# coding=utf-8
# Copyright 2018 Google T5 Authors and HuggingFace Inc. team.
#
# 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 json
import os
import re
import tempfile
import unittest
from functools import lru_cache
from transformers import SPIECE_UNDERLINE, AddedToken, BatchEncoding, T5Tokenizer, T5TokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_seqio, require_tokenizers, slow
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin, use_cache_if_possible
SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
if is_torch_available():
FRAMEWORK = "pt"
elif is_tf_available():
FRAMEWORK = "tf"
else:
FRAMEWORK = "jax"
@require_sentencepiece
@require_tokenizers
class T5TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
from_pretrained_id = "google-t5/t5-small"
tokenizer_class = T5Tokenizer
rust_tokenizer_class = T5TokenizerFast
test_rust_tokenizer = True
test_sentencepiece = True
@classmethod
def setUpClass(cls):
super().setUpClass()
# We have a SentencePiece fixture for testing
tokenizer = T5Tokenizer(SAMPLE_VOCAB)
tokenizer.save_pretrained(cls.tmpdirname)
def test_convert_token_and_id(self):
"""Test ``_convert_token_to_id`` and ``_convert_id_to_token``."""
token = ""
token_id = 1
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], "")
self.assertEqual(vocab_keys[1], "")
self.assertEqual(vocab_keys[1100], "")
self.assertEqual(len(vocab_keys), 1_101)
def test_vocab_size(self):
self.assertEqual(self.get_tokenizer().vocab_size, 1000)
self.assertEqual(len(self.get_tokenizer()), 1101)
def test_full_tokenizer(self):
tokenizer = T5Tokenizer(SAMPLE_VOCAB)
tokens = tokenizer.tokenize("This is a test")
self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [285, 46, 10, 170, 382])
tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
self.assertListEqual(
tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
],
)
ids = tokenizer.convert_tokens_to_ids(tokens)
self.assertListEqual(ids, [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4])
back_tokens = tokenizer.convert_ids_to_tokens(ids)
self.assertListEqual(
back_tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"",
".",
],
)
@cached_property
def t5_base_tokenizer(self):
return T5Tokenizer.from_pretrained("google-t5/t5-base")
@cached_property
def t5_base_tokenizer_fast(self):
return T5TokenizerFast.from_pretrained("google-t5/t5-base")
@classmethod
@use_cache_if_possible
@lru_cache(maxsize=64)
def get_tokenizer(cls, pretrained_name=None, **kwargs) -> T5Tokenizer:
pretrained_name = pretrained_name or cls.tmpdirname
return cls.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
@classmethod
@use_cache_if_possible
@lru_cache(maxsize=64)
def get_rust_tokenizer(cls, pretrained_name=None, **kwargs) -> T5TokenizerFast:
pretrained_name = pretrained_name or cls.tmpdirname
return cls.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
def test_rust_and_python_full_tokenizers(self):
if not self.test_rust_tokenizer:
self.skipTest(reason="test_rust_tokenizer is set to False")
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_eos_treatment(self):
tokenizer = self.t5_base_tokenizer
batch_with_eos_added = tokenizer(["hi", "I went to the gym", ""])
batch_without_eos_added = tokenizer(["hi", "I went to the gym", ""])
self.assertListEqual(batch_with_eos_added["input_ids"], batch_without_eos_added["input_ids"])
def test_prepare_batch(self):
tokenizer = self.t5_base_tokenizer
src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
expected_src_tokens = [71, 307, 8986, 21, 4505, 1635, 1707, 5, tokenizer.eos_token_id]
