



'''simple docstring'''





import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowerCAmelCase      						=	logging.get_logger(__name__)
_lowerCAmelCase      						=	{'''vocab_file''': '''spiece.model'''}

_lowerCAmelCase      						=	{
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    }
}

_lowerCAmelCase      						=	{
    '''albert-base-v1''': 512,
    '''albert-large-v1''': 512,
    '''albert-xlarge-v1''': 512,
    '''albert-xxlarge-v1''': 512,
    '''albert-base-v2''': 512,
    '''albert-large-v2''': 512,
    '''albert-xlarge-v2''': 512,
    '''albert-xxlarge-v2''': 512,
}

_lowerCAmelCase      						=	'''▁'''






class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  __lowercase							:      Any				=      VOCAB_FILES_NAMES
  __lowercase							:      Optional[Any]				=      PRETRAINED_VOCAB_FILES_MAP
  __lowercase							:      int				=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




  def __init__(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=True  ,__UpperCAmelCase=True  ,__UpperCAmelCase=False  ,__UpperCAmelCase="[CLS]"  ,__UpperCAmelCase="[SEP]"  ,__UpperCAmelCase="<unk>"  ,__UpperCAmelCase="[SEP]"  ,__UpperCAmelCase="<pad>"  ,__UpperCAmelCase="[CLS]"  ,__UpperCAmelCase="[MASK]"  ,__UpperCAmelCase = None  ,**__UpperCAmelCase  ,)    ->		None:
      # Mask token behave like a normal word, i.e. include the space before it and
      # is included in the raw text, there should be a match in a non-normalized sentence.
      lowerCAmelCase__ :  Tuple							      =					(
          AddedToken(__UpperCAmelCase  ,lstrip=__UpperCAmelCase  ,rstrip=__UpperCAmelCase  ,normalized=__UpperCAmelCase      )
          if isinstance(__UpperCAmelCase  ,__UpperCAmelCase      )
          else mask_token
      )

      lowerCAmelCase__ :  Any							      =					{} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          do_lower_case=__UpperCAmelCase  ,remove_space=__UpperCAmelCase  ,keep_accents=__UpperCAmelCase  ,bos_token=__UpperCAmelCase  ,eos_token=__UpperCAmelCase  ,unk_token=__UpperCAmelCase  ,sep_token=__UpperCAmelCase  ,pad_token=__UpperCAmelCase  ,cls_token=__UpperCAmelCase  ,mask_token=__UpperCAmelCase  ,sp_model_kwargs=self.sp_model_kwargs  ,**__UpperCAmelCase  ,)

      lowerCAmelCase__ :  str							      =					do_lower_case
      lowerCAmelCase__ :  int							      =					remove_space
      lowerCAmelCase__ :  Tuple							      =					keep_accents
      lowerCAmelCase__ :  Any							      =					vocab_file

      lowerCAmelCase__ :  Tuple							      =					spm.SentencePieceProcessor(**self.sp_model_kwargs      )
      self.sp_model.Load(__UpperCAmelCase      )




  @property
  def 							UpperCAmelCase_	(    self      )    ->		Optional[int]:
      return len(self.sp_model      )




  def 							UpperCAmelCase_	(    self      )    ->		Optional[Any]:
      lowerCAmelCase__ :  Union[str, Any]							      =					{self.convert_ids_to_tokens(__UpperCAmelCase      ): i for i in range(self.vocab_size      )}
      vocab.update(self.added_tokens_encoder      )
      return vocab




  def __getstate__(    self      )    ->		Any:
      lowerCAmelCase__ :  Optional[Any]							      =					self.__dict__.copy()
      lowerCAmelCase__ :  Optional[Any]							      =					None
      return state




  def __setstate__(    self  ,__UpperCAmelCase      )    ->		List[Any]:
      lowerCAmelCase__ :  List[str]							      =					d

      # for backward compatibility
      if not hasattr(self  ,"""sp_model_kwargs"""      ):
          lowerCAmelCase__ :  Union[str, Any]							      =					{}

      lowerCAmelCase__ :  Any							      =					spm.SentencePieceProcessor(**self.sp_model_kwargs      )
      self.sp_model.Load(self.vocab_file      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Optional[int]:
      if self.remove_space:
          lowerCAmelCase__ :  int							      =					""" """.join(inputs.strip().split()      )
      else:
          lowerCAmelCase__ :  str							      =					inputs
      lowerCAmelCase__ :  Tuple							      =					outputs.replace("""``"""  ,"""\""""      ).replace("""''"""  ,"""\""""      )

