# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddlenlp.utils.log import logger
logger.set_level("WARNING")
import paddle
import argparse
import contextlib
import gc
import hashlib
import math
import os
import sys
import warnings
from pathlib import Path
from typing import Optional

import numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
import requests
from huggingface_hub import HfFolder, create_repo, upload_folder, whoami
from paddle.distributed.fleet.utils.hybrid_parallel_util import (
    fused_allreduce_gradients,
)
from utils import context_nologging, _retry
from paddle.io import BatchSampler, DataLoader, Dataset, DistributedBatchSampler
from paddle.optimizer import AdamW
from paddle.vision import BaseTransform, transforms
from PIL import Image
from tqdm.auto import tqdm

from paddlenlp.trainer import set_seed
from paddlenlp.transformers import AutoTokenizer, PretrainedConfig
from ppdiffusers import (
    AutoencoderKL,
    DDPMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    UNet2DConditionModel,
)
from ppdiffusers.loaders import AttnProcsLayers
from ppdiffusers.modeling_utils import freeze_params, unwrap_model
from ppdiffusers.models.cross_attention import LoRACrossAttnProcessor
from ppdiffusers.optimization import get_scheduler
from ppdiffusers.utils import image_grid

def str2bool(v):
    if v.lower() in ("yes", "true", "t", "y", "1"):
        return True
    elif v.lower() in ("no", "false", "f", "n", "0"):
        return False
    else:
        raise argparse.ArgumentTypeError("Unsupported value encountered.")

def url_or_path_join(*path_list):
    return os.path.join(*path_list) if os.path.isdir(os.path.join(*path_list)) else "/".join(path_list)


def save_model_card(repo_name, images=None, base_model=str, prompt=str, repo_folder=None):
    img_str = ""
    for i, image in enumerate(images):
        image.save(os.path.join(repo_folder, f"image_{i}.png"))
        img_str += f"![img_{i}](./image_{i}.png)\n"

    yaml = f"""
---
license: creativeml-openrail-m
base_model: {base_model}
instance_prompt: {prompt}
tags:
- stable-diffusion
- stable-diffusion-ppdiffusers
- text-to-image
- ppdiffusers
- lora
inference: false
---
    """
    model_card = f"""
# LoRA DreamBooth - {repo_name}
These are LoRA adaption weights for {base_model}. The weights were trained on {prompt} using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. \n
{img_str}
"""
    with open(os.path.join(repo_folder, "README.md"), "w") as f:
        f.write(yaml + model_card)


def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str):
    try:
        text_encoder_config = PretrainedConfig.from_pretrained(
            url_or_path_join(pretrained_model_name_or_path, "text_encoder")
        )
        model_class = text_encoder_config.architectures[0]
    except Exception:
        model_class = "LDMBertModel"
    if model_class == "CLIPTextModel":
        from paddlenlp.transformers import CLIPTextModel

        return CLIPTextModel
    elif model_class == "RobertaSeriesModelWithTransformation":
        from ppdiffusers.pipelines.alt_diffusion.modeling_roberta_series import (
            RobertaSeriesModelWithTransformation,
        )

        return RobertaSeriesModelWithTransformation
    elif model_class == "BertModel":
        from paddlenlp.transformers import BertModel

        return BertModel
    elif model_class == "LDMBertModel":
        from ppdiffusers.pipelines.latent_diffusion.pipeline_latent_diffusion import (
            LDMBertModel,
        )

        return LDMBertModel
    else:
        raise ValueError(f"{model_class} is not supported.")


class Lambda(BaseTransform):
    def __init__(self, fn, keys=None):
        super().__init__(keys)
        self.fn = fn

    def _apply_image(self, img):
        return self.fn(img)


def get_report_to(args):
    if args.report_to == "visualdl":
        from visualdl import LogWriter

        writer = LogWriter(logdir=args.logging_dir)
    elif args.report_to == "tensorboard":
        from tensorboardX import SummaryWriter

        writer = SummaryWriter(logdir=args.logging_dir)
    else:
        raise ValueError("report_to must be in ['visualdl', 'tensorboard']")
    return writer


