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4b8ec667e9
* remove it from all py files * remove it from the doc * remove it from examples * style * remove traces of _fast_init * Update test_peft_integration.py * CIs
61 lines
1.9 KiB
Python
61 lines
1.9 KiB
Python
# Copyright 2025 The HuggingFace Inc. 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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"""Testing suite for the PyTorch Gemma3 model."""
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import unittest
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from io import BytesIO
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import requests
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from PIL import Image
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from transformers import is_torch_available
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from transformers.testing_utils import (
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cleanup,
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require_torch_accelerator,
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slow,
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torch_device,
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)
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if is_torch_available():
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import torch
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from transformers import ShieldGemma2ForImageClassification, ShieldGemma2Processor
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@slow
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@require_torch_accelerator
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# @require_read_token
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class ShieldGemma2IntegrationTest(unittest.TestCase):
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def tearDown(self):
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cleanup(torch_device, gc_collect=True)
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def test_model(self):
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model_id = "google/shieldgemma-2-4b-it"
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processor = ShieldGemma2Processor.from_pretrained(model_id, padding_side="left")
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url = "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png"
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response = requests.get(url)
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image = Image.open(BytesIO(response.content))
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model = ShieldGemma2ForImageClassification.from_pretrained(model_id, torch_dtype=torch.bfloat16).to(
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torch_device
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)
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inputs = processor(images=[image]).to(torch_device)
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output = model(**inputs)
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self.assertEqual(len(output.probabilities), 3)
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for element in output.probabilities:
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self.assertEqual(len(element), 2)
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