import collections
import os.path
import sys
import threading

import torch
import re
import safetensors.torch
from omegaconf import OmegaConf, ListConfig
from os import mkdir
from urllib import request
import ldm.modules.midas as midas
import gc

from ldm.util import instantiate_from_config

from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet, sd_models_xl, cache, extra_networks, processing, lowvram, sd_hijack, patches
from modules.timer import Timer
import tomesd
import numpy as np
from modules_forge import forge_loader
import modules_forge.ops as forge_ops
from ldm_patched.modules.ops import manual_cast
from ldm_patched.modules import model_management as model_management
import ldm_patched.modules.model_patcher


model_dir = "Stable-diffusion"
model_path = os.path.abspath(os.path.join(paths.models_path, model_dir))

checkpoints_list = {}
checkpoint_aliases = {}
checkpoint_alisases = checkpoint_aliases  # for compatibility with old name
checkpoints_loaded = collections.OrderedDict()


def replace_key(d, key, new_key, value):
    keys = list(d.keys())

    d[new_key] = value

    if key not in keys:
        return d

    index = keys.index(key)
    keys[index] = new_key

    new_d = {k: d[k] for k in keys}

    d.clear()
    d.update(new_d)
    return d


class CheckpointInfo:
    def __init__(self, filename):
        self.filename = filename
        abspath = os.path.abspath(filename)
        abs_ckpt_dir = os.path.abspath(shared.cmd_opts.ckpt_dir) if shared.cmd_opts.ckpt_dir is not None else None

        self.is_safetensors = os.path.splitext(filename)[1].lower() == ".safetensors"

        if abs_ckpt_dir and abspath.startswith(abs_ckpt_dir):
            name = abspath.replace(abs_ckpt_dir, '')
        elif abspath.startswith(model_path):
            name = abspath.replace(model_path, '')
        else:
            name = os.path.basename(filename)

        if name.startswith("\\") or name.startswith("/"):
            name = name[1:]

        def read_metadata():
            metadata = read_metadata_from_safetensors(filename)
            self.modelspec_thumbnail = metadata.pop('modelspec.thumbnail', None)

            return metadata

        self.metadata = {}
        if self.is_safetensors:
            try:
                self.metadata = cache.cached_data_for_file('safetensors-metadata', "checkpoint/" + name, filename, read_metadata)
            except Exception as e:
                errors.display(e, f"reading metadata for {filename}")

        self.name = name
        self.name_for_extra = os.path.splitext(os.path.basename(filename))[0]
        self.model_name = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
        self.hash = model_hash(filename)

        self.sha256 = hashes.sha256_from_cache(self.filename, f"checkpoint/{name}")
        self.shorthash = self.sha256[0:10] if self.sha256 else None

        self.title = name if self.shorthash is None else f'{name} [{self.shorthash}]'
        self.short_title = self.name_for_extra if self.shorthash is None else f'{self.name_for_extra} [{self.shorthash}]'

        self.ids = [self.hash, self.model_name, self.title, name, self.name_for_extra, f'{name} [{self.hash}]']
        if self.shorthash:
            self.ids += [self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]', f'{self.name_for_extra} [{self.shorthash}]']

    def register(self):
        checkpoints_list[self.title] = self
        for id in self.ids:
            checkpoint_aliases[id] = self

    def calculate_shorthash(self):
        self.sha256 = hashes.sha256(self.filename, f"checkpoint/{self.name}")
        if self.sha256 is None:
            return

        shorthash = self.sha256[0:10]
        if self.shorthash == self.sha256[0:10]:
            return self.shorthash

        self.shorthash = shorthash

        if self.shorthash not in self.ids:
            self.ids += [self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]', f'{self.name_for_extra} [{self.shorthash}]']

        old_title = self.title
        self.title = f'{self.name} [{self.shorthash}]'
        self.short_title = f'{self.name_for_extra} [{self.shorthash}]'

        replace_key(checkpoints_list, old_title, self.title, self)
        self.register()

        return self.shorthash


try:
    # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
    from transformers import logging, CLIPModel  # noqa: F401

    logging.set_verbosity_error()
except Exception:
    pass


def setup_model():
    """called once at startup to do various one-time tasks related to SD models"""

