import os.path as osp
import random

import torch
import numpy as np
import cv2
import sys


from PIL import Image
import subprocess

import importlib.metadata

cur_dir = osp.dirname(osp.abspath(__file__))

installed_packages = [package.name for package in importlib.metadata.distributions()]

REQUIRED = {
  'diffusers', 'transformers', 'einops', 'opencv-python', 'tqdm', 'pillow', 'onnxruntime-gpu', 'onnx', 'safetensors', 'accelerate', 'peft'
}

missing = [name for name in REQUIRED if name not in installed_packages]
print("missing pkgs", missing)

if missing:
    python = sys.executable
    subprocess.check_call([python, '-m', 'pip', 'install', *missing], stdout=subprocess.DEVNULL)

from .hellomeme.utils import (get_drive_expression,
                              get_drive_expression_pd_fgc,
                              gen_control_heatmaps,
                              get_drive_pose,
                              crop_and_resize,
                              det_landmarks,
                              load_data_list,
                              load_unet_from_safetensors
                              )

from .hellomeme.tools import Hello3DMMPred, HelloARKitBSPred, HelloFaceAlignment, HelloCameraDemo, FanEncoder
from .hellomeme import HMImagePipeline, HMVideoPipeline
from transformers import CLIPVisionModelWithProjection

DEFAULT_PROMPT = '(best quality), highly detailed, ultra-detailed, headshot, person, well-placed five sense organs, looking at the viewer, centered composition, sharp focus, realistic skin texture'


class HMImagePipelineLoader:
    @classmethod
    def INPUT_TYPES(s):
        checkpoint_files = sorted(load_data_list(osp.join(cur_dir, '../../models/checkpoints'), '.pt;.pth;.ckpt;.safetensors'))
        lora_files = sorted(load_data_list(osp.join(cur_dir, '../../models/loras'), '.safetensors'))

        return {
            "optional": {
                "checkpoint_path": (['SD1.5'] + checkpoint_files, ),
                "lora_path": (['None'] + lora_files, ),
                "gpu_id": ("INT", {"default": 0}),
            }
        }
    RETURN_TYPES = ("HMIMAGEPIPELINE", )
    RETURN_NAMES = ("hm_image_pipeline", )
    FUNCTION = "load_pipeline"
    CATEGORY = "hellomeme"
    def load_pipeline(self, checkpoint_path=None, lora_path=None, gpu_id=0):
        dtype = torch.float16
        if gpu_id >= 0:
            device = torch.device("cuda:{}".format(gpu_id))
        else:
            device = torch.device("cpu")
        pipeline = HMImagePipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5").to(dtype=dtype, device=device)
        pipeline.caryomitosis()

        ### load customized checkpoint or lora here:
        ## checkpoints

        if checkpoint_path and osp.isfile(checkpoint_path):
            if checkpoint_path.endswith('.safetensors'):
                state_dict = load_unet_from_safetensors(checkpoint_path, pipeline.unet_ref.config)
                pipeline.unet.load_state_dict(state_dict, strict=False)
            elif osp.splitext(checkpoint_path)[-1] in ['.pt', '.pth', '.ckpt']:
                state_dict = torch.load(checkpoint_path)
                pipeline.unet.load_state_dict(state_dict, strict=False)
            else:
                print("Invalid checkpoint path", checkpoint_path)

        ### lora
        if lora_path and osp.isfile(lora_path):
            pipeline.load_lora_weights(osp.dirname(lora_path), weight_name=osp.basename(lora_path), adapter_name="lora")

        pipeline.insert_hm_modules(dtype=dtype, device=device)
        
        return (pipeline, )


class HMVideoPipelineLoader:
    @classmethod
    def INPUT_TYPES(s):
        checkpoint_files = sorted(load_data_list(osp.join(cur_dir, '../../models/checkpoints'), '.pt;.pth;.ckpt;.safetensors'))
        lora_files = sorted(load_data_list(osp.join(cur_dir, '../../models/loras'), '.safetensors'))

        return {
            "optional": {
                "checkpoint_path": (['SD1.5'] + checkpoint_files, ),
                "lora_path": (['None'] + lora_files, ),
                "patch_frames": ([12, 16], ),
                "gpu_id": ("INT", {"default": 0}, ),
            }
        }

    RETURN_TYPES = ("HMVIDEOPIPELINE",)
    RETURN_NAMES = ("hm_video_pipeline",)
    FUNCTION = "load_pipeline"
    CATEGORY = "hellomeme"

    def load_pipeline(self, checkpoint_path=None, lora_path=None, patch_frames=12, gpu_id=0):
        dtype = torch.float16
        if gpu_id >= 0:
            device = torch.device("cuda:{}".format(gpu_id))
        else:
            device = torch.device("cpu")
        pipeline = HMVideoPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5").to(dtype=dtype,
                                                                                                     device=device)
        pipeline.caryomitosis(patch_frames=patch_frames)

