



"""simple docstring"""
from ... import PretrainedConfig


_UpperCAmelCase     =			{
    """sijunhe/nezha-cn-base""": """https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json""",
}
class 					a				(  UpperCAmelCase__       ):
  UpperCamelCase	:				str     						=					NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
  UpperCamelCase	:				Optional[Any]     						=					'nezha'



  def __init__(						self :					Dict       ,		lowerCAmelCase :					List[str]=2_1128       ,		lowerCAmelCase :					Any=768       ,		lowerCAmelCase :					Optional[Any]=12       ,		lowerCAmelCase :					List[str]=12       ,		lowerCAmelCase :					Optional[Any]=3072       ,		lowerCAmelCase :					Optional[int]="gelu"       ,		lowerCAmelCase :					Any=0.1       ,		lowerCAmelCase :					List[str]=0.1       ,		lowerCAmelCase :					Tuple=512       ,		lowerCAmelCase :					Any=64       ,		lowerCAmelCase :					Optional[int]=2       ,		lowerCAmelCase :					Union[str, Any]=0.0_2       ,		lowerCAmelCase :					List[Any]=1E-12       ,		lowerCAmelCase :					int=0.1       ,		lowerCAmelCase :					int=0       ,		lowerCAmelCase :					Any=2       ,		lowerCAmelCase :					Optional[int]=3       ,		lowerCAmelCase :					int=True       ,		**lowerCAmelCase :					Dict       ,		)     ->					Optional[int]:


     '''simple docstring'''




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

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








"""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)



