import argparse
import itertools
import math
import os
from pathlib import Path

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint
from torch.utils.data import Dataset

import PIL
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from diffusers import AutoencoderKL, DDPMScheduler, PNDMScheduler, StableDiffusionPipeline, UNet2DConditionModel, LMSDiscreteScheduler
from diffusers.optimization import get_scheduler
from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
from huggingface_hub import HfFolder, Repository, whoami

# TODO: remove and import from diffusers.utils when the new version of diffusers is released
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer, CLIPVisionModel


from typing import Optional
from train_global import inj_forward_text, th2image, Mapper
from datasets import OpenImagesDatasetWithMask


class MapperLocal(nn.Module):
    def __init__(self,
         input_dim: int,
         output_dim: int,
    ):
        super(MapperLocal, self).__init__()

        for i in range(5):
            setattr(self, f'mapping_{i}', nn.Sequential(nn.Linear(input_dim, 1024),
                                                        nn.LayerNorm(1024),
                                                        nn.LeakyReLU(),
                                                        nn.Linear(1024, 1024),
                                                        nn.LayerNorm(1024),
                                                        nn.LeakyReLU(),
                                                        nn.Linear(1024, output_dim)))

            setattr(self, f'mapping_patch_{i}', nn.Sequential(nn.Linear(input_dim, 1024),
                                                              nn.LayerNorm(1024),
                                                              nn.LeakyReLU(),
                                                              nn.Linear(1024, 1024),
                                                              nn.LayerNorm(1024),
                                                              nn.LeakyReLU(),
                                                              nn.Linear(1024, output_dim)))

    def forward(self, embs):
        hidden_states = ()
        for i, emb in enumerate(embs):
            hidden_state = getattr(self, f'mapping_{i}')(emb[:, :1]) + getattr(self, f'mapping_patch_{i}')(emb[:, 1:])
            hidden_states += (hidden_state.unsqueeze(0),)
        hidden_states = torch.cat(hidden_states, dim=0).mean(dim=0)
        return hidden_states

value_local_list = []

def inj_forward_crossattention(self, hidden_states, encoder_hidden_states=None, attention_mask=None):

    context = encoder_hidden_states
    hidden_states_local = hidden_states.clone()
    if context is not None:
        context_tensor = context["CONTEXT_TENSOR"]
    else:
        context_tensor = hidden_states

    batch_size, sequence_length, _ = hidden_states.shape

    query = self.to_q(hidden_states)

    if context is not None:
        key = self.to_k_global(context_tensor)
        value = self.to_v_global(context_tensor)
    else:
        key = self.to_k(context_tensor)
        value = self.to_v(context_tensor)

    dim = query.shape[-1]

    query = self.reshape_heads_to_batch_dim(query)
    key = self.reshape_heads_to_batch_dim(key)
    value = self.reshape_heads_to_batch_dim(value)


    attention_scores = torch.matmul(query, key.transpose(-1, -2))
    attention_scores = attention_scores * self.scale

    attention_probs = attention_scores.softmax(dim=-1)

    hidden_states = torch.matmul(attention_probs, value)

    if context is not None and "LOCAL" in context:
        # Perform cross attention with the local context
        query_local = self.to_q(hidden_states_local)
        key_local = self.to_k_local(context["LOCAL"])
        value_local = self.to_v_local(context["LOCAL"])

        query_local = self.reshape_heads_to_batch_dim(query_local)
        key_local = self.reshape_heads_to_batch_dim(key_local)
        value_local = self.reshape_heads_to_batch_dim(value_local)

        attention_scores_local = torch.matmul(query_local, key_local.transpose(-1, -2))
        attention_scores_local = attention_scores_local * self.scale
        attention_probs_local = attention_scores_local.softmax(dim=-1)

