first commit

tests/models/pp_formulanet/test_modeling_pp_formulanet.py

@@ -0,0 +1,323 @@
+# coding = utf-8
+# Copyright 2026 The PaddlePaddle Team and 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 PPFormulaNet model."""
+
+import copy
+import unittest
+
+import pytest
+from parameterized import parameterized
+
+from transformers import (
+    AutoProcessor,
+    PPFormulaNetConfig,
+    PPFormulaNetForConditionalGeneration,
+    PPFormulaNetModel,
+    PPFormulaNetTextConfig,
+    PPFormulaNetVisionConfig,
+    is_torch_available,
+)
+from transformers.image_utils import load_image
+from transformers.testing_utils import (
+    require_torch,
+    require_vision,
+    slow,
+    torch_device,
+)
+
+from ...test_modeling_common import floats_tensor
+from ...test_processing_common import url_to_local_path
+from ...vlm_tester import VLMModelTest, VLMModelTester
+
+
+if is_torch_available():
+    import torch
+
+
+# NOTE: PPFormulaNet is not a typical VLM; it follows an encoder-decoder architecture.
+class PPFormulaNetModelTester(VLMModelTester):
+    base_model_class = PPFormulaNetModel
+    config_class = PPFormulaNetConfig
+    text_config_class = PPFormulaNetTextConfig
+    vision_config_class = PPFormulaNetVisionConfig
+    conditional_generation_class = PPFormulaNetForConditionalGeneration
+
+    def __init__(self, parent, **kwargs):
+        kwargs.setdefault("batch_size", 2)
+        kwargs.setdefault("hidden_size", 48)
+        kwargs.setdefault("image_size", 768)
+        kwargs.setdefault("patch_size", 768)
+        kwargs.setdefault("num_attention_heads", 2)
+        kwargs.setdefault("num_channels", 3)
+        kwargs.setdefault("num_hidden_layers", 1)
+        kwargs.setdefault("is_training", False)
+        kwargs.setdefault(
+            "vision_config",
+            {
+                "image_size": 768,
+                "patch_size": 16,
+                "hidden_size": 48,
+                "windows_size": 14,
+                "num_hidden_layers": 1,
+                "output_channels": 16,
+                "num_attention_heads": 2,
+                "global_attn_indexes": [1, 1, 1, 1],
+                "mlp_dim": 1,
+                "post_conv_in_channels": 16,
+                "post_conv_mid_channels": 16,
+                "post_conv_out_channels": 16,
+                "decoder_hidden_size": 48,
+            },
+        )
+        kwargs.setdefault(
+            "text_config",
+            {
+                "decoder_ffn_dim": 16,
+                "decoder_layers": 1,
+                "d_model": 48,
+                "vocab_size": 99,
+            },
+        )
+        super().__init__(parent, **kwargs)
+        self.seq_length = self.image_size // self.patch_size
+        self.encoder_seq_length = self.vision_config["windows_size"] ** 2
+        self.decoder_seq_length = 1
+
+    def prepare_config_and_inputs_for_common(self):
+        config, inputs_dict = self.prepare_config_and_inputs()
+        return config, inputs_dict
+
+    def prepare_config_and_inputs(self):
+        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
+        config = self.get_config()
+        decoder_input_ids = torch.full((self.batch_size, 1), 2, dtype=torch.long, device=torch_device)
+        inputs_dict = {
+            "pixel_values": pixel_values,
+            "decoder_input_ids": decoder_input_ids,
+            "input_ids": decoder_input_ids,
+        }
+        return config, inputs_dict
+
+    def get_config(self) -> PPFormulaNetConfig:
+        config = PPFormulaNetConfig(
+            text_config=self.text_config,
+            vision_config=self.vision_config,
+            num_hidden_layers=self.num_hidden_layers,
+        )
+
+        return config
+
+
+@require_torch
+class PPFormulaNetModelTest(VLMModelTest, unittest.TestCase):
+    model_tester_class = PPFormulaNetModelTester
+    all_model_classes = (PPFormulaNetForConditionalGeneration,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {"image-text-to-text": PPFormulaNetForConditionalGeneration} if is_torch_available() else {}
+    )
+
+    test_resize_embeddings = False
+    is_encoder_decoder = True
+
+    def _check_encoder_attention_for_generate(self, attentions, batch_size, config, prompt_length):
+        # Ignoring batch size for now as it is dynamically changed during window partitioning
+        encoder_config = self.model_tester.vision_config
+        prompt_length = encoder_config["windows_size"] ** 2
+        encoder_expected_shape = (prompt_length, prompt_length)
+        self.assertIsInstance(attentions, tuple)
