
"""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					:	str							=		parser.parse_args()

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

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

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

 a					:	Dict							=		0
 for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
  for w in ["weight", "bias"]:
   a					:	List[str]							=		state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"
   ]
   a					:	Optional[int]							=		state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"
   ]
   a					:	int							=		state_dict[
       F"{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"
   ]

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

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

 a					:	Tuple							=		state_dict['''cls.predictions.decoder.weight''']
 a					:	Optional[Any]							=		state_dict['''cls.predictions.bias''']
 if args.vocab_transform:
  for w in ["weight", "bias"]:
   a					:	Optional[Any]							=		state_dict[F"cls.predictions.transform.dense.{w}"]
   a					:	int							=		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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a					:	Tuple							=		{
    '''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a					:	List[str]							=		[
     '''PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''PegasusXForConditionalGeneration''',
     '''PegasusXModel''',
     '''PegasusXPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_pegasus_x import (
      PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
      PegasusXForConditionalGeneration,
      PegasusXModel,
      PegasusXPreTrainedModel,
  )


else:
 import sys

 a					:	Dict							=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)