import json
from datetime import datetime, timezone

from llm_connect.cli import main
from llm_connect.quality import QualityLedger, QualityObservation


def test_rates_show_json_outputs_default_registry(capsys):
    assert main(["rates", "show", "--json"]) == 0

    payload = json.loads(capsys.readouterr().out)

    assert payload["openai/gpt-4o-mini"]["prompt_per_1k"] == 0.00015


def test_classes_show_lists_builtins(capsys):
    assert main(["classes", "show"]) == 0

    output = capsys.readouterr().out

    assert "chunk-summarization" in output
    assert "entity-extraction" in output


def test_classes_fit_reads_quality_ledger(tmp_path, capsys):
    ledger = QualityLedger(tmp_path / "quality.jsonl")
    for _ in range(3):
        ledger.append(
            QualityObservation(
                task_type="extract",
                adapter_id="openrouter",
                model_id="openai/gpt-4o-mini",
                cost_usd=0.001,
                quality_score=0.9,
                latency_ms=100,
                tokens_in=500,
                tokens_out=350,
                recorded_at=datetime(2026, 5, 19, tzinfo=timezone.utc),
                tags={
                    "problem_class": "entity-extraction",
                    "dimensions": {
                        "chunk_words": 300,
                        "template_words": 100,
                        "expected_entities": 5,
                    },
                },
            )
        )

    assert main(["classes", "fit", str(ledger.path), "--class", "entity-extraction", "--json"]) == 0

    payload = json.loads(capsys.readouterr().out)

    assert payload["entity-extraction"]["params"]["tokens_per_entity"] == 70