



"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A:   Optional[int]								=		logging.get_logger(__name__)

A:   List[Any]								=		{
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__      ,			UpperCAmelCase__ ):
   __lowerCAmelCase  :   Optional[int]						=						'bit'
   __lowerCAmelCase  :   Union[str, Any]						=						['preactivation', 'bottleneck']
   __lowerCAmelCase  :   Union[str, Any]						=						['SAME', 'VALID']

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE=3      ,     _SCREAMING_SNAKE_CASE=64      ,     _SCREAMING_SNAKE_CASE=[256, 512, 1024, 2048]      ,     _SCREAMING_SNAKE_CASE=[3, 4, 6, 3]      ,     _SCREAMING_SNAKE_CASE="preactivation"      ,     _SCREAMING_SNAKE_CASE="relu"      ,     _SCREAMING_SNAKE_CASE=None      ,     _SCREAMING_SNAKE_CASE=32      ,     _SCREAMING_SNAKE_CASE=0.0      ,     _SCREAMING_SNAKE_CASE=False      ,     _SCREAMING_SNAKE_CASE=32      ,     _SCREAMING_SNAKE_CASE=1      ,     _SCREAMING_SNAKE_CASE=None      ,     _SCREAMING_SNAKE_CASE=None      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      int:





    '''simple docstring'''




    super().__init__(**_SCREAMING_SNAKE_CASE						)
    if layer_type not in self.layer_types:
     raise ValueError(F"layer_type={layer_type} is not one of {','.join(self.layer_types						)}"						)
    if global_padding is not None:
     if global_padding.upper() in self.supported_padding:
      UpperCAmelCase	:					List[str]	=	global_padding.upper()
     else:
      raise ValueError(F"Padding strategy {global_padding} not supported"						)
    UpperCAmelCase	:					Optional[Any]	=	num_channels
    UpperCAmelCase	:					Optional[int]	=	embedding_size
    UpperCAmelCase	:					List[str]	=	hidden_sizes
    UpperCAmelCase	:					int	=	depths
    UpperCAmelCase	:					Tuple	=	layer_type
    UpperCAmelCase	:					Tuple	=	hidden_act
    UpperCAmelCase	:					Optional[int]	=	global_padding
    UpperCAmelCase	:					Any	=	num_groups
    UpperCAmelCase	:					Any	=	drop_path_rate
    UpperCAmelCase	:					List[str]	=	embedding_dynamic_padding
    UpperCAmelCase	:					Dict	=	output_stride
    UpperCAmelCase	:					Union[str, Any]	=	width_factor

    UpperCAmelCase	:					Tuple	=	["""stem"""] + [F"stage{idx}" for idx in range(1      ,     len(_SCREAMING_SNAKE_CASE						) + 1						)]
    UpperCAmelCase	,		UpperCAmelCase	:					Optional[Any]	=	get_aligned_output_features_output_indices(
        out_features=_SCREAMING_SNAKE_CASE      ,     out_indices=_SCREAMING_SNAKE_CASE      ,     stage_names=self.stage_names						)







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__					:							Optional[int]													=     parser.parse_args()

       if args.model_type == "bert":
              snake_case__					:							Dict													=     BertForMaskedLM.from_pretrained(args.model_name)
              snake_case__					:							Union[str, Any]													=     'bert'
       else:
              raise ValueError('args.model_type should be "bert".')

       snake_case__					:							Optional[int]													=     model.state_dict()
       snake_case__					:							List[Any]													=     {}

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

       snake_case__					:							int													=     0
       for teacher_idx in [0, 2, 4, 7, 9, 11]:
              for w in ["weight", "bias"]:
                     snake_case__					:							Union[str, Any]													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'
                     ]
                     snake_case__					:							Dict													=     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__					:							int													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'
                     ]
                     snake_case__					:							Optional[int]													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'
                     ]

                     snake_case__					:							Optional[Any]													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'
                     ]
                     snake_case__					:							List[str]													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'
                     ]
                     snake_case__					:							int													=     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__					:							str													=     state_dict['cls.predictions.bias']
       if args.vocab_transform:
              for w in ["weight", "bias"]:
                     snake_case__					:							int													=     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)





