import torch
from xora.models.autoencoders.causal_video_autoencoder import CausalVideoAutoencoder
from xora.models.transformers.transformer3d import Transformer3DModel
from xora.models.transformers.symmetric_patchifier import SymmetricPatchifier
from xora.schedulers.rf import RectifiedFlowScheduler
from xora.pipelines.pipeline_video_pixart_alpha import VideoPixArtAlphaPipeline
from pathlib import Path
from transformers import T5EncoderModel


model_name_or_path = "PixArt-alpha/PixArt-XL-2-1024-MS"
vae_local_path = Path("/opt/models/checkpoints/vae_training/causal_vvae_32x32x8_420m_cont_32/step_2296000")
dtype = torch.float32
vae = CausalVideoAutoencoder.from_pretrained(
            pretrained_model_name_or_path=vae_local_path,
            revision=False,
            torch_dtype=torch.bfloat16,
            load_in_8bit=False,
).cuda()
transformer_config_path = Path("/opt/txt2img/txt2img/config/transformer3d/xora_v1.2-L.json")
transformer_config = Transformer3DModel.load_config(transformer_config_path)
transformer = Transformer3DModel.from_config(transformer_config)
transformer_local_path = Path("/opt/models/logs/v1.2-vae-mf-medHR-mr-cvae-nl/ckpt/01760000/model.pt")
transformer_ckpt_state_dict = torch.load(transformer_local_path)
transformer.load_state_dict(transformer_ckpt_state_dict, True)
transformer = transformer.cuda()
unet = transformer
scheduler_config_path = Path("/opt/txt2img/txt2img/config/scheduler/RF_SD3_shifted.json")
scheduler_config = RectifiedFlowScheduler.load_config(scheduler_config_path)
scheduler = RectifiedFlowScheduler.from_config(scheduler_config)
patchifier = SymmetricPatchifier(patch_size=1)
# text_encoder = T5EncoderModel.from_pretrained("t5-v1_1-xxl")

submodel_dict = {
    "unet": unet,
    "transformer": transformer,
    "patchifier": patchifier,
    "text_encoder": None,
    "scheduler": scheduler,
    "vae": vae,

}

pipeline = VideoPixArtAlphaPipeline.from_pretrained(model_name_or_path,
                                                    safety_checker=None,
            revision=None,
            torch_dtype=dtype,
            **submodel_dict,
        )

num_inference_steps=20
num_images_per_prompt=2
guidance_scale=3
height=512
width=768
num_frames=57
frame_rate=25
# sample = {
#     "prompt": "A cat", # (B, L, E)
#     'prompt_attention_mask': None, # (B , L)
#     'negative_prompt': "Ugly deformed",
#     'negative_prompt_attention_mask': None # (B , L)
# }

sample = torch.load("/opt/sample.pt")
for _, item in sample.items():
    if item is not None:
        item = item.cuda()



images = pipeline(
    num_inference_steps=num_inference_steps,
    num_images_per_prompt=num_images_per_prompt,
    guidance_scale=guidance_scale,
    generator=None,
    output_type="pt",
    callback_on_step_end=None,
    height=height,
    width=width,
    num_frames=num_frames,
    frame_rate=frame_rate,
    **sample,
    is_video=True,
    vae_per_channel_normalize=True,
).images

print()