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	import argparse
	import datetime
	import logging
	import inspect
	import math
	import os
	from typing import Dict, Optional, Tuple
	from omegaconf import OmegaConf

	import torch
	import torch.nn.functional as F
	import torch.utils.checkpoint

	import diffusers
	import transformers
	from accelerate import Accelerator
	from accelerate.logging import get_logger
	from accelerate.utils import set_seed
	from diffusers import AutoencoderKL, DDPMScheduler, DDIMScheduler
	from diffusers.optimization import get_scheduler
	from diffusers.utils import check_min_version
	from diffusers.utils.import_utils import is_xformers_available
	from tqdm.auto import tqdm
	from transformers import CLIPTextModel, CLIPTokenizer

	from tuneavideo.models.unet import UNet3DConditionModel
	from tuneavideo.data.dataset import TuneAVideoDataset
	from tuneavideo.pipelines.pipeline_tuneavideo import TuneAVideoPipeline
	from tuneavideo.util import save_videos_grid, ddim_inversion
	from einops import rearrange


	# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
	check_min_version("0.10.0.dev0")

	logger = get_logger(__name__, log_level="INFO")


	def main(
	pretrained_model_path: str,
	output_dir: str,
	train_data: Dict,
	validation_data: Dict,
	validation_steps: int = 100,
	trainable_modules: Tuple[str] = (
	"attn1.to_q",
	"attn2.to_q",
	"attn_temp",
	),
	train_batch_size: int = 1,
	max_train_steps: int = 500,
	learning_rate: float = 3e-5,
	scale_lr: bool = False,
	lr_scheduler: str = "constant",
	lr_warmup_steps: int = 0,
	adam_beta1: float = 0.9,
	adam_beta2: float = 0.999,
	adam_weight_decay: float = 1e-2,
	adam_epsilon: float = 1e-08,
	max_grad_norm: float = 1.0,
	gradient_accumulation_steps: int = 1,
	gradient_checkpointing: bool = True,
	checkpointing_steps: int = 500,
	resume_from_checkpoint: Optional[str] = None,
	mixed_precision: Optional[str] = "fp16",
	use_8bit_adam: bool = False,
	enable_xformers_memory_efficient_attention: bool = True,
	seed: Optional[int] = None,
	):
	*_, config = inspect.getargvalues(inspect.currentframe())

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

	# Make one log on every process with the configuration for debugging.
	logging.basicConfig(
	format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
	datefmt="%m/%d/%Y %H:%M:%S",
	level=logging.INFO,
	)
	logger.info(accelerator.state, main_process_only=False)
	if accelerator.is_local_main_process:
	transformers.utils.logging.set_verbosity_warning()
	diffusers.utils.logging.set_verbosity_info()
	else:
	transformers.utils.logging.set_verbosity_error()
	diffusers.utils.logging.set_verbosity_error()

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

	# Handle the output folder creation
	if accelerator.is_main_process:
	# now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
	# output_dir = os.path.join(output_dir, now)
	os.makedirs(output_dir, exist_ok=True)
	os.makedirs(f"{output_dir}/samples", exist_ok=True)
	os.makedirs(f"{output_dir}/inv_latents", exist_ok=True)
	OmegaConf.save(config, os.path.join(output_dir, 'config.yaml'))

	# Load scheduler, tokenizer and models.
	noise_scheduler = DDPMScheduler.from_pretrained(pretrained_model_path, subfolder="scheduler")
	tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer")
	text_encoder = CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder")
	vae = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae")
	unet = UNet3DConditionModel.from_pretrained_2d(pretrained_model_path, subfolder="unet")

	# Freeze vae and text_encoder
	vae.requires_grad_(False)
	text_encoder.requires_grad_(False)

	unet.requires_grad_(False)
	for name, module in unet.named_modules():
	if name.endswith(tuple(trainable_modules)):
	for params in module.parameters():
	params.requires_grad = True

	if enable_xformers_memory_efficient_attention:
	if is_xformers_available():
	unet.enable_xformers_memory_efficient_attention()
	else:
	raise ValueError("xformers is not available. Make sure it is installed correctly")

	if gradient_checkpointing:
	unet.enable_gradient_checkpointing()

