Add SigLIP (#26522)

* Add first draft

* Use appropriate gelu function

* More improvements

* More improvements

* More improvements

* Convert checkpoint

* More improvements

* Improve docs, remove print statements

* More improvements

* Add link

* remove unused masking function

* begin tokenizer

* do_lower_case

* debug

* set split_special_tokens=True

* Remove script

* Fix style

* Fix rebase

* Use same design as CLIP

* Add fast tokenizer

* Add SiglipTokenizer to init, remove extra_ids

* Improve conversion script

* Use smaller inputs in conversion script

* Update conversion script

* More improvements

* Add processor to conversion script

* Add tests

* Remove print statements

* Add tokenizer tests

* Fix more tests

* More improvements related to weight initialization

* More improvements

* Make more tests pass

* More improvements

* More improvements

* Add copied from

* Add canonicalize_text

* Enable fast tokenizer tests

* More improvements

* Fix most slow tokenizer tests

* Address comments

* Fix style

* Remove script

* Address some comments

* Add copied from to tests

* Add more copied from

* Add more copied from

* Add more copied from

* Remove is_flax_available

* More updates

* Address comment

* Remove SiglipTokenizerFast for now

* Add caching

* Remove umt5 test

* Add canonicalize_text inside _tokenize, thanks Arthur

* Fix image processor tests

* Skip tests which are not applicable

* Skip test_initialization

* More improvements

* Compare pixel values

* Fix doc tests, add integration test

* Add do_normalize

* Remove causal mask and leverage ignore copy

* Fix attention_mask

* Fix remaining tests

* Fix dummies

* Rename temperature and bias

* Address comments

* Add copied from to tokenizer tests

* Add SiglipVisionModel to auto mapping

* Add copied from to image processor tests

* Improve doc

* Remove SiglipVisionModel from index

* Address comments

* Improve docs

* Simplify config

* Add first draft

* Make it like mistral

* More improvements

* Fix attention_mask

* Fix output_attentions

* Add note in docs

* Convert multilingual model

* Convert large checkpoint

* Convert more checkpoints

* Add pipeline support, correct image_mean and image_std

* Use padding=max_length by default

* Make processor like llava

* Add code snippet

* Convert more checkpoints

* Set keep_punctuation_string=None as in OpenCLIP

* Set normalized=False for special tokens

* Fix doc test

* Update integration test

* Add figure

* Update organization

* Happy new year

* Use AutoModel everywhere

---------

Co-authored-by: patil-suraj <surajp815@gmail.com>

tests/pipelines/test_pipelines_zero_shot_image_classification.py

@@ -241,3 +241,37 @@ class ZeroShotImageClassificationPipelineTests(unittest.TestCase):
             ]
             * 5,
         )
+
+    @slow
+    @require_torch
+    def test_siglip_model_pt(self):
+        image_classifier = pipeline(
+            task="zero-shot-image-classification",
+            model="google/siglip-base-patch16-224",
+        )
+        # This is an image of 2 cats with remotes and no planes
+        image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
+        output = image_classifier(image, candidate_labels=["2 cats", "a plane", "a remote"])
+
+        self.assertEqual(
+            nested_simplify(output),
+            [
+                {"score": 0.198, "label": "2 cats"},
+                {"score": 0.0, "label": "a remote"},
+                {"score": 0.0, "label": "a plane"},
+            ],
+        )
+
+        output = image_classifier([image] * 5, candidate_labels=["2 cats", "a plane", "a remote"], batch_size=2)
+
+        self.assertEqual(
+            nested_simplify(output),
+            [
+                [
+                    {"score": 0.198, "label": "2 cats"},
+                    {"score": 0.0, "label": "a remote"},
+                    {"score": 0.0, "label": "a plane"},
+                ]
+            ]
+            * 5,
+        )