# General LyCORIS wrapper based on kohya-ss/sd-scripts' style
import os
import fnmatch
import re
import logging

from typing import Any, List

import torch
import torch.nn as nn

from .modules.locon import LoConModule
from .modules.loha import LohaModule
from .modules.lokr import LokrModule
from .modules.dylora import DyLoraModule
from .modules.glora import GLoRAModule
from .modules.norms import NormModule
from .modules.full import FullModule
from .modules.diag_oft import DiagOFTModule
from .modules.boft import ButterflyOFTModule
from .modules import get_module, make_module

from .config import PRESET
from .utils.preset import read_preset
from .utils import str_bool
from .logging import logger


VALID_PRESET_KEYS = [
    "enable_conv",
    "target_module",
    "target_name",
    "module_algo_map",
    "name_algo_map",
    "lora_prefix",
    "use_fnmatch",
    "unet_target_module",
    "unet_target_name",
    "text_encoder_target_module",
    "text_encoder_target_name",
    "exclude_name",
]


network_module_dict = {
    "lora": LoConModule,
    "locon": LoConModule,
    "loha": LohaModule,
    "lokr": LokrModule,
    "dylora": DyLoraModule,
    "glora": GLoRAModule,
    "full": FullModule,
    "diag-oft": DiagOFTModule,
    "boft": ButterflyOFTModule,
}
deprecated_arg_dict = {
    "disable_conv_cp": "use_tucker",
    "use_cp": "use_tucker",
    "use_conv_cp": "use_tucker",
    "constrain": "constraint",
}


def create_lycoris(module, multiplier=1.0, linear_dim=4, linear_alpha=1, **kwargs):
    for key, value in list(kwargs.items()):
        if key in deprecated_arg_dict:
            logger.warning(
                f"{key} is deprecated. Please use {deprecated_arg_dict[key]} instead.",
                stacklevel=2,
            )
            kwargs[deprecated_arg_dict[key]] = value
    if linear_dim is None:
        linear_dim = 4  # default
    conv_dim = int(kwargs.get("conv_dim", linear_dim) or linear_dim)
    conv_alpha = float(kwargs.get("conv_alpha", linear_alpha) or linear_alpha)
    dropout = float(kwargs.get("dropout", 0.0) or 0.0)
    rank_dropout = float(kwargs.get("rank_dropout", 0.0) or 0.0)
    module_dropout = float(kwargs.get("module_dropout", 0.0) or 0.0)
    algo = (kwargs.get("algo", "lora") or "lora").lower()
    use_tucker = str_bool(
        not kwargs.get("disable_conv_cp", True)
        or kwargs.get("use_conv_cp", False)
        or kwargs.get("use_cp", False)
        or kwargs.get("use_tucker", False)
    )
    use_scalar = str_bool(kwargs.get("use_scalar", False))
    block_size = int(kwargs.get("block_size", 4) or 4)
    train_norm = str_bool(kwargs.get("train_norm", False))
    constraint = float(kwargs.get("constraint", 0) or 0)
    rescaled = str_bool(kwargs.get("rescaled", False))
    weight_decompose = str_bool(kwargs.get("dora_wd", False))
    wd_on_output = str_bool(kwargs.get("wd_on_output", False))
    full_matrix = str_bool(kwargs.get("full_matrix", False))
    bypass_mode = str_bool(kwargs.get("bypass_mode", None))
    unbalanced_factorization = str_bool(kwargs.get("unbalanced_factorization", False))

    if unbalanced_factorization:
        logger.info("Unbalanced factorization for LoKr is enabled")

    if bypass_mode:
        logger.info("Bypass mode is enabled")

    if weight_decompose:
        logger.info("Weight decomposition is enabled")

    if full_matrix:
        logger.info("Full matrix mode for LoKr is enabled")

    preset = kwargs.get("preset", "full")
    if preset not in PRESET:
        preset = read_preset(preset)
    else:
        preset = PRESET[preset]
    assert preset is not None
    LycorisNetwork.apply_preset(preset)

