##################################################################################################
# Adapted from https://github.com/TheAgentCompany/TheAgentCompany/blob/main/evaluation/run_eval.py
##################################################################################################

import asyncio
import base64
import json
import os
import shutil
import tempfile
from typing import List

import yaml
from browsing import pre_login

from evaluation.utils.shared import get_default_sandbox_config_for_eval
from openhands.controller.state.state import State
from openhands.core.config import (
    AppConfig,
    LLMConfig,
    get_agent_config_arg,
    get_llm_config_arg,
    get_parser,
)
from openhands.core.config.agent_config import AgentConfig
from openhands.core.logger import openhands_logger as logger
from openhands.core.main import create_runtime, run_controller
from openhands.events.action import CmdRunAction, MessageAction
from openhands.events.observation import BrowserOutputObservation, CmdOutputObservation
from openhands.runtime.base import Runtime
from openhands.utils.async_utils import call_async_from_sync


def get_config(
    base_container_image: str,
    task_short_name: str,
    mount_path_on_host: str,
    llm_config: LLMConfig,
    agent_config: AgentConfig | None,
) -> AppConfig:
    search_api_key = os.environ.get('SEARCH_API_KEY', None)
    assert search_api_key is not None, 'Environment variable SEARCH_API_KEY is not set.'

    sandbox_config = get_default_sandbox_config_for_eval()
    sandbox_config.base_container_image = base_container_image
    sandbox_config.runtime_startup_env_vars = {
        'SEARCH_API_KEY': search_api_key,
    }
    sandbox_config.enable_auto_lint = True
    # If the web services are running on the host machine, this must be set to True
    sandbox_config.use_host_network = True

    config = AppConfig(
        run_as_openhands=False,
        max_budget_per_task=10,
        max_iterations=100,
        save_trajectory_path=os.path.join(
            mount_path_on_host, f'traj_{task_short_name}.json'
        ),
        sandbox=sandbox_config,
        # we mount trajectories path so that trajectories, generated by OpenHands
        # controller, can be accessible to the evaluator file in the runtime container
        workspace_mount_path=mount_path_on_host,
        workspace_mount_path_in_sandbox='/outputs',
    )
    config.set_llm_config(llm_config)
    if agent_config:
        config.set_agent_config(agent_config)
    else:
        logger.info('Agent config not provided, using default settings')
        agent_config = AgentConfig(
            enable_prompt_extensions=False,
        )
        config.set_agent_config(agent_config)
    print(agent_config)
    return config


def load_dependencies(runtime: Runtime) -> List[str]:
    """
    Every task has a dependencies.yml file, which lists all the services that the
    task depends on. This function loads the file and returns all dependent service names.
    """
    command = 'cat /utils/dependencies.yml'
    action = CmdRunAction(command=command)
    logger.info(action, extra={'msg_type': 'ACTION'})
    obs: CmdOutputObservation = runtime.run_action(action)
    logger.info(obs, extra={'msg_type': 'OBSERVATION'})
    assert obs.exit_code == 0
    dependencies = yaml.safe_load(obs.content)
    if dependencies is None:
        dependencies = []
    return dependencies


def init_task_env(runtime: Runtime, hostname: str, env_llm_config: LLMConfig):
    command = (
        f'SERVER_HOSTNAME={hostname} '
        f'LITELLM_API_KEY={env_llm_config.api_key.get_secret_value() if env_llm_config.api_key else None} '
        f'LITELLM_BASE_URL={env_llm_config.base_url} '
        f'LITELLM_MODEL={env_llm_config.model} '
        'bash /utils/init.sh'
    )
    action = CmdRunAction(command=command)
    action.set_hard_timeout(1800)
    logger.info(action, extra={'msg_type': 'ACTION'})
    obs = runtime.run_action(action)
    logger.info(obs, extra={'msg_type': 'OBSERVATION'})
    assert obs.exit_code == 0


def codeact_user_response(state: State) -> str:
    msg = (
        'Please continue working on the task on whatever approach you think is suitable.\n'
        'If you think you have solved the task, please use the finish tool to end the interaction.\n'
        'IMPORTANT: YOU SHOULD NEVER ASK FOR HUMAN HELP.\n'
    )
    return msg


