#from comet_ml import Experiment
import os
import math
import torch
import numpy as np
import matplotlib.pyplot as plt
from torch.utils.data import DataLoader, Sampler
from torch.utils.data.distributed import DistributedSampler
from torch.optim.lr_scheduler import LambdaLR
from collections import defaultdict
from diffusers import UNet2DConditionModel, AutoencoderKL
from accelerate import Accelerator
from datasets import load_from_disk
from tqdm import tqdm
from PIL import Image, ImageOps
import wandb
import random
import gc
from accelerate.state import DistributedType
from torch.distributed import broadcast_object_list
from torch.utils.checkpoint import checkpoint
from diffusers.models.attention_processor import AttnProcessor2_0
from datetime import datetime
import bitsandbytes as bnb
import torch.nn.functional as F
from collections import deque
from transformers import AutoTokenizer, AutoModel

# --------------------------- Параметры ---------------------------
ds_path = "/workspace/sdxs/datasets/768"
project = "unet"
batch_size = 36
base_learning_rate = 2.7e-5 #4e-5
min_learning_rate = 1e-5 #2.7e-5
num_epochs = 80
sample_interval_share = 5
max_length = 192
use_wandb = True
use_comet_ml = False
save_model = True
use_decay = True
fbp = False
optimizer_type = "adam8bit"
torch_compile = False
unet_gradient = True
fixed_seed = False
shuffle = True
comet_ml_api_key = "Agctp26mbqnoYrrlvQuKSTk6r" 
comet_ml_workspace = "recoilme" 
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
#torch.backends.cuda.enable_mem_efficient_sdp(False)
dtype = torch.float32
save_barrier = 1.01
warmup_percent = 0.01
percentile_clipping = 96 #97
betta2 = 0.999
eps = 1e-7
clip_grad_norm = 1.0
limit = 0
checkpoints_folder = ""
mixed_precision = "no" 
gradient_accumulation_steps = 1

accelerator = Accelerator(
    mixed_precision=mixed_precision,
    gradient_accumulation_steps=gradient_accumulation_steps
)
device = accelerator.device

# Параметры для диффузии
n_diffusion_steps = 40
samples_to_generate = 12
guidance_scale = 4

# Папки для сохранения результатов
generated_folder = "samples"
os.makedirs(generated_folder, exist_ok=True)

# Настройка seed
current_date = datetime.now()
seed = int(current_date.strftime("%Y%m%d"))
if fixed_seed:
    torch.manual_seed(seed)
    np.random.seed(seed)
    random.seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(seed)

# --------------------------- Параметры LoRA ---------------------------
lora_name = "" 
lora_rank = 32
lora_alpha = 64

print("init")

loss_ratios = {
    "mse":   1.,
}
median_coeff_steps = 256 

# Нормализация лоссов по медианам: считаем КОЭФФИЦИЕНТЫ
class MedianLossNormalizer:
    def __init__(self, desired_ratios: dict, window_steps: int):
        # нормируем доли на случай, если сумма != 1
        s = sum(desired_ratios.values())
        self.ratios = {k: (v / s) for k, v in desired_ratios.items()}
        self.buffers = {k: deque(maxlen=window_steps) for k in self.ratios.keys()}
        self.window = window_steps

    def update_and_total(self, losses: dict):
        """
        losses: dict ключ->тензор (значения лоссов)
        Поведение:
          - буферим ABS(l) только для активных (ratio>0) лоссов
          - coeff = ratio / median(abs(loss))
          - total = sum(coeff * loss) по активным лоссам
        CHANGED: буферим abs() — чтобы медиана была положительной и не ломала деление.
        """
        # буферим только активные лоссы
        for k, v in losses.items():
            if k in self.buffers and self.ratios.get(k, 0) > 0:
                self.buffers[k].append(float(v.detach().abs().cpu()))

        meds = {k: (np.median(self.buffers[k]) if len(self.buffers[k]) > 0 else 1.0) for k in self.buffers}
        coeffs = {k: (self.ratios[k] / max(meds[k], 1e-12)) for k in self.ratios}

        # суммируем только по активным (ratio>0)
        total = sum(coeffs[k] * losses[k] for k in coeffs if self.ratios.get(k, 0) > 0)
        return total, coeffs, meds