        # To extract the attmap of learned [w]
        index_local = context["LOCAL_INDEX"]
        index_local = index_local.reshape(index_local.shape[0], 1).repeat((1, self.heads)).reshape(-1)
        attention_probs_clone = attention_probs.clone().permute((0, 2, 1))
        attention_probs_mask = attention_probs_clone[torch.arange(index_local.shape[0]), index_local]
        # Normalize the attention map
        attention_probs_mask = attention_probs_mask.unsqueeze(2) / attention_probs_mask.max()

        if "LAMBDA" in context:
            _lambda = context["LAMBDA"]
        else:
            _lambda = 1

        attention_probs_local = attention_probs_local * attention_probs_mask * _lambda
        hidden_states += torch.matmul(attention_probs_local, value_local)
        value_local_list.append(value_local)

    hidden_states = self.reshape_batch_dim_to_heads(hidden_states)

    # linear proj
    hidden_states = self.to_out[0](hidden_states)
    # dropout
    hidden_states = self.to_out[1](hidden_states)

    return hidden_states

# ------------------------------------------------------------------------------

logger = get_logger(__name__)


def save_progress(mapper, accelerator, args, step=None):
    logger.info("Saving embeddings")

    state_dict = accelerator.unwrap_model(mapper).state_dict()

    if step is not None:
        torch.save(state_dict, os.path.join(args.output_dir, f"local_mapper_{str(step).zfill(6)}.pt"))
    else:
        torch.save(state_dict, os.path.join(args.output_dir, "local_mapper.pt"))


def parse_args():
    parser = argparse.ArgumentParser(description="Simple example of a training script.")
    parser.add_argument(
        "--save_steps",
        type=int,
        default=500,
        help="Save learned_embeds.bin every X updates steps.",
    )
    parser.add_argument(
        "--pretrained_model_name_or_path",
        type=str,
        default=None,
        required=True,
        help="Path to pretrained model or model identifier from huggingface.co/models.",
    )
    parser.add_argument(
        "--tokenizer_name",
        type=str,
        default=None,
        help="Pretrained tokenizer name or path if not the same as model_name",
    )
    parser.add_argument(
        "--train_data_dir", type=str, default=None, required=True, help="A folder containing the training data."
    )
    parser.add_argument(
        "--global_mapper_path", type=str, default=None,
        help="If not none, the training will start from the given checkpoints."
    )
    parser.add_argument(
        "--local_mapper_path", type=str, default=None,
        help="If not none, the training will start from the given checkpoints."
    )
    parser.add_argument(
        "--placeholder_token",
        type=str,
        default=None,
        required=True,
        help="A token to use as a placeholder for the concept.",
    )
    parser.add_argument(
        "--output_dir",
        type=str,
        default="text-inversion-model",
        help="The output directory where the model predictions and checkpoints will be written.",
    )
    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
    parser.add_argument(
        "--resolution",
        type=int,
        default=512,
        help=(
            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
            " resolution"
        ),
    )
    parser.add_argument(
        "--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
    )
    parser.add_argument("--num_train_epochs", type=int, default=100)
    parser.add_argument(
        "--max_train_steps",
        type=int,
        default=5000,
        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
    )
    parser.add_argument(
        "--gradient_accumulation_steps",
        type=int,
        default=1,
        help="Number of updates steps to accumulate before performing a backward/update pass.",
    )
    parser.add_argument(
        "--learning_rate",
        type=float,
        default=1e-4,
        help="Initial learning rate (after the potential warmup period) to use.",
    )
    parser.add_argument(
        "--scale_lr",
        action="store_true",
        default=True,
        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
    )
    parser.add_argument(
        "--lr_scheduler",
        type=str,
        default="constant",
        help=(
            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
            ' "constant", "constant_with_warmup"]'
        ),
    )
    parser.add_argument(
        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
    )
    parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
    parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
    parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
    parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
    parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
    parser.add_argument(
        "--hub_model_id",
        type=str,
        default=None,
        help="The name of the repository to keep in sync with the local `output_dir`.",
    )
    parser.add_argument(
        "--logging_dir",
        type=str,
        default="logs",
        help=(
            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
        ),
    )
    parser.add_argument(
        "--mixed_precision",
        type=str,
        default="no",
        choices=["no", "fp16", "bf16"],
        help=(
            "Whether to use mixed precision. Choose"
            "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
            "and an Nvidia Ampere GPU."
        ),
    )
    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")

    args = parser.parse_args()
    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
    if env_local_rank != -1 and env_local_rank != args.local_rank:
        args.local_rank = env_local_rank

    if args.train_data_dir is None:
        raise ValueError("You must specify a train data directory.")

