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| # coding=utf-8 | |
| # Copyright 2019 Hugging Face inc. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import unittest | |
| from transformers import FNetTokenizer, FNetTokenizerFast | |
| from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow, tooslow | |
| from transformers.tokenization_utils import AddedToken | |
| from ...test_tokenization_common import TokenizerTesterMixin | |
| SAMPLE_VOCAB = get_tests_dir("fixtures/spiece.model") | |
| class FNetTokenizationTest(TokenizerTesterMixin, unittest.TestCase): | |
| tokenizer_class = FNetTokenizer | |
| rust_tokenizer_class = FNetTokenizerFast | |
| test_rust_tokenizer = True | |
| test_sentencepiece = True | |
| test_sentencepiece_ignore_case = True | |
| test_seq2seq = False | |
| def setUp(self): | |
| super().setUp() | |
| # We have a SentencePiece fixture for testing | |
| tokenizer = FNetTokenizer(SAMPLE_VOCAB) | |
| tokenizer.save_pretrained(self.tmpdirname) | |
| def get_input_output_texts(self, tokenizer): | |
| input_text = "this is a test" | |
| output_text = "this is a test" | |
| return input_text, output_text | |
| def test_convert_token_and_id(self): | |
| """Test ``_convert_token_to_id`` and ``_convert_id_to_token``.""" | |
| token = "<pad>" | |
| token_id = 0 | |
| self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id) | |
| self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token) | |
| def test_get_vocab(self): | |
| vocab_keys = list(self.get_tokenizer().get_vocab().keys()) | |
| self.assertEqual(vocab_keys[0], "<pad>") | |
| self.assertEqual(vocab_keys[1], "<unk>") | |
| self.assertEqual(vocab_keys[-1], "▁eloquent") | |
| self.assertEqual(len(vocab_keys), 30_000) | |
| def test_vocab_size(self): | |
| self.assertEqual(self.get_tokenizer().vocab_size, 30_000) | |
| def test_rust_and_python_full_tokenizers(self): | |
| if not self.test_rust_tokenizer: | |
| return | |
| tokenizer = self.get_tokenizer() | |
| rust_tokenizer = self.get_rust_tokenizer() | |
| sequence = "I was born in 92000, and this is falsé." | |
| tokens = tokenizer.tokenize(sequence) | |
| rust_tokens = rust_tokenizer.tokenize(sequence) | |
| self.assertListEqual(tokens, rust_tokens) | |
| ids = tokenizer.encode(sequence, add_special_tokens=False) | |
| rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False) | |
| self.assertListEqual(ids, rust_ids) | |
| rust_tokenizer = self.get_rust_tokenizer() | |
| ids = tokenizer.encode(sequence) | |
| rust_ids = rust_tokenizer.encode(sequence) | |
| self.assertListEqual(ids, rust_ids) | |
| def test_full_tokenizer(self): | |
| tokenizer = FNetTokenizer(SAMPLE_VOCAB, keep_accents=True) | |
| tokens = tokenizer.tokenize("This is a test") | |
| self.assertListEqual(tokens, ["▁", "T", "his", "▁is", "▁a", "▁test"]) | |
| self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [13, 1, 4398, 25, 21, 1289]) | |
| tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.") | |
| self.assertListEqual( | |
| tokens, | |
| ["▁", "I", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."], | |
| ) | |
| ids = tokenizer.convert_tokens_to_ids(tokens) | |
| self.assertListEqual(ids, [13, 1, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]) | |
| back_tokens = tokenizer.convert_ids_to_tokens(ids) | |
| self.assertListEqual( | |
| back_tokens, | |
| [ | |
| "▁", | |
| "<unk>", | |
| "▁was", | |
| "▁born", | |
| "▁in", | |
| "▁9", | |
| "2000", | |
| ",", | |
| "▁and", | |
| "▁this", | |
| "▁is", | |
| "▁fal", | |
| "s", | |
| "<unk>", | |
| ".", | |
| ], | |
| ) | |
| def test_sequence_builders(self): | |
| tokenizer = FNetTokenizer(SAMPLE_VOCAB) | |
| text = tokenizer.encode("sequence builders") | |
| text_2 = tokenizer.encode("multi-sequence build") | |
| encoded_sentence = tokenizer.build_inputs_with_special_tokens(text) | |
| encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2) | |
| assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] | |
| assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_2 + [ | |
| tokenizer.sep_token_id | |
| ] | |
| # Overriden Tests - loading the fast tokenizer from slow just takes too long | |
| def test_special_tokens_initialization(self): | |
| for tokenizer, pretrained_name, kwargs in self.tokenizers_list: | |
| with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): | |
| added_tokens = [AddedToken("<special>", lstrip=True)] | |
