first commit

tests/models/idefics/test_image_processing_idefics.py

@@ -0,0 +1,226 @@
+# Copyright 2021 HuggingFace Inc.
+#
+# 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 io
+import unittest
+
+import httpx
+import pytest
+
+from transformers.testing_utils import (
+    require_torch,
+    require_torch_accelerator,
+    require_torchvision,
+    require_vision,
+    slow,
+    torch_device,
+)
+from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available
+
+from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
+
+
+if is_torch_available():
+    import torch
+
+if is_torchvision_available():
+    from torchvision import transforms
+
+if is_vision_available():
+    from PIL import Image
+
+
+class IdeficsImageProcessingTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        num_channels=3,
+        image_size=18,
+        min_resolution=30,
+        max_resolution=400,
+        image_mean=[0.48145466, 0.4578275, 0.40821073],
+        image_std=[0.26862954, 0.26130258, 0.27577711],
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.image_size = image_size
+        self.min_resolution = min_resolution
+        self.max_resolution = max_resolution
+        self.image_mean = image_mean
+        self.image_std = image_std
+
+    def prepare_image_processor_dict(self):
+        return {
+            "image_mean": self.image_mean,
+            "image_std": self.image_std,
+            "image_size": self.image_size,
+        }
+
+    def expected_output_image_shape(self, images):
+        return (self.num_channels, self.image_size, self.image_size)
+
+    def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
+        return prepare_image_inputs(
+            batch_size=self.batch_size,
+            num_channels=self.num_channels,
+            min_resolution=self.min_resolution,
+            max_resolution=self.max_resolution,
+            equal_resolution=equal_resolution,
+            numpify=numpify,
+            torchify=torchify,
+        )
+
+
+@require_torch
+@require_vision
+class IdeficsImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
+    def setUp(self):
+        super().setUp()
+        self.image_processor_tester = IdeficsImageProcessingTester(self)
+
+    @property
+    def image_processor_dict(self):
+        return self.image_processor_tester.prepare_image_processor_dict()
+
+    def test_image_processor_properties(self):
+        for image_processing_class in self.image_processing_classes.values():
+            image_processing = image_processing_class(**self.image_processor_dict)
+            self.assertTrue(hasattr(image_processing, "image_mean"))
+            self.assertTrue(hasattr(image_processing, "image_std"))
+            self.assertTrue(hasattr(image_processing, "image_size"))
+
+    def test_image_processor_from_dict_with_kwargs(self):
+        for image_processing_class in self.image_processing_classes.values():
+            image_processor = image_processing_class.from_dict(self.image_processor_dict)
+            self.assertNotEqual(image_processor.image_size, 30)
+
+            image_processor = image_processing_class.from_dict(self.image_processor_dict, image_size=42)
+            self.assertEqual(image_processor.image_size, 42)
+
+    @require_torchvision
+    def test_torchvision_numpy_transforms_equivalency(self):
+        def convert_to_rgb(image):
+            if image.mode == "RGB":
+                return image
+            image_rgba = image.convert("RGBA")
+            background = Image.new("RGBA", image_rgba.size, (255, 255, 255))
+            alpha_composite = Image.alpha_composite(background, image_rgba)
+            alpha_composite = alpha_composite.convert("RGB")
+            return alpha_composite
+
+        # Verify that the default inference transforms match an equivalent torchvision.Compose pipeline.
+        for image_processing_class in self.image_processing_classes.values():
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
+            image_processor = image_processing_class(**self.image_processor_dict)
+
+            image_size = image_processor.image_size
+            image_mean = image_processor.image_mean
+            image_std = image_processor.image_std
+
+            transform = transforms.Compose(
+                [
+                    convert_to_rgb,
+                    transforms.Resize((image_size, image_size), interpolation=transforms.InterpolationMode.BICUBIC),
+                    transforms.ToTensor(),
+                    transforms.Normalize(mean=image_mean, std=image_std),
+                ]
+            )
+
+            pixel_values_transform_implied = image_processor(image_inputs, transform=None, return_tensors="pt")
+            pixel_values_transform_supplied = image_processor(image_inputs, transform=transform, return_tensors="pt")
+
+            torch.testing.assert_close(
+                pixel_values_transform_implied, pixel_values_transform_supplied, rtol=1e-2, atol=2e-2
+            )
+
+    @require_vision
+    @require_torch
+    def test_backends_equivalence(self):
+        if len(self.image_processing_classes) < 2:
+            self.skipTest(reason="Skipping backends equivalence test as there are less than 2 backends")
+
+        dummy_image = Image.open(
+            io.BytesIO(
+                httpx.get("http://images.cocodataset.org/val2017/000000039769.jpg", follow_redirects=True).content
+            )
+        )
+
+        # Create processors for each backend
+        encodings = {}
+        for backend_name, image_processing_class in self.image_processing_classes.items():
+            image_processor = image_processing_class(**self.image_processor_dict)
+            encodings[backend_name] = image_processor(dummy_image, return_tensors="pt")
+
+        # Compare all backends to the first one (reference backend)
+        backend_names = list(encodings.keys())
+        reference_backend = backend_names[0]
+        reference_encoding = encodings[reference_backend]
+        for backend_name in backend_names[1:]:
+            self._assert_tensors_equivalence(reference_encoding, encodings[backend_name])
+
+    @require_vision
+    @require_torch
+    def test_backends_equivalence_batched(self):
+        if len(self.image_processing_classes) < 2:
+            self.skipTest(reason="Skipping backends equivalence test as there are less than 2 backends")
+
+        dummy_images = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
+
+        # Create processors for each backend
+        encodings = {}
+        for backend_name, image_processing_class in self.image_processing_classes.items():
+            image_processor = image_processing_class(**self.image_processor_dict)
+            encodings[backend_name] = image_processor(dummy_images, return_tensors="pt")
+
+        # Compare all backends to the first one (reference backend)
+        backend_names = list(encodings.keys())
+        reference_backend = backend_names[0]
+        reference_encoding = encodings[reference_backend]
+        for backend_name in backend_names[1:]:
+            self._assert_tensors_equivalence(reference_encoding, encodings[backend_name])
+
+    @slow
+    @require_torch_accelerator
+    @require_vision
+    @pytest.mark.torch_compile_test
+    def test_can_compile_torchvision_backend(self):
+        # Test compilation with torchvision backend (equivalent to fast processor)
+        if "torchvision" not in self.image_processing_classes:
+            self.skipTest("Skipping compilation test as torchvision backend is not available")
+
+        torch.compiler.reset()
+        input_image = torch.randint(0, 255, (3, 224, 224), dtype=torch.uint8)
+        image_processor = self.image_processing_classes["torchvision"](**self.image_processor_dict)
+        output_eager = image_processor(input_image, device=torch_device, return_tensors="pt")
+
+        image_processor = torch.compile(image_processor, mode="reduce-overhead")
+        output_compiled = image_processor(input_image, device=torch_device, return_tensors="pt")
+        self._assert_tensors_equivalence(output_eager, output_compiled, atol=1e-4, rtol=1e-4, mean_atol=1e-5)
+
+    @unittest.skip(reason="not supported")
+    def test_call_numpy(self):
+        pass
+
+    @unittest.skip(reason="not supported")
+    def test_call_numpy_4_channels(self):
+        pass
+
+    @unittest.skip(reason="not supported")
+    def test_call_pil(self):
+        pass
+
+    @unittest.skip(reason="not supported")
+    def test_call_pytorch(self):
+        pass

tests/models/idefics/test_modeling_idefics.py

@@ -0,0 +1,971 @@
+# Copyright 2023 The HuggingFace Inc. 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.