        ### load customized checkpoint or lora here:
        ## checkpoints

        if checkpoint_path and osp.isfile(checkpoint_path):
            if checkpoint_path.endswith('.safetensors'):
                state_dict = load_unet_from_safetensors(checkpoint_path, pipeline.unet_ref.config)
                pipeline.unet.load_state_dict(state_dict, strict=False)
            elif osp.splitext(checkpoint_path)[-1] in ['.pt', '.pth', '.ckpt']:
                state_dict = torch.load(checkpoint_path)
                pipeline.unet.load_state_dict(state_dict, strict=False)
            else:
                print("Invalid checkpoint path", checkpoint_path)

        ### lora
        if lora_path and osp.isfile(lora_path):
            pipeline.load_lora_weights(osp.dirname(lora_path), weight_name=osp.basename(lora_path), adapter_name="lora")

        pipeline.insert_hm_modules(dtype=dtype, device=device)

        return (pipeline,)


class HMFaceToolkitsLoader:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "gpu_id": ("INT", {"default": 0}),
            }
        }

    RETURN_TYPES = ("FACE_TOOLKITS",)
    RETURN_NAMES = ("face_toolkits",)
    FUNCTION = "load_face_toolkits"
    CATEGORY = "hellomeme"
    def load_face_toolkits(self, gpu_id):
        dtype = torch.float16
        if gpu_id < 0:
            device = torch.device('cpu')
        else:
            device = torch.device(f'cuda:{gpu_id}')
        return (
            dict(
                 device=device,
                 dtype=dtype,
                 pd_fpg_motion=FanEncoder.from_pretrained("songkey/pd_fgc_motion").to(dtype=dtype),
                 face_aligner=HelloCameraDemo(face_alignment_module=HelloFaceAlignment(gpu_id=gpu_id), reset=False),
                 harkit_bs=HelloARKitBSPred(gpu_id=gpu_id),
                 h3dmm=Hello3DMMPred(gpu_id=gpu_id),
                 image_encoder=CLIPVisionModelWithProjection.from_pretrained(
                    'h94/IP-Adapter', subfolder='models/image_encoder').to(dtype=dtype)
            ),
        )

class CropPortrait:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "image": ("IMAGE",),
                "face_toolkits": ("FACE_TOOLKITS",),
            }
        }

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "crop_portrait"
    CATEGORY = "hellomeme"

    def crop_portrait(self, image, face_toolkits):
        image_np = cv2.cvtColor((image[0] * 255).cpu().numpy().astype(np.uint8), cv2.COLOR_BGR2RGB)
        image_np = cv2.resize(image_np, (512, 512))
        # print(image_np.shape)
        face_toolkits['face_aligner'].reset_track()
        faces = face_toolkits['face_aligner'].forward(image_np)
        assert len(faces) > 0
        face = sorted(faces, key=lambda x: (x['face_rect'][2] - x['face_rect'][0]) * (
                x['face_rect'][3] - x['face_rect'][1]))[-1]
        ref_landmark = face['pre_kpt_222']

        new_image = crop_and_resize(image_np[np.newaxis, :,:,:], ref_landmark[np.newaxis, :,:], 512, crop=True)[0]
        new_image = cv2.cvtColor(new_image[0], cv2.COLOR_RGB2BGR)
        return (torch.from_numpy(new_image[np.newaxis, :,:,:]).float() / 255., )


class GetFaceLandmarks:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "face_toolkits": ("FACE_TOOLKITS",),
                "images": ("IMAGE",),
            }
        }

    RETURN_TYPES = ("FACELANDMARKS222",)
    RETURN_NAMES = ("landmarks",)
    FUNCTION = "get_face_landmarks"
    CATEGORY = "hellomeme"

    def get_face_landmarks(self, face_toolkits, images):
        frame_list = [cv2.cvtColor((frame * 255).cpu().numpy().astype(np.uint8), cv2.COLOR_BGR2RGB) for frame in images]
        frame_num = len(frame_list)
        assert frame_num > 0
        _, landmark_list = det_landmarks(face_toolkits['face_aligner'], frame_list)
        assert len(frame_list) == frame_num

        return (torch.from_numpy(landmark_list).float(), )


class GetDrivePose:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "face_toolkits": ("FACE_TOOLKITS",),
                "images": ("IMAGE",),
                "landmarks": ("FACELANDMARKS222",),
            }
        }
    RETURN_TYPES = ("DRIVE_POSE",)
    RETURN_NAMES = ("drive_pose",)
    FUNCTION = "get_drive_pose"
    CATEGORY = "hellomeme"
    def get_drive_pose(self, face_toolkits, images, landmarks):
        frame_list = [cv2.cvtColor((frame * 255).cpu().numpy().astype(np.uint8), cv2.COLOR_BGR2RGB) for frame in images]
        landmarks = landmarks.cpu().numpy()