| tokenizer_r = self.rust_tokenizer_class.from_pretrained( | |
| pretrained_name, additional_special_tokens=added_tokens, **kwargs | |
| ) | |
| r_output = tokenizer_r.encode("Hey this is a <special> token") | |
| special_token_id = tokenizer_r.encode("<special>", add_special_tokens=False)[0] | |
| self.assertTrue(special_token_id in r_output) | |
| if self.test_slow_tokenizer: | |
| tokenizer_p = self.tokenizer_class.from_pretrained( | |
| pretrained_name, additional_special_tokens=added_tokens, **kwargs | |
| ) | |
| p_output = tokenizer_p.encode("Hey this is a <special> token") | |
| cr_output = tokenizer_r.encode("Hey this is a <special> token") | |
| self.assertEqual(p_output, r_output) | |
| self.assertEqual(cr_output, r_output) | |
| self.assertTrue(special_token_id in p_output) | |
| self.assertTrue(special_token_id in cr_output) | |
| def test_special_tokens_initialization_from_slow(self): | |
| for tokenizer, pretrained_name, kwargs in self.tokenizers_list: | |
| with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): | |
| added_tokens = [AddedToken("<special>", lstrip=True)] | |
| tokenizer_r = self.rust_tokenizer_class.from_pretrained( | |
| pretrained_name, additional_special_tokens=added_tokens, **kwargs, from_slow=True | |
| ) | |
| special_token_id = tokenizer_r.encode("<special>", add_special_tokens=False)[0] | |
| tokenizer_p = self.tokenizer_class.from_pretrained( | |
| pretrained_name, additional_special_tokens=added_tokens, **kwargs | |
| ) | |
| p_output = tokenizer_p.encode("Hey this is a <special> token") | |
| cr_output = tokenizer_r.encode("Hey this is a <special> token") | |
| self.assertEqual(p_output, cr_output) | |
| self.assertTrue(special_token_id in p_output) | |
| self.assertTrue(special_token_id in cr_output) | |
| # Overriden Tests | |
| def test_padding(self, max_length=50): | |
| if not self.test_slow_tokenizer: | |
| # as we don't have a slow version, we can't compare the outputs between slow and fast versions | |
| return | |
| for tokenizer, pretrained_name, kwargs in self.tokenizers_list: | |
| with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): | |
| tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs) | |
| tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs) | |
| self.assertEqual(tokenizer_p.pad_token_id, tokenizer_r.pad_token_id) | |
| pad_token_id = tokenizer_p.pad_token_id | |
| # Encode - Simple input | |
| input_r = tokenizer_r.encode("This is a simple input", max_length=max_length, pad_to_max_length=True) | |
| input_p = tokenizer_p.encode("This is a simple input", max_length=max_length, pad_to_max_length=True) | |
| self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id) | |
| input_r = tokenizer_r.encode("This is a simple input", max_length=max_length, padding="max_length") | |
| input_p = tokenizer_p.encode("This is a simple input", max_length=max_length, padding="max_length") | |
| self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id) | |
| input_r = tokenizer_r.encode("This is a simple input", padding="longest") | |
| input_p = tokenizer_p.encode("This is a simple input", padding=True) | |
| self.assert_padded_input_match(input_r, input_p, len(input_r), pad_token_id) | |
| # Encode - Pair input | |
| input_r = tokenizer_r.encode( | |
| "This is a simple input", "This is a pair", max_length=max_length, pad_to_max_length=True | |
| ) | |
| input_p = tokenizer_p.encode( | |
| "This is a simple input", "This is a pair", max_length=max_length, pad_to_max_length=True | |
| ) | |
| self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id) | |
| input_r = tokenizer_r.encode( | |
| "This is a simple input", "This is a pair", max_length=max_length, padding="max_length" | |
| ) | |
| input_p = tokenizer_p.encode( | |
| "This is a simple input", "This is a pair", max_length=max_length, padding="max_length" | |
| ) | |
| self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id) | |
| input_r = tokenizer_r.encode("This is a simple input", "This is a pair", padding=True) | |
| input_p = tokenizer_p.encode("This is a simple input", "This is a pair", padding="longest") | |
| self.assert_padded_input_match(input_r, input_p, len(input_r), pad_token_id) | |
| # Encode_plus - Simple input | |
| input_r = tokenizer_r.encode_plus( | |
| "This is a simple input", max_length=max_length, pad_to_max_length=True | |
