



'''simple docstring'''





import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
 _lowerCAmelCase										=     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										=     parser.parse_args()

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

 _lowerCAmelCase										=     model.state_dict()
 _lowerCAmelCase										=     {}

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

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

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

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

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


_lowerCAmelCase										=     {
    '''configuration_blip_2''': [
        '''BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Blip2Config''',
        '''Blip2QFormerConfig''',
        '''Blip2VisionConfig''',
    ],
    '''processing_blip_2''': ['''Blip2Processor'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase										=     [
     '''BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''Blip2Model''',
     '''Blip2QFormerModel''',
     '''Blip2PreTrainedModel''',
     '''Blip2ForConditionalGeneration''',
     '''Blip2VisionModel''',
 ]

if TYPE_CHECKING:
 from .configuration_blip_a import (
     BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BlipaConfig,
     BlipaQFormerConfig,
     BlipaVisionConfig,
 )
 from .processing_blip_a import BlipaProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_blip_a import (
      BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
      BlipaForConditionalGeneration,
      BlipaModel,
      BlipaPreTrainedModel,
      BlipaQFormerModel,
      BlipaVisionModel,
  )

else:
 import sys

 _lowerCAmelCase										=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


