import datetime
import html
import os
from collections import deque
import inspect
from statistics import stdev, mean
from rich import progress
import tqdm
import torch
from torch import einsum
from torch.nn.init import normal_, xavier_normal_, xavier_uniform_, kaiming_normal_, kaiming_uniform_, zeros_
from einops import rearrange, repeat
from ldm.util import default
from modules import devices, processing, sd_models, shared, hashes, sd_hijack_checkpoint, errors
import modules.textual_inversion.dataset
from modules.textual_inversion import textual_inversion, ti_logging
from modules.textual_inversion.learn_schedule import LearnRateScheduler


optimizer_dict = {optim_name : cls_obj for optim_name, cls_obj in inspect.getmembers(torch.optim, inspect.isclass) if optim_name != "Optimizer"}

class HypernetworkModule(torch.nn.Module):
    activation_dict = {
        "linear": torch.nn.Identity,
        "relu": torch.nn.ReLU,
        "leakyrelu": torch.nn.LeakyReLU,
        "elu": torch.nn.ELU,
        "swish": torch.nn.Hardswish,
        "tanh": torch.nn.Tanh,
        "sigmoid": torch.nn.Sigmoid,
    }
    activation_dict.update({cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'})

    def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal',
                 add_layer_norm=False, activate_output=False, dropout_structure=None):
        super().__init__()
        self.multiplier = 1.0
        assert layer_structure is not None, "layer_structure must not be None"
        assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!"
        assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!"
        linears = []
        for i in range(len(layer_structure) - 1):
            # Add a fully-connected layer
            linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
            # Add an activation func except last layer
            if activation_func == "linear" or activation_func is None or (i >= len(layer_structure) - 2 and not activate_output):
                pass
            elif activation_func in self.activation_dict:
                linears.append(self.activation_dict[activation_func]())
            else:
                raise RuntimeError(f'hypernetwork uses an unsupported activation function: {activation_func}')
            # Add layer normalization
            if add_layer_norm:
                linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
            # Everything should be now parsed into dropout structure, and applied here.
            # Since we only have dropouts after layers, dropout structure should start with 0 and end with 0.
            if dropout_structure is not None and dropout_structure[i+1] > 0:
                assert 0 < dropout_structure[i+1] < 1, "Dropout probability should be 0 or float between 0 and 1!"
                linears.append(torch.nn.Dropout(p=dropout_structure[i+1]))
            # Code explanation : [1, 2, 1] -> dropout is missing when last_layer_dropout is false. [1, 2, 2, 1] -> [0, 0.3, 0, 0], when its True, [0, 0.3, 0.3, 0].
        self.linear = torch.nn.Sequential(*linears)
        if state_dict is not None:
            self.fix_old_state_dict(state_dict)
            self.load_state_dict(state_dict)
        else:
            for layer in self.linear:
                if type(layer) == torch.nn.Linear or type(layer) == torch.nn.LayerNorm:
                    w, b = layer.weight.data, layer.bias.data
                    if weight_init == "Normal" or type(layer) == torch.nn.LayerNorm:
                        normal_(w, mean=0.0, std=0.01)
                        normal_(b, mean=0.0, std=0)
                    elif weight_init == 'XavierUniform':
                        xavier_uniform_(w)
                        zeros_(b)
                    elif weight_init == 'XavierNormal':
                        xavier_normal_(w)
                        zeros_(b)
                    elif weight_init == 'KaimingUniform':
                        kaiming_uniform_(w, nonlinearity='leaky_relu' if 'leakyrelu' == activation_func else 'relu')
                        zeros_(b)
                    elif weight_init == 'KaimingNormal':
                        kaiming_normal_(w, nonlinearity='leaky_relu' if 'leakyrelu' == activation_func else 'relu')
                        zeros_(b)
                    else:
                        raise KeyError(f"Key {weight_init} is not defined as initialization!")
        self.to(devices.device)

    def fix_old_state_dict(self, state_dict):
        changes = {
            'linear1.bias': 'linear.0.bias',
            'linear1.weight': 'linear.0.weight',
            'linear2.bias': 'linear.1.bias',
            'linear2.weight': 'linear.1.weight',
        }
        for fr, to in changes.items():
            x = state_dict.get(fr, None)
            if x is None:
                continue
            del state_dict[fr]
            state_dict[to] = x

    def forward(self, x):
        return x + self.linear(x) * (self.multiplier if not self.training else 1)

    def trainables(self):
        layer_structure = []
        for layer in self.linear:
            if type(layer) == torch.nn.Linear or type(layer) == torch.nn.LayerNorm:
                layer_structure += [layer.weight, layer.bias]
        return layer_structure


