# Copyright 2021 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 shutil import tempfile import unittest from transformers import AutoProcessor, AutoTokenizer, LlamaTokenizerFast, LlavaProcessor from transformers.testing_utils import require_vision from transformers.utils import is_vision_available from ...test_processing_common import ProcessorTesterMixin if is_vision_available(): from transformers import CLIPImageProcessor @require_vision class LlavaProcessorTest(ProcessorTesterMixin, unittest.TestCase): processor_class = LlavaProcessor def setUp(self): self.tmpdirname = tempfile.mkdtemp() image_processor = CLIPImageProcessor(do_center_crop=False) tokenizer = LlamaTokenizerFast.from_pretrained("huggyllama/llama-7b") processor_kwargs = self.prepare_processor_dict() processor = LlavaProcessor(image_processor, tokenizer, **processor_kwargs) processor.save_pretrained(self.tmpdirname) def get_tokenizer(self, **kwargs): return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).tokenizer def get_image_processor(self, **kwargs): return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor def tearDown(self): shutil.rmtree(self.tmpdirname) def prepare_processor_dict(self): return {"chat_template": "dummy_template"} @unittest.skip( "Skip because the model has no processor kwargs except for chat template and" "chat template is saved as a separate file. Stop skipping this test when the processor" "has new kwargs saved in config file." ) def test_processor_to_json_string(self): pass def test_chat_template_is_saved(self): processor_loaded = self.processor_class.from_pretrained(self.tmpdirname) processor_dict_loaded = json.loads(processor_loaded.to_json_string()) # chat templates aren't serialized to json in processors self.assertFalse("chat_template" in processor_dict_loaded.keys()) # they have to be saved as separate file and loaded back from that file # so we check if the same template is loaded processor_dict = self.prepare_processor_dict() self.assertTrue(processor_loaded.chat_template == processor_dict.get("chat_template", None)) def test_can_load_various_tokenizers(self): for checkpoint in ["Intel/llava-gemma-2b", "llava-hf/llava-1.5-7b-hf"]: processor = LlavaProcessor.from_pretrained(checkpoint) tokenizer = AutoTokenizer.from_pretrained(checkpoint) self.assertEqual(processor.tokenizer.__class__, tokenizer.__class__) def test_chat_template(self): processor = LlavaProcessor.from_pretrained("llava-hf/llava-1.5-7b-hf") expected_prompt = "USER: \nWhat is shown in this image? ASSISTANT:" messages = [ { "role": "user", "content": [ {"type": "image"}, {"type": "text", "text": "What is shown in this image?"}, ], }, ] formatted_prompt = processor.apply_chat_template(messages, add_generation_prompt=True) self.assertEqual(expected_prompt, formatted_prompt)