





import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
      __UpperCAmelCase       :   Dict				       =							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       :   Dict				       =							parser.parse_args()

      if args.model_type == "roberta":
            __UpperCAmelCase       :   str				       =							RobertaForMaskedLM.from_pretrained(args.model_name)
            __UpperCAmelCase       :   Tuple				       =							"roberta"
      elif args.model_type == "gpt2":
            __UpperCAmelCase       :   Dict				       =							GPTaLMHeadModel.from_pretrained(args.model_name)
            __UpperCAmelCase       :   Optional[int]				       =							"transformer"

      __UpperCAmelCase       :   Dict				       =							model.state_dict()
      __UpperCAmelCase       :   Any				       =							{}

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

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













def 			A__							(    SCREAMING_SNAKE_CASE__ = 200)   ->    int:
  __snake_case:						Optional[int]   =						[1, 2, 5, 10, 20, 50, 100, 200]
  __snake_case:						List[Any]   =						[0] * (pence + 1)
  __snake_case:						int   =						1  # base case: 1 way to make 0 pence

  for coin in coins:
    for i in range(SCREAMING_SNAKE_CASE__					,					pence + 1					,					1):
      number_of_ways[i] += number_of_ways[i - coin]
  return number_of_ways[pence]


if __name__ == "__main__":
      assert solution(200) == 73_682