batch = tokenizer(src_text, padding=True, return_tensors=FRAMEWORK)
self.assertIsInstance(batch, BatchEncoding)
if FRAMEWORK != "jax":
result = list(batch.input_ids.numpy()[0])
else:
result = list(batch.input_ids.tolist()[0])
self.assertListEqual(expected_src_tokens, result)
self.assertEqual((2, 9), batch.input_ids.shape)
self.assertEqual((2, 9), batch.attention_mask.shape)
def test_empty_target_text(self):
tokenizer = self.t5_base_tokenizer
src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
batch = tokenizer(src_text, padding=True, return_tensors=FRAMEWORK)
# check if input_ids are returned and no decoder_input_ids
self.assertIn("input_ids", batch)
self.assertIn("attention_mask", batch)
self.assertNotIn("decoder_input_ids", batch)
self.assertNotIn("decoder_attention_mask", batch)
def test_max_length(self):
tokenizer = self.t5_base_tokenizer
tgt_text = [
"Summary of the text.",
"Another summary.",
]
targets = tokenizer(
text_target=tgt_text, max_length=32, padding="max_length", truncation=True, return_tensors=FRAMEWORK
)
self.assertEqual(32, targets["input_ids"].shape[1])
def test_outputs_not_longer_than_maxlen(self):
tokenizer = self.t5_base_tokenizer
batch = tokenizer(
["I am a small frog" * 1000, "I am a small frog"], padding=True, truncation=True, return_tensors=FRAMEWORK
)
self.assertIsInstance(batch, BatchEncoding)
# Since T5 does NOT have a max input length,
# this test should be changed to the following in Transformers v5:
# self.assertEqual(batch.input_ids.shape, (2, 8001))
self.assertEqual(batch.input_ids.shape, (2, 8001))
def test_eos_in_input(self):
tokenizer = self.t5_base_tokenizer
src_text = ["A long paragraph for summarization. "]
tgt_text = ["Summary of the text. "]
expected_src_tokens = [71, 307, 8986, 21, 4505, 1635, 1707, 5, 1]
expected_tgt_tokens = [20698, 13, 8, 1499, 5, 1]
batch = tokenizer(src_text, text_target=tgt_text)
self.assertEqual(expected_src_tokens, batch["input_ids"][0])
self.assertEqual(expected_tgt_tokens, batch["labels"][0])
def test_token_type_ids(self):
src_text_1 = ["A first paragraph for summarization."]
src_text_2 = ["A second paragraph for summarization."]
fast_token_type_ids = self.t5_base_tokenizer_fast(
src_text_1, src_text_2, add_special_tokens=True, return_token_type_ids=True
).token_type_ids
slow_token_type_ids = self.t5_base_tokenizer(
src_text_1, src_text_2, add_special_tokens=True, return_token_type_ids=True
).token_type_ids
self.assertEqual(slow_token_type_ids, fast_token_type_ids)
self.assertEqual(len(slow_token_type_ids[0]), 18)
def test_fast_and_slow_same_result(self):
src_text = " Today is nice day "
tgt_ids = [0, 1960, 19, 2, 1245, 239, 1]
tgt_text = " Today is nice day"
fast_ids = self.t5_base_tokenizer_fast(src_text, add_special_tokens=False).input_ids
slow_ids = self.t5_base_tokenizer(src_text, add_special_tokens=False).input_ids
self.assertEqual(tgt_ids, fast_ids)
self.assertEqual(tgt_ids, slow_ids)
fast_text = self.t5_base_tokenizer_fast.decode(fast_ids)
slow_text = self.t5_base_tokenizer.decode(fast_ids)
self.assertEqual(tgt_text, fast_text)
self.assertEqual(tgt_text, slow_text)
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 = [f"" for i in range(100)] + [AddedToken("", lstrip=True)]
tokenizer_r = self.get_rust_tokenizer(
pretrained_name, additional_special_tokens=added_tokens, **kwargs
)
tokenizer_cr = self.get_rust_tokenizer(
pretrained_name, additional_special_tokens=added_tokens, **kwargs, from_slow=True
)
tokenizer_p = self.tokenizer_class.from_pretrained(
