

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_camembert import CamembertTokenizer
else:
     _lowerCamelCase				:   List[Any]					   =							None


_lowerCamelCase				:   Union[str, Any]					   =							logging.get_logger(__name__)

_lowerCamelCase				:   List[Any]					   =							{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

_lowerCamelCase				:   Any					   =							{
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/tokenizer.json",
    },
}

_lowerCamelCase				:   Optional[int]					   =							{
    "camembert-base": 5_1_2,
}

_lowerCamelCase				:   str					   =							"▁"





class 					__UpperCAmelCase     (       UpperCAmelCase_						):
   UpperCamelCase        =     VOCAB_FILES_NAMES
   UpperCamelCase        =     PRETRAINED_VOCAB_FILES_MAP
   UpperCamelCase        =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   UpperCamelCase        =     ['input_ids', 'attention_mask']
   UpperCamelCase        =     CamembertTokenizer



   def __init__(							self							:     Optional[Any],		__A							:     List[str]=None,		__A							:     Any=None,		__A							:     Tuple="<s>",		__A							:     int="</s>",		__A							:     Union[str, Any]="</s>",		__A							:     Optional[int]="<s>",		__A							:     str="<unk>",		__A							:     int="<pad>",		__A							:     Dict="<mask>",		__A							:     Union[str, Any]=["<s>NOTUSED", "</s>NOTUSED"],		**__A							:     List[Any],		):
     UpperCAmelCase       :							str  = AddedToken(__UpperCAmelCase,		lstrip=__UpperCAmelCase,		rstrip=__UpperCAmelCase						) if isinstance(__UpperCAmelCase,		__UpperCAmelCase						) else mask_token

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

     UpperCAmelCase       :							Union[str, Any]  = vocab_file
     UpperCAmelCase       :							Any  = False if not self.vocab_file else True



   def        __magic_name__    (							self							:     Any,		__A							:     List[int],		__A							:     Optional[List[int]] = None						):



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



   def        __magic_name__    (							self							:     Any,		__A							:     List[int],		__A							:     Optional[List[int]] = None						):
     UpperCAmelCase       :							Tuple  = [self.sep_token_id]
     UpperCAmelCase       :							List[str]  = [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        __magic_name__    (							self							:     Optional[Any],		__A							:     str,		__A							:     Optional[str] = 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(__UpperCAmelCase						):
       logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
       return
     UpperCAmelCase       :							Optional[Any]  = 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						):
       copyfile(self.vocab_file,		__UpperCAmelCase						)

     return (out_vocab_file,)






import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property





@unittest.skipUnless(os.path.exists(UpperCAmelCase_     )		,				'Tatoeba directory does not exist.'     )
class      A							(				unittest.TestCase     ):


   @cached_property
   def 			lowercase_      (self     :						Optional[int]		)						->				List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  tempfile.mkdtemp()
         return TatoebaConverter(save_dir=__UpperCAmelCase		)


   @slow
   def 			lowercase_      (self     :						List[Any]		)						->				Optional[int]:
         """simple docstring"""



         self.resolver.convert_models(["heb-eng"]		)





   @slow
   def 			lowercase_      (self     :						Dict		)						->				List[str]:
         """simple docstring"""



         UpperCAmelCase__		,      UpperCAmelCase__								=  self.resolver.write_model_card("opus-mt-he-en"       ,  dry_run=__UpperCAmelCase		)
         assert mmeta["long_pair"] == "heb-eng"