# создаём normalizer после определения loss_ratios
normalizer = MedianLossNormalizer(loss_ratios, median_coeff_steps)

# --------------------------- Инициализация WandB ---------------------------
if accelerator.is_main_process:
    if use_wandb:
        wandb.init(project=project+lora_name, config={
            "batch_size": batch_size,
            "base_learning_rate": base_learning_rate,
            "num_epochs": num_epochs,
            "optimizer_type": optimizer_type,
        })
    if use_comet_ml:
        from comet_ml import Experiment
        comet_experiment = Experiment(
            api_key=comet_ml_api_key,
            project_name=project,
            workspace=comet_ml_workspace
        )
        hyper_params = {
            "batch_size": batch_size,
            "base_learning_rate": base_learning_rate,
            "num_epochs": num_epochs,
        }
        comet_experiment.log_parameters(hyper_params)

# Включение Flash Attention 2/SDPA
torch.backends.cuda.enable_flash_sdp(True)

# --------------------------- Загрузка моделей ---------------------------
vae = AutoencoderKL.from_pretrained("vae1x", torch_dtype=dtype).to("cpu").eval()
tokenizer = AutoTokenizer.from_pretrained("tokenizer")
text_model = AutoModel.from_pretrained("text_encoder").to(device).eval()

# --- [UPDATED] Функция кодирования текста (с маской и пулингом) ---
def encode_texts(texts, max_length=max_length):
    # Если тексты пустые (для unconditional), создаем заглушки
    if texts is None: 
        # В случае None возвращаем нули (логика для get_negative_embedding)
        # Но здесь мы обычно ожидаем список строк.
        pass

    with torch.no_grad():
        if isinstance(texts, str):
            texts = [texts]

        for i, prompt_item in enumerate(texts):
            messages = [
                {"role": "user", "content": prompt_item},
            ]
            prompt_item = tokenizer.apply_chat_template(
                messages,
                tokenize=False,
                add_generation_prompt=True,
                #enable_thinking=True,
            )
            #print(prompt_item+"\n")
            texts[i] = prompt_item
            
        toks = tokenizer(
            texts,
            return_tensors="pt",
            padding="max_length",
            truncation=True,
            max_length=max_length
        ).to(device)
        
        outs = text_model(**toks, output_hidden_states=True, return_dict=True)
        
        # Используем last_hidden_state или hidden_states[-1] (если Qwen, лучше last_hidden_state - прим человека: ХУЙ)
        hidden = outs.hidden_states[-2] 
        
        # 2. Маска внимания
        attention_mask = toks["attention_mask"] 
        
        # 3. Пулинг-эмбеддинг (Последний токен)
        sequence_lengths = attention_mask.sum(dim=1) - 1
        batch_size = hidden.shape[0]
        pooled = hidden[torch.arange(batch_size, device=hidden.device), sequence_lengths]

        #return hidden, attention_mask
        # --- НОВАЯ ЛОГИКА: ОБЪЕДИНЕНИЕ ДЛЯ КРОСС-ВНИМАНИЯ ---
        # 1. Расширяем пулинг-вектор до последовательности [B, 1, emb]
        pooled_expanded = pooled.unsqueeze(1) 
                    
        # 2. Объединяем последовательность токенов и пулинг-вектор
        # !!! ИЗМЕНЕНИЕ ЗДЕСЬ !!!: Пулинг идет ПЕРВЫМ
        # Теперь: [B, 1 + L, emb]. Пулинг стал токеном в НАЧАЛЕ.
        new_encoder_hidden_states = torch.cat([pooled_expanded, hidden], dim=1) 
                    
        # 3. Обновляем маску внимания для нового токена
        # Маска внимания: [B, 1 + L]. Добавляем 1 в НАЧАЛО.
        # torch.ones((batch_size, 1), device=device) создает маску [B, 1] со значениями 1.
        new_attention_mask = torch.cat([torch.ones((batch_size, 1), device=device), attention_mask], dim=1)
        
        return new_encoder_hidden_states, new_attention_mask

shift_factor = getattr(vae.config, "shift_factor", 0.0)
if shift_factor is None: shift_factor = 0.0
scaling_factor = getattr(vae.config, "scaling_factor", 1.0)
if scaling_factor is None: scaling_factor = 1.0