    logger.info(f"Using rank adaptation algo: {algo}")

    network = LycorisNetwork(
        module,
        multiplier=multiplier,
        lora_dim=linear_dim,
        conv_lora_dim=conv_dim,
        alpha=linear_alpha,
        conv_alpha=conv_alpha,
        dropout=dropout,
        rank_dropout=rank_dropout,
        module_dropout=module_dropout,
        use_tucker=use_tucker,
        use_scalar=use_scalar,
        network_module=algo,
        train_norm=train_norm,
        decompose_both=kwargs.get("decompose_both", False),
        factor=kwargs.get("factor", -1),
        block_size=block_size,
        constraint=constraint,
        rescaled=rescaled,
        weight_decompose=weight_decompose,
        wd_on_out=wd_on_output,
        full_matrix=full_matrix,
        bypass_mode=bypass_mode,
        unbalanced_factorization=unbalanced_factorization,
    )

    return network


def create_lycoris_from_weights(multiplier, file, module, weights_sd=None, **kwargs):
    if weights_sd is None:
        if os.path.splitext(file)[1] == ".safetensors":
            from safetensors.torch import load_file

            weights_sd = load_file(file)
        else:
            weights_sd = torch.load(file, map_location="cpu")

    # get dim/alpha mapping
    loras = {}
    for key in weights_sd:
        if "." not in key:
            continue

        lora_name = key.split(".")[0]
        loras[lora_name] = None

    for name, modules in module.named_modules():
        lora_name = f"{LycorisNetwork.LORA_PREFIX}_{name}".replace(".", "_")
        if lora_name in loras:
            loras[lora_name] = modules

    original_level = logger.level
    logger.setLevel(logging.ERROR)
    network = LycorisNetwork(module, init_only=True)
    network.multiplier = multiplier
    network.loras = []
    logger.setLevel(original_level)

    logger.info("Loading Modules from state dict...")
    for lora_name, orig_modules in loras.items():
        if orig_modules is None:
            continue
        lyco_type, params = get_module(weights_sd, lora_name)
        module = make_module(lyco_type, params, lora_name, orig_modules)
        if module is not None:
            network.loras.append(module)
            network.algo_table[module.__class__.__name__] = (
                network.algo_table.get(module.__class__.__name__, 0) + 1
            )
    logger.info(f"{len(network.loras)} Modules Loaded")

    for lora in network.loras:
        lora.multiplier = multiplier

    return network, weights_sd


class LycorisNetwork(torch.nn.Module):
    ENABLE_CONV = True
    TARGET_REPLACE_MODULE = [
        "Linear",
        "Conv1d",
        "Conv2d",
        "Conv3d",
        "GroupNorm",
        "LayerNorm",
    ]
    TARGET_REPLACE_NAME = []
    LORA_PREFIX = "lycoris"
    MODULE_ALGO_MAP = {}
    NAME_ALGO_MAP = {}
    USE_FNMATCH = False
    TARGET_EXCLUDE_NAME = []

    @classmethod
    def apply_preset(cls, preset):
        for preset_key in preset.keys():
            if preset_key not in VALID_PRESET_KEYS:
                raise KeyError(
                    f'Unknown preset key "{preset_key}". Valid keys: {VALID_PRESET_KEYS}'
                )

        if "enable_conv" in preset:
            cls.ENABLE_CONV = preset["enable_conv"]
        if "target_module" in preset:
            cls.TARGET_REPLACE_MODULE = preset["target_module"]
        if "target_name" in preset:
            cls.TARGET_REPLACE_NAME = preset["target_name"]
        if "module_algo_map" in preset:
            cls.MODULE_ALGO_MAP = preset["module_algo_map"]
        if "name_algo_map" in preset:
            cls.NAME_ALGO_MAP = preset["name_algo_map"]
        if "lora_prefix" in preset:
            cls.LORA_PREFIX = preset["lora_prefix"]
        if "use_fnmatch" in preset:
            cls.USE_FNMATCH = preset["use_fnmatch"]
        if "exclude_name" in preset:
            cls.TARGET_EXCLUDE_NAME = preset["exclude_name"]
        return cls

