import os
import cv2
import math
import numpy as np
from PIL import Image, ImageOps

import torch
from torchvision.transforms import InterpolationMode
from torchvision.transforms.functional import normalize, resize
from transformers import T5EncoderModel, T5Tokenizer
from typing import List, Optional, Tuple, Union
from diffusers.models.embeddings import get_3d_rotary_pos_embed
from diffusers.pipelines.cogvideo.pipeline_cogvideox import get_resize_crop_region_for_grid
from diffusers.utils import load_image

import insightface
from insightface.app import FaceAnalysis
from facexlib.parsing import init_parsing_model
from facexlib.utils.face_restoration_helper import FaceRestoreHelper

from models.eva_clip import create_model_and_transforms
from models.eva_clip.constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD
from models.eva_clip.utils_qformer import resize_numpy_image_long

def tensor_to_pil(src_img_tensor):
    img = src_img_tensor.clone().detach()
    if img.dtype == torch.bfloat16:
        img = img.to(torch.float32)
    img = img.cpu().numpy()
    img = np.transpose(img, (1, 2, 0))
    img = img.astype(np.uint8)
    pil_image = Image.fromarray(img)
    return pil_image
    

def _get_t5_prompt_embeds(
    tokenizer: T5Tokenizer,
    text_encoder: T5EncoderModel,
    prompt: Union[str, List[str]],
    num_videos_per_prompt: int = 1,
    max_sequence_length: int = 226,
    device: Optional[torch.device] = None,
    dtype: Optional[torch.dtype] = None,
    text_input_ids=None,
):
    prompt = [prompt] if isinstance(prompt, str) else prompt
    batch_size = len(prompt)

    if tokenizer is not None:
        text_inputs = tokenizer(
            prompt,
            padding="max_length",
            max_length=max_sequence_length,
            truncation=True,
            add_special_tokens=True,
            return_tensors="pt",
        )
        text_input_ids = text_inputs.input_ids
    else:
        if text_input_ids is None:
            raise ValueError("`text_input_ids` must be provided when the tokenizer is not specified.")

    prompt_embeds = text_encoder(text_input_ids.to(device))[0]
    prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)

    # duplicate text embeddings for each generation per prompt, using mps friendly method
    _, seq_len, _ = prompt_embeds.shape
    prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
    prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)

    return prompt_embeds


def encode_prompt(
    tokenizer: T5Tokenizer,
    text_encoder: T5EncoderModel,
    prompt: Union[str, List[str]],
    num_videos_per_prompt: int = 1,
    max_sequence_length: int = 226,
    device: Optional[torch.device] = None,
    dtype: Optional[torch.dtype] = None,
    text_input_ids=None,
):
    prompt = [prompt] if isinstance(prompt, str) else prompt
    prompt_embeds = _get_t5_prompt_embeds(
        tokenizer,
        text_encoder,
        prompt=prompt,
        num_videos_per_prompt=num_videos_per_prompt,
        max_sequence_length=max_sequence_length,
        device=device,
        dtype=dtype,
        text_input_ids=text_input_ids,
    )
    return prompt_embeds


def compute_prompt_embeddings(
    tokenizer, text_encoder, prompt, max_sequence_length, device, dtype, requires_grad: bool = False
):
    if requires_grad:
        prompt_embeds = encode_prompt(
            tokenizer,
            text_encoder,
            prompt,
            num_videos_per_prompt=1,
            max_sequence_length=max_sequence_length,
            device=device,
            dtype=dtype,
        )
    else:
        with torch.no_grad():
            prompt_embeds = encode_prompt(
                tokenizer,
                text_encoder,
                prompt,
                num_videos_per_prompt=1,
                max_sequence_length=max_sequence_length,
                device=device,
                dtype=dtype,
            )
    return prompt_embeds