+"""Testing suite for the PyTorch Idefics model."""
+
+import unittest
+from functools import cached_property
+
+import pytest
+from parameterized import parameterized
+
+from transformers import BitsAndBytesConfig, IdeficsConfig, is_torch_available, is_vision_available
+from transformers.testing_utils import (
+    TestCasePlus,
+    require_bitsandbytes,
+    require_torch,
+    require_vision,
+    set_config_for_less_flaky_test,
+    set_model_for_less_flaky_test,
+    slow,
+    torch_device,
+)
+
+from ...generation.test_utils import GenerationTesterMixin
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import (
+    TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION,
+    ModelTesterMixin,
+    floats_tensor,
+    ids_tensor,
+    random_attention_mask,
+)
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import IdeficsForVisionText2Text, IdeficsModel, IdeficsProcessor
+    from transformers.models.idefics.configuration_idefics import IdeficsPerceiverConfig, IdeficsVisionConfig
+
+if is_vision_available():
+    from PIL import Image
+
+
+class IdeficsModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=1,
+        seq_length=7,
+        image_size=30,
+        patch_size=2,
+        num_channels=3,
+        is_training=True,
+        use_input_mask=True,
+        use_token_type_ids=True,
+        use_labels=True,
+        vocab_size=99,
+        hidden_size=32,
+        num_hidden_layers=2,
+        num_attention_heads=4,
+        intermediate_size=37,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        max_position_embeddings=512,
+        type_vocab_size=16,
+        type_sequence_label_size=2,
+        initializer_range=0.02,
+        alpha_initializer="ones",
+        num_labels=3,
+        scope=None,
+        modality_type_vocab_size=2,
+        vision_embed_dim=32,
+        vision_patch_size=2,
+        vision_image_size=30,
+        vision_num_attention_heads=4,
+        vision_num_hidden_layers=2,
+        vision_intermediate_size=37,
+        perceiver_qk_layer_norms_perceiver=False,
+        perceiver_resampler_depth=2,
+        perceiver_resampler_head_dim=8,
+        perceiver_resampler_n_heads=2,
+        perceiver_resampler_n_latents=16,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.seq_length = seq_length
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.num_channels = num_channels
+        self.is_training = is_training
+        self.use_input_mask = use_input_mask
+        self.use_token_type_ids = use_token_type_ids
+        self.use_labels = use_labels
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.type_vocab_size = type_vocab_size
+        self.type_sequence_label_size = type_sequence_label_size
+        self.initializer_range = initializer_range
+        self.alpha_initializer = alpha_initializer
+        self.num_labels = num_labels
+        self.scope = scope
+        self.modality_type_vocab_size = modality_type_vocab_size
+
+        self.vision_embed_dim = vision_embed_dim
+        self.vision_patch_size = vision_patch_size
+        self.vision_image_size = vision_image_size
+        self.vision_num_attention_heads = vision_num_attention_heads
+        self.vision_num_hidden_layers = vision_num_hidden_layers
+        self.vision_intermediate_size = vision_intermediate_size
+
+        self.vision_config = IdeficsVisionConfig(
+            embed_dim=self.vision_embed_dim,
+            patch_size=self.vision_patch_size,
+            image_size=self.vision_image_size,
+            num_attention_heads=self.vision_num_attention_heads,
+            num_hidden_layers=self.vision_num_hidden_layers,
+            intermediate_size=self.vision_intermediate_size,
+        ).to_dict()
+
+        self.perceiver_qk_layer_norms_perceiver = perceiver_qk_layer_norms_perceiver
+        self.perceiver_resampler_depth = perceiver_resampler_depth
+        self.perceiver_resampler_head_dim = perceiver_resampler_head_dim
+        self.perceiver_resampler_n_heads = perceiver_resampler_n_heads
+        self.perceiver_resampler_n_latents = perceiver_resampler_n_latents
+
+        self.perceiver_config = IdeficsPerceiverConfig(
+            qk_layer_norms_perceiver=self.perceiver_qk_layer_norms_perceiver,
+            resampler_depth=self.perceiver_resampler_depth,
+            resampler_head_dim=self.perceiver_resampler_head_dim,
+            resampler_n_heads=self.perceiver_resampler_n_heads,
+            resampler_n_latents=self.perceiver_resampler_n_latents,
+        )
+
+        # we set the expected sequence length (which is used in several tests)
+        # this is equal to the seq length of the text tokens + number of image patches + 1 for the CLS token
+        self.expected_seq_len = self.seq_length + (self.image_size // self.patch_size) ** 2 + 1
+
+    def prepare_config_and_inputs(self, num_images=1, interpolate_pos_encoding=False, image_expansion=0):
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+
+        pixel_values = floats_tensor(
+            [
+                self.batch_size,
+                num_images,
+                self.num_channels,
+                self.image_size + image_expansion,
+                self.image_size + image_expansion,
+            ]
+        )
+        input_mask = None
+        if self.use_input_mask:
+            input_mask = random_attention_mask([self.batch_size, self.seq_length])
+
+        image_attention_mask = random_attention_mask([self.batch_size, self.seq_length, num_images])
+
+        config = self.get_config()
+        return (config, input_ids, input_mask, pixel_values, image_attention_mask, interpolate_pos_encoding)
+
+    def prepare_config_and_inputs_gate_tests(self):
+        # Create a list of configs and inputs, to test 2 things:
+        # 1. For the same image, the output should be different when image_attention_mask is filled with 0s vs filled with 1s.
