
"""simple docstring"""



import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase__          = logging.get_logger(__name__)

UpperCAmelCase__          = {'vocab_file': 'sentencepiece.model'}

UpperCAmelCase__          = {
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
}

UpperCAmelCase__          = {
    'google/rembert': 256,
}

class       lowerCAmelCase__				(						A_	):
     __a						=					VOCAB_FILES_NAMES
     __a						=					PRETRAINED_VOCAB_FILES_MAP
     __a						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







     def __init__(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			Any    ,      _lowerCamelCase			:			Union[str, Any]=False    ,      _lowerCamelCase			:			Any=True    ,      _lowerCamelCase			:			Optional[Any]=True    ,      _lowerCamelCase			:			int="[CLS]"    ,      _lowerCamelCase			:			Optional[int]="[SEP]"    ,      _lowerCamelCase			:			Optional[int]="[UNK]"    ,      _lowerCamelCase			:			Optional[Any]="[SEP]"    ,      _lowerCamelCase			:			str="[PAD]"    ,      _lowerCamelCase			:			List[Any]="[CLS]"    ,      _lowerCamelCase			:			Any="[MASK]"    ,      **_lowerCamelCase			:			Optional[int]    ,      ):
       super().__init__(
           do_lower_case=_lowerCamelCase    ,      remove_space=_lowerCamelCase    ,      keep_accents=_lowerCamelCase    ,      bos_token=_lowerCamelCase    ,      eos_token=_lowerCamelCase    ,      unk_token=_lowerCamelCase    ,      sep_token=_lowerCamelCase    ,      pad_token=_lowerCamelCase    ,      cls_token=_lowerCamelCase    ,      mask_token=_lowerCamelCase    ,      **_lowerCamelCase    ,      )

       _snake_case					     =       do_lower_case
       _snake_case					     =       remove_space
       _snake_case					     =       keep_accents
       _snake_case					     =       vocab_file

       _snake_case					     =       spm.SentencePieceProcessor()
       self.sp_model.Load(_lowerCamelCase			)







     @property
     def      lowercase	(							self			:			int			):
       return len(self.sp_model			)







     def      lowercase	(							self			:			Any			):
       _snake_case					     =       {self.convert_ids_to_tokens(_lowerCamelCase			): i for i in range(self.vocab_size			)}
       vocab.update(self.added_tokens_encoder			)
       return vocab







     def __getstate__(							self			:			List[str]			):
       _snake_case					     =       self.__dict__.copy()
       _snake_case					     =       None
       return state







     def __setstate__(							self			:			List[str]    ,      _lowerCamelCase			:			Tuple			):
       _snake_case					     =       d
       _snake_case					     =       spm.SentencePieceProcessor()
       self.sp_model.Load(self.vocab_file			)







     def      lowercase	(							self			:			str    ,      _lowerCamelCase			:			List[str]    ,      _lowerCamelCase			:			Tuple=False			):
       _snake_case					     =       self.sp_model.EncodeAsPieces(_lowerCamelCase			)
       return pieces







     def      lowercase	(							self			:			str    ,      _lowerCamelCase			:			str			):
       return self.sp_model.PieceToId(_lowerCamelCase			)







     def      lowercase	(							self			:			List[str]    ,      _lowerCamelCase			:			int			):
       return self.sp_model.IdToPiece(_lowerCamelCase			)







     def      lowercase	(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			Any			):
       _snake_case					     =       self.sp_model.decode_pieces(_lowerCamelCase			)
       return out_string







     def      lowercase	(							self			:			Optional[Any]    ,      _lowerCamelCase			:			List[int]    ,      _lowerCamelCase			:			Optional[List[int]] = None			):
       _snake_case					     =       [self.sep_token_id]
       _snake_case					     =       [self.cls_token_id]
       if token_ids_a is None:
         return cls + token_ids_a + sep
       return cls + token_ids_a + sep + token_ids_a + sep







     def      lowercase	(							self			:			Tuple    ,      _lowerCamelCase			:			List[int]    ,      _lowerCamelCase			:			Optional[List[int]] = None    ,      _lowerCamelCase			:			bool = False			):

       if already_has_special_tokens:
         if token_ids_a is not None:
           raise ValueError(
               '''You should not supply a second sequence if the provided sequence of '''
               '''ids is already formatted with special tokens for the model.'''			)
         return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

       if token_ids_a is not None:
         return [1] + ([0] * len(_lowerCamelCase			)) + [1] + ([0] * len(_lowerCamelCase			)) + [1]
       return [1] + ([0] * len(_lowerCamelCase			)) + [1]







     def      lowercase	(							self			:			Optional[int]    ,      _lowerCamelCase			:			List[int]    ,      _lowerCamelCase			:			Optional[List[int]] = None			):
       _snake_case					     =       [self.sep_token_id]
       _snake_case					     =       [self.cls_token_id]

       if token_ids_a is None:
         return len(cls + token_ids_a + sep			) * [0]
       return len(cls + token_ids_a + sep			) * [0] + len(token_ids_a + sep			) * [1]







     def      lowercase	(							self			:			List[str]    ,      _lowerCamelCase			:			str    ,      _lowerCamelCase			:			Optional[str] = None			):
       if not os.path.isdir(_lowerCamelCase			):
         logger.error('''Vocabulary path ({}) should be a directory'''.format(_lowerCamelCase			)			)
         return
       _snake_case					     =       os.path.join(
           _lowerCamelCase    ,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

       if os.path.abspath(self.vocab_file			) != os.path.abspath(_lowerCamelCase			):
         copyfile(self.vocab_file    ,      _lowerCamelCase			)

       return (out_vocab_file,)





"""simple docstring"""



import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
 UpperCAmelCase__          = 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')
 UpperCAmelCase__          = parser.parse_args()

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

 UpperCAmelCase__          = model.state_dict()
 UpperCAmelCase__          = {}

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

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

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

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

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



