830 lines
38 KiB
Python
830 lines
38 KiB
Python
# coding=utf-8
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# Copyright 2023 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 os
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import pickle
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import shutil
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import tempfile
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import unittest
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from datasets import load_dataset
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from transformers import (
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SPIECE_UNDERLINE,
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AddedToken,
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LlamaTokenizer,
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LlamaTokenizerFast,
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is_torch_available,
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)
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from transformers.convert_slow_tokenizer import convert_slow_tokenizer
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from transformers.testing_utils import (
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get_tests_dir,
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nested_simplify,
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require_jinja,
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require_sentencepiece,
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require_tokenizers,
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require_torch,
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slow,
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)
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from ...test_tokenization_common import TokenizerTesterMixin
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SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
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if is_torch_available():
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pass
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@require_sentencepiece
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@require_tokenizers
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class LlamaTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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from_pretrained_id = ["hf-internal-testing/llama-tokenizer", "meta-llama/Llama-2-7b-hf"]
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tokenizer_class = LlamaTokenizer
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rust_tokenizer_class = LlamaTokenizerFast
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test_rust_tokenizer = False
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test_sentencepiece = True
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from_pretrained_kwargs = {}
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def setUp(self):
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super().setUp()
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# We have a SentencePiece fixture for testing
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tokenizer = LlamaTokenizer(SAMPLE_VOCAB, keep_accents=True)
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tokenizer.pad_token = tokenizer.eos_token
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tokenizer.save_pretrained(self.tmpdirname)
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def get_tokenizers(self, **kwargs):
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kwargs.update({"pad_token": "<PAD>"})
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return super().get_tokenizers(**kwargs)
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def test_full_tokenizer(self):
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tokenizer = LlamaTokenizer(SAMPLE_VOCAB, keep_accents=True)
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tokens = tokenizer.tokenize("This is a test")
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self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
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self.assertListEqual(
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tokenizer.convert_tokens_to_ids(tokens),
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[285, 46, 10, 170, 382],
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)
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tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
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self.assertListEqual(
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tokens,
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[
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SPIECE_UNDERLINE + "I",
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SPIECE_UNDERLINE + "was",
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SPIECE_UNDERLINE + "b",
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"or",
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"n",
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SPIECE_UNDERLINE + "in",
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SPIECE_UNDERLINE + "",
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"9",
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"2",
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"0",
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"0",
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"0",
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",",
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SPIECE_UNDERLINE + "and",
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SPIECE_UNDERLINE + "this",
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SPIECE_UNDERLINE + "is",
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SPIECE_UNDERLINE + "f",
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"al",
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"s",
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"é",
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".",
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],
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)
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ids = tokenizer.convert_tokens_to_ids(tokens)
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self.assertListEqual(
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ids,
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[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4],
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)
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back_tokens = tokenizer.convert_ids_to_tokens(ids)
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self.assertListEqual(
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back_tokens,
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[
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SPIECE_UNDERLINE + "I",
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SPIECE_UNDERLINE + "was",
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SPIECE_UNDERLINE + "b",
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"or",
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"n",
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SPIECE_UNDERLINE + "in",
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SPIECE_UNDERLINE + "",
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"<unk>",
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"2",
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"0",
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"0",
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"0",
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",",
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SPIECE_UNDERLINE + "and",
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SPIECE_UNDERLINE + "this",
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SPIECE_UNDERLINE + "is",
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SPIECE_UNDERLINE + "f",
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"al",
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"s",
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"<unk>",
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".",
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],
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)
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@unittest.skip("Let's wait for the fast tokenizer!")
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def test_save_pretrained(self):
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self.tokenizers_list += (self.rust_tokenizer_class, "hf-internal-testing/llama-tokenizer", {})
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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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tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
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tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
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tmpdirname2 = tempfile.mkdtemp()
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tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2)
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tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
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# Checks it save with the same files + the tokenizer.json file for the fast one
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self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))
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tokenizer_r_files = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f)
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self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files)
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# Checks everything loads correctly in the same way
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tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
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tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
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# Check special tokens are set accordingly on Rust and Python
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for key in tokenizer_pp.special_tokens_map:
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self.assertTrue(hasattr(tokenizer_rp, key))
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shutil.rmtree(tmpdirname2)
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# Save tokenizer rust, legacy_format=True
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tmpdirname2 = tempfile.mkdtemp()
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tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=True)
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tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
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# Checks it save with the same files
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self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files)
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# Checks everything loads correctly in the same way
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tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
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tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
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# Check special tokens are set accordingly on Rust and Python
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for key in tokenizer_pp.special_tokens_map:
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self.assertTrue(hasattr(tokenizer_rp, key))
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shutil.rmtree(tmpdirname2)
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# Save tokenizer rust, legacy_format=False
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tmpdirname2 = tempfile.mkdtemp()
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tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=False)
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tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
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# Checks it saved the tokenizer.json file
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self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))
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# Checks everything loads correctly in the same way
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tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
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tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
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# Check special tokens are set accordingly on Rust and Python
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for key in tokenizer_pp.special_tokens_map:
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self.assertTrue(hasattr(tokenizer_rp, key))
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shutil.rmtree(tmpdirname2)
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@require_torch
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def test_batch_tokenization(self):
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if not self.test_seq2seq:
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return
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tokenizers = self.get_tokenizers()
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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# Longer text that will definitely require truncation.