    return args

def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
    if token is None:
        token = HfFolder.get_token()
    if organization is None:
        username = whoami(token)["name"]
        return f"{username}/{model_id}"
    else:
        return f"{organization}/{model_id}"


def freeze_params(params):
    for param in params:
        param.requires_grad = False

def unfreeze_params(params):
    for param in params:
        param.requires_grad = True


@torch.no_grad()
def validation(example, tokenizer, image_encoder, text_encoder, unet, mapper, mapper_local, vae, device, guidance_scale, seed=None, llambda=1):
    scheduler = LMSDiscreteScheduler(
        beta_start=0.00085,
        beta_end=0.012,
        beta_schedule="scaled_linear",
        num_train_timesteps=1000,
    )

    uncond_input = tokenizer(
        [''] * example["pixel_values"].shape[0],
        padding="max_length",
        max_length=tokenizer.model_max_length,
        return_tensors="pt",
    )
    uncond_embeddings = text_encoder({'input_ids':uncond_input.input_ids.to(device)})[0]

    if seed is None:
        latents = torch.randn(
            (example["pixel_values"].shape[0], unet.in_channels, 64, 64)
        )
    else:
        generator = torch.manual_seed(seed)
        latents = torch.randn(
            (example["pixel_values"].shape[0], unet.in_channels, 64, 64), generator=generator,
        )

    latents = latents.to(example["pixel_values_clip"])
    scheduler.set_timesteps(100)
    latents = latents * scheduler.init_noise_sigma

    placeholder_idx = example["index"]

    image = F.interpolate(example["pixel_values_clip"], (224, 224), mode='bilinear')
    image_features = image_encoder(image, output_hidden_states=True)
    image_embeddings = [image_features[0], image_features[2][4], image_features[2][8], image_features[2][12], image_features[2][16]]
    image_embeddings = [emb.detach() for emb in image_embeddings]
    inj_embedding = mapper(image_embeddings)

    inj_embedding = inj_embedding[:, 0:1, :]
    encoder_hidden_states = text_encoder({'input_ids': example["input_ids"],
                                          "inj_embedding": inj_embedding,
                                          "inj_index": placeholder_idx})[0]

    image_obj = F.interpolate(example["pixel_values_obj"], (224, 224), mode='bilinear')
    image_features_obj = image_encoder(image_obj, output_hidden_states=True)
    image_embeddings_obj = [image_features_obj[0], image_features_obj[2][4], image_features_obj[2][8],
                            image_features_obj[2][12], image_features_obj[2][16]]
    image_embeddings_obj = [emb.detach() for emb in image_embeddings_obj]

    inj_embedding_local = mapper_local(image_embeddings_obj)
    mask = F.interpolate(example["pixel_values_seg"], (16, 16), mode='nearest')
    mask = mask[:, 0].reshape(mask.shape[0], -1, 1)
    inj_embedding_local = inj_embedding_local * mask


    for t in tqdm(scheduler.timesteps):
        latent_model_input = scheduler.scale_model_input(latents, t)
        noise_pred_text = unet(
            latent_model_input,
            t,
            encoder_hidden_states={
                "CONTEXT_TENSOR": encoder_hidden_states,
                "LOCAL": inj_embedding_local,
                "LOCAL_INDEX": placeholder_idx.detach(),
                "LAMBDA": llambda
            }
        ).sample
        value_local_list.clear()
        latent_model_input = scheduler.scale_model_input(latents, t)

        noise_pred_uncond = unet(
            latent_model_input,
            t,
            encoder_hidden_states={
                "CONTEXT_TENSOR": uncond_embeddings,
            }
        ).sample
        value_local_list.clear()
        noise_pred = noise_pred_uncond + guidance_scale * (
                noise_pred_text - noise_pred_uncond
        )

        # compute the previous noisy sample x_t -> x_t-1
        latents = scheduler.step(noise_pred, t, latents).prev_sample

    _latents = 1 / 0.18215 * latents.clone()
    images = vae.decode(_latents).sample
    ret_pil_images = [th2image(image) for image in images]

    return ret_pil_images

def main():
    args = parse_args()
    logging_dir = os.path.join(args.output_dir, args.logging_dir)

    accelerator = Accelerator(
        gradient_accumulation_steps=args.gradient_accumulation_steps,
        mixed_precision=args.mixed_precision,
        log_with="tensorboard",
        logging_dir=logging_dir,
    )

    # If passed along, set the training seed now.
    if args.seed is not None:
        set_seed(args.seed)

    # Handle the repository creation
    if accelerator.is_main_process:
        if args.push_to_hub:
            if args.hub_model_id is None:
                repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
            else:
                repo_name = args.hub_model_id
            repo = Repository(args.output_dir, clone_from=repo_name)

            with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                if "step_*" not in gitignore:
                    gitignore.write("step_*\n")
                if "epoch_*" not in gitignore:
                    gitignore.write("epoch_*\n")
        elif args.output_dir is not None:
            os.makedirs(args.output_dir, exist_ok=True)

    # Load the tokenizer and add the placeholder token as a additional special token

    tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
    # Load models and create wrapper for stable diffusion
    text_encoder = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14")

    for _module in text_encoder.modules():
        if _module.__class__.__name__ == "CLIPTextTransformer":
            _module.__class__.__call__ = inj_forward_text

    image_encoder = CLIPVisionModel.from_pretrained("openai/clip-vit-large-patch14")

    mapper = Mapper(input_dim=1024, output_dim=768)
    mapper_local = MapperLocal(input_dim=1024, output_dim=768)

    vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
    unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")

    # replace the forward method of the crossattention to finetune the to_k and to_v layers
    for _name, _module in unet.named_modules():
        if _module.__class__.__name__ == "CrossAttention":
            if 'attn1' in _name: continue
            _module.__class__.__call__ = inj_forward_crossattention

            shape = _module.to_k.weight.shape
            to_k_global = nn.Linear(shape[1], shape[0], bias=False)
            to_k_global.weight.data = _module.to_k.weight.data.clone()
            mapper.add_module(f'{_name.replace(".", "_")}_to_k', to_k_global)

            shape = _module.to_v.weight.shape
            to_v_global = nn.Linear(shape[1], shape[0], bias=False)
            to_v_global.weight.data = _module.to_v.weight.data.clone()
            mapper.add_module(f'{_name.replace(".", "_")}_to_v', to_v_global)

            to_k_local = nn.Linear(shape[1], shape[0], bias=False)
            to_k_local.weight.data = _module.to_k.weight.data.clone()
            mapper_local.add_module(f'{_name.replace(".", "_")}_to_k', to_k_local)
            _module.add_module('to_k_local', to_k_local)

            to_v_local = nn.Linear(shape[1], shape[0], bias=False)
            to_v_local.weight.data = _module.to_v.weight.data.clone()
            mapper_local.add_module(f'{_name.replace(".", "_")}_to_v', to_v_local)
            _module.add_module('to_v_local', to_v_local)

            if args.global_mapper_path is None:
                _module.add_module('to_k_global', to_k_global)
                _module.add_module('to_v_global', to_v_global)

            if args.local_mapper_path is None:
                _module.add_module('to_k_local', to_k_local)
                _module.add_module('to_v_local', to_v_local)

    if args.global_mapper_path is not None:
        mapper.load_state_dict(torch.load(args.global_mapper_path, map_location='cpu'))
        for _name, _module in unet.named_modules():
            if _module.__class__.__name__ == "CrossAttention":
                if 'attn1' in _name: continue
                _module.add_module('to_k_global', getattr(mapper, f'{_name.replace(".", "_")}_to_k'))
                _module.add_module('to_v_global', getattr(mapper, f'{_name.replace(".", "_")}_to_v'))

    if args.local_mapper_path is not None:
        mapper_local.load_state_dict(torch.load(args.local_mapper_path, map_location='cpu'))
        for _name, _module in unet.named_modules():
            if _module.__class__.__name__ == "CrossAttention":
                if 'attn1' in _name: continue
                _module.add_module('to_k_local', getattr(mapper_local, f'{_name.replace(".", "_")}_to_k'))
                _module.add_module('to_v_local', getattr(mapper_local, f'{_name.replace(".", "_")}_to_v'))

