"""Shared fixtures and helpers for report-export tests."""

import json
import tempfile
import unittest
from pathlib import Path
from analysis.letters_reports import (
    analyze_modular,
    export_reports,
    load_profile as load,
)


def vlm(letter, qid=1, score=1.0, protocol="base", model="m", frames=32):
    r = {
        "model": model,
        "protocol": protocol,
        "condition": protocol,
        "question_id": qid,
        "scene": "s",
        "dataset": "d",
        "question_type": "count",
        "score": score,
        "frame_count": frames,
        "input_token_count": 10,
        "output_token_count": 2,
        "generation_seconds": 1.0,
        "answer_given": "x",
        "full_prompt": "p",
    }
    if letter == "A":
        r["frame_selection"] = "uniform"
    elif letter in "BC":
        r.update(
            input_selection="uniform",
            spatial_code_format="explicit",
            depth="metric",
            tracking="tracking",
        )
    return r


def put(root, relative, record):
    p = root / relative
    p.parent.mkdir(parents=True, exist_ok=True)
    p.write_text(json.dumps(record))
    return p


class ReportTestCase(unittest.TestCase):
    def setUp(self):
        self.temp = tempfile.TemporaryDirectory()
        self.root = Path(self.temp.name)

    def tearDown(self):
        self.temp.cleanup()

    def directory(self, letter, records):
        d = self.root / letter
        d.mkdir()
        for i, r in enumerate(records):
            put(d, f"{i}.json", r)
        return d

    def symbolic(self, future=False):
        d = self.root / ("F_future" if future else "F")
        d.mkdir(exist_ok=True)
        prefix = (
            "perceived/metric/tracking/uniform/32/explicit"
            if future
            else "metric/tracking/uniform/32/explicit"
        )
        put(
            d,
            f"{prefix}/s/1.json",
            {
                "model": "symbolic",
                "condition": "metric:tracking:uniform:32:explicit",
                "question_id": 1,
                "scene": "s",
                "dataset": "d",
                "question_type": "count",
                "score": 1.0,
                "spatial_code_format": "explicit",
                "depth": "metric",
                "tracking": "tracking",
                "input": "uniform",
                "number_of_frames": 32,
            },
        )
        return d

    def ground_truth_symbolic(self):
        d = self.root / "F"
        d.mkdir()
        put(
            d,
            "ground truth/explicit/s/1.json",
            {
                "model": "symbolic",
                "condition": "ground truth:explicit",
                "question_id": 1,
                "scene": "s",
                "dataset": "d",
                "question_type": "count",
                "score": 1.0,
                "spatial_code_format": "explicit",
            },
        )
        return d

    def analyze(self, letters, dirs, pairs=(), protocols=("base",)):
        profiles = {l: load(l) for l in letters}
        per, combined = analyze_modular(
            {l: dirs[l] for l in letters}, profiles, protocols, pairs
        )
        paths = export_reports(per, combined, self.root / "reports")
        return per, combined, {p.name for p in paths}