from diffusers import FlowMatchEulerDiscreteScheduler
num_train_timesteps = 1000
scheduler = FlowMatchEulerDiscreteScheduler(num_train_timesteps=num_train_timesteps)

class DistributedResolutionBatchSampler(Sampler):
    def __init__(self, dataset, batch_size, num_replicas, rank, shuffle=True, drop_last=True):
        self.dataset = dataset
        self.batch_size = max(1, batch_size // num_replicas)
        self.num_replicas = num_replicas
        self.rank = rank
        self.shuffle = shuffle
        self.drop_last = drop_last
        self.epoch = 0
        
        try:
            widths = np.array(dataset["width"])
            heights = np.array(dataset["height"])
        except KeyError:
            widths = np.zeros(len(dataset))
            heights = np.zeros(len(dataset))
        
        self.size_keys = np.unique(np.stack([widths, heights], axis=1), axis=0)
        self.size_groups = {}
        for w, h in self.size_keys:
            mask = (widths == w) & (heights == h)
            self.size_groups[(w, h)] = np.where(mask)[0]
            
        self.group_num_batches = {}
        total_batches = 0
        for size, indices in self.size_groups.items():
            num_full_batches = len(indices) // (self.batch_size * self.num_replicas)
            self.group_num_batches[size] = num_full_batches
            total_batches += num_full_batches
            
        self.num_batches = (total_batches // self.num_replicas) * self.num_replicas
        
    def __iter__(self):
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        all_batches = []
        rng = np.random.RandomState(self.epoch)
        
        for size, indices in self.size_groups.items():
            indices = indices.copy()
            if self.shuffle:
                rng.shuffle(indices)
            num_full_batches = self.group_num_batches[size]
            if num_full_batches == 0:
                continue
            valid_indices = indices[:num_full_batches * self.batch_size * self.num_replicas]
            batches = valid_indices.reshape(-1, self.batch_size * self.num_replicas)
            start_idx = self.rank * self.batch_size
            end_idx = start_idx + self.batch_size
            gpu_batches = batches[:, start_idx:end_idx]
            all_batches.extend(gpu_batches)
        
        if self.shuffle:
            rng.shuffle(all_batches)
        accelerator.wait_for_everyone()
        return iter(all_batches)

    def __len__(self):
        return self.num_batches 

    def set_epoch(self, epoch):
        self.epoch = epoch

# --- [UPDATED] Функция для фиксированных семплов ---
def get_fixed_samples_by_resolution(dataset, samples_per_group=1):
    size_groups = defaultdict(list)
    try:
        widths = dataset["width"]
        heights = dataset["height"]
    except KeyError:
        widths = [0] * len(dataset)
        heights = [0] * len(dataset)
    for i, (w, h) in enumerate(zip(widths, heights)):
        size = (w, h)
        size_groups[size].append(i)
    
    fixed_samples = {}
    for size, indices in size_groups.items():
        n_samples = min(samples_per_group, len(indices))
        if len(size_groups)==1:
            n_samples = samples_to_generate
        if n_samples == 0:
            continue
        sample_indices = random.sample(indices, n_samples)
        samples_data = [dataset[idx] for idx in sample_indices]
        
        latents = torch.tensor(np.array([item["vae"] for item in samples_data])).to(device=device, dtype=dtype)
        texts = [item["text"] for item in samples_data]
        
        # Кодируем тексты на лету, чтобы получить маски и пулинг
        embeddings, masks = encode_texts(texts)
        
        fixed_samples[size] = (latents, embeddings, masks, texts)
    
    print(f"Создано {len(fixed_samples)} групп фиксированных семплов по разрешениям")
    return fixed_samples

if limit > 0:
    dataset = load_from_disk(ds_path).select(range(limit))
else:
    dataset = load_from_disk(ds_path)

# --- [UPDATED] Collate Function ---
def collate_fn_simple(batch):
    # 1. Латенты (VAE)
    latents = torch.tensor(np.array([item["vae"] for item in batch])).to(device, dtype=dtype)
    
    # 2. Текст берем сырой из датасета
    raw_texts = [item["text"] for item in batch]
    texts = [
        "" if t.lower().startswith("zero") 
        else "" if random.random() < 0.05
        else t[1:].lstrip() if t.startswith(".")
        else t.replace("The image shows ", "").replace("The image is ", "").replace("This image captures ","").strip()
        for t in raw_texts
    ]
    