    # Freeze vae and unet
    freeze_params(vae.parameters())
    freeze_params(unet.parameters())
    freeze_params(text_encoder.parameters())
    freeze_params(image_encoder.parameters())
    unfreeze_params(mapper_local.parameters())

    if args.scale_lr:
        args.learning_rate = (
            args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
        )

    # Initialize the optimizer
    optimizer = torch.optim.AdamW(
        itertools.chain(mapper_local.parameters()),  # only optimize the embeddings
        lr=args.learning_rate,
        betas=(args.adam_beta1, args.adam_beta2),
        weight_decay=args.adam_weight_decay,
        eps=args.adam_epsilon,
    )

    noise_scheduler = DDPMScheduler.from_config(args.pretrained_model_name_or_path, subfolder="scheduler")

    train_dataset = OpenImagesDatasetWithMask(
        data_root=args.train_data_dir,
        tokenizer=tokenizer,
        size=args.resolution,
        placeholder_token=args.placeholder_token,
        set="test"
    )
    train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=args.train_batch_size, shuffle=True)

    # Scheduler and math around the number of training steps.
    overrode_max_train_steps = False
    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
    if args.max_train_steps is None:
        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
        overrode_max_train_steps = True

    lr_scheduler = get_scheduler(
        args.lr_scheduler,
        optimizer=optimizer,
        num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
        num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
    )

    mapper_local, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
        mapper_local, optimizer, train_dataloader, lr_scheduler
    )

    # Move vae and unet to device
    vae.to(accelerator.device)
    unet.to(accelerator.device)
    image_encoder.to(accelerator.device)
    text_encoder.to(accelerator.device)
    mapper.to(accelerator.device)
    # Keep vae and unet in eval model as we don't train these
    vae.eval()
    unet.eval()
    image_encoder.eval()
    mapper.eval()

    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
    if overrode_max_train_steps:
        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
    # Afterwards we recalculate our number of training epochs
    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)

    # We need to initialize the trackers we use, and also store our configuration.
    # The trackers initialize automatically on the main process.
    if accelerator.is_main_process:
        accelerator.init_trackers("elite", config=vars(args))

    # Train!
    total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps

    logger.info("***** Running training *****")
    logger.info(f"  Num examples = {len(train_dataset)}")
    logger.info(f"  Num Epochs = {args.num_train_epochs}")
    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
    logger.info(f"  Total optimization steps = {args.max_train_steps}")
    # Only show the progress bar once on each machine.
    progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
    progress_bar.set_description("Steps")
    global_step = 0

    for epoch in range(args.num_train_epochs):
        mapper_local.train()
        for step, batch in enumerate(train_dataloader):
            with accelerator.accumulate(mapper_local):
                # Convert images to latent space
                latents = vae.encode(batch["pixel_values"]).latent_dist.sample().detach()
                latents = latents * 0.18215

                # Sample noise that we'll add to the latents
                noise = torch.randn(latents.shape).to(latents.device)
                bsz = latents.shape[0]
                # Sample a random timestep for each image
                timesteps = torch.randint(
                    0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device
                ).long()

                # Add noise to the latents according to the noise magnitude at each timestep
                # (this is the forward diffusion process)
                noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)

                placeholder_idx = batch["index"]
                image = F.interpolate(batch["pixel_values_clip"], (224, 224), mode='bilinear')
                image_obj = F.interpolate(batch["pixel_values_obj"], (224, 224), mode='bilinear')

                mask = F.interpolate(batch["pixel_values_seg"], (16, 16), mode='nearest')
                mask = mask[:, 0].reshape(mask.shape[0], -1, 1)

                image_features = image_encoder(image, output_hidden_states=True)
                image_embeddings = [image_features[0], image_features[2][4], image_features[2][8], image_features[2][12], image_features[2][16]]
                image_embeddings = [emb.detach() for emb in image_embeddings]
                inj_embedding = mapper(image_embeddings)

