





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

import sentencepiece as spm

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


_UpperCamelCase				=       logging.get_logger(__name__)

_UpperCamelCase				=       '''▁'''

_UpperCamelCase				=       {'''vocab_file''': '''sentencepiece.bpe.model'''}

_UpperCamelCase				=       {
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    }
}

_UpperCamelCase				=       {
    '''facebook/xglm-564M''': 2048,
}




class  _lowerCamelCase       (			__SCREAMING_SNAKE_CASE			):



      """simple docstring"""






      UpperCAmelCase_  :					str      			=VOCAB_FILES_NAMES
      UpperCAmelCase_  :					List[Any]      			=PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase_  :					int      			=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCAmelCase_  :					Optional[Any]      			=["input_ids", "attention_mask"]







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






        '''simple docstring'''






        __snake_case      : Dict						=				{} if sp_model_kwargs is None else sp_model_kwargs

        # Compatibility with the original tokenizer
        __snake_case      : Tuple						=				7
        __snake_case      : int						=				[F"""<madeupword{i}>""" for i in range(self.num_madeup_words      )]

        __snake_case      : str						=				kwargs.get("additional_special_tokens" ,  []      )
        kwargs["additional_special_tokens"] += [
            word for word in madeup_words if word not in kwargs["additional_special_tokens"]
        ]

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

        __snake_case      : Any						=				spm.SentencePieceProcessor(**self.sp_model_kwargs      )
        self.sp_model.Load(str(UpperCAmelCase_      )      )
        __snake_case      : List[str]						=				vocab_file

        # Original fairseq vocab and spm vocab must be "aligned":
        # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
        # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
        # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
        # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

        # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
        __snake_case      : Union[str, Any]						=				1

        # Mimic fairseq token-to-id alignment for the first 4 token
        __snake_case      : Union[str, Any]						=				{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

        __snake_case      : Any						=				len(self.sp_model      )
        __snake_case      : Tuple						=				{F"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words      )}
        self.fairseq_tokens_to_ids.update(UpperCAmelCase_      )

        __snake_case      : int						=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}







      def __getstate__(       self      )				->      List[str]:






        '''simple docstring'''






        __snake_case      : List[Any]						=				self.__dict__.copy()
        __snake_case      : str						=				None
        __snake_case      : Any						=				self.sp_model.serialized_model_proto()
        return state







      def __setstate__(       self ,  UpperCAmelCase      )				->      Optional[int]:






        '''simple docstring'''






        __snake_case      : Optional[Any]						=				d

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

        __snake_case      : str						=				spm.SentencePieceProcessor(**self.sp_model_kwargs      )
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto      )







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






        '''simple docstring'''






        if token_ids_a is None:
          return [self.sep_token_id] + token_ids_a
        __snake_case      : Any						=				[self.sep_token_id]
        return sep + token_ids_a + sep + sep + token_ids_a







      def   UpperCAmelCase						(       self ,  UpperCAmelCase ,  UpperCAmelCase = None ,  UpperCAmelCase = False      )				->      List[int]:






        '''simple docstring'''






        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 None:
          return [1] + ([0] * len(UpperCAmelCase_      ))
        return [1] + ([0] * len(UpperCAmelCase_      )) + [1, 1] + ([0] * len(UpperCAmelCase_      ))







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






        '''simple docstring'''






        __snake_case      : List[str]						=				[self.sep_token_id]

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







      @property
      def   UpperCAmelCase						(       self      )				->      Optional[int]:






        '''simple docstring'''






        return len(self.sp_model      ) + self.fairseq_offset + self.num_madeup_words







      def   UpperCAmelCase						(       self      )				->      str:






        '''simple docstring'''






        __snake_case      : str						=				{self.convert_ids_to_tokens(UpperCAmelCase_      ): i for i in range(self.vocab_size      )}
        vocab.update(self.added_tokens_encoder      )
        return vocab







      def   UpperCAmelCase						(       self ,  UpperCAmelCase      )				->      List[str]:






        '''simple docstring'''






        return self.sp_model.encode(UpperCAmelCase_ ,  out_type=UpperCAmelCase_      )







      def   UpperCAmelCase						(       self ,  UpperCAmelCase      )				->      Dict:






        '''simple docstring'''






        if token in self.fairseq_tokens_to_ids:
          return self.fairseq_tokens_to_ids[token]
        __snake_case      : Optional[int]						=				self.sp_model.PieceToId(UpperCAmelCase_      )

        # Need to return unknown token if the SP model returned 0
        return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







      def   UpperCAmelCase						(       self ,  UpperCAmelCase      )				->      Optional[Any]:






        '''simple docstring'''






        if index in self.fairseq_ids_to_tokens:
          return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset      )







      def   UpperCAmelCase						(       self ,  UpperCAmelCase      )				->      Dict:






        '''simple docstring'''






        __snake_case      : str						=				"".join(UpperCAmelCase_      ).replace(UpperCAmelCase_ ,  " "      ).strip()
        return out_string







      def   UpperCAmelCase						(       self ,  UpperCAmelCase ,  UpperCAmelCase = None      )				->      Tuple[str]:






        '''simple docstring'''






        if not os.path.isdir(UpperCAmelCase_      ):
          logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
          return
        __snake_case      : List[str]						=				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:
            __snake_case      : Optional[int]						=				self.sp_model.serialized_model_proto()
            fi.write(UpperCAmelCase_      )

        return (out_vocab_file,)








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


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



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ,							unittest.TestCase    ):







 '''simple docstring'''
 lowercase_		=			BartphoTokenizer
 lowercase_		=			False
 lowercase_		=			True

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any])       ->Tuple:



   '''simple docstring'''



   super().setUp()

   lowerCamelCase__:      int			  =["▁This", "▁is", "▁a", "▁t", "est"]
   lowerCamelCase__:      Tuple			  =dict(zip(UpperCAmelCase_       , range(len(UpperCAmelCase_))))
   lowerCamelCase__:      List[Any]			  ={"unk_token": "<unk>"}

   lowerCamelCase__:      Dict			  =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""")

   lowerCamelCase__:      Dict			  =BartphoTokenizer(UpperCAmelCase_       , self.monolingual_vocab_file       , **self.special_tokens_map)
   tokenizer.save_pretrained(self.tmpdirname)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[int]       , **UpperCAmelCase_				:   Optional[Any])       ->str:



   '''simple docstring'''



   kwargs.update(self.special_tokens_map)
   return BartphoTokenizer.from_pretrained(self.tmpdirname       , **UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any]       , UpperCAmelCase_				:   Optional[Any])       ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  ="This is a là test"
   lowerCamelCase__:      Optional[Any]			  ="This is a<unk><unk> test"
   return input_text, output_text







 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any])       ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:      str			  =BartphoTokenizer(UpperCAmelCase_       , self.monolingual_vocab_file       , **self.special_tokens_map)
   lowerCamelCase__:      List[Any]			  ="This is a là test"
   lowerCamelCase__:      Optional[int]			  ="▁This ▁is ▁a ▁l à ▁t est".split()
   lowerCamelCase__:      Optional[int]			  =tokenizer.tokenize(UpperCAmelCase_)
   self.assertListEqual(UpperCAmelCase_       , UpperCAmelCase_)

   lowerCamelCase__:      Tuple			  =tokens + [tokenizer.unk_token]
   lowerCamelCase__:      List[Any]			  =[4, 5, 6, 3, 3, 7, 8, 3]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_)       , UpperCAmelCase_)

