

'''simple docstring'''







import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


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

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

      __lowerCamelCase		       =    model.state_dict()
      __lowerCamelCase		       =    {}

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

    # Transformer Blocks #
      __lowerCamelCase		       =    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"]:
                              __lowerCamelCase		       =    state_dict[
                                  f"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
                              ]
                  __lowerCamelCase		       =    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		       =    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		       =    state_dict[f"""{layer}"""]
            if args.vocab_transform:
                  for w in ["weight", "bias"]:
                        __lowerCamelCase		       =    state_dict[f"""lm_head.dense.{w}"""]
                        __lowerCamelCase		       =    state_dict[f"""lm_head.layer_norm.{w}"""]
      elif args.model_type == "gpt2":
            for w in ["weight", "bias"]:
                  __lowerCamelCase		       =    state_dict[f"""{prefix}.ln_f.{w}"""]
            __lowerCamelCase		       =    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       UpperCAmelCase__    (      UpperCAmelCase__				)			->			int:
       assert (
           isinstance(UpperCAmelCase__,       UpperCAmelCase__				) and number_of_steps > 0
       ), F'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
       if number_of_steps == 1:
              return 1
       A_					,			A_						=      1, 1
       for _ in range(number_of_steps - 1				):
              A_					,			A_						=      current + previous, current
       return current


if __name__ == "__main__":
      import doctest

      doctest.testmod()
