import gradio as gr
import logging
import os
import re

import lora_patches
import network
import network_lora
import network_glora
import network_hada
import network_ia3
import network_lokr
import network_full
import network_norm
import network_oft

import torch
from typing import Union

from modules import shared, devices, sd_models, errors, scripts, sd_hijack
import modules.textual_inversion.textual_inversion as textual_inversion

from lora_logger import logger

module_types = [
    network_lora.ModuleTypeLora(),
    network_hada.ModuleTypeHada(),
    network_ia3.ModuleTypeIa3(),
    network_lokr.ModuleTypeLokr(),
    network_full.ModuleTypeFull(),
    network_norm.ModuleTypeNorm(),
    network_glora.ModuleTypeGLora(),
    network_oft.ModuleTypeOFT(),
]


re_digits = re.compile(r"\d+")
re_x_proj = re.compile(r"(.*)_([qkv]_proj)$")
re_compiled = {}

suffix_conversion = {
    "attentions": {},
    "resnets": {
        "conv1": "in_layers_2",
        "conv2": "out_layers_3",
        "norm1": "in_layers_0",
        "norm2": "out_layers_0",
        "time_emb_proj": "emb_layers_1",
        "conv_shortcut": "skip_connection",
    }
}


def convert_diffusers_name_to_compvis(key, is_sd2):
    def match(match_list, regex_text):
        regex = re_compiled.get(regex_text)
        if regex is None:
            regex = re.compile(regex_text)
            re_compiled[regex_text] = regex

        r = re.match(regex, key)
        if not r:
            return False

        match_list.clear()
        match_list.extend([int(x) if re.match(re_digits, x) else x for x in r.groups()])
        return True

    m = []

    if match(m, r"lora_unet_conv_in(.*)"):
        return f'diffusion_model_input_blocks_0_0{m[0]}'

    if match(m, r"lora_unet_conv_out(.*)"):
        return f'diffusion_model_out_2{m[0]}'

    if match(m, r"lora_unet_time_embedding_linear_(\d+)(.*)"):
        return f"diffusion_model_time_embed_{m[0] * 2 - 2}{m[1]}"

    if match(m, r"lora_unet_down_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
        suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
        return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"

    if match(m, r"lora_unet_mid_block_(attentions|resnets)_(\d+)_(.+)"):
        suffix = suffix_conversion.get(m[0], {}).get(m[2], m[2])
        return f"diffusion_model_middle_block_{1 if m[0] == 'attentions' else m[1] * 2}_{suffix}"

    if match(m, r"lora_unet_up_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
        suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
        return f"diffusion_model_output_blocks_{m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"

    if match(m, r"lora_unet_down_blocks_(\d+)_downsamplers_0_conv"):
        return f"diffusion_model_input_blocks_{3 + m[0] * 3}_0_op"

    if match(m, r"lora_unet_up_blocks_(\d+)_upsamplers_0_conv"):
        return f"diffusion_model_output_blocks_{2 + m[0] * 3}_{2 if m[0]>0 else 1}_conv"

    if match(m, r"lora_te_text_model_encoder_layers_(\d+)_(.+)"):
        if is_sd2:
            if 'mlp_fc1' in m[1]:
                return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}"
            elif 'mlp_fc2' in m[1]:
                return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}"
            else:
                return f"model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}"

        return f"transformer_text_model_encoder_layers_{m[0]}_{m[1]}"

    if match(m, r"lora_te2_text_model_encoder_layers_(\d+)_(.+)"):
        if 'mlp_fc1' in m[1]:
            return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}"
        elif 'mlp_fc2' in m[1]:
            return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}"
        else:
            return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}"

    return key


def assign_network_names_to_compvis_modules(sd_model):
    network_layer_mapping = {}

    if shared.sd_model.is_sdxl:
        for i, embedder in enumerate(shared.sd_model.conditioner.embedders):
            if not hasattr(embedder, 'wrapped'):
                continue

            for name, module in embedder.wrapped.named_modules():
                network_name = f'{i}_{name.replace(".", "_")}'
                network_layer_mapping[network_name] = module
                module.network_layer_name = network_name
    else:
        for name, module in shared.sd_model.cond_stage_model.wrapped.named_modules():
            network_name = name.replace(".", "_")
            network_layer_mapping[network_name] = module
            module.network_layer_name = network_name

    for name, module in shared.sd_model.model.named_modules():
        network_name = name.replace(".", "_")
        network_layer_mapping[network_name] = module
        module.network_layer_name = network_name

    sd_model.network_layer_mapping = network_layer_mapping


def load_network(name, network_on_disk):
    net = network.Network(name, network_on_disk)
    net.mtime = os.path.getmtime(network_on_disk.filename)

    sd = sd_models.read_state_dict(network_on_disk.filename)

