# Copyright (c) 2023 PaddlePaddle Authors. 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.glm.tokenizer import GLMBertTokenizer, GLMGPT2Tokenizer from ..test_tokenizer_common import TokenizerTesterMixin, filter_non_english VOCAB_FILES_NAMES = { "vocab_file": "vocab.json", "merges_file": "merges.txt", } class GLMBertTokenizerTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = GLMBertTokenizer space_between_special_tokens = True from_pretrained_filter = filter_non_english test_seq2seq = True def setUp(self): super().setUp() vocab_tokens = [ "[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest", ] self.special_tokens_map = {"truncation_side": "right"} self.vocab_file = os.path.join(self.tmpdirname, GLMBertTokenizer.resource_files_names["vocab_file"]) with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer: vocab_writer.write("".join([x + "\n" for x in vocab_tokens])) def get_input_output_texts(self, tokenizer): input_text = "UNwant\u00E9d,running" output_text = "unwanted, running" return input_text, output_text def get_tokenizer(self, **kwargs): kwargs.update(self.special_tokens_map) return GLMBertTokenizer.from_pretrained(self.tmpdirname, **kwargs) def test_full_tokenizer(self): tokenizer = self.tokenizer_class(self.vocab_file, **self.special_tokens_map) tokens = tokenizer.tokenize("UNwant\u00E9d,running") self.assertListEqual(tokens, ["un", "##want", "##ed", ",", "runn", "##ing"]) self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [9, 6, 7, 12, 10, 11]) class GLMGPT2TokenizerTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = GLMGPT2Tokenizer from_pretrained_kwargs = {"add_prefix_space": True} test_seq2seq = False def setUp(self): super().setUp() vocab = [ "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "\u0120", "\u0120l", "\u0120n", "\u0120lo", "\u0120low", "er", "\u0120lowest", "\u0120newer", "\u0120wider", "", "<|endoftext|>", "[CLS]", ] 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": "", "truncation_side": "right"} 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) return GLMGPT2Tokenizer.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_pretokenized_inputs(self, *args, **kwargs): pass def test_full_tokenizer(self): tokenizer = GLMGPT2Tokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map) text = "lower newer" bpe_tokens = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"] tokens = tokenizer.tokenize(text, add_prefix_space=True) self.assertListEqual(tokens, bpe_tokens) input_tokens = tokens + [tokenizer.unk_token] input_bpe_tokens = [14, 15, 10, 9, 3, 2, 15, 19] self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens) def test_offsets_mapping(self): if not self.test_offsets: return for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): tokenizer = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs) text = "Wonderful no inspiration example with subtoken" # No pair tokens_with_offsets = tokenizer.encode( text, return_special_tokens_mask=True, return_offsets_mapping=True, add_special_tokens=True ) added_tokens = tokenizer.num_special_tokens_to_add(False) offsets = tokens_with_offsets["offset_mapping"] print(offsets) print(added_tokens) print(tokens_with_offsets["input_ids"], tokenizer.decode(tokens_with_offsets["input_ids"])) # Assert there is the same number of tokens and offsets self.assertEqual(len(offsets), len(tokens_with_offsets["input_ids"])) # Assert there is online added_tokens special_tokens self.assertEqual(sum(tokens_with_offsets["special_tokens_mask"]), added_tokens) def test_padding_different_model_input_name(self): pass def test_padding_if_pad_token_set_slow(self): tokenizer = GLMGPT2Tokenizer.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"] 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) # 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], 35) # 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]) def test_add_bos_token_slow(self): pass def test_maximum_encoding_length_pair_input(self): pass def test_special_tokens_mask_input_pairs(self): pass def test_number_of_added_tokens(self): pass def test_pretrained_model_lists(self): # No max_model_input_sizes self.assertGreaterEqual(len(self.tokenizer_class.pretrained_resource_files_map), 1) self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_resource_files_map.values())[0]), 1) def test_consecutive_unk_string(self): tokenizers = self.get_tokenizers(fast=True, do_lower_case=True) for tokenizer in tokenizers: tokens = [tokenizer.unk_token for _ in range(2)] string = tokenizer.convert_tokens_to_string(tokens) encoding = tokenizer( text=string, truncation=True, return_offsets_mapping=True, ) self.assertEqual(len(encoding["input_ids"]), 4) self.assertEqual(len(encoding["offset_mapping"]), 4) if __name__ == "__main__": unittest.main()