




'''simple docstring'''


import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
  UpperCAmelCase_ 		=      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")
  UpperCAmelCase_ 		=      parser.parse_args()

  if args.model_type == "roberta":
    UpperCAmelCase_ 		=      RobertaForMaskedLM.from_pretrained(args.model_name)
    UpperCAmelCase_ 		=      "roberta"
  elif args.model_type == "gpt2":
    UpperCAmelCase_ 		=      GPTaLMHeadModel.from_pretrained(args.model_name)
    UpperCAmelCase_ 		=      "transformer"

  UpperCAmelCase_ 		=      model.state_dict()
  UpperCAmelCase_ 		=      {}

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

    # Transformer Blocks #
  UpperCAmelCase_ 		=      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"]:
          UpperCAmelCase_ 		=      state_dict[
              f"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
          ]
      UpperCAmelCase_ 		=      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"]:
          UpperCAmelCase_ 		=      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"]:
      UpperCAmelCase_ 		=      state_dict[f"""{layer}"""]
    if args.vocab_transform:
      for w in ["weight", "bias"]:
        UpperCAmelCase_ 		=      state_dict[f"""lm_head.dense.{w}"""]
        UpperCAmelCase_ 		=      state_dict[f"""lm_head.layer_norm.{w}"""]
  elif args.model_type == "gpt2":
    for w in ["weight", "bias"]:
      UpperCAmelCase_ 		=      state_dict[f"""{prefix}.ln_f.{w}"""]
    UpperCAmelCase_ 		=      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 						SCREAMING_SNAKE_CASE       (      a_	:							int				,					a_	:							int			):
  return int((input_a, input_a).count(0			) != 0			)


def 						SCREAMING_SNAKE_CASE       (      ):
  assert nand_gate(0				,					0			) == 1
  assert nand_gate(0				,					1			) == 1
  assert nand_gate(1				,					0			) == 1
  assert nand_gate(1				,					1			) == 0


if __name__ == "__main__":
  print(nand_gate(0, 0))
  print(nand_gate(0, 1))
  print(nand_gate(1, 0))
  print(nand_gate(1, 1))