#param layer_structure : sequence used for length, use_dropout : controlling boolean, last_layer_dropout : for compatibility check.
def parse_dropout_structure(layer_structure, use_dropout, last_layer_dropout):
    if layer_structure is None:
        layer_structure = [1, 2, 1]
    if not use_dropout:
        return [0] * len(layer_structure)
    dropout_values = [0]
    dropout_values.extend([0.3] * (len(layer_structure) - 3))
    if last_layer_dropout:
        dropout_values.append(0.3)
    else:
        dropout_values.append(0)
    dropout_values.append(0)
    return dropout_values


class Hypernetwork:
    filename = None
    name = None

    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, activate_output=False, **kwargs):
        self.filename = None
        self.name = name
        self.layers = {}
        self.step = 0
        self.sd_checkpoint = None
        self.sd_checkpoint_name = None
        self.layer_structure = layer_structure
        self.activation_func = activation_func
        self.weight_init = weight_init
        self.add_layer_norm = add_layer_norm
        self.use_dropout = use_dropout
        self.activate_output = activate_output
        self.last_layer_dropout = kwargs.get('last_layer_dropout', True)
        self.dropout_structure = kwargs.get('dropout_structure', None)
        if self.dropout_structure is None:
            self.dropout_structure = parse_dropout_structure(self.layer_structure, self.use_dropout, self.last_layer_dropout)
        self.optimizer_name = None
        self.optimizer_state_dict = None
        self.optional_info = None
        for size in enable_sizes or []:
            self.layers[size] = (
                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init,
                                   self.add_layer_norm, self.activate_output, dropout_structure=self.dropout_structure),
                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init,
                                   self.add_layer_norm, self.activate_output, dropout_structure=self.dropout_structure),
            )
        self.eval()

    def weights(self):
        res = []
        for layers in self.layers.values():
            for layer in layers:
                res += layer.parameters()
        return res

    def train(self, mode=True):
        for layers in self.layers.values():
            for layer in layers:
                layer.train(mode=mode)
                for param in layer.parameters():
                    param.requires_grad = mode

    def to(self, device):
        for layers in self.layers.values():
            for layer in layers:
                layer.to(device)

        return self

    def set_multiplier(self, multiplier):
        for layers in self.layers.values():
            for layer in layers:
                layer.multiplier = multiplier

        return self

    def eval(self):
        for layers in self.layers.values():
            for layer in layers:
                layer.eval()
                for param in layer.parameters():
                    param.requires_grad = False

    def save(self, filename):
        state_dict = {}
        optimizer_saved_dict = {}
        for k, v in self.layers.items():
            state_dict[k] = (v[0].state_dict(), v[1].state_dict())
        state_dict['step'] = self.step
        state_dict['name'] = self.name
        state_dict['layer_structure'] = self.layer_structure
        state_dict['activation_func'] = self.activation_func
        state_dict['is_layer_norm'] = self.add_layer_norm
        state_dict['weight_initialization'] = self.weight_init
        state_dict['sd_checkpoint'] = self.sd_checkpoint
        state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
        state_dict['activate_output'] = self.activate_output
        state_dict['use_dropout'] = self.use_dropout
        state_dict['dropout_structure'] = self.dropout_structure
        state_dict['last_layer_dropout'] = (self.dropout_structure[-2] != 0) if self.dropout_structure is not None else self.last_layer_dropout
        state_dict['optional_info'] = self.optional_info if self.optional_info else None
        if self.optimizer_name is not None:
            optimizer_saved_dict['optimizer_name'] = self.optimizer_name
        torch.save(state_dict, filename)
        if shared.opts.save_optimizer_state and self.optimizer_state_dict:
            optimizer_saved_dict['hash'] = self.shorthash()
            optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict
            torch.save(optimizer_saved_dict, f"{filename}.optim")

