# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.

import inspect
import os
import socket
import subprocess
import tempfile
import time

import pytest
import requests
import yaml
from llama_stack_client import LlamaStackClient
from openai import OpenAI

from llama_stack import LlamaStackAsLibraryClient
from llama_stack.distribution.stack import run_config_from_adhoc_config_spec
from llama_stack.env import get_env_or_fail

DEFAULT_PORT = 8321


def is_port_available(port: int, host: str = "localhost") -> bool:
    """Check if a port is available for binding."""
    try:
        with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
            sock.bind((host, port))
            return True
    except OSError:
        return False


def start_llama_stack_server(config_name: str) -> subprocess.Popen:
    """Start a llama stack server with the given config."""
    cmd = ["llama", "stack", "run", config_name]

    # Start server in background
    process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
    return process


def wait_for_server_ready(base_url: str, timeout: int = 120) -> bool:
    """Wait for the server to be ready by polling the health endpoint."""
    health_url = f"{base_url}/v1/health"
    start_time = time.time()

    while time.time() - start_time < timeout:
        try:
            response = requests.get(health_url, timeout=5)
            if response.status_code == 200:
                return True
        except (requests.exceptions.ConnectionError, requests.exceptions.Timeout):
            pass
        time.sleep(0.5)

    return False


@pytest.fixture(scope="session")
def provider_data():
    # TODO: this needs to be generalized so each provider can have a sample provider data just
    # like sample run config on which we can do replace_env_vars()
    keymap = {
        "TAVILY_SEARCH_API_KEY": "tavily_search_api_key",
        "BRAVE_SEARCH_API_KEY": "brave_search_api_key",
        "FIREWORKS_API_KEY": "fireworks_api_key",
        "GEMINI_API_KEY": "gemini_api_key",
        "OPENAI_API_KEY": "openai_api_key",
        "TOGETHER_API_KEY": "together_api_key",
        "ANTHROPIC_API_KEY": "anthropic_api_key",
        "GROQ_API_KEY": "groq_api_key",
        "WOLFRAM_ALPHA_API_KEY": "wolfram_alpha_api_key",
    }
    provider_data = {}
    for key, value in keymap.items():
        if os.environ.get(key):
            provider_data[value] = os.environ[key]
    return provider_data


@pytest.fixture(scope="session")
def inference_provider_type(llama_stack_client):
    providers = llama_stack_client.providers.list()
    inference_providers = [p for p in providers if p.api == "inference"]
    assert len(inference_providers) > 0, "No inference providers found"
    return inference_providers[0].provider_type


@pytest.fixture(scope="session")
def client_with_models(
    llama_stack_client,
    text_model_id,
    vision_model_id,
    embedding_model_id,
    embedding_dimension,
    judge_model_id,
):
    client = llama_stack_client

    providers = [p for p in client.providers.list() if p.api == "inference"]
    assert len(providers) > 0, "No inference providers found"
    inference_providers = [p.provider_id for p in providers if p.provider_type != "inline::sentence-transformers"]

    model_ids = {m.identifier for m in client.models.list()}
    model_ids.update(m.provider_resource_id for m in client.models.list())

    if text_model_id and text_model_id not in model_ids:
        client.models.register(model_id=text_model_id, provider_id=inference_providers[0])
    if vision_model_id and vision_model_id not in model_ids:
        client.models.register(model_id=vision_model_id, provider_id=inference_providers[0])
    if judge_model_id and judge_model_id not in model_ids:
        client.models.register(model_id=judge_model_id, provider_id=inference_providers[0])

    if embedding_model_id and embedding_model_id not in model_ids:
        # try to find a provider that supports embeddings, if sentence-transformers is not available
        selected_provider = None
        for p in providers:
            if p.provider_type == "inline::sentence-transformers":
                selected_provider = p
                break

        selected_provider = selected_provider or providers[0]
        client.models.register(
            model_id=embedding_model_id,
            provider_id=selected_provider.provider_id,
            model_type="embedding",
            metadata={"embedding_dimension": embedding_dimension or 384},
        )
    return client


@pytest.fixture(scope="session")
def available_shields(llama_stack_client):
    return [shield.identifier for shield in llama_stack_client.shields.list()]


@pytest.fixture(scope="session")
def model_providers(llama_stack_client):
    return {x.provider_id for x in llama_stack_client.providers.list() if x.api == "inference"}


@pytest.fixture(autouse=True)
def skip_if_no_model(request):
    model_fixtures = ["text_model_id", "vision_model_id", "embedding_model_id", "judge_model_id"]
    test_func = request.node.function

    actual_params = inspect.signature(test_func).parameters.keys()
    for fixture in model_fixtures:
        # Only check fixtures that are actually in the test function's signature
        if fixture in actual_params and fixture in request.fixturenames and not request.getfixturevalue(fixture):
            pytest.skip(f"{fixture} empty - skipping test")


@pytest.fixture(scope="session")
def llama_stack_client(request, provider_data):
    config = request.config.getoption("--stack-config")
    if not config:
        config = get_env_or_fail("LLAMA_STACK_CONFIG")

    if not config:
        raise ValueError("You must specify either --stack-config or LLAMA_STACK_CONFIG")

    # Handle server:<config_name> format or server:<config_name>:<port>
    if config.startswith("server:"):
        parts = config.split(":")
        config_name = parts[1]
        port = int(parts[2]) if len(parts) > 2 else int(os.environ.get("LLAMA_STACK_PORT", DEFAULT_PORT))
        base_url = f"http://localhost:{port}"

        # Check if port is available
        if is_port_available(port):
            print(f"Starting llama stack server with config '{config_name}' on port {port}...")

            # Start server
            server_process = start_llama_stack_server(config_name)

            # Wait for server to be ready
            if not wait_for_server_ready(base_url, timeout=120):
                print("Server failed to start within timeout")
                server_process.terminate()
                raise RuntimeError(
                    f"Server failed to start within timeout. Check that config '{config_name}' exists and is valid."
                )

            print(f"Server is ready at {base_url}")

            # Store process for potential cleanup (pytest will handle termination at session end)
            request.session._llama_stack_server_process = server_process
        else:
            print(f"Port {port} is already in use, assuming server is already running...")

        return LlamaStackClient(
            base_url=base_url,
            provider_data=provider_data,
        )

    # check if this looks like a URL
    if config.startswith("http") or "//" in config:
        return LlamaStackClient(
            base_url=config,
            provider_data=provider_data,
        )

    if "=" in config:
        run_config = run_config_from_adhoc_config_spec(config)
        run_config_file = tempfile.NamedTemporaryFile(delete=False, suffix=".yaml")
        with open(run_config_file.name, "w") as f:
            yaml.dump(run_config.model_dump(), f)
        config = run_config_file.name

    client = LlamaStackAsLibraryClient(
        config,
        provider_data=provider_data,
        skip_logger_removal=True,
    )
    if not client.initialize():
        raise RuntimeError("Initialization failed")

    return client


@pytest.fixture(scope="session")
def openai_client(client_with_models):
    base_url = f"{client_with_models.base_url}/v1/openai/v1"
    return OpenAI(base_url=base_url, api_key="fake")