def parse_args(input_args=None):
    parser = argparse.ArgumentParser(description="Simple example of a training dreambooth lora script.")
    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(
        "--instance_data_dir",
        type=str,
        default=None,
        required=True,
        help="A folder containing the training data of instance images.",
    )
    parser.add_argument(
        "--class_data_dir",
        type=str,
        default=None,
        required=False,
        help="A folder containing the training data of class images.",
    )
    parser.add_argument(
        "--instance_prompt",
        type=str,
        default=None,
        required=True,
        help="The prompt with identifier specifying the instance",
    )
    parser.add_argument(
        "--class_prompt",
        type=str,
        default=None,
        help="The prompt to specify images in the same class as provided instance images.",
    )
    parser.add_argument(
        "--validation_prompt", type=str, default=None, help="A prompt that is sampled during training for inference."
    )
    parser.add_argument(
        "--num_validation_images",
        type=int,
        default=4,
        help="Number of images that should be generated during validation with `validation_prompt`.",
    )
    parser.add_argument(
        "--validation_steps",
        type=int,
        default=50,
        help=(
            "Run dreambooth validation every X global steps. Dreambooth validation consists of running the prompt"
            " `args.validation_prompt` multiple times: `args.num_validation_images`."
        ),
    )
    parser.add_argument(
        "--with_prior_preservation",
        default=False,
        action="store_true",
        help="Flag to add prior preservation loss.",
    )
    parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
    parser.add_argument(
        "--num_class_images",
        type=int,
        default=100,
        help=(
            "Minimal class images for prior preservation loss. If there are not enough images already present in"
            " class_data_dir, additional images will be sampled with class_prompt."
        ),
    )
    parser.add_argument(
        "--lora_rank",
        type=int,
        default=4,
        help=(
            "lora_rank"
        ),
    )  

    parser.add_argument(
        "--output_dir",
        type=str,
        default="lora-dreambooth-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(
        "--height",
        type=int,
        default=None,
        help=(
            "The height for input images, all the images in the train/validation dataset will be resized to this"
            " height"
        ),
    )
    parser.add_argument(
        "--width",
        type=int,
        default=None,
        help=(
            "The width for input images, all the images in the train/validation dataset will be resized to this"
            " width"
        ),
    )
    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(
        "--center_crop",
        default=False,
        action="store_true",
        help=(
            "Whether to center crop the input images to the resolution. If not set, the images will be randomly"
            " cropped. The images will be resized to the resolution first before cropping."
        ),
    )
    parser.add_argument(
        "--random_flip",
        action="store_true",
        help="whether to randomly flip images horizontally",
    )
    parser.add_argument(
        "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
    )
    parser.add_argument(
        "--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
    )
    parser.add_argument("--num_train_epochs", type=int, default=1)
    parser.add_argument(
        "--max_train_steps",
        type=int,
        default=500,
        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
    )
    parser.add_argument(
        "--checkpointing_steps",
        type=int,
        default=100,
        help=("Save a checkpoint of the training state every X updates."),
    )
    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(
        "--gradient_checkpointing",
        action="store_true",
        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
    )
    parser.add_argument(
        "--learning_rate",
        type=float,
        default=5e-4,
        help="Initial learning rate (after the potential warmup period) to use.",
    )
    parser.add_argument(
        "--scale_lr",
        action="store_true",
        default=False,
        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(
        "--lr_num_cycles",
        type=int,
        default=1,
        help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
    )
    parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
    parser.add_argument(
        "--dataloader_num_workers",
        type=int,
        default=0,
        help=(
            "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
        ),
    )
    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("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
    parser.add_argument("--push_to_hub", type=str2bool, nargs="?", const=False, 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) or [VisualDL](https://www.paddlepaddle.org.cn/paddle/visualdl) log directory. Will default to"
            "*output_dir/logs"
        ),
    )
    parser.add_argument(
        "--report_to",
        type=str,
        default="visualdl",
        choices=["tensorboard", "visualdl"],
        help="Log writer type.",
    )
    if input_args is not None:
        args = parser.parse_args(input_args)
    else:
        args = parser.parse_args()

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

    if args.with_prior_preservation:
        if args.class_data_dir is None:
            raise ValueError("You must specify a data directory for class images.")
        if args.class_prompt is None:
            raise ValueError("You must specify prompt for class images.")
    else:
        # logger is not available yet
        if args.class_data_dir is not None:
            warnings.warn("You need not use --class_data_dir without --with_prior_preservation.")
        if args.class_prompt is not None:
            warnings.warn("You need not use --class_prompt without --with_prior_preservation.")

