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convert_to_sharegpt.py

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+import os
+import re
+import sys
+import json
+import argparse
+from datasets import load_dataset
+from tqdm import tqdm
+import random
+from multiprocessing import cpu_count
+from pathlib import Path
+
+
+markdown_prompts = [
+    "Please transform the document’s contents into Markdown format.",
+    "Extract the core information of the document and present it in markdown form.",
+    "Reconstruct the document in markdown format, paying attention to title hierarchy and list usage.",
+    "Task: Parse the main body of the document and convert it to markdown. Requirements: Retain the original logical structure, use elements such as titles, lists, and quotes appropriately, and ensure that the output document is clear and easy to read.",
+    "Reorganize the document using markdown syntax, ensuring clear structure and logical coherence.",
+]
+
+
+html_table_prompts = [
+    "Please encode the table from the image into HTML format.",
+    "Render the table in the image as HTML code, please.",
+    "Please transform the table from the image into HTML format.",
+    "Convert the image’s table data into the HTML structure.",
+    "Transform the image’s table into the HTML format, please.",
+    "Convert the table found in the image into HTML format.",
+]
+
+
+def get_random_prompt(task_type):
+    prompts = {
+        "document_parsing": markdown_prompts,
+        "table_parsing": html_table_prompts,
+    }
+    return random.choice(prompts.get(task_type, [""]))
+
+
+def build_res_batch(item):
+    idx, img, gt, attr = item["id"], item["image"], item["gt"], item["attributes"]
+
+    info = json.loads(attr)
+    task_type = info.get("task", "unknown")
+    doc_type = info.get("document_type", "unknown")
+    save_path = os.path.join(args.image_path, idx + ".png")
+    if not os.path.exists(save_path):
+        img.save(save_path, quality=100)
+
+    results = {
+        "images": [str(Path(save_path).resolve())],
+        "conversations": [
+            {"from": "human", "value": get_random_prompt(task_type)},
+            {"from": "gpt", "value": gt},
+        ],
+        "attributes": {"document_type": doc_type, "task": task_type},
+    }
+
+    if "bbox" in item:
+        bbox = item["bbox"]
+        results["bbox"] = (
+            json.dumps(json.loads(bbox), ensure_ascii=False) if bbox != "" else ""
+        )
+
+    return results
+
+
+def main(args):
+    file_dir = args.input
+    dataset = load_dataset(
+        "parquet",
+        data_files=os.path.join(file_dir, "train-*.parquet"),
+        split="train",
+        cache_dir=file_dir,
+    )
+    print(dataset)
+    os.makedirs(args.image_path, exist_ok=True)
+
+    processed = dataset.map(
+        build_res_batch,
+        batched=False,
+        num_proc=32,
+        remove_columns=dataset.column_names,
+        desc="Converting to sharegpt format",
+    )
+
+    df = processed.to_pandas()
+    df.to_json(args.output, orient="records", force_ascii=False, indent=2)
+
+
+if __name__ == "__main__":
+
+    def parse_args():
+        parser = argparse.ArgumentParser(
+            description="Convert parquet format to sharegpt format"
+        )
+        parser.add_argument("--input", type=str, required=True, help="Input directory")
+        parser.add_argument(
+            "--output", type=str, required=True, help="Output json file"
+        )
+        parser.add_argument(
+            "--image_path", required=True, help="output image directory"
+        )
+        return parser.parse_args()
+
+    args = parse_args()
+    main(args)

train-00000-of-00005.parquet

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+size 5634346684

train-00001-of-00005.parquet

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train-00002-of-00005.parquet

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train-00003-of-00005.parquet

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train-00004-of-00005.parquet

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