examples/load_dataset.py

"""
The dataset ships two Parquet configs under `data/`:

    canonical.parquet        652 rows — full benchmark; drives file-level eval
    function_level.parquet   343 rows — subset where edit_functions is non-empty

This script walks through both evaluation granularities (file-level and
function-level), decodes an image, reads a 20-row VCE+ preview from
`examples/preview/vce_plus_preview.jsonl`, and shows how to materialize
the underlying GitHub repository snapshot required at scoring time.

Run:
    python3 examples/load_dataset.py
"""

from __future__ import annotations

import json
from pathlib import Path

from datasets import Dataset

ROOT = Path(__file__).resolve().parent.parent
DATA = ROOT / "data"

ds = Dataset.from_parquet(str(DATA / "canonical.parquet"))
ds_fn = Dataset.from_parquet(str(DATA / "function_level.parquet"))

print(f"canonical:      {len(ds):4d} rows   (file-level eval — all edit_files non-empty)")
print(f"function_level: {len(ds_fn):4d} rows   (function-level eval — strict subset)")


# ===========================================================================
# 1. File-level evaluation
# ===========================================================================
# Input : issue_title + issue_body + images + (repo snapshot at base_commit)
# Output: ranked list of files to edit
# Gold  : row["edit_files"]  — always non-empty in the canonical config.
# ===========================================================================

row = ds[0]
print(f"\n── file-level example ──────────────────────────────────────────────")
print(f"  instance_id  : {row['instance_id']}")
print(f"  repo @ commit: {row['repo_full_name']} @ {row['base_commit'][:12]}")
print(f"  language     : {row['language']} ({row['language_category']})")
print(f"  difficulty   : {row['difficulty']}  (changed_files={row['changed_files']})")
print(f"  # images     : {len(row['images'])}")
print(f"  gold files   : {row['edit_files']}")
print(f"  (your model must rank {row['edit_files'][0]!r} near the top)")

# Decode the first screenshot — HF Image feature returns PIL.Image automatically.
img = row["images"][0]
print(f"  image[0]     : {img.size} px, mode={img.mode}, caption={row['image_alts'][0]!r}")
# img.save("first_issue_screenshot.png")  # persist for visual inspection


# ===========================================================================
# 2. Function-level evaluation
# ===========================================================================
# Input : same as file-level
# Output: ranked list of `path/to/file:function` identifiers
# Gold  : row["edit_functions"] (always non-empty in the function_level config)
# Caveat: exclude row["added_functions"] when scoring — those functions do
#         not exist at base_commit, so they cannot be retrieved from the
#         repository tree.
# ===========================================================================

fn_row = ds_fn[0]
print(f"\n── function-level example ──────────────────────────────────────────")
print(f"  instance_id    : {fn_row['instance_id']}")
print(f"  edit_functions : {fn_row['edit_functions']}")
print(f"  added_functions: {fn_row['added_functions']}  ← exclude from scoring")
print(f"  supports_function_level: {fn_row['supports_function_level']}")

# Pick any instance that is ONLY file-level (no function annotation) to
# illustrate the filter that separates the two configs.
file_only = next(r for r in ds if not r["supports_function_level"] or not r["edit_functions"])
print(f"\n  (a file-level-only instance in canonical but NOT in function_level)")
print(f"  instance_id                   : {file_only['instance_id']}")
print(f"  supports_function_level       : {file_only['supports_function_level']}")
print(f"  edit_functions (empty here!)  : {file_only['edit_functions']}")


# ===========================================================================
# 3. VCE+ preview (illustrative; not a shipped config)
# ===========================================================================
# VCE+ is a 7-dimensional structured extraction per screenshot (OCR, error
# signal, UI elements, user action, code hints, visual saliency, confidence)
# produced by a multimodal LLM. The FULL cache is a rerunnable byproduct and
# is NOT shipped in data/. A 20-row preview aligned by instance_id with
# examples/preview/preview.jsonl ships at examples/preview/vce_plus_preview.jsonl
# so the schema stays discoverable without running the extractor.
# ===========================================================================

vce_preview_path = ROOT / "examples" / "preview" / "vce_plus_preview.jsonl"
vce_rows = [json.loads(l) for l in vce_preview_path.open()]
print(f"\n── VCE+ preview (20 records) ──────────────────────────────────────")
print(f"  path: {vce_preview_path.relative_to(ROOT)}  ({len(vce_rows)} rows)")
vce_row = vce_rows[0]
print(f"  sample instance_id: {vce_row['instance_id']}  (category={vce_row['category']})")
for i, rec in enumerate(vce_row["records"]):
    err = rec["error_signal"] or {}
    ch = rec["code_hint"] or {}
    print(
        f"  record[{i}]: conf={rec.get('confidence', 0.0):.2f}  "
        f"error={err.get('kind', '')}/{err.get('type', '')!r}  "
        f"ui_elements={(rec.get('ui_elements') or [])[:3]}  "
        f"frameworks={ch.get('frameworks', [])}"
    )


# ===========================================================================
# 4. Repository snapshots
# ===========================================================================
# Scoring requires the repository tree at each row's base_commit. Repos are
# not embedded in the Parquet (they would total several GB and carry
# heterogeneous upstream licenses). Use commit_cache.json + the bundled
# script to fetch them:
#
#     export GITHUB_TOKEN=ghp_xxx
#     python3 scripts/download_repos.py         # fetch all 653 repos
#     python3 scripts/download_repos.py --only 5  # dry-run slice
# ===========================================================================

cache = json.loads((ROOT / "commit_cache.json").read_text())
entry = cache[row["instance_id"]]
print(f"\n── repo snapshot for the file-level example ─────────────────────────")
print(f"  expected directory: repos/{entry['dir_name']}/")
print(f"  equivalent to     :")
print(f"    git clone https://github.com/{entry['repo']} {entry['dir_name']}")
print(f"    git -C {entry['dir_name']} checkout {entry['sha']}")
print(f"  (or run: python3 scripts/download_repos.py  — see scripts/download_repos.py)")