



"""simple docstring"""







import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
     lowercase__		:    Optional[Any]				   =     argparse.ArgumentParser(
         description=(
             """Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"""
             """ Distillation"""
         )
     )
     parser.add_argument("""--model_type""", default="""roberta""", choices=["""roberta""", """gpt2"""])
     parser.add_argument("""--model_name""", default="""roberta-large""", type=str)
     parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_roberta_048131723.pth""", type=str)
     parser.add_argument("""--vocab_transform""", action="""store_true""")
     lowercase__		:    str				   =     parser.parse_args()

     if args.model_type == "roberta":
          lowercase__		:    Any				   =     RobertaForMaskedLM.from_pretrained(args.model_name)
          lowercase__		:    Any				   =     """roberta"""
     elif args.model_type == "gpt2":
          lowercase__		:    Tuple				   =     GPTaLMHeadModel.from_pretrained(args.model_name)
          lowercase__		:    Optional[Any]				   =     """transformer"""

     lowercase__		:    Dict				   =     model.state_dict()
     lowercase__		:    str				   =     {}

     # Embeddings #
     if args.model_type == "gpt2":
          for param_name in ["wte.weight", "wpe.weight"]:
               lowercase__		:    List[Any]				   =     state_dict[f'{prefix}.{param_name}']
     else:
          for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
               lowercase__		:    List[Any]				   =     f'{prefix}.embeddings.{w}.weight'
               lowercase__		:    str				   =     state_dict[param_name]
          for w in ["weight", "bias"]:
               lowercase__		:    Tuple				   =     f'{prefix}.embeddings.LayerNorm.{w}'
               lowercase__		:    str				   =     state_dict[param_name]

    # Transformer Blocks #
     lowercase__		:    Optional[Any]				   =     0
     for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
          if args.model_type == "gpt2":
               for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
                    for w in ["weight", "bias"]:
                         lowercase__		:    int				   =     state_dict[
                             f'{prefix}.h.{teacher_idx}.{layer}.{w}'
                         ]
               lowercase__		:    List[str]				   =     state_dict[f'{prefix}.h.{teacher_idx}.attn.bias']
          else:
               for layer in [
                   "attention.self.query",
                   "attention.self.key",
                   "attention.self.value",
                   "attention.output.dense",
                   "attention.output.LayerNorm",
                   "intermediate.dense",
                   "output.dense",
                   "output.LayerNorm",
               ]:
                    for w in ["weight", "bias"]:
                         lowercase__		:    Optional[Any]				   =     state_dict[
                             f'{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'
                         ]
          std_idx += 1

     # Language Modeling Head ###s
     if args.model_type == "roberta":
          for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
               lowercase__		:    Tuple				   =     state_dict[f'{layer}']
          if args.vocab_transform:
               for w in ["weight", "bias"]:
                    lowercase__		:    Union[str, Any]				   =     state_dict[f'lm_head.dense.{w}']
                    lowercase__		:    Optional[Any]				   =     state_dict[f'lm_head.layer_norm.{w}']
     elif args.model_type == "gpt2":
          for w in ["weight", "bias"]:
               lowercase__		:    int				   =     state_dict[f'{prefix}.ln_f.{w}']
          lowercase__		:    str				   =     state_dict["""lm_head.weight"""]

     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"""







import logging

from transformers.configuration_utils import PretrainedConfig


lowercase__		:    List[Any]				   =     logging.getLogger(__name__)







class 				UpperCamelCase__       (     lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					"""masked_bert"""



      def __init__(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=3_0_5_2_2   ,      SCREAMING_SNAKE_CASE_    :				int=7_6_8   ,      SCREAMING_SNAKE_CASE_    :				Tuple=1_2   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=1_2   ,      SCREAMING_SNAKE_CASE_    :				Tuple=3_0_7_2   ,      SCREAMING_SNAKE_CASE_    :				List[str]="gelu"   ,      SCREAMING_SNAKE_CASE_    :				Tuple=0.1   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=0.1   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=5_1_2   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=2   ,      SCREAMING_SNAKE_CASE_    :				Dict=0.02   ,      SCREAMING_SNAKE_CASE_    :				Any=1E-12   ,      SCREAMING_SNAKE_CASE_    :				List[str]=0   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]="topK"   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]="constant"   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=0.0   ,      **SCREAMING_SNAKE_CASE_    :				Any   ,      ):
        super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_   ,      **SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    List[Any]								=			vocab_size
        lowerCAmelCase_       :    str								=			hidden_size
        lowerCAmelCase_       :    Optional[int]								=			num_hidden_layers
        lowerCAmelCase_       :    Dict								=			num_attention_heads
        lowerCAmelCase_       :    List[str]								=			hidden_act
        lowerCAmelCase_       :    List[Any]								=			intermediate_size
        lowerCAmelCase_       :    Any								=			hidden_dropout_prob
        lowerCAmelCase_       :    str								=			attention_probs_dropout_prob
        lowerCAmelCase_       :    Any								=			max_position_embeddings
        lowerCAmelCase_       :    Dict								=			type_vocab_size
        lowerCAmelCase_       :    Tuple								=			initializer_range
        lowerCAmelCase_       :    List[Any]								=			layer_norm_eps
        lowerCAmelCase_       :    str								=			pruning_method
        lowerCAmelCase_       :    Optional[Any]								=			mask_init
        lowerCAmelCase_       :    int								=			mask_scale





