"""OpenTelemetry integration tests for `llm.Model.context_call`."""

from __future__ import annotations

from collections.abc import Generator

import pytest
from inline_snapshot import snapshot
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export.in_memory_span_exporter import InMemorySpanExporter

from mirascope import llm, ops
from tests.ops.utils import span_snapshot


@pytest.fixture(autouse=True, scope="function")
def initialize(tracer_provider: TracerProvider) -> Generator[None, None, None]:
    """Initialize ops configuration and LLM instrumentation for each test."""
    ops.configure(tracer_provider=tracer_provider)
    ops.instrument_llm()
    yield
    ops.uninstrument_llm()


@pytest.mark.vcr()
def test_model_context_call_exports_genai_span(
    span_exporter: InMemorySpanExporter,
) -> None:
    """Ensure context_call emits a GenAI span with expected attributes."""
    model = llm.Model(model_id="openai/gpt-4o-mini")
    ctx = llm.Context(deps={"tenant": "kai"})
    messages = [
        llm.messages.system("You are a concise assistant."),
        llm.messages.user("Say hello to the user named Kai."),
    ]

    response = model.context_call(messages, ctx=ctx)
    assert "Kai" in response.pretty()

    spans = span_exporter.get_finished_spans()
    assert len(spans) == 1
    span_dict = span_snapshot(spans[0])
    assert span_dict == snapshot(
        {
            "name": "chat openai/gpt-4o-mini",
            "kind": "CLIENT",
            "status": "UNSET",
            "attributes": {
                "gen_ai.operation.name": "chat",
                "gen_ai.provider.name": "openai",
                "gen_ai.request.model": "openai/gpt-4o-mini",
                "gen_ai.output.type": "text",
                "gen_ai.response.model": "openai/gpt-4o-mini",
                "gen_ai.response.finish_reasons": ["stop"],
                "gen_ai.response.id": "resp_0232fb1fc58d743a00691ae4934c6c8197a687889ce65332e6",
                "gen_ai.system_instructions": '[{"type":"text","content":"You are a concise assistant."}]',
                "gen_ai.input.messages": '[{"role":"user","parts":[{"type":"text","content":"Say hello to the user named Kai."}]}]',
                "gen_ai.output.messages": '[{"role":"assistant","parts":[{"type":"text","content":"Hello, Kai! How can I assist you today?"}],"finish_reason":"stop"}]',
                "gen_ai.usage.input_tokens": 25,
                "gen_ai.usage.output_tokens": 12,
                "mirascope.response.messages": '[{"role":"system","content":{"type":"text","text":"You are a concise assistant."}},{"role":"user","content":[{"type":"text","text":"Say hello to the user named Kai."}],"name":null}]',
                "mirascope.response.content": '[{"type":"text","text":"Hello, Kai! How can I assist you today?"}]',
                "mirascope.response.usage": '{"input_tokens":25,"output_tokens":12,"cache_read_tokens":0,"cache_write_tokens":0,"reasoning_tokens":0,"total_tokens":37}',
            },
        }
    )
