from unittest.mock import Mock, patch

import pytest

from mem0.configs.llms.base import BaseLlmConfig
from mem0.configs.llms.lmstudio import LMStudioConfig
from mem0.llms.lmstudio import LMStudioLLM


@pytest.fixture
def mock_lm_studio_client():
    with patch("mem0.llms.lmstudio.OpenAI") as mock_openai:  # Corrected path
        mock_client = Mock()
        mock_client.chat.completions.create.return_value = Mock(
            choices=[Mock(message=Mock(content="I'm doing well, thank you for asking!"))]
        )
        mock_openai.return_value = mock_client
        yield mock_client


def test_generate_response_without_tools(mock_lm_studio_client):
    config = LMStudioConfig(
        model="lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf",
        temperature=0.7,
        max_tokens=100,
        top_p=1.0,
    )
    llm = LMStudioLLM(config)
    messages = [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello, how are you?"},
    ]

    response = llm.generate_response(messages)

    mock_lm_studio_client.chat.completions.create.assert_called_once_with(
        model="lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf",
        messages=messages,
        temperature=0.7,
        max_tokens=100,
        top_p=1.0,
        response_format={"type": "json_object"},
    )

    assert response == "I'm doing well, thank you for asking!"


def test_generate_response_specifying_response_format(mock_lm_studio_client):
    config = LMStudioConfig(
        model="lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf",
        temperature=0.7,
        max_tokens=100,
        top_p=1.0,
        lmstudio_response_format={"type": "json_schema"},  # Specifying the response format in config
    )
    llm = LMStudioLLM(config)
    messages = [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello, how are you?"},
    ]

    response = llm.generate_response(messages)

    mock_lm_studio_client.chat.completions.create.assert_called_once_with(
        model="lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf",
        messages=messages,
        temperature=0.7,
        max_tokens=100,
        top_p=1.0,
        response_format={"type": "json_schema"},
    )

    assert response == "I'm doing well, thank you for asking!"


def test_base_url_honors_lmstudio_base_url_env(monkeypatch):
    monkeypatch.setenv("LMSTUDIO_BASE_URL", "http://lmstudio.internal:9000/v1")

    with patch("mem0.llms.lmstudio.OpenAI") as mock_openai:
        LMStudioLLM(LMStudioConfig())

    assert mock_openai.call_args.kwargs["base_url"] == "http://lmstudio.internal:9000/v1"


def test_base_url_prefers_explicit_config_over_env(monkeypatch):
    monkeypatch.setenv("LMSTUDIO_BASE_URL", "http://lmstudio.internal:9000/v1")

    with patch("mem0.llms.lmstudio.OpenAI") as mock_openai:
        LMStudioLLM(LMStudioConfig(lmstudio_base_url="http://explicit:1234/v1"))

    assert mock_openai.call_args.kwargs["base_url"] == "http://explicit:1234/v1"


def test_base_url_falls_back_to_localhost_default(monkeypatch):
    monkeypatch.delenv("LMSTUDIO_BASE_URL", raising=False)

    with patch("mem0.llms.lmstudio.OpenAI") as mock_openai:
        llm = LMStudioLLM(LMStudioConfig())

    assert mock_openai.call_args.kwargs["base_url"] == "http://localhost:1234/v1"
    assert llm.config.lmstudio_base_url == "http://localhost:1234/v1"


def test_base_url_honors_env_via_base_llm_config(monkeypatch):
    monkeypatch.setenv("LMSTUDIO_BASE_URL", "http://lmstudio.internal:9000/v1")

    with patch("mem0.llms.lmstudio.OpenAI") as mock_openai:
        LMStudioLLM(BaseLlmConfig(model="some-model"))

    assert mock_openai.call_args.kwargs["base_url"] == "http://lmstudio.internal:9000/v1"