+        self.assertListEqual(
+            [layer_attentions.shape[-2:] for layer_attentions in attentions],
+            [encoder_expected_shape] * len(attentions),
+        )
+
+    def _check_encoder_hidden_states_for_generate(self, hidden_states, batch_size, config, prompt_length):
+        # update encoder_expected_shape
+        encoder_config = self.model_tester.vision_config
+        patched_image_size = encoder_config["image_size"] // encoder_config["patch_size"]
+        encoder_expected_shape = (patched_image_size, patched_image_size, encoder_config["hidden_size"])
+        self.assertIsInstance(hidden_states, tuple)
+        self.assertListEqual(
+            [layer_hidden_states.shape[-3:] for layer_hidden_states in hidden_states],
+            [encoder_expected_shape] * len(hidden_states),
+        )
+
+    # use encoder_seq_length and decoder_seq_length to replace seq_len
+    def test_attention_outputs(self):
+        if not self.has_attentions:
+            self.skipTest(reason="Model does not output attentions")
+
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.return_dict = True
+        # force eager attention to support output attentions
+        config._attn_implementation = "eager"
+
+        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.encoder_attentions if config.is_encoder_decoder else 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
+            self._set_subconfig_attributes(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.encoder_attentions
+            self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
+            # Ignoring batch size for now as it is dynamically changed during window partitioning
+            self.assertListEqual(
+                list(attentions[0].shape[-2:]),
+                [self.model_tester.encoder_seq_length, self.model_tester.encoder_seq_length],
+            )
+
+            attentions = outputs.decoder_attentions
+            self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
+            # Ignoring batch size for now as it is dynamically changed during window partitioning
+            self.assertListEqual(
+                list(attentions[0].shape[-2:]),
+                [self.model_tester.decoder_seq_length, self.model_tester.decoder_seq_length],
+            )
+
+    def test_hidden_states_output(self):
+        def check_hidden_states_output(inputs_dict, config, model_class):
+            model = model_class(copy.deepcopy(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)
+
+            encoder_config = self.model_tester.vision_config
+            seq_length = encoder_config["image_size"] // encoder_config["patch_size"]
+
+            self.assertListEqual(
+                list(hidden_states[0].shape[-2:]),
+                [seq_length, self.model_tester.hidden_size],
+            )
+
+            if config.is_encoder_decoder:
+                hidden_states = outputs.decoder_hidden_states
+
+                self.assertIsInstance(hidden_states, (list, tuple))
+                self.assertEqual(len(hidden_states), expected_num_layers)
+                self.assertListEqual(
+                    list(hidden_states[0].shape[-2:]),
+                    [self.model_tester.decoder_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
+            self._set_subconfig_attributes(config, "output_hidden_states", True)
+            check_hidden_states_output(inputs_dict, config, model_class)
+
+    @unittest.skip(reason="PPFormulaNet does not use inputs_embeds")
+    def test_enable_input_require_grads(self):
+        pass
+
+    @unittest.skip(reason="PPFormulaNet does not use inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="PPFormulaNetTextModel has no attribute `shared`")
+    def test_tied_weights_keys(self):
+        pass
+
+    @pytest.mark.generate
+    @unittest.skip(reason="PPFormulaNet does not support generation from no inputs")
+    def test_generate_without_input_ids(self):
+        pass
+
+    @unittest.skip(reason="PPFormulaNet does not support input and output embeddings")
+    def test_model_get_set_embeddings(self):
+        pass
+
+    @unittest.skip(reason="PPFormulaNet does not support image_token")
+    def test_mismatching_num_image_tokens(self):
+        pass
+
+    @unittest.skip(reason="PPFormulaNet does not support data parallel")
+    def test_multi_gpu_data_parallel_forward(self):
+        pass
+
+    @parameterized.expand([("random",), ("same",)])
+    @pytest.mark.generate
+    @unittest.skip(reason="PPFormulaNet does not support assisted decoding.")