def run_solver(
    runtime: Runtime,
    task_name: str,
    config: AppConfig,
    dependencies: List[str],
    save_final_state: bool,
    state_dir: str,
    save_screenshots: bool,
    screenshots_dir: str,
) -> State:
    instruction = """Complete the following task: """
    cmd_action = CmdRunAction(command='cat /instruction/task.md')
    obs = runtime.run_action(cmd_action)
    task_description: str = obs.content
    instruction += task_description
    instruction += '\n\nIMPORTANT: If there are the-agent-company.com websites mentioned in the task description, NOTE that these websites are privately hosted.\n'
    instruction += (
        'IMPORTANT: If you want to close pop-ups, please press the escape key.\n'
    )
    instruction += 'IMPORTANT: You should NEVER ask for Human Help.\n'
    if 'gitlab' in dependencies:
        instruction += "IMPORTANT: You are already signed in to Gitlab, but here are the sign-in credentials for your reference - Gitlab username is 'root' and password is 'theagentcompany'"

    state: State | None = asyncio.run(
        run_controller(
            config=config,
            sid=task_name,
            initial_user_action=MessageAction(content=instruction),
            runtime=runtime,
            fake_user_response_fn=codeact_user_response,
        )
    )
    logger.info(state)

    if save_screenshots:
        screenshots_dir = os.path.join(screenshots_dir, task_name)
        os.makedirs(screenshots_dir, exist_ok=True)
        for image_id, obs in enumerate(state.history):
            if isinstance(obs, BrowserOutputObservation):
                if obs.screenshot is None:
                    continue
                image_data = base64.b64decode(
                    obs.screenshot.replace('data:image/png;base64,', '')
                )
                with open(
                    os.path.join(screenshots_dir, f'{image_id}.png'), 'wb'
                ) as file:
                    file.write(image_data)
                if obs.set_of_marks:
                    som_image_data = base64.b64decode(
                        obs.set_of_marks.replace('data:image/png;base64,', '')
                    )
                    with open(
                        os.path.join(screenshots_dir, f'{image_id}_som.png'), 'wb'
                    ) as file:
                        file.write(som_image_data)

    if save_final_state:
        os.makedirs(state_dir, exist_ok=True)
        with open(os.path.join(state_dir, f'state_{task_name}.json'), 'w') as file:
            json.dump(str(state), file)

    return state


def run_evaluator(
    runtime: Runtime, env_llm_config: LLMConfig, trajectory_path: str, result_path: str
):
    # HACK: this allows us to use the anthropic API for evaluator LLM.
    if 'sde-add-wiki-page' in trajectory_path:
        command = 'python_default -m pip install -U litellm'
        action = CmdRunAction(command=command)
        obs = runtime.run_action(action)
        logger.info(obs, extra={'msg_type': 'OBSERVATION'})
    command = (
        f'LITELLM_API_KEY={env_llm_config.api_key.get_secret_value() if env_llm_config.api_key else None} '
        f'LITELLM_BASE_URL={env_llm_config.base_url} '
        f'LITELLM_MODEL={env_llm_config.model} '
        f"DECRYPTION_KEY='theagentcompany is all you need' "  # Hardcoded Key
        f'python_default /utils/eval.py --trajectory_path {trajectory_path} --result_path {result_path}'
    )
    action = CmdRunAction(command=command)
    action.set_hard_timeout(600)
    logger.info(action, extra={'msg_type': 'ACTION'})
    obs = runtime.run_action(action)
    logger.info(obs, extra={'msg_type': 'OBSERVATION'})
    assert obs.exit_code == 0


if __name__ == '__main__':
    parser = get_parser()
    parser.add_argument(
        '--task-image-name',
        type=str,
        default='ghcr.io/theagentcompany/example-image:1.0.0',
        help='Task image name',
    )
    parser.add_argument(
        '--outputs-path',
        type=str,
        default='./outputs',
        help='Folder path to save trajectories and evaluation results',
    )
    parser.add_argument(
        '--server-hostname',
        type=str,
        default='localhost',
        help='Server hostname, e.g. localhost to access the host machine from the container, '
        'assuming the task docker container is run with `--network host` flag',
    )
    parser.add_argument(
        '--agent-llm-config',
        type=str,
        default=None,
        help='LLM config for agent',
    )
    parser.add_argument(
        '--env-llm-config',
        type=str,
        default=None,
        help='LLM config for evaluation environment (NPC & llm-based evaluator)',
    )
    args, _ = parser.parse_known_args()