    # this should not be needed but is here as an emergency fix for an unknown error people are experiencing in 1.2.0
    if not hasattr(shared.sd_model, 'network_layer_mapping'):
        assign_network_names_to_compvis_modules(shared.sd_model)

    keys_failed_to_match = {}
    is_sd2 = 'model_transformer_resblocks' in shared.sd_model.network_layer_mapping

    matched_networks = {}
    bundle_embeddings = {}

    for key_network, weight in sd.items():
        key_network_without_network_parts, _, network_part = key_network.partition(".")

        if key_network_without_network_parts == "bundle_emb":
            emb_name, vec_name = network_part.split(".", 1)
            emb_dict = bundle_embeddings.get(emb_name, {})
            if vec_name.split('.')[0] == 'string_to_param':
                _, k2 = vec_name.split('.', 1)
                emb_dict['string_to_param'] = {k2: weight}
            else:
                emb_dict[vec_name] = weight
            bundle_embeddings[emb_name] = emb_dict

        key = convert_diffusers_name_to_compvis(key_network_without_network_parts, is_sd2)
        sd_module = shared.sd_model.network_layer_mapping.get(key, None)

        if sd_module is None:
            m = re_x_proj.match(key)
            if m:
                sd_module = shared.sd_model.network_layer_mapping.get(m.group(1), None)

        # SDXL loras seem to already have correct compvis keys, so only need to replace "lora_unet" with "diffusion_model"
        if sd_module is None and "lora_unet" in key_network_without_network_parts:
            key = key_network_without_network_parts.replace("lora_unet", "diffusion_model")
            sd_module = shared.sd_model.network_layer_mapping.get(key, None)
        elif sd_module is None and "lora_te1_text_model" in key_network_without_network_parts:
            key = key_network_without_network_parts.replace("lora_te1_text_model", "0_transformer_text_model")
            sd_module = shared.sd_model.network_layer_mapping.get(key, None)

            # some SD1 Loras also have correct compvis keys
            if sd_module is None:
                key = key_network_without_network_parts.replace("lora_te1_text_model", "transformer_text_model")
                sd_module = shared.sd_model.network_layer_mapping.get(key, None)

        # kohya_ss OFT module
        elif sd_module is None and "oft_unet" in key_network_without_network_parts:
            key = key_network_without_network_parts.replace("oft_unet", "diffusion_model")
            sd_module = shared.sd_model.network_layer_mapping.get(key, None)

        # KohakuBlueLeaf OFT module
        if sd_module is None and "oft_diag" in key:
            key = key_network_without_network_parts.replace("lora_unet", "diffusion_model")
            key = key_network_without_network_parts.replace("lora_te1_text_model", "0_transformer_text_model")
            sd_module = shared.sd_model.network_layer_mapping.get(key, None)

        if sd_module is None:
            keys_failed_to_match[key_network] = key
            continue

        if key not in matched_networks:
            matched_networks[key] = network.NetworkWeights(network_key=key_network, sd_key=key, w={}, sd_module=sd_module)

        matched_networks[key].w[network_part] = weight

    for key, weights in matched_networks.items():
        net_module = None
        for nettype in module_types:
            net_module = nettype.create_module(net, weights)
            if net_module is not None:
                break

        if net_module is None:
            raise AssertionError(f"Could not find a module type (out of {', '.join([x.__class__.__name__ for x in module_types])}) that would accept those keys: {', '.join(weights.w)}")

        net.modules[key] = net_module

    embeddings = {}
    for emb_name, data in bundle_embeddings.items():
        embedding = textual_inversion.create_embedding_from_data(data, emb_name, filename=network_on_disk.filename + "/" + emb_name)
        embedding.loaded = None
        embeddings[emb_name] = embedding

    net.bundle_embeddings = embeddings

    if keys_failed_to_match:
        logging.debug(f"Network {network_on_disk.filename} didn't match keys: {keys_failed_to_match}")