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text = [
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" UN Chief Says There Is No Military Solution in Syria",
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" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"
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" Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"
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" will only worsen the violence and misery for millions of people.",
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]
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try:
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batch = tokenizer(
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text=text,
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max_length=3,
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max_target_length=10,
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return_tensors="pt",
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)
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except NotImplementedError:
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return
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self.assertEqual(batch.input_ids.shape[1], 3)
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# max_target_length will default to max_length if not specified
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batch = tokenizer(text, max_length=3, return_tensors="pt")
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self.assertEqual(batch.input_ids.shape[1], 3)
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batch_encoder_only = tokenizer(text=text, max_length=3, max_target_length=10, return_tensors="pt")
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self.assertEqual(batch_encoder_only.input_ids.shape[1], 3)
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self.assertEqual(batch_encoder_only.attention_mask.shape[1], 3)
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self.assertNotIn("decoder_input_ids", batch_encoder_only)
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@unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece.")
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def test_save_slow_from_fast_and_reload_fast(self):
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pass
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def test_special_tokens_initialization(self):
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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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added_tokens = [AddedToken("<special>", lstrip=True)]
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tokenizer_r = self.rust_tokenizer_class.from_pretrained(
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pretrained_name, additional_special_tokens=added_tokens, **kwargs
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)
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r_output = tokenizer_r.encode("Hey this is a <special> token")
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special_token_id = tokenizer_r.encode("<special>", add_special_tokens=False)[0]
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self.assertTrue(special_token_id in r_output)
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if self.test_slow_tokenizer:
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tokenizer_cr = self.rust_tokenizer_class.from_pretrained(
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pretrained_name,
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additional_special_tokens=added_tokens,
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**kwargs, # , from_slow=True <- unfortunately too slow to convert
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)
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tokenizer_p = self.tokenizer_class.from_pretrained(
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pretrained_name, additional_special_tokens=added_tokens, **kwargs
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)
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p_output = tokenizer_p.encode("Hey this is a <special> token")
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cr_output = tokenizer_cr.encode("Hey this is a <special> token")
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self.assertEqual(p_output, r_output)
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self.assertEqual(cr_output, r_output)
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self.assertTrue(special_token_id in p_output)
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self.assertTrue(special_token_id in cr_output)
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@slow
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def test_tokenizer_integration(self):
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expected_encoding = {'input_ids': [[1, 4103, 689, 414, 313, 24784, 368, 2998, 408, 282, 3637, 25350, 29899, 9067, 414, 322, 282, 3637, 25350, 29899, 1457, 3018, 1312, 29899, 2151, 29897, 8128, 2498, 29899, 15503, 4220, 6956, 1973, 313, 13635, 29911, 29892, 402, 7982, 29899, 29906, 29892, 1528, 13635, 29911, 29874, 29892, 1060, 26369, 29892, 6652, 309, 29933, 814, 29892, 1060, 29931, 6779, 11410, 363, 18385, 17088, 7634, 11235, 313, 25103, 29965, 29897, 322, 18385, 17088, 28203, 313, 25103, 29954, 29897, 411, 975, 29871, 29941, 29906, 29974, 758, 3018, 1312, 4733, 297, 29871, 29896, 29900, 29900, 29974, 10276, 322, 6483, 1006, 3372, 3097, 1546, 435, 1165, 29892, 10772, 29911, 25350, 322, 323, 6073, 17907, 29889], [1, 350, 20161, 338, 8688, 304, 758, 29899, 14968, 6483, 21000, 8684, 284, 22540, 515, 443, 29880, 24025, 1426, 491, 14002, 368, 4195, 292, 373, 1716, 2175, 322, 1492, 3030, 297, 599, 15359, 29889], [1, 450, 4996, 17354, 1701, 29916, 432, 17204, 975, 278, 17366, 11203, 29889]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # fmt: skip
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self.tokenizer_integration_test_util(
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expected_encoding=expected_encoding,
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model_name="hf-internal-testing/llama-tokenizer",
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revision="0984d03108b1a041ed679bd253b6519b7e1a4778",
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padding=False,
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)
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def test_picklable(self):
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with tempfile.NamedTemporaryFile() as f:
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shutil.copyfile(SAMPLE_VOCAB, f.name)
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tokenizer = LlamaTokenizer(f.name, keep_accents=True)
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pickled_tokenizer = pickle.dumps(tokenizer)
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pickle.loads(pickled_tokenizer)
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@unittest.skip("worker 'gw4' crashed on CI, passing locally.")
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def test_pickle_subword_regularization_tokenizer(self):
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pass
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@unittest.skip("worker 'gw4' crashed on CI, passing locally.")