    def load(self, filename):
        self.filename = filename if os.path.exists(filename) else os.path.join(shared.opts.hypernetwork_dir, filename)
        if self.name is None:
            self.name = os.path.splitext(os.path.basename(self.filename))[0]
        with progress.open(self.filename, 'rb', description=f'Load hypernetwork: [cyan]{self.filename}', auto_refresh=True, console=shared.console) as f:
            state_dict = torch.load(f, map_location='cpu')
        self.layer_structure = state_dict.get('layer_structure', [1, 2, 1])
        self.optional_info = state_dict.get('optional_info', None)
        self.activation_func = state_dict.get('activation_func', None)
        self.weight_init = state_dict.get('weight_initialization', 'Normal')
        self.add_layer_norm = state_dict.get('is_layer_norm', False)
        self.dropout_structure = state_dict.get('dropout_structure', None)
        self.use_dropout = True if self.dropout_structure is not None and any(self.dropout_structure) else state_dict.get('use_dropout', False)
        self.activate_output = state_dict.get('activate_output', True)
        self.last_layer_dropout = state_dict.get('last_layer_dropout', False)
        # Dropout structure should have same length as layer structure, Every digits should be in [0,1), and last digit must be 0.
        if self.dropout_structure is None:
            self.dropout_structure = parse_dropout_structure(self.layer_structure, self.use_dropout, self.last_layer_dropout)
        if shared.opts.print_hypernet_extra:
            if self.optional_info is not None:
                print(f"  INFO:\n {self.optional_info}\n")
            print(f"  Layer structure: {self.layer_structure}")
            print(f"  Activation function: {self.activation_func}")
            print(f"  Weight initialization: {self.weight_init}")
            print(f"  Layer norm: {self.add_layer_norm}")
            print(f"  Dropout usage: {self.use_dropout}" )
            print(f"  Activate last layer: {self.activate_output}")
            print(f"  Dropout structure: {self.dropout_structure}")
        optimizer_saved_dict = torch.load(self.filename + '.optim', map_location='cpu') if os.path.exists(self.filename + '.optim') else {}
        if self.shorthash() == optimizer_saved_dict.get('hash', None):
            self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)
        else:
            self.optimizer_state_dict = None
        if self.optimizer_state_dict:
            self.optimizer_name = optimizer_saved_dict.get('optimizer_name', 'AdamW')
            if shared.opts.print_hypernet_extra:
                print("Load existing optimizer from checkpoint")
                print(f"Optimizer name is {self.optimizer_name}")
        else:
            self.optimizer_name = "AdamW"
            if shared.opts.print_hypernet_extra:
                print("No saved optimizer exists in checkpoint")
        for size, sd in state_dict.items():
            if type(size) == int:
                self.layers[size] = (
                    HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init,
                                       self.add_layer_norm, self.activate_output, self.dropout_structure),
                    HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init,
                                       self.add_layer_norm, self.activate_output, self.dropout_structure),
                )
        self.name = state_dict.get('name', self.name)
        self.step = state_dict.get('step', 0)
        self.sd_checkpoint = state_dict.get('sd_checkpoint', None)
        self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None)
        self.eval()

    def shorthash(self):
        sha256 = hashes.sha256(self.filename, f'hypernet/{self.name}')
        return sha256[0:10] if sha256 else None


def list_hypernetworks(path):
    res = {}
    def list_folder(folder):
        for filename in os.listdir(folder):
            fn = os.path.join(folder, filename)
            if os.path.isfile(fn) and fn.lower().endswith(".pt"):
                name = os.path.splitext(os.path.basename(fn))[0]
                res[name] = fn
            elif os.path.isdir(fn) and not fn.startswith('.'):
                list_folder(fn)

    list_folder(path)
    return res


def load_hypernetwork(name):
    path = shared.hypernetworks.get(name, None)
    if path is None:
        return None
    hypernetwork = Hypernetwork()
    try:
        hypernetwork.load(path)
    except Exception as e:
        errors.display(e, f'hypernetwork load: {path}')
        return None
    return hypernetwork


def load_hypernetworks(names, multipliers=None):
    already_loaded = {}
    for hypernetwork in shared.loaded_hypernetworks:
        if hypernetwork.name in names:
            already_loaded[hypernetwork.name] = hypernetwork
    shared.loaded_hypernetworks.clear()
    for i, name in enumerate(names):
        hypernetwork = already_loaded.get(name, None)
        if hypernetwork is None:
            hypernetwork = load_hypernetwork(name)
        if hypernetwork is None:
            continue
        hypernetwork.set_multiplier(multipliers[i] if multipliers else 1.0)
        shared.loaded_hypernetworks.append(hypernetwork)