pretrained_name, additional_special_tokens=added_tokens, **kwargs
)
p_output = tokenizer_p.encode("Hey this is a token")
r_output = tokenizer_r.encode("Hey this is a token")
cr_output = tokenizer_cr.encode("Hey this is a token")
special_token_id = tokenizer_r.encode("", add_special_tokens=False)[0]
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 r_output)
self.assertTrue(special_token_id in cr_output)
def test_special_tokens_initialization_with_non_empty_additional_special_tokens(self):
tokenizer_list = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(tmp_dir)
with open(os.path.join(tmp_dir, "special_tokens_map.json"), encoding="utf-8") as json_file:
special_tokens_map = json.load(json_file)
with open(os.path.join(tmp_dir, "tokenizer_config.json"), encoding="utf-8") as json_file:
tokenizer_config = json.load(json_file)
added_tokens_extra_ids = [f"" for i in range(100)]
special_tokens_map["additional_special_tokens"] = added_tokens_extra_ids + [
"an_additional_special_token"
]
tokenizer_config["additional_special_tokens"] = added_tokens_extra_ids + [
"an_additional_special_token"
]
with open(os.path.join(tmp_dir, "special_tokens_map.json"), "w", encoding="utf-8") as outfile:
json.dump(special_tokens_map, outfile)
with open(os.path.join(tmp_dir, "tokenizer_config.json"), "w", encoding="utf-8") as outfile:
json.dump(tokenizer_config, outfile)
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
tokenizer_without_change_in_init = tokenizer_class.from_pretrained(
tmp_dir,
)
self.assertIn(
"an_additional_special_token", tokenizer_without_change_in_init.additional_special_tokens
)
# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
["an_additional_special_token"],
tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(["an_additional_special_token"])
),
)
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
new_added_tokens = added_tokens_extra_ids + [AddedToken("a_new_additional_special_token", lstrip=True)]
tokenizer = tokenizer_class.from_pretrained(
tmp_dir,
additional_special_tokens=new_added_tokens,
)
self.assertIn("a_new_additional_special_token", tokenizer.additional_special_tokens)
self.assertEqual(
["a_new_additional_special_token"],
tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(["a_new_additional_special_token"])
),
)
# overwritten from `test_tokenization_common` since T5 has no max length
@slow
def test_tokenizer_integration(self):
expected_encoding = {'input_ids': [[31220, 7, 41, 14034, 801, 38, 3, 102, 63, 17, 127, 524, 18, 7031, 2032, 277, 11, 3, 102, 63, 17, 127, 524, 18, 2026, 17, 10761, 18, 7041, 61, 795, 879, 18, 19681, 4648, 7, 41, 12920, 382, 6, 350, 6383, 4949, 6, 2158, 12920, 382, 9, 6, 3, 4, 11160, 6, 2043, 17153, 279, 49, 17, 6, 3, 4, 434, 9688, 11439, 21, 6869, 10509, 17725, 41, 567, 9138, 61, 11, 6869, 10509, 11946, 41, 18207, 517, 61, 28, 147, 3538, 1220, 7140, 10761, 2250, 16, 910, 1220, 8024, 11, 1659, 1413, 32, 883, 2020, 344, 2215, 226, 6, 12901, 382, 127, 524, 11, 4738, 7, 127, 15390, 5, 1], [272, 24203, 19, 876, 12, 554, 18, 9719, 1659, 2647, 26352, 6497, 7, 45, 73, 9339, 400, 26, 1499, 57, 22801, 10760, 30, 321, 646, 11, 269, 2625, 16, 66, 7500, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [37, 1704, 4216, 3, 20400, 4418, 7, 147, 8, 19743, 1782, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip
self.tokenizer_integration_test_util(
expected_encoding=expected_encoding,
model_name="google-t5/t5-base",
revision="5a7ff2d8f5117c194c7e32ec1ccbf04642cca99b",
)
def test_get_sentinel_tokens(self):
tokenizer = T5Tokenizer(SAMPLE_VOCAB, extra_ids=10)
