transformers/tests/models/codegen/test_tokenization_codegen.py

# 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 functools import lru_cache

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, use_cache_if_possible


@require_tokenizers
class CodeGenTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
    from_pretrained_id = "Salesforce/codegen-350M-mono"
    tokenizer_class = CodeGenTokenizer
    rust_tokenizer_class = CodeGenTokenizerFast
    test_rust_tokenizer = True
    from_pretrained_kwargs = {"add_prefix_space": True}
    test_seq2seq = False

    @classmethod
    def setUpClass(cls):
        super().setUpClass()

        # 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",
            "<unk>",
            "<|endoftext|>",
        ]
        vocab_tokens = dict(zip(vocab, range(len(vocab))))
        merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
        cls.special_tokens_map = {"unk_token": "<unk>"}

        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(cls.merges_file, "w", encoding="utf-8") as fp:
            fp.write("\n".join(merges))

    @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 CodeGenTokenizer.from_pretrained(pretrained_name, **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 CodeGenTokenizerFast.from_pretrained(pretrained_name, **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:
            self.skipTest(reason="test_rust_tokenizer is set to False")

        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)

    @unittest.skip
    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.get_rust_tokenizer(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="<pad>")

        # 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.assertTrue(decode_s.startswith(bos_token))
        self.assertTrue(all(d.startswith(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_truncated_text = "\nif len_a > len_b:\n      result = a\nelse:\n      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_truncated_text)
        # TODO @ArthurZ outputs of the fast tokenizer are different in this case, un-related to the PR

    # tokenizer has no padding token
    @unittest.skip(reason="tokenizer has no padding token")
    def test_padding_different_model_input_name(self):
        pass

    @slow
    def test_tokenizer_integration(self):
        # Custom test since this tokenizer takes return_token_type_ids as an init argument for backward compatibility.

        sequences = [
            "Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
            "general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
            "Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained "
            "models in 100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.",
            "BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
            "conditioning on both left and right context in all layers.",
            "The quick brown fox jumps over the lazy dog.",
        ]

        tokenizer_classes = [self.tokenizer_class]
        if self.test_rust_tokenizer:
            tokenizer_classes.append(self.rust_tokenizer_class)

        # Test default case. i.e. return_token_type_ids is False.
        for tokenizer_class in tokenizer_classes:
            tokenizer = tokenizer_class.from_pretrained("Salesforce/codegen-350M-mono")

            encoding = tokenizer(sequences)
            decoded_sequences = [tokenizer.decode(seq, skip_special_tokens=True) for seq in encoding["input_ids"]]

            # fmt: off
            expected_encoding = {'input_ids': [[41762, 364, 357, 36234, 1900, 355, 12972, 13165, 354, 12, 35636, 364, 290, 12972, 13165, 354, 12, 5310, 13363, 12, 4835, 8, 3769, 2276, 12, 29983, 45619, 357, 13246, 51, 11, 402, 11571, 12, 17, 11, 5564, 13246, 38586, 11, 16276, 44, 11, 4307, 346, 33, 861, 11, 16276, 7934, 23029, 329, 12068, 15417, 28491, 357, 32572, 52, 8, 290, 12068, 15417, 16588, 357, 32572, 38, 8, 351, 625, 3933, 10, 2181, 13363, 4981, 287, 1802, 10, 8950, 290, 2769, 48817, 1799, 1022, 449, 897, 11, 9485, 15884, 354, 290, 309, 22854, 37535, 13], [13246, 51, 318, 3562, 284, 662, 12, 27432, 2769, 8406, 4154, 282, 24612, 422, 9642, 9608, 276, 2420, 416, 26913, 21143, 319, 1111, 1364, 290, 826, 4732, 287, 477, 11685, 13], [464, 2068, 7586, 21831, 18045, 625, 262, 16931, 3290, 13]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
            # fmt: on

            encoding_data = encoding.data
            self.assertDictEqual(encoding_data, expected_encoding)

            for expected, decoded in zip(sequences, decoded_sequences):
                self.assertEqual(expected, decoded)

        # Test return_token_type_ids is True case.
        for tokenizer_class in tokenizer_classes:
            tokenizer = tokenizer_class.from_pretrained("Salesforce/codegen-350M-mono", return_token_type_ids=True)

            encoding = tokenizer(sequences)
            decoded_sequences = [tokenizer.decode(seq, skip_special_tokens=True) for seq in encoding["input_ids"]]

            # fmt: off
            expected_encoding = {'input_ids': [[41762, 364, 357, 36234, 1900, 355, 12972, 13165, 354, 12, 35636, 364, 290, 12972, 13165, 354, 12, 5310, 13363, 12, 4835, 8, 3769, 2276, 12, 29983, 45619, 357, 13246, 51, 11, 402, 11571, 12, 17, 11, 5564, 13246, 38586, 11, 16276, 44, 11, 4307, 346, 33, 861, 11, 16276, 7934, 23029, 329, 12068, 15417, 28491, 357, 32572, 52, 8, 290, 12068, 15417, 16588, 357, 32572, 38, 8, 351, 625, 3933, 10, 2181, 13363, 4981, 287, 1802, 10, 8950, 290, 2769, 48817, 1799, 1022, 449, 897, 11, 9485, 15884, 354, 290, 309, 22854, 37535, 13], [13246, 51, 318, 3562, 284, 662, 12, 27432, 2769, 8406, 4154, 282, 24612, 422, 9642, 9608, 276, 2420, 416, 26913, 21143, 319, 1111, 1364, 290, 826, 4732, 287, 477, 11685, 13], [464, 2068, 7586, 21831, 18045, 625, 262, 16931, 3290, 13]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
            # fmt: on

            encoding_data = encoding.data
            self.assertDictEqual(encoding_data, expected_encoding)

            for expected, decoded in zip(sequences, decoded_sequences):
                self.assertEqual(expected, decoded)