    args.logging_dir = os.path.join(args.output_dir, args.logging_dir)
    if args.height is None or args.width is None and args.resolution is not None:
        args.height = args.width = args.resolution

    return args


class DreamBoothDataset(Dataset):
    """
    A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
    It pre-processes the images and the tokenizes prompts.
    """

    def __init__(
        self,
        instance_data_root,
        instance_prompt,
        tokenizer,
        class_data_root=None,
        class_prompt=None,
        height=512,
        width=512,
        center_crop=False,
        interpolation="bilinear",
        random_flip=False,
    ):
        self.height = height
        self.width = width
        self.center_crop = center_crop
        self.tokenizer = tokenizer

        self.instance_data_root = Path(instance_data_root)
        if not self.instance_data_root.exists():
            raise ValueError("Instance images root doesn't exists.")
        ext = ["png", "jpg", "jpeg", "bmp", "PNG", "JPG", "JPEG", "BMP"]
        self.instance_images_path = []
        for p in Path(instance_data_root).iterdir():
            if any(suffix in p.name for suffix in ext):
                self.instance_images_path.append(p)
        self.num_instance_images = len(self.instance_images_path)
        self.instance_prompt = instance_prompt
        self._length = self.num_instance_images

        if class_data_root is not None:
            self.class_data_root = Path(class_data_root)
            self.class_data_root.mkdir(parents=True, exist_ok=True)
            self.class_images_path = []
            for p in Path(class_data_root).iterdir():
                if any(suffix in p.name for suffix in ext):
                    self.class_images_path.append(p)
            self.num_class_images = len(self.class_images_path)
            self._length = max(self.num_class_images, self.num_instance_images)
            self.class_prompt = class_prompt
        else:
            self.class_data_root = None

        self.image_transforms = transforms.Compose(
            [
                transforms.Resize((height, width), interpolation=interpolation),
                transforms.CenterCrop((height, width)) if center_crop else transforms.RandomCrop((height, width)),
                transforms.RandomHorizontalFlip() if random_flip else Lambda(lambda x: x),
                transforms.ToTensor(),
                transforms.Normalize([0.5], [0.5]),
            ]
        )

    def __len__(self):
        return self._length

    def __getitem__(self, index):
        example = {}
        instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])
        if not instance_image.mode == "RGB":
            instance_image = instance_image.convert("RGB")
        example["instance_images"] = self.image_transforms(instance_image)
        example["instance_prompt_ids"] = self.tokenizer(
            self.instance_prompt,
            padding="do_not_pad",
            truncation=True,
            max_length=self.tokenizer.model_max_length,
            return_attention_mask=False,
        ).input_ids

        if self.class_data_root:
            class_image = Image.open(self.class_images_path[index % self.num_class_images])
            if not class_image.mode == "RGB":
                class_image = class_image.convert("RGB")
            example["class_images"] = self.image_transforms(class_image)
            example["class_prompt_ids"] = self.tokenizer(
                self.class_prompt,
                padding="do_not_pad",
                truncation=True,
                max_length=self.tokenizer.model_max_length,
                return_attention_mask=False,
            ).input_ids

        return example


class PromptDataset(Dataset):
    "A simple dataset to prepare the prompts to generate class images on multiple GPUs."

    def __init__(self, prompt, num_samples):
        self.prompt = prompt
        self.num_samples = num_samples

    def __len__(self):
        return self.num_samples

    def __getitem__(self, index):
        example = {}
        example["prompt"] = self.prompt
        example["index"] = index
        return example


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 main():
    paddle.randn((1,))
    args = parse_args()
    rank = paddle.distributed.get_rank()
    is_main_process = rank == 0
    num_processes = paddle.distributed.get_world_size()
    if num_processes > 1:
        paddle.distributed.init_parallel_env()

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

    # Generate class images if prior preservation is enabled.
    if args.with_prior_preservation:
        class_images_dir = Path(args.class_data_dir)
        if not class_images_dir.exists():
            class_images_dir.mkdir(parents=True)
        cur_class_images = len(list(class_images_dir.iterdir()))

        if cur_class_images < args.num_class_images:
            with context_nologging():
                pipeline = DiffusionPipeline.from_pretrained(
                    args.pretrained_model_name_or_path,
                    safety_checker=None,
                )
                pipeline.set_progress_bar_config(disable=True)

            num_new_images = args.num_class_images - cur_class_images
            logger.info(f"Number of class images to sample: {num_new_images}.")

