





import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


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

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

      _SCREAMING_SNAKE_CASE      	=		model.state_dict()
      _SCREAMING_SNAKE_CASE      	=		{}

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

    # Transformer Blocks #
      _SCREAMING_SNAKE_CASE      	=		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"]:
                              _SCREAMING_SNAKE_CASE      	=		state_dict[
                                  F'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
                              ]
                  _SCREAMING_SNAKE_CASE      	=		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"]:
                              _SCREAMING_SNAKE_CASE      	=		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"]:
                  _SCREAMING_SNAKE_CASE      	=		state_dict[F'''{layer}''']
            if args.vocab_transform:
                  for w in ["weight", "bias"]:
                        _SCREAMING_SNAKE_CASE      	=		state_dict[F'''lm_head.dense.{w}''']
                        _SCREAMING_SNAKE_CASE      	=		state_dict[F'''lm_head.layer_norm.{w}''']
      elif args.model_type == "gpt2":
            for w in ["weight", "bias"]:
                  _SCREAMING_SNAKE_CASE      	=		state_dict[F'''{prefix}.ln_f.{w}''']
            _SCREAMING_SNAKE_CASE      	=		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 numpy as np


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	return (2 / (1 + np.exp(-2 * vector   ))) - 1


if __name__ == "__main__":
						import doctest

						doctest.testmod()