    return net


def purge_networks_from_memory():
    while len(networks_in_memory) > shared.opts.lora_in_memory_limit and len(networks_in_memory) > 0:
        name = next(iter(networks_in_memory))
        networks_in_memory.pop(name, None)

    devices.torch_gc()


def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=None):
    emb_db = sd_hijack.model_hijack.embedding_db
    already_loaded = {}

    for net in loaded_networks:
        if net.name in names:
            already_loaded[net.name] = net
        for emb_name, embedding in net.bundle_embeddings.items():
            if embedding.loaded:
                emb_db.register_embedding_by_name(None, shared.sd_model, emb_name)

    loaded_networks.clear()

    networks_on_disk = [available_networks.get(name, None) if name.lower() in forbidden_network_aliases else available_network_aliases.get(name, None) for name in names]
    if any(x is None for x in networks_on_disk):
        list_available_networks()

        networks_on_disk = [available_networks.get(name, None) if name.lower() in forbidden_network_aliases else available_network_aliases.get(name, None) for name in names]

    failed_to_load_networks = []

    for i, (network_on_disk, name) in enumerate(zip(networks_on_disk, names)):
        net = already_loaded.get(name, None)

        if network_on_disk is not None:
            if net is None:
                net = networks_in_memory.get(name)

            if net is None or os.path.getmtime(network_on_disk.filename) > net.mtime:
                try:
                    net = load_network(name, network_on_disk)

                    networks_in_memory.pop(name, None)
                    networks_in_memory[name] = net
                except Exception as e:
                    errors.display(e, f"loading network {network_on_disk.filename}")
                    continue

            net.mentioned_name = name

            network_on_disk.read_hash()

        if net is None:
            failed_to_load_networks.append(name)
            logging.info(f"Couldn't find network with name {name}")
            continue

        net.te_multiplier = te_multipliers[i] if te_multipliers else 1.0
        net.unet_multiplier = unet_multipliers[i] if unet_multipliers else 1.0
        net.dyn_dim = dyn_dims[i] if dyn_dims else 1.0
        loaded_networks.append(net)

        for emb_name, embedding in net.bundle_embeddings.items():
            if embedding.loaded is None and emb_name in emb_db.word_embeddings:
                logger.warning(
                    f'Skip bundle embedding: "{emb_name}"'
                    ' as it was already loaded from embeddings folder'
                )
                continue

            embedding.loaded = False
            if emb_db.expected_shape == -1 or emb_db.expected_shape == embedding.shape:
                embedding.loaded = True
                emb_db.register_embedding(embedding, shared.sd_model)
            else:
                emb_db.skipped_embeddings[name] = embedding

    if failed_to_load_networks:
        lora_not_found_message = f'Lora not found: {", ".join(failed_to_load_networks)}'
        sd_hijack.model_hijack.comments.append(lora_not_found_message)
        if shared.opts.lora_not_found_warning_console:
            print(f'\n{lora_not_found_message}\n')
        if shared.opts.lora_not_found_gradio_warning:
            gr.Warning(lora_not_found_message)

    purge_networks_from_memory()


def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]):
    weights_backup = getattr(self, "network_weights_backup", None)
    bias_backup = getattr(self, "network_bias_backup", None)

    if weights_backup is None and bias_backup is None:
        return

    if weights_backup is not None:
        if isinstance(self, torch.nn.MultiheadAttention):
            self.in_proj_weight.copy_(weights_backup[0])
            self.out_proj.weight.copy_(weights_backup[1])
        else:
            self.weight.copy_(weights_backup)

    if bias_backup is not None:
        if isinstance(self, torch.nn.MultiheadAttention):
            self.out_proj.bias.copy_(bias_backup)
        else:
            self.bias.copy_(bias_backup)
    else:
        if isinstance(self, torch.nn.MultiheadAttention):
            self.out_proj.bias = None
        else:
            self.bias = None


def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]):
    """
    Applies the currently selected set of networks to the weights of torch layer self.
    If weights already have this particular set of networks applied, does nothing.
    If not, restores original weights from backup and alters weights according to networks.
    """

    network_layer_name = getattr(self, 'network_layer_name', None)
    if network_layer_name is None:
        return

    current_names = getattr(self, "network_current_names", ())
    wanted_names = tuple((x.name, x.te_multiplier, x.unet_multiplier, x.dyn_dim) for x in loaded_networks)

