import os
import platform
import re
from collections import defaultdict

import gradio as gr
from cachetools import TTLCache, cached
from cytoolz import groupby
from huggingface_hub import get_collection, list_datasets, list_models
from tqdm.auto import tqdm

os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
is_macos = platform.system() == "Darwin"
LIMIT = None
CACHE_TIME = 60 * 5  # 5 minutes


@cached(cache=TTLCache(maxsize=100, ttl=CACHE_TIME))
def get_models():
    return list(tqdm(iter(list_models(full=True, limit=LIMIT))))


@cached(cache=TTLCache(maxsize=100, ttl=CACHE_TIME))
def get_datasets():
    return list(tqdm(iter(list_datasets(full=True, limit=LIMIT))))


get_models()  # warm up the cache
get_datasets()  # warm up the cache


def check_for_arxiv_id(model):
    return [tag for tag in model.tags if "arxiv" in tag] if model.tags else False


def extract_arxiv_id(input_string: str) -> str:
    # Define the regular expression pattern
    pattern = re.compile(r"\barxiv:(\d+\.\d+)\b")

    # Search for the pattern in the input string
    match = pattern.search(input_string)

    # If a match is found, return the numeric part of the ARXIV ID, else return None
    return match[1] if match else None


@cached(cache=TTLCache(maxsize=100, ttl=CACHE_TIME))
def create_model_to_arxiv_id_dict():
    models = get_models()
    model_to_arxiv_id = {}
    for model in models:
        if arxiv_papers := check_for_arxiv_id(model):
            clean_arxiv_ids = []
            for paper in arxiv_papers:
                if arxiv_id := extract_arxiv_id(paper):
                    clean_arxiv_ids.append(arxiv_id)
            model_to_arxiv_id[model.modelId] = clean_arxiv_ids
    return model_to_arxiv_id


@cached(cache=TTLCache(maxsize=100, ttl=CACHE_TIME))
def create_dataset_to_arxiv_id_dict():
    datasets = get_datasets()
    dataset_to_arxiv_id = {}
    for dataset in datasets:
        if arxiv_papers := check_for_arxiv_id(dataset):
            clean_arxiv_ids = []
            for paper in arxiv_papers:
                if arxiv_id := extract_arxiv_id(paper):
                    clean_arxiv_ids.append(arxiv_id)
            dataset_to_arxiv_id[dataset.id] = clean_arxiv_ids
    return dataset_to_arxiv_id


placeholder_url = "lunarflu/ai-podcasts-and-talks-65119866353a60593bf99c58"


def group_collection_items(collection_slug: str):
    collection = get_collection(collection_slug)
    items = collection.items
    return groupby(lambda x: f"{x.repoType}s", items)


def get_papers_for_collection(collection_slug: str):
    dataset_to_arxiv_id = create_dataset_to_arxiv_id_dict()
    models_to_arxiv_id = create_model_to_arxiv_id_dict()
    collection = group_collection_items(collection_slug)
    collection_datasets = collection.get("datasets", None)
    collection_models = collection.get("models", None)
    dataset_papers = defaultdict(dict)
    model_papers = defaultdict(dict)
    if collection_datasets is not None:
        for dataset in collection_datasets:
            if arxiv_ids := dataset_to_arxiv_id.get(dataset.item_id, None):
                data = {
                    "arxiv_ids": arxiv_ids,
                    "hub_paper_links": [
                        f"https://huggingface.co/papers/{arxiv_id}"
                        for arxiv_id in arxiv_ids
                    ],
                }
                dataset_papers[dataset.item_id] = data
    if collection_models is not None:
        for model in collection.get("models", []):
            if arxiv_ids := models_to_arxiv_id.get(model.item_id, None):
                data = {
                    "arxiv_ids": arxiv_ids,
                    "hub_paper_links": [
                        f"https://huggingface.co/papers/{arxiv_id}"
                        for arxiv_id in arxiv_ids
                    ],
                }
                model_papers[model.item_id] = data
    return {"dataset papers": dataset_papers, "model papers": model_papers}


placeholder_url = "HF-IA-archiving/models-to-archive-65006a7fdadb8c628f33aac9"
slug_input = gr.Textbox(
    placeholder=placeholder_url, interactive=True, label="Collection slug", max_lines=1
)
description = (
    "Enter a collection slug to get a list of papers associated with models and"
    " datasets in the collection."
)

examples = [
    placeholder_url,
    "davanstrien/historic-language-modeling-64f99e243188ade79d7ad74b",
]
gr.Interface(
    get_papers_for_collection,
    slug_input,
    "json",
    description=description,
    examples=examples,
    cache_examples=True,
).launch()