# coding=utf-8 # 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 transformers import CodeGenTokenizer, CodeGenTokenizerFast from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES from transformers.testing_utils import require_tokenizers, slow from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers class CodeGenTokenizationTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = CodeGenTokenizer rust_tokenizer_class = CodeGenTokenizerFast test_rust_tokenizer = True 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_rust_tokenizer(self, **kwargs): kwargs.update(self.special_tokens_map) return CodeGenTokenizerFast.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_rust_and_python_full_tokenizers(self): if not self.test_rust_tokenizer: return 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) 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(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.rust_tokenizer_class.from_pretrained(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", ) 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") 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]) 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) 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