    def __init__(
        self,
        module: nn.Module,
        multiplier=1.0,
        lora_dim=4,
        conv_lora_dim=4,
        alpha=1,
        conv_alpha=1,
        use_tucker=False,
        dropout=0,
        rank_dropout=0,
        module_dropout=0,
        network_module: str = "locon",
        norm_modules=NormModule,
        train_norm=False,
        init_only=False,
        **kwargs,
    ) -> None:
        super().__init__()
        root_kwargs = kwargs
        self.weights_sd = None
        if init_only:
            self.multiplier = 1
            self.lora_dim = 0
            self.alpha = 1
            self.conv_lora_dim = 0
            self.conv_alpha = 1
            self.dropout = 0
            self.rank_dropout = 0
            self.module_dropout = 0
            self.use_tucker = False
            self.loras = []
            self.algo_table = {}
            return
        self.multiplier = multiplier
        self.lora_dim = lora_dim

        if not self.ENABLE_CONV:
            conv_lora_dim = 0

        self.conv_lora_dim = int(conv_lora_dim)
        if self.conv_lora_dim and self.conv_lora_dim != self.lora_dim:
            logger.info("Apply different lora dim for conv layer")
            logger.info(f"Conv Dim: {conv_lora_dim}, Linear Dim: {lora_dim}")
        elif self.conv_lora_dim == 0:
            logger.info("Disable conv layer")

        self.alpha = alpha
        self.conv_alpha = float(conv_alpha)
        if self.conv_lora_dim and self.alpha != self.conv_alpha:
            logger.info("Apply different alpha value for conv layer")
            logger.info(f"Conv alpha: {conv_alpha}, Linear alpha: {alpha}")

        if 1 >= dropout >= 0:
            logger.info(f"Use Dropout value: {dropout}")
        self.dropout = dropout
        self.rank_dropout = rank_dropout
        self.module_dropout = module_dropout

        self.use_tucker = use_tucker

        def create_single_module(
            lora_name: str,
            module: torch.nn.Module,
            algo_name,
            dim=None,
            alpha=None,
            use_tucker=self.use_tucker,
            **kwargs,
        ):
            for k, v in root_kwargs.items():
                if k in kwargs:
                    continue
                kwargs[k] = v

            if train_norm and "Norm" in module.__class__.__name__:
                return norm_modules(
                    lora_name,
                    module,
                    self.multiplier,
                    self.rank_dropout,
                    self.module_dropout,
                    **kwargs,
                )
            lora = None
            if isinstance(module, torch.nn.Linear) and lora_dim > 0:
                dim = dim or lora_dim
                alpha = alpha or self.alpha
            elif isinstance(
                module, (torch.nn.Conv1d, torch.nn.Conv2d, torch.nn.Conv3d)
            ):
                k_size, *_ = module.kernel_size
                if k_size == 1 and lora_dim > 0:
                    dim = dim or lora_dim
                    alpha = alpha or self.alpha
                elif conv_lora_dim > 0 or dim:
                    dim = dim or conv_lora_dim
                    alpha = alpha or self.conv_alpha
                else:
                    return None
            else:
                return None
            lora = network_module_dict[algo_name](
                lora_name,
                module,
                self.multiplier,
                dim,
                alpha,
                self.dropout,
                self.rank_dropout,
                self.module_dropout,
                use_tucker,
                **kwargs,
            )
            return lora

