


import argparse

import torch

from transformers import BertForMaskedLM


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

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

   _snake_case             =     model.state_dict()
   _snake_case             =     {}

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

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

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

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

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







import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Optional[int]     		=		"M-CLIP"






   def __init__(		self						,				__A=1024						,				__A=768						,				**__A ):






     """simple docstring"""





     lowerCamelCase :					str    =				transformerDimSize
     lowerCamelCase :					Any    =				imageDimSize
     super().__init__(**__A )



class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Tuple     		=		MCLIPConfig






   def __init__(		self						,				__A						,				*__A						,				**__A ):






     """simple docstring"""





     super().__init__(__A						,				*__A						,				**__A )
     lowerCamelCase :					Tuple    =				XLMRobertaModel(__A )
     lowerCamelCase :					Optional[Any]    =				torch.nn.Linear(
         in_features=config.transformerDimensions						,				out_features=config.numDims )






   def _snake_case    (		self						,				__A						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Any    =				self.transformer(input_ids=__A						,				attention_mask=__A )[0]
     lowerCamelCase :					int    =				(embs * attention_mask.unsqueeze(2 )).sum(dim=1 ) / attention_mask.sum(dim=1 )[:, None]
     return self.LinearTransformation(__A ), embs



