# this is the huggingface handler file

from diffusers import AutoencoderKL, DDPMScheduler, DDIMScheduler
from transformers import CLIPTextModel, CLIPTokenizer
from omegaconf import OmegaConf

from diffusers.utils.import_utils import is_xformers_available
from typing import Any
import torch
import einops
import torchvision

import numpy as np

from diffusers import AutoPipelineForText2Image

from animatediff.models.unet import UNet3DConditionModel
from animatediff.pipelines.pipeline_animation import AnimationPipeline
from animatediff.utils.util import save_videos_grid
from animatediff.utils.util import load_weights


class EndpointHandler():
    def __init__(self, model_path: str = "bluestarburst/AnimateDiff-SceneFusion", inference_config_path: str = "configs/inference/inference-v3.yaml", motion_module: str = "models/Motion_Module/mm_sd_v15.ckpt"):
        
        # inference_config = OmegaConf.load(inference_config_path)
        
        inference_config = {'unet_additional_kwargs': {'unet_use_cross_frame_attention': False, 'unet_use_temporal_attention': False, 'use_motion_module': True, 'motion_module_resolutions': [1, 2, 4, 8], 'motion_module_mid_block': False, 'motion_module_decoder_only': False, 'motion_module_type': 'Vanilla', 'motion_module_kwargs': {'num_attention_heads': 8, 'num_transformer_block': 1, 'attention_block_types': ['Temporal_Self', 'Temporal_Self'], 'temporal_position_encoding': True, 'temporal_position_encoding_max_len': 24, 'temporal_attention_dim_div': 1}}, 'noise_scheduler_kwargs': {'DDIMScheduler': {'num_train_timesteps': 1000, 'beta_start': 0.00085, 'beta_end': 0.012, 'beta_schedule': 'linear', 'steps_offset': 1, 'clip_sample': False}, 'EulerAncestralDiscreteScheduler': {'num_train_timesteps': 1000, 'beta_start': 0.00085, 'beta_end': 0.012, 'beta_schedule': 'linear'}, 'KDPM2AncestralDiscreteScheduler': {'num_train_timesteps': 1000, 'beta_start': 0.00085, 'beta_end': 0.012, 'beta_schedule': 'linear'}}}
        
        ### >>> create validation pipeline >>> ###
        tokenizer    = CLIPTokenizer.from_pretrained(model_path, subfolder="models/StableDiffusion/tokenizer")
        text_encoder = CLIPTextModel.from_pretrained(model_path, subfolder="models/StableDiffusion/text_encoder")
        vae          = AutoencoderKL.from_pretrained(model_path, subfolder="models/StableDiffusion/vae")            
        unet         = UNet3DConditionModel.from_pretrained_2d(model_path, subfolder="models/StableDiffusion/unet", unet_additional_kwargs=OmegaConf.to_container(inference_config.unet_additional_kwargs))

        if is_xformers_available(): unet.enable_xformers_memory_efficient_attention()
        else: assert False

        self.pipeline = AnimationPipeline(
            vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet,
            scheduler=DDIMScheduler(**OmegaConf.to_container(inference_config.noise_scheduler_kwargs.DDIMScheduler)),
        ).to("cuda")

        self.pipeline = load_weights(
            self.pipeline,
            # motion module
            motion_module_path         = motion_module,
            motion_module_lora_configs = [],
            # image layers
            dreambooth_model_path      = "",
            lora_model_path            = "",
            lora_alpha                 = 0.8,
        ).to("cuda")
    
    def __call__(self, prompt, negative_prompt, steps, guidance_scale):
        """
        __call__ method will be called once per request. This can be used to
        run inference.
        """
        vids = self.pipeline(
            prompt=prompt, 
            negative_prompt=negative_prompt, 
            num_inference_steps=steps, 
            guidance_scale=guidance_scale,
            width= 256,
            height= 256,
            video_length= 5,
            ).videos
        
        videos = einops.rearrange(vids, "b c t h w -> t b c h w")
        n_rows=6
        fps=1
        loop = True
        rescale=False
        outputs = []
        for x in videos:
            x = torchvision.utils.make_grid(x, nrow=n_rows)
            x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
            if rescale:
                x = (x + 1.0) / 2.0  # -1,1 -> 0,1
            x = (x * 255).numpy().astype(np.uint8)
            outputs.append(x)
            
        # imageio.mimsave(path, outputs, fps=fps)
        
        # return a gif file as bytes
        return outputs
    

# This is the entry point for the serverless function.
# This function will be called during inference time.


# new_handler = EndpointHandler()