Uniformize kwargs for chameleon processor (#32181)

* uniformize kwargs of Chameleon

* fix linter nit

* rm stride default

* add tests for chameleon processor

* fix tests

* add comment on get_component

* rm Chameleon's slow tokenizer

* add check order images text + nit

* update docs and tests

* Fix LlamaTokenizer tests

* fix gated repo access

* fix wrong import

---------

Co-authored-by: yonigozlan <yoni.gozlan@huggingface.co>

tests/models/chameleon/test_modeling_chameleon.py

@@ -350,7 +350,7 @@ class ChameleonModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTester
 
         processor.tokenizer.padding_side = "right"
 
-        inputs = processor(texts, return_tensors="pt", padding=True).to(0)
+        inputs = processor(text=texts, return_tensors="pt", padding=True).to(0)
 
         output_native = model.generate(**inputs, max_new_tokens=20, do_sample=False)
         output_native = processor.tokenizer.batch_decode(output_native)
@@ -392,7 +392,7 @@ class ChameleonIntegrationTest(unittest.TestCase):
         )
         prompt = "<image>Describe what do you see here and tell me about the history behind it?"
 
-        inputs = processor(prompt, images=image, return_tensors="pt").to(model.device, torch.float16)
+        inputs = processor(images=image, text=prompt, return_tensors="pt").to(model.device, torch.float16)
 
         # greedy generation outputs
         EXPECTED_TEXT_COMPLETION = ['Describe what do you see here and tell me about the history behind it?The image depicts a star map, with a bright blue line extending across the center of the image. The line is labeled "390 light years" and is accompanied by a small black and']  # fmt: skip
@@ -420,7 +420,7 @@ class ChameleonIntegrationTest(unittest.TestCase):
             "What constellation is this image showing?<image>",
         ]
 
-        inputs = processor(prompts, images=[image, image_2], padding=True, return_tensors="pt").to(
+        inputs = processor(images=[image, image_2], text=prompts, padding=True, return_tensors="pt").to(
             model.device, torch.float16
         )
 
@@ -450,7 +450,7 @@ class ChameleonIntegrationTest(unittest.TestCase):
         )
         prompt = "What do these two images have in common?<image><image>"
 
-        inputs = processor(prompt, images=[image, image_2], return_tensors="pt").to(model.device, torch.float16)
+        inputs = processor(images=[image, image_2], text=prompt, return_tensors="pt").to(model.device, torch.float16)
 
         # greedy generation outputs
         EXPECTED_TEXT_COMPLETION = ['What do these two images have in common?The two images show a connection between two things that are not necessarily related. The first image shows a group of stars, while the second image shows a network of lines connecting two points. The connection between']  # fmt: skip

tests/models/chameleon/test_processor_chameleon.py

@@ -0,0 +1,44 @@
+# coding=utf-8
+# Copyright 2024 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 chameleon model."""
+
+import tempfile
+import unittest
+
+from transformers import ChameleonProcessor, LlamaTokenizer
+from transformers.testing_utils import get_tests_dir
+from transformers.utils import is_vision_available
+
+from ...test_processing_common import ProcessorTesterMixin
+
+
+if is_vision_available():
+    from transformers import ChameleonImageProcessor
+
+
+SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
+
+
+class ChameleonProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = ChameleonProcessor
+
+    def setUp(self):
+        self.tmpdirname = tempfile.mkdtemp()
+        image_processor = ChameleonImageProcessor()
+        tokenizer = LlamaTokenizer(vocab_file=SAMPLE_VOCAB)
+        tokenizer.pad_token_id = 0
+        tokenizer.sep_token_id = 1
+        processor = self.processor_class(image_processor=image_processor, tokenizer=tokenizer)
+        processor.save_pretrained(self.tmpdirname)