


import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
     snake_case				:    Optional[int]       				=     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				:    List[str]       				=     parser.parse_args()

     if args.model_type == "bert":
          snake_case				:    List[Any]       				=     BertForMaskedLM.from_pretrained(args.model_name)
          snake_case				:    int       				=     '''bert'''
     else:
          raise ValueError('''args.model_type should be "bert".''')

     snake_case				:    List[str]       				=     model.state_dict()
     snake_case				:    int       				=     {}

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

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

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

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

     snake_case				:    Optional[int]       				=     state_dict['''cls.predictions.decoder.weight''']
     snake_case				:    Dict       				=     state_dict['''cls.predictions.bias''']
     if args.vocab_transform:
          for w in ["weight", "bias"]:
               snake_case				:    List[str]       				=     state_dict[F"""cls.predictions.transform.dense.{w}"""]
               snake_case				:    Optional[int]       				=     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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__    :      List[str]						=    logging.get_logger(__name__)

lowercase__    :      Dict						=    {
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}


class  SCREAMING_SNAKE_CASE    (a__							):
     lowerCAmelCase					=       '''roberta'''


     def __init__(						self						,	_UpperCAmelCase=5_0265						,	_UpperCAmelCase=768						,	_UpperCAmelCase=12						,	_UpperCAmelCase=12						,	_UpperCAmelCase=3072						,	_UpperCAmelCase="gelu"						,	_UpperCAmelCase=0.1						,	_UpperCAmelCase=0.1						,	_UpperCAmelCase=512						,	_UpperCAmelCase=2						,	_UpperCAmelCase=0.02						,	_UpperCAmelCase=1e-1_2						,	_UpperCAmelCase=1						,	_UpperCAmelCase=0						,	_UpperCAmelCase=2						,	_UpperCAmelCase="absolute"						,	_UpperCAmelCase=True						,	_UpperCAmelCase=None						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






          super().__init__(pad_token_id=_UpperCAmelCase						,	bos_token_id=_UpperCAmelCase						,	eos_token_id=_UpperCAmelCase						,	**_UpperCAmelCase)

          __A						: Optional[int]      =       vocab_size
          __A						: int      =       hidden_size
          __A						: Union[str, Any]      =       num_hidden_layers
          __A						: List[str]      =       num_attention_heads
          __A						: Optional[int]      =       hidden_act
          __A						: str      =       intermediate_size
          __A						: Union[str, Any]      =       hidden_dropout_prob
          __A						: Dict      =       attention_probs_dropout_prob
          __A						: int      =       max_position_embeddings
          __A						: str      =       type_vocab_size
          __A						: Any      =       initializer_range
          __A						: int      =       layer_norm_eps
          __A						: Optional[int]      =       position_embedding_type
          __A						: int      =       use_cache
          __A						: Union[str, Any]      =       classifier_dropout




class  SCREAMING_SNAKE_CASE    (a__							):


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          if self.task == "multiple-choice":
               __A						: str      =       {0: 'batch', 1: 'choice', 2: 'sequence'}
          else:
               __A						: List[str]      =       {0: 'batch', 1: 'sequence'}
          return OrderedDict(
              [
                  ('input_ids', dynamic_axis),
                  ('attention_mask', dynamic_axis),
              ])