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# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# 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 unittest
from functools import lru_cache
from transformers import ClvpTokenizer
from ...test_tokenization_common import TokenizerTesterMixin, slow, use_cache_if_possible
class ClvpTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
from_pretrained_id = "susnato/clvp_dev"
tokenizer_class = ClvpTokenizer
test_rust_tokenizer = False
from_pretrained_kwargs = {"add_prefix_space": True}
test_seq2seq = False
test_sentencepiece_ignore_case = True
@classmethod
def setUpClass(cls):
super().setUpClass()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
vocab = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
"<|endoftext|>",
"[SPACE]",
]
vocab_tokens = dict(zip(vocab, range(len(vocab))))
merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
cls.special_tokens_map = {"unk_token": "<unk>"}
cls.vocab_file = os.path.join(cls.tmpdirname, "vocab.json")
cls.merges_file = os.path.join(cls.tmpdirname, "merges.txt")
with open(cls.vocab_file, "w", encoding="utf-8") as fp:
fp.write(json.dumps(vocab_tokens) + "\n")
with open(cls.merges_file, "w", encoding="utf-8") as fp:
fp.write("\n".join(merges))
# Copied from transformers.tests.models.gpt2.test_tokenization_gpt2.GPT2TokenizationTest.get_tokenizer with GPT2->Clvp
@classmethod
@use_cache_if_possible
@lru_cache(maxsize=64)
def get_tokenizer(cls, pretrained_name=None, **kwargs):
kwargs.update(cls.special_tokens_map)
pretrained_name = pretrained_name or cls.tmpdirname
return ClvpTokenizer.from_pretrained(pretrained_name, **kwargs)
# Copied from transformers.tests.models.gpt2.test_tokenization_gpt2.GPT2TokenizationTest.get_input_output_texts
def get_input_output_texts(self, tokenizer):
input_text = "lower newer"
output_text = "lower[SPACE]newer"
return input_text, output_text
# Copied from transformers.tests.models.layoutxlm.test_tokenization_layoutxlm.LayoutXLMTokenizationTest.test_add_special_tokens
def test_add_special_tokens(self):
tokenizers: list[ClvpTokenizer] = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
special_token = "[SPECIAL_TOKEN]"
special_token_box = [1000, 1000, 1000, 1000]
tokenizer.add_special_tokens({"cls_token": special_token})
encoded_special_token = tokenizer.encode(
[special_token], boxes=[special_token_box], add_special_tokens=False
)
self.assertEqual(len(encoded_special_token), 1)
decoded = tokenizer.decode(encoded_special_token, skip_special_tokens=True)
self.assertTrue(special_token not in decoded)
# Copied from transformers.tests.models.gpt2.test_tokenization_gpt2.GPT2TokenizationTest.test_rust_and_python_full_tokenizers
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(add_prefix_space=True)
sequence = "lower newer"
# Testing tokenization
tokens = tokenizer.tokenize(sequence, add_prefix_space=True)
rust_tokens = rust_tokenizer.tokenize(sequence)
self.assertListEqual(tokens, rust_tokens)
# Testing conversion to ids without special tokens
ids = tokenizer.encode(sequence, add_special_tokens=False, add_prefix_space=True)
rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
self.assertListEqual(ids, rust_ids)
# Testing conversion to ids with special tokens
rust_tokenizer = self.get_rust_tokenizer(add_prefix_space=True)
ids = tokenizer.encode(sequence, add_prefix_space=True)
rust_ids = rust_tokenizer.encode(sequence)
self.assertListEqual(ids, rust_ids)
# Testing the unknown token
input_tokens = tokens + [rust_tokenizer.unk_token]
input_bpe_tokens = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
# Copied from transformers.tests.models.gpt2.test_tokenization_gpt2.GPT2TokenizationTest.test_padding
def test_padding(self, max_length=15):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.get_rust_tokenizer(pretrained_name, **kwargs)
# Simple input
s = "This is a simple input"
s2 = ["This is a simple input 1", "This is a simple input 2"]
p = ("This is a simple input", "This is a pair")
