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Use lru_cache for tokenization tests (#36818)

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* fix

* fix

* fix

* fix

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
2025-03-28 15:09:35 +01:00
committed by GitHub
parent 3af425d4c6
commit 1fcaad6df9
92 changed files with 1301 additions and 884 deletions
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43
tests/models/led/test_tokenization_led.py
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@@ -14,13 +14,14 @@
import json
import os
import unittest
from functools import lru_cache
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
from ...test_tokenization_common import TokenizerTesterMixin, use_cache_if_possible
@require_tokenizers
@@ -30,8 +31,10 @@ class TestTokenizationLED(TokenizerTesterMixin, unittest.TestCase):
rust_tokenizer_class = LEDTokenizerFast
test_rust_tokenizer = True
def setUp(self):
super().setUp()
@classmethod
def setUpClass(cls):
super().setUpClass()
vocab = [
"l",
"o",
@@ -56,22 +59,30 @@ class TestTokenizationLED(TokenizerTesterMixin, unittest.TestCase):
]
vocab_tokens = dict(zip(vocab, range(len(vocab))))
merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
self.special_tokens_map = {"unk_token": "<unk>"}
cls.special_tokens_map = {"unk_token": "<unk>"}
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
with open(self.vocab_file, "w", encoding="utf-8") as fp:
cls.vocab_file = os.path.join(cls.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
cls.merges_file = os.path.join(cls.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
with open(cls.vocab_file, "w", encoding="utf-8") as fp:
fp.write(json.dumps(vocab_tokens) + "\n")
with open(self.merges_file, "w", encoding="utf-8") as fp:
with open(cls.merges_file, "w", encoding="utf-8") as fp:
fp.write("\n".join(merges))
def get_tokenizer(self, **kwargs):
kwargs.update(self.special_tokens_map)
return self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)
@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 cls.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
def get_rust_tokenizer(self, **kwargs):
kwargs.update(self.special_tokens_map)
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)
@classmethod
@use_cache_if_possible
@lru_cache(maxsize=64)
def get_rust_tokenizer(cls, pretrained_name=None, **kwargs):
kwargs.update(cls.special_tokens_map)
pretrained_name = pretrained_name or cls.tmpdirname
return cls.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
def get_input_output_texts(self, tokenizer):
return "lower newer", "lower newer"
@@ -161,8 +172,8 @@ class TestTokenizationLED(TokenizerTesterMixin, unittest.TestCase):
def test_embeded_special_tokens(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_r = self.get_rust_tokenizer(pretrained_name, **kwargs)
tokenizer_p = self.get_tokenizer(pretrained_name, **kwargs)
sentence = "A, <mask> AllenNLP sentence."
tokens_r = tokenizer_r.encode_plus(sentence, add_special_tokens=True, return_token_type_ids=True)
tokens_p = tokenizer_p.encode_plus(sentence, add_special_tokens=True, return_token_type_ids=True)