





import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 			__UpperCamelCase    (							A__	, unittest.TestCase    ):
     __A	:  Optional[int]							  =   BarthezTokenizer
     __A	:  Union[str, Any]							  =   BarthezTokenizerFast
     __A	:  List[Any]							  =   True
     __A	:  str							  =   True





     def 		UpperCamelCase(     self					):
        super().setUp()

        _UpperCAmelCase           =   BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''					)
        tokenizer.save_pretrained(self.tmpdirname					)
        tokenizer.save_pretrained(self.tmpdirname				,							legacy_format=_UpperCamelCase					)
        _UpperCAmelCase           =   tokenizer





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   '''<pad>'''
        _UpperCAmelCase           =   1

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCamelCase					)				,							_UpperCamelCase					)
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCamelCase					)				,							_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   list(self.get_tokenizer().get_vocab().keys()					)

        self.assertEqual(vocab_keys[0]				,							'''<s>'''					)
        self.assertEqual(vocab_keys[1]				,							'''<pad>'''					)
        self.assertEqual(vocab_keys[-1]				,							'''<mask>'''					)
        self.assertEqual(len(_UpperCamelCase					)				,							101122					)





     def 		UpperCamelCase(     self					):
        self.assertEqual(self.get_tokenizer().vocab_size				,							101122					)





     @require_torch
     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        _UpperCAmelCase           =   [0, 57, 3018, 70307, 91, 2]

        _UpperCAmelCase           =   self.tokenizer(
            _UpperCamelCase				,							max_length=len(_UpperCamelCase					)				,							padding=_UpperCamelCase				,							truncation=_UpperCamelCase				,							return_tensors='''pt'''					)
        self.assertIsInstance(_UpperCamelCase				,							_UpperCamelCase					)

        self.assertEqual((2, 6)				,							batch.input_ids.shape					)
        self.assertEqual((2, 6)				,							batch.attention_mask.shape					)
        _UpperCAmelCase           =   batch.input_ids.tolist()[0]
        self.assertListEqual(_UpperCamelCase				,							_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        if not self.test_rust_tokenizer:
           return

        _UpperCAmelCase           =   self.get_tokenizer()
        _UpperCAmelCase           =   self.get_rust_tokenizer()

        _UpperCAmelCase           =   '''I was born in 92000, and this is falsé.'''

        _UpperCAmelCase           =   tokenizer.tokenize(_UpperCamelCase					)
        _UpperCAmelCase           =   rust_tokenizer.tokenize(_UpperCamelCase					)
        self.assertListEqual(_UpperCamelCase				,							_UpperCamelCase					)

        _UpperCAmelCase           =   tokenizer.encode(_UpperCamelCase				,							add_special_tokens=_UpperCamelCase					)
        _UpperCAmelCase           =   rust_tokenizer.encode(_UpperCamelCase				,							add_special_tokens=_UpperCamelCase					)
        self.assertListEqual(_UpperCamelCase				,							_UpperCamelCase					)

        _UpperCAmelCase           =   self.get_rust_tokenizer()
        _UpperCAmelCase           =   tokenizer.encode(_UpperCamelCase					)
        _UpperCAmelCase           =   rust_tokenizer.encode(_UpperCamelCase					)
        self.assertListEqual(_UpperCamelCase				,							_UpperCamelCase					)





     @slow
     def 		UpperCamelCase(     self					):
        # fmt: off
        _UpperCAmelCase           =   {'''input_ids''': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
        # fmt: on

        # moussaKam/mbarthez is a french model. So we also use french texts.
        _UpperCAmelCase           =   [
            '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
            '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
            '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
            '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
            '''telles que la traduction et la synthèse de texte.''',
        ]

        self.tokenizer_integration_test_util(
            expected_encoding=_UpperCamelCase				,							model_name='''moussaKam/mbarthez'''				,							revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''				,							sequences=_UpperCamelCase				,							)






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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
   from ...tokenization_utils_base import TextInput
from ...utils import logging


UpperCAmelCase_           =	logging.get_logger(__name__)

UpperCAmelCase_           =	{"vocab_file": "spiece.model"}

UpperCAmelCase_           =	{
    "vocab_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
    }
}


# TODO(PVP) - this should be removed in Transformers v5
UpperCAmelCase_           =	{
    "t5-small": 5_12,
    "t5-base": 5_12,
    "t5-large": 5_12,
    "t5-3b": 5_12,
    "t5-11b": 5_12,
}

UpperCAmelCase_           =	"▁"




class 			__UpperCamelCase    (							A__    ):
     __A	:  Any							  =   VOCAB_FILES_NAMES
     __A	:  Union[str, Any]							  =   PRETRAINED_VOCAB_FILES_MAP
     __A	:  Any							  =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __A	:  Tuple							  =   ["""input_ids""", """attention_mask"""]





     def __init__(     self				,							_UpperCamelCase				,							_UpperCamelCase="</s>"				,							_UpperCamelCase="<unk>"				,							_UpperCamelCase="<pad>"				,							_UpperCamelCase=100				,							_UpperCamelCase=None				,							_UpperCamelCase = None				,							_UpperCamelCase=True				,							**_UpperCamelCase				,							):
        # Add extra_ids to the special token list
        if extra_ids > 0 and additional_special_tokens is None:
           _UpperCAmelCase           =   [f'''<extra_id_{i}>''' for i in range(_UpperCamelCase					)]
        elif extra_ids > 0 and additional_special_tokens is not None:
           # Check that we have the right number of extra_id special tokens
           _UpperCAmelCase           =   len(set(filter(lambda _UpperCamelCase					: bool('''extra_id''' in str(_UpperCamelCase					)					)				,							_UpperCamelCase					)					)					)
           if extra_tokens != extra_ids:
              raise ValueError(
                  f'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
                  ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
                  ''' tokens'''					)
        if legacy:
           logger.warning_once(
               f'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
               ''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'''					)

