from typing import Union
import os
import cv2
import numpy as np
import torch
import torch.nn as nn
from PIL import Image
from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline, StableDiffusionXLInpaintPipeline
from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput

from huggingface_hub import hf_hub_download, snapshot_download
from safetensors.torch import load_file
from torchvision.transforms import InterpolationMode
from torchvision.transforms.functional import normalize, resize

import insightface
from basicsr.utils import img2tensor, tensor2img
from facexlib.parsing import init_parsing_model
from facexlib.utils.face_restoration_helper import FaceRestoreHelper
from insightface.app import FaceAnalysis

from eva_clip import create_model_and_transforms
from eva_clip.constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD
from encoders_transformer import IDFormer, IDEncoder
from modules.errors import log


debug = log.trace if os.environ.get('SD_PULID_DEBUG', None) is not None else lambda *args, **kwargs: None


class StableDiffusionXLPuLIDPipeline:
    def __init__(self,
                 pipe: Union[StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline, StableDiffusionXLInpaintPipeline],
                 device: torch.device,
                 dtype: torch.dtype=None,
                 providers: list=None,
                 offload: bool=True,
                 sampler=None,
                 cache_dir=None,
                 sdp: bool=True,
                 version: str='v1.1',
                ):
        super().__init__()
        self.device = device
        self.dtype = dtype or torch.float16
        self.pipe = pipe
        self.cache_dir = cache_dir
        self.offload = offload
        self.sdp = sdp
        self.version = version
        self.folder = 'models--ToTheBeginning--PuLID'
        debug(f'PulID init: device={self.device} dtype={self.dtype} dir={self.cache_dir} offload={self.offload} sdp={self.sdp} version={self.version}')

        # self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(self.pipe.scheduler.config)
        self.hack_unet_attn_layers(self.pipe.unet)
        if self.version == 'v1.1':
            self.id_adapter = IDFormer().to(self.device, self.dtype)
        else:
            self.id_adapter = IDEncoder().to(self.device, self.dtype)
        debug(f'PulID load: adapter={self.id_adapter.__class__.__name__}')
        self.providers = providers or ['CUDAExecutionProvider', 'CPUExecutionProvider']
        debug(f'PulID load: providers={self.providers}')

        # preprocessors
        # face align and parsing
        self.face_helper = FaceRestoreHelper(
            upscale_factor=1,
            face_size=512,
            crop_ratio=(1, 1),
            det_model='retinaface_resnet50',
            save_ext='png',
            device=self.device,
        )
        self.face_helper.face_parse = init_parsing_model(model_name='bisenet', device=self.device)
        debug(f'PulID load: facehelper={self.face_helper.__class__.__name__}')

        # clip-vit backbone
        eva_precision = 'fp16' if self.dtype == torch.float16 or self.dtype == torch.bfloat16 else 'fp32'
        eva_model, _, _ = create_model_and_transforms('EVA02-CLIP-L-14-336', 'eva_clip', force_custom_clip=True, precision=eva_precision, device=self.device)
        self.clip_vision_model = eva_model.visual.to(dtype=self.dtype)
        debug(f'PulID load: evaclip={self.clip_vision_model.__class__.__name__} precision={eva_precision}')
        eva_transform_mean = getattr(self.clip_vision_model, 'image_mean', OPENAI_DATASET_MEAN)
        eva_transform_std = getattr(self.clip_vision_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
        self.eva_transform_mean = eva_transform_mean
        self.eva_transform_std = eva_transform_std

        # antelopev2
        local_dir = os.path.join(self.cache_dir, self.folder, 'models', 'antelopev2')
        _loc = snapshot_download('DIAMONIK7777/antelopev2', local_dir=local_dir)
        self.app = FaceAnalysis(
            name='antelopev2',
            root=os.path.join(self.cache_dir, self.folder),
            providers=self.providers,
        )
        debug(f'PulID load: faceanalysis={_loc}')
        self.app.prepare(ctx_id=0, det_size=(640, 640))
        self.handler_ante = insightface.model_zoo.get_model(os.path.join(local_dir, 'glintr100.onnx'))
        self.handler_ante.prepare(ctx_id=0)
        debug(f'PulID load: handler={self.handler_ante.__class__.__name__}')

        self.load_pretrain()

        # other configs
        self.debug_img_list = []

        # karras schedule related code, borrow from lllyasviel/Omost
        linear_start = 0.00085
        linear_end = 0.012
        timesteps = 1000
        betas = torch.linspace(linear_start**0.5, linear_end**0.5, timesteps, dtype=torch.float64) ** 2
        alphas = 1.0 - betas
        alphas_cumprod = torch.tensor(np.cumprod(alphas, axis=0), dtype=torch.float32)

        self.sigmas = ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5
        self.log_sigmas = self.sigmas.log()
        self.sigma_data = 1.0

