




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       a__	( __lowercase       , unittest.TestCase				):
     a		:      Optional[Any]		 =     BarthezTokenizer
     a		:      List[Any]		 =     BarthezTokenizerFast
     a		:      int		 =     True
     a		:      Union[str, Any]		 =     True







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



          '''simple docstring'''



          super().setUp()

          a     =				BarthezTokenizerFast.from_pretrained("moussaKam/mbarthez"			)
          tokenizer.save_pretrained(self.tmpdirname			)
          tokenizer.save_pretrained(self.tmpdirname  ,		legacy_format=_a			)
          a     =				tokenizer







     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



          a     =				"<pad>"
          a     =				1

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







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



          '''simple docstring'''



          a     =				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(_a			)  ,		101122			)







     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



          self.assertEqual(self.get_tokenizer().vocab_size  ,		101122			)







     @require_torch
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          a     =				["A long paragraph for summarization.", "Another paragraph for summarization."]
          a     =				[0, 57, 3018, 70307, 91, 2]

          a     =				self.tokenizer(
              _a  ,		max_length=len(_a			)  ,		padding=_a  ,		truncation=_a  ,		return_tensors="pt"			)
          self.assertIsInstance(_a  ,		_a			)

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







     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          if not self.test_rust_tokenizer:
               return

          a     =				self.get_tokenizer()
          a     =				self.get_rust_tokenizer()

          a     =				"I was born in 92000, and this is falsé."

          a     =				tokenizer.tokenize(_a			)
          a     =				rust_tokenizer.tokenize(_a			)
          self.assertListEqual(_a  ,		_a			)

          a     =				tokenizer.encode(_a  ,		add_special_tokens=_a			)
          a     =				rust_tokenizer.encode(_a  ,		add_special_tokens=_a			)
          self.assertListEqual(_a  ,		_a			)

          a     =				self.get_rust_tokenizer()
          a     =				tokenizer.encode(_a			)
          a     =				rust_tokenizer.encode(_a			)
          self.assertListEqual(_a  ,		_a			)







     @slow
     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          a     =				{"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.
          a     =				[
              "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=_a  ,		model_name="moussaKam/mbarthez"  ,		revision="c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"  ,		sequences=_a  ,		)







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


lowercase__				:      int	   = logging.get_logger(__name__)


lowercase__				:      Dict	   = "▁"

lowercase__				:      Union[str, Any]	   = {"vocab_file": "spiece.model"}

lowercase__				:      Union[str, Any]	   = {
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    }
}

lowercase__				:      Tuple	   = {
    "google/reformer-crime-and-punishment": 524_288,
}




class       a__	( UpperCamelCase__				):
     a		:      List[Any]		 =     VOCAB_FILES_NAMES
     a		:      List[Any]		 =     PRETRAINED_VOCAB_FILES_MAP
     a		:      Any		 =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     a		:      Dict		 =     ["""input_ids""", """attention_mask"""]







     def __init__(     self  ,		A  ,		A="</s>"  ,		A="<unk>"  ,		A=[]  ,		A = None  ,		**A  ,		)  ->   None:



          '''simple docstring'''



          a     =				{} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              eos_token=A  ,		unk_token=A  ,		additional_special_tokens=A  ,		sp_model_kwargs=self.sp_model_kwargs  ,		**A  ,		)

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







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



          '''simple docstring'''



          return self.sp_model.get_piece_size()







     def       lowerCAmelCase_    (     self			)  ->   Dict[str, int]:



          '''simple docstring'''



          a     =				{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			)  ->   Optional[int]:



          '''simple docstring'''



          a     =				self.__dict__.copy()
          a     =				None
          return state







     def __setstate__(     self  ,		A			)  ->   Union[str, Any]:



          '''simple docstring'''



          a     =				d

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

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







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



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self  ,		A			)  ->   Union[str, Any]:



          '''simple docstring'''



          return self.sp_model.piece_to_id(A			)







     def       lowerCAmelCase_    (     self  ,		A			)  ->   Optional[int]:



          '''simple docstring'''



          if index < self.sp_model.get_piece_size():
               a     =				self.sp_model.IdToPiece(A			)
          return token







     def       lowerCAmelCase_    (     self  ,		A			)  ->   Union[str, Any]:



          '''simple docstring'''



          a     =				[]
          a     =				""
          for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                    out_string += self.sp_model.decode(A			) + token
                    a     =				[]
               else:
                    current_sub_tokens.append(A			)
          out_string += self.sp_model.decode(A			)
          return out_string.strip()







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



          '''simple docstring'''



          if not os.path.isdir(A			):
               logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''			)
               return
          a     =				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			) and os.path.isfile(self.vocab_file			):
               copyfile(self.vocab_file  ,		A			)
          elif not os.path.isfile(self.vocab_file			):
               with open(A  ,		"wb"			) as fi:
                    a     =				self.sp_model.serialized_model_proto()
                    fi.write(A			)

          return (out_vocab_file,)

