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tools/schema.py

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+"""Planned Parquet schema and a synthetic local Hugging Face compatibility check."""
+
+import argparse
+import hashlib
+import json
+from pathlib import Path
+
+from datasets import Dataset, Features, Value
+
+try:
+    from datasets import List
+except ImportError:
+    # datasets 3.x represents an Arrow list with a one-element Python list.
+    # Sequence(dict) would instead transpose a list of records into records of lists.
+    def List(feature):
+        return [feature]
+
+
+def features():
+    text, number = Value("string"), Value("float64")
+    media = {
+        "media_id": text, "source_dataset": text, "source_video_id": text,
+        "kind": text, "video_path": text, "frame_keys": List(text),
+        "source_frame_indices": List(Value("int64")),
+        "source_timestamps_sec": List(number), "presentation_timestamps_sec": List(number),
+        "clip_start_sec": number, "clip_end_sec": number, "duration_sec": number,
+        "source_fps": number, "effective_fps": number, "ordering": text,
+    }
+    return Features({
+        "row_id": text, "sample_id": text, "source_sample_id": text,
+        "dataset_version": text, "source_dataset": text, "source_video_ids": List(text),
+        "task_family": text, "problem_type": text, "data_type": text, "level": Value("int8"),
+        "prompt_mode": text, "reasoning_tag": text, "parent_sample_id": text,
+        "parent_dataset_revision": text, "prompt": text,
+        "messages": List({"role": text, "content": text}),
+        "answer": text, "answer_format": text, "answer_time_coordinate": text,
+        "media": List(media), "split_role": text, "mix_id": text,
+        "mix_row_index": Value("int64"), "annotation_id": text, "build_id": text,
+        "license_ids": List(text), "prompt_sha256": text, "answer_sha256": text,
+        "training_metadata_json": text,
+    })
+
+
+def fixture_row(row_number=0):
+    prompt = "<video>\nChoose A or B. Provide your answer inside <answer></answer> tags."
+    row = dict.fromkeys(features())
+    row.update({
+        "row_id": f"fixture_row_{row_number}", "sample_id": f"fixture_sample_{row_number}",
+        "dataset_version": "synthetic_schema_fixture", "source_dataset": "synthetic",
+        "source_video_ids": ["fixture_video"], "task_family": "mcq", "problem_type": "llava_mcq", "data_type": "video",
+        "prompt_mode": "no_cot", "prompt": prompt,
+        "messages": [{"role": "user", "content": prompt}], "answer": "A", "answer_format": "choice_letter",
+        "media": [{
+            "media_id": "fixture_sequence", "source_dataset": "synthetic", "source_video_id": "fixture_video",
+            "kind": "frames", "video_path": None,
+            "frame_keys": ["fixture/video/000001.jpg", "fixture/video/000002.jpg"],
+            "source_frame_indices": [1, 2], "source_timestamps_sec": [0.0, 0.5],
+            "presentation_timestamps_sec": [0.0, 0.5], "clip_start_sec": 0.0, "clip_end_sec": 1.0,
+            "duration_sec": 1.0, "source_fps": 2.0, "effective_fps": 2.0, "ordering": "forward",
+        }],
+        "split_role": "train", "mix_id": "fixture", "mix_row_index": row_number,
+        "build_id": "synthetic", "license_ids": ["synthetic_fixture"],
+        "prompt_sha256": hashlib.sha256(prompt.encode()).hexdigest(),
+        "answer_sha256": hashlib.sha256(b"A").hexdigest(), "training_metadata_json": "{}",
+    })
+    return row
+
+
+def write_fixture(destination):
+    destination = Path(destination).resolve()
+    if destination.exists():
+        raise ValueError("Fixture directory exists; choose a fresh path")
+    directory = destination / "data/paper_mt_opd"
+    directory.mkdir(parents=True)
+    train = Dataset.from_list([fixture_row(0), fixture_row(1)], features=features())
+    monitor = fixture_row(2)
+    monitor["split_role"] = "monitor_val"
+    Dataset.from_list([monitor], features=features()).to_parquet(str(directory / "monitor_val-00000-of-00001.parquet"))
+    train.to_parquet(str(directory / "train-00000-of-00001.parquet"))
+    card = """---
+pretty_name: EventLens Synthetic Schema Fixture
+configs:
+- config_name: paper_mt_opd
+  default: true
+  data_files:
+  - split: train
+    path: data/paper_mt_opd/train-*.parquet
+  - split: monitor_val
+    path: data/paper_mt_opd/monitor_val-*.parquet
+---
+# Synthetic schema fixture
+
+Three synthetic rows; no paper data or real media is included.
+Frame references intentionally have no JPEG payload. This checks Parquet
+schema, nested fields, and local dataset-card config/split loading only.
+"""
+    (destination / "README.md").write_text(card)
+    return destination
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--schema-output", type=Path)
+    parser.add_argument("--fixture-dir", type=Path)
+    args = parser.parse_args()
+    if args.schema_output:
+        args.schema_output.write_text(json.dumps(features().to_dict(), indent=2) + "\n")
+    if args.fixture_dir:
+        print(write_fixture(args.fixture_dir))
+    if not args.schema_output and not args.fixture_dir:
+        print(json.dumps({"columns": len(features()), "schema": "eventlens_parquet_v1"}))
+
+
+if __name__ == "__main__":
+    main()

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