        rot_list, trans_list = get_drive_pose(face_toolkits, frame_list, landmarks, save_size=512)
        return (dict(rot=np.stack(rot_list), trans=np.stack(trans_list)), )


class GetDriveExpression:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "face_toolkits": ("FACE_TOOLKITS",),
                "images": ("IMAGE",),
                "landmarks": ("FACELANDMARKS222",),
            }
        }
    RETURN_TYPES = ("DRIVE_EXPRESSION",)
    RETURN_NAMES = ("drive_exp",)
    FUNCTION = "get_drive_expression"
    CATEGORY = "hellomeme"
    def get_drive_expression(self, face_toolkits, images, landmarks):
        frame_list = [cv2.cvtColor((frame * 255).cpu().numpy().astype(np.uint8), cv2.COLOR_BGR2RGB) for frame in images]
        landmarks = landmarks.cpu().numpy()
        exp_dict = get_drive_expression(face_toolkits, frame_list, landmarks)
        return (exp_dict, )


class GetDriveExpression2:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "face_toolkits": ("FACE_TOOLKITS",),
                "images": ("IMAGE",),
                "landmarks": ("FACELANDMARKS222",),
            }
        }
    RETURN_TYPES = ("DRIVE_EXPRESSION2",)
    RETURN_NAMES = ("drive_exp2",)
    FUNCTION = "get_drive_expression"
    CATEGORY = "hellomeme"
    def get_drive_expression(self, face_toolkits, images, landmarks):
        frame_list = [cv2.cvtColor((frame * 255).cpu().numpy().astype(np.uint8), cv2.COLOR_BGR2RGB) for frame in images]
        landmarks = landmarks.cpu().numpy()
        exp_dict = get_drive_expression_pd_fgc(face_toolkits, frame_list, landmarks)
        return (exp_dict, )


class HMPipelineImage:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "hm_image_pipeline": ("HMIMAGEPIPELINE",),
                "face_toolkits": ("FACE_TOOLKITS",),
                "ref_image": ("IMAGE",),
                "drive_pose": ("DRIVE_POSE",),
                "trans_ratio": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
                "prompt": ("STRING", {"default": DEFAULT_PROMPT}),
                "negative_prompt": ("STRING", {"default": ''}),
                "steps": ("INT", {"default": 25, "min": 1, "max": 1000}),
                "seed": ("INT", {"default": -1, "min": -1, "max": 100000}),
                "guidance_scale": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 100.0, "step": 0.01}),
            },
                "optional": {
                    "drive_exp": ("DRIVE_EXPRESSION", {"default": None},),
                    "drive_exp2": ("DRIVE_EXPRESSION2", {"default": None},),
                }
        }

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "sample"
    CATEGORY = "hellomeme"

    def sample(self,
               hm_image_pipeline,
               face_toolkits,
               ref_image,
               drive_pose,
               drive_exp=None,
               drive_exp2=None,
               trans_ratio='0.0',
               prompt=DEFAULT_PROMPT,
               negative_prompt='',
               steps=25,
               seed=-1,
               guidance_scale=2.0
               ):
        image_np = (ref_image[0] * 255).cpu().numpy().astype(np.uint8)
        image_np = cv2.resize(image_np, (512, 512))
        image_pil = Image.fromarray(image_np)

        face_toolkits['face_aligner'].reset_track()
        faces = face_toolkits['face_aligner'].forward(image_np)
        assert len(faces) > 0
        face = sorted(faces, key=lambda x: (x['face_rect'][2] - x['face_rect'][0]) * (
                x['face_rect'][3] - x['face_rect'][1]))[-1]
        ref_landmark = face['pre_kpt_222']

        _, ref_trans = face_toolkits['h3dmm'].forward_params(image_np, ref_landmark)

        drive_rot, drive_trans = drive_pose['rot'], drive_pose['trans']
        condition = gen_control_heatmaps(drive_rot, drive_trans, ref_trans, 512, trans_ratio)
        drive_params = dict(condition=condition.unsqueeze(0).to(dtype=torch.float16, device='cpu'))

        if isinstance(drive_exp, dict):
            drive_params.update(drive_exp)
        if isinstance(drive_exp2, dict):
            drive_params.update(drive_exp2)

        if seed < 0:
            generator = torch.Generator().manual_seed(random.randint(0, 100000))
        else:
            generator = torch.Generator().manual_seed(seed)

        result_img = hm_image_pipeline(
            prompt=[prompt],
            strength=1.0,
            image=image_pil,
            drive_params=drive_params,
            num_inference_steps=steps,
            negative_prompt=[negative_prompt],
            guidance_scale=guidance_scale,
            generator=generator,
            output_type='np'
        )
        return (torch.from_numpy(np.clip(result_img[0], 0, 1)), )


