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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_barthez import BarthezTokenizer
else:
      UpperCAmelCase_								=							None

UpperCAmelCase_								=							logging.get_logger(__name__)

UpperCAmelCase_								=							{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase_								=							{
    '''vocab_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCAmelCase_								=							{
    '''moussaKam/mbarthez''': 1_024,
    '''moussaKam/barthez''': 1_024,
    '''moussaKam/barthez-orangesum-title''': 1_024,
}

UpperCAmelCase_								=							'''▁'''





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     int		=VOCAB_FILES_NAMES
     _SCREAMING_SNAKE_CASE     :     Union[str, Any]		=PRETRAINED_VOCAB_FILES_MAP
     _SCREAMING_SNAKE_CASE     :     Optional[int]		=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _SCREAMING_SNAKE_CASE     :     Optional[int]		=["""input_ids""", """attention_mask"""]
     _SCREAMING_SNAKE_CASE     :     str		=BarthezTokenizer



     def __init__(					self				,      lowerCAmelCase__=None				,      lowerCAmelCase__=None				,      lowerCAmelCase__="<s>"				,      lowerCAmelCase__="</s>"				,      lowerCAmelCase__="</s>"				,      lowerCAmelCase__="<s>"				,      lowerCAmelCase__="<unk>"				,      lowerCAmelCase__="<pad>"				,      lowerCAmelCase__="<mask>"				,      **lowerCAmelCase__				,      ):
            # Mask token behave like a normal word, i.e. include the space before it
            _A=	AddedToken(lowerCAmelCase__				,      lstrip=lowerCAmelCase__				,      rstrip=lowerCAmelCase__       ) if isinstance(lowerCAmelCase__				,      lowerCAmelCase__       ) else mask_token

            super().__init__(
                lowerCAmelCase__				,      tokenizer_file=lowerCAmelCase__				,      bos_token=lowerCAmelCase__				,      eos_token=lowerCAmelCase__				,      unk_token=lowerCAmelCase__				,      sep_token=lowerCAmelCase__				,      cls_token=lowerCAmelCase__				,      pad_token=lowerCAmelCase__				,      mask_token=lowerCAmelCase__				,      **lowerCAmelCase__				,      )

            _A=	vocab_file
            _A=	False if not self.vocab_file else True



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__ = None       ):

            if token_ids_a is None:
                   return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
            _A=	[self.cls_token_id]
            _A=	[self.sep_token_id]
            return cls + token_ids_a + sep + sep + token_ids_a + sep



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__ = None       ):
            _A=	[self.sep_token_id]
            _A=	[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 + sep + token_ids_a + sep       ) * [0]





     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__ = None       ):
            if not self.can_save_slow_tokenizer:
                   raise ValueError(
                       'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                       'tokenizer.'       )

            if not os.path.isdir(lowerCAmelCase__       ):
                   logger.error(f"Vocabulary path ({save_directory}) should be a directory"       )
                   return
            _A=	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,)
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)