def find_closest_hypernetwork_name(search: str):
    if not search:
        return None
    search = search.lower()
    applicable = [name for name in shared.hypernetworks if search in name.lower()]
    if not applicable:
        return None
    applicable = sorted(applicable, key=lambda name: len(name))
    return applicable[0]


def apply_single_hypernetwork(hypernetwork, context_k, context_v, layer=None):
    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context_k.shape[2], None)
    if hypernetwork_layers is None:
        return context_k, context_v
    if layer is not None:
        layer.hyper_k = hypernetwork_layers[0]
        layer.hyper_v = hypernetwork_layers[1]
    context_k = devices.cond_cast_unet(hypernetwork_layers[0](devices.cond_cast_float(context_k)))
    context_v = devices.cond_cast_unet(hypernetwork_layers[1](devices.cond_cast_float(context_v)))
    return context_k, context_v


def apply_hypernetworks(hypernetworks, context, layer=None):
    context_k = context
    context_v = context
    for hypernetwork in hypernetworks:
        context_k, context_v = apply_single_hypernetwork(hypernetwork, context_k, context_v, layer)
    return context_k, context_v


def attention_CrossAttention_forward(self, x, context=None, mask=None):
    h = self.heads
    q = self.to_q(x)
    context = default(context, x)
    context_k, context_v = apply_hypernetworks(shared.loaded_hypernetworks, context, self)
    k = self.to_k(context_k)
    v = self.to_v(context_v)
    q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q, k, v))
    sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
    if mask is not None:
        mask = rearrange(mask, 'b ... -> b (...)')
        max_neg_value = -torch.finfo(sim.dtype).max
        mask = repeat(mask, 'b j -> (b h) () j', h=h)
        sim.masked_fill_(~mask, max_neg_value)
    # attention, what we cannot get enough of
    attn = sim.softmax(dim=-1)
    out = einsum('b i j, b j d -> b i d', attn, v)
    out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
    return self.to_out(out)


def stack_conds(conds):
    if len(conds) == 1:
        return torch.stack(conds)
    # same as in reconstruct_multicond_batch
    token_count = max([x.shape[0] for x in conds])
    for i in range(len(conds)):
        if conds[i].shape[0] != token_count:
            last_vector = conds[i][-1:]
            last_vector_repeated = last_vector.repeat([token_count - conds[i].shape[0], 1])
            conds[i] = torch.vstack([conds[i], last_vector_repeated])
    return torch.stack(conds)


def statistics(data):
    if len(data) < 2:
        std = 0
    else:
        std = stdev(data)
    total_information = f"loss:{mean(data):.3f}" + "\u00B1" + f"({std/ (len(data) ** 0.5):.3f})"
    recent_data = data[-32:]
    if len(recent_data) < 2:
        std = 0
    else:
        std = stdev(recent_data)
    recent_information = f"recent 32 loss:{mean(recent_data):.3f}" + "\u00B1" + f"({std / (len(recent_data) ** 0.5):.3f})"
    return total_information, recent_information


def report_statistics(loss_info:dict):
    keys = sorted(loss_info.keys(), key=lambda x: sum(loss_info[x]) / len(loss_info[x]))
    for key in keys:
        try:
            print("Loss statistics for file " + key)
            info, recent = statistics(list(loss_info[key]))
            print(info)
            print(recent)
        except Exception as e:
            print(e)


def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, dropout_structure=None):
    # Remove illegal characters from name.
    name = "".join( x for x in name if (x.isalnum() or x in "._- "))
    assert name, "Name cannot be empty!"
    fn = os.path.join(shared.opts.hypernetwork_dir, f"{name}.pt")
    if not overwrite_old:
        assert not os.path.exists(fn), f"file {fn} already exists"
    if type(layer_structure) == str:
        layer_structure = [float(x.strip()) for x in layer_structure.split(",")]
    if use_dropout and dropout_structure and type(dropout_structure) == str:
        dropout_structure = [float(x.strip()) for x in dropout_structure.split(",")]
    else:
        dropout_structure = [0] * len(layer_structure)
    hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(
        name=name,
        enable_sizes=[int(x) for x in enable_sizes],
        layer_structure=layer_structure,
        activation_func=activation_func,
        weight_init=weight_init,
        add_layer_norm=add_layer_norm,
        use_dropout=use_dropout,
        dropout_structure=dropout_structure
    )
    hypernet.save(fn)
    shared.reload_hypernetworks()
    return name


