import os
import logging
import picklescan.scanner
import rich

from sd_internal import app, TaskData
from modules import model_loader
from modules.types import Context

log = logging.getLogger()

KNOWN_MODEL_TYPES = ['stable-diffusion', 'vae', 'hypernetwork', 'gfpgan', 'realesrgan']
MODEL_EXTENSIONS = {
    'stable-diffusion': ['.ckpt', '.safetensors'],
    'vae': ['.vae.pt', '.ckpt'],
    'hypernetwork': ['.pt'],
    'gfpgan': ['.pth'],
    'realesrgan': ['.pth'],
}
DEFAULT_MODELS = {
    'stable-diffusion': [ # needed to support the legacy installations
        'custom-model', # only one custom model file was supported initially, creatively named 'custom-model'
        'sd-v1-4', # Default fallback.
    ],
    'gfpgan': ['GFPGANv1.3'],
    'realesrgan': ['RealESRGAN_x4plus'],
}

known_models = {}

def init():
    make_model_folders()
    getModels() # run this once, to cache the picklescan results

def load_default_models(context: Context):
    # init default model paths
    for model_type in KNOWN_MODEL_TYPES:
        context.model_paths[model_type] = resolve_model_to_use(model_type=model_type)

    # disable TURBO initially (this should be read from the config eventually)
    context.vram_optimizations -= {'TURBO'}

    # load mandatory models
    model_loader.load_model(context, 'stable-diffusion')

def unload_all(context: Context):
    for model_type in KNOWN_MODEL_TYPES:
        model_loader.unload_model(context, model_type)

def resolve_model_to_use(model_name:str=None, model_type:str=None):
    model_extensions = MODEL_EXTENSIONS.get(model_type, [])
    default_models = DEFAULT_MODELS.get(model_type, [])
    config = app.getConfig()

    model_dirs = [os.path.join(app.MODELS_DIR, model_type), app.SD_DIR]
    if not model_name: # When None try user configured model.
        # config = getConfig()
        if 'model' in config and model_type in config['model']:
            model_name = config['model'][model_type]

    if model_name:
        is_sd2 = config.get('test_sd2', False)
        if model_name.startswith('sd2_') and not is_sd2: # temp hack, until SD2 is unified with 1.4
            log.error('ERROR: Cannot use SD 2.0 models with SD 1.0 code. Using the sd-v1-4 model instead!')
            model_name = 'sd-v1-4'

        # Check models directory
        models_dir_path = os.path.join(app.MODELS_DIR, model_type, model_name)
        for model_extension in model_extensions:
            if os.path.exists(models_dir_path + model_extension):
                return models_dir_path + model_extension
            if os.path.exists(model_name + model_extension):
                return os.path.abspath(model_name + model_extension)

    # Default locations
    if model_name in default_models:
        default_model_path = os.path.join(app.SD_DIR, model_name)
        for model_extension in model_extensions:
            if os.path.exists(default_model_path + model_extension):
                return default_model_path + model_extension

    # Can't find requested model, check the default paths.
    for default_model in default_models:
        for model_dir in model_dirs:
            default_model_path = os.path.join(model_dir, default_model)
            for model_extension in model_extensions:
                if os.path.exists(default_model_path + model_extension):
                    if model_name is not None:
                        log.warn(f'Could not find the configured custom model {model_name}{model_extension}. Using the default one: {default_model_path}{model_extension}')
                    return default_model_path + model_extension

    return None

def reload_models_if_necessary(context: Context, task_data: TaskData):
    model_paths_in_req = (
        ('hypernetwork', task_data.use_hypernetwork_model),
        ('gfpgan', task_data.use_face_correction),
        ('realesrgan', task_data.use_upscale),
    )

    if context.model_paths.get('stable-diffusion') != task_data.use_stable_diffusion_model or context.model_paths.get('vae') != task_data.use_vae_model:
        context.model_paths['stable-diffusion'] = task_data.use_stable_diffusion_model
        context.model_paths['vae'] = task_data.use_vae_model

        model_loader.load_model(context, 'stable-diffusion')

