add sp_model_kwargs to unpickle of xlm roberta tok (#11430)

add test for pickle

simplify test

fix test code style

add missing pickle import

fix test

fix test

fix test

src/transformers/models/xlm_roberta/tokenization_xlm_roberta.py

@@ -135,7 +135,7 @@ class XLMRobertaTokenizer(PreTrainedTokenizer):
         # Mask token behave like a normal word, i.e. include the space before it
         mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
 
-        sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
 
         super().__init__(
             bos_token=bos_token,
@@ -145,11 +145,11 @@ class XLMRobertaTokenizer(PreTrainedTokenizer):
             cls_token=cls_token,
             pad_token=pad_token,
             mask_token=mask_token,
-            sp_model_kwargs=sp_model_kwargs,
+            sp_model_kwargs=self.sp_model_kwargs,
             **kwargs,
         )
 
-        self.sp_model = spm.SentencePieceProcessor(**sp_model_kwargs)
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
         self.sp_model.Load(str(vocab_file))
         self.vocab_file = vocab_file
 
@@ -175,7 +175,12 @@ class XLMRobertaTokenizer(PreTrainedTokenizer):
 
     def __setstate__(self, d):
         self.__dict__ = d
-        self.sp_model = spm.SentencePieceProcessor()
+
+        # for backward compatibility
+        if not hasattr(self, "sp_model_kwargs"):
+            self.sp_model_kwargs = {}
+
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
         self.sp_model.Load(self.vocab_file)
 
     def build_inputs_with_special_tokens(

tests/test_tokenization_xlm_roberta.py

@@ -16,6 +16,7 @@
 
 import itertools
 import os
+import pickle
 import unittest
 
 from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
@@ -142,6 +143,18 @@ class XLMRobertaTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
 
         self.assertFalse(all_equal)
 
+    def test_pickle_subword_regularization_tokenizer(self):
+        """Google pickle __getstate__ __setstate__ if you are struggling with this."""
+        # Subword regularization is only available for the slow tokenizer.
+        sp_model_kwargs = {"enable_sampling": True, "alpha": 0.1, "nbest_size": -1}
+        tokenizer = XLMRobertaTokenizer(SAMPLE_VOCAB, keep_accents=True, sp_model_kwargs=sp_model_kwargs)
+        tokenizer_bin = pickle.dumps(tokenizer)
+        tokenizer_new = pickle.loads(tokenizer_bin)
+
+        self.assertIsNotNone(tokenizer_new.sp_model_kwargs)
+        self.assertTrue(isinstance(tokenizer_new.sp_model_kwargs, dict))
+        self.assertEqual(tokenizer_new.sp_model_kwargs, sp_model_kwargs)
+
     @cached_property
     def big_tokenizer(self):
         return XLMRobertaTokenizer.from_pretrained("xlm-roberta-base")