# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. # Copyright 2022 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 re import unittest from paddlenlp.transformers import CodeGenTokenizer from paddlenlp.transformers.codegen.tokenizer import VOCAB_FILES_NAMES from ...testing_utils import slow from ..test_tokenizer_common import TokenizerTesterMixin class CodeGenTokenizationTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = CodeGenTokenizer from_pretrained_kwargs = {"add_prefix_space": True} test_seq2seq = False def setUp(self): super().setUp() # 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", "", "<|endoftext|>", ] 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": ""} 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 CodeGenTokenizer.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 = CodeGenTokenizer(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_pretokenized_inputs(self, *args, **kwargs): # It's very difficult to mix/test pretokenization with byte-level # And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string) pass def test_padding_if_pad_token_set_slow(self): tokenizer = CodeGenTokenizer.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], 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]) def test_add_bos_token_slow(self): bos_token = "$$$" tokenizer = CodeGenTokenizer.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)) @slow def test_truncation(self): tokenizer = CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono") text = "\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#" expected_trucated_text = "\nif len_a > len_b: result = a\nelse: result = b" input_ids = tokenizer.encode(text)["input_ids"] truncation_pattern = ["^#", re.escape("<|endoftext|>"), "^'''", '^"""', "\n\n\n"] decoded_text = tokenizer.decode(input_ids, truncate_before_pattern=truncation_pattern) self.assertEqual(decoded_text, expected_trucated_text) # tokenizer has no padding token def test_padding_different_model_input_name(self): pass def test_pretrained_model_lists(self): # We should have at least one default checkpoint for each tokenizer # We should specify the max input length as well (used in some part to list the pretrained checkpoints) self.assertGreaterEqual(len(self.tokenizer_class.pretrained_resource_files_map), 1) self.assertEqual( len(list(self.tokenizer_class.pretrained_resource_files_map.values())[0]), len(self.tokenizer_class.max_model_input_sizes), ) weights_list = list(self.tokenizer_class.max_model_input_sizes.keys()) weights_lists_2 = [] for file_id, map_list in self.tokenizer_class.pretrained_resource_files_map.items(): weights_lists_2.append(list(map_list.keys())) for weights_list_2 in weights_lists_2: self.assertListEqual(weights_list, weights_list_2) 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, runcation=True, return_offsets_mapping=True, ) self.assertEqual(len(encoding["input_ids"]), 2) self.assertEqual(len(encoding["offset_mapping"]), 2)