sentinel_tokens = tokenizer.get_sentinel_tokens()
self.assertEqual(len(sentinel_tokens), 10)
self.assertListEqual(sorted(sentinel_tokens), sorted([f"" for i in range(0, 10)]))
self.assertTrue([re.search(r"", token) is not None for token in sentinel_tokens])
def test_get_sentinel_token_ids(self):
tokenizer = T5Tokenizer(SAMPLE_VOCAB, extra_ids=10)
self.assertListEqual(sorted(tokenizer.get_sentinel_token_ids()), sorted(range(1000, 1010)))
def test_get_sentinel_tokens_for_fasttokenizer(self):
tokenizer = T5TokenizerFast(SAMPLE_VOCAB, extra_ids=10)
sentinel_tokens = tokenizer.get_sentinel_tokens()
self.assertEqual(len(sentinel_tokens), 10)
self.assertListEqual(sorted(sentinel_tokens), sorted([f"" for i in range(0, 10)]))
self.assertTrue([re.search(r"", token) is not None for token in sentinel_tokens])
def test_get_sentinel_token_ids_for_fasttokenizer(self):
tokenizer = T5TokenizerFast(SAMPLE_VOCAB, extra_ids=10)
self.assertListEqual(sorted(tokenizer.get_sentinel_token_ids()), sorted(range(1000, 1010)))
def test_some_edge_cases(self):
tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-base", legacy=False)
sp_tokens = tokenizer.sp_model.encode(">", out_type=str)
self.assertEqual(sp_tokens, ["<", "/", "s", ">", ">"])
tokens = tokenizer.tokenize(">")
self.assertNotEqual(sp_tokens, tokens)
self.assertEqual(tokens, ["", ">"])
tokens = tokenizer.tokenize("")
self.assertEqual(tokens, [])
self.assertEqual(tokens, tokenizer.sp_model.encode("", out_type=str))
tokens = tokenizer.tokenize(" ")
self.assertEqual(tokens, [])
self.assertEqual(tokens, tokenizer.sp_model.encode(" ", out_type=str))
tokens = tokenizer.tokenize("▁")
self.assertEqual(tokens, [])
self.assertEqual(tokens, tokenizer.sp_model.encode("▁", out_type=str))
tokens = tokenizer.tokenize(" ▁")
self.assertEqual(tokens, [])
self.assertEqual(tokens, tokenizer.sp_model.encode("▁", out_type=str))
def test_fast_slow_edge_cases(self):
# We are testing spaces before and spaces after special tokens + space transformations
slow_tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-base", legacy=False)
fast_tokenizer = T5TokenizerFast.from_pretrained("google-t5/t5-base", legacy=False, from_slow=True)
slow_tokenizer.add_tokens(AddedToken("", rstrip=False, lstrip=False, normalized=False))
fast_tokenizer.add_tokens(AddedToken("", rstrip=False, lstrip=False, normalized=False))
edge_case = "Hey!. HowHey !"
EXPECTED_SLOW = ["▁Hey", "!", "", ".", "▁How", "", "He", "y", "", "!"] # fmt: skip
with self.subTest(f"slow {edge_case} normalized = False"):
self.assertEqual(slow_tokenizer.tokenize(edge_case), EXPECTED_SLOW)
with self.subTest(f"Fast {edge_case} normalized = False"):
self.assertEqual(fast_tokenizer.tokenize(edge_case), EXPECTED_SLOW)
hard_case = "Hey! . How Hey ! . "
EXPECTED_SLOW = ["▁Hey", "!", "", ".", "▁How", "", "▁Hey", "", "▁", "!", "▁", "."] # fmt: skip
with self.subTest(f"slow {edge_case} normalized = False"):
self.assertEqual(slow_tokenizer.tokenize(hard_case), EXPECTED_SLOW)
with self.subTest(f"fast {edge_case} normalized = False"):
self.assertEqual(fast_tokenizer.tokenize(hard_case), EXPECTED_SLOW)
fast_tokenizer = T5TokenizerFast.from_pretrained("google-t5/t5-base", legacy=False, from_slow=True)
fast_tokenizer.add_tokens(AddedToken("", rstrip=False, lstrip=False, normalized=True))
# `normalized=True` is the default normalization scheme when adding a token. Normalize -> don't strip the space.
# the issue now is that our slow tokenizer should NOT strip the space if we want to simulate sentencepiece token addition.