+        # 2. For 2 different images, the output should be the same when image_attention_mask is filled with 0s.
+
+        interpolate_pos_encoding = False
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+        pixel_values = floats_tensor(
+            [
+                self.batch_size,
+                1,
+                self.num_channels,
+                self.image_size,
+                self.image_size,
+            ]
+        )
+        pixel_values_list = [
+            pixel_values.clone(),
+            pixel_values.clone(),
+            pixel_values.clone().fill_(0.6),
+            pixel_values.clone().fill_(0.3),
+        ]
+        attention_mask = None
+        if self.use_input_mask:
+            attention_mask = random_attention_mask([self.batch_size, self.seq_length])
+
+        image_attention_mask = random_attention_mask([self.batch_size, self.seq_length, 1])
+        image_attention_mask_list = [
+            image_attention_mask.clone().fill_(0),
+            image_attention_mask.clone().fill_(1),
+            image_attention_mask.clone().fill_(0),
+            image_attention_mask.clone().fill_(0),
+        ]
+
+        config = self.get_config()
+        inputs_list = []
+        for pixel_values, image_attention_mask in zip(pixel_values_list, image_attention_mask_list):
+            inputs_list.append(
+                {
+                    "input_ids": input_ids,
+                    "attention_mask": attention_mask,
+                    "pixel_values": pixel_values,
+                    "image_attention_mask": image_attention_mask,
+                    "interpolate_pos_encoding": interpolate_pos_encoding,
+                }
+            )
+
+        inputs_w_same_img = inputs_list[:2]
+        inputs_w_0_img_attn = inputs_list[2:]
+        return config, inputs_w_same_img, inputs_w_0_img_attn
+
+    def get_config(self):
+        return IdeficsConfig(
+            image_size=self.image_size,
+            patch_size=self.patch_size,
+            num_channels=self.num_channels,
+            vocab_size=self.vocab_size,
+            hidden_size=self.hidden_size,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            intermediate_size=self.intermediate_size,
+            hidden_act=self.hidden_act,
+            hidden_dropout_prob=self.hidden_dropout_prob,
+            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
+            max_position_embeddings=self.max_position_embeddings,
+            type_vocab_size=self.type_vocab_size,
+            is_decoder=False,
+            initializer_range=self.initializer_range,
+            alpha_initializer=self.alpha_initializer,
+            num_labels=self.num_labels,
+            modality_type_vocab_size=self.modality_type_vocab_size,
+            vision_config=self.vision_config,
+        )
+
+    def create_and_check_model(
+        self,
+        config,
+        input_ids,
+        input_mask,
+        pixel_values,
+        image_attention_mask,
+        interpolate_pos_encoding,
+    ):
+        model = IdeficsModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(
+            input_ids,
+            attention_mask=input_mask,
+            pixel_values=pixel_values,
+            image_attention_mask=image_attention_mask,
+            interpolate_pos_encoding=interpolate_pos_encoding,
+        )
+        self.parent.assertEqual(
+            result.last_hidden_state.shape, (self.batch_size, input_ids.shape[1], self.hidden_size)
+        )
+
+    def create_and_check_model_gen(
+        self,
+        config,
+        input_ids,
+        input_mask,
+        pixel_values,
+        image_attention_mask,
+        interpolate_pos_encoding,
+    ):
+        model = IdeficsForVisionText2Text(config)
+        model.to(torch_device)
+        model.eval()
+        model.generate(
+            input_ids,
+            attention_mask=input_mask,
+            pixel_values=pixel_values,
+            image_attention_mask=image_attention_mask,
+            interpolate_pos_encoding=interpolate_pos_encoding,
+            max_length=self.seq_length + 2,
+        )
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        (
+            config,
+            input_ids,
+            input_mask,
+            pixel_values,
+            image_attention_mask,
+            interpolate_pos_encoding,
+        ) = config_and_inputs
+        inputs_dict = {
+            "input_ids": input_ids,
+            "attention_mask": input_mask,
+            "pixel_values": pixel_values,
+            "image_attention_mask": image_attention_mask,
+            "interpolate_pos_encoding": interpolate_pos_encoding,
+        }
+        return config, inputs_dict
+
+    def prepare_pixel_values(self):
+        return floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
+
+
+@require_torch
+class IdeficsModelTest(ModelTesterMixin, PipelineTesterMixin, GenerationTesterMixin, unittest.TestCase):
+    all_model_classes = (IdeficsModel, IdeficsForVisionText2Text) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": IdeficsModel,
+            "image-text-to-text": IdeficsForVisionText2Text,
+            "any-to-any": IdeficsForVisionText2Text,
+        }
+        if is_torch_available()
+        else {}
+    )
+
+    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
+        inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
+        # XXX: IdeficsForVisionText2TextTest has no MODEL_FOR group yet, but it should be the same
+        # as MODEL_FOR_CAUSAL_LM_MAPPING_NAMES, so for now manually changing to do the right thing
+        # as super won't do it
+        if return_labels:
+            inputs_dict["labels"] = torch.zeros(
+                (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
+            )
+
+        return inputs_dict
+
+    @parameterized.expand(TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION)
+    @unittest.skip("Idefics requires both text and image inputs which is currently not done in this test.")