        _UpperCAmelCase           =   legacy
        _UpperCAmelCase           =   {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            eos_token=_UpperCamelCase				,							unk_token=_UpperCamelCase				,							pad_token=_UpperCamelCase				,							extra_ids=_UpperCamelCase				,							additional_special_tokens=_UpperCamelCase				,							sp_model_kwargs=self.sp_model_kwargs				,							legacy=_UpperCamelCase				,							**_UpperCamelCase				,							)

        _UpperCAmelCase           =   vocab_file
        _UpperCAmelCase           =   extra_ids

        _UpperCAmelCase           =   spm.SentencePieceProcessor(**self.sp_model_kwargs					)
        self.sp_model.Load(_UpperCamelCase					)





     @staticmethod
     def 		UpperCamelCase(     _UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase					):
        if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
           _UpperCAmelCase           =   TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
           if init_max_model_length is not None and init_max_model_length != max_model_length:
              return init_max_model_length
           elif init_max_model_length is None:
              warnings.warn(
                  '''This tokenizer was incorrectly instantiated with a model max length of'''
                  f''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
                  ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
                  ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
                  f''' {pretrained_model_name_or_path} automatically truncating your input to'''
                  f''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
                  f''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
                  ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
                  ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''				,							_UpperCamelCase				,							)

        return max_model_length





     @property
     def 		UpperCamelCase(     self					):
        return self.sp_model.get_piece_size() + self._extra_ids





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   {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				,							_UpperCamelCase = None				,							_UpperCamelCase = False					):
        if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=_UpperCamelCase				,							token_ids_a=_UpperCamelCase				,							already_has_special_tokens=_UpperCamelCase					)

        # normal case: some special tokens
        if token_ids_a is None:
           return ([0] * len(_UpperCamelCase					)) + [1]
        return ([0] * len(_UpperCamelCase					)) + [1] + ([0] * len(_UpperCamelCase					)) + [1]





     def 		UpperCamelCase(     self					):
        return list(
            set(filter(lambda _UpperCamelCase					: bool(re.search(R'''<extra_id_\d+>'''				,							_UpperCamelCase					)					) is not None				,							self.additional_special_tokens					)					)					)





     def 		UpperCamelCase(     self					):
        return [self._convert_token_to_id(_UpperCamelCase					) for token in self.get_sentinel_tokens()]





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        if len(_UpperCamelCase					) > 0 and token_ids[-1] == self.eos_token_id:
           warnings.warn(
               f'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
               ''' eos tokens being added.'''					)
           return token_ids
        else:
           return token_ids + [self.eos_token_id]





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase = None					):
        _UpperCAmelCase           =   [self.eos_token_id]

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





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase = None					):
        _UpperCAmelCase           =   self._add_eos_if_not_present(_UpperCamelCase					)
        if token_ids_a is None:
           return token_ids_a
        else:
           _UpperCAmelCase           =   self._add_eos_if_not_present(_UpperCamelCase					)
           return token_ids_a + token_ids_a





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





     def __setstate__(     self				,							_UpperCamelCase					):
        _UpperCAmelCase           =   d

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

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





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							**_UpperCamelCase					):
        # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
        # the beginning of the text
        if not self.legacy:
           _UpperCAmelCase           =   SPIECE_UNDERLINE + text.replace(_UpperCamelCase				,							''' '''					)
        return super().tokenize(_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							**_UpperCamelCase					):
        if not self.legacy:
           _UpperCAmelCase           =   text.startswith(_UpperCamelCase					)
           if is_first:
              _UpperCAmelCase           =   text[1:]

        _UpperCAmelCase           =   self.sp_model.encode(_UpperCamelCase				,							out_type=_UpperCamelCase					)

        if not self.legacy and not is_first and not text.startswith(''' '''					) and tokens[0].startswith(_UpperCamelCase					):
           _UpperCAmelCase           =   ([tokens[0][1:]] if len(tokens[0]					) > 1 else []) + tokens[1:]
        return tokens





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        if token.startswith('''<extra_id_'''					):
           _UpperCAmelCase           =   re.match(R'''<extra_id_(\d+)>'''				,							_UpperCamelCase					)
           _UpperCAmelCase           =   int(match.group(1					)					)
           return self.vocab_size - num - 1
        return self.sp_model.piece_to_id(_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        if index < self.sp_model.get_piece_size():
           _UpperCAmelCase           =   self.sp_model.IdToPiece(_UpperCamelCase					)
        else:
           _UpperCAmelCase           =   f'''<extra_id_{self.vocab_size - 1 - index}>'''
        return token





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        _UpperCAmelCase           =   []
        _UpperCAmelCase           =   ''''''
        _UpperCAmelCase           =   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(_UpperCamelCase					) + token
              _UpperCAmelCase           =   True
              _UpperCAmelCase           =   []
           else:
              current_sub_tokens.append(_UpperCamelCase					)
              _UpperCAmelCase           =   False
        out_string += self.sp_model.decode(_UpperCamelCase					)
        return out_string.strip()





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase = None					):
        if not os.path.isdir(_UpperCamelCase					):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
           return
        _UpperCAmelCase           =   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:
              _UpperCAmelCase           =   self.sp_model.serialized_model_proto()
              fi.write(_UpperCamelCase					)

        return (out_vocab_file,)