    agent_config: AgentConfig | None = None
    if args.agent_config:
        agent_config = get_agent_config_arg(args.agent_config)

    agent_llm_config: LLMConfig | None = None
    if args.agent_llm_config:
        agent_llm_config = get_llm_config_arg(args.agent_llm_config)

    if agent_llm_config is None:
        raise ValueError(
            f'Could not find LLM config for agent: --agent-llm-config {args.agent_llm_config}'
        )

    if agent_llm_config.api_key is None:
        raise ValueError('LLM API key is not set for agent')

    env_llm_config: LLMConfig | None = None
    if args.env_llm_config:
        env_llm_config = get_llm_config_arg(args.env_llm_config)

    if env_llm_config is None:
        raise ValueError(
            f'Could not find LLM config for evaluation environment: --env-llm-config {args.env_llm_config}'
        )

    if env_llm_config.api_key is None:
        raise ValueError('LLM API key is not set for evaluation environment')

    task_short_name = args.task_image_name.split('/')[-1].split(':')[0]
    logger.info(
        f'Task image name is {args.task_image_name}, short name is {task_short_name}'
    )

    # mount a temporary directory to pass trajectory from host to container, and to
    # pass the evaluation result from container to host
    # 1) trajectory is dumped by OpenHands library (on host machine), but it's needed by
    # evaluator (in container), so we mount a temporary directory to pass it in
    # 2) evaluation result is written by evaluator (in container), but we need to persist
    # it on host machine, so we mount a temporary directory to pass it out
    if os.getenv('TMPDIR') and os.path.exists(os.getenv('TMPDIR')):
        temp_dir = os.path.abspath(os.getenv('TMPDIR'))
    else:
        temp_dir = tempfile.mkdtemp()
    config: AppConfig = get_config(
        args.task_image_name, task_short_name, temp_dir, agent_llm_config, agent_config
    )
    runtime: Runtime = create_runtime(config)
    call_async_from_sync(runtime.connect)
    init_task_env(runtime, args.server_hostname, env_llm_config)

    dependencies = load_dependencies(runtime)
    logger.info(f'Service dependencies: {dependencies}')

    try:
        pre_login(
            runtime,
            dependencies,
            save_screenshots=True,
            screenshots_dir=os.path.join(
                os.path.abspath(args.outputs_path), 'screenshots'
            ),
        )
    except Exception as e:
        logger.error(f'Failed to pre-login: {e}')

        # before giving up, let's try to init and login again
        init_task_env(runtime, args.server_hostname, env_llm_config)
        pre_login(
            runtime,
            dependencies,
            save_screenshots=True,
            screenshots_dir=os.path.join(
                os.path.abspath(args.outputs_path), 'screenshots'
            ),
        )

    state = run_solver(
        runtime,
        task_short_name,
        config,
        dependencies,
        save_final_state=True,
        state_dir=os.path.abspath(args.outputs_path),
        save_screenshots=True,
        screenshots_dir=os.path.join(os.path.abspath(args.outputs_path), 'screenshots'),
    )

    # this path is the absolute path in the runtime container
    trajectory_path = f'/outputs/traj_{task_short_name}.json'
    result_path = f'/outputs/eval_{task_short_name}.json'

    run_evaluator(runtime, env_llm_config, trajectory_path, result_path)

    # finally, move trajectory file and evaluation result from mount path on host (temp dir) to outputs path
    shutil.move(
        os.path.join(temp_dir, f'traj_{task_short_name}.json'),
        os.path.join(
            os.path.abspath(args.outputs_path), f'traj_{task_short_name}.json'
        ),
    )
    shutil.move(
        os.path.join(temp_dir, f'eval_{task_short_name}.json'),
        os.path.join(
            os.path.abspath(args.outputs_path), f'eval_{task_short_name}.json'
        ),
    )