    os.makedirs(model_path, exist_ok=True)

    enable_midas_autodownload()
    patch_given_betas()


def checkpoint_tiles(use_short=False):
    return [x.short_title if use_short else x.title for x in checkpoints_list.values()]


def list_models():
    checkpoints_list.clear()
    checkpoint_aliases.clear()

    cmd_ckpt = shared.cmd_opts.ckpt
    if shared.cmd_opts.no_download_sd_model or cmd_ckpt != shared.sd_model_file or os.path.exists(cmd_ckpt):
        model_url = None
    else:
        model_url = "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors"

    model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], download_name="v1-5-pruned-emaonly.safetensors", ext_blacklist=[".vae.ckpt", ".vae.safetensors"])

    if os.path.exists(cmd_ckpt):
        checkpoint_info = CheckpointInfo(cmd_ckpt)
        checkpoint_info.register()

        shared.opts.data['sd_model_checkpoint'] = checkpoint_info.title
    elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
        print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)

    for filename in model_list:
        checkpoint_info = CheckpointInfo(filename)
        checkpoint_info.register()


re_strip_checksum = re.compile(r"\s*\[[^]]+]\s*$")


def get_closet_checkpoint_match(search_string):
    if not search_string:
        return None

    checkpoint_info = checkpoint_aliases.get(search_string, None)
    if checkpoint_info is not None:
        return checkpoint_info

    found = sorted([info for info in checkpoints_list.values() if search_string in info.title], key=lambda x: len(x.title))
    if found:
        return found[0]

    search_string_without_checksum = re.sub(re_strip_checksum, '', search_string)
    found = sorted([info for info in checkpoints_list.values() if search_string_without_checksum in info.title], key=lambda x: len(x.title))
    if found:
        return found[0]

    return None


def model_hash(filename):
    """old hash that only looks at a small part of the file and is prone to collisions"""

    try:
        with open(filename, "rb") as file:
            import hashlib
            m = hashlib.sha256()

            file.seek(0x100000)
            m.update(file.read(0x10000))
            return m.hexdigest()[0:8]
    except FileNotFoundError:
        return 'NOFILE'


def select_checkpoint():
    """Raises `FileNotFoundError` if no checkpoints are found."""
    model_checkpoint = shared.opts.sd_model_checkpoint

    checkpoint_info = checkpoint_aliases.get(model_checkpoint, None)
    if checkpoint_info is not None:
        return checkpoint_info

    if len(checkpoints_list) == 0:
        error_message = "No checkpoints found. When searching for checkpoints, looked at:"
        if shared.cmd_opts.ckpt is not None:
            error_message += f"\n - file {os.path.abspath(shared.cmd_opts.ckpt)}"
        error_message += f"\n - directory {model_path}"
        if shared.cmd_opts.ckpt_dir is not None:
            error_message += f"\n - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}"
        error_message += "Can't run without a checkpoint. Find and place a .ckpt or .safetensors file into any of those locations."
        raise FileNotFoundError(error_message)

    checkpoint_info = next(iter(checkpoints_list.values()))
    if model_checkpoint is not None:
        print(f"Checkpoint {model_checkpoint} not found; loading fallback {checkpoint_info.title}", file=sys.stderr)

    return checkpoint_info


checkpoint_dict_replacements_sd1 = {
    'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.',
    'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.',
    'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.',
}

checkpoint_dict_replacements_sd2_turbo = { # Converts SD 2.1 Turbo from SGM to LDM format.
    'conditioner.embedders.0.': 'cond_stage_model.',
}


def transform_checkpoint_dict_key(k, replacements):
    for text, replacement in replacements.items():
        if k.startswith(text):
            k = replacement + k[len(text):]

    return k


def get_state_dict_from_checkpoint(pl_sd):
    pl_sd = pl_sd.pop("state_dict", pl_sd)
    pl_sd.pop("state_dict", None)

    is_sd2_turbo = 'conditioner.embedders.0.model.ln_final.weight' in pl_sd and pl_sd['conditioner.embedders.0.model.ln_final.weight'].size()[0] == 1024

    sd = {}
    for k, v in pl_sd.items():
        if is_sd2_turbo:
            new_key = transform_checkpoint_dict_key(k, checkpoint_dict_replacements_sd2_turbo)
        else:
            new_key = transform_checkpoint_dict_key(k, checkpoint_dict_replacements_sd1)