    # 3. Кодируем на лету
    # Возвращает: hidden (B, L, D), mask (B, L)
    embeddings, attention_mask = encode_texts(texts)
    
    # attention_mask от токенизатора уже имеет нужный формат, но на всякий случай приведем к long
    attention_mask = attention_mask.to(dtype=torch.int64)

    return latents, embeddings, attention_mask

batch_sampler = DistributedResolutionBatchSampler(
        dataset=dataset,
        batch_size=batch_size,
        num_replicas=accelerator.num_processes,
        rank=accelerator.process_index,
        shuffle=shuffle
    )

dataloader = DataLoader(dataset, batch_sampler=batch_sampler, collate_fn=collate_fn_simple)
print("Total samples", len(dataloader))
dataloader = accelerator.prepare(dataloader)

start_epoch = 0
global_step = 0
total_training_steps = (len(dataloader) * num_epochs)
world_size = accelerator.state.num_processes

# Загрузка UNet
latest_checkpoint = os.path.join(checkpoints_folder, project)
if os.path.isdir(latest_checkpoint):
    print("Загружаем UNet из чекпоинта:", latest_checkpoint)
    unet = UNet2DConditionModel.from_pretrained(latest_checkpoint).to(device=device, dtype=dtype)
    if unet_gradient:
        unet.enable_gradient_checkpointing()
    unet.set_use_memory_efficient_attention_xformers(False)
    try:
        unet.set_attn_processor(AttnProcessor2_0())
    except Exception as e:
        print(f"Ошибка при включении SDPA: {e}")
        unet.set_use_memory_efficient_attention_xformers(True)
else:
    raise FileNotFoundError(f"UNet checkpoint not found at {latest_checkpoint}")

if lora_name:
    # ... (Код LoRA без изменений, опущен для краткости, если не используется, иначе раскомментируйте оригинальный блок) ...
    pass 

# Оптимизатор
if lora_name:
    trainable_params = [p for p in unet.parameters() if p.requires_grad]
else:
    if fbp:
        trainable_params = list(unet.parameters())

def create_optimizer(name, params):
    if name == "adam8bit":
        return bnb.optim.AdamW8bit(
            params, lr=base_learning_rate, betas=(0.9, betta2), eps=eps, weight_decay=0.01,
            percentile_clipping=percentile_clipping
        )
    elif name == "adam":
        return torch.optim.AdamW(
            params, lr=base_learning_rate, betas=(0.9, betta2), eps=1e-8, weight_decay=0.01
        )
    elif name == "muon":
        from muon import MuonWithAuxAdam
        trainable_params = [p for p in params if p.requires_grad]
        hidden_weights = [p for p in trainable_params if p.ndim >= 2]
        hidden_gains_biases = [p for p in trainable_params if p.ndim < 2]
    
        param_groups = [ 
            dict(params=hidden_weights, use_muon=True,
                lr=1e-3, weight_decay=1e-4),
            dict(params=hidden_gains_biases, use_muon=False,
                lr=1e-4, betas=(0.9, 0.95), weight_decay=1e-4),
        ]
        optimizer = MuonWithAuxAdam(param_groups)
        from snooc import SnooC
        return SnooC(optimizer)
    else:
        raise ValueError(f"Unknown optimizer: {name}")

if fbp:
    optimizer_dict = {p: create_optimizer(optimizer_type, [p]) for p in trainable_params}
    def optimizer_hook(param):
        optimizer_dict[param].step()
        optimizer_dict[param].zero_grad(set_to_none=True)
    for param in trainable_params:
        param.register_post_accumulate_grad_hook(optimizer_hook)
    unet, optimizer = accelerator.prepare(unet, optimizer_dict)
else:
    optimizer = create_optimizer(optimizer_type, unet.parameters())
    def lr_schedule(step):
        x = step / (total_training_steps * world_size)
        warmup = warmup_percent
        if not use_decay:
            return base_learning_rate
        if x < warmup:
            return min_learning_rate + (base_learning_rate - min_learning_rate) * (x / warmup)
        decay_ratio = (x - warmup) / (1 - warmup)
        return min_learning_rate + 0.5 * (base_learning_rate - min_learning_rate) * \
               (1 + math.cos(math.pi * decay_ratio))
    lr_scheduler = LambdaLR(optimizer, lambda step: lr_schedule(step) / base_learning_rate)
    unet, optimizer, lr_scheduler = accelerator.prepare(unet, optimizer, lr_scheduler)

if torch_compile:
    print("compiling")
    unet = torch.compile(unet)
    print("compiling - ok")

# Фиксированные семплы
fixed_samples = get_fixed_samples_by_resolution(dataset)

# --- [UPDATED] Функция для негативного эмбеддинга (возвращает 3 элемента) ---
def get_negative_embedding(neg_prompt="", batch_size=1):
    if not neg_prompt:
        hidden_dim = 2048 
        seq_len = max_length
        empty_emb = torch.zeros((batch_size, seq_len, hidden_dim), dtype=dtype, device=device)
        empty_mask = torch.ones((batch_size, seq_len), dtype=torch.int64, device=device)
        return empty_emb, empty_mask

    uncond_emb, uncond_mask = encode_texts([neg_prompt])
    uncond_emb = uncond_emb.to(dtype=dtype, device=device).repeat(batch_size, 1, 1)
    uncond_mask = uncond_mask.to(device=device).repeat(batch_size, 1)

    return uncond_emb, uncond_mask
    
# Получаем негативные (пустые) условия для валидации
uncond_emb, uncond_mask = get_negative_embedding("low quality")

# --- Функция генерации семплов  ---
@torch.compiler.disable()
@torch.no_grad()
def generate_and_save_samples(fixed_samples_cpu, uncond_data, step):
    uncond_emb, uncond_mask = uncond_data
    
    original_model = None
    try:
        if not torch_compile:
            original_model = accelerator.unwrap_model(unet, keep_torch_compile=True).eval()
        else:
            original_model = unet.eval()

        vae.to(device=device).eval() 
        
        all_generated_images = []
        all_captions = [] 
        
        # Распаковываем 5 элементов (добавились mask)
        for size, (sample_latents, sample_text_embeddings, sample_mask, sample_text) in fixed_samples_cpu.items():
            width, height = size
            sample_latents = sample_latents.to(dtype=dtype, device=device)
            sample_text_embeddings = sample_text_embeddings.to(dtype=dtype, device=device)
            sample_mask = sample_mask.to(device=device)
        
            latents = torch.randn(
                sample_latents.shape,
                device=device,
                dtype=sample_latents.dtype,
                generator=torch.Generator(device=device).manual_seed(seed)
            )
        
            scheduler.set_timesteps(n_diffusion_steps, device=device)
        
            for t in scheduler.timesteps:
                if guidance_scale != 1:
                    latent_model_input = torch.cat([latents, latents], dim=0)
                
                    # Подготовка батчей для CFG (Negative + Positive)
                    # 1. Embeddings
                    curr_batch_size = sample_text_embeddings.shape[0]
                    seq_len = sample_text_embeddings.shape[1]
                    hidden_dim = sample_text_embeddings.shape[2]
                    
                    neg_emb_batch = uncond_emb[0:1].expand(curr_batch_size, -1, -1)
                    text_embeddings_batch = torch.cat([neg_emb_batch, sample_text_embeddings], dim=0)
                    
                    # 2. Masks
                    neg_mask_batch = uncond_mask[0:1].expand(curr_batch_size, -1)
                    attention_mask_batch = torch.cat([neg_mask_batch, sample_mask], dim=0)

                else:
                    latent_model_input = latents
                    text_embeddings_batch = sample_text_embeddings
                    attention_mask_batch = sample_mask

                # Предсказание с передачей всех условий
                model_out = original_model(
                    latent_model_input, 
                    t, 
                    encoder_hidden_states=text_embeddings_batch, 
                    encoder_attention_mask=attention_mask_batch,
                )
                flow = getattr(model_out, "sample", model_out)
        
                if guidance_scale != 1:
                    flow_uncond, flow_cond = flow.chunk(2)
                    flow = flow_uncond + guidance_scale * (flow_cond - flow_uncond)
        
                latents = scheduler.step(flow, t, latents).prev_sample
        
            current_latents = latents

            latent_for_vae = current_latents.detach() / scaling_factor + shift_factor
            decoded = vae.decode(latent_for_vae.to(torch.float32)).sample
            decoded_fp32 = decoded.to(torch.float32)
            
            for img_idx, img_tensor in enumerate(decoded_fp32):
                img = (img_tensor / 2 + 0.5).clamp(0, 1).cpu().numpy()
                img = img.transpose(1, 2, 0)
    
                if np.isnan(img).any():
                    print("NaNs found, saving stopped! Step:", step)
                pil_img = Image.fromarray((img * 255).astype("uint8"))
                
                max_w_overall = max(s[0] for s in fixed_samples_cpu.keys())
                max_h_overall = max(s[1] for s in fixed_samples_cpu.keys())
                max_w_overall = max(255, max_w_overall)
                max_h_overall = max(255, max_h_overall)
            
                padded_img = ImageOps.pad(pil_img, (max_w_overall, max_h_overall), color='white')
                all_generated_images.append(padded_img)

                caption_text = sample_text[img_idx][:300] if img_idx < len(sample_text) else ""
                all_captions.append(caption_text)
                
                sample_path = f"{generated_folder}/{project}_{width}x{height}_{img_idx}.jpg"
                pil_img.save(sample_path, "JPEG", quality=96)
        
        if use_wandb and accelerator.is_main_process:
            wandb_images = [
                wandb.Image(img, caption=f"{all_captions[i]}")
                for i, img in enumerate(all_generated_images)
            ]
            wandb.log({"generated_images": wandb_images})
        if use_comet_ml and accelerator.is_main_process:
            for i, img in enumerate(all_generated_images):
                comet_experiment.log_image(
                    image_data=img,
                    name=f"step_{step}_img_{i}",
                    step=step,
                    metadata={"caption": all_captions[i]}
                )
    finally:
        vae.to("cpu")
        torch.cuda.empty_cache()
        gc.collect()

# --------------------------- Генерация сэмплов перед обучением ---------------------------
if accelerator.is_main_process:
    if save_model:
        print("Генерация сэмплов до старта обучения...")
        generate_and_save_samples(fixed_samples, (uncond_emb, uncond_mask), 0)
accelerator.wait_for_everyone()

def save_checkpoint(unet, variant=""):
    if accelerator.is_main_process:
        if lora_name:
            save_lora_checkpoint(unet)
        else:
            model_to_save = None
            if not torch_compile:
                model_to_save = accelerator.unwrap_model(unet)
            else:
                model_to_save = unet

            if variant != "":
                model_to_save.to(dtype=torch.float16).save_pretrained(
                    os.path.join(checkpoints_folder, f"{project}"), variant=variant
                )
            else:
                model_to_save.save_pretrained(os.path.join(checkpoints_folder, f"{project}"))

            unet = unet.to(dtype=dtype)

# --------------------------- Тренировочный цикл ---------------------------
if accelerator.is_main_process:
    print(f"Total steps per GPU: {total_training_steps}")

epoch_loss_points = []
progress_bar = tqdm(total=total_training_steps, disable=not accelerator.is_local_main_process, desc="Training", unit="step")

steps_per_epoch = len(dataloader)
sample_interval = max(1, steps_per_epoch // sample_interval_share)
min_loss = 2.

for epoch in range(start_epoch, start_epoch + num_epochs):
    batch_losses = []
    batch_grads = []
    batch_sampler.set_epoch(epoch)
    accelerator.wait_for_everyone()
    unet.train()
    
    for step, (latents, embeddings, attention_mask) in enumerate(dataloader):
        with accelerator.accumulate(unet):
            if save_model == False and step == 5 :
                used_gb = torch.cuda.max_memory_allocated() / 1024**3
                print(f"Шаг {step}: {used_gb:.2f} GB")
                
            # шум
            noise = torch.randn_like(latents, dtype=latents.dtype)
            # берём t из [0, 1]
            t = torch.rand(latents.shape[0], device=latents.device, dtype=latents.dtype)
            # интерполяция между x0 и шумом
            noisy_latents = (1.0 - t.view(-1, 1, 1, 1)) * latents + t.view(-1, 1, 1, 1) * noise
            # делаем integer timesteps для UNet
            timesteps = (t * scheduler.config.num_train_timesteps).long()
            
            # --- Вызов UNet с маской  ---
            model_pred = unet(
                noisy_latents, 
                timesteps, 
                encoder_hidden_states=embeddings,
                encoder_attention_mask=attention_mask
            ).sample
            
            target = noise - latents 
            mse_loss = F.mse_loss(model_pred.float(), target.float())
            batch_losses.append(mse_loss.detach().item())

            if (global_step % 100 == 0) or (global_step % sample_interval == 0):
                accelerator.wait_for_everyone()

            losses_dict = {}
            losses_dict["mse"] = mse_loss

            # === Нормализация всех лоссов ===
            abs_for_norm = {k: losses_dict.get(k, torch.tensor(0.0, device=device)) for k in normalizer.ratios.keys()}
            total_loss, coeffs, meds = normalizer.update_and_total(abs_for_norm)

            if (global_step % 100 == 0) or (global_step % sample_interval == 0):
                accelerator.wait_for_everyone()
                
            accelerator.backward(total_loss)

            if (global_step % 100 == 0) or (global_step % sample_interval == 0):
                accelerator.wait_for_everyone()
                
            grad = 0.0
            if not fbp:
                if accelerator.sync_gradients:
                    #with torch.amp.autocast('cuda', enabled=False):
                    grad_val = accelerator.clip_grad_norm_(unet.parameters(), clip_grad_norm)
                    grad = float(grad_val)
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad(set_to_none=True)

            if accelerator.sync_gradients:
                global_step += 1
                progress_bar.update(1)
                if accelerator.is_main_process:
                    if fbp:
                        current_lr = base_learning_rate
                    else:
                        current_lr = lr_scheduler.get_last_lr()[0]
                    batch_grads.append(grad)
    
                    log_data = {}
                    log_data["loss"] = mse_loss.detach().item()
                    log_data["lr"] = current_lr
                    log_data["grad"] = grad
                    log_data["loss_total"] = float(total_loss.item())
                    for k, c in coeffs.items():
                        log_data[f"coeff_{k}"] = float(c)
                    if accelerator.sync_gradients:
                        if use_wandb:
                            wandb.log(log_data, step=global_step)
                        if use_comet_ml:
                            comet_experiment.log_metrics(log_data, step=global_step)
    
                    if global_step % sample_interval == 0:
                        # Передаем tuple (emb, mask) для негатива
                        generate_and_save_samples(fixed_samples, (uncond_emb, uncond_mask), global_step)
                        last_n = sample_interval
                        
                        if save_model:
                            has_losses = len(batch_losses) > 0
                            avg_sample_loss = np.mean(batch_losses[-sample_interval:]) if has_losses else 0.0
                            last_loss = batch_losses[-1] if has_losses else 0.0
                            max_loss = max(avg_sample_loss, last_loss)    
                            should_save = max_loss < min_loss * save_barrier
                            print(
                                f"Saving: {should_save} | Max: {max_loss:.4f} | "
                                f"Last: {last_loss:.4f} | Avg: {avg_sample_loss:.4f}"
                            )
                            # 6. Сохранение и обновление
                            if should_save:
                                min_loss = max_loss
                                save_checkpoint(unet)

    if accelerator.is_main_process:
        avg_epoch_loss = np.mean(batch_losses) if len(batch_losses) > 0 else 0.0
        avg_epoch_grad = np.mean(batch_grads) if len(batch_grads) > 0 else 0.0

        print(f"\nЭпоха {epoch} завершена. Средний лосс: {avg_epoch_loss:.6f}")
        log_data_ep = {
                        "epoch_loss": avg_epoch_loss,
                        "epoch_grad": avg_epoch_grad,
                        "epoch": epoch + 1,
                    }
        if use_wandb:
            wandb.log(log_data_ep)
        if use_comet_ml:
            comet_experiment.log_metrics(log_data_ep)

if accelerator.is_main_process:
    print("Обучение завершено! Сохраняем финальную модель...")
    if save_model:
        save_checkpoint(unet,"fp16")
    if use_comet_ml:
        comet_experiment.end()
accelerator.free_memory()
if torch.distributed.is_initialized():
    torch.distributed.destroy_process_group()
    
print("Готово!")