| ) | |
| input_p = tokenizer_p.encode_plus( | |
| "This is a simple input", max_length=max_length, pad_to_max_length=True | |
| ) | |
| self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id) | |
| input_r = tokenizer_r.encode_plus( | |
| "This is a simple input", max_length=max_length, padding="max_length" | |
| ) | |
| input_p = tokenizer_p.encode_plus( | |
| "This is a simple input", max_length=max_length, padding="max_length" | |
| ) | |
| self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id) | |
| input_r = tokenizer_r.encode_plus("This is a simple input", padding="longest") | |
| input_p = tokenizer_p.encode_plus("This is a simple input", padding=True) | |
| self.assert_padded_input_match( | |
| input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id | |
| ) | |
| # Encode_plus - Pair input | |
| input_r = tokenizer_r.encode_plus( | |
| "This is a simple input", "This is a pair", max_length=max_length, pad_to_max_length=True | |
| ) | |
| input_p = tokenizer_p.encode_plus( | |
| "This is a simple input", "This is a pair", max_length=max_length, pad_to_max_length=True | |
| ) | |
| self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id) | |
| input_r = tokenizer_r.encode_plus( | |
| "This is a simple input", "This is a pair", max_length=max_length, padding="max_length" | |
| ) | |
| input_p = tokenizer_p.encode_plus( | |
| "This is a simple input", "This is a pair", max_length=max_length, padding="max_length" | |
| ) | |
| self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id) | |
| input_r = tokenizer_r.encode_plus("This is a simple input", "This is a pair", padding="longest") | |
| input_p = tokenizer_p.encode_plus("This is a simple input", "This is a pair", padding=True) | |
| self.assert_padded_input_match( | |
| input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id | |
| ) | |
| # Batch_encode_plus - Simple input | |
| input_r = tokenizer_r.batch_encode_plus( | |
| ["This is a simple input 1", "This is a simple input 2"], | |
| max_length=max_length, | |
| pad_to_max_length=True, | |
| ) | |
| input_p = tokenizer_p.batch_encode_plus( | |
| ["This is a simple input 1", "This is a simple input 2"], | |
| max_length=max_length, | |
| pad_to_max_length=True, | |
| ) | |
| self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id) | |
| input_r = tokenizer_r.batch_encode_plus( | |
| ["This is a simple input 1", "This is a simple input 2"], | |
| max_length=max_length, | |
| padding="max_length", | |
| ) | |
| input_p = tokenizer_p.batch_encode_plus( | |
| ["This is a simple input 1", "This is a simple input 2"], | |
| max_length=max_length, | |
| padding="max_length", | |
| ) | |
| self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id) | |
| input_r = tokenizer_r.batch_encode_plus( | |
| ["This is a simple input 1", "This is a simple input 2"], | |
| max_length=max_length, | |
| padding="longest", | |
| ) | |
| input_p = tokenizer_p.batch_encode_plus( | |
| ["This is a simple input 1", "This is a simple input 2"], | |
| max_length=max_length, | |
| padding=True, | |
| ) | |
| self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id) | |
| input_r = tokenizer_r.batch_encode_plus( | |
| ["This is a simple input 1", "This is a simple input 2"], padding="longest" | |
| ) | |
| input_p = tokenizer_p.batch_encode_plus( | |
| ["This is a simple input 1", "This is a simple input 2"], padding=True | |
| ) | |
| self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id) | |
| # Batch_encode_plus - Pair input | |
| input_r = tokenizer_r.batch_encode_plus( | |
| [ | |
| ("This is a simple input 1", "This is a simple input 2"), | |
| ("This is a simple pair 1", "This is a simple pair 2"), | |
| ], | |
| max_length=max_length, | |
| truncation=True, | |
| padding="max_length", | |
| ) | |
| input_p = tokenizer_p.batch_encode_plus( | |
| [ | |
| ("This is a simple input 1", "This is a simple input 2"), | |
| ("This is a simple pair 1", "This is a simple pair 2"), | |
| ], | |
| max_length=max_length, | |
| truncation=True, | |
| padding="max_length", | |
| ) | |
| self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id) | |
| input_r = tokenizer_r.batch_encode_plus( | |
| [ | |
| ("This is a simple input 1", "This is a simple input 2"), | |
| ("This is a simple pair 1", "This is a simple pair 2"), | |
| ], | |
| padding=True, | |
| ) | |
