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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
	from collections import OrderedDict

	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 animatediff.models.unet import UNet3DConditionModel
	from tuneavideo.data.frames_dataset import FramesDataset
	from animatediff.data.dataset import ImgSeqDataset
	from tuneavideo.data.multi_dataset import MultiTuneAVideoDataset
	from animatediff.pipelines.pipeline_animation import AnimationPipeline
	from tuneavideo.util import save_videos_grid, ddim_inversion
	from einops import rearrange, repeat


	# 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,
	train_whole_module: bool = False,
	trainable_modules: Tuple[str] = (
	"to_q",
	),
	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,
	start_global_step: int = 0,
	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,

	motion_module: str = "models/Motion_Module/mm_sd_v15.ckpt",
	inference_config_path: str = "configs/inference/inference-v3.yaml",
	motion_module_pe_multiplier: int = 1,
	dataset_class: str = 'MultiTuneAVideoDataset',

	# extra args

	image_finetune: bool = False,

	name: str = "scenefusion",
	use_wandb: bool = True,
	launcher: str = "launcher",

	cfg_random_null_text: bool = True,
	cfg_random_null_text_ratio: float = 0.1,

	unet_checkpoint_path: str = "",
	unet_additional_kwargs: Dict = {},
	ema_decay: float = 0.9999,
	noise_scheduler_kwargs = None,

	max_train_epoch: int = -1,
	validation_steps_tuple: Tuple = (-1,),

	num_workers: int = 32,
	checkpointing_epochs: int = 5,

	mixed_precision_training: bool = True,

	global_seed: int = 42,
	is_debug: bool = False,
	):
	*_, config = inspect.getargvalues(inspect.currentframe())

	inference_config = OmegaConf.load(inference_config_path)

	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",
	unet_additional_kwargs=OmegaConf.to_container(inference_config.unet_additional_kwargs)
	)

	# unet_path = "unet"
	# unet = UNet3DConditionModel.from_pretrained_2d(
	# unet_path, subfolder="unet",
	# unet_additional_kwargs=OmegaConf.to_container(inference_config.unet_additional_kwargs)
	# )


	motion_module_state_dict = torch.load(motion_module, map_location="cpu")

	# Multiply pe weights by multiplier for training more than 24 frames
	if motion_module_pe_multiplier > 1:
	for key in motion_module_state_dict:
	if 'pe' in key:
	t = motion_module_state_dict[key]
	t = repeat(t, "b f d -> b (f m) d", m=motion_module_pe_multiplier)
	motion_module_state_dict[key] = t

	if "global_step" in motion_module_state_dict: func_args.update({"global_step": motion_module_state_dict["global_step"]})
	missing, unexpected = unet.load_state_dict(motion_module_state_dict, strict=False)
	assert len(unexpected) == 0

	# 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 "motion_modules" in name and (train_whole_module or name.endswith(tuple(trainable_modules))):
	for params in module.parameters():
	print("trainable", name)
	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
	)


	print("optimizer values", learning_rate, adam_beta1, adam_beta2, adam_weight_decay, adam_epsilon)
	# 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 = None
	if dataset_class == 'MultiTuneAVideoDataset':
	train_dataset = ImgSeqDataset(**train_data)

	# Preprocessing the dataset
	train_dataset.prompt_ids = [None] * len(train_dataset.prompt)
	for index, prompt in enumerate(train_dataset.prompt):
	train_dataset.prompt_ids[index] = tokenizer(
	prompt,max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
	).input_ids[0]
	else:
	train_dataset = FramesDataset(tokenizer=tokenizer, **train_data)
	train_dataset.load()

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

	# Get the validation pipeline
	validation_pipeline = AnimationPipeline(
	vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet,
	scheduler=DDIMScheduler(**OmegaConf.to_container(inference_config.noise_scheduler_kwargs['DDIMScheduler'])),
	)
	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)

	print("DATA LEN:", len(train_dataloader))

	# 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

	if start_global_step > 0:
	global_step = start_global_step
	first_epoch = global_step // num_update_steps_per_epoch
	resume_step = global_step % num_update_steps_per_epoch

	# 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")
	optimizer.zero_grad()

	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
	print("Model Output:", model_pred)
	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

	print("Loss:", loss)

	# Backpropagate
	accelerator.backward(loss)

	if accelerator.sync_gradients:
	accelerator.clip_grad_norm_(unet.parameters(), max_grad_norm)

	print("grad: ")
	for param in unet.parameters():
	if param.grad is not None:
	print(param.grad)
	break

	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"mm-{global_step}.pth")
	save_checkpoint(unet, 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(set(validation_data.prompts)):
	sample = validation_pipeline(prompt, generator=generator,
	latents=ddim_inv_latent,
	fp16=True,
	**validation_data).videos
	save_videos_grid(sample, f"{output_dir}/samples/sample-{global_step}/{idx}.gif", fps=1)
	samples.append(sample)
	samples = torch.concat(samples)
	save_path = f"{output_dir}/samples/sample-{global_step}.gif"
	save_videos_grid(samples, save_path, fps=1)
	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 = AnimationPipeline.from_pretrained(
	pretrained_model_path,
	text_encoder=text_encoder,
	vae=vae,
	unet=unet,
	)

	mm_path = "%s/mm.pth" % output_dir
	save_checkpoint(unet, mm_path)

	accelerator.end_training()

	def save_checkpoint(unet, mm_path):
	mm_state_dict = OrderedDict()
	state_dict = unet.state_dict()
	for key in state_dict:
	if "motion_module" in key:
	mm_state_dict[key] = state_dict[key]

	torch.save(mm_state_dict, mm_path)


	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))