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def test_subword_regularization_tokenizer(self):
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pass
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def test_add_prefix_space(self):
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pretrained_name = "hf-internal-testing/llama-tokenizer-non-normalized"
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inputs = "Hey how are you doing"
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EXPECTED_WITH_SPACE = [1, 18637, 920, 526, 366, 2599]
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EXPECTED_WO_SPACE = [1, 29950, 1032, 920, 526, 366, 2599]
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slow_ = self.tokenizer_class.from_pretrained(pretrained_name, add_prefix_space=False, legacy=False)
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fast_ = self.rust_tokenizer_class.from_pretrained(pretrained_name, add_prefix_space=False, legacy=False)
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self.assertEqual(slow_.encode(inputs), EXPECTED_WO_SPACE)
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self.assertEqual(slow_.encode(inputs), fast_.encode(inputs))
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self.assertEqual(slow_.tokenize(inputs), ["H", "ey", "▁how", "▁are", "▁you", "▁doing"])
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self.assertEqual(slow_.decode(EXPECTED_WO_SPACE, skip_special_tokens=True), inputs)
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self.assertEqual(
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slow_.decode(EXPECTED_WO_SPACE, skip_special_tokens=True),
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fast_.decode(EXPECTED_WO_SPACE, skip_special_tokens=True),
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)
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slow_ = self.tokenizer_class.from_pretrained(pretrained_name, add_prefix_space=True, legacy=False)
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fast_ = self.rust_tokenizer_class.from_pretrained(pretrained_name, add_prefix_space=True, legacy=False)
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self.assertEqual(slow_.encode(inputs), EXPECTED_WITH_SPACE)
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self.assertEqual(slow_.encode(inputs), fast_.encode(inputs))
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self.assertEqual(slow_.tokenize(inputs), ["▁Hey", "▁how", "▁are", "▁you", "▁doing"])
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self.assertEqual(slow_.decode(EXPECTED_WITH_SPACE, skip_special_tokens=True), inputs)
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self.assertEqual(
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slow_.decode(EXPECTED_WITH_SPACE, skip_special_tokens=True),
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fast_.decode(EXPECTED_WITH_SPACE, skip_special_tokens=True),
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)
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@require_torch
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@require_sentencepiece
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@require_tokenizers
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class LlamaIntegrationTest(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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checkpoint_name = "hf-internal-testing/llama-tokenizer-non-normalized"
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cls.tokenizer: LlamaTokenizer = LlamaTokenizer.from_pretrained(checkpoint_name)
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cls.rust_tokenizer = LlamaTokenizerFast.from_pretrained(checkpoint_name)
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return cls
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@require_torch
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def integration_tests(self):
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inputs = self.tokenizer(
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["The following string should be properly encoded: Hello.", "But ird and ปี ird ด"],
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return_tensors="pt",
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)
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self.assertEqual(
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nested_simplify(inputs),
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{
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"input_ids": [
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[1, 450, 1494, 1347, 881, 367, 6284, 18511, 29901, 15043, 29889],
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[1, 1205, 29871, 1823, 322, 29871, 31010, 30691, 1678, 1823, 1678, 30718],
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],
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"attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]],
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},
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)
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def test_fast_special_tokens(self):
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slow_tokenizer = self.tokenizer
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fast_tokenizer = self.rust_tokenizer
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slow = slow_tokenizer.encode("A sample test", add_special_tokens=True)
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assert slow == [1, 319, 4559, 1243]
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fast_tokenizer.add_eos_token = False
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fast = fast_tokenizer.encode("A sample test", add_special_tokens=True)
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assert fast == [1, 319, 4559, 1243]
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fast_tokenizer.add_eos_token = True
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fast = fast_tokenizer.encode("A sample test", add_special_tokens=True)
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assert fast == [1, 319, 4559, 1243, 2]
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slow_tokenizer.add_eos_token = True
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slow = slow_tokenizer.encode("A sample test", add_special_tokens=True)
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assert slow == [1, 319, 4559, 1243, 2]
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fast_tokenizer = LlamaTokenizerFast.from_pretrained(
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"hf-internal-testing/llama-tokenizer", add_eos_token=True, add_bos_token=False
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)
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fast = fast_tokenizer.encode("A sample test", add_special_tokens=True)
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assert fast == [319, 4559, 1243, 2]
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slow_tokenizer = LlamaTokenizer.from_pretrained(
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"hf-internal-testing/llama-tokenizer", add_eos_token=True, add_bos_token=False
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)
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slow = slow_tokenizer.encode("A sample test", add_special_tokens=True)
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assert slow == [319, 4559, 1243, 2]
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self.tokenizer.add_eos_token = False
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self.rust_tokenizer.add_eos_token = False
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@slow
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def test_conversion(self):
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# This is excruciatingly slow since it has to recreate the entire merge
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# list from the original vocabulary in spm
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self.rust_tokenizer.save_pretrained("./out")
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with tempfile.TemporaryDirectory() as dirname:
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self.rust_tokenizer.save_pretrained(dirname)
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with open(os.path.join(dirname, "tokenizer.json"), "r") as f:
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old_serialized = f.read()
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new_tokenizer = convert_slow_tokenizer(self.tokenizer)
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with tempfile.NamedTemporaryFile() as f:
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new_tokenizer.save(f.name)
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# Re-opening since `f` is in bytes.
