import asyncio
import json
import logging
import multiprocessing as mp
import os
import pathlib
import subprocess
import time
from concurrent.futures import ProcessPoolExecutor
from typing import Any, Awaitable, Callable

import pandas as pd
from pydantic import BaseModel
from tqdm import tqdm

from openhands.controller.state.state import State
from openhands.core.config import LLMConfig
from openhands.core.logger import get_console_handler
from openhands.core.logger import openhands_logger as logger
from openhands.events.action import Action
from openhands.events.action.message import MessageAction


class EvalMetadata(BaseModel):
    agent_class: str
    llm_config: LLMConfig
    max_iterations: int
    eval_output_dir: str
    start_time: str
    git_commit: str
    dataset: str | None = None
    data_split: str | None = None
    details: dict[str, Any] | None = None

    def model_dump(self, *args, **kwargs):
        dumped_dict = super().model_dump(*args, **kwargs)
        # avoid leaking sensitive information
        dumped_dict['llm_config'] = self.llm_config.to_safe_dict()
        return dumped_dict

    def model_dump_json(self, *args, **kwargs):
        dumped = super().model_dump_json(*args, **kwargs)
        dumped_dict = json.loads(dumped)
        logger.debug(f'Dumped metadata: {dumped_dict}')
        # avoid leaking sensitive information
        dumped_dict['llm_config'] = self.llm_config.to_safe_dict()
        return json.dumps(dumped_dict)


class EvalOutput(BaseModel):
    # NOTE: User-specified
    instance_id: str
    instruction: str
    # output of the evaluation
    # store anything that is needed for the score calculation
    test_result: dict[str, Any]

    # Interaction info
    metadata: EvalMetadata
    history: list[tuple[dict[str, Any], dict[str, Any]]]
    metrics: dict[str, Any]
    error: str | None = None

    # Optionally save the input test instance
    instance: dict[str, Any] | None = None

    def model_dump(self, *args, **kwargs):
        dumped_dict = super().model_dump(*args, **kwargs)
        # Apply custom serialization for metadata (to avoid leaking sensitive information)
        dumped_dict['metadata'] = self.metadata.model_dump()
        return dumped_dict

    def model_dump_json(self, *args, **kwargs):
        dumped = super().model_dump_json(*args, **kwargs)
        dumped_dict = json.loads(dumped)
        # Apply custom serialization for metadata (to avoid leaking sensitive information)
        dumped_dict['metadata'] = json.loads(self.metadata.model_dump_json())
        return json.dumps(dumped_dict)


def codeact_user_response(
    state: State,
    encapsulate_solution: bool = False,
    try_parse: Callable[[Action], str] | None = None,
) -> str:
    encaps_str = (
        (
            'Please encapsulate your final answer (answer ONLY) within <solution> and </solution>.\n'
            'For example: The answer to the question is <solution> 42 </solution>.\n'
        )
        if encapsulate_solution
        else ''
    )
    msg = (
        'Please continue working on the task on whatever approach you think is suitable.\n'
        'If you think you have solved the task, please first send your answer to user through message and then <execute_bash> exit </execute_bash>.\n'
        f'{encaps_str}'
        'IMPORTANT: YOU SHOULD NEVER ASK FOR HUMAN HELP.\n'
    )

    if state.history:
        # check if the last action has an answer, if so, early exit
        if try_parse is not None:
            last_action = state.history.get_last_action()
            ans = try_parse(last_action)
            if ans is not None:
                return '/exit'

        # check if the agent has tried to talk to the user 3 times, if so, let the agent know it can give up
        user_msgs = [
            event
            for event in state.history.get_events()
            if isinstance(event, MessageAction) and event.source == 'user'
        ]
        if len(user_msgs) >= 2:
            # let the agent know that it can give up when it has tried 3 times
            return (
                msg
                + 'If you want to give up, run: <execute_bash> exit </execute_bash>.\n'
            )
    return msg


def cleanup():
    print('Cleaning up child processes...')
    for process in mp.active_children():
        print(f'Terminating child process: {process.name}')
        process.terminate()
        process.join()


def make_metadata(
    llm_config: LLMConfig,
    dataset_name: str,
    agent_class: str,
    max_iterations: int,
    eval_note: str | None,
    eval_output_dir: str,
    data_split: str | None = None,
    details: dict[str, Any] | None = None,
) -> EvalMetadata:
    model_name = llm_config.model.split('/')[-1]
    eval_note = f'_N_{eval_note}' if eval_note else ''

    eval_output_path = os.path.join(
        eval_output_dir,
        dataset_name,
        agent_class,
        f'{model_name}_maxiter_{max_iterations}{eval_note}',
    )

    pathlib.Path(eval_output_path).mkdir(parents=True, exist_ok=True)
    pathlib.Path(os.path.join(eval_output_path, 'logs')).mkdir(
        parents=True, exist_ok=True
    )
    logger.info(f'Using evaluation output directory: {eval_output_path}')

