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# Copyright 2024 The HuggingFace Inc. 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.
"""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
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