wehub-resource-sync
181 lines
6.9 KiB
Python
181 lines
6.9 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2020 The HuggingFace Team. All rights reserved.
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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 unittest
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from paddlenlp.transformers.mamba.tokenizer import MambaTokenizer
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from paddlenlp.transformers.tokenizer_utils import PretrainedTokenizer
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from ...transformers.test_tokenizer_common import TokenizerTesterMixin
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class MambaTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = MambaTokenizer
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test_decode_token = True
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def get_tokenizer(self, **kwargs) -> PretrainedTokenizer:
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tokenizer = MambaTokenizer.from_pretrained("state-spaces/mamba-2.8b-hf", **kwargs)
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return tokenizer
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def get_input_output_texts(self, tokenizer):
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input_text = "lower newer"
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output_text = "lower newer"
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return input_text, output_text
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def test_full_tokenizer(self):
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tokenizer = self.get_tokenizer()
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text = "lower newer"
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bpe_tokens = ["Ġlower", "Ġnewer"]
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tokens = tokenizer.tokenize(text, add_prefix_space=True)
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self.assertListEqual(tokens, bpe_tokens)
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input_tokens = tokens + [tokenizer.unk_token]
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input_bpe_tokens = [2406, 21629, 0]
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self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
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def test_pretokenized_inputs(self, *args, **kwargs):
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pass
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def test_tokenizers_common_ids_setters(self, *args, **kwargs):
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pass
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def test_mask_output(self):
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pass
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def test_offsets_mapping(self):
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pass
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def test_offsets_mapping_with_unk(self):
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pass
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def test_special_tokens_mask(self):
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pass
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def test_special_tokens_mask_input_pairs(self):
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pass
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def test_padding_side_in_kwargs(self):
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tokenizer = self.get_tokenizer(padding_side="left")
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self.assertEqual(tokenizer.padding_side, "left")
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tokenizer = self.get_tokenizer(padding_side="right")
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self.assertEqual(tokenizer.padding_side, "right")
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def test_truncation_side_in_kwargs(self):
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tokenizer = self.get_tokenizer(truncation_side="left")
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self.assertEqual(tokenizer.truncation_side, "left")
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tokenizer = self.get_tokenizer(truncation_side="right")
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self.assertEqual(tokenizer.truncation_side, "right")
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def test_add_tokens(self):
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tokenizer = self.get_tokenizer()
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vocab_size = len(tokenizer)
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self.assertEqual(tokenizer.add_tokens(""), 0)
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self.assertEqual(tokenizer.add_tokens("testoken"), 1)
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self.assertEqual(tokenizer.add_tokens(["testoken1", "testtoken2"]), 2)
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self.assertEqual(len(tokenizer), vocab_size + 3)
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self.assertEqual(tokenizer.add_special_tokens({}), 0)
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self.assertRaises(AssertionError, tokenizer.add_special_tokens, {"additional_special_tokens": "<testtoken1>"})
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self.assertEqual(tokenizer.add_special_tokens({"additional_special_tokens": ["<testtoken2>"]}), 1)
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self.assertEqual(
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tokenizer.add_special_tokens({"additional_special_tokens": ["<testtoken3>", "<testtoken4>"]}), 2
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)
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self.assertIn("<testtoken3>", tokenizer.special_tokens_map["additional_special_tokens"])
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self.assertIsInstance(tokenizer.special_tokens_map["additional_special_tokens"], list)
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self.assertGreaterEqual(len(tokenizer.special_tokens_map["additional_special_tokens"]), 2)
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self.assertEqual(len(tokenizer), vocab_size + 6)
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def test_add_tokens_tokenizer(self):
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tokenizer = self.get_tokenizer()
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vocab_size = tokenizer.vocab_size
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all_size = len(tokenizer)
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self.assertNotEqual(vocab_size, 0)
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new_toks = ["aaaaa bbbbbb", "cccccccccdddddddd"]
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added_toks = tokenizer.add_tokens(new_toks)
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vocab_size_2 = tokenizer.vocab_size
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all_size_2 = len(tokenizer)
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self.assertNotEqual(vocab_size_2, 0)
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self.assertEqual(vocab_size, vocab_size_2)
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self.assertEqual(added_toks, len(new_toks))
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self.assertEqual(all_size_2, all_size + len(new_toks))
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tokens = tokenizer.encode(
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"aaaaa bbbbbb low cccccccccdddddddd l", return_token_type_ids=None, add_special_tokens=False
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)["input_ids"]
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self.assertGreaterEqual(len(tokens), 4)
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self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
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self.assertGreater(tokens[-2], tokenizer.vocab_size - 1)
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def test_consecutive_unk_string(self):
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tokenizer = self.get_tokenizer(add_bos_token=False)
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tokens = [tokenizer.unk_token for _ in range(2)]
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string = tokenizer.convert_tokens_to_string(tokens)
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encoding = tokenizer(
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text=string,
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runcation=True,
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return_offsets_mapping=True,
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)
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self.assertEqual(len(encoding["input_ids"]), 2)
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self.assertEqual(len(encoding["offset_mapping"]), 2)
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def test_padding_if_pad_token_set_slow(self):
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tokenizer = self.get_tokenizer()
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# Simple input
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s = "This is a simple input"
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s2 = ["This is a simple input looooooooong", "This is a simple input"]
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p = ("This is a simple input", "This is a pair")
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pad_token_id = tokenizer.pad_token_id
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out_s = tokenizer(s, padding="max_length", max_length=30, return_tensors="np", return_attention_mask=True)
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out_s2 = tokenizer(s2, padding=True, truncate=True, return_tensors="np", return_attention_mask=True)
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out_p = tokenizer(*p, padding="max_length", max_length=60, return_tensors="np", return_attention_mask=True)
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# s
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# test single string max_length padding
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self.assertEqual(out_s["input_ids"].shape[-1], 30)
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self.assertTrue(pad_token_id in out_s["input_ids"])
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self.assertTrue(0 in out_s["attention_mask"])
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# s2
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# test automatic padding
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self.assertEqual(out_s2["input_ids"].shape[-1], 9)
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# long slice doesn't have padding
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self.assertFalse(pad_token_id in out_s2["input_ids"][0])
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self.assertFalse(0 in out_s2["attention_mask"][0])
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# short slice does have padding
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self.assertTrue(pad_token_id in out_s2["input_ids"][1])
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self.assertTrue(0 in out_s2["attention_mask"][1])
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# p
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# test single pair max_length padding
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self.assertEqual(out_p["input_ids"].shape[-1], 60)
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self.assertTrue(pad_token_id in out_p["input_ids"])
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self.assertTrue(0 in out_p["attention_mask"])
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def test_pretrained_model_lists(self):
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pass
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