"""Verify run_with_tool_loop supports a custom send_func for vendors
that don't use send_openai_compatible (gemini_cli, gemini, anthropic,
deepseek). The vendor provides a send_func that returns a
NormalizedResponse, and the helper handles history + dispatch.
"""
from __future__ import annotations
from typing import Any
from unittest.mock import MagicMock, patch
from src.openai_compatible import NormalizedResponse
from src.ai_client import run_with_tool_loop
from src.vendor_capabilities import VendorCapabilities

def _make_normalized_response(text: str = "ok", tool_calls: list[dict[str, Any]] | None = None) -> NormalizedResponse:
 return NormalizedResponse(
  text=text, tool_calls=tool_calls or [],
  usage_input_tokens=10, usage_output_tokens=5,
  usage_cache_read_tokens=0, usage_cache_creation_tokens=0,
  raw_response=None,
 )

def test_run_with_tool_loop_uses_send_func_when_provided() -> None:
 client = MagicMock()
 def send_func(_round_idx: int) -> NormalizedResponse:
  return _make_normalized_response(f"from-send-func-{_round_idx}")
 result = run_with_tool_loop(
  client, request=lambda _i: MagicMock(),  # should be IGNORED
  base_dir=".", vendor_name="custom",
  send_func=send_func,
 )
 assert result == "from-send-func-0"

def test_run_with_tool_loop_dispatches_via_send_func() -> None:
 client = MagicMock()
 tool_resp = _make_normalized_response(
  "first", tool_calls=[{"id": "c1", "type": "function", "function": {"name": "t", "arguments": "{}"}}]
 )
 final = _make_normalized_response("done")
 def send_func(round_idx: int) -> NormalizedResponse:
  return [tool_resp, final][round_idx]
 with patch("src.ai_client._execute_tool_calls_concurrently", return_value=[("t", "c1", "r", "")]) as dispatch:
  result = run_with_tool_loop(
   client, request=lambda _i: MagicMock(),
   base_dir=".", vendor_name="custom",
   send_func=send_func,
  )
  assert result == "done"
  assert dispatch.call_count == 1