        if new_key is not None:
            sd[new_key] = v

    pl_sd.clear()
    pl_sd.update(sd)

    return pl_sd


def read_metadata_from_safetensors(filename):
    import json

    with open(filename, mode="rb") as file:
        metadata_len = file.read(8)
        metadata_len = int.from_bytes(metadata_len, "little")
        json_start = file.read(2)

        assert metadata_len > 2 and json_start in (b'{"', b"{'"), f"{filename} is not a safetensors file"
        json_data = json_start + file.read(metadata_len-2)
        json_obj = json.loads(json_data)

        res = {}
        for k, v in json_obj.get("__metadata__", {}).items():
            res[k] = v
            if isinstance(v, str) and v[0:1] == '{':
                try:
                    res[k] = json.loads(v)
                except Exception:
                    pass

        return res


def read_state_dict(checkpoint_file, print_global_state=False, map_location=None):
    _, extension = os.path.splitext(checkpoint_file)
    if extension.lower() == ".safetensors":
        device = map_location or shared.weight_load_location or devices.get_optimal_device_name()

        if not shared.opts.disable_mmap_load_safetensors:
            pl_sd = safetensors.torch.load_file(checkpoint_file, device=device)
        else:
            pl_sd = safetensors.torch.load(open(checkpoint_file, 'rb').read())
            pl_sd = {k: v.to(device) for k, v in pl_sd.items()}
    else:
        pl_sd = torch.load(checkpoint_file, map_location=map_location or shared.weight_load_location)

    if print_global_state and "global_step" in pl_sd:
        print(f"Global Step: {pl_sd['global_step']}")

    sd = get_state_dict_from_checkpoint(pl_sd)
    return sd


def get_checkpoint_state_dict(checkpoint_info: CheckpointInfo, timer):
    sd_model_hash = checkpoint_info.calculate_shorthash()
    timer.record("calculate hash")

    if checkpoint_info in checkpoints_loaded:
        # use checkpoint cache
        print(f"Loading weights [{sd_model_hash}] from cache")
        # move to end as latest
        checkpoints_loaded.move_to_end(checkpoint_info)
        return checkpoints_loaded[checkpoint_info]

    print(f"Loading weights [{sd_model_hash}] from {checkpoint_info.filename}")
    res = read_state_dict(checkpoint_info.filename)
    timer.record("load weights from disk")

    return res


class SkipWritingToConfig:
    """This context manager prevents load_model_weights from writing checkpoint name to the config when it loads weight."""

    skip = False
    previous = None

    def __enter__(self):
        self.previous = SkipWritingToConfig.skip
        SkipWritingToConfig.skip = True
        return self

    def __exit__(self, exc_type, exc_value, exc_traceback):
        SkipWritingToConfig.skip = self.previous


def check_fp8(model):
    if model is None:
        return None
    if devices.get_optimal_device_name() == "mps":
        enable_fp8 = False
    elif shared.opts.fp8_storage == "Enable":
        enable_fp8 = True
    elif getattr(model, "is_sdxl", False) and shared.opts.fp8_storage == "Enable for SDXL":
        enable_fp8 = True
    else:
        enable_fp8 = False
    return enable_fp8


def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer):
    sd_model_hash = checkpoint_info.calculate_shorthash()
    timer.record("calculate hash")

    if not SkipWritingToConfig.skip:
        shared.opts.data["sd_model_checkpoint"] = checkpoint_info.title

    if state_dict is None:
        state_dict = get_checkpoint_state_dict(checkpoint_info, timer)

    if shared.opts.sd_checkpoint_cache > 0:
        # cache newly loaded model
        checkpoints_loaded[checkpoint_info] = state_dict.copy()

    model.load_state_dict(state_dict, strict=False)
    timer.record("apply weights to model")

    del state_dict

    # clean up cache if limit is reached
    while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
        checkpoints_loaded.popitem(last=False)

    model.sd_model_hash = sd_model_hash
    model.sd_model_checkpoint = checkpoint_info.filename
    model.sd_checkpoint_info = checkpoint_info
    shared.opts.data["sd_checkpoint_hash"] = checkpoint_info.sha256

    if hasattr(model, 'logvar'):
        model.logvar = model.logvar.to(devices.device)  # fix for training

    sd_vae.delete_base_vae()
    sd_vae.clear_loaded_vae()
    vae_file, vae_source = sd_vae.resolve_vae(checkpoint_info.filename).tuple()
    sd_vae.load_vae(model, vae_file, vae_source)
    timer.record("load VAE")


def enable_midas_autodownload():
    """
    Gives the ldm.modules.midas.api.load_model function automatic downloading.

