

'''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  :		List[Any]  =		logging.get_logger(__name__)

_UpperCAmelCase  :		List[Any]  =		{'''vocab_file''': '''sentencepiece.bpe.model'''}

_UpperCAmelCase  :		Optional[int]  =		{
    '''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  :		Tuple  =		{
    '''moussaKam/mbarthez''': 10_24,
    '''moussaKam/barthez''': 10_24,
    '''moussaKam/barthez-orangesum-title''': 10_24,
}

_UpperCAmelCase  :		Any  =		'''▁'''

class 						__magic_name__   (							__SCREAMING_SNAKE_CASE ):
 UpperCamelCase__     =       VOCAB_FILES_NAMES
 UpperCamelCase__     =       PRETRAINED_VOCAB_FILES_MAP
 UpperCamelCase__     =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCamelCase__     =       ['input_ids', 'attention_mask']







 def __init__(       self  , snake_case_  , snake_case_="<s>"  , snake_case_="</s>"  , snake_case_="</s>"  , snake_case_="<s>"  , snake_case_="<unk>"  , snake_case_="<pad>"  , snake_case_="<mask>"  , snake_case_ = None  , **snake_case_  , ):
   # Mask token behave like a normal word, i.e. include the space before it
   lowercase									=AddedToken(snake_case_  , lstrip=snake_case_  , rstrip=snake_case_   ) if isinstance(snake_case_  , snake_case_   ) else mask_token

   lowercase									={} if sp_model_kwargs is None else sp_model_kwargs

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

   lowercase									=vocab_file
   lowercase									=spm.SentencePieceProcessor(**self.sp_model_kwargs   )
   self.sp_model.Load(str(snake_case_   )   )

   lowercase									={'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

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







 def 						_A(       self  , snake_case_  , snake_case_ = None   ):

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







 def 						_A(       self  , snake_case_  , snake_case_ = None  , snake_case_ = False   ):
   if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=snake_case_  , token_ids_a=snake_case_  , already_has_special_tokens=snake_case_   )

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







 def 						_A(       self  , snake_case_  , snake_case_ = None   ):
   lowercase									=[self.sep_token_id]
   lowercase									=[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 						_A(       self   ):
   return len(self.sp_model   )







 def 						_A(       self   ):
   lowercase									={self.convert_ids_to_tokens(snake_case_   ): i for i in range(self.vocab_size   )}
   vocab.update(self.added_tokens_encoder   )
   return vocab







 def 						_A(       self  , snake_case_   ):
   return self.sp_model.encode(snake_case_  , out_type=snake_case_   )







 def 						_A(       self  , snake_case_   ):
   if token in self.fairseq_tokens_to_ids:
     return self.fairseq_tokens_to_ids[token]
   lowercase									=self.sp_model.PieceToId(snake_case_   )

   return spm_id if spm_id else self.unk_token_id







 def 						_A(       self  , snake_case_   ):
   if index in self.fairseq_ids_to_tokens:
     return self.fairseq_ids_to_tokens[index]
   return self.sp_model.IdToPiece(snake_case_   )







 def 						_A(       self  , snake_case_   ):
   lowercase									=[]
   lowercase									=''''''
   lowercase									=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(snake_case_   ) + token
       lowercase									=True
       lowercase									=[]
     else:
       current_sub_tokens.append(snake_case_   )
       lowercase									=False
   out_string += self.sp_model.decode(snake_case_   )
   return out_string.strip()







 def __getstate__(       self   ):
   lowercase									=self.__dict__.copy()
   lowercase									=None
   return state







 def __setstate__(       self  , snake_case_   ):
   lowercase									=d

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

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







 def 						_A(       self  , snake_case_  , snake_case_ = None   ):
   if not os.path.isdir(snake_case_   ):
     logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
     return
   lowercase									=os.path.join(
       snake_case_  , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

   if os.path.abspath(self.vocab_file   ) != os.path.abspath(snake_case_   ) and os.path.isfile(self.vocab_file   ):
     copyfile(self.vocab_file  , snake_case_   )
   elif not os.path.isfile(self.vocab_file   ):
     with open(snake_case_  , '''wb'''   ) as fi:
       lowercase									=self.sp_model.serialized_model_proto()
       fi.write(snake_case_   )

   return (out_vocab_file,)





import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class 			__lowerCAmelCase  (						lowerCAmelCase__	,						unittest.TestCase ):
 lowerCAmelCase__							       =	XLMTokenizer
 lowerCAmelCase__							       =	False




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    __lowerCamelCase    				=     [
        '''l''',
        '''o''',
        '''w''',
        '''e''',
        '''r''',
        '''s''',
        '''t''',
        '''i''',
        '''d''',
        '''n''',
        '''w</w>''',
        '''r</w>''',
        '''t</w>''',
        '''lo''',
        '''low''',
        '''er</w>''',
        '''low</w>''',
        '''lowest</w>''',
        '''newer</w>''',
        '''wider</w>''',
        '''<unk>''',
    ]
    __lowerCamelCase    				=     dict(zip(__UpperCAmelCase						,					range(len(__UpperCAmelCase					)					)					)					)
    __lowerCamelCase    				=     ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

    __lowerCamelCase    				=     os.path.join(self.tmpdirname						,					VOCAB_FILES_NAMES['''vocab_file''']					)
    __lowerCamelCase    				=     os.path.join(self.tmpdirname						,					VOCAB_FILES_NAMES['''merges_file''']					)
    with open(self.vocab_file						,					'''w'''					) as fp:
       fp.write(json.dumps(__UpperCAmelCase					)					)
    with open(self.merges_file						,					'''w'''					) as fp:
       fp.write('''\n'''.join(__UpperCAmelCase					)					)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     '''lower newer'''
    __lowerCamelCase    				=     '''lower newer'''
    return input_text, output_text




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     XLMTokenizer(self.vocab_file						,					self.merges_file					)

    __lowerCamelCase    				=     '''lower'''
    __lowerCamelCase    				=     ['''low''', '''er</w>''']
    __lowerCamelCase    				=     tokenizer.tokenize(__UpperCAmelCase					)
    self.assertListEqual(__UpperCAmelCase						,					__UpperCAmelCase					)

    __lowerCamelCase    				=     tokens + ['''<unk>''']
    __lowerCamelCase    				=     [14, 15, 20]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase					)						,					__UpperCAmelCase					)




 @slow
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     XLMTokenizer.from_pretrained('''xlm-mlm-en-2048'''					)

    __lowerCamelCase    				=     tokenizer.encode('''sequence builders'''						,					add_special_tokens=__UpperCAmelCase					)
    __lowerCamelCase    				=     tokenizer.encode('''multi-sequence build'''						,					add_special_tokens=__UpperCAmelCase					)

    __lowerCamelCase    				=     tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase					)
    __lowerCamelCase    				=     tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase						,					__UpperCAmelCase					)

    assert encoded_sentence == [0] + text + [1]
    assert encoded_pair == [0] + text + [1] + text_a + [1]