def prepare_rotary_positional_embeddings(
    height: int,
    width: int,
    num_frames: int,
    vae_scale_factor_spatial: int = 8,
    patch_size: int = 2,
    attention_head_dim: int = 64,
    device: Optional[torch.device] = None,
    base_height: int = 480,
    base_width: int = 720,
) -> Tuple[torch.Tensor, torch.Tensor]:
    grid_height = height // (vae_scale_factor_spatial * patch_size)
    grid_width = width // (vae_scale_factor_spatial * patch_size)
    base_size_width = base_width // (vae_scale_factor_spatial * patch_size)
    base_size_height = base_height // (vae_scale_factor_spatial * patch_size)

    grid_crops_coords = get_resize_crop_region_for_grid((grid_height, grid_width), base_size_width, base_size_height)
    freqs_cos, freqs_sin = get_3d_rotary_pos_embed(
        embed_dim=attention_head_dim,
        crops_coords=grid_crops_coords,
        grid_size=(grid_height, grid_width),
        temporal_size=num_frames,
    )

    freqs_cos = freqs_cos.to(device=device)
    freqs_sin = freqs_sin.to(device=device)
    return freqs_cos, freqs_sin


def img2tensor(imgs, bgr2rgb=True, float32=True):
    """Numpy array to tensor.

    Args:
        imgs (list[ndarray] | ndarray): Input images.
        bgr2rgb (bool): Whether to change bgr to rgb.
        float32 (bool): Whether to change to float32.

    Returns:
        list[tensor] | tensor: Tensor images. If returned results only have
            one element, just return tensor.
    """

    def _totensor(img, bgr2rgb, float32):
        if img.shape[2] == 3 and bgr2rgb:
            if img.dtype == 'float64':
                img = img.astype('float32')
            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        img = torch.from_numpy(img.transpose(2, 0, 1))
        if float32:
            img = img.float()
        return img

    if isinstance(imgs, list):
        return [_totensor(img, bgr2rgb, float32) for img in imgs]
    return _totensor(imgs, bgr2rgb, float32)


def to_gray(img):
    x = 0.299 * img[:, 0:1] + 0.587 * img[:, 1:2] + 0.114 * img[:, 2:3]
    x = x.repeat(1, 3, 1, 1)
    return x


def draw_kps(image_pil, kps, color_list=[(255,0,0), (0,255,0), (0,0,255), (255,255,0), (255,0,255)]):
    stickwidth = 4
    limbSeq = np.array([[0, 2], [1, 2], [3, 2], [4, 2]])
    kps = np.array(kps)

    w, h = image_pil.size
    out_img = np.zeros([h, w, 3])

    for i in range(len(limbSeq)):
        index = limbSeq[i]
        color = color_list[index[0]]

        x = kps[index][:, 0]
        y = kps[index][:, 1]
        length = ((x[0] - x[1]) ** 2 + (y[0] - y[1]) ** 2) ** 0.5
        angle = math.degrees(math.atan2(y[0] - y[1], x[0] - x[1]))
        polygon = cv2.ellipse2Poly((int(np.mean(x)), int(np.mean(y))), (int(length / 2), stickwidth), int(angle), 0, 360, 1)
        out_img = cv2.fillConvexPoly(out_img.copy(), polygon, color)
    out_img = (out_img * 0.6).astype(np.uint8)

    for idx_kp, kp in enumerate(kps):
        color = color_list[idx_kp]
        x, y = kp
        out_img = cv2.circle(out_img.copy(), (int(x), int(y)), 10, color, -1)

    out_img_pil = Image.fromarray(out_img.astype(np.uint8))
    return out_img_pil


def process_face_embeddings(face_helper_1, clip_vision_model, face_helper_2, eva_transform_mean, eva_transform_std, app, device, weight_dtype, image, original_id_image=None, is_align_face=True):
    """
    Args:
        image: numpy rgb image, range [0, 255]
    """
    face_helper_1.clean_all()
    image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)  # (724, 502, 3)
    # get antelopev2 embedding
    face_info = app.get(image_bgr)
    if len(face_info) > 0:
        face_info = sorted(face_info, key=lambda x: (x['bbox'][2] - x['bbox'][0]) * (x['bbox'][3] - x['bbox'][1]))[
            -1
        ]  # only use the maximum face
        id_ante_embedding = face_info['embedding']  # (512,)
        face_kps = face_info['kps']
    else:
        id_ante_embedding = None
        face_kps = None