    weights_backup = getattr(self, "network_weights_backup", None)
    if weights_backup is None and wanted_names != ():
        if current_names != ():
            raise RuntimeError("no backup weights found and current weights are not unchanged")

        if isinstance(self, torch.nn.MultiheadAttention):
            weights_backup = (self.in_proj_weight.to(devices.cpu, copy=True), self.out_proj.weight.to(devices.cpu, copy=True))
        else:
            weights_backup = self.weight.to(devices.cpu, copy=True)

        self.network_weights_backup = weights_backup

    bias_backup = getattr(self, "network_bias_backup", None)
    if bias_backup is None:
        if isinstance(self, torch.nn.MultiheadAttention) and self.out_proj.bias is not None:
            bias_backup = self.out_proj.bias.to(devices.cpu, copy=True)
        elif getattr(self, 'bias', None) is not None:
            bias_backup = self.bias.to(devices.cpu, copy=True)
        else:
            bias_backup = None
        self.network_bias_backup = bias_backup

    if current_names != wanted_names:
        network_restore_weights_from_backup(self)

        for net in loaded_networks:
            module = net.modules.get(network_layer_name, None)
            if module is not None and hasattr(self, 'weight'):
                try:
                    with torch.no_grad():
                        if getattr(self, 'fp16_weight', None) is None:
                            weight = self.weight
                            bias = self.bias
                        else:
                            weight = self.fp16_weight.clone().to(self.weight.device)
                            bias = getattr(self, 'fp16_bias', None)
                            if bias is not None:
                                bias = bias.clone().to(self.bias.device)
                        updown, ex_bias = module.calc_updown(weight)

                        if len(weight.shape) == 4 and weight.shape[1] == 9:
                            # inpainting model. zero pad updown to make channel[1]  4 to 9
                            updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5))

                        self.weight.copy_((weight.to(dtype=updown.dtype) + updown).to(dtype=self.weight.dtype))
                        if ex_bias is not None and hasattr(self, 'bias'):
                            if self.bias is None:
                                self.bias = torch.nn.Parameter(ex_bias).to(self.weight.dtype)
                            else:
                                self.bias.copy_((bias + ex_bias).to(dtype=self.bias.dtype))
                except RuntimeError as e:
                    logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
                    extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1

                continue

            module_q = net.modules.get(network_layer_name + "_q_proj", None)
            module_k = net.modules.get(network_layer_name + "_k_proj", None)
            module_v = net.modules.get(network_layer_name + "_v_proj", None)
            module_out = net.modules.get(network_layer_name + "_out_proj", None)

            if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out:
                try:
                    with torch.no_grad():
                        updown_q, _ = module_q.calc_updown(self.in_proj_weight)
                        updown_k, _ = module_k.calc_updown(self.in_proj_weight)
                        updown_v, _ = module_v.calc_updown(self.in_proj_weight)
                        updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
                        updown_out, ex_bias = module_out.calc_updown(self.out_proj.weight)

                        self.in_proj_weight += updown_qkv
                        self.out_proj.weight += updown_out
                    if ex_bias is not None:
                        if self.out_proj.bias is None:
                            self.out_proj.bias = torch.nn.Parameter(ex_bias)
                        else:
                            self.out_proj.bias += ex_bias

                except RuntimeError as e:
                    logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
                    extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1

                continue

            if module is None:
                continue

            logging.debug(f"Network {net.name} layer {network_layer_name}: couldn't find supported operation")
            extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1

        self.network_current_names = wanted_names


def network_forward(org_module, input, original_forward):
    """
    Old way of applying Lora by executing operations during layer's forward.
    Stacking many loras this way results in big performance degradation.
    """

    if len(loaded_networks) == 0:
        return original_forward(org_module, input)

    input = devices.cond_cast_unet(input)

    network_restore_weights_from_backup(org_module)
    network_reset_cached_weight(org_module)

    y = original_forward(org_module, input)

    network_layer_name = getattr(org_module, 'network_layer_name', None)
    for lora in loaded_networks:
        module = lora.modules.get(network_layer_name, None)
        if module is None:
            continue

        y = module.forward(input, y)

    return y


def network_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):
    self.network_current_names = ()
    self.network_weights_backup = None
    self.network_bias_backup = None


