docs/transformers/tests/models/chameleon/test_processor_chameleon.py

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# Licensed under the Apache License, Version 2.0 (the "License");
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"""Testing suite for the PyTorch chameleon model."""

import tempfile
import unittest

from transformers import ChameleonProcessor, LlamaTokenizer
from transformers.testing_utils import get_tests_dir
from transformers.utils import is_vision_available

from ...test_processing_common import ProcessorTesterMixin


if is_vision_available():
    from transformers import ChameleonImageProcessor


SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")


class ChameleonProcessorTest(ProcessorTesterMixin, unittest.TestCase):
    processor_class = ChameleonProcessor

    @classmethod
    def setUpClass(cls):
        cls.tmpdirname = tempfile.mkdtemp()
        image_processor = ChameleonImageProcessor()
        tokenizer = LlamaTokenizer(vocab_file=SAMPLE_VOCAB)
        tokenizer.pad_token_id = 0
        tokenizer.sep_token_id = 1
        tokenizer.add_special_tokens({"additional_special_tokens": ["<image>"]})
        processor = cls.processor_class(image_processor=image_processor, tokenizer=tokenizer, image_seq_length=2)
        processor.save_pretrained(cls.tmpdirname)
        cls.image_token = processor.image_token

    def test_special_mm_token_truncation(self):
        """Tests that special vision tokens do not get truncated when `truncation=True` is set."""

        processor = self.get_processor()

        input_str = self.prepare_text_inputs(batch_size=2, modality="image")
        image_input = self.prepare_image_inputs(batch_size=2)

        _ = processor(
            text=input_str,
            images=image_input,
            return_tensors="pt",
            truncation=None,
            padding=True,
        )

        with self.assertRaises(ValueError):
            _ = processor(
                text=input_str,
                images=image_input,
                return_tensors="pt",
                truncation=True,
                padding=True,
                max_length=20,
            )

    @staticmethod
    def prepare_processor_dict():
        return {"image_seq_length": 2}  # fmt: skip