EXPECTED_FAST = ["▁Hey", "!", "", ".", "▁How", "", "He", "y", "▁", "", "!"] # fmt: skip
with self.subTest(f"fast {edge_case} normalized = True"):
self.assertEqual(fast_tokenizer.tokenize(edge_case), EXPECTED_FAST)
EXPECTED_FAST = ['▁Hey', '!', '▁', '', '.', '▁How', '', '▁Hey','▁', '', '▁', '!', '▁', '.'] # fmt: skip
with self.subTest(f"fast {edge_case} normalized = False"):
self.assertEqual(fast_tokenizer.tokenize(hard_case), EXPECTED_FAST)
def test_add_prefix_space(self):
pretrained_name = "google-t5/t5-base"
inputs = "Hey how are you doing"
EXPECTED_WITH_SPACE = [9459, 149, 33, 25, 692, 1]
EXPECTED_WO_SPACE = [3845, 63, 149, 33, 25, 692, 1]
slow_ = self.get_tokenizer(pretrained_name, add_prefix_space=False, legacy=False)
fast_ = self.get_rust_tokenizer(pretrained_name, add_prefix_space=False, legacy=False, from_slow=True)
self.assertEqual(slow_.encode(inputs), EXPECTED_WO_SPACE)
self.assertEqual(slow_.encode(inputs), fast_.encode(inputs))
self.assertEqual(slow_.tokenize(inputs), ["He", "y", "▁how", "▁are", "▁you", "▁doing"])
self.assertEqual(slow_.decode(EXPECTED_WO_SPACE, skip_special_tokens=True), inputs)
self.assertEqual(
slow_.decode(EXPECTED_WO_SPACE, skip_special_tokens=True),
fast_.decode(EXPECTED_WO_SPACE, skip_special_tokens=True),
)
slow_ = self.get_tokenizer(pretrained_name, add_prefix_space=True, legacy=False)
fast_ = self.get_rust_tokenizer(pretrained_name, add_prefix_space=True, legacy=False)
self.assertEqual(slow_.encode(inputs), EXPECTED_WITH_SPACE)
self.assertEqual(slow_.encode(inputs), fast_.encode(inputs))
self.assertEqual(slow_.tokenize(inputs), ["▁Hey", "▁how", "▁are", "▁you", "▁doing"])
self.assertEqual(slow_.decode(EXPECTED_WITH_SPACE, skip_special_tokens=True), inputs)
self.assertEqual(
slow_.decode(EXPECTED_WITH_SPACE, skip_special_tokens=True),
fast_.decode(EXPECTED_WITH_SPACE, skip_special_tokens=True),
)
@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 = T5Tokenizer(SAMPLE_VOCAB, extra_ids=0, legacy=False)
tokenizer.add_special_tokens(
{"additional_special_tokens": [AddedToken("", rstrip=False, lstrip=False)]}
)
