import os

import torch
from diffusers import (AutoencoderKL, DDIMScheduler,
                       DPMSolverMultistepScheduler,
                       EulerAncestralDiscreteScheduler, EulerDiscreteScheduler,
                       PNDMScheduler)
from omegaconf import OmegaConf

from easyanimate.models.autoencoder_magvit import AutoencoderKLMagvit
from easyanimate.models.transformer3d import Transformer3DModel
from easyanimate.pipeline.pipeline_easyanimate import EasyAnimatePipeline
from easyanimate.utils.lora_utils import merge_lora, unmerge_lora
from easyanimate.utils.utils import save_videos_grid

# Config and model path
config_path         = "config/easyanimate_video_magvit_motion_module_v2.yaml"
model_name          = "models/Diffusion_Transformer/EasyAnimateV2-XL-2-512x512"

# Choose the sampler in "Euler" "Euler A" "DPM++" "PNDM" and "DDIM"
sampler_name        = "DPM++"

# Load pretrained model if need
transformer_path    = None
# V2 does not need a motion module
motion_module_path  = None 
vae_path            = None
lora_path           = None

# Other params
sample_size         = [384, 672]
# In EasyAnimateV1, the video_length of video is 40 ~ 80.
# In EasyAnimateV2, the video_length of video is 1 ~ 144. If u want to generate a image, please set the video_length = 1.
video_length        = 144
fps                 = 24

weight_dtype        = torch.bfloat16
prompt              = "The video features a young woman with with black eyes and blonde hair standing in a forest wearing a crown. She seems to be lost in thought, and the camera focuses on her face. The atmosphere is serene, and the shot is in slow motion. The video is of high quality, and the view is very clear. High quality, masterpiece, best quality, highres, ultra-detailed, fantastic. "
negative_prompt     = "The video is not of a high quality, it has a low resolution, and the audio quality is not clear. Strange motion trajectory, a poor composition and deformed video, low resolution, duplicate and ugly, strange body structure, long and strange neck, bad teeth, bad eyes, bad limbs, bad hands, rotating camera, blurry camera, shaking camera. Deformation, low-resolution, blurry, ugly, distortion. " 
guidance_scale      = 6.0
seed                = 43
num_inference_steps = 50
lora_weight         = 0.55
save_path           = "samples/easyanimate-videos"

config = OmegaConf.load(config_path)

# Get Transformer
transformer = Transformer3DModel.from_pretrained_2d(
    model_name, 
    subfolder="transformer",
    transformer_additional_kwargs=OmegaConf.to_container(config['transformer_additional_kwargs'])
).to(weight_dtype)

if transformer_path is not None:
    print(f"From checkpoint: {transformer_path}")
    if transformer_path.endswith("safetensors"):
        from safetensors.torch import load_file, safe_open
        state_dict = load_file(transformer_path)
    else:
        state_dict = torch.load(transformer_path, map_location="cpu")
    state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict

    m, u = transformer.load_state_dict(state_dict, strict=False)
    print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")

if motion_module_path is not None:
    print(f"From Motion Module: {motion_module_path}")
    if motion_module_path.endswith("safetensors"):
        from safetensors.torch import load_file, safe_open
        state_dict = load_file(motion_module_path)
    else:
        state_dict = torch.load(motion_module_path, map_location="cpu")
    state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict

    m, u = transformer.load_state_dict(state_dict, strict=False)
    print(f"missing keys: {len(m)}, unexpected keys: {len(u)}, {u}")

# Get Vae
if OmegaConf.to_container(config['vae_kwargs'])['enable_magvit']:
    Choosen_AutoencoderKL = AutoencoderKLMagvit
else:
    Choosen_AutoencoderKL = AutoencoderKL
vae = Choosen_AutoencoderKL.from_pretrained(
    model_name, 
    subfolder="vae"
).to(weight_dtype)

if vae_path is not None:
    print(f"From checkpoint: {vae_path}")
    if vae_path.endswith("safetensors"):
        from safetensors.torch import load_file, safe_open
        state_dict = load_file(vae_path)
    else:
        state_dict = torch.load(vae_path, map_location="cpu")
    state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict

    m, u = vae.load_state_dict(state_dict, strict=False)
    print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")

# Get Scheduler
Choosen_Scheduler = scheduler_dict = {
    "Euler": EulerDiscreteScheduler,
    "Euler A": EulerAncestralDiscreteScheduler,
    "DPM++": DPMSolverMultistepScheduler, 
    "PNDM": PNDMScheduler,
    "DDIM": DDIMScheduler,
}[sampler_name]
scheduler = Choosen_Scheduler(**OmegaConf.to_container(config['noise_scheduler_kwargs']))

pipeline = EasyAnimatePipeline.from_pretrained(
    model_name,
    vae=vae,
    transformer=transformer,
    scheduler=scheduler,
    torch_dtype=weight_dtype
)
pipeline.to("cuda")
pipeline.enable_model_cpu_offload()

generator = torch.Generator(device="cuda").manual_seed(seed)

if lora_path is not None:
    pipeline = merge_lora(pipeline, lora_path, lora_weight)

with torch.no_grad():
    sample = pipeline(
        prompt, 
        video_length = video_length,
        negative_prompt = negative_prompt,
        height      = sample_size[0],
        width       = sample_size[1],
        generator   = generator,
        guidance_scale = guidance_scale,
        num_inference_steps = num_inference_steps,
    ).videos

if not os.path.exists(save_path):
    os.makedirs(save_path, exist_ok=True)

index = len([path for path in os.listdir(save_path)]) + 1
prefix = str(index).zfill(8)
video_path = os.path.join(save_path, prefix + ".gif")
save_videos_grid(sample, video_path, fps=fps)