import json

from llm_connect.gemini import GeminiAdapter
from llm_connect.models import RunConfig
from llm_connect.openai import OpenAIAdapter
from llm_connect.openrouter import OpenRouterAdapter


STRUCTURED_SCHEMA = {
    "type": "object",
    "properties": {
        "summary": {"type": "string"},
        "recommendations": {"type": "array", "items": {"type": "string"}},
    },
    "required": ["summary", "recommendations"],
}


SMOKE_CONFIG = RunConfig(
    model_name="gpt-4",
    temperature=0.1,
    max_tokens=300,
    model_params={
        "reasoning_effort": "medium",
        "max_depth": 3,
        "json_schema": STRUCTURED_SCHEMA,
    },
)


def test_openrouter_structured_output_payload_and_model_routing(monkeypatch):
    captured: dict[str, object] = {}

    def fake_post_json(url, payload, headers=None, timeout=300):  # noqa: ANN001
        captured["url"] = url
        captured["payload"] = payload
        captured["headers"] = headers
        captured["timeout"] = timeout
        return {
            "id": "or-response",
            "model": payload["model"],
            "choices": [
                {
                    "message": {
                        "content": json.dumps(
                            {"summary": "ok", "recommendations": ["keep payload clean"]}
                        )
                    },
                    "finish_reason": "stop",
                }
            ],
            "usage": {"prompt_tokens": 1, "completion_tokens": 2, "total_tokens": 3},
        }

    monkeypatch.setattr("llm_connect.openrouter.post_json", fake_post_json)
    adapter = OpenRouterAdapter(
        model="anthropic/claude-sonnet-4",
        api_key="or-test",
        api_base="https://openrouter.example/api/v1",
    )

    response = adapter.execute_prompt("Return JSON.", SMOKE_CONFIG)
    payload = captured["payload"]

    assert response.model == "anthropic/claude-sonnet-4"
    assert payload["model"] == "anthropic/claude-sonnet-4"
    assert payload["response_format"]["json_schema"]["schema"] == STRUCTURED_SCHEMA
    assert payload["response_format"]["json_schema"]["strict"] is False
    assert "reasoning_effort" not in payload
    assert "max_depth" not in payload
    assert "json_schema" not in payload


def test_openai_structured_output_payload(monkeypatch):
    captured: dict[str, object] = {}

    def fake_post_json(url, payload, headers=None, timeout=300):  # noqa: ANN001
        captured["payload"] = payload
        return {
            "id": "oa-response",
            "model": payload["model"],
            "choices": [
                {
                    "message": {
                        "content": json.dumps({"summary": "ok", "recommendations": []})
                    },
                    "finish_reason": "stop",
                }
            ],
            "usage": {"prompt_tokens": 1, "completion_tokens": 2, "total_tokens": 3},
        }

    monkeypatch.setattr("llm_connect.openai.post_json", fake_post_json)
    adapter = OpenAIAdapter(model="gpt-4.1-mini", api_key="sk-test")

    response = adapter.execute_prompt("Return JSON.", SMOKE_CONFIG)
    payload = captured["payload"]

    assert response.model == "gpt-4.1-mini"
    assert payload["model"] == "gpt-4.1-mini"
    assert payload["response_format"]["json_schema"]["schema"] == STRUCTURED_SCHEMA
    assert "reasoning_effort" not in payload
    assert "max_depth" not in payload
    assert "json_schema" not in payload


def test_gemini_structured_output_payload(monkeypatch):
    captured: dict[str, object] = {}

    def fake_post_json(url, payload, headers=None, timeout=300):  # noqa: ANN001
        captured["url"] = url
        captured["payload"] = payload
        return {
            "candidates": [
                {
                    "content": {
                        "parts": [
                            {"text": json.dumps({"summary": "ok", "recommendations": []})}
                        ]
                    },
                    "finishReason": "STOP",
                }
            ],
            "usageMetadata": {
                "promptTokenCount": 1,
                "candidatesTokenCount": 2,
                "totalTokenCount": 3,
            },
        }

    monkeypatch.setattr("llm_connect.gemini.post_json", fake_post_json)
    adapter = GeminiAdapter(model="gemini-2.5-flash", api_key="gemini-test")

    response = adapter.execute_prompt("Return JSON.", SMOKE_CONFIG)
    payload = captured["payload"]

    assert response.model == "gemini-2.5-flash"
    assert payload["generationConfig"]["responseMimeType"] == "application/json"
    assert payload["generationConfig"]["responseSchema"] == STRUCTURED_SCHEMA
    assert "reasoning_effort" not in payload
    assert "max_depth" not in payload
    assert "json_schema" not in payload