
from typing import Any, Dict, Union
import torchvision.transforms as transforms
import torch
from torch.utils.data import DataLoader, TensorDataset
import numpy as np
from tqdm.auto import tqdm
from PIL import Image
from diffusers import (
    DiffusionPipeline,
    ControlNetModel,
    DDIMScheduler,
    AutoencoderKL,
)
from diffusers.utils import BaseOutput
from transformers import CLIPTextModel, CLIPTokenizer
from transformers import CLIPImageProcessor
from transformers import CLIPVisionModelWithProjection

from utils.image_util import resize_max_res,chw2hwc
from src.point_network import PointNet
from src.models.mutual_self_attention_multi_scale import ReferenceAttentionControl
from src.models.unet_2d_condition import UNet2DConditionModel
from src.models.refunet_2d_condition import RefUNet2DConditionModel


class MangaNinjiaPipelineOutput(BaseOutput):
    img_np: np.ndarray
    img_pil: Image.Image
    to_save_dict: dict


class MangaNinjiaPipeline(DiffusionPipeline):
    rgb_latent_scale_factor = 0.18215
    
    def __init__(self,
        reference_unet: RefUNet2DConditionModel,
        controlnet: ControlNetModel,
        denoising_unet: UNet2DConditionModel,
        vae: AutoencoderKL,
        refnet_tokenizer: CLIPTokenizer,
        refnet_text_encoder: CLIPTextModel,
        refnet_image_encoder: CLIPVisionModelWithProjection,
        controlnet_tokenizer: CLIPTokenizer,
        controlnet_text_encoder: CLIPTextModel,
        controlnet_image_encoder: CLIPVisionModelWithProjection,
        scheduler: DDIMScheduler,
        point_net: PointNet
    ):
        super().__init__()
            
        self.register_modules(
            reference_unet=reference_unet,
            controlnet=controlnet,
            denoising_unet=denoising_unet,       
            vae=vae,
            refnet_tokenizer=refnet_tokenizer,
            refnet_text_encoder=refnet_text_encoder,
            refnet_image_encoder=refnet_image_encoder,
            controlnet_tokenizer=controlnet_tokenizer,
            controlnet_text_encoder=controlnet_text_encoder,
            controlnet_image_encoder=controlnet_image_encoder,
            point_net=point_net,
            scheduler=scheduler,
        )
        self.empty_text_embed = None
        self.clip_image_processor = CLIPImageProcessor()
        
    @torch.no_grad()
    def __call__(
        self,
        is_lineart: bool,
        ref1: Image.Image,
        raw2: Image.Image,
        edit2: Image.Image,
        denosing_steps: int = 20,
        processing_res: int = 512,
        match_input_res: bool = True,
        batch_size: int = 0,
        show_progress_bar: bool = True,
        guidance_scale_ref: float = 7,
        guidance_scale_point: float = 12,
        preprocessor=None,
        generator=None,
        point_ref=None,
        point_main=None,
    ) -> MangaNinjiaPipelineOutput:

        device = self.device
        
        input_size = raw2.size
        point_ref=point_ref.float().to(device)
        point_main=point_main.float().to(device)
        def img2embeds(img, image_enc):
            clip_image = self.clip_image_processor.preprocess(
                img, return_tensors="pt"
            ).pixel_values
            clip_image_embeds = image_enc(
                clip_image.to(device, dtype=image_enc.dtype)
            ).image_embeds
            encoder_hidden_states = clip_image_embeds.unsqueeze(1)
            return encoder_hidden_states
        if self.reference_unet:
            refnet_encoder_hidden_states = img2embeds(ref1, self.refnet_image_encoder)
        else:
            refnet_encoder_hidden_states = None
        if self.controlnet:
            controlnet_encoder_hidden_states = img2embeds(ref1, self.controlnet_image_encoder)
        else:
            controlnet_encoder_hidden_states = None

        prompt = ""
        def prompt2embeds(prompt, tokenizer, text_encoder):
            text_inputs = tokenizer(
                prompt,
                padding="do_not_pad",
                max_length=tokenizer.model_max_length,
                truncation=True,
                return_tensors="pt",
            )
            text_input_ids = text_inputs.input_ids.to(device) #[1,2]
            empty_text_embed = text_encoder(text_input_ids)[0].to(self.dtype)
            uncond_encoder_hidden_states = empty_text_embed.repeat((1, 1, 1))[:,0,:].unsqueeze(0)
            return uncond_encoder_hidden_states
        if self.reference_unet:
            refnet_uncond_encoder_hidden_states = prompt2embeds(prompt, self.refnet_tokenizer, self.refnet_text_encoder)
        else:
            refnet_uncond_encoder_hidden_states = None
        if self.controlnet:
            controlnet_uncond_encoder_hidden_states = prompt2embeds(prompt, self.controlnet_tokenizer, self.controlnet_text_encoder)
        else:
            controlnet_uncond_encoder_hidden_states = None

