import sys
import os
import json
import subprocess
from unittest.mock import patch, MagicMock
from src import ai_client

def test_gemini_cli_full_integration() -> None:
    """Integration test for the Gemini CLI provider and tool bridge."""
    from src import ai_client
    
    # 1. Setup mock response with a tool call
    tool_call_json = {
        "kind": "tool_use",
        "payload": {
            "id": "call_123",
            "name": "read_file",
            "input": {"path": "test.txt"}
        }
    }
    
    # 2. Setup mock final response
    final_resp_json = {
        "kind": "message",
        "payload": "Final integrated answer"
    }
    
    # 3. Mock subprocess.Popen
    mock_process = MagicMock()
    mock_process.stdout = [
        (json.dumps(tool_call_json) + "\n").encode('utf-8'),
        (json.dumps(final_resp_json) + "\n").encode('utf-8')
    ]
    mock_process.stderr = []
    mock_process.returncode = 0
    
    with patch('subprocess.Popen', return_value=mock_process), \
         patch('src.mcp_client.dispatch', return_value="file content") as mock_dispatch:
        
        ai_client.set_provider("gemini_cli", "gemini-2.0-flash")
        result = ai_client.send("context", "integrated test")
        
        assert result == "Final integrated answer"
        assert mock_dispatch.called
        mock_dispatch.assert_called_with("read_file", {"path": "test.txt"})

def test_gemini_cli_rejection_and_history() -> None:
    """Integration test for the Gemini CLI provider: Rejection flow and history."""
    from src import ai_client
    
    # Tool call
    tool_call_json = {
        "kind": "tool_use",
        "payload": {"id": "c1", "name": "run_powershell", "input": {"script": "dir"}}
    }
    
    mock_process = MagicMock()
    mock_process.stdout = [(json.dumps(tool_call_json) + "\n").encode('utf-8')]
    mock_process.stderr = []
    mock_process.returncode = 0
    
    with patch('subprocess.Popen', return_value=mock_process):
        ai_client.set_provider("gemini_cli", "gemini-2.0-flash")
        
        # Simulate rejection
        def pre_tool_cb(*args, **kwargs):
            return None # Reject
            
        result = ai_client.send("ctx", "msg", pre_tool_callback=pre_tool_cb)
        # In current impl, if rejected, it returns the accumulated text so far
        # or a message about rejection.
        assert "REJECTED" in result or result == ""