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chore: import upstream snapshot with attribution
2026-07-13 11:57:37 +08:00

47 lines
1.6 KiB
Python

# Copyright 2023 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 unittest
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 Blip2Processor
@require_vision
class Blip2ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
processor_class = Blip2Processor
@classmethod
def _setup_tokenizer(cls):
tokenizer_class = cls._get_component_class_from_processor("tokenizer")
return tokenizer_class.from_pretrained("hf-internal-testing/tiny-random-GPT2Model")
@classmethod
def _setup_image_processor(cls):
image_processor_class = cls._get_component_class_from_processor("image_processor")
return image_processor_class.from_pretrained("hf-internal-testing/tiny-random-ViTModel")
@staticmethod
def prepare_processor_dict():
return {"num_query_tokens": 1}
@classmethod
def _setup_test_attributes(cls, processor):
cls.image_token = processor.image_token.content