"""simple docstring"""




import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
     _a   :	Optional[Any] 					=     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   :	Any 					=     parser.parse_args()

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

     _a   :	Tuple 					=     model.state_dict()
     _a   :	Optional[int] 					=     {}

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

     _a   :	List[str] 					=     0
     for teacher_idx in [0, 2, 4, 7, 9, 11]:
          for w in ["weight", "bias"]:
               _a   :	Dict 					=     state_dict[
                   F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
               ]
               _a   :	Tuple 					=     state_dict[
                   F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
               ]
               _a   :	Optional[Any] 					=     state_dict[
                   F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
               ]

               _a   :	Optional[int] 					=     state_dict[
                   F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
               ]
               _a   :	List[str] 					=     state_dict[
                   F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
               ]

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

     _a   :	int 					=     state_dict['cls.predictions.decoder.weight']
     _a   :	List[str] 					=     state_dict['cls.predictions.bias']
     if args.vocab_transform:
          for w in ["weight", "bias"]:
               _a   :	Optional[int] 					=     state_dict[F"""cls.predictions.transform.dense.{w}"""]
               _a   :	Tuple 					=     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  (    _lowerCamelCase		: Image    ,_lowerCamelCase		: int			) ->   Image:
  _lowerCAmelCase		: Any						    =  (259 * (level + 255)) / (255 * (259 - level))

  def contrast(_lowerCamelCase		: int			) -> int:
    return int(128 + factor * (c - 128)			)

  return img.point(_lowerCamelCase			)


if __name__ == "__main__":
     # Load image
     with Image.open('image_data/lena.jpg') as img:
          # Change contrast to 170
          _a   :	str 					=     change_contrast(img, 170)
          cont_img.save('image_data/lena_high_contrast.png', format='png')




