




import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
  _lowerCAmelCase						:   List[str]		=   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")
  _lowerCAmelCase						:   Optional[int]		=   parser.parse_args()

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

  _lowerCAmelCase						:   Any		=   model.state_dict()
  _lowerCAmelCase						:   List[str]		=   {}

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

    # Transformer Blocks #
  _lowerCAmelCase						:   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"]:
          _lowerCAmelCase						:   List[Any]		=   state_dict[
              F'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
          ]
      _lowerCAmelCase						:   List[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"]:
          _lowerCAmelCase						:   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"]:
      _lowerCAmelCase						:   List[Any]		=   state_dict[F'''{layer}''']
    if args.vocab_transform:
      for w in ["weight", "bias"]:
        _lowerCAmelCase						:   List[Any]		=   state_dict[F'''lm_head.dense.{w}''']
        _lowerCAmelCase						:   Optional[Any]		=   state_dict[F'''lm_head.layer_norm.{w}''']
  elif args.model_type == "gpt2":
    for w in ["weight", "bias"]:
      _lowerCAmelCase						:   Optional[int]		=   state_dict[F'''{prefix}.ln_f.{w}''']
    _lowerCAmelCase						:   List[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)











import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_lowerCAmelCase						:   List[Any]		=   ["small", "medium", "large"]

_lowerCAmelCase						:   List[Any]		=   "lm_head.decoder.weight"
_lowerCAmelCase						:   Optional[int]		=   "lm_head.weight"



def 						lowerCAmelCase		(		_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   str	):




 """simple docstring"""







 UpperCAmelCase__							 =						torch.load(_lowerCAmelCase	)
 UpperCAmelCase__							 =						d.pop(_lowerCAmelCase	)
 os.makedirs(_lowerCAmelCase						,							exist_ok=_lowerCAmelCase	)
 torch.save(_lowerCAmelCase						,							os.path.join(_lowerCAmelCase						,							_lowerCAmelCase	)	)


if __name__ == "__main__":
  _lowerCAmelCase						:   Dict		=   argparse.ArgumentParser()
  parser.add_argument("--dialogpt_path", default=".", type=str)
  _lowerCAmelCase						:   Union[str, Any]		=   parser.parse_args()
  for MODEL in DIALOGPT_MODELS:
    _lowerCAmelCase						:   Union[str, Any]		=   os.path.join(args.dialogpt_path, F'''{MODEL}_ft.pkl''')
    _lowerCAmelCase						:   List[str]		=   F'''./DialoGPT-{MODEL}'''
    convert_dialogpt_checkpoint(
        checkpoint_path,
        pytorch_dump_folder_path,
    )





