
'''simple docstring'''
import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
     A__   :		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')
     A__   :		List[Any] 		=parser.parse_args()

     if args.model_type == "roberta":
          A__   :		str 		=RobertaForMaskedLM.from_pretrained(args.model_name)
          A__   :		List[Any] 		='roberta'
     elif args.model_type == "gpt2":
          A__   :		Union[str, Any] 		=GPTaLMHeadModel.from_pretrained(args.model_name)
          A__   :		int 		='transformer'

     A__   :		Dict 		=model.state_dict()
     A__   :		Any 		={}

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

    # Transformer Blocks #
     A__   :		Union[str, Any] 		=0
     for teacher_idx in [0, 2, 4, 7, 9, 11]:
          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"]:
                         A__   :		Union[str, Any] 		=state_dict[
                             F"{prefix}.h.{teacher_idx}.{layer}.{w}"
                         ]
               A__   :		int 		=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"]:
                         A__   :		Dict 		=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"]:
               A__   :		Dict 		=state_dict[F"{layer}"]
          if args.vocab_transform:
               for w in ["weight", "bias"]:
                    A__   :		str 		=state_dict[F"lm_head.dense.{w}"]
                    A__   :		Any 		=state_dict[F"lm_head.layer_norm.{w}"]
     elif args.model_type == "gpt2":
          for w in ["weight", "bias"]:
               A__   :		Optional[int] 		=state_dict[F"{prefix}.ln_f.{w}"]
          A__   :		List[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'''
A__   :		List[Any] 		=[
    'Audio',
    'Array2D',
    'Array3D',
    'Array4D',
    'Array5D',
    'ClassLabel',
    'Features',
    'Sequence',
    'Value',
    'Image',
    'Translation',
    'TranslationVariableLanguages',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages