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new_serialized = open(f.name, "r").read()
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with open("out_tokenizer.json", "w") as g:
|
||
g.write(new_serialized)
|
||
|
||
self.assertEqual(old_serialized, new_serialized)
|
||
|
||
def test_simple_encode_decode(self):
|
||
pyth_tokenizer = self.tokenizer
|
||
rust_tokenizer = self.rust_tokenizer
|
||
|
||
self.assertEqual(pyth_tokenizer.encode("This is a test"), [1, 910, 338, 263, 1243])
|
||
self.assertEqual(rust_tokenizer.encode("This is a test"), [1, 910, 338, 263, 1243])
|
||
self.assertEqual(pyth_tokenizer.decode([1, 910, 338, 263, 1243], skip_special_tokens=True), "This is a test")
|
||
self.assertEqual(rust_tokenizer.decode([1, 910, 338, 263, 1243], skip_special_tokens=True), "This is a test")
|
||
|
||
# bytefallback showcase
|
||
self.assertEqual(pyth_tokenizer.encode("生活的真谛是"), [1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392]) # fmt: skip
|
||
self.assertEqual(rust_tokenizer.encode("生活的真谛是"), [1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392]) # fmt: skip
|
||
self.assertEqual(
|
||
pyth_tokenizer.decode(
|
||
[1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392], skip_special_tokens=True
|
||
),
|
||
"生活的真谛是",
|
||
)
|
||
self.assertEqual(
|
||
rust_tokenizer.decode(
|
||
[1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392], skip_special_tokens=True
|
||
),
|
||
"生活的真谛是",
|
||
)
|
||
|
||
# Inner spaces showcase
|
||
self.assertEqual(pyth_tokenizer.encode("Hi Hello"), [1, 6324, 29871, 15043])
|
||
self.assertEqual(rust_tokenizer.encode("Hi Hello"), [1, 6324, 29871, 15043])
|
||
self.assertEqual(pyth_tokenizer.decode([1, 6324, 29871, 15043], skip_special_tokens=True), "Hi Hello")
|
||
self.assertEqual(rust_tokenizer.decode([1, 6324, 29871, 15043], skip_special_tokens=True), "Hi Hello")
|
||
|
||
self.assertEqual(pyth_tokenizer.encode("Hi Hello"), [1, 6324, 259, 15043])
|
||
self.assertEqual(rust_tokenizer.encode("Hi Hello"), [1, 6324, 259, 15043])
|
||
self.assertEqual(pyth_tokenizer.decode([1, 6324, 259, 15043], skip_special_tokens=True), "Hi Hello")
|
||
self.assertEqual(rust_tokenizer.decode([1, 6324, 259, 15043], skip_special_tokens=True), "Hi Hello")
|
||
|
||
self.assertEqual(pyth_tokenizer.encode(""), [1])
|
||
self.assertEqual(rust_tokenizer.encode(""), [1])
|
||
|
||
self.assertEqual(pyth_tokenizer.encode(" "), [1, 259])
|
||
self.assertEqual(rust_tokenizer.encode(" "), [1, 259])
|
||
|
||
self.assertEqual(pyth_tokenizer.encode(" "), [1, 1678])
|
||
self.assertEqual(rust_tokenizer.encode(" "), [1, 1678])
|
||
|
||
self.assertEqual(pyth_tokenizer.encode(" Hello"), [1, 29871, 15043])
|
||
self.assertEqual(rust_tokenizer.encode(" Hello"), [1, 29871, 15043])
|
||
|
||
def test_no_differences_showcase(self):
|
||
pyth_tokenizer = self.tokenizer
|
||
rust_tokenizer = self.rust_tokenizer
|
||
self.assertEqual(pyth_tokenizer.encode(""), [1])
|
||
self.assertEqual(rust_tokenizer.encode(""), [1])
|
||
|
||
self.assertEqual(pyth_tokenizer.encode(" "), [1, 259])
|
||
self.assertEqual(rust_tokenizer.encode(" "), [1, 259])
|
||
|
||
self.assertEqual(pyth_tokenizer.encode(" "), [1, 1678])
|
||
self.assertEqual(rust_tokenizer.encode(" "), [1, 1678])
|
||
|
||
self.assertEqual(pyth_tokenizer.encode(" Hello"), [1, 29871, 15043])
|
||
self.assertEqual(rust_tokenizer.encode(" Hello"), [1, 29871, 15043])
|
||
|
||
self.assertEqual(pyth_tokenizer.encode("<s>"), [1, 1])
|
||
self.assertEqual(rust_tokenizer.encode("<s>"), [1, 1])
|
||
|
||
def test_no_differences_decode(self):
|
||
pyth_tokenizer = self.tokenizer
|
||
rust_tokenizer = self.rust_tokenizer
|
||
|
||
self.assertEqual(pyth_tokenizer.decode([869]), ".")
|
||
self.assertEqual(rust_tokenizer.decode([869]), ".")
|
||
|
||
self.assertEqual(pyth_tokenizer.decode([30112, 869]), "ا .")
|
||
self.assertEqual(rust_tokenizer.decode([30112, 869]), "ا .")