def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_hypernetwork_every, template_filename, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): # pylint: disable=unused-argument
    # images allows training previews to have infotext. Importing it at the top causes a circular import problem.
    from modules import images

    save_hypernetwork_every = save_hypernetwork_every or 0
    create_image_every = create_image_every or 0
    template_file = textual_inversion.textual_inversion_templates.get(template_filename, None)
    textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, template_file, template_filename, steps, save_hypernetwork_every, create_image_every, name="hypernetwork")
    template_file = template_file.path

    path = shared.hypernetworks.get(hypernetwork_name, None)
    hypernetwork = Hypernetwork()
    hypernetwork.load(path)
    shared.loaded_hypernetworks = [hypernetwork]

    shared.state.job = "train"
    shared.state.textinfo = "Initializing hypernetwork training..."
    shared.state.job_count = steps

    hypernetwork_name = hypernetwork_name.rsplit('(', 1)[0]
    filename = os.path.join(shared.opts.hypernetwork_dir, f'{hypernetwork_name}.pt')

    log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name)
    unload = shared.opts.unload_models_when_training

    if save_hypernetwork_every > 0:
        hypernetwork_dir = os.path.join(log_directory, "hypernetworks")
        os.makedirs(hypernetwork_dir, exist_ok=True)
    else:
        hypernetwork_dir = None

    if create_image_every > 0:
        images_dir = os.path.join(log_directory, "images")
        os.makedirs(images_dir, exist_ok=True)
    else:
        images_dir = None

    checkpoint = sd_models.select_checkpoint()

    initial_step = hypernetwork.step or 0
    if initial_step >= steps:
        shared.state.textinfo = "Model has already been trained beyond specified max steps"
        return hypernetwork, filename

    scheduler = LearnRateScheduler(learn_rate, steps, initial_step)

    clip_grad = torch.nn.utils.clip_grad_value_ if clip_grad_mode == "value" else torch.nn.utils.clip_grad_norm_ if clip_grad_mode == "norm" else None
    if clip_grad:
        clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, initial_step, verbose=False)

    if shared.opts.training_enable_tensorboard:
        tensorboard_writer = textual_inversion.tensorboard_setup(log_directory)

    # dataset loading may take a while, so input validations and early returns should be done before this
    shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."

    pin_memory = shared.opts.pin_memory

    ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize, use_weight=use_weight)

    if shared.opts.save_training_settings_to_txt:
        saved_params = dict(
            model_name=checkpoint.model_name, model_hash=checkpoint.shorthash, num_of_dataset_images=len(ds),
            **{field: getattr(hypernetwork, field) for field in ['layer_structure', 'activation_func', 'weight_init', 'add_layer_norm', 'use_dropout', ]}
        )
        ti_logging.save_settings_to_file(log_directory, {**saved_params, **locals()})

    latent_sampling_method = ds.latent_sampling_method

    dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, latent_sampling_method=latent_sampling_method, batch_size=ds.batch_size, pin_memory=pin_memory)

    old_parallel_processing_allowed = shared.parallel_processing_allowed

    if unload:
        shared.parallel_processing_allowed = False
        shared.sd_model.cond_stage_model.to(devices.cpu)
        shared.sd_model.first_stage_model.to(devices.cpu)

    weights = hypernetwork.weights()
    hypernetwork.train()

