from ..models import ModelManager, SD3TextEncoder1
from ..models.hunyuan_video_dit import HunyuanVideoDiT
from ..schedulers.flow_match import FlowMatchScheduler
from .base import BasePipeline
from ..prompters import HunyuanVideoPrompter
import torch
from transformers import LlamaModel


class HunyuanVideoPipeline(BasePipeline):

    def __init__(self, device="cuda", torch_dtype=torch.float16):
        super().__init__(device=device, torch_dtype=torch_dtype)
        self.scheduler = FlowMatchScheduler(shift=7.0, sigma_min=0.0, extra_one_step=True)
        self.prompter = HunyuanVideoPrompter()
        self.text_encoder_1: SD3TextEncoder1 = None
        self.text_encoder_2: LlamaModel = None
        self.dit: HunyuanVideoDiT = None
        self.model_names = ['text_encoder_1', 'text_encoder_2', 'dit']
    

    def fetch_models(self, model_manager: ModelManager):
        self.text_encoder_1 = model_manager.fetch_model("sd3_text_encoder_1")
        self.text_encoder_2 = model_manager.fetch_model("hunyuan_video_text_encoder_2")
        self.dit = model_manager.fetch_model("hunyuan_video_dit")
        self.prompter.fetch_models(self.text_encoder_1, self.text_encoder_2)


    @staticmethod
    def from_model_manager(model_manager: ModelManager, torch_dtype=None, device=None, enable_vram_management=True):
        if device is None: device = model_manager.device
        if torch_dtype is None: torch_dtype = model_manager.torch_dtype
        pipe = HunyuanVideoPipeline(device=device, torch_dtype=torch_dtype)
        pipe.fetch_models(model_manager)
        # VRAM management is automatically enabled.
        if enable_vram_management:
            pipe.enable_cpu_offload()
            pipe.dit.enable_auto_offload(dtype=torch_dtype, device=device)
        return pipe


    def encode_prompt(self, prompt, positive=True, clip_sequence_length=77, llm_sequence_length=256):
        prompt_emb, pooled_prompt_emb, text_mask = self.prompter.encode_prompt(
            prompt, device=self.device, positive=positive, clip_sequence_length=clip_sequence_length, llm_sequence_length=llm_sequence_length
        )
        return {"prompt_emb": prompt_emb, "pooled_prompt_emb": pooled_prompt_emb, "text_mask": text_mask}


    def prepare_extra_input(self, latents=None, guidance=1.0):
        freqs_cos, freqs_sin = self.dit.prepare_freqs(latents)
        guidance = torch.Tensor([guidance] * latents.shape[0]).to(device=latents.device, dtype=latents.dtype)
        return {"freqs_cos": freqs_cos, "freqs_sin": freqs_sin, "guidance": guidance}


    @torch.no_grad()
    def __call__(
        self,
        prompt,
        negative_prompt="",
        seed=None,
        height=720,
        width=1280,
        num_frames=129,
        embedded_guidance=6.0,
        cfg_scale=1.0,
        num_inference_steps=30,
        progress_bar_cmd=lambda x: x,
        progress_bar_st=None,
    ):
        latents = self.generate_noise((1, 16, (num_frames - 1) // 4 + 1, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype)
        
        self.load_models_to_device(["text_encoder_1", "text_encoder_2"])
        prompt_emb_posi = self.encode_prompt(prompt, positive=True)
        if cfg_scale != 1.0:
            prompt_emb_nega = self.encode_prompt(negative_prompt, positive=False)

        extra_input = self.prepare_extra_input(latents, guidance=embedded_guidance)

        self.scheduler.set_timesteps(num_inference_steps)

        self.load_models_to_device([])
        for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
            timestep = timestep.unsqueeze(0).to(self.device)
            with torch.autocast(device_type=self.device, dtype=self.torch_dtype):
                print(f"Step {progress_id + 1} / {len(self.scheduler.timesteps)}")

                noise_pred_posi = self.dit(latents, timestep, **prompt_emb_posi, **extra_input)
                if cfg_scale != 1.0:
                    noise_pred_nega = self.dit(latents, timestep, **prompt_emb_nega, **extra_input)
                    noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega)
                else:
                    noise_pred = noise_pred_posi

            latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents)
        
        # TODO: Add VAE decode here.
        
        return latents