        def create_modules_(
            prefix: str,
            root_module: torch.nn.Module,
            algo,
            current_lora_map: dict[str, Any],
            configs={},
        ):
            assert current_lora_map is not None, "No mapping supplied"
            loras = current_lora_map
            lora_names = []
            for name, module in root_module.named_modules():
                module_name = module.__class__.__name__
                if module_name in self.MODULE_ALGO_MAP and module is not root_module:
                    next_config = self.MODULE_ALGO_MAP[module_name]
                    next_algo = next_config.get("algo", algo)
                    new_loras, new_lora_names, new_lora_map = create_modules_(
                        f"{prefix}_{name}" if name else prefix,
                        module,
                        next_algo,
                        loras,
                        configs=next_config,
                    )
                    loras = {**loras, **new_lora_map}
                    for lora_name, lora in zip(new_lora_names, new_loras):
                        if lora_name not in loras and lora_name not in current_lora_map:
                            loras[lora_name] = lora
                        if lora_name not in lora_names:
                            lora_names.append(lora_name)
                    continue

                if name:
                    lora_name = prefix + "." + name
                else:
                    lora_name = prefix

                if f"{self.LORA_PREFIX}_." in lora_name:
                    lora_name = lora_name.replace(
                        f"{self.LORA_PREFIX}_.",
                        f"{self.LORA_PREFIX}.",
                    )

                lora_name = lora_name.replace(".", "_")
                if lora_name in loras:
                    continue

                lora = create_single_module(lora_name, module, algo, **configs)
                if lora is not None:
                    loras[lora_name] = lora
                    lora_names.append(lora_name)
            return [loras[lora_name] for lora_name in lora_names], lora_names, loras

        # create module instances
        def create_modules(
            prefix,
            root_module: torch.nn.Module,
            target_replace_modules,
            target_replace_names=[],
            target_exclude_names=[],
        ) -> List:
            logger.info("Create LyCORIS Module")
            loras = []
            lora_map = {}
            next_config = {}
            for name, module in root_module.named_modules():
                if name in target_exclude_names or any(
                    self.match_fn(t, name) for t in target_exclude_names
                ):
                    continue

                module_name = module.__class__.__name__
                if module_name in target_replace_modules and not any(
                    self.match_fn(t, name) for t in target_replace_names
                ):
                    if module_name in self.MODULE_ALGO_MAP:
                        next_config = self.MODULE_ALGO_MAP[module_name]
                        algo = next_config.get("algo", network_module)
                    else:
                        algo = network_module

                    lora_lst, _, _lora_map = create_modules_(
                        f"{prefix}_{name}",
                        module,
                        algo,
                        lora_map,
                        configs=next_config,
                    )
                    lora_map = {**lora_map, **_lora_map}
                    loras.extend(lora_lst)
                    next_config = {}
                elif name in target_replace_names or any(
                    self.match_fn(t, name) for t in target_replace_names
                ):
                    conf_from_name = self.find_conf_for_name(name)
                    if conf_from_name is not None:
                        next_config = conf_from_name
                        algo = next_config.get("algo", network_module)
                    elif module_name in self.MODULE_ALGO_MAP:
                        next_config = self.MODULE_ALGO_MAP[module_name]
                        algo = next_config.get("algo", network_module)
                    else:
                        algo = network_module
                    lora_name = prefix + "." + name
                    lora_name = lora_name.replace(".", "_")

                    if lora_name in lora_map:
                        continue

                    lora = create_single_module(lora_name, module, algo, **next_config)
                    next_config = {}
                    if lora is not None:
                        lora_map[lora.lora_name] = lora
                        loras.append(lora)
            return loras

        self.loras = create_modules(
            LycorisNetwork.LORA_PREFIX,
            module,
            list(
                set(
                    [
                        *LycorisNetwork.TARGET_REPLACE_MODULE,
                        *LycorisNetwork.MODULE_ALGO_MAP.keys(),
                    ]
                )
            ),
            list(
                set(
                    [
                        *LycorisNetwork.TARGET_REPLACE_NAME,
                        *LycorisNetwork.NAME_ALGO_MAP.keys(),
                    ]
                )
            ),
            target_exclude_names=LycorisNetwork.TARGET_EXCLUDE_NAME,
        )
        logger.info(f"create LyCORIS: {len(self.loras)} modules.")