    When the 512-depth-ema model, and other future models like it, is loaded,
    it calls midas.api.load_model to load the associated midas depth model.
    This function applies a wrapper to download the model to the correct
    location automatically.
    """

    midas_path = os.path.join(paths.models_path, 'midas')

    # stable-diffusion-stability-ai hard-codes the midas model path to
    # a location that differs from where other scripts using this model look.
    # HACK: Overriding the path here.
    for k, v in midas.api.ISL_PATHS.items():
        file_name = os.path.basename(v)
        midas.api.ISL_PATHS[k] = os.path.join(midas_path, file_name)

    midas_urls = {
        "dpt_large": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
        "dpt_hybrid": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid-midas-501f0c75.pt",
        "midas_v21": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21-f6b98070.pt",
        "midas_v21_small": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21_small-70d6b9c8.pt",
    }

    midas.api.load_model_inner = midas.api.load_model

    def load_model_wrapper(model_type):
        path = midas.api.ISL_PATHS[model_type]
        if not os.path.exists(path):
            if not os.path.exists(midas_path):
                mkdir(midas_path)

            print(f"Downloading midas model weights for {model_type} to {path}")
            request.urlretrieve(midas_urls[model_type], path)
            print(f"{model_type} downloaded")

        return midas.api.load_model_inner(model_type)

    midas.api.load_model = load_model_wrapper


def patch_given_betas():
    import ldm.models.diffusion.ddpm

    def patched_register_schedule(*args, **kwargs):
        """a modified version of register_schedule function that converts plain list from Omegaconf into numpy"""

        if isinstance(args[1], ListConfig):
            args = (args[0], np.array(args[1]), *args[2:])

        original_register_schedule(*args, **kwargs)

    original_register_schedule = patches.patch(__name__, ldm.models.diffusion.ddpm.DDPM, 'register_schedule', patched_register_schedule)


def repair_config(sd_config):

    if not hasattr(sd_config.model.params, "use_ema"):
        sd_config.model.params.use_ema = False

    if hasattr(sd_config.model.params, 'unet_config'):
        if shared.cmd_opts.no_half:
            sd_config.model.params.unet_config.params.use_fp16 = False
        elif shared.cmd_opts.upcast_sampling:
            sd_config.model.params.unet_config.params.use_fp16 = True

    if getattr(sd_config.model.params.first_stage_config.params.ddconfig, "attn_type", None) == "vanilla-xformers" and not shared.xformers_available:
        sd_config.model.params.first_stage_config.params.ddconfig.attn_type = "vanilla"

    # For UnCLIP-L, override the hardcoded karlo directory
    if hasattr(sd_config.model.params, "noise_aug_config") and hasattr(sd_config.model.params.noise_aug_config.params, "clip_stats_path"):
        karlo_path = os.path.join(paths.models_path, 'karlo')
        sd_config.model.params.noise_aug_config.params.clip_stats_path = sd_config.model.params.noise_aug_config.params.clip_stats_path.replace("checkpoints/karlo_models", karlo_path)


sd1_clip_weight = 'cond_stage_model.transformer.text_model.embeddings.token_embedding.weight'
sd2_clip_weight = 'cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight'
sdxl_clip_weight = 'conditioner.embedders.1.model.ln_final.weight'
sdxl_refiner_clip_weight = 'conditioner.embedders.0.model.ln_final.weight'


class SdModelData:
    def __init__(self):
        self.sd_model = None
        self.loaded_sd_models = []
        self.was_loaded_at_least_once = False
        self.lock = threading.Lock()

    def get_sd_model(self):
        if self.was_loaded_at_least_once:
            return self.sd_model