+    def test_eager_matches_sdpa_inference(
+        self, name, dtype, padding_side, use_attention_mask, output_attentions, enable_kernels
+    ):
+        pass
+
+    def test_model_outputs_equivalence(self):
+        try:
+            orig = self.all_model_classes
+            # IdeficsModel.forward doesn't have labels input arg - only IdeficsForVisionText2Text does
+            self.all_model_classes = (IdeficsForVisionText2Text,) if is_torch_available() else ()
+            super().test_model_outputs_equivalence()
+        finally:
+            self.all_model_classes = orig
+
+    def setUp(self):
+        self.model_tester = IdeficsModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=IdeficsConfig, hidden_size=32)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_model_single_image(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs(
+            num_images=1, interpolate_pos_encoding=False, image_expansion=0
+        )
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_model_multiple_images(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs(
+            num_images=2, interpolate_pos_encoding=False, image_expansion=0
+        )
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_model_with_image_pos_embeddings_interpolation_single_image(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs(
+            num_images=1, interpolate_pos_encoding=True, image_expansion=2
+        )
+        self.model_tester.create_and_check_model(*config_and_inputs)
+        config_and_inputs = self.model_tester.prepare_config_and_inputs(
+            num_images=1, interpolate_pos_encoding=True, image_expansion=0
+        )
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_model_with_image_pos_embeddings_interpolation_multiple_images(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs(
+            num_images=2, interpolate_pos_encoding=True, image_expansion=2
+        )
+        self.model_tester.create_and_check_model(*config_and_inputs)
+        config_and_inputs = self.model_tester.prepare_config_and_inputs(
+            num_images=2, interpolate_pos_encoding=True, image_expansion=0
+        )
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_generate_with_image_pos_embeddings_interpolation_single_image(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs(
+            num_images=1, interpolate_pos_encoding=True, image_expansion=2
+        )
+        self.model_tester.create_and_check_model_gen(*config_and_inputs)
+
+    def test_generate_with_image_pos_embeddings_interpolation_multiple_images(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs(
+            num_images=2, interpolate_pos_encoding=True, image_expansion=2
+        )
+        self.model_tester.create_and_check_model_gen(*config_and_inputs)
+
+    def test_cross_attention_gates(self):
+        config, inputs_w_same_img, inputs_w_0_img_attn = self.model_tester.prepare_config_and_inputs_gate_tests()
+
+        model = IdeficsModel(config=config).to(torch_device)
+        model.eval()
+        test_1_results = []
+        for inputs in inputs_w_same_img:
+            with torch.no_grad():
+                last_hidden_states = model(**inputs).last_hidden_state
+            last_hidden_states = model(**inputs).last_hidden_state
+            test_1_results.append(last_hidden_states)
+        self.assertNotEqual(test_1_results[0].sum().item(), test_1_results[1].sum().item())
+
+        test_2_results = []
+        for inputs in inputs_w_0_img_attn:
+            with torch.no_grad():
+                last_hidden_states = model(**inputs).last_hidden_state
+            test_2_results.append(last_hidden_states)
+        self.assertEqual(test_2_results[0].sum().item(), test_2_results[1].sum().item())
+
+    def test_training(self):
+        if not self.model_tester.is_training:
+            self.skipTest(reason="model_tester.is_training is set to False")
+
+        for model_class in self.all_model_classes:
+            # IdeficsModel does not support training, users should use
+            # IdeficsForVisionText2Text for this purpose
+            if model_class == IdeficsModel:
+                self.skipTest(reason="IdeficsModel does not support training")
+
+            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+            config.return_dict = True
+
+            model = model_class(config)
+            model.to(torch_device)
+            model.train()
+            inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
+            loss = model(**inputs).loss
+            loss.backward()
+
+    def check_training_gradient_checkpointing(self, gradient_checkpointing_kwargs=None):
+        if not self.model_tester.is_training:
+            self.skipTest(reason="model_tester.is_training is set to False")
+
+        for model_class in self.all_model_classes:
+            # IdeficsModel does not support training, users should use
+            # IdeficsForVisionText2Text for this purpose
+            if model_class == IdeficsModel:
+                self.skipTest(reason="IdeficsModel does not support training")
+
+            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+            config.use_cache = False
+            config.return_dict = True
+
+            model = model_class(config)
+            model.to(torch_device)
+            model.gradient_checkpointing_enable(gradient_checkpointing_kwargs=gradient_checkpointing_kwargs)
+            model.train()
+            inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
+            loss = model(**inputs).loss
+            loss.backward()
+
+    @unittest.skip(reason="""IDEFICS does not support retaining the gradients of the hidden states and attention""")
+    def test_retain_grad_hidden_states_attentions(self):
+        return
+
+    @pytest.mark.generate
+    @unittest.skip(reason="""IDEFICS cannot generate with no images provided!""")
+    def test_generate_without_input_ids(self):
+        pass
+
+    @pytest.mark.generate
+    @unittest.skip(reason="""IDEFICS cannot generate with no images provided!""")
+    def test_generate_continue_from_inputs_embeds(self):
+        pass
+
+    @pytest.mark.generate
+    def test_generate_continue_from_past_key_values(self):
+        """Overwrite because IDEFICS needs image attention mask to be also processed"""
+
+        # Tests that we can continue generating from past key values, returned from a previous `generate` call
+        for model_class in self.all_generative_model_classes:
+            config, inputs = self.model_tester.prepare_config_and_inputs_for_common()
+
+            # Ensure left padding in the mask because otherwise position ids will
+            # not be consecutive. Randomly mask leftmost tokens
+            if (
+                (attention_mask := inputs.get("attention_mask")) is not None
+                and attention_mask.ndim == 2
+                and 0 in attention_mask[:, -1]
+            ):
+                attention_mask = torch.ones_like(attention_mask)
+                attention_mask[0, :1] = 0
+                attention_mask[1:, :2] = 0
+                inputs["attention_mask"] = attention_mask
+
+            # Let's make it always:
+            # 1. use cache (for obvious reasons)
+            # 2. generate to max length (which can be achieved by setting the eos token to an invalid value), which
+            #    would make the test flaky (e.g. EOS is generated on iteration 1 on both generations, but the
+            #    continuation would force it to generate beyond an EOS token)
+            # 3. ignore `token_type_ids` for simplicity
+            # 4. ignore `forced_eos_token_id`, which requires further manipulation of the continuation inputs and is
+            #    active by default on some models
+            # 5. ignore `encoder_no_repeat_ngram_size`, which is set by default in some encoder-decoder models. When
+            #    we use their decoder as a stand-alone model, `encoder_no_repeat_ngram_size` actually prevents
+            #    repetition exclusively from the prompt. This test relies on comparing one call vs 2 calls
+            #    with cache, what is considered a prompt is different in the two cases.