    for model_type, model_path_in_req in model_paths_in_req:
        if context.model_paths.get(model_type) != model_path_in_req:
            context.model_paths[model_type] = model_path_in_req

            action_fn = model_loader.unload_model if context.model_paths[model_type] is None else model_loader.load_model
            action_fn(context, model_type)

def resolve_model_paths(task_data: TaskData):
    task_data.use_stable_diffusion_model = resolve_model_to_use(task_data.use_stable_diffusion_model, model_type='stable-diffusion')
    task_data.use_vae_model = resolve_model_to_use(task_data.use_vae_model, model_type='vae')
    task_data.use_hypernetwork_model = resolve_model_to_use(task_data.use_hypernetwork_model, model_type='hypernetwork')

    if task_data.use_face_correction: task_data.use_face_correction = resolve_model_to_use(task_data.use_face_correction, 'gfpgan')
    if task_data.use_upscale: task_data.use_upscale = resolve_model_to_use(task_data.use_upscale, 'gfpgan')

def set_vram_optimizations(context: Context, task_data: TaskData):
    if task_data.turbo:
        context.vram_optimizations.add('TURBO')
    else:
        context.vram_optimizations.remove('TURBO')

def make_model_folders():
    for model_type in KNOWN_MODEL_TYPES:
        model_dir_path = os.path.join(app.MODELS_DIR, model_type)

        os.makedirs(model_dir_path, exist_ok=True)

        help_file_name = f'Place your {model_type} model files here.txt'
        help_file_contents = f'Supported extensions: {" or ".join(MODEL_EXTENSIONS.get(model_type))}'

        with open(os.path.join(model_dir_path, help_file_name), 'w', encoding='utf-8') as f:
            f.write(help_file_contents)

def is_malicious_model(file_path):
    try:
        scan_result = picklescan.scanner.scan_file_path(file_path)
        if scan_result.issues_count > 0 or scan_result.infected_files > 0:
            log.warn(":warning: [bold red]Scan %s: %d scanned, %d issue, %d infected.[/bold red]" % (file_path, scan_result.scanned_files, scan_result.issues_count, scan_result.infected_files))
            return True
        else:
            log.debug("Scan %s: [green]%d scanned, %d issue, %d infected.[/green]" % (file_path, scan_result.scanned_files, scan_result.issues_count, scan_result.infected_files))
            return False
    except Exception as e:
        log.error(f'error while scanning: {file_path}, error: {e}')
    return False

def getModels():
    models = {
        'active': {
            'stable-diffusion': 'sd-v1-4',
            'vae': '',
            'hypernetwork': '',
        },
        'options': {
            'stable-diffusion': ['sd-v1-4'],
            'vae': [],
            'hypernetwork': [],
        },
    }

    models_scanned = 0
    def listModels(model_type):
        nonlocal models_scanned

        model_extensions = MODEL_EXTENSIONS.get(model_type, [])
        models_dir = os.path.join(app.MODELS_DIR, model_type)
        if not os.path.exists(models_dir):
            os.makedirs(models_dir)

        for file in os.listdir(models_dir):
            for model_extension in model_extensions:
                if not file.endswith(model_extension):
                    continue

                model_path = os.path.join(models_dir, file)
                mtime = os.path.getmtime(model_path)
                mod_time = known_models[model_path] if model_path in known_models else -1
                if mod_time != mtime:
                    models_scanned += 1
                    if is_malicious_model(model_path):
                        models['scan-error'] = file
                        return
                known_models[model_path] = mtime

                model_name = file[:-len(model_extension)]
                models['options'][model_type].append(model_name)

        models['options'][model_type] = [*set(models['options'][model_type])] # remove duplicates
        models['options'][model_type].sort()

    # custom models
    listModels(model_type='stable-diffusion')
    listModels(model_type='vae')
    listModels(model_type='hypernetwork')

    if models_scanned > 0: log.info(f'[green]Scanned {models_scanned} models. Nothing infected[/]')

    # legacy
    custom_weight_path = os.path.join(app.SD_DIR, 'custom-model.ckpt')
    if os.path.exists(custom_weight_path):
        models['options']['stable-diffusion'].append('custom-model')

    return models