        do_classifier_free_guidance = guidance_scale_ref > 1.0
        
        # adjust the input resolution.
        if not match_input_res:
            assert (
                processing_res is not None                
            )," Value Error: `resize_output_back` is only valid with "
        
        assert processing_res >= 0
        assert denosing_steps >= 1
        
        # --------------- Image Processing ------------------------
        # Resize image
        if processing_res > 0:
            def resize_img(img):
                img = resize_max_res(img, max_edge_resolution=processing_res)
                return img
            ref1 = resize_img(ref1)
            raw2 = resize_img(raw2)
            edit2 = resize_img(edit2)
        
        # Normalize image
        def normalize_img(img):
            img = img.convert("RGB")
            img = np.array(img)

            # Normalize RGB Values.
            rgb = np.transpose(img,(2,0,1))
            rgb_norm = rgb / 255.0 * 2.0 - 1.0
            rgb_norm = torch.from_numpy(rgb_norm).to(self.dtype)
            rgb_norm = rgb_norm.to(device)
            img = rgb_norm
            assert img.min() >= -1.0 and img.max() <= 1.0
            return img
        raw2_real = raw2.convert('L')
        ref1 = normalize_img(ref1)
        raw2 = normalize_img(raw2)
        edit2 = normalize_img(edit2)
        single_rgb_dataset = TensorDataset(ref1[None], raw2[None], edit2[None])

        
        # find the batch size
        if batch_size>0:
            _bs = batch_size
        else:
            _bs = 1
        point_ref=self.point_net(point_ref)
        point_main=self.point_net(point_main)
        single_rgb_loader = DataLoader(single_rgb_dataset,batch_size=_bs,shuffle=False)

        # classifier guidance
        if do_classifier_free_guidance:
            if self.reference_unet:
                refnet_encoder_hidden_states = torch.cat(
                    [refnet_uncond_encoder_hidden_states, refnet_encoder_hidden_states,refnet_encoder_hidden_states], dim=0
                )
            else:
                refnet_encoder_hidden_states = None

            if self.controlnet:
                controlnet_encoder_hidden_states = torch.cat(
                    [controlnet_uncond_encoder_hidden_states, controlnet_encoder_hidden_states,controlnet_encoder_hidden_states], dim=0
                )
            else:
                controlnet_encoder_hidden_states = None

        if self.reference_unet:
            reference_control_writer = ReferenceAttentionControl(
                self.reference_unet,
                do_classifier_free_guidance=do_classifier_free_guidance,
                mode="write",
                batch_size=batch_size,
                fusion_blocks="full",
            )
            reference_control_reader = ReferenceAttentionControl(
                self.denoising_unet,
                do_classifier_free_guidance=do_classifier_free_guidance,
                mode="read",
                batch_size=batch_size,
                fusion_blocks="full",
            )
        else:
            reference_control_writer = None
            reference_control_reader = None
            
        if show_progress_bar:
            iterable_bar = tqdm(
                single_rgb_loader, desc=" " * 2 + "Inference batches", leave=False
            )
        else:
            iterable_bar = single_rgb_loader
        
        assert len(iterable_bar) == 1
        for batch in iterable_bar:
            (ref1, raw2, edit2) = batch  # here the image is still around 0-1
            if is_lineart:
                raw2 = raw2_real
            img_pred, to_save_dict = self.single_infer(
                is_lineart=is_lineart,
                ref1=ref1,
                raw2=raw2,
                edit2=edit2,
                num_inference_steps=denosing_steps,
                show_pbar=show_progress_bar,
                guidance_scale_ref=guidance_scale_ref,
                guidance_scale_point=guidance_scale_point,
                refnet_encoder_hidden_states=refnet_encoder_hidden_states,
                controlnet_encoder_hidden_states=controlnet_encoder_hidden_states,
                reference_control_writer=reference_control_writer,
                reference_control_reader=reference_control_reader,
                preprocessor=preprocessor,
                generator=generator,
                point_ref=point_ref,
                point_main=point_main
            )
            for k, v in to_save_dict.items():
                if k =='edge2_black':
                    to_save_dict[k] = Image.fromarray(
                   ((to_save_dict['edge2_black'][:,0].squeeze().detach().cpu().numpy() + 1.) / 2 * 255).astype(np.uint8)
                    )
                else:
                    try:
                        to_save_dict[k] = Image.fromarray(
                        chw2hwc(((v.squeeze().detach().cpu().numpy() + 1.) / 2 * 255).astype(np.uint8))
                        )
                    except:
                        import ipdb;ipdb.set_trace()
        
