codingutf8 2022 the huggingface team 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 orglicenseslicense2 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 forward pass coding utf 8 2022 the huggingface team 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 forward pass
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
    import torch

    from transformers import AutoModelForImageClassification

if is_vision_available():
    from transformers import AutoImageProcessor


@require_torch
@require_vision
class DiTIntegrationTest(unittest.TestCase):
    @slow
    def test_for_image_classification(self):
        image_processor = AutoImageProcessor.from_pretrained("microsoft/dit-base-finetuned-rvlcdip")
        model = AutoModelForImageClassification.from_pretrained("microsoft/dit-base-finetuned-rvlcdip")
        model.to(torch_device)

        from datasets import load_dataset

        dataset = load_dataset("nielsr/rvlcdip-demo")

        image = dataset["train"][0]["image"].convert("RGB")

        inputs = image_processor(image, return_tensors="pt").to(torch_device)

        
        with torch.no_grad():
            outputs = model(**inputs)
            logits = outputs.logits

        expected_shape = torch.Size((1, 16))
        self.assertEqual(logits.shape, expected_shape)

        expected_slice = torch.tensor(
            [-0.4158, -0.4092, -0.4347],
            device=torch_device,
            dtype=torch.float,
        )
        self.assertTrue(torch.allclose(logits[0, :3], expected_slice, atol=1e-4))