"""Regression tests: OpenAI reasoning models reject a non-default temperature.

o1/o3/o4/gpt-5 only accept the default temperature (1); sending an explicit
value — even 0.0 — returns HTTP 400 "Only the default (1) value is supported".
The OpenAI-compatible payload builders must omit the temperature field for these
models so chat (with a non-default preset) and endpoint probing don't break.
"""
import httpx
import pytest

from src import llm_core


@pytest.mark.parametrize(
    "model",
    ["o1", "o1-mini", "o3", "o3-mini", "o4-mini", "gpt-5", "gpt-5-mini",
     "openrouter/openai/o3-mini", "OpenAI/GPT-5", "kimi-for-coding"],
)
def test_reasoning_models_restrict_temperature(model):
    assert llm_core._restricts_temperature(model) is True


@pytest.mark.parametrize(
    "model",
    ["gpt-4o", "gpt-4.1", "gpt-3.5-turbo", "gpt-4.5-preview",
     "claude-3-5-sonnet", "llama3.1", "", None],
)
def test_normal_models_allow_temperature(model):
    assert llm_core._restricts_temperature(model) is False


def _capture_openai_payload(
    monkeypatch,
    model,
    temperature,
    url="https://api.openai.com/v1/chat/completions",
):
    """Run a synchronous OpenAI-compatible call and return the posted JSON body."""
    llm_core._response_cache.clear()
    seen = {}

    def fake_post(url, headers=None, json=None, timeout=None):
        seen["json"] = json
        request = httpx.Request("POST", url)
        return httpx.Response(
            200,
            request=request,
            json={"choices": [{"message": {"content": "OK"}}]},
        )

    monkeypatch.setattr(llm_core.httpx, "post", fake_post)
    result = llm_core.llm_call(
        url,
        model,
        [{"role": "user", "content": "Say OK"}],
        temperature=temperature,
        max_tokens=5,
    )
    assert result == "OK"
    return seen["json"]


def test_reasoning_model_payload_omits_temperature(monkeypatch):
    payload = _capture_openai_payload(monkeypatch, "o3-mini", 0.0)
    assert "temperature" not in payload
    # Reasoning models also use max_completion_tokens, which must survive.
    assert payload["max_completion_tokens"] == 5


def test_kimi_for_coding_payload_omits_temperature(monkeypatch):
    payload = _capture_openai_payload(monkeypatch, "kimi-for-coding", 0.1)
    assert "temperature" not in payload
    assert payload["max_tokens"] == 5


def test_normal_model_payload_keeps_temperature(monkeypatch):
    payload = _capture_openai_payload(monkeypatch, "gpt-4o", 0.2)
    assert payload["temperature"] == 0.2
    assert payload["max_tokens"] == 5


def test_normal_model_payload_keeps_temperature_above_one(monkeypatch):
    # OpenAI/local providers may validly use temperatures above 1.0; the clamp
    # is Anthropic-only and must not touch this path.
    payload = _capture_openai_payload(monkeypatch, "gpt-4o", 1.2)
    assert payload["temperature"] == 1.2


def test_local_minimax_mlx_payload_gets_stability_defaults(monkeypatch):
    import src.model_context as model_context

    monkeypatch.setattr(model_context, "is_local_endpoint", lambda _url: True)
    payload = {
        "model": "example-org/MiniMax-M2.7-BF16-mlx-4Bit",
        "temperature": 0.9,
    }

    llm_core._apply_local_generation_stability(
        payload,
        "http://192.168.1.22:8091/v1/chat/completions",
        "example-org/MiniMax-M2.7-BF16-mlx-4Bit",
    )

    assert payload["temperature"] == 0.2
    assert payload["top_p"] == 0.9
    assert payload["top_k"] == 20
    assert payload["max_tokens"] == 2048
    assert payload["repetition_penalty"] == 1.12


def test_chatgpt_subscription_payload_omits_max_output_tokens():
    # ChatGPT Subscription Codex API does not support max_output_tokens —
    # passing it returns HTTP 400 "Unsupported parameter: max_output_tokens".
    # The payload should NOT include max_output_tokens regardless of max_tokens.
    payload = llm_core._build_chatgpt_responses_payload(
        "gpt-5.1-codex",
        [{"role": "user", "content": "Say OK"}],
        temperature=0.2,
        max_tokens=37,
    )

    assert "max_output_tokens" not in payload


def test_chatgpt_subscription_payload_omits_max_output_tokens_when_zero():
    payload = llm_core._build_chatgpt_responses_payload(
        "gpt-5.1-codex",
        [{"role": "user", "content": "Say OK"}],
        temperature=0.2,
        max_tokens=0,
    )

    assert "max_output_tokens" not in payload
