chore: import upstream snapshot with attribution

docs/source/en/model_doc/pp_chart2table.md

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+<!--Copyright 2026 The HuggingFace Team. All rights reserved.
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+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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+http://www.apache.org/licenses/LICENSE-2.0
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+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+*This model was contributed to Hugging Face Transformers on 2026-03-20.*
+
+# PP-Chart2Table
+
+
+## Overview
+
+**PP-Chart2Table** is a SOTA multimodal model developed by the PaddlePaddle team, specializing in chart parsing for both Chinese and English. Its high performance is driven by a novel "Shuffled Chart Data Retrieval" training task, which, combined with a refined token masking strategy, significantly improves its efficiency in converting charts to data tables. The model is further strengthened by an advanced data synthesis pipeline that uses high-quality seed data, RAG, and LLMs persona design to create a richer, more diverse training set. To address the challenge of large-scale unlabeled, out-of-distribution (OOD) data, the team implemented a two-stage distillation process, ensuring robust adaptability and generalization on real-world data.
+
+## Model Architecture 
+PP-Chart2Table adopts a multimodal fusion architecture that combines a vision tower for chart feature extraction and a language model for table structure generation, enabling end-to-end chart-to-table conversion.
+
+
+## Usage
+
+### Single input inference
+
+The example below demonstrates how to classify image with PP-Chart2Table using [`Pipeline`] or the [`AutoModel`].
+
+<hfoptions id="usage">
+<hfoption id="Pipeline">
+
+```python
+from transformers import pipeline
+
+
+pipe = pipeline("image-text-to-text", model="PaddlePaddle/PP-Chart2Table_safetensors")
+
+# PPChart2TableProcessor uses hardcoded "Chart to table" instruction internally via chat template
+conversation = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "url": "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/chart_parsing_02.png",
+            },
+        ],
+    },
+]
+result = pipe(text=conversation)
+print(result[0]["generated_text"])
+```
+
+</hfoption>
+
+<hfoption id="AutoModel">
+
+```python
+from transformers import AutoModelForImageTextToText, AutoProcessor
+
+
+model_path = "PaddlePaddle/PP-Chart2Table_safetensors"
+model = AutoModelForImageTextToText.from_pretrained(
+    model_path,
+    device_map="auto",
+)
+processor = AutoProcessor.from_pretrained(model_path)
+
+# PPChart2TableProcessor uses hardcoded "Chart to table" instruction internally via chat template
+conversation = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "url": "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/chart_parsing_02.png",
+            },
+        ],
+    },
+]
+
+inputs = processor.apply_chat_template(
+    conversation,
+    tokenize=True,
+    add_generation_prompt=True,
+    truncation=True,
+    return_dict=True,
+    return_tensors="pt",
+).to(model.device)
+
+generated_ids = model.generate(**inputs, do_sample=False, max_new_tokens=256)
+generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
+result = processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)
+print(result)
+```
+
+</hfoption>
+</hfoptions>
+
+### Batched inference
+
+Here is how you can do it with PP-Chart2Table using [`Pipeline`] or the [`AutoModel`]:
+
+<hfoptions id="usage">
+<hfoption id="Pipeline">
+
+```python
+from transformers import pipeline
+
+
+pipe = pipeline("image-text-to-text", model="PaddlePaddle/PP-Chart2Table_safetensors")
+
+# PPChart2TableProcessor uses hardcoded "Chart to table" instruction internally via chat template
+conversation = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "url": "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/chart_parsing_02.png",
+            },
+        ],
+    },
+]
+result = pipe(text=[conversation, conversation])
+print(result[0][0]["generated_text"])
+```
+
+</hfoption>
+
+<hfoption id="AutoModel">
+
+```python
+from transformers import AutoModelForImageTextToText, AutoProcessor
+
+
+model_path = "PaddlePaddle/PP-Chart2Table_safetensors"
+model = AutoModelForImageTextToText.from_pretrained(
+    model_path,
+    device_map="auto",
+)
+processor = AutoProcessor.from_pretrained(model_path)
+
+# PPChart2TableProcessor uses hardcoded "Chart to table" instruction internally via chat template
+conversation = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "url": "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/chart_parsing_02.png",
+            },
+        ],
+    },
+]
+
+batch_conversation = [conversation, conversation]
+inputs = processor.apply_chat_template(
+    batch_conversation,
+    tokenize=True,
+    add_generation_prompt=True,
+    truncation=True,
+    return_dict=True,
+    return_tensors="pt",
+).to(model.device)
+
+generated_ids = model.generate(**inputs, do_sample=False, max_new_tokens=256)
+generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
+result = processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)
+print(result)
+```
+
+</hfoption>
+</hfoptions>
+
+
+## PPChart2TableConfig
+
+[[autodoc]] PPChart2TableConfig
+
+## PPChart2TableImageProcessor
+
+[[autodoc]] PPChart2TableImageProcessor
+
+## PPChart2TableImageProcessorPil
+
+[[autodoc]] PPChart2TableImageProcessorPil
+
+## PPChart2TableProcessor
+
+[[autodoc]] PPChart2TableProcessor