import os
import sys

import numpy as np
import torch
from diffusers import (CogVideoXDDIMScheduler, DDIMScheduler,
                       DPMSolverMultistepScheduler,
                       EulerAncestralDiscreteScheduler, EulerDiscreteScheduler,
                       PNDMScheduler)
from PIL import Image
from transformers import T5EncoderModel

current_file_path = os.path.abspath(__file__)
project_roots = [os.path.dirname(current_file_path), os.path.dirname(os.path.dirname(current_file_path)), os.path.dirname(os.path.dirname(os.path.dirname(current_file_path)))]
for project_root in project_roots:
    sys.path.insert(0, project_root) if project_root not in sys.path else None

from cogvideox.models import (AutoencoderKLCogVideoX,
                              CogVideoXTransformer3DModel, T5EncoderModel,
                              T5Tokenizer)
from cogvideox.pipeline import (CogVideoXFunPipeline,
                                CogVideoXFunInpaintPipeline)
from cogvideox.utils.fp8_optimization import convert_weight_dtype_wrapper
from cogvideox.utils.lora_utils import merge_lora, unmerge_lora
from cogvideox.utils.utils import get_image_to_video_latent, save_videos_grid

# GPU memory mode, which can be choosen in [model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
# model_cpu_offload means that the entire model will be moved to the CPU after use, which can save some GPU memory.
# 
# model_cpu_offload_and_qfloat8 indicates that the entire model will be moved to the CPU after use, 
# and the transformer model has been quantized to float8, which can save more GPU memory. 
# 
# sequential_cpu_offload means that each layer of the model will be moved to the CPU after use, 
# resulting in slower speeds but saving a large amount of GPU memory.
GPU_memory_mode     = "model_cpu_offload_and_qfloat8"

# model path
model_name          = "models/Diffusion_Transformer/CogVideoX-Fun-V1.1-2b-InP"

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

# Load pretrained model if need
transformer_path    = None
vae_path            = None
lora_path           = None

# Other params
sample_size         = [384, 672]
# V1.0 and V1.1 support up to 49 frames of video generation,
# while V1.5 supports up to 85 frames.  
video_length        = 49
fps                 = 8

# Use torch.float16 if GPU does not support torch.bfloat16
# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
weight_dtype        = torch.bfloat16
prompt              = "A young woman with beautiful and clear eyes and blonde hair standing and white dress in a forest wearing a crown. She seems to be lost in thought, and the camera focuses on her face. 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. Watermark present in each frame. The background is solid. Strange body and strange trajectory. Distortion. "
guidance_scale      = 6.0
seed                = 43
num_inference_steps = 50
lora_weight         = 0.55
save_path           = "samples/cogvideox-fun-videos-t2v"

transformer = CogVideoXTransformer3DModel.from_pretrained(
    model_name, 
    subfolder="transformer",
    low_cpu_mem_usage=True,
    torch_dtype=torch.float8_e4m3fn if GPU_memory_mode == "model_cpu_offload_and_qfloat8" else weight_dtype,
).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)}")

# Get Vae
vae = AutoencoderKLCogVideoX.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 tokenizer and text_encoder
tokenizer = T5Tokenizer.from_pretrained(
    model_name, subfolder="tokenizer"
)
text_encoder = T5EncoderModel.from_pretrained(
    model_name, subfolder="text_encoder", torch_dtype=weight_dtype
)

# Get Scheduler
Choosen_Scheduler = scheduler_dict = {
    "Euler": EulerDiscreteScheduler,
    "Euler A": EulerAncestralDiscreteScheduler,
    "DPM++": DPMSolverMultistepScheduler, 
    "PNDM": PNDMScheduler,
    "DDIM_Cog": CogVideoXDDIMScheduler,
    "DDIM_Origin": DDIMScheduler,
}[sampler_name]
scheduler = Choosen_Scheduler.from_pretrained(
    model_name, 
    subfolder="scheduler"
)

if transformer.config.in_channels != vae.config.latent_channels:
    pipeline = CogVideoXFunInpaintPipeline(
        vae=vae,
        tokenizer=tokenizer,
        text_encoder=text_encoder,
        transformer=transformer,
        scheduler=scheduler,
    )
else:
    pipeline = CogVideoXFunPipeline(
        vae=vae,
        tokenizer=tokenizer,
        text_encoder=text_encoder,
        transformer=transformer,
        scheduler=scheduler,
    )
if GPU_memory_mode == "sequential_cpu_offload":
    pipeline.enable_sequential_cpu_offload()
elif GPU_memory_mode == "model_cpu_offload_and_qfloat8":
    convert_weight_dtype_wrapper(transformer, weight_dtype)
    pipeline.enable_model_cpu_offload()
else:
    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():
    video_length = int((video_length - 1) // vae.config.temporal_compression_ratio * vae.config.temporal_compression_ratio) + 1 if video_length != 1 else 1
    latent_frames = (video_length - 1) // vae.config.temporal_compression_ratio + 1
    if video_length != 1 and transformer.config.patch_size_t is not None and latent_frames % transformer.config.patch_size_t != 0:
        additional_frames = transformer.config.patch_size_t - latent_frames % transformer.config.patch_size_t
        video_length += additional_frames * vae.config.temporal_compression_ratio

    if transformer.config.in_channels != vae.config.latent_channels:
        input_video, input_video_mask, _ = get_image_to_video_latent(None, None, video_length=video_length, sample_size=sample_size)

        sample = pipeline(
            prompt, 
            num_frames = 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,

            video        = input_video,
            mask_video   = input_video_mask,
        ).videos
    else:
        sample = pipeline(
            prompt, 
            num_frames = 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 lora_path is not None:
    pipeline = unmerge_lora(pipeline, lora_path, lora_weight)

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)

if video_length == 1:
    video_path = os.path.join(save_path, prefix + ".png")

    image = sample[0, :, 0]
    image = image.transpose(0, 1).transpose(1, 2)
    image = (image * 255).numpy().astype(np.uint8)
    image = Image.fromarray(image)
    image.save(video_path)
else:
    video_path = os.path.join(save_path, prefix + ".mp4")
    save_videos_grid(sample, video_path, fps=fps)