238 lines
12 KiB
Python
238 lines
12 KiB
Python
# coding=utf-8
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# Copyright 2019 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import concurrent.futures
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import json
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import os
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import shutil
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import tempfile
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import unittest
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from transformers import AutoTokenizer, PreTrainedTokenizerFast
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from transformers.testing_utils import require_tokenizers
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from ..test_tokenization_common import TokenizerTesterMixin
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@require_tokenizers
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class PreTrainedTokenizationFastTest(TokenizerTesterMixin, unittest.TestCase):
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rust_tokenizer_class = PreTrainedTokenizerFast
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test_slow_tokenizer = False
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test_rust_tokenizer = True
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from_pretrained_vocab_key = "tokenizer_file"
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def setUp(self):
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self.test_rust_tokenizer = False # because we don't have pretrained_vocab_files_map
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super().setUp()
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self.test_rust_tokenizer = True
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model_paths = ["robot-test/dummy-tokenizer-fast", "robot-test/dummy-tokenizer-wordlevel"]
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self.bytelevel_bpe_model_name = "SaulLu/dummy-tokenizer-bytelevel-bpe"
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# Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
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self.tokenizers_list = [(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]
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tokenizer = PreTrainedTokenizerFast.from_pretrained(model_paths[0])
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tokenizer.save_pretrained(self.tmpdirname)
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def test_tokenizer_mismatch_warning(self):
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# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
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# model
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pass
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@unittest.skip(
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"We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any model"
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)
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def test_encode_decode_with_spaces(self):
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pass
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@unittest.skip(
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"We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any model"
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)
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def test_added_tokens_serialization(self):
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pass
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@unittest.skip(
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"We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any model"
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)
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def test_additional_special_tokens_serialization(self):
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pass
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def test_prepare_for_model(self):
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# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
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# model
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pass
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def test_rust_tokenizer_signature(self):
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# PreTrainedTokenizerFast doesn't have tokenizer_file in its signature
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pass
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def test_training_new_tokenizer(self):
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tmpdirname_orig = self.tmpdirname
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# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
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for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
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with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
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try:
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self.tmpdirname = tempfile.mkdtemp()
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tokenizer = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
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tokenizer.save_pretrained(self.tmpdirname)
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super().test_training_new_tokenizer()
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finally:
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# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
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# is restored
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shutil.rmtree(self.tmpdirname)
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self.tmpdirname = tmpdirname_orig
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def test_training_new_tokenizer_with_special_tokens_change(self):
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tmpdirname_orig = self.tmpdirname
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# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
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for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
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with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
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try:
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self.tmpdirname = tempfile.mkdtemp()
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tokenizer = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
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tokenizer.save_pretrained(self.tmpdirname)
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super().test_training_new_tokenizer_with_special_tokens_change()
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finally:
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# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
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# is restored
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shutil.rmtree(self.tmpdirname)
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self.tmpdirname = tmpdirname_orig
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def test_training_new_tokenizer_with_bytelevel(self):
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tokenizer = self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name)
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toy_text_iterator = ("a" for _ in range(1000))
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new_tokenizer = tokenizer.train_new_from_iterator(text_iterator=toy_text_iterator, length=1000, vocab_size=50)
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encoding_ids = new_tokenizer.encode("a🤗")
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self.assertEqual(encoding_ids, [64, 172, 253, 97, 245])
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def test_init_from_tokenizers_model(self):
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from tokenizers import Tokenizer
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sentences = ["Hello, y'all!", "How are you 😁 ? There should not be any issue right?"]
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tokenizer = Tokenizer.from_pretrained("google-t5/t5-base")
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# Enable padding
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tokenizer.enable_padding(pad_id=0, pad_token="<pad>", length=512, pad_to_multiple_of=8)
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self.assertEqual(
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tokenizer.padding,
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{
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"length": 512,
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"pad_to_multiple_of": 8,
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"pad_id": 0,
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"pad_token": "<pad>",
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"pad_type_id": 0,
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"direction": "right",
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},
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)
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fast_tokenizer = PreTrainedTokenizerFast(tokenizer_object=tokenizer)
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tmpdirname = tempfile.mkdtemp()
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fast_tokenizer.save_pretrained(tmpdirname)
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fast_from_saved = PreTrainedTokenizerFast.from_pretrained(tmpdirname)
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for tok in [fast_tokenizer, fast_from_saved]:
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self.assertEqual(tok.pad_token_id, 0)
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self.assertEqual(tok.padding_side, "right")
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self.assertEqual(tok.pad_token, "<pad>")
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self.assertEqual(tok.init_kwargs["max_length"], 512)
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self.assertEqual(tok.init_kwargs["pad_to_multiple_of"], 8)
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self.assertEqual(tok(sentences, padding = True), {'input_ids': [[8774, 6, 3, 63, 31, 1748, 55, 1, 0, 0, 0, 0,0, 0, 0, 0],[ 571, 33, 25, 3, 2, 3, 58, 290, 225, 59, 36, 136, 962, 269, 58, 1]], 'token_type_ids': [[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, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}) # fmt: skip
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tokenizer.enable_truncation(8, stride=0, strategy="longest_first", direction="right")
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self.assertEqual(
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tokenizer.truncation, {"max_length": 8, "stride": 0, "strategy": "longest_first", "direction": "right"}
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)
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fast_tokenizer = PreTrainedTokenizerFast(tokenizer_object=tokenizer)
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tmpdirname = tempfile.mkdtemp()
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fast_tokenizer.save_pretrained(tmpdirname)
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fast_from_saved = PreTrainedTokenizerFast.from_pretrained(tmpdirname)
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for tok in [fast_tokenizer, fast_from_saved]:
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self.assertEqual(tok.truncation_side, "right")
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self.assertEqual(tok.init_kwargs["truncation_strategy"], "longest_first")
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self.assertEqual(tok.init_kwargs["max_length"], 8)
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self.assertEqual(tok.init_kwargs["stride"], 0)
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# NOTE even if the model has a default max_length, it is not used...