|
||
|
||
def test_no_differences_special_tokens(self):
|
||
pyth_tokenizer = self.tokenizer
|
||
rust_tokenizer = self.rust_tokenizer
|
||
self.assertEqual(pyth_tokenizer.encode(""), [1])
|
||
self.assertEqual(rust_tokenizer.encode(""), [1])
|
||
|
||
self.assertEqual(pyth_tokenizer.encode("<s>"), [1, 1])
|
||
self.assertEqual(rust_tokenizer.encode("<s>"), [1, 1])
|
||
|
||
@unittest.skipIf(
|
||
os.getenv("RUN_TOKENIZER_INTEGRATION", "0") == "0",
|
||
"RUN_TOKENIZER_INTEGRATION=1 to run tokenizer integration tests",
|
||
)
|
||
def test_integration_test_xnli(self):
|
||
import tqdm
|
||
|
||
pyth_tokenizer = self.tokenizer
|
||
rust_tokenizer = self.rust_tokenizer
|
||
|
||
dataset = load_dataset("code_x_glue_ct_code_to_text", "go")
|
||
for item in tqdm.tqdm(dataset["validation"]):
|
||
string = item["code"]
|
||
encoded1 = pyth_tokenizer.encode(string)
|
||
encoded2 = rust_tokenizer.encode(string)
|
||
|
||
self.assertEqual(encoded1, encoded2)
|
||
|
||
decoded1 = pyth_tokenizer.decode(encoded1, skip_special_tokens=True)
|
||
decoded2 = rust_tokenizer.decode(encoded2, skip_special_tokens=True)
|
||
|
||
self.assertEqual(decoded1, decoded2)
|
||
|
||
dataset = load_dataset("xnli", "all_languages")
|
||
|
||
for item in tqdm.tqdm(dataset["train"]):
|
||
for string in item["premise"].values():
|
||
encoded1 = pyth_tokenizer.encode(string)
|
||
encoded2 = rust_tokenizer.encode(string)
|
||
|
||
self.assertEqual(encoded1, encoded2)
|
||
|
||
decoded1 = pyth_tokenizer.decode(encoded1, skip_special_tokens=True)
|
||
decoded2 = rust_tokenizer.decode(encoded2, skip_special_tokens=True)
|
||
|
||
self.assertEqual(decoded1, decoded2)
|
||
|
||
def test_special_token_special_word(self):
|
||
# the word inform should be split as ['in', 'form']
|
||
tokenizer = LlamaTokenizerFast.from_pretrained("huggyllama/llama-7b", legacy=False, from_slow=True)
|
||
tokenizer.add_tokens([AddedToken("<REPR_END>", rstrip=True, lstrip=True)], special_tokens=False)
|
||
|
||
example_inputs = tokenizer.tokenize("<REPR_END>inform<s>. Hey. .")
|
||
self.assertEqual(example_inputs, ["<REPR_END>", "in", "form", "<s>", ".", "▁Hey", ".", "▁▁▁▁▁▁", "▁."])
|
||
|
||
# Make sure dummy space is added if it is indeed the first word
|
||
example_inputs = tokenizer.tokenize("inform<s>. Hey. .")
|
||
self.assertEqual(example_inputs, ["▁inform", "<s>", ".", "▁Hey", ".", "▁▁▁▁▁▁", "▁."])
|
||
out1 = tokenizer.decode(
|
||
tokenizer.encode("<REPR_END>inform", add_special_tokens=False), spaces_between_special_tokens=False
|
||
)
|
||
self.assertEqual(out1, "<REPR_END>inform")
|
||
out2 = tokenizer.decode(
|
||
tokenizer.encode("<REPR_END>inform", add_special_tokens=False), spaces_between_special_tokens=True
|
||
)
|
||
# decoding strips the added prefix space.
|
||
self.assertEqual(out2, "<REPR_END>inform")
|
||
input_ids = tokenizer.encode("<REPR_END>inform", add_special_tokens=False)
|
||
self.assertEqual(input_ids, [32000, 262, 689]) # 29871 is the spiece underline, '▁' added as it should
|
||
|
||
out2 = tokenizer.decode(
|
||
tokenizer.encode(" <REPR_END>inform", add_special_tokens=False), spaces_between_special_tokens=False
|
||
)
|
||
# TODO @ArthurZ currently we strip left and right, so this will not keep the spaces
|
||
self.assertEqual(out2, "<REPR_END>inform")
|
||
|
||
### Let's make sure decoding does not add extra spaces here and there
|
||
# TODO @ArthurZ this should be affected by the lstrip/rstrip/single word /normalize refactoring
|
||
# Since currently we always strip left and right of the token, results are as such
|
||
input_ids = tokenizer.encode("<s> Hello<s>how", add_special_tokens=False)
|
||
self.assertEqual(input_ids, [1, 15043, 1, 3525])
|
||
tokens = tokenizer.tokenize("<s> Hello<s>how", add_special_tokens=False)
|
||
self.assertEqual(tokens, ["<s>", "▁Hello", "<s>", "how"])
|
||
decoded_tokens = tokenizer.decode(input_ids)
|
||
self.assertEqual(decoded_tokens, "<s> Hello<s>how")