            sample_dataset = PromptDataset(args.class_prompt, num_new_images)
            batch_sampler = (
                DistributedBatchSampler(sample_dataset, batch_size=args.sample_batch_size, shuffle=False)
                if num_processes > 1
                else BatchSampler(sample_dataset, batch_size=args.sample_batch_size, shuffle=False)
            )
            sample_dataloader = DataLoader(
                sample_dataset, batch_sampler=batch_sampler, num_workers=args.dataloader_num_workers
            )

            for example in tqdm(sample_dataloader, desc="Generating class images", disable=not is_main_process, ncols=100):
                images = pipeline(example["prompt"]).images

                for i, image in enumerate(images):
                    hash_image = hashlib.sha1(image.tobytes()).hexdigest()
                    image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
                    image.save(image_filename)
            pipeline.to("cpu")
            del pipeline
            gc.collect()

    if is_main_process:
        if args.output_dir is not None:
            os.makedirs(args.output_dir, exist_ok=True)

    print("正在下载模型权重，请耐心等待。。。。。。。。。。")
    with context_nologging():
        # Load the tokenizer
        if args.tokenizer_name:
            tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name)
        elif args.pretrained_model_name_or_path:
            tokenizer = AutoTokenizer.from_pretrained(url_or_path_join(args.pretrained_model_name_or_path, "tokenizer"))

        # import correct text encoder class
        text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path)

        # Load scheduler and models
        noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
        text_encoder = text_encoder_cls.from_pretrained(
            url_or_path_join(args.pretrained_model_name_or_path, "text_encoder")
        )
        text_config = text_encoder.config if isinstance(text_encoder.config, dict) else text_encoder.config.to_dict()
        if text_config.get("use_attention_mask", None) is not None and text_config["use_attention_mask"]:
            use_attention_mask = True
        else:
            use_attention_mask = False
        vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
        unet = UNet2DConditionModel.from_pretrained(
            args.pretrained_model_name_or_path,
            subfolder="unet",
        )

    # We only train the additional adapter LoRA layers
    freeze_params(vae.parameters())
    freeze_params(text_encoder.parameters())
    freeze_params(unet.parameters())

    # now we will add new LoRA weights to the attention layers
    # It's important to realize here how many attention weights will be added and of which sizes
    # The sizes of the attention layers consist only of two different variables:
    # 1) - the "hidden_size", which is increased according to `unet.config.block_out_channels`.
    # 2) - the "cross attention size", which is set to `unet.config.cross_attention_dim`.

    # Let's first see how many attention processors we will have to set.
    # For Stable Diffusion, it should be equal to:
    # - down blocks (2x attention layers) * (2x transformer layers) * (3x down blocks) = 12
    # - mid blocks (2x attention layers) * (1x transformer layers) * (1x mid blocks) = 2
    # - up blocks (2x attention layers) * (3x transformer layers) * (3x down blocks) = 18
    # => 32 layers

    # Set correct lora layers
    lora_attn_procs = {}
    for name in unet.attn_processors.keys():
        cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
        if name.startswith("mid_block"):
            hidden_size = unet.config.block_out_channels[-1]
        elif name.startswith("up_blocks"):
            block_id = int(name[len("up_blocks.")])
            hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
        elif name.startswith("down_blocks"):
            block_id = int(name[len("down_blocks.")])
            hidden_size = unet.config.block_out_channels[block_id]

        lora_attn_procs[name] = LoRACrossAttnProcessor(
            hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=args.lora_rank
        )

    unet.set_attn_processor(lora_attn_procs)
    lora_layers = AttnProcsLayers(unet.attn_processors)

    # Dataset and DataLoaders creation:
    train_dataset = DreamBoothDataset(
        instance_data_root=args.instance_data_dir,
        instance_prompt=args.instance_prompt,
        class_data_root=args.class_data_dir if args.with_prior_preservation else None,
        class_prompt=args.class_prompt,
        tokenizer=tokenizer,
        height=args.height,
        width=args.width,
        center_crop=args.center_crop,
        interpolation="bilinear",
        random_flip=args.random_flip,
    )

    def collate_fn(examples):
        input_ids = [example["instance_prompt_ids"] for example in examples]
        pixel_values = [example["instance_images"] for example in examples]

