



"""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, 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"]:
               _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"""
import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
   _UpperCAmelCase     =			argparse.ArgumentParser(
       description=(
           """Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"""
           """ Distillation"""
       )
   )
   parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""])
   parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str)
   parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str)
   parser.add_argument("""--vocab_transform""", action="""store_true""")
   _UpperCAmelCase     =			parser.parse_args()

   if args.model_type == "bert":
      _UpperCAmelCase     =			BertForMaskedLM.from_pretrained(args.model_name)
      _UpperCAmelCase     =			"""bert"""
   else:
      raise ValueError("""args.model_type should be \"bert\".""")

   _UpperCAmelCase     =			model.state_dict()
   _UpperCAmelCase     =			{}

   for w in ["word_embeddings", "position_embeddings"]:
      _UpperCAmelCase     =			state_dict[f"""{prefix}.embeddings.{w}.weight"""]
   for w in ["weight", "bias"]:
      _UpperCAmelCase     =			state_dict[f"""{prefix}.embeddings.LayerNorm.{w}"""]

   _UpperCAmelCase     =			0
   for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
      for w in ["weight", "bias"]:
         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
         ]
         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
         ]
         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
         ]

         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
         ]
         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
         ]

         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
         ]
         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
         ]
         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
         ]
      std_idx += 1

   _UpperCAmelCase     =			state_dict["""cls.predictions.decoder.weight"""]
   _UpperCAmelCase     =			state_dict["""cls.predictions.bias"""]
   if args.vocab_transform:
      for w in ["weight", "bias"]:
         _UpperCAmelCase     =			state_dict[f"""cls.predictions.transform.dense.{w}"""]
         _UpperCAmelCase     =			state_dict[f"""cls.predictions.transform.LayerNorm.{w}"""]

   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)