+    def test_assisted_decoding_matches_greedy_search(self, assistant_type):
+        pass
+
+    @pytest.mark.generate
+    @unittest.skip(reason="PPFormulaNet does not support assisted decoding.")
+    def test_assisted_decoding_sample(self):
+        pass
+
+    @unittest.skip(
+        reason="PPFormulaNet does not support continuing generation from past_key_values across generate calls."
+    )
+    def test_generate_continue_from_past_key_values(self):
+        pass
+
+
+@require_torch
+@require_vision
+@slow
+class PPFormulaNetModelIntegrationTest(unittest.TestCase):
+    def setUp(self):
+        model_path = "PaddlePaddle/PP-FormulaNet_plus-L_safetensors"
+        self.model = PPFormulaNetForConditionalGeneration.from_pretrained(model_path).to(torch_device)
+        self.processor = AutoProcessor.from_pretrained(model_path)
+        img_url = url_to_local_path(
+            "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_formula_rec_001.png"
+        )
+        self.image = load_image(img_url)
+
+    def test_inference_formula_recognition_head(self):
+        inputs = self.processor(images=self.image, return_tensors="pt").to(torch_device)
+
+        with torch.no_grad():
+            outputs = self.model.generate(**inputs)
+
+        formula_text = self.processor.post_process(outputs)
+        expected_formula_text = [
+            "\\zeta_{0}(\\nu)=-\\frac{\\nu\\varrho^{-2\\nu}}{\\pi}\\int_{\\mu}^{\\infty}d\\omega\\int_{C_{+}}d z\\frac{2z^{2}}{(z^{2}+\\omega^{2})^{\\nu+1}}\\breve{\\Psi}(\\omega;z)e^{i\\epsilon z}\\quad,"
+        ]
+
+        self.assertEqual(formula_text, expected_formula_text)

tests/models/pp_formulanet/test_processing_pp_formulanet.py

@@ -0,0 +1,59 @@
+# Copyright 2026 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 PPFormulaNetProcessor
+from transformers.testing_utils import require_vision
+
+from ...test_processing_common import ProcessorTesterMixin
+
+
+# PPFormulaNet is an encoder-decoder VLM that uses pixel_values as encoder inputs.
+# It does not consume text input_ids, so processor tests that require input_ids should be skipped.
+@require_vision
+class PPFormulaNetProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = PPFormulaNetProcessor
+
+    @unittest.skip(reason="PPFormulaNet does not need input_ids")
+    def test_model_input_names(self):
+        pass
+
+    @unittest.skip(reason="PPFormulaNet does not need input_ids")
+    def test_processor_with_multiple_inputs(self):
+        pass
+
+    @unittest.skip(reason="PPFormulaNet does not need input_ids")
+    def test_structured_kwargs_nested(self):
+        pass
+
+    @unittest.skip(reason="PPFormulaNet does not need input_ids")
+    def test_structured_kwargs_nested_from_dict(self):
+        pass
+
+    @unittest.skip(reason="PPFormulaNet does not need input_ids")
+    def test_tokenizer_defaults_preserved_by_kwargs(self):
+        pass
+
+    @unittest.skip(reason="PPFormulaNet does not need input_ids")
+    def test_unstructured_kwargs(self):
+        pass
+
+    @unittest.skip(reason="PPFormulaNet does not need input_ids")
+    def test_unstructured_kwargs_batched(self):
+        pass
+
+    @unittest.skip(reason="PPFormulaNet does not need input_ids")
+    def test_kwargs_overrides_default_tokenizer_kwargs(self):
+        pass