mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-16 02:58:23 +06:00
1ae8d54b04
* fix tests and some clean up * make one general test for each modality * remove redundant merging of kwargs * edge cases * dont enforce slow when reloading * fix gemma3 tests * has to adapt llama 4 after rebase * remove also from overriden tests * should be green now
89 lines
4.0 KiB
Python
89 lines
4.0 KiB
Python
# Copyright 2021 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import json
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import shutil
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import tempfile
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import unittest
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from transformers import AutoProcessor, AutoTokenizer, LlamaTokenizerFast, LlavaProcessor
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from transformers.testing_utils import require_vision
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from transformers.utils import is_torch_available, is_vision_available
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from ...test_processing_common import ProcessorTesterMixin
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if is_vision_available():
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from transformers import CLIPImageProcessor
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if is_torch_available:
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pass
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@require_vision
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class LlavaProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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processor_class = LlavaProcessor
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@classmethod
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def setUpClass(cls):
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cls.tmpdirname = tempfile.mkdtemp()
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image_processor = CLIPImageProcessor(do_center_crop=False)
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tokenizer = LlamaTokenizerFast.from_pretrained("huggyllama/llama-7b")
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processor_kwargs = cls.prepare_processor_dict()
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processor = LlavaProcessor(image_processor, tokenizer, **processor_kwargs)
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processor.save_pretrained(cls.tmpdirname)
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def get_tokenizer(self, **kwargs):
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return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).tokenizer
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def get_image_processor(self, **kwargs):
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return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor
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@classmethod
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def tearDownClass(cls):
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shutil.rmtree(cls.tmpdirname, ignore_errors=True)
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@staticmethod
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def prepare_processor_dict():
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return {
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"chat_template": "{% for message in messages %}{% if message['role'] != 'system' %}{{ message['role'].upper() + ': '}}{% endif %}{# Render all images first #}{% for content in message['content'] | selectattr('type', 'equalto', 'image') %}{{ '<image>\n' }}{% endfor %}{# Render all text next #}{% if message['role'] != 'assistant' %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{{ content['text'] + ' '}}{% endfor %}{% else %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{% generation %}{{ content['text'] + ' '}}{% endgeneration %}{% endfor %}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'ASSISTANT:' }}{% endif %}",
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"patch_size": 3,
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"vision_feature_select_strategy": "default"
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} # fmt: skip
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@unittest.skip(
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"Skip because the model has no processor kwargs except for chat template and"
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"chat template is saved as a separate file. Stop skipping this test when the processor"
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"has new kwargs saved in config file."
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)
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def test_processor_to_json_string(self):
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pass
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def test_chat_template_is_saved(self):
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processor_loaded = self.processor_class.from_pretrained(self.tmpdirname)
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processor_dict_loaded = json.loads(processor_loaded.to_json_string())
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# chat templates aren't serialized to json in processors
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self.assertFalse("chat_template" in processor_dict_loaded.keys())
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# they have to be saved as separate file and loaded back from that file
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# so we check if the same template is loaded
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processor_dict = self.prepare_processor_dict()
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self.assertTrue(processor_loaded.chat_template == processor_dict.get("chat_template", None))
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def test_can_load_various_tokenizers(self):
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for checkpoint in ["Intel/llava-gemma-2b", "llava-hf/llava-1.5-7b-hf"]:
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processor = LlavaProcessor.from_pretrained(checkpoint)
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tokenizer = AutoTokenizer.from_pretrained(checkpoint)
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self.assertEqual(processor.tokenizer.__class__, tokenizer.__class__)
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