+
+            model = model_class(config).to(torch_device)
+            model.eval()
+            model.generation_config.pad_token_id = model.generation_config.eos_token_id = -1
+            model.generation_config.forced_eos_token_id = None
+            model.generation_config.encoder_no_repeat_ngram_size = 0
+            model.generation_config.use_cache = True
+
+            # Traditional way of generating text, with `return_dict_in_generate` to return the past key values
+            outputs = model.generate(**inputs, do_sample=False, max_new_tokens=4, return_dict_in_generate=True)
+
+            # Let's generate again, but passing the past key values in between (3 + 1 = 4 tokens). Note that the
+            # inputs may need to be tweaked across `generate` calls (like the attention mask).
+            outputs_cached = model.generate(**inputs, do_sample=False, max_new_tokens=3, return_dict_in_generate=True)
+
+            # Continue from the tokens generated above, preparing the inputs accordingly
+            inputs["past_key_values"] = outputs_cached.past_key_values
+            new_attention_len = outputs_cached.sequences.shape[-1]
+            inputs["input_ids"] = outputs_cached.sequences
+            if "attention_mask" in inputs:
+                inputs["attention_mask"] = torch.nn.functional.pad(
+                    inputs["attention_mask"],
+                    (0, new_attention_len - inputs["attention_mask"].shape[1]),
+                    mode="constant",
+                    value=1,
+                )
+            if "image_attention_mask" in inputs:
+                inputs["image_attention_mask"] = inputs["image_attention_mask"][:, -1:, :]
+
+            outputs_cached = model.generate(**inputs, do_sample=False, max_new_tokens=1, return_dict_in_generate=True)
+
+            # The two sets of generated text and past kv should be equal to each other
+            self.assertListEqual(outputs.sequences.tolist(), outputs_cached.sequences.tolist())
+            self._check_caches_are_equal(outputs.past_key_values, outputs_cached.past_key_values)
+
+    def test_attention_outputs(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.return_dict = True
+
+        for model_class in self.all_model_classes:
+            inputs_dict["output_attentions"] = True
+            inputs_dict["output_hidden_states"] = False
+            config.return_dict = True
+            model = model_class._from_config(config, attn_implementation="eager")
+            config = model.config
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+            attentions = outputs.attentions
+
+            self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
+
+            # check that output_attentions also work using config
+            del inputs_dict["output_attentions"]
+            config.output_attentions = True
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+            attentions = outputs.attentions
+            self.assertFalse(attentions[0] is None)
+            self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
+            out_len = len(outputs)
+
+            # Check attention is always last and order is fine
+            inputs_dict["output_attentions"] = True
+            inputs_dict["output_hidden_states"] = True
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+
+            self.assertEqual(out_len + 1, len(outputs))
+
+            self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
+
+            self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
+            self.assertFalse(self_attentions[0] is None)
+
+    def test_hidden_states_output(self):
+        def check_hidden_states_output(inputs_dict, config, model_class):
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+
+            hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
+
+            expected_num_layers = getattr(
+                self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
+            )
+            self.assertEqual(len(hidden_states), expected_num_layers)
+
+            seq_length = self.model_tester.seq_length
+
+            self.assertListEqual(
+                list(hidden_states[0].shape[-2:]),
+                [seq_length, self.model_tester.hidden_size],
+            )
+
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            inputs_dict["output_hidden_states"] = True
+            check_hidden_states_output(inputs_dict, config, model_class)
+
+            # check that output_hidden_states also work using config
+            del inputs_dict["output_hidden_states"]
+            config.output_hidden_states = True
+
+            check_hidden_states_output(inputs_dict, config, model_class)
+
+    @slow
+    def test_model_from_pretrained(self):
+        model_name = "HuggingFaceM4/idefics-9b"
+        model = IdeficsModel.from_pretrained(model_name)
+        self.assertIsNotNone(model)
+
+    @unittest.skip("Idefics has a hard requirement on SDPA")
+    def test_sdpa_can_dispatch_non_composite_models(self):
+        pass
+
+    @unittest.skip(reason="Idefics can't do text-only inference")
+    def test_generate_from_random_inputs_embeds(
+        self, name, dtype, padding_side, use_attention_mask, output_attentions, enable_kernels
+    ):
+        pass
+
+    @pytest.mark.generate
+    def test_left_padding_compatibility(self):
+        # Overwrite -- Idefics needs to prepare `image_attention_mask`, and it must be padded accordingly
+        _, inputs_dict = self.prepare_config_and_inputs_for_generate()
+        input_ids = inputs_dict["input_ids"]
+        image_attention_mask = inputs_dict["image_attention_mask"]
+
+        pad_size_img = (input_ids.shape[0], 32, image_attention_mask.shape[-1])
+        extra_img_mask = torch.zeros(pad_size_img, dtype=image_attention_mask.dtype, device=torch_device)
+        padded_image_attention_mask = torch.cat([extra_img_mask, image_attention_mask], dim=1)
+
+        # `image_attention_mask` is randomly generated in `prepare_config_and_inputs_for_generate`, and it must match
+        # its padded version for the test to be valid -- we need to pass both
+        unpadded_custom_inputs = {"image_attention_mask": image_attention_mask}
+        padded_custom_inputs = {"image_attention_mask": padded_image_attention_mask}
+        super().test_left_padding_compatibility(
+            unpadded_custom_inputs=unpadded_custom_inputs, padded_custom_inputs=padded_custom_inputs
+        )
+
+    @unittest.skip(reason="Idefics can't do text-only inference (test filters non-text inputs)")
+    def test_eager_padding_matches_padding_free_with_position_ids(self):
+        pass
+
+    @unittest.skip(reason="Idefics can't do text-only inference (test filters non-text inputs)")
+    def test_sdpa_padding_matches_padding_free_with_position_ids(self):
+        pass
+
+
+@require_torch
+class IdeficsForVisionText2TextTest(IdeficsModelTest, GenerationTesterMixin, unittest.TestCase):
+    all_model_classes = (IdeficsForVisionText2Text,) if is_torch_available() else ()
+
+    def setUp(self):
+        self.model_tester = IdeficsModelTester(
+            self,
+            modality_type_vocab_size=3,
+        )
+        self.config_tester = ConfigTester(self, config_class=IdeficsConfig, hidden_size=32)
+
+    @parameterized.expand(TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION)
+    @unittest.skip("Idefics requires both text and image inputs which is currently not done in this test.")
