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

import sentencepiece as spm

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


snake_case__				= logging.get_logger(__name__)

snake_case__				= {'''vocab_file''': '''sentencepiece.model'''}

snake_case__				= {
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
}

snake_case__				= {
    '''google/rembert''': 256,
}



class  UpperCamelCase				( UpperCamelCase__ ):

     '''simple docstring'''

     A_										=  VOCAB_FILES_NAMES
     A_										=  PRETRAINED_VOCAB_FILES_MAP
     A_										=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



     def __init__(					self				,				A_				,				A_=False				,				A_=True				,				A_=True				,				A_="[CLS]"				,				A_="[SEP]"				,				A_="[UNK]"				,				A_="[SEP]"				,				A_="[PAD]"				,				A_="[CLS]"				,				A_="[MASK]"				,				**A_				,				) ->				Union[str, Any]:






           """simple docstring"""
           super().__init__(
               do_lower_case=A_				,				remove_space=A_				,				keep_accents=A_				,				bos_token=A_				,				eos_token=A_				,				unk_token=A_				,				sep_token=A_				,				pad_token=A_				,				cls_token=A_				,				mask_token=A_				,				**A_				,				)

           _lowerCamelCase			    =				do_lower_case
           _lowerCamelCase			    =				remove_space
           _lowerCamelCase			    =				keep_accents
           _lowerCamelCase			    =				vocab_file

           _lowerCamelCase			    =				spm.SentencePieceProcessor()
           self.sp_model.Load(A_						)



     @property
     def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






           """simple docstring"""
           return len(self.sp_model						)



     def 		UpperCamelCase_						(					self						) ->				Optional[int]:






           """simple docstring"""
           _lowerCamelCase			    =				{self.convert_ids_to_tokens(A_						): i for i in range(self.vocab_size						)}
           vocab.update(self.added_tokens_encoder						)
           return vocab



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






           """simple docstring"""
           _lowerCamelCase			    =				self.__dict__.copy()
           _lowerCamelCase			    =				None
           return state



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






           """simple docstring"""
           _lowerCamelCase			    =				d
           _lowerCamelCase			    =				spm.SentencePieceProcessor()
           self.sp_model.Load(self.vocab_file						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_=False						) ->				Any:






           """simple docstring"""
           _lowerCamelCase			    =				self.sp_model.EncodeAsPieces(A_						)
           return pieces



     def 		UpperCamelCase_						(					self				,				A_						) ->				List[Any]:






           """simple docstring"""
           return self.sp_model.PieceToId(A_						)



     def 		UpperCamelCase_						(					self				,				A_						) ->				Tuple:






           """simple docstring"""
           return self.sp_model.IdToPiece(A_						)



     def 		UpperCamelCase_						(					self				,				A_						) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				self.sp_model.decode_pieces(A_						)
           return out_string



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






           """simple docstring"""
           _lowerCamelCase			    =				[self.sep_token_id]
           _lowerCamelCase			    =				[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				,				A_				,				A_ = None				,				A_ = False						) ->				List[int]:






           """simple docstring"""

           if already_has_special_tokens:
                 if token_ids_a is not None:
                       raise ValueError(
                           '''You should not supply a second sequence if the provided sequence of '''
                           '''ids is already formatted with special tokens for the model.'''						)
                 return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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



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






           """simple docstring"""
           _lowerCamelCase			    =				[self.sep_token_id]
           _lowerCamelCase			    =				[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				,				A_				,				A_ = None						) ->				Tuple[str]:






           """simple docstring"""
           if not os.path.isdir(A_						):
                 logger.error('''Vocabulary path ({}) should be a directory'''.format(A_						)						)
                 return
           _lowerCamelCase			    =				os.path.join(
               A_				,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)

           if os.path.abspath(self.vocab_file						) != os.path.abspath(A_						):
                 copyfile(self.vocab_file				,				A_						)

           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(__lowercase )   ,  'Tatoeba directory does not exist.' )
class  UpperCamelCase				( unittest.TestCase ):

     '''simple docstring'''

     @cached_property
     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				tempfile.mkdtemp()
           return TatoebaConverter(save_dir=A_						)



     @slow
     def 		UpperCamelCase_						(					self						) ->				Any:






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





     @slow
     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase    ,					_lowerCamelCase			    =				self.resolver.write_model_card('''opus-mt-he-en'''				,				dry_run=A_						)
           assert mmeta["long_pair"] == "heb-eng"