from dataclasses import dataclass
from enum import Enum


@dataclass
class Task:
    benchmark: str
    metric: str
    col_name: str


# Select your tasks here
# ---------------------------------------------------
class Tasks(Enum):
    # task_key in the json file, metric_key in the json file, name to display in the leaderboard 
    # task0 = Task("boolq", "acc", "BoolQA")
    task1 = Task("trivia", "EM", "TriviaQA")
    task2 = Task("truthfulqa", "EM", "TruthfulQA")
    task3 = Task("popqa", "acc", "PopQA")
    task4 = Task("hpqa", "EM", "HotpotQA")
    task5 = Task("nq", "EM", "Natural Questions")
    task6 = Task("2wiki", "EM", "2WikiMultiHop")
    task7 = Task("musique", "EM", "MuSiQue")
    # task0 = Task("anli_r1", "acc", "ANLI")
    # task1 = Task("logiqa", "acc_norm", "LogiQA")


NUM_FEWSHOT = 0  # Change with your few shot
# ---------------------------------------------------


# Your leaderboard name
TITLE = """<h1 align="center" id="space-title">GIFT-Eval Time Series Forecasting Leaderboard</h1>"""

# What does your leaderboard evaluate?
INTRODUCTION_TEXT = """
We introduce the **G**eneral T**I**me Series **F**orecas**T**ing Model Evaluation, GIFT-Eval,
a pioneering benchmark aimed at promoting evaluation across diverse datasets.
GIFT-Eval encompasses 24 datasets over 144,000 time series and 177 million data
points, spanning seven domains, 10 frequencies, multivariate inputs, and prediction lengths ranging from short to long-term forecasts.
"""

# Which evaluations are you running? how can people reproduce what you have?
LLM_BENCHMARKS_TEXT = f"""
## Update Log

### 2025‑07‑24
- Corrected the Naive and Seasonal Naive scores to match the latest GIFT‑Eval notebooks. Most model rankings remain unchanged; only a few near the bottom shifted slightly (AutoETS and Timer each dropped two places now at 35th and 36th places respectively, while NBEATS moved up one now at 27th place).

### 2025-08-05
- Added new columns to the leaderboard: Organization, TestData Leakage, and MASE_Rank. TestData Leakage is a binary indicator specifying whether any test data was present in the training set. MASE_Rank reflects the model's ranking based on the MASE metric, aligned with the ranking scheme used for CRPS_Rank. These additions were made in response to multiple requests from independent groups seeking fairer comparisons. With these updates, the leaderboard now supports sorting by models that do not leak test data, and viewers can choose to rank models based on either MASE_Rank or CRPS_Rank, depending on their use case.
- Added new model type: Agentic to indicate submissions that use agentic system to generate the forecasts.

## How It Works

To participate in the GIFT-Eval leaderboard, follow these steps to evaluate your Time Series Model:

Clone the Repository: Start by cloning the GIFT-Eval GitHub repository to your local machine using the following command:

```bash
git clone https://github.com/SalesforceAIResearch/gift-eval
```

Navigate to the Directory: Move into the cloned repository's directory:


```bash
cd gift-eval
```

Follow the instruction in the README.md file to install the required dependencies, set up your environment and obtain the evaluation results. 

"""

EVALUATION_QUEUE_TEXT = """

"""

CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
CITATION_BUTTON_TEXT = r"""
@article{
aksu2024gifteval,
title={{GIFT}-Eval: A Benchmark for General Time Series Forecasting Model Evaluation},
author={Taha Aksu and Gerald Woo and Juncheng Liu and Xu Liu and Chenghao Liu and Silvio Savarese and Caiming Xiong and Doyen Sahoo},
booktitle={NeurIPS Workshop on Time Series in the Age of Large Models},
year={2024},
url={https://openreview.net/forum?id=Z2cMOOANFX}
}
"""