class HMPipelineVideo:
    @classmethod
    def INPUT_TYPES(s):
        return {
                    "required":{
                        "hm_video_pipeline": ("HMVIDEOPIPELINE",),
                        "face_toolkits": ("FACE_TOOLKITS",),
                        "ref_image": ("IMAGE",),
                        "drive_pose": ("DRIVE_POSE",),
                        "trans_ratio": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
                        "patch_overlap": ("INT", {"default": 4, "min": 0, "max": 5}),
                        "prompt": ("STRING", {"default": DEFAULT_PROMPT}),
                        "negative_prompt": ("STRING", {"default": ''}),
                        "steps": ("INT", {"default": 25, "min": 1, "max": 1000}),
                        "seed": ("INT", {"default": -1, "min": -1, "max": 100000}),
                        "guidance_scale": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 100.0, "step": 0.01}),
                     },
                    "optional": {
                        "drive_exp": ("DRIVE_EXPRESSION", {"default": None},),
                        "drive_exp2": ("DRIVE_EXPRESSION2", {"default": None},),
                    }
                }

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "sample"
    CATEGORY = "hellomeme"

    def sample(self,
                hm_video_pipeline,
                face_toolkits,
                ref_image,
                drive_pose,
                drive_exp=None,
                drive_exp2=None,
                trans_ratio=0.0,
                patch_overlap=4,
                prompt=DEFAULT_PROMPT,
                negative_prompt="",
                steps=25,
                seed=-1,
                guidance_scale=2.0
        ):
        image_np = (ref_image[0] * 255).cpu().numpy().astype(np.uint8)
        image_np = cv2.resize(image_np, (512, 512))
        image_pil = Image.fromarray(image_np)

        face_toolkits['face_aligner'].reset_track()
        faces = face_toolkits['face_aligner'].forward(image_np)
        face_toolkits['face_aligner'].reset_track()
        assert len(faces) > 0
        face = sorted(faces, key=lambda x: (x['face_rect'][2] - x['face_rect'][0]) * (
                x['face_rect'][3] - x['face_rect'][1]))[-1]
        ref_landmark = face['pre_kpt_222']

        _, ref_trans = face_toolkits['h3dmm'].forward_params(image_np, ref_landmark)

        if seed < 0:
            generator = torch.Generator().manual_seed(random.randint(0, 100000))
        else:
            generator = torch.Generator().manual_seed(seed)

        drive_rot, drive_trans = drive_pose['rot'], drive_pose['trans']
        condition = gen_control_heatmaps(drive_rot, drive_trans, ref_trans, 512, trans_ratio)
        drive_params = dict(condition=condition.unsqueeze(0).to(dtype=torch.float16, device='cpu'))

        if isinstance(drive_exp, dict):
            drive_params.update(drive_exp)
        if isinstance(drive_exp2, dict):
            drive_params.update(drive_exp2)

        res_frames = hm_video_pipeline(
            prompt=[prompt],
            strength=1.0,
            image=image_pil,
            chunk_overlap=patch_overlap,
            drive_params=drive_params,
            num_inference_steps=steps,
            negative_prompt=[negative_prompt],
            guidance_scale=guidance_scale,
            generator=generator,
            output_type='np'
        )
        res_frames = [np.clip(x[0], 0, 1) for x in res_frames]
        return (torch.from_numpy(np.array(res_frames)), )


NODE_CLASS_MAPPINGS = {
    "HMImagePipelineLoader": HMImagePipelineLoader,
    "HMVideoPipelineLoader": HMVideoPipelineLoader,
    "HMFaceToolkitsLoader": HMFaceToolkitsLoader,
    "HMPipelineImage": HMPipelineImage,
    "HMPipelineVideo": HMPipelineVideo,
    "CropPortrait": CropPortrait,
    "GetFaceLandmarks": GetFaceLandmarks,
    "GetDrivePose": GetDrivePose,
    "GetDriveExpression": GetDriveExpression,
    "GetDriveExpression2": GetDriveExpression2,
}

NODE_DISPLAY_NAME_MAPPINGS = {
    "HMImagePipelineLoader": "Load HelloMemeImage Pipeline",
    "HMVideoPipelineLoader": "Load HelloMemeVideo Pipeline",
    "HMFaceToolkitsLoader": "Load Face Toolkits",
    "HMPipelineImage": "HelloMeme Image Pipeline",
    "HMPipelineVideo": "HelloMeme Video Pipeline",
    "CropPortrait": "Crop Portrait",
    "GetFaceLandmarks": "Get Face Landmarks",
    "GetDrivePose": "Get Drive Pose",
    "GetDriveExpression": "Get Drive Expression",
    "GetDriveExpression2": "Get Drive Expression V2",
}