                # only use the first word
                inj_embedding = inj_embedding[:, 0:1, :]

                # Get the text embedding for conditioning
                encoder_hidden_states = text_encoder({'input_ids': batch["input_ids"],
                                                      "inj_embedding": inj_embedding,
                                                      "inj_index": placeholder_idx.detach()})[0]

                image_features_obj = image_encoder(image_obj, output_hidden_states=True)
                image_embeddings_obj = [image_features_obj[0], image_features_obj[2][4], image_features_obj[2][8], image_features_obj[2][12], image_features_obj[2][16]]
                image_embeddings_obj = [emb.detach() for emb in image_embeddings_obj]

                inj_embedding_local = mapper_local(image_embeddings_obj)
                inj_embedding_local = inj_embedding_local * mask


                noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states={
                    "CONTEXT_TENSOR": encoder_hidden_states,
                    "LOCAL": inj_embedding_local,
                    "LOCAL_INDEX": placeholder_idx.detach()
                }).sample

                mask_values = batch["mask_values"]
                loss_mle = F.mse_loss(noise_pred, noise, reduction="none")
                loss_mle = ((loss_mle*mask_values).sum([1, 2, 3])/mask_values.sum([1, 2, 3])).mean()

                loss_reg = 0
                for vvv in value_local_list:
                    loss_reg += torch.mean(torch.abs(vvv))
                loss_reg = loss_reg / len(value_local_list) * 0.0001

                loss = loss_mle + loss_reg

                accelerator.backward(loss)

                if accelerator.sync_gradients:
                    accelerator.clip_grad_norm_(mapper_local.parameters(), 1)

                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()
                value_local_list.clear()


            # Checks if the accelerator has performed an optimization step behind the scenes
            if accelerator.sync_gradients:
                progress_bar.update(1)
                global_step += 1
                if global_step % args.save_steps == 0:
                    save_progress(mapper_local, accelerator, args, global_step)
                    syn_images = validation(batch, tokenizer, image_encoder, text_encoder, unet, mapper, mapper_local, vae, batch["pixel_values_clip"].device, 5)
                    input_images = [th2image(img) for img in batch["pixel_values"]]
                    clip_images = [th2image(img).resize((512, 512)) for img in batch["pixel_values_clip"]]
                    obj_images = [th2image(img).resize((512, 512)) for img in batch["pixel_values_obj"]]
                    input_masks = torch.cat([mask_values, mask_values, mask_values], dim=1)
                    input_masks = [th2image(img).resize((512, 512)) for img in input_masks]
                    obj_masks = [th2image(img).resize((512, 512)) for img in batch["pixel_values_seg"]]
                    img_list = []
                    for syn, input_img, input_mask, clip_image, obj_image, obj_mask in zip(syn_images, input_images, input_masks, clip_images, obj_images, obj_masks):
                        img_list.append(np.concatenate((np.array(syn), np.array(input_img), np.array(input_mask), np.array(clip_image), np.array(obj_image), np.array(obj_mask)), axis=1))
                    img_list = np.concatenate(img_list, axis=0)
                    Image.fromarray(img_list).save(os.path.join(args.output_dir, f"{str(global_step).zfill(5)}.jpg"))

            logs = {"loss_mle": loss_mle.detach().item(), "loss_reg": loss_reg.detach().item(),  "lr": lr_scheduler.get_last_lr()[0]}
            progress_bar.set_postfix(**logs)
            accelerator.log(logs, step=global_step)

            if global_step >= args.max_train_steps:
                break

        accelerator.wait_for_everyone()

    if accelerator.is_main_process:
        save_progress(mapper_local, accelerator, args)

    accelerator.end_training()


if __name__ == "__main__":
    main()