Add Document Image Transformer (DiT) (#15984)

* Add conversion script

* Improve script

* Fix bug

* Add option to push to hub

* Add support for classification models

* Update model name

* Upload feature extractor files first

* Remove hash checking

* Fix config

* Add id2label

* Add import

* Fix id2label file name

* Fix expected shape

* Add model to README

* Improve docs

* Add integration test and fix CI

* Fix code style

* Add missing init

* Add model to SPECIAL_MODULE_TO_TEST_MAP

Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>

tests/dit/test_modeling_dit.py

@@ -0,0 +1,61 @@
+# coding=utf-8
+# Copyright 2022 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 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 AutoFeatureExtractor
+
+
+@require_torch
+@require_vision
+class DiTIntegrationTest(unittest.TestCase):
+    @slow
+    def test_for_image_classification(self):
+        feature_extractor = AutoFeatureExtractor.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 = feature_extractor(image, return_tensors="pt")
+
+        # forward pass
+        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))