


import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
       __UpperCamelCase       :       Union[str, Any]						       =				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       :       List[Any]						       =				parser.parse_args()

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

       __UpperCamelCase       :       Optional[Any]						       =				model.state_dict()
       __UpperCamelCase       :       List[str]						       =				{}

       # Embeddings #
       if args.model_type == "gpt2":
              for param_name in ["wte.weight", "wpe.weight"]:
                     __UpperCamelCase       :       str						       =				state_dict[f"{prefix}.{param_name}"]
       else:
              for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
                     __UpperCamelCase       :       Union[str, Any]						       =				f"{prefix}.embeddings.{w}.weight"
                     __UpperCamelCase       :       str						       =				state_dict[param_name]
              for w in ["weight", "bias"]:
                     __UpperCamelCase       :       int						       =				f"{prefix}.embeddings.LayerNorm.{w}"
                     __UpperCamelCase       :       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       :       int						       =				state_dict[
                                       f"{prefix}.h.{teacher_idx}.{layer}.{w}"
                                   ]
                     __UpperCamelCase       :       Union[str, 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       :       List[Any]						       =				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       :       Tuple						       =				state_dict[f"{layer}"]
              if args.vocab_transform:
                     for w in ["weight", "bias"]:
                            __UpperCamelCase       :       int						       =				state_dict[f"lm_head.dense.{w}"]
                            __UpperCamelCase       :       Tuple						       =				state_dict[f"lm_head.layer_norm.{w}"]
       elif args.model_type == "gpt2":
              for w in ["weight", "bias"]:
                     __UpperCamelCase       :       Dict						       =				state_dict[f"{prefix}.ln_f.{w}"]
              __UpperCamelCase       :       Optional[int]						       =				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 functools

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    str          =   len(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Optional[Any]          =   len(SCREAMING_SNAKE_CASE     )

 @functools.cache
 def min_distance(SCREAMING_SNAKE_CASE	:				int    , SCREAMING_SNAKE_CASE	:				int     ) -> int:
  # if first word index is overflow - delete all from the second word
  if indexa >= len_worda:
   return len_worda - indexa
  # if second word index is overflow - delete all from the first word
  if indexa >= len_worda:
   return len_worda - indexa
  UpperCamelCase__					:    Tuple          =   int(worda[indexa] != worda[indexa]     )  # current letters not identical
  return min(
      1 + min_distance(indexa + 1    , SCREAMING_SNAKE_CASE     )    , 1 + min_distance(SCREAMING_SNAKE_CASE    , indexa + 1     )    , diff + min_distance(indexa + 1    , indexa + 1     )    , )

 return min_distance(0    , 0     )


if __name__ == "__main__":
       import doctest

       doctest.testmod()


