

"""simple docstring"""


import os
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


UpperCAmelCase													=logging.get_logger(__name__)

UpperCAmelCase													={"vocab_file": "sentencepiece.bpe.model"}

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"
        ),
    },
}

UpperCAmelCase													={
    "moussaKam/mbarthez": 1_024,
    "moussaKam/barthez": 1_024,
    "moussaKam/barthez-orangesum-title": 1_024,
}

UpperCAmelCase													="▁"





class lowerCamelCase__   (				SCREAMING_SNAKE_CASE    ):



    '''simple docstring'''




    _lowerCamelCase					=   VOCAB_FILES_NAMES
    _lowerCamelCase					=   PRETRAINED_VOCAB_FILES_MAP
    _lowerCamelCase					=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _lowerCamelCase					=   ['''input_ids''', '''attention_mask''']




    def __init__( self ,lowerCamelCase_ ,lowerCamelCase_="<s>" ,lowerCamelCase_="</s>" ,lowerCamelCase_="</s>" ,lowerCamelCase_="<s>" ,lowerCamelCase_="<unk>" ,lowerCamelCase_="<pad>" ,lowerCamelCase_="<mask>" ,lowerCamelCase_ = None ,**lowerCamelCase_ ,)    ->      None:
          # 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

          A				  =		{} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              bos_token=lowerCamelCase_ ,eos_token=lowerCamelCase_ ,unk_token=lowerCamelCase_ ,sep_token=lowerCamelCase_ ,cls_token=lowerCamelCase_ ,pad_token=lowerCamelCase_ ,mask_token=lowerCamelCase_ ,sp_model_kwargs=self.sp_model_kwargs ,**lowerCamelCase_ ,)

          A				  =		vocab_file
          A				  =		spm.SentencePieceProcessor(**self.sp_model_kwargs    )
          self.sp_model.Load(str(lowerCamelCase_    )    )

          A				  =		{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

          A				  =		len(self.sp_model    ) - 1
          A				  =		{v: k for k, v in self.fairseq_tokens_to_ids.items()}




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_ = None    )    ->      List[int]:

          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 UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_ = None ,lowerCamelCase_ = False    )    ->      List[int]:
          if already_has_special_tokens:
                return super().get_special_tokens_mask(
                    token_ids_a=lowerCamelCase_ ,token_ids_a=lowerCamelCase_ ,already_has_special_tokens=lowerCamelCase_    )

          if token_ids_a is None:
                return [1] + ([0] * len(lowerCamelCase_    )) + [1]
          return [1] + ([0] * len(lowerCamelCase_    )) + [1, 1] + ([0] * len(lowerCamelCase_    )) + [1]




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_ = None    )    ->      List[int]:
          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]




    @property
    def UpperCamelCase__		( self    )    ->      Dict:
          return len(self.sp_model    )




    def UpperCamelCase__		( self    )    ->      int:
          A				  =		{self.convert_ids_to_tokens(lowerCamelCase_    ): i for i in range(self.vocab_size    )}
          vocab.update(self.added_tokens_encoder    )
          return vocab




    def UpperCamelCase__		( self ,lowerCamelCase_    )    ->      List[str]:
          return self.sp_model.encode(lowerCamelCase_ ,out_type=lowerCamelCase_    )




    def UpperCamelCase__		( self ,lowerCamelCase_    )    ->      List[str]:
          if token in self.fairseq_tokens_to_ids:
                return self.fairseq_tokens_to_ids[token]
          A				  =		self.sp_model.PieceToId(lowerCamelCase_    )

          return spm_id if spm_id else self.unk_token_id




    def UpperCamelCase__		( self ,lowerCamelCase_    )    ->      List[Any]:
          if index in self.fairseq_ids_to_tokens:
                return self.fairseq_ids_to_tokens[index]
          return self.sp_model.IdToPiece(lowerCamelCase_    )




    def UpperCamelCase__		( self ,lowerCamelCase_    )    ->      Any:
          A				  =		[]
          A				  =		""""""
          A				  =		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(lowerCamelCase_    ) + token
                      A				  =		True
                      A				  =		[]
                else:
                      current_sub_tokens.append(lowerCamelCase_    )
                      A				  =		False
          out_string += self.sp_model.decode(lowerCamelCase_    )
          return out_string.strip()




    def __getstate__( self    )    ->      int:
          A				  =		self.__dict__.copy()
          A				  =		None
          return state




    def __setstate__( self ,lowerCamelCase_    )    ->      Tuple:
          A				  =		d

          # for backward compatibility
          if not hasattr(self ,"""sp_model_kwargs"""    ):
                A				  =		{}

          A				  =		spm.SentencePieceProcessor(**self.sp_model_kwargs    )
          self.sp_model.Load(self.vocab_file    )




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_ = None    )    ->      Tuple[str]:
          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_    ) and os.path.isfile(self.vocab_file    ):
                copyfile(self.vocab_file ,lowerCamelCase_    )
          elif not os.path.isfile(self.vocab_file    ):
                with open(lowerCamelCase_ ,"""wb"""    ) as fi:
                      A				  =		self.sp_model.serialized_model_proto()
                      fi.write(lowerCamelCase_    )

          return (out_vocab_file,)







"""simple docstring"""


import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase													=get_tests_dir("fixtures/test_sentencepiece_bpe.model")





class lowerCamelCase__   (				SCREAMING_SNAKE_CASE     ,   unittest.TestCase    ):



    '''simple docstring'''




    _lowerCamelCase					=   BartphoTokenizer
    _lowerCamelCase					=   False
    _lowerCamelCase					=   True




    def UpperCamelCase__		( self    )    ->      str:
          super().setUp()

          A				  =		["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]
          A				  =		dict(zip(lowerCamelCase_ ,range(len(lowerCamelCase_    )    )    )    )
          A				  =		{"""unk_token""": """<unk>"""}

          A				  =		os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""monolingual_vocab_file"""]    )
          with open(self.monolingual_vocab_file ,"""w""" ,encoding="""utf-8"""    ) as fp:
                for token in vocab_tokens:
                      fp.write(f'{token} {vocab_tokens[token]}\n'    )

          A				  =		BartphoTokenizer(lowerCamelCase_ ,self.monolingual_vocab_file ,**self.special_tokens_map    )
          tokenizer.save_pretrained(self.tmpdirname    )




    def UpperCamelCase__		( self ,**lowerCamelCase_    )    ->      Tuple:
          kwargs.update(self.special_tokens_map    )
          return BartphoTokenizer.from_pretrained(self.tmpdirname ,**lowerCamelCase_    )




    def UpperCamelCase__		( self ,lowerCamelCase_    )    ->      Optional[Any]:
          A				  =		"""This is a là test"""
          A				  =		"""This is a<unk><unk> test"""
          return input_text, output_text




    def UpperCamelCase__		( self    )    ->      int:
          A				  =		BartphoTokenizer(lowerCamelCase_ ,self.monolingual_vocab_file ,**self.special_tokens_map    )
          A				  =		"""This is a là test"""
          A				  =		"""▁This ▁is ▁a ▁l à ▁t est""".split()
          A				  =		tokenizer.tokenize(lowerCamelCase_    )
          self.assertListEqual(lowerCamelCase_ ,lowerCamelCase_    )

          A				  =		tokens + [tokenizer.unk_token]
          A				  =		[4, 5, 6, 3, 3, 7, 8, 3]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_    ) ,lowerCamelCase_    )




