
"""simple docstring"""







import argparse

import torch

from transformers import BertForMaskedLM


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

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

       lowercase_         =       model.state_dict()
       lowercase_         =       {}

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

       lowercase_         =       0
       for teacher_idx in [0, 2, 4, 7, 9, 11]:
              for w in ["weight", "bias"]:
                     lowercase_         =       state_dict[
                         f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
                     ]
                     lowercase_         =       state_dict[
                         f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
                     ]
                     lowercase_         =       state_dict[
                         f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
                     ]

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

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

       lowercase_         =       state_dict["cls.predictions.decoder.weight"]
       lowercase_         =       state_dict["cls.predictions.bias"]
       if args.vocab_transform:
              for w in ["weight", "bias"]:
                     lowercase_         =       state_dict[f"""cls.predictions.transform.dense.{w}"""]
                     lowercase_         =       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 json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing



class 		UpperCAmelCase_						(lowerCamelCase_    ):
 """simple docstring"""




 def __init__(			self							:			List[Any]  , a_							:			str = "▁"  , a_							:			bool = True  , a_							:			Union[str, AddedToken] = "<unk>"  , a_							:			Union[str, AddedToken] = "</s>"  , a_							:			Union[str, AddedToken] = "<pad>"  , )->       List[Any]:



      """simple docstring"""

      UpperCAmelCase_    :     Dict            =	{
          """pad""": {"""id""": 0, """token""": pad_token},
          """eos""": {"""id""": 1, """token""": eos_token},
          """unk""": {"""id""": 2, """token""": unk_token},
      }

      UpperCAmelCase_    :     Dict            =	[None] * len(self.special_tokens			)
      for token_dict in self.special_tokens.values():
           UpperCAmelCase_    :     Optional[int]            =	token_dict["""token"""]

      UpperCAmelCase_    :     Tuple            =	Tokenizer(Unigram()			)

      UpperCAmelCase_    :     Optional[Any]            =	normalizers.Sequence(
          [
              normalizers.Nmt(),
              normalizers.NFKC(),
              normalizers.Replace(Regex(""" {2,}"""			)  , """ """			),
              normalizers.Lowercase(),
          ]			)
      UpperCAmelCase_    :     Any            =	pre_tokenizers.Sequence(
          [
              pre_tokenizers.Metaspace(replacement=a_  , add_prefix_space=a_			),
              pre_tokenizers.Digits(individual_digits=a_			),
              pre_tokenizers.Punctuation(),
          ]			)
      UpperCAmelCase_    :     str            =	decoders.Metaspace(replacement=a_  , add_prefix_space=a_			)

      UpperCAmelCase_    :     List[Any]            =	TemplateProcessing(
          single=f'''$A {self.special_tokens['eos']['token']}'''  , special_tokens=[(self.special_tokens["""eos"""]["""token"""], self.special_tokens["""eos"""]["""id"""])]  , )

      UpperCAmelCase_    :     Dict            =	{
          """model""": """SentencePieceUnigram""",
          """replacement""": replacement,
          """add_prefix_space""": add_prefix_space,
      }

      super().__init__(a_  , a_			)




 def 	a   (			self							:			int  , a_							:			Union[str, List[str]]  , a_							:			int = 80_00  , a_							:			bool = True  , )->       int:



      """simple docstring"""

      UpperCAmelCase_    :     int            =	trainers.UnigramTrainer(
          vocab_size=a_  , special_tokens=self.special_tokens_list  , show_progress=a_  , )

      if isinstance(a_  , a_			):
           UpperCAmelCase_    :     str            =	[files]
      self._tokenizer.train(a_  , trainer=a_			)

      self.add_unk_id()




 def 	a   (			self							:			List[Any]  , a_							:			Union[Iterator[str], Iterator[Iterator[str]]]  , a_							:			int = 80_00  , a_							:			bool = True  , )->       Any:



      """simple docstring"""

      UpperCAmelCase_    :     Union[str, Any]            =	trainers.UnigramTrainer(
          vocab_size=a_  , special_tokens=self.special_tokens_list  , show_progress=a_  , )

      self._tokenizer.train_from_iterator(a_  , trainer=a_			)

      self.add_unk_id()






 def 	a   (			self							:			Dict			)->       Union[str, Any]:



      """simple docstring"""

      UpperCAmelCase_    :     int            =	json.loads(self._tokenizer.to_str()			)

      UpperCAmelCase_    :     Any            =	self.special_tokens["""unk"""]["""id"""]

      UpperCAmelCase_    :     Tuple            =	Tokenizer.from_str(json.dumps(a_			)			)