      if not self.keep_accents:
          lowerCAmelCase__ :  Any							      =					unicodedata.normalize("""NFKD"""  ,__UpperCAmelCase      )
          lowerCAmelCase__ :  Dict							      =					"""""".join([c for c in outputs if not unicodedata.combining(__UpperCAmelCase      )]      )
      if self.do_lower_case:
          lowerCAmelCase__ :  Tuple							      =					outputs.lower()

      return outputs




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		List[str]:
      lowerCAmelCase__ :  List[str]							      =					self.preprocess_text(__UpperCAmelCase      )
      lowerCAmelCase__ :  Optional[int]							      =					self.sp_model.encode(__UpperCAmelCase  ,out_type=__UpperCAmelCase      )
      lowerCAmelCase__ :  str							      =					[]
      for piece in pieces:
          if len(__UpperCAmelCase      ) > 1 and piece[-1] == str(""","""      ) and piece[-2].isdigit():
              lowerCAmelCase__ :  List[Any]							      =					self.sp_model.EncodeAsPieces(piece[:-1].replace(__UpperCAmelCase  ,""""""      )      )
              if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                  if len(cur_pieces[0]      ) == 1:
                      lowerCAmelCase__ :  str							      =					cur_pieces[1:]
                  else:
                      lowerCAmelCase__ :  int							      =					cur_pieces[0][1:]
              cur_pieces.append(piece[-1]      )
              new_pieces.extend(__UpperCAmelCase      )
          else:
              new_pieces.append(__UpperCAmelCase      )

      return new_pieces




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		List[str]:
      return self.sp_model.PieceToId(__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Any:
      return self.sp_model.IdToPiece(__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		str:
      lowerCAmelCase__ :  str							      =					[]
      lowerCAmelCase__ :  Tuple							      =					""""""
      lowerCAmelCase__ :  Tuple							      =					False
      for token in tokens:
          # make sure that special tokens are not decoded using sentencepiece model
          if token in self.all_special_tokens:
              if not prev_is_special:
                  out_string += " "
              out_string += self.sp_model.decode(__UpperCAmelCase      ) + token
              lowerCAmelCase__ :  Union[str, Any]							      =					True
              lowerCAmelCase__ :  List[str]							      =					[]
          else:
              current_sub_tokens.append(__UpperCAmelCase      )
              lowerCAmelCase__ :  Optional[Any]							      =					False
      out_string += self.sp_model.decode(__UpperCAmelCase      )
      return out_string.strip()




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase = None      )    ->		List[int]:
      lowerCAmelCase__ :  int							      =					[self.sep_token_id]
      lowerCAmelCase__ :  Dict							      =					[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 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase = None  ,__UpperCAmelCase = False      )    ->		List[int]:

      if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_a=__UpperCAmelCase  ,token_ids_a=__UpperCAmelCase  ,already_has_special_tokens=__UpperCAmelCase      )

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




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase = None      )    ->		List[int]:
      lowerCAmelCase__ :  List[str]							      =					[self.sep_token_id]
      lowerCAmelCase__ :  Tuple							      =					[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 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase = None      )    ->		Tuple[str]:
      if not os.path.isdir(__UpperCAmelCase      ):
          logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
          return
      lowerCAmelCase__ :  int							      =					os.path.join(
          __UpperCAmelCase  ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )

      if os.path.abspath(self.vocab_file      ) != os.path.abspath(__UpperCAmelCase      ) and os.path.isfile(self.vocab_file      ):
          copyfile(self.vocab_file  ,__UpperCAmelCase      )
      elif not os.path.isfile(self.vocab_file      ):
          with open(__UpperCAmelCase  ,"""wb"""      ) as fi:
              lowerCAmelCase__ :  List[Any]							      =					self.sp_model.serialized_model_proto()
              fi.write(__UpperCAmelCase      )

      return (out_vocab_file,)









"""simple docstring"""





import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->Dict:





  """simple docstring"""


  lowerCAmelCase__			:str											=							BertConfig.from_json_file(_SCREAMING_SNAKE_CASE			)
  print(F"Building PyTorch model from configuration: {config}"			)
  lowerCAmelCase__			:int											=							BertForPreTraining(_SCREAMING_SNAKE_CASE			)

  # Load weights from tf checkpoint
  load_tf_weights_in_bert(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)

  # Save pytorch-model
  print(F"Save PyTorch model to {pytorch_dump_path}"			)
  torch.save(model.state_dict()     ,   _SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
  __A											=		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
  )
  parser.add_argument(
      """--bert_config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained BERT model. \n"""
          """This specifies the model architecture."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  __A											=		parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