        # Concat class and instance examples for prior preservation.
        # We do this to avoid doing two forward passes.
        if args.with_prior_preservation:
            input_ids += [example["class_prompt_ids"] for example in examples]
            pixel_values += [example["class_images"] for example in examples]

        pixel_values = paddle.stack(pixel_values).astype("float32")

        input_ids = tokenizer.pad(
            {"input_ids": input_ids}, padding="max_length", max_length=tokenizer.model_max_length, return_tensors="pd"
        ).input_ids

        return {
            "input_ids": input_ids,
            "pixel_values": pixel_values,
        }

    train_sampler = (
        DistributedBatchSampler(train_dataset, batch_size=args.train_batch_size, shuffle=True)
        if num_processes > 1
        else BatchSampler(train_dataset, batch_size=args.train_batch_size, shuffle=True)
    )
    train_dataloader = DataLoader(
        train_dataset, batch_sampler=train_sampler, collate_fn=collate_fn, num_workers=args.dataloader_num_workers
    )

    # Scheduler and math around the number of training steps.
    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
    # Afterwards we recalculate our number of training epochs
    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)

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

    lr_scheduler = get_scheduler(
        args.lr_scheduler,
        learning_rate=args.learning_rate,
        num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
        num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
        num_cycles=args.lr_num_cycles,
        power=args.lr_power,
    )

    # Optimizer creation
    optimizer = AdamW(
        learning_rate=lr_scheduler,
        parameters=lora_layers.parameters(),
        beta1=args.adam_beta1,
        beta2=args.adam_beta2,
        weight_decay=args.adam_weight_decay,
        epsilon=args.adam_epsilon,
        grad_clip=nn.ClipGradByGlobalNorm(args.max_grad_norm) if args.max_grad_norm > 0 else None,
    )

    if num_processes > 1:
        unet = paddle.DataParallel(unet)

    if is_main_process:
        logger.info("-----------  Configuration Arguments -----------")
        for arg, value in sorted(vars(args).items()):
            logger.info("%s: %s" % (arg, value))
        logger.info("------------------------------------------------")
        writer = get_report_to(args)

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

    logger.info("***** Running training *****")
    logger.info(f"  Num examples = {len(train_dataset)}")
    logger.info(f"  Num batches each epoch = {len(train_dataloader)}")
    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 is_main_process, ncols=100)
    progress_bar.set_description("Train Steps")
    global_step = 0
    vae.eval()
    text_encoder.eval()

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

            # Sample noise that we'll add to the latents
            noise = paddle.randn(latents.shape)
            batch_size = latents.shape[0]
            # Sample a random timestep for each image
            timesteps = paddle.randint(0, noise_scheduler.config.num_train_timesteps, (batch_size,)).cast("int64")

            # 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)

            if num_processes > 1 and (
                args.gradient_checkpointing or ((step + 1) % args.gradient_accumulation_steps != 0)
            ):
                # grad acc, no_sync when (step + 1) % args.gradient_accumulation_steps != 0:
                # gradient_checkpointing, no_sync every where
                # gradient_checkpointing + grad_acc, no_sync every where
                unet_ctx_manager = unet.no_sync()
            else:
                unet_ctx_manager = contextlib.nullcontext() if sys.version_info >= (3, 7) else contextlib.suppress()

            if use_attention_mask:
                attention_mask = (batch["input_ids"] != tokenizer.pad_token_id).cast("int64")
            else:
                attention_mask = None
            encoder_hidden_states = text_encoder(batch["input_ids"], attention_mask=attention_mask)[0]

            with unet_ctx_manager:
                # Predict the noise residual / sample
                model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample

                # Get the target for loss depending on the prediction type
                if noise_scheduler.config.prediction_type == "epsilon":
                    target = noise
                elif noise_scheduler.config.prediction_type == "v_prediction":
                    target = noise_scheduler.get_velocity(latents, noise, timesteps)
                else:
                    raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")

                if args.with_prior_preservation:
                    # Chunk the noise and model_pred into two parts and compute the loss on each part separately.
                    model_pred, model_pred_prior = model_pred.chunk(2, axis=0)
                    target, target_prior = target.chunk(2, axis=0)

