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29fef1e7aa
* nits * Fixing deps. * Ruff update. * Import order matters. * Fix. * Revert ruff fix. * Visualizer. * Putting back the imports. --------- Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
59 lines
2.2 KiB
Python
59 lines
2.2 KiB
Python
from tokenizers import CharBPETokenizer
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from ..utils import data_dir, multiprocessing_with_parallelism, openai_files
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class TestCharBPETokenizer:
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def test_basic_encode(self, openai_files):
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tokenizer = CharBPETokenizer.from_file(openai_files["vocab"], openai_files["merges"])
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output = tokenizer.encode("My name is John", "pair")
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assert output.ids == [0, 253, 1362, 544, 0, 7, 12662, 2688]
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assert output.tokens == [
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"<unk>",
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"y</w>",
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"name</w>",
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"is</w>",
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"<unk>",
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"o",
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"hn</w>",
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"pair</w>",
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]
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assert output.offsets == [
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(0, 1),
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(1, 2),
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(3, 7),
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(8, 10),
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(11, 12),
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(12, 13),
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(13, 15),
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(0, 4),
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]
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assert output.type_ids == [0, 0, 0, 0, 0, 0, 0, 1]
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def test_lowercase(self, openai_files):
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tokenizer = CharBPETokenizer.from_file(openai_files["vocab"], openai_files["merges"], lowercase=True)
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output = tokenizer.encode("My name is John", "pair", add_special_tokens=False)
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assert output.ids == [547, 1362, 544, 2476, 2688]
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assert output.tokens == ["my</w>", "name</w>", "is</w>", "john</w>", "pair</w>"]
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assert output.offsets == [(0, 2), (3, 7), (8, 10), (11, 15), (0, 4)]
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assert output.type_ids == [0, 0, 0, 0, 1]
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def test_decoding(self, openai_files):
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tokenizer = CharBPETokenizer.from_file(openai_files["vocab"], openai_files["merges"], lowercase=True)
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decoded = tokenizer.decode(tokenizer.encode("my name is john").ids)
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assert decoded == "my name is john"
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def test_multiprocessing_with_parallelism(self, openai_files):
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tokenizer = CharBPETokenizer.from_file(openai_files["vocab"], openai_files["merges"])
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multiprocessing_with_parallelism(tokenizer, False)
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multiprocessing_with_parallelism(tokenizer, True)
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def test_train_from_iterator(self):
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text = ["A first sentence", "Another sentence", "And a last one"]
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tokenizer = CharBPETokenizer()
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tokenizer.train_from_iterator(text, show_progress=False)
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output = tokenizer.encode("A sentence")
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assert output.tokens == ["A</w>", "sentence</w>"]
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