from __future__ import annotations

import os
import re
from urllib.parse import urlparse

# from modules import shared


def natural_sort_key(s, regex=re.compile('([0-9]+)')):
    return [int(text) if text.isdigit() else text.lower() for text in regex.split(s)]

def walk_files(path, allowed_extensions=None):
    if not os.path.exists(path):
        return

    if allowed_extensions is not None:
        allowed_extensions = set(allowed_extensions)

    items = list(os.walk(path, followlinks=True))
    items = sorted(items, key=lambda x: natural_sort_key(x[0]))

    for root, _, files in items:
        for filename in sorted(files, key=natural_sort_key):
            if allowed_extensions is not None:
                _, ext = os.path.splitext(filename)
                if ext not in allowed_extensions:
                    continue

            yield os.path.join(root, filename)

def load_file_from_url(
    url: str,
    *,
    model_dir: str,
    progress: bool = True,
    file_name: str | None = None,
) -> str:
    """Download a file from `url` into `model_dir`, using the file present if possible.

    Returns the path to the downloaded file.
    """
    os.makedirs(model_dir, exist_ok=True)
    if not file_name:
        parts = urlparse(url)
        file_name = os.path.basename(parts.path)
    cached_file = os.path.abspath(os.path.join(model_dir, file_name))
    if not os.path.exists(cached_file):
        print(f'Downloading: "{url}" to {cached_file}\n')
        from torch.hub import download_url_to_file
        download_url_to_file(url, cached_file, progress=progress)
    return cached_file


def load_models(model_path: str, model_url: str = None, command_path: str = None, ext_filter=None, download_name=None, ext_blacklist=None) -> list:
    """
    A one-and done loader to try finding the desired models in specified directories.

    @param download_name: Specify to download from model_url immediately.
    @param model_url: If no other models are found, this will be downloaded on upscale.
    @param model_path: The location to store/find models in.
    @param command_path: A command-line argument to search for models in first.
    @param ext_filter: An optional list of filename extensions to filter by
    @return: A list of paths containing the desired model(s)
    """
    output = []

    try:
        places = []

        if command_path is not None and command_path != model_path:
            pretrained_path = os.path.join(command_path, 'experiments/pretrained_models')
            if os.path.exists(pretrained_path):
                print(f"Appending path: {pretrained_path}")
                places.append(pretrained_path)
            elif os.path.exists(command_path):
                places.append(command_path)

        places.append(model_path)

        for place in places:
            for full_path in walk_files(place, allowed_extensions=ext_filter):
                if os.path.islink(full_path) and not os.path.exists(full_path):
                    print(f"Skipping broken symlink: {full_path}")
                    continue
                if ext_blacklist is not None and any(full_path.endswith(x) for x in ext_blacklist):
                    continue
                if full_path not in output:
                    output.append(full_path)

        if model_url is not None and len(output) == 0:
            if download_name is not None:
                output.append(load_file_from_url(model_url, model_dir=places[0], file_name=download_name))
            else:
                output.append(model_url)

    except Exception:
        pass

    return output