# coding=utf-8 # Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. # Copyright 2021 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 paddlenlp.transformers import CLIPTokenizer from ...transformers.test_tokenizer_common import TokenizerTesterMixin VOCAB_FILES_NAMES = CLIPTokenizer.resource_files_names class CLIPTokenizationTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = CLIPTokenizer test_rust_tokenizer = True from_pretrained_kwargs = {} test_seq2seq = False def setUp(self): super().setUp() # fmt: off vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l", "w", "r", "t", "low", "er", "lowest", "newer", "wider", "", "<|startoftext|>", "<|endoftext|>"] # fmt: on vocab_tokens = dict(zip(vocab, range(len(vocab)))) merges = ["#version: 0.2", "l o", "lo w", "e r"] self.special_tokens_map = {"unk_token": ""} 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: fp.write(json.dumps(vocab_tokens) + "\n") with open(self.merges_file, "w", encoding="utf-8") as fp: fp.write("\n".join(merges)) def get_tokenizer(self, **kwargs): kwargs.update(self.special_tokens_map) if "model_max_length" not in kwargs: kwargs.update({"model_max_length": 512}) return CLIPTokenizer.from_pretrained(self.tmpdirname, **kwargs) def get_input_output_texts(self, tokenizer): input_text = "lower newer" output_text = "lower newer" return input_text, output_text def test_full_tokenizer(self): tokenizer = self.get_tokenizer() text = "lower newer" bpe_tokens = ["lo", "w", "er", "n", "e", "w", "er"] tokens = tokenizer.tokenize(text) self.assertListEqual(tokens, bpe_tokens) input_tokens = tokens + [tokenizer.unk_token] input_bpe_tokens = [10, 2, 16, 9, 3, 2, 16, 20] self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens) def test_padding_if_pad_token_set_slow(self): tokenizer = CLIPTokenizer.from_pretrained(self.tmpdirname, pad_token="") # 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", return_attention_mask=True) out_s2 = tokenizer(s2, padding=True, truncate=True, return_tensors="np", return_attention_mask=True) out_p = tokenizer(*p, padding="max_length", max_length=60, return_tensors="np", return_attention_mask=True) out_p2 = tokenizer(p2, padding=True, truncate=True, return_tensors="np", return_attention_mask=True) # 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], 31) # 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], 48) # 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]) def test_add_bos_token_slow(self): bos_token = "$$$" tokenizer = CLIPTokenizer.from_pretrained(self.tmpdirname, bos_token=bos_token, add_bos_token=True) s = "This is a simple input" s2 = ["This is a simple input 1", "This is a simple input 2"] bos_token_id = tokenizer.bos_token_id out_s = tokenizer(s) out_s2 = tokenizer(s2) self.assertEqual(out_s.input_ids[0], bos_token_id) self.assertTrue(all(o[0] == bos_token_id for o in out_s2.input_ids)) decode_s = tokenizer.decode(out_s.input_ids) decode_s2 = tokenizer.batch_decode(out_s2.input_ids) self.assertEqual(decode_s.split()[0], bos_token) self.assertTrue(all(d.split()[0] == bos_token for d in decode_s2)) @unittest.skip(reason="CLIP always lower cases letters") def test_added_tokens_do_lower_case(self): # CLIP always lower cases letters pass @unittest.skip(reason="CLIP do not check pretrained_model_lists") def test_pretrained_model_lists(self): pass