# TODO ArthurZ the above is necessary as addedTokens / initialization sucks. Trie is not correctly created
# So the extra ids are split....
cls.tokenizer = tokenizer
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", add_special_tokens=False)
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
# sentencepiece.NormalizerSpec.remove_extra_whitespaces attribute
input_ids = self.tokenizer.encode(" . Hello", add_special_tokens=False)
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, 2])
tokens = self.tokenizer.tokenize("▁He is not")
self.assertEqual(tokens, ["▁He", "▁is", "▁not"]) # no extra space added
input_ids = self.tokenizer.encode("▁He is not ▁He")
# here t5x does not eat with lstrip, so there is and extra ▁He in the original one
self.assertEqual(input_ids, [156, 46, 44, 1001, 156, 2])
tokens = self.tokenizer.tokenize("▁He is not ▁He")
self.assertEqual(tokens, ["▁He", "▁is", "▁not", "", "▁He"]) # spaces are eaten by spm
# 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, 2])
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 I")
self.assertEqual(input_ids, [156, 30, 1001, 100, 2])
tokens = self.tokenizer.tokenize("Hey I")
self.assertEqual(tokens, ["▁He", "y", "", "I"])
input_ids = self.tokenizer.encode("Hello, ,")
self.assertEqual(input_ids, [156, 86, 20, 3, 1001, 3, 2])
tokens = self.tokenizer.tokenize("Hello, ,")
self.assertEqual(tokens, ["▁He", "ll", "o", ",", "", ","])
def test_special_tokens_strip(self):
input_ids = self.tokenizer.encode(" ,")
self.assertEqual(input_ids, [1001, 7, 3, 2])
tokens = self.tokenizer.tokenize(" ,")
# spaces are not longer eaten by rstrip and lstrip
self.assertEqual(tokens, ["", "▁", ","])
# test with a begin of word like `▁He`
input_ids = self.tokenizer.encode("No He")
self.assertEqual(input_ids, [284, 1001, 156, 2])
# spaces are eaten by rstrip / lstrip, so this is expected. Don't strip otherwise you break
tokens = self.tokenizer.tokenize("No He")
self.assertEqual(tokens, ["▁No", "", "▁He"])
# Make sure this does not happen if we don't strip
tokenizer = T5Tokenizer(SAMPLE_VOCAB, extra_ids=0)
tokenizer.add_special_tokens({"bos_token": AddedToken("")})
input_ids = tokenizer.encode("No He")
self.assertEqual(input_ids, [284, 1001, 156, 2])
tokens = tokenizer.tokenize("No He")
# the first `' '` after `'No'` is eaten by spm:
self.assertEqual(tokenizer.sp_model.encode("No ", out_type=str), ["▁No"])
self.assertEqual(tokens, ["▁No", "", "▁He"])
@require_seqio
@unittest.skipIf(
os.getenv("RUN_TOKENIZER_INTEGRATION", "0") == "0",
"RUN_TOKENIZER_INTEGRATION=1 to run tokenizer integration tests",
)
def test_integration_seqio(self):
from datasets import load_dataset
from seqio import SentencePieceVocabulary
ds = load_dataset("facebook/xnli", "all_languages", split="train+test+validation")
# TODO @ArthurZucker fix the 3 commented tests with #23909
input_texts = [
"Bonjour .",
# "Bonjour.", # this will fail. In T5 the special token has to be at the end.
# because in T5 they add `_` to the vocab, not ``.
" Hey I love you",
# "Hey I love you", # this will fail, we strip left, to _I vs I
# "Hey ▁He", # this will fail for the same reason, we replace `_` then strip
]
import tqdm
# Test with umt5
vocab_path = "gs://t5-data/vocabs/umt5.256000/sentencepiece.model"
t5x_tokenizer = SentencePieceVocabulary(vocab_path, extra_ids=300)
hf_tokenizer = T5Tokenizer.from_pretrained("google/umt5-small", legacy=False)
for text in input_texts:
self.assertEqual(
hf_tokenizer.encode(text, add_special_tokens=False), t5x_tokenizer.tokenizer.tokenize(text), f"{text}"
)
for texts in tqdm.tqdm(ds["premise"]):
for text in texts:
self.assertEqual(
hf_tokenizer.encode(text, add_special_tokens=False),
t5x_tokenizer.tokenizer.tokenize(text),
f"{text}",
)
# Test with T5
hf_tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-small")
vocab_path = "gs://t5-data/vocabs/cc_all.32000/sentencepiece.model"
t5x_tokenizer = SentencePieceVocabulary(vocab_path, extra_ids=300)
for text in input_texts:
self.assertEqual(
hf_tokenizer.encode(text, add_special_tokens=False), t5x_tokenizer.tokenizer.tokenize(text), f"{text}"
)
for texts in tqdm.tqdm(ds["premise"]):
for text in texts:
self.assertEqual(
hf_tokenizer.encode(text, add_special_tokens=False),
t5x_tokenizer.tokenizer.tokenize(text),
f"{text}",
)