import random
import sys
import time
from pathlib import Path

import cv2
import numpy as np
import torch
import torch.nn.functional as F
from diffusers import AutoencoderKL, UniPCMultistepScheduler
from PIL import Image
from transformers import (
    AutoProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionModelWithProjection,
)

# sys.path.append(str(Path(__file__).parent))
from . import pipelines_ootd

#! Necessary for OotdPipeline.from_pretrained
sys.modules["pipelines_ootd"] = pipelines_ootd

from .pipelines_ootd.pipeline_ootd import OotdPipeline
from .pipelines_ootd.unet_garm_2d_condition import UNetGarm2DConditionModel
from .pipelines_ootd.unet_vton_2d_condition import UNetVton2DConditionModel


class OOTDiffusion:

    def __init__(self, hg_root: str):
        self.device = "cuda" if torch.cuda.is_available() else "cpu"

        # hg_root = str(Path.cwd().resolve() / hg_root)
        VIT_PATH = f"openai/clip-vit-large-patch14"
        VAE_PATH = f"{hg_root}/checkpoints/ootd"
        UNET_PATH = f"{hg_root}/checkpoints/ootd/ootd_hd/checkpoint-36000"
        MODEL_PATH = f"{hg_root}/checkpoints/ootd"

        vae = AutoencoderKL.from_pretrained(
            VAE_PATH,
            subfolder="vae",
            torch_dtype=torch.float16,
        )
        unet_garm = UNetGarm2DConditionModel.from_pretrained(
            UNET_PATH,
            subfolder="unet_garm",
            torch_dtype=torch.float16,
            use_safetensors=True,
        )
        unet_vton = UNetVton2DConditionModel.from_pretrained(
            UNET_PATH,
            subfolder="unet_vton",
            torch_dtype=torch.float16,
            use_safetensors=True,
        )
        self.pipe = OotdPipeline.from_pretrained(
            MODEL_PATH,
            unet_garm=unet_garm,
            unet_vton=unet_vton,
            vae=vae,
            torch_dtype=torch.float16,
            variant="fp16",
            use_safetensors=True,
            safety_checker=None,
            requires_safety_checker=False,
        ).to(self.device)

        self.pipe.scheduler = UniPCMultistepScheduler.from_config(
            self.pipe.scheduler.config
        )
        self.auto_processor = AutoProcessor.from_pretrained(VIT_PATH)
        self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(VIT_PATH).to(
            self.device
        )
        self.tokenizer = CLIPTokenizer.from_pretrained(
            MODEL_PATH,
            subfolder="tokenizer",
        )
        self.text_encoder = CLIPTextModel.from_pretrained(
            MODEL_PATH,
            subfolder="text_encoder",
        ).to(self.device)

    def tokenize_captions(self, captions, max_length):
        inputs = self.tokenizer(
            captions,
            max_length=max_length,
            padding="max_length",
            truncation=True,
            return_tensors="pt",
        )
        return inputs.input_ids

    def __call__(
        self,
        model_type="hd",
        category="upperbody",
        image_garm=None,
        image_vton=None,
        mask=None,
        image_ori=None,
        num_samples=1,
        num_steps=20,
        image_scale=1.0,
        seed=-1,
    ):
        if seed == -1:
            random.seed(time.time())
            seed = random.randint(0, 2147483647)
        print("Initial seed: " + str(seed))
        generator = torch.manual_seed(seed)

        with torch.no_grad():
            prompt_image = self.auto_processor(
                images=image_garm, return_tensors="pt"
            ).to(self.device)
            prompt_image = self.image_encoder(
                prompt_image.data["pixel_values"]
            ).image_embeds
            prompt_image = prompt_image.unsqueeze(1)
            if model_type == "hd":
                prompt_embeds = self.text_encoder(
                    self.tokenize_captions([""], 2).to(self.device)
                )[0]
                prompt_embeds[:, 1:] = prompt_image[:]
            elif model_type == "dc":
                prompt_embeds = self.text_encoder(
                    self.tokenize_captions([category], 3).to(self.device)
                )[0]
                prompt_embeds = torch.cat([prompt_embeds, prompt_image], dim=1)
            else:
                raise ValueError("model_type must be 'hd' or 'dc'!")

            images = self.pipe(
                prompt_embeds=prompt_embeds,
                image_garm=image_garm,
                image_vton=image_vton,
                mask=mask,
                image_ori=image_ori,
                num_inference_steps=num_steps,
                image_guidance_scale=image_scale,
                num_images_per_prompt=num_samples,
                generator=generator,
            ).images

        return images