import argparse
import copy
import fnmatch
import os.path as osp
import random
import time
from typing import List, Optional, Union

import mmengine
from mmengine.config import Config, ConfigDict
from mmengine.utils import mkdir_or_exist

from opencompass.registry import ICL_EVALUATORS, MODELS, TEXT_POSTPROCESSORS
from opencompass.tasks.base import BaseTask
from opencompass.utils import (build_dataset_from_cfg, dataset_abbr_from_cfg,
                               get_infer_output_path, get_logger,
                               task_abbr_from_cfg)


class SubjectiveEvalTask(BaseTask):
    """Subjective Evaluation Task.

    This task is used to evaluate the metric between predictions and
    references.

    Args:
        cfg (ConfigDict): The configuration of the entire evaluation task.
    """

    name_prefix = 'SubjectiveEval'
    log_subdir = 'logs/eval'
    output_subdir = 'results'

    def __init__(self, cfg: ConfigDict):
        super().__init__(cfg)
        self.logger = get_logger()
        judge_cfg = cfg.eval.runner.task.get('judge_cfg', {})
        run_cfg = judge_cfg.get('run_cfg', {})
        self.num_gpus = run_cfg.get('num_gpus', 0)
        self.num_procs = run_cfg.get('num_procs', 1)
        self.judge_cfg = copy.deepcopy(judge_cfg)

    def get_command(self, cfg_path, template):
        """Get the command template for the task.

        Args:
            cfg_path (str): The path to the config file of the task.
            template (str): The template which have '{task_cmd}' to format
                the command.
        """
        script_path = __file__
        if self.num_gpus > 0:
            port = random.randint(12000, 32000)
            command = (f'torchrun --master_port={port} '
                       f'--nproc_per_node {self.num_procs} '
                       f'{script_path} {cfg_path}')
        else:
            command = f'python {script_path} {cfg_path}'

        return template.format(task_cmd=command)

    def run(self):
        # model_cfg can be a list of model configs
        for model_cfg, dataset_cfgs in zip(self.model_cfgs, self.dataset_cfgs):
            for dataset_cfg in dataset_cfgs:
                # self.model_cfg = model_cfg
                # self.dataset_cfg = dataset_cfg

                # Load Dataset
                eval_cfg = dataset_cfg.get('eval_cfg')
                output_column = dataset_cfg['reader_cfg']['output_column']
                if type(model_cfg) == ConfigDict:
                    model_cfg = (model_cfg, )
                model_cfg += ({
                    'abbr': 'judged-by--' + self.judge_cfg['abbr']
                }, )
                out_path = get_infer_output_path(
                    model_cfg, dataset_cfg, osp.join(self.work_dir, 'results'))
                if osp.exists(out_path):
                    continue
                self._score(model_cfg, dataset_cfg, eval_cfg, output_column)

    def _load_model_pred(self, model_cfg: Union[ConfigDict, List[ConfigDict]],
                         dataset_cfg: ConfigDict,
                         eval_cfg: ConfigDict) -> Union[None, List[str]]:
        if isinstance(model_cfg, (tuple, list)):
            return [
                self._load_model_pred(m, dataset_cfg, eval_cfg)
                for m in model_cfg
            ]

        # Load predictions
        filename = get_infer_output_path(
            model_cfg, dataset_cfg, osp.join(self.work_dir, 'predictions'))
        # in case the prediction is partial
        root, ext = osp.splitext(filename)
        partial_filename = root + '_0' + ext
        pred_strs = None
        if osp.exists(osp.realpath(filename)) or osp.exists(
                osp.realpath(partial_filename)):
            if osp.exists(osp.realpath(filename)):
                preds = mmengine.load(filename)
                pred_strs = [
                    preds[str(i)]['prediction'] for i in range(len(preds))
                ]
            else:
                filename = partial_filename
                pred_strs = []
                i = 1
                while osp.exists(osp.realpath(filename)):
                    preds = mmengine.load(filename)
                    filename = root + f'_{i}' + ext
                    i += 1
                    pred_strs += [
                        preds[str(i)]['prediction'] for i in range(len(preds))
                    ]

            if ('pred_role' in eval_cfg and 'meta_template' in model_cfg
                    and not MODELS.get(model_cfg['type']).is_api):
                # Create a prompt template for role config parsing
                from opencompass.models.base import LMTemplateParser
                parser = LMTemplateParser(model_cfg['meta_template'])
                role = parser.roles[eval_cfg['pred_role']]
                pred_strs = [
                    self._extract_role_pred(pred, role.get('begin', None),
                                            role.get('end', None))
                    for pred in pred_strs
                ]

