





'''simple docstring'''





import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
     _UpperCAmelCase        =				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')
     _UpperCAmelCase        =				parser.parse_args()

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

     _UpperCAmelCase        =				model.state_dict()
     _UpperCAmelCase        =				{}

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

     _UpperCAmelCase        =				0
     for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
          for w in ["weight", "bias"]:
               _UpperCAmelCase        =				state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
               ]
               _UpperCAmelCase        =				state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
               ]
               _UpperCAmelCase        =				state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
               ]

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

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

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








_UpperCAmelCase        =				{
    'a': 'AAAAA',
    'b': 'AAAAB',
    'c': 'AAABA',
    'd': 'AAABB',
    'e': 'AABAA',
    'f': 'AABAB',
    'g': 'AABBA',
    'h': 'AABBB',
    'i': 'ABAAA',
    'j': 'BBBAA',
    'k': 'ABAAB',
    'l': 'ABABA',
    'm': 'ABABB',
    'n': 'ABBAA',
    'o': 'ABBAB',
    'p': 'ABBBA',
    'q': 'ABBBB',
    'r': 'BAAAA',
    's': 'BAAAB',
    't': 'BAABA',
    'u': 'BAABB',
    'v': 'BBBAB',
    'w': 'BABAA',
    'x': 'BABAB',
    'y': 'BABBA',
    'z': 'BABBB',
    ' ': ' ',
}


_UpperCAmelCase        =				{value: key for key, value in encode_dict.items()}




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		str:
       UpperCamelCase_          =  ""
       for letter in word.lower():
              if letter.isalpha() or letter == " ":
                     encoded += encode_dict[letter]
              else:
                     raise Exception("encode() accepts only letters of the alphabet and spaces"  )
       return encoded




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		str:
       if set(UpperCamelCase_  ) - {"A", "B", " "} != set():
              raise Exception("decode() accepts only 'A', 'B' and spaces"  )
       UpperCamelCase_          =  ""
       for word in coded.split():
              while len(UpperCamelCase_  ) != 0:
                     decoded += decode_dict[word[:5]]
                     UpperCamelCase_          =  word[5:]
              decoded += " "
       return decoded.strip()


if __name__ == "__main__":
     from doctest import testmod

     testmod()