        algo_table = {}
        for lora in self.loras:
            algo_table[lora.__class__.__name__] = (
                algo_table.get(lora.__class__.__name__, 0) + 1
            )
        logger.info(f"module type table: {algo_table}")

        # Assertion to ensure we have not accidentally wrapped some layers
        # multiple times.
        names = set()
        for lora in self.loras:
            assert (
                lora.lora_name not in names
            ), f"duplicated lora name: {lora.lora_name}"
            names.add(lora.lora_name)

    def match_fn(self, pattern: str, name: str) -> bool:
        if self.USE_FNMATCH:
            return fnmatch.fnmatch(name, pattern)
        return bool(re.match(pattern, name))

    def find_conf_for_name(
        self,
        name: str,
    ) -> dict[str, Any]:
        if name in self.NAME_ALGO_MAP.keys():
            return self.NAME_ALGO_MAP[name]

        for key, value in self.NAME_ALGO_MAP.items():
            if self.match_fn(key, name):
                return value

        return None

    def set_multiplier(self, multiplier):
        self.multiplier = multiplier
        for lora in self.loras:
            lora.multiplier = self.multiplier

    def load_weights(self, file):
        if os.path.splitext(file)[1] == ".safetensors":
            from safetensors.torch import load_file, safe_open

            self.weights_sd = load_file(file)
        else:
            self.weights_sd = torch.load(file, map_location="cpu")
        missing, unexpected = self.load_state_dict(self.weights_sd, strict=False)
        state = {}
        if missing:
            state["missing keys"] = missing
        if unexpected:
            state["unexpected keys"] = unexpected
        return state

    def apply_to(self):
        """
        Register to modules to the subclass so that torch sees them.
        """
        for lora in self.loras:
            lora.apply_to()
            self.add_module(lora.lora_name, lora)

        if self.weights_sd:
            # if some weights are not in state dict, it is ok because initial LoRA does nothing (lora_up is initialized by zeros)
            info = self.load_state_dict(self.weights_sd, False)
            logger.info(f"weights are loaded: {info}")

    def is_mergeable(self):
        return True

    def restore(self):
        for lora in self.loras:
            lora.restore()

    def merge_to(self, weight=1.0):
        for lora in self.loras:
            lora.merge_to(weight)

    def apply_max_norm_regularization(self, max_norm_value, device):
        key_scaled = 0
        norms = []
        for module in self.loras:
            scaled, norm = module.apply_max_norm(max_norm_value, device)
            if scaled is None:
                continue
            norms.append(norm)
            key_scaled += scaled

        if key_scaled == 0:
            return key_scaled, 0, 0

        return key_scaled, sum(norms) / len(norms), max(norms)

    def enable_gradient_checkpointing(self):
        # not supported
        def make_ckpt(module):
            if isinstance(module, torch.nn.Module):
                module.grad_ckpt = True

        self.apply(make_ckpt)
        pass

    def prepare_optimizer_params(self, lr):
        def enumerate_params(loras):
            params = []
            for lora in loras:
                params.extend(lora.parameters())
            return params

        self.requires_grad_(True)
        all_params = []

        param_data = {"params": enumerate_params(self.loras)}
        if lr is not None:
            param_data["lr"] = lr
        all_params.append(param_data)
        return all_params

    def prepare_grad_etc(self, *args):
        self.requires_grad_(True)

    def on_epoch_start(self, *args):
        self.train()

    def get_trainable_params(self, *args):
        return self.parameters()

    def save_weights(self, file, dtype, metadata):
        if metadata is not None and len(metadata) == 0:
            metadata = None

        state_dict = self.state_dict()

        if dtype is not None:
            for key in list(state_dict.keys()):
                v = state_dict[key]
                v = v.detach().clone().to("cpu").to(dtype)
                state_dict[key] = v

        if os.path.splitext(file)[1] == ".safetensors":
            from safetensors.torch import save_file

            # Precalculate model hashes to save time on indexing
            if metadata is None:
                metadata = {}
            save_file(state_dict, file, metadata)
        else:
            torch.save(state_dict, file)