            # Postprocess predictions if necessary
            ds_abbr = dataset_abbr_from_cfg(dataset_cfg)
            model_postprocessors = model_cfg.get('pred_postprocessor', {})
            pred_postprocessor = None
            for pattern in model_postprocessors.keys():
                if fnmatch.fnmatch(ds_abbr, pattern):
                    pred_postprocessor = model_postprocessors[pattern]
                    break
            if 'pred_postprocessor' in eval_cfg or pred_postprocessor:
                kwargs = pred_postprocessor or eval_cfg['pred_postprocessor']
                proc = TEXT_POSTPROCESSORS.get(kwargs.pop('type'))
                pred_strs = [proc(s, **kwargs) for s in pred_strs]
        return {'model_name': model_cfg['abbr'], 'model_preds': pred_strs}

    def _score(self, model_cfg, dataset_cfg, eval_cfg, output_column):
        test_set = build_dataset_from_cfg(dataset_cfg).test
        # Postprocess dataset if necessary
        if 'dataset_postprocessor' in eval_cfg:
            proc = TEXT_POSTPROCESSORS.get(
                eval_cfg['dataset_postprocessor']['type'])

            def postprocess(sample):
                s = sample[output_column]
                sample[output_column] = proc(s)
                return sample

            test_set = test_set.map(postprocess)
        # Get out_path
        out_path = get_infer_output_path(model_cfg, dataset_cfg,
                                         osp.join(self.work_dir, 'results'))
        new_model_cfg = []
        for m_cfg in model_cfg:
            if len(m_cfg) > 1:
                new_model_cfg.append(m_cfg)
        if len(new_model_cfg) == 1:
            new_model_cfg = new_model_cfg[0]
        model_preds = self._load_model_pred(new_model_cfg, dataset_cfg,
                                            eval_cfg)
        if not self.judge_cfg:
            raise ValueError('missing "eval.runner.task.judge_cfg"')
        eval_cfg['evaluator']['judge_cfg'] = self.judge_cfg
        eval_cfg['evaluator']['dataset_cfg'] = dataset_cfg
        eval_cfg['evaluator']['output_path'] = out_path
        icl_evaluator = ICL_EVALUATORS.build(eval_cfg['evaluator'])
        references = (test_set[output_column] if output_column else None)
        result = icl_evaluator.score(predictions=model_preds,
                                     references=references)

        if 'error' in result:
            self.logger.error(
                f'Task {task_abbr_from_cfg(self.cfg)}: {result["error"]}')
            return
        else:
            self.logger.info(
                f'Task {task_abbr_from_cfg(self.cfg)}')  #: {result}')

        # Save result
        mkdir_or_exist(osp.split(out_path)[0])
        mmengine.dump(result,
                      open(out_path, 'w', encoding='utf-8'),
                      file_format='json',
                      ensure_ascii=False,
                      indent=4)

    def _extract_role_pred(self, s: str, begin_str: Optional[str],
                           end_str: Optional[str]) -> str:
        """Extract the role prediction from the full prediction string. The
        role prediction may be the substring between the begin and end string.

        Args:
            s (str): Full prediction string.
            begin_str (str): The beginning string of the role
            end_str (str): The ending string of the role.

        Returns:
            str: The extracted role prediction.
        """
        start = 0
        end = len(s)

        if begin_str:
            begin_idx = s.find(begin_str)
            if begin_idx != -1:
                start = begin_idx + len(begin_str)

        if end_str:
            # TODO: Support calling tokenizer for the accurate eos token
            # and avoid such hardcode
            end_idx = s.find(end_str[:1], start)
            if end_idx != -1:
                end = end_idx

        return s[start:end]

    def get_output_paths(self, file_extension: str = 'json') -> List[str]:
        """Get the paths to the output files. Every file should exist if the
        task succeeds.

        Args:
            file_extension (str): The file extension of the output files.
                Default: 'json'.
        """
        output_paths = []
        for model, datasets in zip(self.model_cfgs, self.dataset_cfgs):
            for dataset in datasets:
                if type(model) == ConfigDict:
                    model = (model, )
                model += ({'abbr': 'judged-by--' + self.judge_cfg['abbr']}, )
                output_paths.append(
                    get_infer_output_path(
                        model, dataset,
                        osp.join(self.work_dir, self.output_subdir),
                        file_extension))
        return output_paths


def parse_args():
    parser = argparse.ArgumentParser(description='Score Calculator')
    parser.add_argument('config', help='Config file path')
    args = parser.parse_args()
    return args


if __name__ == '__main__':
    args = parse_args()
    cfg = Config.fromfile(args.config)
    start_time = time.time()
    inferencer = SubjectiveEvalTask(cfg)
    inferencer.run()
    end_time = time.time()
    get_logger().info(f'time elapsed: {end_time - start_time:.2f}s')