|
||
|
||
# Let's make sure that if there are any spaces, we don't remove them!
|
||
input_ids = tokenizer.encode(" <s> Hello<s> how", add_special_tokens=False)
|
||
self.assertEqual(input_ids, [29871, 1, 15043, 1, 920])
|
||
tokens = tokenizer.tokenize(" <s> Hello<s> how", add_special_tokens=False)
|
||
self.assertEqual(tokens, ["▁", "<s>", "▁Hello", "<s>", "▁how"])
|
||
decoded_tokens = tokenizer.decode(input_ids)
|
||
self.assertEqual(decoded_tokens, "<s> Hello<s> how")
|
||
|
||
# Let's make sure the space is preserved
|
||
input_ids = tokenizer.encode("hello", add_special_tokens=True)
|
||
self.assertEqual(input_ids, [1, 22172])
|
||
tokens = tokenizer.tokenize("hello")
|
||
self.assertEqual(tokens, ["▁hello"])
|
||
decoded_tokens = tokenizer.decode(input_ids)
|
||
self.assertEqual(decoded_tokens, "<s> hello")
|
||
|
||
input_ids = tokenizer.encode("hello", add_special_tokens=False)
|
||
self.assertEqual(input_ids, [22172])
|
||
decoded_tokens = tokenizer.decode(input_ids)
|
||
self.assertEqual(decoded_tokens, "hello")
|
||
|
||
def test_no_prefix_space(self):
|
||
tokenizer_no_prefix_space = LlamaTokenizerFast.from_pretrained("huggyllama/llama-7b", add_prefix_space=False)
|
||
no_prefix_space_tokens = tokenizer_no_prefix_space.tokenize("Hey")
|
||
self.assertEqual(no_prefix_space_tokens, ["H", "ey"])
|
||
|
||
tokenizer = LlamaTokenizerFast.from_pretrained(
|
||
"huggyllama/llama-7b", legacy=False, from_slow=True, add_prefix_space=False
|
||
)
|
||
tokenizer.add_tokens([AddedToken("<REPR_END>", rstrip=True, lstrip=True)], special_tokens=False)
|
||
|
||
example_inputs = tokenizer.tokenize("<REPR_END>inform<s>. Hey. .")
|
||
self.assertEqual(example_inputs, ["<REPR_END>", "in", "form", "<s>", ".", "▁Hey", ".", "▁▁▁▁▁▁", "▁."])
|
||
|
||
# Make sure dummy space is added if it is indeed the first word
|
||
example_inputs = tokenizer.tokenize("inform<s>. Hey. .")
|
||
self.assertEqual(example_inputs, ["in", "form", "<s>", ".", "▁Hey", ".", "▁▁▁▁▁▁", "▁."])
|
||
out1 = tokenizer.decode(
|
||
tokenizer.encode("<REPR_END>inform", add_special_tokens=False), spaces_between_special_tokens=False
|
||
)
|
||
self.assertEqual(out1, "<REPR_END>inform")
|
||
out2 = tokenizer.decode(
|
||
tokenizer.encode("<REPR_END>inform", add_special_tokens=False), spaces_between_special_tokens=True
|
||
)
|
||
# decoding strips the added prefix space.
|
||
self.assertEqual(out2, "<REPR_END>inform")
|
||
input_ids = tokenizer.encode("<REPR_END>inform", add_special_tokens=False)
|
||
self.assertEqual(input_ids, [32000, 262, 689]) # 29871 is the spiece underline, '▁' added as it should
|
||
|
||
out2 = tokenizer.decode(
|
||
tokenizer.encode(" <REPR_END>inform", add_special_tokens=False), spaces_between_special_tokens=False
|
||
)
|
||
self.assertEqual(out2, "<REPR_END>inform")
|
||
|
||
input_ids = tokenizer.encode("<s> Hello<s>how", add_special_tokens=False)
|
||
self.assertEqual(input_ids, [1, 15043, 1, 3525])
|
||
tokens = tokenizer.tokenize("<s> Hello<s>how", add_special_tokens=False)
|
||
self.assertEqual(tokens, ["<s>", "▁Hello", "<s>", "how"])
|
||
decoded_tokens = tokenizer.decode(input_ids)
|
||
self.assertEqual(decoded_tokens, "<s> Hello<s>how")