        if self.sd_model is None:
            with self.lock:
                if self.sd_model is not None or self.was_loaded_at_least_once:
                    return self.sd_model

                try:
                    load_model()

                except Exception as e:
                    errors.display(e, "loading stable diffusion model", full_traceback=True)
                    print("", file=sys.stderr)
                    print("Stable diffusion model failed to load", file=sys.stderr)
                    self.sd_model = None

        return self.sd_model

    def set_sd_model(self, v, already_loaded=False):
        self.sd_model = v
        if already_loaded:
            sd_vae.base_vae = getattr(v, "base_vae", None)
            sd_vae.loaded_vae_file = getattr(v, "loaded_vae_file", None)
            sd_vae.checkpoint_info = v.sd_checkpoint_info


model_data = SdModelData()


def get_empty_cond(sd_model):

    p = processing.StableDiffusionProcessingTxt2Img()
    extra_networks.activate(p, {})

    if hasattr(sd_model, 'conditioner'):
        d = sd_model.get_learned_conditioning([""])
        return d['crossattn']
    else:
        return sd_model.cond_stage_model([""])


def send_model_to_cpu(m):
    pass


def model_target_device(m):
    return devices.device


def send_model_to_device(m):
    pass


def send_model_to_trash(m):
    pass


def load_model(checkpoint_info=None, already_loaded_state_dict=None):
    from modules import sd_hijack
    checkpoint_info = checkpoint_info or select_checkpoint()

    timer = Timer()

    if model_data.sd_model:
        if model_data.sd_model.filename == checkpoint_info.filename:
            return model_data.sd_model

        model_data.sd_model = None
        model_data.loaded_sd_models = []
        model_management.unload_all_models()
        model_management.soft_empty_cache()
        gc.collect()

    timer.record("unload existing model")

    if already_loaded_state_dict is not None:
        state_dict = already_loaded_state_dict
    else:
        state_dict = get_checkpoint_state_dict(checkpoint_info, timer)

    if shared.opts.sd_checkpoint_cache > 0:
        # cache newly loaded model
        checkpoints_loaded[checkpoint_info] = state_dict.copy()

    sd_model = forge_loader.load_model_for_a1111(timer=timer, checkpoint_info=checkpoint_info, state_dict=state_dict)
    sd_model.filename = checkpoint_info.filename

    del state_dict

    # clean up cache if limit is reached
    while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
        checkpoints_loaded.popitem(last=False)

    shared.opts.data["sd_checkpoint_hash"] = checkpoint_info.sha256

    sd_vae.delete_base_vae()
    sd_vae.clear_loaded_vae()
    vae_file, vae_source = sd_vae.resolve_vae(checkpoint_info.filename).tuple()
    sd_vae.load_vae(sd_model, vae_file, vae_source)
    timer.record("load VAE")

    model_data.set_sd_model(sd_model)
    model_data.was_loaded_at_least_once = True

    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True)  # Reload embeddings after model load as they may or may not fit the model

    timer.record("load textual inversion embeddings")

    script_callbacks.model_loaded_callback(sd_model)

    timer.record("scripts callbacks")

    with torch.no_grad():
        sd_model.cond_stage_model_empty_prompt = get_empty_cond(sd_model)

    timer.record("calculate empty prompt")

    print(f"Model loaded in {timer.summary()}.")

    return sd_model


def reuse_model_from_already_loaded(sd_model, checkpoint_info, timer):
    pass


def reload_model_weights(sd_model=None, info=None, forced_reload=False):
    return load_model(info)


def unload_model_weights(sd_model=None, info=None):
    return sd_model


def apply_token_merging(sd_model, token_merging_ratio):
    """
    Applies speed and memory optimizations from tomesd.
    """

    current_token_merging_ratio = getattr(sd_model, 'applied_token_merged_ratio', 0)

    if current_token_merging_ratio == token_merging_ratio:
        return

    if current_token_merging_ratio > 0:
        tomesd.remove_patch(sd_model)

    if token_merging_ratio > 0:
        tomesd.apply_patch(
            sd_model,
            ratio=token_merging_ratio,
            use_rand=False,  # can cause issues with some samplers
            merge_attn=True,
            merge_crossattn=False,
            merge_mlp=False
        )

    sd_model.applied_token_merged_ratio = token_merging_ratio