        torch.cuda.empty_cache()  # clear vram cache for ensembling
        
        # ----------------- Post processing -----------------        
        # Convert to numpy
        img_pred = img_pred.squeeze().cpu().numpy().astype(np.float32)
        img_pred_np = (((img_pred + 1.) / 2.) * 255).astype(np.uint8)
        img_pred_np = chw2hwc(img_pred_np)
        img_pred_pil = Image.fromarray(img_pred_np)

        # Resize back to original resolution
        if match_input_res:
            img_pred_pil = img_pred_pil.resize(input_size)
            img_pred_np = np.asarray(img_pred_pil)        

        return MangaNinjiaPipelineOutput(
            img_np=img_pred_np,
            img_pil=img_pred_pil,
            to_save_dict=to_save_dict
        )

    
    def __encode_empty_text(self):
        """
        Encode text embedding for empty prompt
        """
        prompt = ""
        text_inputs = self.tokenizer(
            prompt,
            padding="do_not_pad",
            max_length=self.tokenizer.model_max_length,
            truncation=True,
            return_tensors="pt",
        )
        text_input_ids = text_inputs.input_ids.to(self.text_encoder.device) #[1,2]
        # print(text_input_ids.shape)
        self.empty_text_embed = self.text_encoder(text_input_ids)[0].to(self.dtype) #[1,2,1024]

    def get_timesteps(self, num_inference_steps, strength, device, denoising_start=None):
        # get the original timestep using init_timestep
        if denoising_start is None:
            init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
            t_start = max(num_inference_steps - init_timestep, 0)
        else:
            t_start = 0

        timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]

        # Strength is irrelevant if we directly request a timestep to start at;
        # that is, strength is determined by the denoising_start instead.
        if denoising_start is not None:
            discrete_timestep_cutoff = int(
                round(
                    self.scheduler.config.num_train_timesteps
                    - (denoising_start * self.scheduler.config.num_train_timesteps)
                )
            )
            timesteps = list(filter(lambda ts: ts < discrete_timestep_cutoff, timesteps))
            return torch.tensor(timesteps), len(timesteps)

        return timesteps, num_inference_steps - t_start
        
    @torch.no_grad()
    def single_infer(
        self,
        is_lineart: bool,
        ref1: torch.Tensor,
        raw2: torch.Tensor,
        edit2: torch.Tensor,
        num_inference_steps: int,
        show_pbar: bool,
        guidance_scale_ref: float,
        guidance_scale_point: float,
        refnet_encoder_hidden_states: torch.Tensor,
        controlnet_encoder_hidden_states: torch.Tensor,
        reference_control_writer: ReferenceAttentionControl,
        reference_control_reader: ReferenceAttentionControl,
        preprocessor,
        generator,
        point_ref,
        point_main
    ):
        do_classifier_free_guidance = guidance_scale_ref > 1.0
        device = ref1.device
        to_save_dict = {
            'ref1': ref1,
        }
        
        # Set timesteps: inherit from the diffuison pipeline
        self.scheduler.set_timesteps(num_inference_steps, device=device) # here the numbers of the steps is only 10.
        timesteps = self.scheduler.timesteps  # [T]
        
        # encode image
        ref1_latents = self.encode_RGB(ref1, generator=generator) # 1/8 Resolution with a channel nums of 4. 
        edge2_src = raw2

        timesteps_add,_=self.get_timesteps(num_inference_steps, 1.0, device, denoising_start=None)
        if is_lineart is not True:
            edge2 = preprocessor(edge2_src)
        else:
            gray_image_np = np.array(edge2_src)
            gray_image_np = gray_image_np / 255.0
            edge2 = torch.from_numpy(gray_image_np.astype(np.float32)).unsqueeze(0).unsqueeze(0).cuda()
        edge2[edge2<=0.24]=0
        edge2_black = edge2.repeat(1, 3, 1, 1) * 2 - 1.
        to_save_dict['edge2_black']=edge2_black

        edge2 = edge2.repeat(1, 3, 1, 1) * 2 - 1.
        to_save_dict['edge2'] = (1-((edge2+1.)/2))*2-1
        
        noisy_edit2_latents = torch.randn(
            ref1_latents.shape, device=device, dtype=self.dtype
        )  # [B, 4, H/8, W/8]
            