|
||
|
||
# Let's make sure that if there are any spaces, we don't remove them!
|
||
input_ids = tokenizer.encode(" <s> Hello<s> how", add_special_tokens=False)
|
||
self.assertEqual(input_ids, [29871, 1, 15043, 1, 920])
|
||
tokens = tokenizer.tokenize(" <s> Hello<s> how", add_special_tokens=False)
|
||
self.assertEqual(tokens, ["▁", "<s>", "▁Hello", "<s>", "▁how"])
|
||
decoded_tokens = tokenizer.decode(input_ids)
|
||
self.assertEqual(decoded_tokens, " <s> Hello<s> how")
|
||
|
||
# Let's make sure the space is preserved
|
||
input_ids = tokenizer.encode("hello", add_special_tokens=True)
|
||
self.assertEqual(input_ids, [1, 12199])
|
||
tokens = tokenizer.tokenize("hello")
|
||
self.assertEqual(tokens, ["hello"])
|
||
decoded_tokens = tokenizer.decode(input_ids)
|
||
self.assertEqual(decoded_tokens, "<s>hello")
|
||
|
||
input_ids = tokenizer.encode("hello", add_special_tokens=False)
|
||
self.assertEqual(input_ids, [12199])
|
||
decoded_tokens = tokenizer.decode(input_ids)
|
||
self.assertEqual(decoded_tokens, "hello")
|
||
|
||
def test_some_edge_cases(self):
|
||
tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", legacy=False)
|
||
|
||
sp_tokens = tokenizer.sp_model.encode("<s>>", out_type=str)
|
||
self.assertEqual(sp_tokens, ["<", "s", ">>"])
|
||
tokens = tokenizer.tokenize("<s>>")
|
||
self.assertNotEqual(sp_tokens, tokens)
|
||
self.assertEqual(tokens, ["<s>", ">"])
|
||
|
||
tokens = tokenizer.tokenize("")
|
||
self.assertEqual(tokens, [])
|
||
self.assertEqual(tokens, tokenizer.sp_model.encode("", out_type=str))
|
||
|
||
tokens = tokenizer.tokenize(" ")
|
||
self.assertEqual(tokens, ["▁▁"])
|
||
# a dummy prefix space is not added by the sp_model as it was de-activated
|
||
self.assertEqual(tokens, tokenizer.sp_model.encode(" ", out_type=str))
|
||
|
||
tokens = tokenizer.tokenize("▁")
|
||
self.assertEqual(tokens, ["▁▁"])
|
||
# a dummy prefix space is not added by the sp_model as it was de-activated
|
||
self.assertEqual(tokens, tokenizer.sp_model.encode("▁▁", out_type=str))
|
||
|
||
tokens = tokenizer.tokenize(" ▁")
|
||
self.assertEqual(tokens, ["▁▁▁"])
|
||
# a dummy prefix space is not added by the sp_model as it was de-activated
|
||
self.assertEqual(tokens, tokenizer.sp_model.encode("▁▁▁", out_type=str))
|
||
|
||
def test_fast_post_processor(self):
|
||
tokenizer = LlamaTokenizerFast(
|
||
SAMPLE_VOCAB, eos_token=None, bos_token=None, add_bos_token=False, add_eos_token=False
|
||
)
|
||
tokenizer.encode(" Hey ")
|
||
|
||
with self.assertRaises(ValueError):
|
||
tokenizer = LlamaTokenizerFast(
|
||
SAMPLE_VOCAB, bos_token=None, eos_token="<s>", add_bos_token=True, add_eos_token=False
|
||
)
|
||
with self.assertRaises(ValueError):
|
||
tokenizer = LlamaTokenizerFast(SAMPLE_VOCAB, eos_token=None, add_bos_token=True, add_eos_token=True)
|
||
|
||
@require_jinja
|
||
def test_tokenization_for_chat(self):
|
||
tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", legacy=False)
|
||
|
||
test_chats = [
|
||
[{"role": "system", "content": "You are a helpful chatbot."}, {"role": "user", "content": "Hello!"}],
|
||
[
|
||
{"role": "system", "content": "You are a helpful chatbot."},
|
||
{"role": "user", "content": "Hello!"},
|
||
{"role": "assistant", "content": "Nice to meet you."},
|
||
],
|
||
[{"role": "user", "content": "Hello!"}],
|
||
]
|
||
# Matt: The third test case tests the default system message, but if this is ever changed in the
|
||
# class/repo code then that test will fail, and the case will need to be updated.
|
||
tokenized_chats = [tokenizer.apply_chat_template(test_chat) for test_chat in test_chats]
|
||
# fmt: off
|
||
expected_tokens = [
|
||
[1, 29961, 25580, 29962, 3532, 14816, 29903, 6778, 13, 3492, 526, 263, 8444, 13563, 7451, 29889, 13, 29966, 829, 14816, 29903, 6778, 13, 13, 10994, 29991, 518, 29914, 25580, 29962],
|
||
[1, 29961, 25580, 29962, 3532, 14816, 29903, 6778, 13, 3492, 526, 263, 8444, 13563, 7451, 29889, 13, 29966, 829, 14816, 29903, 6778, 13, 13, 10994, 29991, 518, 29914, 25580, 29962, 20103, 304, 5870, 366, 29889, 29871, 2],
|
||
[1, 29961, 25580, 29962, 15043, 29991, 518, 29914, 25580, 29962]
|
||
]
|
||
# fmt: on
|
||
for tokenized_chat, expected_tokens in zip(tokenized_chats, expected_tokens):
|
||
self.assertListEqual(tokenized_chat, expected_tokens)
|
||
|
||
|
||
@require_sentencepiece
|
||
@require_tokenizers
|
||
class CommonSpmIntegrationTests(unittest.TestCase):
|
||
"""
|
||
A class that regroups important test to make sure that we properly handle the special tokens.
