





"""simple docstring"""





import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
  lowercase__				:Optional[int]				   =       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__				:Optional[Any]				   =       parser.parse_args()

  if args.model_type == "roberta":
    lowercase__				:List[str]				   =       RobertaForMaskedLM.from_pretrained(args.model_name)
    lowercase__				:Dict				   =       'roberta'
  elif args.model_type == "gpt2":
    lowercase__				:List[Any]				   =       GPTaLMHeadModel.from_pretrained(args.model_name)
    lowercase__				:str				   =       'transformer'

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

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

    # Transformer Blocks #
  lowercase__				:List[str]				   =       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__				:Any				   =       state_dict[
              f"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
          ]
      lowercase__				:Optional[Any]				   =       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__				:Tuple				   =       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__				:Dict				   =       state_dict[f"""{layer}"""]
    if args.vocab_transform:
      for w in ["weight", "bias"]:
        lowercase__				:Tuple				   =       state_dict[f"""lm_head.dense.{w}"""]
        lowercase__				:List[Any]				   =       state_dict[f"""lm_head.layer_norm.{w}"""]
  elif args.model_type == "gpt2":
    for w in ["weight", "bias"]:
      lowercase__				:str				   =       state_dict[f"""{prefix}.ln_f.{w}"""]
    lowercase__				:Union[str, Any]				   =       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"""





def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->bool:





  """simple docstring"""


  return numa ^ numa < 0


if __name__ == "__main__":
  import doctest

  doctest.testmod()