from __future__ import annotations


def last_frame_prompt(row: dict) -> str:
    return (
        "You are evaluating an image-to-video generation result.\n"
        f"Task: {row['task_group_name']}.\n"
        "Compare the supplied final frame with the expected final state below.\n"
        f"Expected final state: {row['last_frame_goal']}\n"
        "Use an integer score from 1 to 5, where 1 is wholly inconsistent and "
        "5 fully satisfies the expected state. Judge only visible evidence.\n"
        "Return one JSON object with exactly these fields:\n"
        '{"last_frame_goal_score": 1, '
        '"reason_for_last_frame_goal_score": "brief evidence-based reason"}'
    )


def _progress_consistency_criteria(row: dict) -> str:
    criteria = [
        value
        for value in (row.get("foreground_rule"), row.get("background_rule"))
        if value
    ]
    return " ".join(criteria) if criteria else (
        "Unprompted objects and background regions should remain stable, and "
        "intended changes should evolve coherently."
    )


def video_prompt(row: dict) -> tuple[str, list[str]]:
    sections = [
        (
            "video_quality",
            "Evaluate visual and temporal coherence: continuity, stability, "
            "flicker, jitter, warping, identity drift, frame repetition, abrupt "
            "cuts, motion discontinuity, blur changes, and background deformation.",
        ),
        (
            "progress_consistency",
            "Evaluate consistency with these foreground/background constraints: "
            + _progress_consistency_criteria(row),
        ),
    ]
    if row.get("implicit_rule"):
        sections.append(
            (
                "implicit_rule",
                "Evaluate adherence to this implicit physical or logical rule: "
                + row["implicit_rule"],
            )
        )
    if row.get("progress_goal"):
        sections.append(
            (
                "progress_goal",
                "Evaluate whether the visible process realizes this expected "
                "intermediate progression: " + row["progress_goal"],
            )
        )

    keys = [name for name, _ in sections]
    lines = [
        "You are evaluating an image-to-video generation result.",
        f"Task: {row['task_group_name']}.",
        f"Generation prompt: {row['user_prompt']}",
        "Judge only visible evidence in the supplied frames.",
        "For every requested metric, use an integer score from 1 to 5.",
        "1 means severe failure; 3 means partial success with clear issues; "
        "5 means fully successful.",
    ]
    for index, (name, criterion) in enumerate(sections, start=1):
        lines.append(f"{index}. {name}: {criterion}")

    fields = []
    for name in keys:
        fields.append(f'"{name}_score": 1')
        fields.append(f'"reason_for_{name}_score": "brief evidence-based reason"')
    lines.append("Return one JSON object with exactly these fields:")
    lines.append("{" + ", ".join(fields) + "}")
    return "\n".join(lines), keys


def mme_cof_prompt(user_prompt: str) -> str:
    return (
        "Evaluate whether the supplied video faithfully and coherently visualizes "
        f'this image-to-video instruction: "{user_prompt}".\n'
        "Judge only visible evidence. Rate each aspect on a 0–4 integer scale "
        "(0 poor, 4 excellent): instruction alignment, temporal consistency, "
        "visual stability, content fidelity, and focus relevance.\n"
        "Return one JSON object with exactly these numeric fields:\n"
        '{"instruction_alignment": 0, "temporal_consistency": 0, '
        '"visual_stability": 0, "content_fidelity": 0, "focus_relevance": 0}'
    )