    metadata = EvalMetadata(
        agent_class=agent_class,
        llm_config=llm_config,
        max_iterations=max_iterations,
        eval_output_dir=eval_output_path,
        start_time=time.strftime('%Y-%m-%d %H:%M:%S'),
        git_commit=subprocess.check_output(['git', 'rev-parse', 'HEAD'])
        .decode('utf-8')
        .strip(),
        dataset=dataset_name,
        data_split=data_split,
        details=details,
    )
    metadata_json = metadata.model_dump_json()
    logger.info(f'Metadata: {metadata_json}')
    with open(os.path.join(eval_output_path, 'metadata.json'), 'w') as f:
        f.write(metadata_json)

    return metadata


def prepare_dataset(
    dataset: pd.DataFrame,
    output_file: str,
    eval_n_limit: int,
    eval_ids: list[str] | None = None,
):
    assert (
        'instance_id' in dataset.columns
    ), "Expected 'instance_id' column in the dataset. You should define your own unique identifier for each instance and use it as the 'instance_id' column."
    id_column = 'instance_id'
    logger.info(f'Writing evaluation output to {output_file}')
    finished_ids = set()
    if os.path.exists(output_file):
        with open(output_file, 'r') as f:
            for line in f:
                data = json.loads(line)
                finished_ids.add(data[id_column])
        logger.warning(
            f'Output file {output_file} already exists. Loaded {len(finished_ids)} finished instances.'
        )

    if eval_ids:
        eval_ids_converted = [dataset[id_column].dtype.type(id) for id in eval_ids]
        dataset = dataset[dataset[id_column].isin(eval_ids_converted)]
        logger.info(f'Limiting evaluation to {len(eval_ids)} specific instances.')
    elif eval_n_limit:
        dataset = dataset.head(eval_n_limit)
        logger.info(f'Limiting evaluation to first {eval_n_limit} instances.')

    new_dataset = [
        instance
        for _, instance in dataset.iterrows()
        if instance[id_column] not in finished_ids
    ]
    logger.info(
        f'Finished instances: {len(finished_ids)}, Remaining instances: {len(new_dataset)}'
    )

    return pd.DataFrame(new_dataset)


async def run_evaluation(
    dataset: pd.DataFrame,
    metadata: EvalMetadata,
    output_file: str,
    num_workers: int,
    process_instance_func: Callable[
        [pd.Series, EvalMetadata, bool], Awaitable[EvalOutput]
    ],
):
    use_multiprocessing = num_workers > 1
    logger.info(
        f'Evaluation started with Agent {metadata.agent_class}, '
        f'model {metadata.llm_config.model}, max iterations {metadata.max_iterations}.'
    )
    pbar = tqdm(total=len(dataset))
    output_fp = open(output_file, 'a')

    async def update_progress(future):
        pbar.update(1)
        output: EvalOutput = await future if use_multiprocessing else future

        pbar.set_description(f'Instance {output.instance_id}')
        pbar.set_postfix_str(f'Test Result: {output.test_result}')
        logger.info(
            f'Finished evaluation for instance {output.instance_id}: {output.test_result}'
        )
        output_fp.write(json.dumps(output.model_dump()) + '\n')
        output_fp.flush()

    try:
        if use_multiprocessing:
            with ProcessPoolExecutor(num_workers) as executor:
                loop = asyncio.get_event_loop()
                futures = []
                for _, instance in dataset.iterrows():
                    future = loop.run_in_executor(
                        executor,
                        process_instance_func,
                        instance,
                        metadata,
                        bool(num_workers > 1),
                    )
                    futures.append(update_progress(future))

                await asyncio.gather(*futures)
        # Use plain for loop for single process for easier debugging
        else:
            assert num_workers == 1
            for _, instance in dataset.iterrows():
                output = await process_instance_func(instance, metadata, False)
                await update_progress(output)

    except KeyboardInterrupt:
        print('KeyboardInterrupt received. Cleaning up...')
        cleanup()

    output_fp.close()
    logger.info('Evaluation finished.')


def reset_logger_for_multiprocessing(
    logger: logging.Logger, instance_id: str, log_dir: str
):
    """Reset the logger for multiprocessing.

    Save logs to a separate file for each process, instead of trying to write to the
    same file/console from multiple processes.
    """
    # Set up logger
    log_file = os.path.join(
        log_dir,
        f'instance_{instance_id}.log',
    )
    # Remove all existing handlers from logger
    for handler in logger.handlers[:]:
        logger.removeHandler(handler)
    # add back the console handler to print ONE line
    logger.addHandler(get_console_handler())
    logger.info(
        f'Starting evaluation for instance {instance_id}.\n'
        f'Hint: run "tail -f {log_file}" to see live logs in a separate shell'
    )
    # Remove all existing handlers from logger
    for handler in logger.handlers[:]:
        logger.removeHandler(handler)
    os.makedirs(os.path.dirname(log_file), exist_ok=True)
    file_handler = logging.FileHandler(log_file)
    file_handler.setFormatter(
        logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
    )
    logger.addHandler(file_handler)