import os

import pandas as pd
from huggingface_hub import add_collection_item, delete_collection_item, get_collection, update_collection_item
from huggingface_hub.utils._errors import HfHubHTTPError
from pandas import DataFrame

from src.display.utils import AutoEvalColumn, ModelType, NUMERIC_INTERVALS
from src.envs import H4_TOKEN, PATH_TO_COLLECTION

# Specific intervals for the collections
"""
intervals = {
    "1B": pd.Interval(0, 1.5, closed="right"),
    "3B": pd.Interval(2.5, 3.5, closed="neither"),
    "7B": pd.Interval(6, 8, closed="neither"),
    "13B": pd.Interval(10, 14, closed="neither"),
    "30B": pd.Interval(25, 35, closed="neither"),
    "65B": pd.Interval(60, 70, closed="neither"),
}
"""
intervals = {k:v for k,v in NUMERIC_INTERVALS.items() if "?" not in k}

def update_collections(df: DataFrame):
    """This function updates the Open LLM Leaderboard model collection with the latest best models for
    each size category and type.
    """
    collection = get_collection(collection_slug=PATH_TO_COLLECTION, token=H4_TOKEN)
    params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce")

    cur_best_models = []

    ix = 0
    for type in ModelType:
        if type.value.name == "":
            continue
        for size in intervals:
            # We filter the df to gather the relevant models
            type_emoji = [t[0] for t in type.value.symbol]
            filtered_df = df[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]

            numeric_interval = pd.IntervalIndex([intervals[size]])
            mask = params_column.apply(lambda x: any(numeric_interval.contains(x)))
            filtered_df = filtered_df.loc[mask]

            best_models = list(
                filtered_df.sort_values(AutoEvalColumn.average.name, ascending=False)[AutoEvalColumn.dummy.name]
            )
            print(type.value.symbol, size, best_models[:10])

            # We add them one by one to the leaderboard
            for model in best_models:
                ix += 1
                cur_len_collection = len(collection.items)
                try:
                    collection = add_collection_item(
                        PATH_TO_COLLECTION,
                        item_id=model,
                        item_type="model",
                        exists_ok=True,
                        note=f"Best {type.to_str(' ')} model of around {size} on the leaderboard today!",
                        token=H4_TOKEN,
                    )
                    if (
                        len(collection.items) > cur_len_collection
                    ):  # we added an item - we make sure its position is correct
                        item_object_id = collection.items[-1].item_object_id
                        update_collection_item(
                            collection_slug=PATH_TO_COLLECTION, item_object_id=item_object_id, position=ix
                        )
                        cur_len_collection = len(collection.items)
                    cur_best_models.append(model)
                    break
                except HfHubHTTPError:
                    continue

    collection = get_collection(PATH_TO_COLLECTION, token=H4_TOKEN)
    for item in collection.items:
        if item.item_id not in cur_best_models:
            try:
                delete_collection_item(
                    collection_slug=PATH_TO_COLLECTION, item_object_id=item.item_object_id, token=H4_TOKEN
                )
            except HfHubHTTPError:
                continue