p2 = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(ValueError, tokenizer_r.encode, s, max_length=max_length, padding="max_length")
# Simple input
self.assertRaises(ValueError, tokenizer_r.encode_plus, s, max_length=max_length, padding="max_length")
# Simple input
self.assertRaises(
ValueError,
tokenizer_r.batch_encode_plus,
s2,
max_length=max_length,
padding="max_length",
)
# Pair input
self.assertRaises(ValueError, tokenizer_r.encode, p, max_length=max_length, padding="max_length")
# Pair input
self.assertRaises(ValueError, tokenizer_r.encode_plus, p, max_length=max_length, padding="max_length")
# Pair input
self.assertRaises(
ValueError,
tokenizer_r.batch_encode_plus,
p2,
max_length=max_length,
padding="max_length",
)
# Copied from transformers.tests.models.gpt2.test_tokenization_gpt2.GPT2TokenizationTest.test_padding_if_pad_token_set_slow
def test_padding_if_pad_token_set_slow(self):
tokenizer = ClvpTokenizer.from_pretrained(self.tmpdirname, pad_token="<pad>")
# Simple input
s = "This is a simple input"
s2 = ["This is a simple input looooooooong", "This is a simple input"]
p = ("This is a simple input", "This is a pair")
p2 = [
("This is a simple input loooooong", "This is a simple input"),
("This is a simple pair loooooong", "This is a simple pair"),
]
pad_token_id = tokenizer.pad_token_id
out_s = tokenizer(s, padding="max_length", max_length=30, return_tensors="np")
out_s2 = tokenizer(s2, padding=True, truncate=True, return_tensors="np")
out_p = tokenizer(*p, padding="max_length", max_length=60, return_tensors="np")
out_p2 = tokenizer(p2, padding=True, truncate=True, return_tensors="np")
# s
# test single string max_length padding
self.assertEqual(out_s["input_ids"].shape[-1], 30)
self.assertTrue(pad_token_id in out_s["input_ids"])
self.assertTrue(0 in out_s["attention_mask"])
# s2
# test automatic padding
self.assertEqual(out_s2["input_ids"].shape[-1], 33)
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_s2["input_ids"][0])
self.assertFalse(0 in out_s2["attention_mask"][0])
# short slice does have padding
self.assertTrue(pad_token_id in out_s2["input_ids"][1])
self.assertTrue(0 in out_s2["attention_mask"][1])
# p
# test single pair max_length padding
self.assertEqual(out_p["input_ids"].shape[-1], 60)
self.assertTrue(pad_token_id in out_p["input_ids"])
self.assertTrue(0 in out_p["attention_mask"])
# p2
# test automatic padding pair
self.assertEqual(out_p2["input_ids"].shape[-1], 52)
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_p2["input_ids"][0])
self.assertFalse(0 in out_p2["attention_mask"][0])
# short slice pair does have padding
self.assertTrue(pad_token_id in out_p2["input_ids"][1])
self.assertTrue(0 in out_p2["attention_mask"][1])
# Copied from transformers.tests.models.gpt2.test_tokenization_gpt2.GPT2TokenizationTest.test_special_tokens_mask_input_pairs_and_bos_token
def test_special_tokens_mask_input_pairs_and_bos_token(self):
# TODO: change to self.get_tokenizers() when the fast version is implemented
tokenizers = [self.get_tokenizer(do_lower_case=False, add_bos_token=True)]
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
sequence_0 = "Encode this."
sequence_1 = "This one too please."
encoded_sequence = tokenizer.encode(sequence_0, add_special_tokens=False)
encoded_sequence += tokenizer.encode(sequence_1, add_special_tokens=False)
encoded_sequence_dict = tokenizer.encode_plus(
sequence_0,
sequence_1,
add_special_tokens=True,
return_special_tokens_mask=True,
)
encoded_sequence_w_special = encoded_sequence_dict["input_ids"]
special_tokens_mask = encoded_sequence_dict["special_tokens_mask"]
self.assertEqual(len(special_tokens_mask), len(encoded_sequence_w_special))
filtered_sequence = [
(x if not special_tokens_mask[i] else None) for i, x in enumerate(encoded_sequence_w_special)
]
filtered_sequence = [x for x in filtered_sequence if x is not None]
self.assertEqual(encoded_sequence, filtered_sequence)
def test_token_type_ids(self):
tokenizer = self.get_tokenizer()
seq_0 = "Test this method."