+    def test_eager_matches_sdpa_inference(
+        self, name, dtype, padding_side, use_attention_mask, output_attentions, enable_kernels
+    ):
+        pass
+
+    @pytest.mark.generate
+    def test_generate_continue_from_past_key_values(self):
+        """Overwrite because IDEFICS needs image attention mask to be also processed"""
+
+        # Tests that we can continue generating from past key values, returned from a previous `generate` call
+        for model_class in self.all_generative_model_classes:
+            config, inputs = self.model_tester.prepare_config_and_inputs_for_common()
+
+            # Ensure left padding in the mask because otherwise position ids will
+            # not be consecutive. Randomly mask leftmost tokens
+            if (
+                (attention_mask := inputs.get("attention_mask")) is not None
+                and attention_mask.ndim == 2
+                and 0 in attention_mask[:, -1]
+            ):
+                attention_mask = torch.ones_like(attention_mask)
+                attention_mask[0, :1] = 0
+                attention_mask[1:, :2] = 0
+                inputs["attention_mask"] = attention_mask
+
+            # Let's make it always:
+            # 1. use cache (for obvious reasons)
+            # 2. generate to max length (which can be achieved by setting the eos token to an invalid value), which
+            #    would make the test flaky (e.g. EOS is generated on iteration 1 on both generations, but the
+            #    continuation would force it to generate beyond an EOS token)
+            # 3. ignore `token_type_ids` for simplicity
+            # 4. ignore `forced_eos_token_id`, which requires further manipulation of the continuation inputs and is
+            #    active by default on some models
+            # 5. ignore `encoder_no_repeat_ngram_size`, which is set by default in some encoder-decoder models. When
+            #    we use their decoder as a stand-alone model, `encoder_no_repeat_ngram_size` actually prevents
+            #    repetition exclusively from the prompt. This test relies on comparing one call vs 2 calls
+            #    with cache, what is considered a prompt is different in the two cases.
+
+            model = model_class(config).to(torch_device)
+            model.eval()
+            model.generation_config.pad_token_id = model.generation_config.eos_token_id = -1
+            model.generation_config.forced_eos_token_id = None
+            model.generation_config.encoder_no_repeat_ngram_size = 0
+            model.generation_config.use_cache = True
+
+            # Traditional way of generating text, with `return_dict_in_generate` to return the past key values
+            outputs = model.generate(**inputs, do_sample=False, max_new_tokens=4, return_dict_in_generate=True)
+
+            # Let's generate again, but passing the past key values in between (3 + 1 = 4 tokens). Note that the
+            # inputs may need to be tweaked across `generate` calls (like the attention mask).
+            outputs_cached = model.generate(**inputs, do_sample=False, max_new_tokens=3, return_dict_in_generate=True)
+
+            # Continue from the tokens generated above, preparing the inputs accordingly
+            inputs["past_key_values"] = outputs_cached.past_key_values
+            new_attention_len = outputs_cached.sequences.shape[-1]
+            inputs["input_ids"] = outputs_cached.sequences
+            if "attention_mask" in inputs:
+                inputs["attention_mask"] = torch.nn.functional.pad(
+                    inputs["attention_mask"],
+                    (0, new_attention_len - inputs["attention_mask"].shape[1]),
+                    mode="constant",
+                    value=1,
+                )
+            if "image_attention_mask" in inputs:
+                inputs["image_attention_mask"] = inputs["image_attention_mask"][:, -1:, :]
+
+            outputs_cached = model.generate(**inputs, do_sample=False, max_new_tokens=1, return_dict_in_generate=True)
+
+            # The two sets of generated text and past kv should be equal to each other
+            self.assertListEqual(outputs.sequences.tolist(), outputs_cached.sequences.tolist())
+            self._check_caches_are_equal(outputs.past_key_values, outputs_cached.past_key_values)
+
+    @pytest.mark.generate
+    def test_generate_without_input_ids(self):
+        """Overwrite because IDEFICS needs image attention mask to be also processed and requires image at input always."""
+
+        config, input_dict = self.prepare_config_and_inputs_for_generate()
+        pixel_values = input_dict["pixel_values"]
+        image_attention_mask = input_dict["image_attention_mask"][:, -1:, :]
+
+        # hack in case they are equal, otherwise the attn mask will be [0]
+        if config.bos_token_id == config.pad_token_id:
+            config.pad_token_id = None
+
+        for model_class in self.all_generative_model_classes:
+            model = model_class(config).to(torch_device)
+            model.eval()
+
+            output_ids_generate = model.generate(
+                pixel_values=pixel_values,
+                image_attention_mask=image_attention_mask,
+                do_sample=False,
+                max_new_tokens=self.max_new_tokens,
+                remove_invalid_values=True,
+            )
+            self.assertIsNotNone(output_ids_generate)
+
+    @pytest.mark.generate
+    def test_generate_continue_from_inputs_embeds(self):
+        """Overwrite for IDEFICS: Ensure image attention mask is processed while continuing from `inputs_embeds`."""
+
+        for model_class in self.all_generative_model_classes:
+            config, inputs = self.model_tester.prepare_config_and_inputs_for_common()
+            set_config_for_less_flaky_test(config)
+
+            model = model_class(config).to(torch_device).eval()
+            set_model_for_less_flaky_test(model)
+
+            model.generation_config.pad_token_id = model.generation_config.eos_token_id = -1
+            model.generation_config.forced_eos_token_id = None
+            model.generation_config.use_cache = True
+
+            input_ids = inputs.pop("input_ids")
+            inputs_embeds = model.get_input_embeddings()(input_ids)
+
+            generation_kwargs = {
+                "return_dict_in_generate": True,
+                "do_sample": False,
+            }
+
+            inputs["inputs_embeds"] = inputs_embeds
+
+            # Traditional way of generating text, with `return_dict_in_generate` to return the past key values
+            outputs = model.generate(**inputs, max_new_tokens=4, **generation_kwargs)
+            # Let's generate again, but passing the past key values in between (3 + 1 = 4 tokens). Note that the
+            # inputs may need to be tweaked across `generate` calls (like the attention mask).