    # Here we use optimizer from saved HN, or we can specify as UI option.
    if hypernetwork.optimizer_name in optimizer_dict:
        optimizer = optimizer_dict[hypernetwork.optimizer_name](params=weights, lr=scheduler.learn_rate)
        optimizer_name = hypernetwork.optimizer_name
    else:
        print(f"Optimizer type {hypernetwork.optimizer_name} is not defined!")
        optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate)
        optimizer_name = 'AdamW'

    if hypernetwork.optimizer_state_dict:  # This line must be changed if Optimizer type can be different from saved optimizer.
        try:
            optimizer.load_state_dict(hypernetwork.optimizer_state_dict)
        except RuntimeError as e:
            print("Cannot resume from saved optimizer!")
            print(e)

    scaler = torch.cuda.amp.GradScaler()

    batch_size = ds.batch_size
    gradient_step = ds.gradient_step
    # n steps = batch_size * gradient_step * n image processed
    steps_per_epoch = len(ds) // batch_size // gradient_step
    max_steps_per_epoch = len(ds) // batch_size - (len(ds) // batch_size) % gradient_step
    loss_step = 0
    _loss_step = 0 #internal
    # size = len(ds.indexes)
    # loss_dict = defaultdict(lambda : deque(maxlen = 1024))
    loss_logging = deque(maxlen=len(ds) * 3)  # this should be configurable parameter, this is 3 * epoch(dataset size)
    # losses = torch.zeros((size,))
    # previous_mean_losses = [0]
    # previous_mean_loss = 0
    # print("Mean loss of {} elements".format(size))

    _steps_without_grad = 0

    last_saved_file = "<none>"
    last_saved_image = "<none>"
    forced_filename = "<none>"

    pbar = tqdm.tqdm(total=steps - initial_step)
    try:
        sd_hijack_checkpoint.add()

        for _i in range((steps-initial_step) * gradient_step):
            if scheduler.finished:
                break
            if shared.state.interrupted:
                break
            for j, batch in enumerate(dl):
                # works as a drop_last=True for gradient accumulation
                if j == max_steps_per_epoch:
                    break
                scheduler.apply(optimizer, hypernetwork.step)
                if scheduler.finished:
                    break
                if shared.state.interrupted:
                    break

                if clip_grad:
                    clip_grad_sched.step(hypernetwork.step)

                with devices.autocast():
                    x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
                    if use_weight:
                        w = batch.weight.to(devices.device, non_blocking=pin_memory)
                    if tag_drop_out != 0 or shuffle_tags:
                        shared.sd_model.cond_stage_model.to(devices.device)
                        c = shared.sd_model.cond_stage_model(batch.cond_text).to(devices.device, non_blocking=pin_memory)
                        shared.sd_model.cond_stage_model.to(devices.cpu)
                    else:
                        c = stack_conds(batch.cond).to(devices.device, non_blocking=pin_memory)
                    if use_weight:
                        loss = shared.sd_model.weighted_forward(x, c, w)[0] / gradient_step
                        del w
                    else:
                        loss = shared.sd_model.forward(x, c)[0] / gradient_step
                    del x
                    del c
                    _loss_step += loss.item()

                scaler.scale(loss).backward()
                # go back until we reach gradient accumulation steps
                if (j + 1) % gradient_step != 0:
                    continue
                loss_logging.append(_loss_step)
                if clip_grad:
                    clip_grad(weights, clip_grad_sched.learn_rate)

                scaler.step(optimizer)
                scaler.update()
                hypernetwork.step += 1
                pbar.update()
                optimizer.zero_grad(set_to_none=True)
                loss_step = _loss_step
                _loss_step = 0
                steps_done = hypernetwork.step + 1
                epoch_num = hypernetwork.step // steps_per_epoch
                epoch_step = hypernetwork.step % steps_per_epoch

                description = f"Training hypernetwork [Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}"
                pbar.set_description(description)
                if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0:
                    # Before saving, change name to match current checkpoint.
                    hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}'
                    last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name_every}.pt')
                    hypernetwork.optimizer_name = optimizer_name
                    if shared.opts.save_optimizer_state:
                        hypernetwork.optimizer_state_dict = optimizer.state_dict()
                    save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file)
                    hypernetwork.optimizer_state_dict = None  # dereference it after saving, to save memory.



