import logging
import os
import re

import pandas as pd  # type: ignore[import]
from datasets import get_dataset_config_names, load_dataset  # type: ignore[import]

from .formatting import model_hyperlink
from .leaderboard_formatting import (
    COLUMNS_PRETTY,
    METRICS_PER_TASK,
    SORT_COLUMN_PER_TASK,
    get_columns_per_task,
)
from .tasks_content import TASKS_PRETTY_REVERSE
from .utils import MD_LINK_PATTERN

try:
    AVAILABLE_TASKS = get_dataset_config_names(os.environ["DATASET_ID"])
except FileNotFoundError as e:
    AVAILABLE_TASKS = []
    logging.warning("Dataset is not available! Check if token is expired.")

AVAILABLE_TASKS_STR = '  ;  '.join(AVAILABLE_TASKS)
logging.warning(f"Available tasks: {AVAILABLE_TASKS_STR}")


def _get_results_stub() -> pd.DataFrame:
    stub_df = pd.DataFrame(
        [
            {
                "Model Name": "GPT-4",
                "Availability": "Proprietary",
                "Context Size": "16k",
                "BLEU": "X",
                "ROUGE": "X",
                "ChrF": "X",
                "BERTScore": "X",
                "BERTScore (Normalized)": "X",
                "Submitted By": "🏟 Long Code Arena Team",
                "Resources": "",
            },
            {
                "Model Name": "CodeLlama-7b (instruct)",
                "Availability": "Llama 2 license",
                "Context Size": "16k",
                "BLEU": "X",
                "ROUGE": "X",
                "ChrF": "X",
                "BERTScore": "X",
                "BERTScore (Normalized)": "X",
                "Submitted By": "🏟 Long Code Arena Team",
                "Resources": "",
            },
        ]
    )
    return stub_df


def _process_urls(raw_urls: str) -> str:
    if not raw_urls:
        return raw_urls
    html_urls = [model_hyperlink(*re.search(MD_LINK_PATTERN, url.strip()).groups()) for url in raw_urls.split(",")]
    return ", ".join(html_urls)

def _extract_dataset_name(raw_urls: str) -> str:
    if not raw_urls:
        return raw_urls
    names = [re.search(MD_LINK_PATTERN, url.strip()).group(1) + ' context' for url in raw_urls.split(",")]
    return ", ".join(names)
    


def _get_results_dataset(task_id: str) -> pd.DataFrame:
    logging.info(f"Loading dataset: {task_id}...")
    results_df = load_dataset(
        os.environ["DATASET_ID"], task_id, split="test", download_mode="force_redownload"
    ).to_pandas()
    results_df = results_df.rename(columns=COLUMNS_PRETTY, errors="ignore")
    results_df["Context Size"] = results_df["Context Size"].map(lambda x: f"{int(x) // 1000}k" if int(x) >= 1000 else x)

    results_df = results_df.sort_values(by=SORT_COLUMN_PER_TASK[task_id], ascending=False)

    for metric_column in METRICS_PER_TASK[task_id]:
        if "BERTScore" in metric_column:
            results_df[metric_column] = results_df[metric_column].map(lambda x: f"{x:.5f}")
        else:
            results_df[metric_column] = results_df[metric_column].map(lambda x: f"{x:.2f}")

    results_df["Model Name"] = [
        model_hyperlink(link=link, model_name=model_name) if link else model_name
        for link, model_name in zip(results_df["model_url"], results_df["Model Name"])
    ]
    if task_id == 'project_code_completion':
        results_df["Dataset Name"] = [_extract_dataset_name(urls) for urls in results_df["Dataset"]]
        results_df["Dataset"] = [_process_urls(urls) for urls in results_df["Dataset"]]
    results_df["Resources"] = [_process_urls(urls) for urls in results_df["Resources"]]
    results_df = results_df[get_columns_per_task(task_id)]
    return results_df


def get_results_for_task(task_pretty: str) -> pd.DataFrame:
    task_id = TASKS_PRETTY_REVERSE[task_pretty]
    if task_id in AVAILABLE_TASKS:
        logging.info(f"Retrieving results for {task_pretty}...")
        return _get_results_dataset(task_id)
    logging.info(f"Generating leaderboard stub for {task_pretty}...")
    return _get_results_stub()