from __future__ import annotations

import logging
import os
import shutil
import importlib
from urllib.parse import urlparse

import torch

from modules import shared
from modules.upscaler import Upscaler, UpscalerLanczos, UpscalerNearest, UpscalerNone
from modules.paths import script_path, models_path


logger = logging.getLogger(__name__)


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 shared.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


def friendly_name(file: str):
    if file.startswith("http"):
        file = urlparse(file).path

    file = os.path.basename(file)
    model_name, extension = os.path.splitext(file)
    return model_name


def cleanup_models():
    # This code could probably be more efficient if we used a tuple list or something to store the src/destinations
    # and then enumerate that, but this works for now. In the future, it'd be nice to just have every "model" scaler
    # somehow auto-register and just do these things...
    root_path = script_path
    src_path = models_path
    dest_path = os.path.join(models_path, "Stable-diffusion")
    move_files(src_path, dest_path, ".ckpt")
    move_files(src_path, dest_path, ".safetensors")
    src_path = os.path.join(root_path, "ESRGAN")
    dest_path = os.path.join(models_path, "ESRGAN")
    move_files(src_path, dest_path)
    src_path = os.path.join(models_path, "BSRGAN")
    dest_path = os.path.join(models_path, "ESRGAN")
    move_files(src_path, dest_path, ".pth")
    src_path = os.path.join(root_path, "gfpgan")
    dest_path = os.path.join(models_path, "GFPGAN")
    move_files(src_path, dest_path)
    src_path = os.path.join(root_path, "SwinIR")
    dest_path = os.path.join(models_path, "SwinIR")
    move_files(src_path, dest_path)
    src_path = os.path.join(root_path, "repositories/latent-diffusion/experiments/pretrained_models/")
    dest_path = os.path.join(models_path, "LDSR")
    move_files(src_path, dest_path)


def move_files(src_path: str, dest_path: str, ext_filter: str = None):
    try:
        os.makedirs(dest_path, exist_ok=True)
        if os.path.exists(src_path):
            for file in os.listdir(src_path):
                fullpath = os.path.join(src_path, file)
                if os.path.isfile(fullpath):
                    if ext_filter is not None:
                        if ext_filter not in file:
                            continue
                    print(f"Moving {file} from {src_path} to {dest_path}.")
                    try:
                        shutil.move(fullpath, dest_path)
                    except Exception:
                        pass
            if len(os.listdir(src_path)) == 0:
                print(f"Removing empty folder: {src_path}")
                shutil.rmtree(src_path, True)
    except Exception:
        pass


def load_upscalers():
    # We can only do this 'magic' method to dynamically load upscalers if they are referenced,
    # so we'll try to import any _model.py files before looking in __subclasses__
    modules_dir = os.path.join(shared.script_path, "modules")
    for file in os.listdir(modules_dir):
        if "_model.py" in file:
            model_name = file.replace("_model.py", "")
            full_model = f"modules.{model_name}_model"
            try:
                importlib.import_module(full_model)
            except Exception:
                pass

    datas = []
    commandline_options = vars(shared.cmd_opts)

    # some of upscaler classes will not go away after reloading their modules, and we'll end
    # up with two copies of those classes. The newest copy will always be the last in the list,
    # so we go from end to beginning and ignore duplicates
    used_classes = {}
    for cls in reversed(Upscaler.__subclasses__()):
        classname = str(cls)
        if classname not in used_classes:
            used_classes[classname] = cls

    for cls in reversed(used_classes.values()):
        name = cls.__name__
        cmd_name = f"{name.lower().replace('upscaler', '')}_models_path"
        commandline_model_path = commandline_options.get(cmd_name, None)
        scaler = cls(commandline_model_path)
        scaler.user_path = commandline_model_path
        scaler.model_download_path = commandline_model_path or scaler.model_path
        datas += scaler.scalers

    shared.sd_upscalers = sorted(
        datas,
        # Special case for UpscalerNone keeps it at the beginning of the list.
        key=lambda x: x.name.lower() if not isinstance(x.scaler, (UpscalerNone, UpscalerLanczos, UpscalerNearest)) else ""
    )


def load_spandrel_model(
    path: str,
    *,
    device: str | torch.device | None,
    half: bool = False,
    dtype: str | None = None,
    expected_architecture: str | None = None,
):
    import spandrel
    model = spandrel.ModelLoader(device=device).load_from_file(path)
    if expected_architecture and model.architecture != expected_architecture:
        logger.warning(
            f"Model {path!r} is not a {expected_architecture!r} model (got {model.architecture!r})",
        )
    if half:
        model = model.model.half()
    if dtype:
        model = model.model.to(dtype=dtype)
    model.eval()
    logger.debug("Loaded %s from %s (device=%s, half=%s, dtype=%s)", model, path, device, half, dtype)
    return model