[ImageGPT] Small fixes (#14460)

* Add integration test

* Fix typo

tests/test_modeling_imagegpt.py

@@ -20,8 +20,9 @@ import os
 import tempfile
 import unittest
 
-from transformers import ImageGPTConfig, is_torch_available
-from transformers.testing_utils import require_torch, slow, torch_device
+from transformers import ImageGPTConfig
+from transformers.file_utils import cached_property, is_torch_available, is_vision_available
+from transformers.testing_utils import require_torch, require_vision, slow, torch_device
 
 from .test_configuration_common import ConfigTester
 from .test_generation_utils import GenerationTesterMixin
@@ -38,6 +39,11 @@ if is_torch_available():
         ImageGPTModel,
     )
 
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import ImageGPTFeatureExtractor
+
 
 class ImageGPTModelTester:
     def __init__(
@@ -496,3 +502,38 @@ class ImageGPTModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCa
                         models_equal = False
 
             self.assertTrue(models_equal)
+
+
+# We will verify our results on an image of cute cats
+def prepare_img():
+    image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
+    return image
+
+
+@require_torch
+@require_vision
+class ImageGPTModelIntegrationTest(unittest.TestCase):
+    @cached_property
+    def default_feature_extractor(self):
+        return ImageGPTFeatureExtractor.from_pretrained("openai/imagegpt-small") if is_vision_available() else None
+
+    @slow
+    def test_inference_causal_lm_head(self):
+        model = ImageGPTForCausalLM.from_pretrained("openai/imagegpt-small").to(torch_device)
+
+        feature_extractor = self.default_feature_extractor
+        image = prepare_img()
+        inputs = feature_extractor(images=image, return_tensors="pt").to(torch_device)
+
+        # forward pass
+        outputs = model(**inputs)
+
+        # verify the logits
+        expected_shape = torch.Size((1, 1024, 512))
+        self.assertEqual(outputs.logits.shape, expected_shape)
+
+        expected_slice = torch.tensor(
+            [[2.3445, 2.6889, 2.7313], [1.0530, 1.2416, 0.5699], [0.2205, 0.7749, 0.3953]]
+        ).to(torch_device)
+
+        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_slice, atol=1e-4))