+            initial_output = model.generate(**inputs, max_new_tokens=3, **generation_kwargs)
+            inputs["past_key_values"] = initial_output.past_key_values
+
+            new_attention_len = input_ids.shape[1] + initial_output.sequences.shape[-1]
+            continued_embeds = torch.cat(
+                [inputs_embeds, model.get_input_embeddings()(initial_output.sequences)], dim=1
+            )
+            inputs["inputs_embeds"] = continued_embeds
+
+            if "attention_mask" in inputs:
+                inputs["attention_mask"] = torch.nn.functional.pad(
+                    inputs["attention_mask"],
+                    (0, new_attention_len - inputs["attention_mask"].shape[1]),
+                    mode="constant",
+                    value=1,
+                )
+            if "image_attention_mask" in inputs:
+                inputs["image_attention_mask"] = inputs["image_attention_mask"][..., -1:, :]
+
+            cached_output = model.generate(**inputs, max_new_tokens=1, **generation_kwargs)
+
+            # Verify that the combined outputs match the full generation.
+            combined_output_sequences = torch.concat([initial_output.sequences, cached_output.sequences], axis=1)
+            self.assertListEqual(outputs.sequences.tolist(), combined_output_sequences.tolist())
+            self._check_caches_are_similar(outputs.past_key_values, cached_output.past_key_values)
+
+    def _check_caches_are_similar(self, cache1, cache2):
+        # In this continuation setup, rare numerical drift appears on the newest cache slot only.
+        # Keep strict checks on earlier slots and use a tolerant check on the newest slot.
+        self.assertEqual(len(cache1), len(cache2))
+        rtol = 5e-2
+        atol = 1e-2
+
+        for idx in range(len(cache1)):
+            keys1 = cache1.layers[idx].keys
+            keys2 = cache2.layers[idx].keys
+            values1 = cache1.layers[idx].values
+            values2 = cache2.layers[idx].values
+
+            self.assertEqual(keys1.shape, keys2.shape)
+            self.assertEqual(values1.shape, values2.shape)
+
+            if keys1.shape[-2] > 1:
+                torch.testing.assert_close(keys1[..., :-1, :], keys2[..., :-1, :])
+                torch.testing.assert_close(values1[..., :-1, :], values2[..., :-1, :])
+
+            torch.testing.assert_close(keys1[..., -1:, :], keys2[..., -1:, :], rtol=rtol, atol=atol)
+            torch.testing.assert_close(values1[..., -1:, :], values2[..., -1:, :], rtol=rtol, atol=atol)
+
+    def _check_attentions_for_generate(
+        self, batch_size, attentions, prompt_length, output_length, config, decoder_past_key_values
+    ):
+        """
+        Overwrite from generation tests because Idefics has only SDPA layers.
+        Do not skip because we still want generation tests to run. Rather we can remove checks for shape.
+        """
+        pass
+
+    @unittest.skip(reason="We only test the model that takes in multiple images")
+    def test_custom_4d_attention_mask(self):
+        pass
+
+    @unittest.skip(reason="IDEFICS cannot compile due to dynamic control flow when checking inputs")
+    def test_generate_with_static_cache(self):
+        pass
+
+    @unittest.skip(reason="We only test the model that takes in multiple images")
+    def test_model(self):
+        pass
+
+    @unittest.skip(reason="We only test the model that takes in multiple images")
+    def test_for_token_classification(self):
+        pass
+
+    @unittest.skip(reason="""IDEFICS does not support retaining the gradients of the hidden states and attention""")
+    def test_retain_grad_hidden_states_attentions(self):
+        pass
+
+    @unittest.skip("Idefics has a hard requirement on SDPA")
+    def test_sdpa_can_dispatch_non_composite_models(self):
+        pass
+
+    @unittest.skip(
+        "Idefics has a separate test runner for generation tests with complex inheritance, causing this check to fail"
+    )
+    def test_generation_tester_mixin_inheritance(self):
+        pass
+
+    @unittest.skip(reason="Idefics can't do text-only inference")
+    def test_generate_from_random_inputs_embeds(
+        self, name, dtype, padding_side, use_attention_mask, output_attentions, enable_kernels
+    ):
+        pass
+
+
+@require_torch
+@require_vision
+class IdeficsModelIntegrationTest(TestCasePlus):
+    @cached_property
+    def default_processor(self):
+        return (
+            IdeficsProcessor.from_pretrained("HuggingFaceM4/idefics-9b", revision="refs/pr/11")
+            if is_vision_available()
+            else None
+        )
+
+    @require_bitsandbytes
+    @slow
+    def test_inference_natural_language_visual_reasoning(self):
+        cat_image_path = self.tests_dir / "fixtures/tests_samples/COCO/000000039769.png"
+        cats_image_obj = Image.open(cat_image_path)  # 2 cats
+        dogs_image_url = "https://huggingface.co/datasets/hf-internal-testing/fixtures_nlvr2/raw/main/image1.jpeg"
+
+        prompts = [
+            [
+                "User:",
+                dogs_image_url,
+                "Describe this image.\nAssistant: An image of two dogs.\n",
+                "User:",
+                cats_image_obj,
+                "Describe this image.\nAssistant:",
+            ],
+            [
+                "User:",
+                cats_image_obj,
+                "Describe this image.\nAssistant: An image of two kittens.\n",
+                "User:",
+                dogs_image_url,
+                "Describe this image.\nAssistant:",
+            ],
+        ]
+
+        # the CI gpu is small so using quantization to fit
+        quantization_config = BitsAndBytesConfig(
+            load_in_4bit=True,
+            bnb_4bit_compute_dtype="float16",
+        )
+        model = IdeficsForVisionText2Text.from_pretrained(
+            "HuggingFaceM4/idefics-9b", quantization_config=quantization_config, device_map="auto"
+        )
+        processor = self.default_processor
+        inputs = processor(text=prompts, return_tensors="pt", padding="longest").to(torch_device)
+        generated_ids = model.generate(**inputs, max_length=100)
+        generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)
+
+        # keep for debugging
+        for i, t in enumerate(generated_text):
+            t = bytes(t, "utf-8").decode("unicode_escape")
+            print(f"{i}:\n{t}\n")
+
+        self.assertIn("image of two cats", generated_text[0])
+        self.assertIn("image of two dogs", generated_text[1])

tests/models/idefics/test_processing_idefics.py

@@ -0,0 +1,173 @@
+# 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
+
