



"""simple docstring"""
import argparse

import torch

from transformers import BertForMaskedLM


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

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

   _lowerCAmelCase     =			model.state_dict()
   _lowerCAmelCase     =			{}

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

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

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

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

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








"""simple docstring"""
import string





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: List[Any]     			=""""""
   for i in sequence:
      SCREAMING_SNAKE_CASE_: List[Any]     			=ord(lowercase )
      if 65 <= extract <= 90:
         output += chr(155 - extract )
      elif 97 <= extract <= 122:
         output += chr(219 - extract )
      else:
         output += i
   return output





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Any     			=string.ascii_letters
   SCREAMING_SNAKE_CASE_: Tuple     			=string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
   return "".join(
       letters_reversed[letters.index(lowercase )] if c in letters else c for c in sequence )





def      __magic_name__				(							):
   from timeit import timeit

   print("""Running performance benchmarks...""" )
   SCREAMING_SNAKE_CASE_: int     			="""from string import printable ; from __main__ import atbash, atbash_slow"""
   print(f'''> atbash_slow(): {timeit("atbash_slow(printable)"							,			setup=lowercase )} seconds''' )
   print(f'''>      atbash(): {timeit("atbash(printable)"							,			setup=lowercase )} seconds''' )


if __name__ == "__main__":
   for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
      print(f"""{example} encrypted in atbash: {atbash(example)}""")
   benchmark()