|
||
"""
|
||
|
||
@classmethod
|
||
def setUpClass(cls):
|
||
tokenizer = LlamaTokenizer(SAMPLE_VOCAB, extra_ids=0, add_bos_token=False, legacy=False)
|
||
tokenizer.add_special_tokens({"additional_special_tokens": [AddedToken("<s>", rstrip=False, lstrip=False)]})
|
||
cls.tokenizer = tokenizer
|
||
return cls
|
||
|
||
def test_add_dummy_prefix(self):
|
||
# make sure `'▁'` is prepended, and outputs match sp_model's
|
||
# `sentencepiece.NormalizerSpec.add_dummy_prefix` attribute
|
||
input_ids = self.tokenizer.encode(". Hello")
|
||
self.assertEqual(input_ids, [7, 4, 156, 86, 20])
|
||
sp_encode = self.tokenizer.sp_model.encode(". Hello")
|
||
self.assertEqual(input_ids, [7] + sp_encode)
|
||
tokens = self.tokenizer.tokenize(". Hello")
|
||
self.assertEqual(tokens, ["▁", ".", "▁He", "ll", "o"])
|
||
|
||
tokens = self.tokenizer.tokenize("")
|
||
self.assertEqual(tokens, [])
|
||
self.assertEqual(tokens, self.tokenizer.sp_model.encode("", out_type=str))
|
||
|
||
tokens = self.tokenizer.tokenize(" ")
|
||
self.assertEqual(tokens, [])
|
||
self.assertEqual(tokens, self.tokenizer.sp_model.encode(" ", out_type=str))
|
||
|
||
tokens = self.tokenizer.tokenize("▁")
|
||
self.assertEqual(tokens, [])
|
||
self.assertEqual(tokens, self.tokenizer.sp_model.encode("▁", out_type=str))
|
||
|
||
def test_remove_extra_whitespaces(self):
|
||
# make sure the extra spaces are eaten. Since the sample vocab does not have
|
||
# `______`. sentencepiece.NormalizerSpec.remove_extra_whitespaces attribute is set to False
|
||
|
||
input_ids = self.tokenizer.encode(" . Hello")
|
||
self.assertEqual(input_ids, [7, 4, 156, 86, 20])
|
||
sp_encode = self.tokenizer.sp_model.encode(" . Hello")
|
||
self.assertEqual(input_ids, [7] + sp_encode)
|
||
tokens = self.tokenizer.tokenize(" . Hello")
|
||
self.assertEqual(tokens, ["▁", ".", "▁He", "ll", "o"])
|
||
|
||
# `'▁'` is also a whitespace
|
||
input_ids = self.tokenizer.encode("▁He is not")
|
||
self.assertEqual(input_ids, [156, 46, 44])
|
||
tokens = self.tokenizer.tokenize("▁He is not")
|
||
sp_encode = [
|
||
self.tokenizer.sp_model.piece_to_id("▁He"),
|
||
self.tokenizer.sp_model.piece_to_id("▁is"),
|
||
self.tokenizer.sp_model.piece_to_id("▁not"),
|
||
]
|
||
self.assertEqual(input_ids, sp_encode)
|
||
self.assertEqual(tokens, ["▁He", "▁is", "▁not"]) # no extra space added
|
||
|
||
input_ids = self.tokenizer.encode("▁He is not<s> ▁He")
|
||
self.assertEqual(input_ids, [156, 46, 44, 1, 156])
|
||
tokens = self.tokenizer.tokenize("▁He is not<s> ▁He")
|
||
self.assertEqual(tokens, ["▁He", "▁is", "▁not", "<s>", "▁He"]) # spaces are eaten by spm + our strip
|
||
# make sure that the output after the extra id is the same as if
|
||
# extra_id was not there
|
||
input_ids = self.tokenizer.encode("▁He is not ▁He")
|
||
self.assertEqual(input_ids, [156, 46, 44, 156])
|
||
tokens = self.tokenizer.tokenize("▁He is not ▁He")
|
||
self.assertEqual(tokens, ["▁He", "▁is", "▁not", "▁He"]) # spaces are eaten by spm even if not start
|
||
|
||
def test_character_after_special_token(self):
|
||
# Make sure that `tokenizer.tokenize` is similar to
|
||
# adding the equivalent special token to the vocab
|
||
input_ids = self.tokenizer.encode("Hey <s>I")
|
||
self.assertEqual(input_ids, [156, 30, 1, 100])
|
||
sp_encode = self.tokenizer.sp_model.encode("Hey .I")
|
||
# the last token should be 100
|
||
self.assertEqual(input_ids[-1], sp_encode[-1])
|
||
tokens = self.tokenizer.tokenize("<s>I")
|
||
self.assertEqual(tokens, ["<s>", "I"])
|
||
|
||
input_ids = self.tokenizer.encode("Hello, <s>,")
|
||
self.assertEqual(input_ids, [156, 86, 20, 3, 1, 3])
|
||
tokens = self.tokenizer.tokenize("Hello, <s>,")
|
||
self.assertEqual(tokens, ["▁He", "ll", "o", ",", "<s>", ","])
|
||
|
||
def test_special_tokens_strip(self):
|
||
input_ids = self.tokenizer.encode(" <s> ,")
|
||
self.assertEqual(input_ids, [1, 7, 3])
|
||
tokens = self.tokenizer.tokenize(" <s> ,")
|
||
# spaces are eaten by rstrip / lstrip + spm sp_model.encode(" ") = []
|
||
self.assertEqual(tokens, ["<s>", "▁", ","])
|
||
|
||
input_ids = self.tokenizer.encode("No <s> ▁He")
|
||
self.assertEqual(input_ids, [284, 1, 156])
|
||
tokens = self.tokenizer.tokenize("No <s> ▁He")
|
||
self.assertEqual(tokens, ["▁No", "<s>", "▁He"]) # spaces are eaten by rstrip / lstrip
|