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

	# Initialize the optimizer
	if use_8bit_adam:
	try:
	import bitsandbytes as bnb
	except ImportError:
	raise ImportError(
	"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
	)

	optimizer_cls = bnb.optim.AdamW8bit
	else:
	optimizer_cls = torch.optim.AdamW

	optimizer = optimizer_cls(
	unet.parameters(),
	lr=learning_rate,
	betas=(adam_beta1, adam_beta2),
	weight_decay=adam_weight_decay,
	eps=adam_epsilon,
	)

	# Get the training dataset
	train_dataset = TuneAVideoDataset(**train_data)

	# Preprocessing the dataset
	train_dataset.prompt_ids = tokenizer(
	train_dataset.prompt, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
	).input_ids[0]

	# DataLoaders creation:
	train_dataloader = torch.utils.data.DataLoader(
	train_dataset, batch_size=train_batch_size
	)

	# Get the validation pipeline
	validation_pipeline = TuneAVideoPipeline(
	vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet,
	scheduler=DDIMScheduler.from_pretrained(pretrained_model_path, subfolder="scheduler")
	)
	validation_pipeline.enable_vae_slicing()
	ddim_inv_scheduler = DDIMScheduler.from_pretrained(pretrained_model_path, subfolder='scheduler')
	ddim_inv_scheduler.set_timesteps(validation_data.num_inv_steps)

	# Scheduler
	lr_scheduler = get_scheduler(
	lr_scheduler,
	optimizer=optimizer,
	num_warmup_steps=lr_warmup_steps * gradient_accumulation_steps,
	num_training_steps=max_train_steps * gradient_accumulation_steps,
	)

	# Prepare everything with our `accelerator`.
	unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
	unet, optimizer, train_dataloader, lr_scheduler
	)

	# For mixed precision training we cast the text_encoder and vae weights to half-precision
	# as these models are only used for inference, keeping weights in full precision is not required.
	weight_dtype = torch.float32
	if accelerator.mixed_precision == "fp16":
	weight_dtype = torch.float16
	elif accelerator.mixed_precision == "bf16":
	weight_dtype = torch.bfloat16

	# Move text_encode and vae to gpu and cast to weight_dtype
	text_encoder.to(accelerator.device, dtype=weight_dtype)
	vae.to(accelerator.device, dtype=weight_dtype)

	# 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) / gradient_accumulation_steps)
	# Afterwards we recalculate our number of training epochs
	num_train_epochs = math.ceil(max_train_steps / num_update_steps_per_epoch)

	# We need to initialize the trackers we use, and also store our configuration.
	# The trackers initializes automatically on the main process.
	if accelerator.is_main_process:
	accelerator.init_trackers("text2video-fine-tune")

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

	logger.info("*** Running training ***")
	logger.info(f" Num examples = {len(train_dataset)}")
	logger.info(f" Num Epochs = {num_train_epochs}")
	logger.info(f" Instantaneous batch size per device = {train_batch_size}")
	logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
	logger.info(f" Gradient Accumulation steps = {gradient_accumulation_steps}")
	logger.info(f" Total optimization steps = {max_train_steps}")
	global_step = 0
	first_epoch = 0

	# Potentially load in the weights and states from a previous save
	if resume_from_checkpoint:
	if resume_from_checkpoint != "latest":
	path = os.path.basename(resume_from_checkpoint)
	else:
	# Get the most recent checkpoint
	dirs = os.listdir(output_dir)
	dirs = [d for d in dirs if d.startswith("checkpoint")]
	dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
	path = dirs[-1]
	accelerator.print(f"Resuming from checkpoint {path}")
	accelerator.load_state(os.path.join(output_dir, path))
	global_step = int(path.split("-")[1])

	first_epoch = global_step // num_update_steps_per_epoch
	resume_step = global_step % num_update_steps_per_epoch