    # using facexlib to detect and align face
    face_helper_1.read_image(image_bgr)
    face_helper_1.get_face_landmarks_5(only_center_face=True)
    if face_kps is None:
        face_kps = face_helper_1.all_landmarks_5[0]
    face_helper_1.align_warp_face()
    if len(face_helper_1.cropped_faces) == 0:
        raise RuntimeError('facexlib align face fail')
    align_face = face_helper_1.cropped_faces[0]  # (512, 512, 3)  # RGB

    # incase insightface didn't detect face
    if id_ante_embedding is None:
        print('fail to detect face using insightface, extract embedding on align face')
        id_ante_embedding = face_helper_2.get_feat(align_face)

    id_ante_embedding = torch.from_numpy(id_ante_embedding).to(device, weight_dtype)  # torch.Size([512])
    if id_ante_embedding.ndim == 1:
        id_ante_embedding = id_ante_embedding.unsqueeze(0)  # torch.Size([1, 512])

    # parsing
    if is_align_face:
        input = img2tensor(align_face, bgr2rgb=True).unsqueeze(0) / 255.0  # torch.Size([1, 3, 512, 512])
        input = input.to(device)
        parsing_out = face_helper_1.face_parse(normalize(input, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]))[0]
        parsing_out = parsing_out.argmax(dim=1, keepdim=True)  # torch.Size([1, 1, 512, 512])
        bg_label = [0, 16, 18, 7, 8, 9, 14, 15]
        bg = sum(parsing_out == i for i in bg_label).bool()
        white_image = torch.ones_like(input)  # torch.Size([1, 3, 512, 512])
        # only keep the face features
        return_face_features_image = torch.where(bg, white_image, to_gray(input))  # torch.Size([1, 3, 512, 512])
        return_face_features_image_2 = torch.where(bg, white_image, input)  # torch.Size([1, 3, 512, 512])
    else:
        original_image_bgr = cv2.cvtColor(original_id_image, cv2.COLOR_RGB2BGR)
        input = img2tensor(original_image_bgr, bgr2rgb=True).unsqueeze(0) / 255.0  # torch.Size([1, 3, 512, 512])
        input = input.to(device)
        return_face_features_image = return_face_features_image_2 = input

    # transform img before sending to eva-clip-vit
    face_features_image = resize(return_face_features_image, clip_vision_model.image_size,
                                 InterpolationMode.BICUBIC)  # torch.Size([1, 3, 336, 336])
    face_features_image = normalize(face_features_image, eva_transform_mean, eva_transform_std)
    id_cond_vit, id_vit_hidden = clip_vision_model(face_features_image.to(weight_dtype), return_all_features=False, return_hidden=True, shuffle=False)  # torch.Size([1, 768]),  list(torch.Size([1, 577, 1024]))
    id_cond_vit_norm = torch.norm(id_cond_vit, 2, 1, True)
    id_cond_vit = torch.div(id_cond_vit, id_cond_vit_norm)

    id_cond = torch.cat([id_ante_embedding, id_cond_vit], dim=-1)  # torch.Size([1, 512]), torch.Size([1, 768])  ->  torch.Size([1, 1280])

    return id_cond, id_vit_hidden, return_face_features_image_2, face_kps    # torch.Size([1, 1280]), list(torch.Size([1, 577, 1024]))