                if shared.opts.training_enable_tensorboard:
                    epoch_num = hypernetwork.step // len(ds)
                    epoch_step = hypernetwork.step - (epoch_num * len(ds)) + 1
                    mean_loss = sum(loss_logging) / len(loss_logging)
                    textual_inversion.tensorboard_add(tensorboard_writer, loss=mean_loss, global_step=hypernetwork.step, step=epoch_step, learn_rate=scheduler.learn_rate, epoch_num=epoch_num)

                textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, steps_per_epoch, {
                    "loss": f"{loss_step:.7f}",
                    "learn_rate": scheduler.learn_rate
                })

                if images_dir is not None and steps_done % create_image_every == 0:
                    forced_filename = f'{hypernetwork_name}-{steps_done}'
                    last_saved_image = os.path.join(images_dir, forced_filename)
                    hypernetwork.eval()
                    rng_state = torch.get_rng_state()
                    cuda_rng_state = None
                    cuda_rng_state = torch.cuda.get_rng_state_all()
                    shared.sd_model.cond_stage_model.to(devices.device)
                    shared.sd_model.first_stage_model.to(devices.device)

                    p = processing.StableDiffusionProcessingTxt2Img(
                        sd_model=shared.sd_model,
                        do_not_save_grid=True,
                        do_not_save_samples=True,
                    )

                    p.disable_extra_networks = True

                    if preview_from_txt2img:
                        p.prompt = preview_prompt
                        p.negative_prompt = preview_negative_prompt
                        p.steps = preview_steps
                        p.sampler_name = processing.get_sampler_name(preview_sampler_index)
                        p.cfg_scale = preview_cfg_scale
                        p.seed = preview_seed
                        p.width = preview_width
                        p.height = preview_height
                    else:
                        p.prompt = batch.cond_text[0]
                        p.steps = 20
                        p.width = training_width
                        p.height = training_height

                    preview_text = p.prompt

                    processed = processing.process_images(p)
                    image = processed.images[0] if len(processed.images) > 0 else None

                    if unload:
                        shared.sd_model.cond_stage_model.to(devices.cpu)
                        shared.sd_model.first_stage_model.to(devices.cpu)
                    torch.set_rng_state(rng_state)
                    torch.cuda.set_rng_state_all(cuda_rng_state)
                    hypernetwork.train()
                    if image is not None:
                        shared.state.assign_current_image(image)
                        if shared.opts.training_enable_tensorboard and shared.opts.training_tensorboard_save_images:
                            textual_inversion.tensorboard_add_image(tensorboard_writer,
                                                                    f"Validation at epoch {epoch_num}", image,
                                                                    hypernetwork.step)
                        last_saved_image, _last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
                        last_saved_image += f", prompt: {preview_text}"

                shared.state.job_no = hypernetwork.step

                shared.state.textinfo = f"""
<p>
Loss: {loss_step:.7f}<br/>
Step: {steps_done}<br/>
Last prompt: {html.escape(batch.cond_text[0])}<br/>
Last saved hypernetwork: {html.escape(last_saved_file)}<br/>
Last saved image: {html.escape(last_saved_image)}<br/>
</p>
"""
    except Exception as e:
        errors.display(e, 'hypernetwork train')
    finally:
        pbar.leave = False
        pbar.close()
        hypernetwork.eval()
        #report_statistics(loss_dict)
        sd_hijack_checkpoint.remove()



    filename = os.path.join(shared.opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
    hypernetwork.optimizer_name = optimizer_name
    if shared.opts.save_optimizer_state:
        hypernetwork.optimizer_state_dict = optimizer.state_dict()
    save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename)

    del optimizer
    hypernetwork.optimizer_state_dict = None  # dereference it after saving, to save memory.
    shared.sd_model.cond_stage_model.to(devices.device)
    shared.sd_model.first_stage_model.to(devices.device)
    shared.parallel_processing_allowed = old_parallel_processing_allowed

    return hypernetwork, filename

def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename):
    old_hypernetwork_name = hypernetwork.name
    old_sd_checkpoint = hypernetwork.sd_checkpoint if hasattr(hypernetwork, "sd_checkpoint") else None
    old_sd_checkpoint_name = hypernetwork.sd_checkpoint_name if hasattr(hypernetwork, "sd_checkpoint_name") else None
    try:
        hypernetwork.sd_checkpoint = checkpoint.shorthash
        hypernetwork.sd_checkpoint_name = checkpoint.model_name
        hypernetwork.name = hypernetwork_name
        hypernetwork.save(filename)
    except Exception:
        hypernetwork.sd_checkpoint = old_sd_checkpoint
        hypernetwork.sd_checkpoint_name = old_sd_checkpoint_name
        hypernetwork.name = old_hypernetwork_name
        raise