   
        # Denoising loop
        if show_pbar:
            iterable = tqdm(
                enumerate(timesteps),
                total=len(timesteps),
                leave=False,
                desc=" " * 4 + "Diffusion denoising",
            )
        else:
            iterable = enumerate(timesteps)

        for i, t in iterable:
            
            refnet_input = ref1_latents
            controlnet_inputs = (noisy_edit2_latents, edge2)
            unet_input = torch.cat([noisy_edit2_latents], dim=1)

            if i == 0:
                if self.reference_unet:
                    self.reference_unet(
                        refnet_input.repeat(
                            (3 if do_classifier_free_guidance else 1), 1, 1, 1
                        ),
                        torch.zeros_like(t),
                        
                        encoder_hidden_states=refnet_encoder_hidden_states,
                        return_dict=False,
                    )
                    reference_control_reader.update(reference_control_writer,point_embedding_ref=point_ref,point_embedding_main=point_main)#size不对

            if self.controlnet:
                noisy_latents, controlnet_cond = controlnet_inputs
                down_block_res_samples, mid_block_res_sample = self.controlnet(
                    noisy_latents.repeat(
                        (3 if do_classifier_free_guidance else 1), 1, 1, 1
                    ),
                    t,
                    encoder_hidden_states=controlnet_encoder_hidden_states,
                    controlnet_cond=controlnet_cond.repeat(
                        (3 if do_classifier_free_guidance else 1), 1, 1, 1
                    ),
                    return_dict=False,
                )
            else:
                down_block_res_samples, mid_block_res_sample = None, None

            # predict the noise residual
            noise_pred = self.denoising_unet(
                unet_input.repeat(
                    (3 if do_classifier_free_guidance else 1), 1, 1, 1
                ).to(dtype=self.denoising_unet.dtype), 
                t, 
                encoder_hidden_states=refnet_encoder_hidden_states,
                down_block_additional_residuals=down_block_res_samples,
                mid_block_additional_residual=mid_block_res_sample,
            ).sample  # [B, 4, h, w]
            noise_pred_uncond, noise_pred_ref, noise_pred_point = noise_pred.chunk(3)
            noise_pred_1 = noise_pred_uncond + guidance_scale_ref * (
                noise_pred_ref - noise_pred_uncond
            )
            noise_pred_2 = noise_pred_ref + guidance_scale_point * (
                noise_pred_point - noise_pred_ref
            )
            noise_pred=(noise_pred_1+noise_pred_2)/2
            noisy_edit2_latents = self.scheduler.step(noise_pred, t, noisy_edit2_latents).prev_sample
        
        reference_control_reader.clear()
        reference_control_writer.clear()
        torch.cuda.empty_cache()

        # clip prediction
        edit2 = self.decode_RGB(noisy_edit2_latents)
        edit2 = torch.clip(edit2, -1.0, 1.0)

        return edit2, to_save_dict
        
    
    def encode_RGB(self, rgb_in: torch.Tensor, generator) -> torch.Tensor:
        """
        Encode RGB image into latent.

        Args:
            rgb_in (`torch.Tensor`):
                Input RGB image to be encoded.

        Returns:
            `torch.Tensor`: Image latent.
        """
        
        # generator = None
        rgb_latent = self.vae.encode(rgb_in).latent_dist.sample(generator)
        rgb_latent = rgb_latent * self.rgb_latent_scale_factor
        return rgb_latent
    
    def decode_RGB(self, rgb_latent: torch.Tensor) -> torch.Tensor:
        """
        Decode depth latent into depth map.

        Args:
            rgb_latent (`torch.Tensor`):
                Depth latent to be decoded.

        Returns:
            `torch.Tensor`: Decoded depth map.
        """

        rgb_latent = rgb_latent / self.rgb_latent_scale_factor
        rgb_out = self.vae.decode(rgb_latent, return_dict=False)[0]
        return rgb_out