# We want to have sequence 0 and sequence 1 are tagged
# respectively with 0 and 1 token_ids
# (regardless of whether the model use token type ids)
# We use this assumption in the QA pipeline among other place
output = tokenizer(seq_0, return_token_type_ids=True, add_special_tokens=True)
self.assertIn(0, output["token_type_ids"])
def test_full_tokenizer(self):
tokenizer = ClvpTokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map)
text = "lower newer"
bpe_tokens = ["l", "o", "w", "er", "[SPACE]", "n", "e", "w", "er"]
tokens = tokenizer.tokenize(text, add_prefix_space=False)
self.assertListEqual(tokens, bpe_tokens)
input_tokens = tokens + [tokenizer.unk_token]
input_bpe_tokens = [0, 1, 2, 15, 21, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
@slow
def test_outputs_with_numbers(self):
text = "hello and this is an example text and I have $1000. my lucky number is 12345."
tokenizer = ClvpTokenizer.from_pretrained("susnato/clvp_dev")
# fmt: off
EXPECTED_OUTPUT = [62, 84, 28, 2, 53, 2,147, 2, 54, 2, 43, 2, 169, 122, 29, 64, 2, 136, 37, 33, 2, 53, 2, 22,
2, 148, 2, 110, 2, 40, 206, 53, 2, 134, 84, 59, 32, 9, 2, 125, 2, 25, 34, 197, 38, 2, 27,
231, 15, 44, 2, 54, 2, 33, 100, 25, 76, 2, 40, 206, 53, 7, 2, 40, 46, 18, 2, 21, 97, 17,
219, 2, 87, 210, 8, 19, 22, 76, 9,
]
# fmt: on
self.assertListEqual(tokenizer.encode(text, add_special_tokens=False), EXPECTED_OUTPUT)
@slow
def test_tokenizer_integration(self):
sequences = [
"Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
"general-purpose architectures (BERT, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
"Language Understanding (NLU) and Natural Language Generation (NLG) with over multiple pretrained "
"models and deep interoperability between Jax, PyTorch and TensorFlow.",
"BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
"conditioning on both left and right context in all layers.",
"The quick brown fox jumps over the lazy dog.",
]
# fmt: off
expected_encoding = {'input_ids': [[144, 43, 32, 87, 26, 173, 2, 5, 87, 26, 44, 70, 2, 209, 27, 2, 55, 2, 29, 38, 51, 31, 71, 8, 144, 43, 32, 87, 26, 173, 2, 53, 2, 29, 38, 51, 31, 71, 8, 29, 46, 144, 137, 49, 8, 15, 44, 33, 6, 2, 187, 35, 83, 61, 2, 20, 50, 44, 56, 8, 29, 121, 139, 66, 2, 59, 71, 60, 18, 16, 33, 34, 175, 2, 5, 15, 44, 33, 7, 2, 89, 15, 44, 33, 14, 7, 2, 37, 25, 26, 7, 2, 17, 54, 78, 25, 15, 44, 33, 7, 2, 37, 25, 111, 33, 9, 9, 9, 6, 2, 87, 2, 27, 48, 121, 56, 2, 25, 43, 20, 34, 14, 112, 2, 97, 234, 63, 53, 52, 2, 5, 27, 25, 34, 6, 2, 53, 2, 27, 48, 121, 56, 2, 25, 43, 20, 34, 14, 112, 2, 20, 50, 44, 158, 2, 5, 27, 25, 20, 6, 2, 103, 2, 253, 2, 26, 167, 78, 29, 64, 2, 29, 46, 144, 137, 49, 2, 115, 126, 25, 32, 2, 53, 2, 126, 18, 29, 2, 41, 114, 161, 44, 109, 151, 240, 2, 67, 33, 100, 50, 2, 23, 14, 37, 7, 2, 29, 38, 51, 31, 71, 2, 53, 2, 33, 50, 32, 57, 19, 25, 69, 9], [ 15, 44, 33, 2, 54, 2, 17, 61, 22, 20, 27, 49, 2, 51, 2, 29, 46, 8, 144, 137, 2, 126, 18, 29, 2, 15, 83, 22, 46, 16, 181, 56, 2, 46, 29, 175, 86, 158, 32, 2, 154, 2, 97, 25, 14, 67, 25, 49, 2, 136, 37, 33, 2, 185, 2, 23, 28, 41, 33, 70, 2, 135, 17, 60, 107, 52, 2, 47, 2, 165, 40, 2, 64, 19, 33, 2, 53, 2, 101, 104, 2, 135, 136, 37, 33, 2, 41, 2, 108, 2, 25, 88, 173, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 42, 2, 194, 91, 24, 2, 243, 190, 2, 182, 37, 2, 23, 231, 29, 32, 2, 253, 2, 42, 2, 25, 14, 39, 38, 2, 134, 20, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], # noqa: E501
'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, 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, 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, 0, 0, 0, 0, 0, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], # noqa: E501
}
# fmt: on
self.tokenizer_integration_test_util(
sequences=sequences, expected_encoding=expected_encoding, model_name="susnato/clvp_dev", padding=True
)
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