from dataclasses import dataclass
from enum import Enum

@dataclass
class Task:
    benchmark: str # Dataset name
    metric: str # Metric name
    col_name: str # Column name


# Tunisian Dialect Tasks
# ---------------------------------------------------
class Tasks(Enum):
    # Example: Sentiment Analysis on TSAC
    accuracy = Task("fbougares/tsac", "accuracy", "Accuracy (TSAC) ⬆️")
    # Example: Text Classification or Corpus Coverage on Tunisian Dialect Corpus
    coverage = Task("arbml/Tunisian_Dialect_Corpus", "coverage", "Coverage (Tunisian Corpus) %")

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



# Your leaderboard name
TITLE = """<h1 align="center" id="space-title">Tunisian Dialect Leaderboard</h1>"""

# What does your leaderboard evaluate?
INTRODUCTION_TEXT = """
This leaderboard evaluates models and datasets focused on the Tunisian dialect of Arabic.\
It highlights performance on key resources such as TSAC (fbougares/tsac) and the Tunisian Dialect Corpus (arbml/Tunisian_Dialect_Corpus).
"""

# Which evaluations are you running? how can people reproduce what you have?
LLM_BENCHMARKS_TEXT = f"""
## How it works

We evaluate models on:
- **TSAC** ([fbougares/tsac](https://huggingface.co/datasets/fbougares/tsac)): Sentiment analysis in Tunisian dialect.
- **Tunisian Dialect Corpus** ([arbml/Tunisian_Dialect_Corpus](https://huggingface.co/datasets/arbml/Tunisian_Dialect_Corpus)): Coverage and language understanding.

## Reproducibility
To reproduce our results, use the following commands (replace with your model):

```python
from transformers import AutoConfig, AutoModel, AutoTokenizer
config = AutoConfig.from_pretrained("your model name", revision=revision)
model = AutoModel.from_pretrained("your model name", revision=revision)
tokenizer = AutoTokenizer.from_pretrained("your model name", revision=revision)
```
"""

EVALUATION_QUEUE_TEXT = """
## Some good practices before submitting a model

### 1) Make sure your model is trained or evaluated on Tunisian dialect data (e.g., TSAC, Tunisian Dialect Corpus).

### 2) Make sure you can load your model and tokenizer using AutoClasses:
```python
from transformers import AutoConfig, AutoModel, AutoTokenizer
config = AutoConfig.from_pretrained("your model name", revision=revision)
model = AutoModel.from_pretrained("your model name", revision=revision)
tokenizer = AutoTokenizer.from_pretrained("your model name", revision=revision)
```
If this step fails, follow the error messages to debug your model before submitting it. It's likely your model has been improperly uploaded.

Note: make sure your model is public!
Note: if your model needs `use_remote_code=True`, we do not support this option yet but we are working on adding it, stay posted!

### 3) Convert your model weights to [safetensors](https://huggingface.co/docs/safetensors/index)
It's a new format for storing weights which is safer and faster to load and use. It will also allow us to add the number of parameters of your model to the `Extended Viewer`!

### 4) Make sure your model has an open license!
This is a leaderboard for Open LLMs, and we'd love for as many people as possible to know they can use your model 🤗
"""

CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
CITATION_BUTTON_TEXT = r"""
"""