                    # Compute instance loss
                    loss = F.mse_loss(model_pred, target, reduction="mean")

                    # Compute prior loss
                    prior_loss = F.mse_loss(model_pred_prior, target_prior, reduction="mean")

                    # Add the prior loss to the instance loss.
                    loss = loss + args.prior_loss_weight * prior_loss
                else:
                    loss = F.mse_loss(model_pred, target, reduction="mean")

                if args.gradient_accumulation_steps > 1:
                    loss = loss / args.gradient_accumulation_steps
                loss.backward()

            if (step + 1) % args.gradient_accumulation_steps == 0:
                if num_processes > 1 and args.gradient_checkpointing:
                    fused_allreduce_gradients(lora_layers.parameters(), None)
                optimizer.step()
                lr_scheduler.step()
                optimizer.clear_grad()
                progress_bar.update(1)
                global_step += 1
                step_loss = loss.item() * args.gradient_accumulation_steps
                logs = {
                    "epoch": str(epoch).zfill(4),
                    "step_loss": round(step_loss, 10),
                    "lr": lr_scheduler.get_lr(),
                }
                progress_bar.set_postfix(**logs)

                if is_main_process:
                    for name, val in logs.items():
                        if name == "epoch":
                            continue
                        writer.add_scalar(f"train/{name}", val, step=global_step)

                    if global_step % args.checkpointing_steps == 0:
                        save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
                        with context_nologging():
                            unwrap_model(unet).save_attn_procs(save_path)
                        print(f"\n Saved lora weights to {save_path}")

                    if args.validation_prompt is not None and global_step % args.validation_steps == 0:
                        with context_nologging():
                            logger.info(
                                    f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
                                    f" {args.validation_prompt}."
                                )
                            # create pipeline
                            pipeline = DiffusionPipeline.from_pretrained(
                                args.pretrained_model_name_or_path,
                                unet=unwrap_model(unet),
                                safety_checker=None,
                            )
                            pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
                            pipeline.set_progress_bar_config(disable=True)

                        # run inference
                        generator = paddle.Generator().manual_seed(args.seed) if args.seed else None
                        images = [
                            pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0]
                            for _ in range(args.num_validation_images)
                        ]
                        png_save_path = os.path.join(args.output_dir, "validation_images")
                        os.makedirs(png_save_path, exist_ok=True)
                        if len(images) == 1:
                            gird_image = images[0]
                        elif len(images) == 2:
                            gird_image = image_grid(images, 1, 2)
                        else:
                            display_images = 2 * (len(images) // 2)
                            gird_image = image_grid(images[:display_images], 2, display_images // 2)
                        gird_image.save(os.path.join(png_save_path, f"{global_step}.png"))

                        np_images = np.stack([np.asarray(img) for img in images])

                        if args.report_to == "tensorboard":
                            writer.add_images("test", np_images, epoch, dataformats="NHWC")
                        else:
                            writer.add_image("test", np_images, epoch, dataformats="NHWC")
                        del pipeline
                        gc.collect()

                if global_step >= args.max_train_steps:
                    break
    # Save the lora layers
    if is_main_process:
        unet = unwrap_model(unet)
        unet.save_attn_procs(args.output_dir)

        # Final inference
        # Load previous pipeline
        with context_nologging():
            pipeline = DiffusionPipeline.from_pretrained(args.pretrained_model_name_or_path, safety_checker=None)
            pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
            pipeline.set_progress_bar_config(disable=True)
        # load attention processors
        pipeline.unet.load_attn_procs(args.output_dir)

        # run inference
        if args.validation_prompt and args.num_validation_images > 0:
            generator = paddle.Generator().manual_seed(args.seed) if args.seed else None
            images = [
                pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0]
                for _ in range(args.num_validation_images)
            ]
            np_images = np.stack([np.asarray(img) for img in images])

            if args.report_to == "tensorboard":
                writer.add_images("test", np_images, epoch, dataformats="NHWC")
            else:
                writer.add_image("test", np_images, epoch, dataformats="NHWC")

        writer.close()

        # logic to push to HF Hub
        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

            _retry(
                create_repo,
                func_kwargs={"repo_id": repo_name, "exist_ok": True, "token": args.hub_token},
                base_wait_time=1.0,
                max_retries=5,
                max_wait_time=10.0,
            )

            save_model_card(
                repo_name,
                images=images,
                base_model=args.pretrained_model_name_or_path,
                prompt=args.instance_prompt,
                repo_folder=args.output_dir,
            )
            # Upload model
            logger.info(f"Pushing to {repo_name}")
            _retry(
                upload_folder,
                func_kwargs={
                    "repo_id": repo_name,
                    "repo_type": "model",
                    "folder_path": args.output_dir,
                    "commit_message": "End of training",
                    "token": args.hub_token,
                    "ignore_patterns": ["checkpoint-*/*", "logs/*"],
                },
                base_wait_time=1.0,
                max_retries=5,
                max_wait_time=20.0,
            )


if __name__ == "__main__":
    main()