| input_p = tokenizer_p.batch_encode_plus( | |
| [ | |
| ("This is a simple input 1", "This is a simple input 2"), | |
| ("This is a simple pair 1", "This is a simple pair 2"), | |
| ], | |
| padding="longest", | |
| ) | |
| self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id) | |
| # Using pad on single examples after tokenization | |
| input_r = tokenizer_r.encode_plus("This is a input 1") | |
| input_r = tokenizer_r.pad(input_r) | |
| input_p = tokenizer_r.encode_plus("This is a input 1") | |
| input_p = tokenizer_r.pad(input_p) | |
| self.assert_padded_input_match( | |
| input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id | |
| ) | |
| # Using pad on single examples after tokenization | |
| input_r = tokenizer_r.encode_plus("This is a input 1") | |
| input_r = tokenizer_r.pad(input_r, max_length=max_length, padding="max_length") | |
| input_p = tokenizer_r.encode_plus("This is a input 1") | |
| input_p = tokenizer_r.pad(input_p, max_length=max_length, padding="max_length") | |
| self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id) | |
| # Using pad after tokenization | |
| input_r = tokenizer_r.batch_encode_plus( | |
| ["This is a input 1", "This is a much longer input whilch should be padded"] | |
| ) | |
| input_r = tokenizer_r.pad(input_r) | |
| input_p = tokenizer_r.batch_encode_plus( | |
| ["This is a input 1", "This is a much longer input whilch should be padded"] | |
| ) | |
| input_p = tokenizer_r.pad(input_p) | |
| self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id) | |
| # Using pad after tokenization | |
| input_r = tokenizer_r.batch_encode_plus( | |
| ["This is a input 1", "This is a much longer input whilch should be padded"] | |
| ) | |
| input_r = tokenizer_r.pad(input_r, max_length=max_length, padding="max_length") | |
| input_p = tokenizer_r.batch_encode_plus( | |
| ["This is a input 1", "This is a much longer input whilch should be padded"] | |
| ) | |
| input_p = tokenizer_r.pad(input_p, max_length=max_length, padding="max_length") | |
| self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id) | |
| def test_save_pretrained(self): | |
| super().test_save_pretrained() | |
| def test_save_slow_from_fast_and_reload_fast(self): | |
| super().test_save_slow_from_fast_and_reload_fast() | |
| def assert_batch_padded_input_match( | |
| self, | |
| input_r: dict, | |
| input_p: dict, | |
| max_length: int, | |
| pad_token_id: int, | |
| model_main_input_name: str = "input_ids", | |
| ): | |
| for i_r in input_r.values(): | |
| self.assertEqual(len(i_r), 2), self.assertEqual(len(i_r[0]), max_length), self.assertEqual( | |
| len(i_r[1]), max_length | |
| ) | |
| self.assertEqual(len(i_r), 2), self.assertEqual(len(i_r[0]), max_length), self.assertEqual( | |
| len(i_r[1]), max_length | |
| ) | |
| for i_r, i_p in zip(input_r[model_main_input_name], input_p[model_main_input_name]): | |
| self.assert_padded_input_match(i_r, i_p, max_length, pad_token_id) | |
| def test_tokenizer_integration(self): | |
| # fmt: off | |
| expected_encoding = {'input_ids': [[4, 4616, 107, 163, 328, 14, 63, 1726, 106, 11954, 16659, 23, 83, 16688, 11427, 328, 107, 36, 11954, 16659, 23, 83, 16688, 6153, 82, 961, 16688, 3474, 16710, 1696, 2306, 16688, 10854, 2524, 3827, 561, 163, 3474, 16680, 62, 226, 2092, 16680, 379, 3474, 16660, 16680, 2436, 16667, 16671, 16680, 999, 87, 3474, 16680, 2436, 16667, 5208, 800, 16710, 68, 2018, 2959, 3037, 163, 16663, 11617, 16710, 36, 2018, 2959, 4737, 163, 16663, 16667, 16674, 16710, 91, 372, 5087, 16745, 2205, 82, 961, 3608, 38, 1770, 16745, 7984, 36, 2565, 751, 9017, 1204, 864, 218, 1244, 16680, 11954, 16659, 23, 83, 36, 14686, 23, 7619, 16678, 5], [4, 28, 532, 65, 1929, 33, 391, 16688, 3979, 9, 2565, 7849, 299, 225, 34, 2040, 305, 167, 289, 16667, 16078, 32, 1966, 181, 4626, 63, 10575, 71, 851, 1491, 36, 624, 4757, 38, 208, 8038, 16678, 5, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [4, 13, 1467, 5187, 26, 2521, 4567, 16664, 372, 13, 16209, 3314, 16678, 5, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]], '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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 0, 0, 0, 0]]} # noqa: E501 | |
| # fmt: on | |
| self.tokenizer_integration_test_util( | |
| expected_encoding=expected_encoding, | |
| model_name="google/fnet-base", | |
| revision="34219a71ca20e280cc6000b89673a169c65d605c", | |
| ) | |