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# thus tok(sentences, truncation = True) does nothing and does not warn either
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self.assertEqual(tok(sentences, truncation = True, max_length = 8), {'input_ids': [[8774, 6, 3, 63, 31, 1748, 55, 1],[ 571, 33, 25, 3, 2, 3, 58, 1]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0],[0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1]]}) # fmt: skip
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@require_tokenizers
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class TokenizerVersioningTest(unittest.TestCase):
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def test_local_versioning(self):
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tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-cased")
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json_tokenizer = json.loads(tokenizer._tokenizer.to_str())
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json_tokenizer["model"]["vocab"]["huggingface"] = len(tokenizer)
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with tempfile.TemporaryDirectory() as tmp_dir:
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# Hack to save this in the tokenizer_config.json
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tokenizer.init_kwargs["fast_tokenizer_files"] = ["tokenizer.4.0.0.json"]
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tokenizer.save_pretrained(tmp_dir)
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json.dump(json_tokenizer, open(os.path.join(tmp_dir, "tokenizer.4.0.0.json"), "w"))
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# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
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new_tokenizer = AutoTokenizer.from_pretrained(tmp_dir)
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self.assertEqual(len(new_tokenizer), len(tokenizer) + 1)
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json_tokenizer = json.loads(new_tokenizer._tokenizer.to_str())
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self.assertIn("huggingface", json_tokenizer["model"]["vocab"])
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# Will need to be adjusted if we reach v42 and this test is still here.
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# Should pick the old tokenizer file as the version of Transformers is < 4.0.0
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shutil.move(os.path.join(tmp_dir, "tokenizer.4.0.0.json"), os.path.join(tmp_dir, "tokenizer.42.0.0.json"))
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tokenizer.init_kwargs["fast_tokenizer_files"] = ["tokenizer.42.0.0.json"]
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tokenizer.save_pretrained(tmp_dir)
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new_tokenizer = AutoTokenizer.from_pretrained(tmp_dir)
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self.assertEqual(len(new_tokenizer), len(tokenizer))
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json_tokenizer = json.loads(new_tokenizer._tokenizer.to_str())
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self.assertNotIn("huggingface", json_tokenizer["model"]["vocab"])
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def test_repo_versioning(self):
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# This repo has two tokenizer files, one for v4.0.0 and above with an added token, one for versions lower.
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repo = "hf-internal-testing/test-two-tokenizers"
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# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
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tokenizer = AutoTokenizer.from_pretrained(repo)
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self.assertEqual(len(tokenizer), 28997)
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json_tokenizer = json.loads(tokenizer._tokenizer.to_str())
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self.assertIn("huggingface", json_tokenizer["model"]["vocab"])
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# Testing an older version by monkey-patching the version in the module it's used.
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import transformers as old_transformers
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old_transformers.tokenization_utils_base.__version__ = "3.0.0"
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old_tokenizer = old_transformers.models.auto.AutoTokenizer.from_pretrained(repo)
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self.assertEqual(len(old_tokenizer), 28996)
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json_tokenizer = json.loads(old_tokenizer._tokenizer.to_str())
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self.assertNotIn("huggingface", json_tokenizer["model"]["vocab"])
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@require_tokenizers
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class ReduceMutableBorrowTests(unittest.TestCase):
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def test_async_share_tokenizer(self):
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# See https://github.com/huggingface/transformers/pull/12550
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# and https://github.com/huggingface/tokenizers/issues/537
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tokenizer = PreTrainedTokenizerFast.from_pretrained("robot-test/dummy-tokenizer-wordlevel")
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text = "The Matrix is a 1999 science fiction action film."
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with concurrent.futures.ThreadPoolExecutor() as executor:
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futures = [executor.submit(self.fetch, tokenizer, text) for i in range(10)]
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return_value = [future.result() for future in futures]
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self.assertEqual(return_value, [[1, 10, 0, 8, 0, 18, 0, 0, 0, 2] for i in range(10)])
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def fetch(self, tokenizer, text):
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return tokenizer.encode(text, truncation="longest_first", padding="longest")
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