



'''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 PIL import Image
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__				,       lowerCAmelCase__			:  Dict											= image.size
    lowerCAmelCase__			:  int											= 0
    lowerCAmelCase__			:  int											= image.load()
    for i in range(UpperCamelCase      ):
        for j in range(UpperCamelCase      ):
            lowerCAmelCase__			:  int											= pixels[j, i]
            mean += pixel
    mean //= width * height

    for j in range(UpperCamelCase      ):
        for i in range(UpperCamelCase      ):
            lowerCAmelCase__			:  Dict											= 255 if pixels[i, j] > mean else 0
    return image


if __name__ == "__main__":
 _lowerCAmelCase										=     mean_threshold(Image.open('''path_to_image''').convert('''L'''))
 image.save('''output_image_path''')