	# Only show the progress bar once on each machine.
	progress_bar = tqdm(range(global_step, max_train_steps), disable=not accelerator.is_local_main_process)
	progress_bar.set_description("Steps")

	for epoch in range(first_epoch, num_train_epochs):
	unet.train()
	train_loss = 0.0
	for step, batch in enumerate(train_dataloader):
	# Skip steps until we reach the resumed step
	if resume_from_checkpoint and epoch == first_epoch and step < resume_step:
	if step % gradient_accumulation_steps == 0:
	progress_bar.update(1)
	continue

	with accelerator.accumulate(unet):
	# Convert videos to latent space
	pixel_values = batch["pixel_values"].to(weight_dtype)
	video_length = pixel_values.shape[1]
	pixel_values = rearrange(pixel_values, "b f c h w -> (b f) c h w")
	latents = vae.encode(pixel_values).latent_dist.sample()
	latents = rearrange(latents, "(b f) c h w -> b c f h w", f=video_length)
	latents = latents * 0.18215

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

	# Get the text embedding for conditioning
	encoder_hidden_states = text_encoder(batch["prompt_ids"])[0]

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

	# Predict the noise residual and compute loss
	model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
	loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")

	# Gather the losses across all processes for logging (if we use distributed training).
	avg_loss = accelerator.gather(loss.repeat(train_batch_size)).mean()
	train_loss += avg_loss.item() / gradient_accumulation_steps

	# Backpropagate
	accelerator.backward(loss)
	if accelerator.sync_gradients:
	accelerator.clip_grad_norm_(unet.parameters(), max_grad_norm)
	optimizer.step()
	lr_scheduler.step()
	optimizer.zero_grad()

	# Checks if the accelerator has performed an optimization step behind the scenes
	if accelerator.sync_gradients:
	progress_bar.update(1)
	global_step += 1
	accelerator.log({"train_loss": train_loss}, step=global_step)
	train_loss = 0.0

	if global_step % checkpointing_steps == 0:
	if accelerator.is_main_process:
	save_path = os.path.join(output_dir, f"checkpoint-{global_step}")
	accelerator.save_state(save_path)
	logger.info(f"Saved state to {save_path}")

	if global_step % validation_steps == 0:
	if accelerator.is_main_process:
	samples = []
	generator = torch.Generator(device=latents.device)
	generator.manual_seed(seed)

	ddim_inv_latent = None
	if validation_data.use_inv_latent:
	inv_latents_path = os.path.join(output_dir, f"inv_latents/ddim_latent-{global_step}.pt")
	ddim_inv_latent = ddim_inversion(
	validation_pipeline, ddim_inv_scheduler, video_latent=latents,
	num_inv_steps=validation_data.num_inv_steps, prompt="")[-1].to(weight_dtype)
	torch.save(ddim_inv_latent, inv_latents_path)

	for idx, prompt in enumerate(validation_data.prompts):
	sample = validation_pipeline(prompt, generator=generator, latents=ddim_inv_latent,
	**validation_data).videos
	save_videos_grid(sample, f"{output_dir}/samples/sample-{global_step}/{prompt}.gif")
	samples.append(sample)
	samples = torch.concat(samples)
	save_path = f"{output_dir}/samples/sample-{global_step}.gif"
	save_videos_grid(samples, save_path)
	logger.info(f"Saved samples to {save_path}")

	logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
	progress_bar.set_postfix(**logs)

	if global_step >= max_train_steps:
	break

	# Create the pipeline using the trained modules and save it.
	accelerator.wait_for_everyone()
	if accelerator.is_main_process:
	unet = accelerator.unwrap_model(unet)
	pipeline = TuneAVideoPipeline.from_pretrained(
	pretrained_model_path,
	text_encoder=text_encoder,
	vae=vae,
	unet=unet,
	)
	pipeline.save_pretrained(output_dir)

	accelerator.end_training()


	if __name__ == "__main__":
	parser = argparse.ArgumentParser()
	parser.add_argument("--config", type=str, default="./configs/tuneavideo.yaml")
	args = parser.parse_args()

	main(**OmegaConf.load(args.config))