"""Public prompts and deterministic answer-slot seeds for UI-TestJev inference."""
import hashlib
import json
from pathlib import Path

MODEL_ID = "google/diffusiongemma-26B-A4B-it"


MODEL_REVISION = "f7f5b7f5fa82ffc52addd066915886d497f5517b"


SEED = 3407


ISSUES = {
    "text_clipping": "Required text glyphs are clipped.",
    "contrast": "Text violates the stated minimum contrast requirement.",
    "required_content": "Required visible content is missing.",
    "occlusion": "The specified target is obstructed.",
    "image_aspect": "An image's aspect ratio differs beyond the allowed tolerance.",
    "target_size": "A control violates the stated minimum rendered dimensions.",
}


SCRY_LABELS = ("Color/Background", "Extra Element", "Icon/Nav", "Image/Asset",
               "Missing Content", "Selected/Disabled State", "Separator", "Shape/Size",
               "Spacing/Layout", "Typography", "badimage", "other")


FIELDS = {
    "requirement_check": ("requirement_type", "requirement", "reference_target", "viewport"),
    "regression_search": ("viewport",),
    "keyboard_access": ("target", "observed_keyboard_trace"),
    "known_ui_differences": (),
}


INSTRUCTIONS = {
    "requirement_check": "Check ONLY the specified target and the single stated requirement. A visible styling change is not automatically a failure. Pass when that requirement remains satisfied; ignore other elements and other requirement categories. If evidence cannot establish the requirement, select unknown. For a failure select only the violated requirement category.",
    "regression_search": "Find the newly introduced regression across the full screen relative to the approved reference. Required visible content must remain present; glyphs must not be clipped; image aspect ratios must stay within 3% of the reference; controls previously at least 24 by 24 CSS pixels must remain above that minimum; text meeting 4.5:1 contrast must keep meeting it. Minor opacity changes still meeting contrast, outlines, one-pixel shifts, and aspect changes within tolerance are allowed. Ignore unchanged pre-existing defects. Select pass if these contracts remain satisfied, unknown if evidence is insufficient, or the violated category. This benchmark contains at most one introduced violation category. Do not infer a location from any supplied answer options.",
    "keyboard_access": "Can keyboard users reach the specified control using Tab in one complete focus cycle? Use the observed trace if supplied. A screenshot alone does not establish reachability: select unknown when the trace is absent. Select pass if reachable, fail if unreachable in a complete cycle.",
}


def digest(path):
    h = hashlib.sha256()
    with open(path, "rb") as f:
        for b in iter(lambda: f.read(4 * 1024 * 1024), b""):
            h.update(b)
    return h.hexdigest()


def stable_seed(*values):
    return int(hashlib.sha256(json.dumps(values, sort_keys=True).encode()).hexdigest()[:15], 16)


def options(task, public=None):
    if task == "keyboard_access":
        return [("pass", "Reachable"), ("fail", "Unreachable"), ("unknown", "Insufficient evidence")]
    if task == "requirement_check" and public and "requirement_type" in public:
        category = public["requirement_type"]
        if category not in ISSUES: raise ValueError("Unsupported public requirement type")
        return [("pass", "Stated requirement remains satisfied"), ("unknown", "Insufficient evidence"), (category, ISSUES[category])]
    issues = [k for k in ISSUES if task != "regression_search" or k != "occlusion"]
    return [("pass", "No violation of the checked contracts"), ("unknown", "Insufficient evidence")] + [(k, ISSUES[k]) for k in issues]


def prompt_spec(public):
    """Only a public input is accepted. IDs/paths/provenance/targets never enter text."""
    task = public["task"]
    state = {k: public[k] for k in FIELDS[task] if k in public}
    state["image_order"] = [dict(index=i+1, role=im["role"], **({"full_image_crop_xyxy": im["full_image_crop_xyxy"]} if "full_image_crop_xyxy" in im else {})) for i, im in enumerate(public["images"])]
    if task == "known_ui_differences":
        questions = [{"instruction": f"Is there a visible difference between the reference and implementation screens in category {tag!r}? Answer yes, no, or unknown. Images may have different dimensions.", "tag": tag,
                      "options": [("yes", "Visible difference in this category"), ("no", "No visible difference in this category"), ("unknown", "Insufficient evidence")]} for tag in SCRY_LABELS]
    else:
        opts = options(task, public)
        questions = [{"instruction": INSTRUCTIONS[task], "options": opts}]
    system = "Answer a fixed set of questions about the supplied UI evidence. Each question lists its allowed answers. Reply with exactly one letter per question.\n"
    for i, q in enumerate(questions):
        system += f"\nQuestion q{i+1}: {q['instruction']}\n"
        for j, (name, description) in enumerate(q["options"]):
            system += f"  {chr(65+j)}: {name} ({description})\n"
    system += "\nReply as lines in the form q1: A, q2: B, using only the allowed letters."
    return {"system": system, "state": json.dumps(state, ensure_ascii=False, sort_keys=True, separators=(",", ":")), "questions": questions}


def public_identity(public, root):
    """Hash actual public prompt + ordered image bytes. No ID, label, path or oracle."""
    spec = prompt_spec(public)
    content = [spec["system"], json.loads(spec["state"]),
               [digest(Path(root)/im["path"]) for im in public["images"]]]
    return hashlib.sha256(json.dumps(content, sort_keys=True, ensure_ascii=False).encode()).hexdigest()