def process_face_embeddings_infer(face_helper_1, clip_vision_model, face_helper_2, eva_transform_mean, eva_transform_std, app, device, weight_dtype, img_file_path, is_align_face=True):
    """
    Args:
        image: numpy rgb image, range [0, 255]
    """
    if isinstance(img_file_path, str):
        image = np.array(load_image(image=img_file_path).convert("RGB"))
    else:   
        image = np.array(ImageOps.exif_transpose(Image.fromarray(img_file_path)).convert("RGB"))
    
    image = resize_numpy_image_long(image, 1024)
    original_id_image = image

    id_cond, id_vit_hidden, align_crop_face_image, face_kps = process_face_embeddings(face_helper_1, clip_vision_model, face_helper_2, eva_transform_mean, eva_transform_std, app, device, weight_dtype, image, original_id_image, is_align_face)
    
    tensor = align_crop_face_image.cpu().detach()
    tensor = tensor.squeeze()
    tensor = tensor.permute(1, 2, 0)
    tensor = tensor.numpy() * 255
    tensor = tensor.astype(np.uint8)
    image = ImageOps.exif_transpose(Image.fromarray(tensor))

    return id_cond, id_vit_hidden, image, face_kps

def prepare_face_models(model_path, device, dtype):
    """
    Prepare all face models for the facial recognition task.

    Parameters:
    - model_path: Path to the directory containing model files.
    - device: The device (e.g., 'cuda', 'cpu') where models will be loaded.
    - dtype: Data type (e.g., torch.float32) for model inference.

    Returns:
    - face_helper_1: First face restoration helper.
    - face_helper_2: Second face restoration helper.
    - face_clip_model: CLIP model for face extraction.
    - eva_transform_mean: Mean value for image normalization.
    - eva_transform_std: Standard deviation value for image normalization.
    - face_main_model: Main face analysis model.
    """
    # get helper model
    face_helper_1 = FaceRestoreHelper(
        upscale_factor=1,
        face_size=512,
        crop_ratio=(1, 1),
        det_model='retinaface_resnet50',
        save_ext='png',
        device=device,
        model_rootpath=os.path.join(model_path, "face_encoder")
    )
    face_helper_1.face_parse = None
    face_helper_1.face_parse = init_parsing_model(model_name='bisenet', device=device, model_rootpath=os.path.join(model_path, "face_encoder"))
    face_helper_2 = insightface.model_zoo.get_model(f'{model_path}/face_encoder/models/antelopev2/glintr100.onnx', providers=['CUDAExecutionProvider'])
    face_helper_2.prepare(ctx_id=0)

    # get local facial extractor part 1
    model, _, _ = create_model_and_transforms('EVA02-CLIP-L-14-336', os.path.join(model_path, "face_encoder", "EVA02_CLIP_L_336_psz14_s6B.pt"), force_custom_clip=True)
    face_clip_model = model.visual
    eva_transform_mean = getattr(face_clip_model, 'image_mean', OPENAI_DATASET_MEAN)
    eva_transform_std = getattr(face_clip_model, 'image_std', OPENAI_DATASET_STD)
    if not isinstance(eva_transform_mean, (list, tuple)):
        eva_transform_mean = (eva_transform_mean,) * 3
    if not isinstance(eva_transform_std, (list, tuple)):
        eva_transform_std = (eva_transform_std,) * 3
    eva_transform_mean = eva_transform_mean
    eva_transform_std = eva_transform_std

    # get local facial extractor part 2
    face_main_model = FaceAnalysis(name='antelopev2', root=os.path.join(model_path, "face_encoder"), providers=['CUDAExecutionProvider'])
    face_main_model.prepare(ctx_id=0, det_size=(640, 640))
    
    # move face models to device
    face_helper_1.face_det.eval()
    face_helper_1.face_parse.eval()
    face_clip_model.eval()
    face_helper_1.face_det.to(device)
    face_helper_1.face_parse.to(device)
    face_clip_model.to(device, dtype=dtype)
    
    return face_helper_1, face_helper_2, face_clip_model, face_main_model, eva_transform_mean, eva_transform_std