def network_Linear_forward(self, input):
    if shared.opts.lora_functional:
        return network_forward(self, input, originals.Linear_forward)

    network_apply_weights(self)

    return originals.Linear_forward(self, input)


def network_Linear_load_state_dict(self, *args, **kwargs):
    network_reset_cached_weight(self)

    return originals.Linear_load_state_dict(self, *args, **kwargs)


def network_Conv2d_forward(self, input):
    if shared.opts.lora_functional:
        return network_forward(self, input, originals.Conv2d_forward)

    network_apply_weights(self)

    return originals.Conv2d_forward(self, input)


def network_Conv2d_load_state_dict(self, *args, **kwargs):
    network_reset_cached_weight(self)

    return originals.Conv2d_load_state_dict(self, *args, **kwargs)


def network_GroupNorm_forward(self, input):
    if shared.opts.lora_functional:
        return network_forward(self, input, originals.GroupNorm_forward)

    network_apply_weights(self)

    return originals.GroupNorm_forward(self, input)


def network_GroupNorm_load_state_dict(self, *args, **kwargs):
    network_reset_cached_weight(self)

    return originals.GroupNorm_load_state_dict(self, *args, **kwargs)


def network_LayerNorm_forward(self, input):
    if shared.opts.lora_functional:
        return network_forward(self, input, originals.LayerNorm_forward)

    network_apply_weights(self)

    return originals.LayerNorm_forward(self, input)


def network_LayerNorm_load_state_dict(self, *args, **kwargs):
    network_reset_cached_weight(self)

    return originals.LayerNorm_load_state_dict(self, *args, **kwargs)


def network_MultiheadAttention_forward(self, *args, **kwargs):
    network_apply_weights(self)

    return originals.MultiheadAttention_forward(self, *args, **kwargs)


def network_MultiheadAttention_load_state_dict(self, *args, **kwargs):
    network_reset_cached_weight(self)

    return originals.MultiheadAttention_load_state_dict(self, *args, **kwargs)


def list_available_networks():
    available_networks.clear()
    available_network_aliases.clear()
    forbidden_network_aliases.clear()
    available_network_hash_lookup.clear()
    forbidden_network_aliases.update({"none": 1, "Addams": 1})

    os.makedirs(shared.cmd_opts.lora_dir, exist_ok=True)

    candidates = list(shared.walk_files(shared.cmd_opts.lora_dir, allowed_extensions=[".pt", ".ckpt", ".safetensors"]))
    candidates += list(shared.walk_files(shared.cmd_opts.lyco_dir_backcompat, allowed_extensions=[".pt", ".ckpt", ".safetensors"]))
    for filename in candidates:
        if os.path.isdir(filename):
            continue

        name = os.path.splitext(os.path.basename(filename))[0]
        try:
            entry = network.NetworkOnDisk(name, filename)
        except OSError:  # should catch FileNotFoundError and PermissionError etc.
            errors.report(f"Failed to load network {name} from {filename}", exc_info=True)
            continue

        available_networks[name] = entry

        if entry.alias in available_network_aliases:
            forbidden_network_aliases[entry.alias.lower()] = 1

        available_network_aliases[name] = entry
        available_network_aliases[entry.alias] = entry


re_network_name = re.compile(r"(.*)\s*\([0-9a-fA-F]+\)")


def infotext_pasted(infotext, params):
    if "AddNet Module 1" in [x[1] for x in scripts.scripts_txt2img.infotext_fields]:
        return  # if the other extension is active, it will handle those fields, no need to do anything

    added = []

    for k in params:
        if not k.startswith("AddNet Model "):
            continue

        num = k[13:]

        if params.get("AddNet Module " + num) != "LoRA":
            continue

        name = params.get("AddNet Model " + num)
        if name is None:
            continue

        m = re_network_name.match(name)
        if m:
            name = m.group(1)

        multiplier = params.get("AddNet Weight A " + num, "1.0")

        added.append(f"<lora:{name}:{multiplier}>")

    if added:
        params["Prompt"] += "\n" + "".join(added)


originals: lora_patches.LoraPatches = None

extra_network_lora = None

available_networks = {}
available_network_aliases = {}
loaded_networks = []
loaded_bundle_embeddings = {}
networks_in_memory = {}
available_network_hash_lookup = {}
forbidden_network_aliases = {}

list_available_networks()