+import numpy as np
+
+from transformers import (
+    IdeficsProcessor,
+)
+from transformers.testing_utils import require_torch, require_vision
+from transformers.utils import is_torch_available, is_vision_available
+
+from ...test_processing_common import ProcessorTesterMixin
+
+
+if is_torch_available():
+    pass
+
+if is_vision_available():
+    from PIL import Image
+
+
+@require_torch
+@require_vision
+class IdeficsProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = IdeficsProcessor
+    input_keys = ["pixel_values", "input_ids", "attention_mask", "image_attention_mask"]
+
+    @classmethod
+    def _setup_image_processor(cls):
+        image_processor_class = cls._get_component_class_from_processor("image_processor")
+        return image_processor_class(return_tensors="pt")
+
+    @classmethod
+    def _setup_tokenizer(cls):
+        tokenizer_class = cls._get_component_class_from_processor("tokenizer")
+        return tokenizer_class.from_pretrained("HuggingFaceM4/tiny-random-idefics")
+
+    def prepare_prompts(self):
+        """This function prepares a list of PIL images"""
+
+        num_images = 2
+        images = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8) for x in range(num_images)]
+        images = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in images]
+
+        # print([type(x) for x in images])
+        # die
+
+        prompts = [
+            # text and 1 image
+            [
+                "User:",
+                images[0],
+                "Describe this image.\nAssistant:",
+            ],
+            # text and images
+            [
+                "User:",
+                images[0],
+                "Describe this image.\nAssistant: An image of two dogs.\n",
+                "User:",
+                images[1],
+                "Describe this image.\nAssistant:",
+            ],
+            # only text
+            [
+                "User:",
+                "Describe this image.\nAssistant: An image of two kittens.\n",
+                "User:",
+                "Describe this image.\nAssistant:",
+            ],
+            # only images
+            [
+                images[0],
+                images[1],
+            ],
+        ]
+
+        return prompts
+
+    def test_save_load_pretrained_additional_features(self):
+        tokenizer_add_kwargs = self.get_component("tokenizer", bos_token="(BOS)", eos_token="(EOS)")
+        image_processor_add_kwargs = self.get_component("image_processor", do_normalize=False, padding_value=1.0)
+        processor = IdeficsProcessor.from_pretrained(
+            self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
+        )
+
+        self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
+        self.assertIsInstance(processor.tokenizer, self._get_component_class_from_processor("tokenizer"))
+
+        self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string())
+        self.assertIsInstance(processor.image_processor, self._get_component_class_from_processor("image_processor"))
+
+    def test_tokenizer_padding(self):
+        image_processor = self.get_component("image_processor")
+        tokenizer = self.get_component("tokenizer", padding_side="right")
+
+        processor = IdeficsProcessor(tokenizer=tokenizer, image_processor=image_processor, return_tensors="pt")
+
+        predicted_tokens = [
+            "<s>Describe this image.\nAssistant:<unk><unk><unk><unk><unk><unk><unk><unk><unk>",
+            "<s>Describe this image.\nAssistant:<unk><unk><unk><unk><unk><unk><unk><unk><unk><unk>",
+        ]
+        predicted_attention_masks = [
+            ([1] * 10) + ([0] * 9),
+            ([1] * 10) + ([0] * 10),
+        ]
+        prompts = [[prompt] for prompt in self.prepare_prompts()[2]]
+
+        max_length = processor(text=prompts, padding="max_length", truncation=True, max_length=20, return_tensors="pt")
+        longest = processor(text=prompts, padding="longest", truncation=True, max_length=30, return_tensors="pt")
+
+        decoded_max_length = processor.tokenizer.decode(max_length["input_ids"][-1])
+        decoded_longest = processor.tokenizer.decode(longest["input_ids"][-1])
+
+        self.assertEqual(decoded_max_length, predicted_tokens[1])
+        self.assertEqual(decoded_longest, predicted_tokens[0])
+
+        self.assertListEqual(max_length["attention_mask"][-1].tolist(), predicted_attention_masks[1])
+        self.assertListEqual(longest["attention_mask"][-1].tolist(), predicted_attention_masks[0])
+
+    def test_tokenizer_left_padding(self):
+        """Identical to test_tokenizer_padding, but with padding_side not explicitly set."""
+        processor = self.get_processor()
+
+        predicted_tokens = [
+            "<unk><unk><unk><unk><unk><unk><unk><unk><unk><s>Describe this image.\nAssistant:",
+            "<unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><s>Describe this image.\nAssistant:",
+        ]
+        predicted_attention_masks = [
+            ([0] * 9) + ([1] * 10),
+            ([0] * 10) + ([1] * 10),
+        ]
+        prompts = [[prompt] for prompt in self.prepare_prompts()[2]]
+        max_length = processor(text=prompts, padding="max_length", truncation=True, max_length=20)
+        longest = processor(text=prompts, padding="longest", truncation=True, max_length=30)
+
+        decoded_max_length = processor.tokenizer.decode(max_length["input_ids"][-1])
+        decoded_longest = processor.tokenizer.decode(longest["input_ids"][-1])
+
+        self.assertEqual(decoded_max_length, predicted_tokens[1])
+        self.assertEqual(decoded_longest, predicted_tokens[0])
+
+        self.assertListEqual(max_length["attention_mask"][-1].tolist(), predicted_attention_masks[1])
+        self.assertListEqual(longest["attention_mask"][-1].tolist(), predicted_attention_masks[0])
+
+    def test_tokenizer_defaults(self):
+        # Override to account for the processor prefixing the BOS token to prompts.
+        components = {attribute: self.get_component(attribute) for attribute in self.processor_class.get_attributes()}
+        processor = self.processor_class(**components)
+        tokenizer = components["tokenizer"]
+
+        input_str = ["lower newer"]
+        encoded_processor = processor(text=input_str, padding=False, return_tensors="pt")
+        encoded_tok = tokenizer(
+            [f"{tokenizer.bos_token}{input_str[0]}"], padding=False, add_special_tokens=False, return_tensors="pt"
+        )
+
+        for key in encoded_tok:
+            if key in encoded_processor:
+                self.assertListEqual(encoded_tok[key].tolist(), encoded_processor[key].tolist())