''



   return default_hp_space_wandb(a_		)







A_						=				{
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}


def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		Optional[int]								=  [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
  if len(snake_case__    ) > 0:
    _snake_case							:		Any								=  available_backends[0].name
    if len(snake_case__    ) > 1:
      logger.info(
          F"{len(snake_case__    )} hyperparameter search backends available. Using {name} as the default."    )
    return name
  raise RuntimeError(
      """No hyperparameter search backend available.\n"""
      + """\n""".join(
          F" - To install {backend.name} run {backend.pip_install()}"
          for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()    )    )