        # default scheduler
        if sampler is not None:
            self.sampler = sampler
        else:
            from scripts.pulid import sampling
            self.sampler = sampling.sample_dpmpp_sde

    @property
    def sigma_min(self):
        return self.sigmas[0]

    @property
    def sigma_max(self):
        return self.sigmas[-1]

    def timestep(self, sigma):
        log_sigma = sigma.log()
        dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None]
        return dists.abs().argmin(dim=0).view(sigma.shape).to(sigma.device)

    def get_sigmas_karras(self, n, rho=7.0):
        ramp = torch.linspace(0, 1, n)
        min_inv_rho = self.sigma_min ** (1 / rho)
        max_inv_rho = self.sigma_max ** (1 / rho)
        sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
        return torch.cat([sigmas, sigmas.new_zeros([1])])

    def hack_unet_attn_layers(self, unet):
        if self.sdp:
            from attention_processor import AttnProcessor2_0 as AttnProcessor
            from attention_processor import IDAttnProcessor2_0 as IDAttnProcessor
        else:
            from attention_processor import AttnProcessor
            from attention_processor import IDAttnProcessor
        id_adapter_attn_procs = {}
        for name, _ in unet.attn_processors.items():
            cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
            if name.startswith("mid_block"):
                hidden_size = unet.config.block_out_channels[-1]
            elif name.startswith("up_blocks"):
                block_id = int(name[len("up_blocks.")])
                hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
            elif name.startswith("down_blocks"):
                block_id = int(name[len("down_blocks.")])
                hidden_size = unet.config.block_out_channels[block_id]
            else:
                hidden_size = None
            if cross_attention_dim is not None:
                id_adapter_attn_procs[name] = IDAttnProcessor(
                    hidden_size=hidden_size,
                    cross_attention_dim=cross_attention_dim,
                ).to(unet.device, unet.dtype)
            else:
                id_adapter_attn_procs[name] = AttnProcessor()
        debug(f'PulID attention: cls={IDAttnProcessor} std={AttnProcessor} len={len(id_adapter_attn_procs.keys())}')
        unet.set_attn_processor(id_adapter_attn_procs)
        self.id_adapter_attn_layers = nn.ModuleList(unet.attn_processors.values())

    def load_pretrain(self):
        if self.version == 'v1.1':
            ckpt_path = hf_hub_download('guozinan/PuLID', 'pulid_v1.1.safetensors', local_dir=os.path.join(self.cache_dir, self.folder))
            state_dict = load_file(ckpt_path)
        else:
            ckpt_path = hf_hub_download('guozinan/PuLID', 'pulid_v1.bin', local_dir=os.path.join(self.cache_dir, self.folder))
            state_dict = torch.load(ckpt_path, map_location="cpu")
        debug(f'PulID load: fn="{ckpt_path}"')
        state_dict_dict = {}
        for k, v in state_dict.items():
            module = k.split('.')[0]
            state_dict_dict.setdefault(module, {})
            new_k = k[len(module) + 1 :]
            state_dict_dict[module][new_k] = v.to(self.dtype)

        for module in state_dict_dict:
            getattr(self, module).load_state_dict(state_dict_dict[module], strict=True)

    def to_gray(self, 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 get_id_embedding(self, image_list):
        """
        Args:
            image in image_list: numpy rgb image, range [0, 255]
        """
        id_cond_list = []
        id_vit_hidden_list = []
        self.face_helper.face_det.to(self.device)
        self.clip_vision_model.to(self.device)
        for _ii, image in enumerate(image_list):
            self.face_helper.clean_all()
            image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
            # get antelopev2 embedding
            face_info = self.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']
                self.debug_img_list.append(image[int(face_info['bbox'][1]) : int(face_info['bbox'][3]), int(face_info['bbox'][0]) : int(face_info['bbox'][2])])
            else:
                id_ante_embedding = None

            # using facexlib to detect and align face
            self.face_helper.read_image(image_bgr)
            self.face_helper.get_face_landmarks_5(only_center_face=True)
            self.face_helper.align_warp_face()
            if len(self.face_helper.cropped_faces) == 0:
                raise RuntimeError('facexlib align face fail')
            align_face = self.face_helper.cropped_faces[0]
            # incase insightface didn't detect face
            if id_ante_embedding is None:
                id_ante_embedding = self.handler_ante.get_feat(align_face)

            id_ante_embedding = torch.from_numpy(id_ante_embedding).to(self.device)
            if id_ante_embedding.ndim == 1:
                id_ante_embedding = id_ante_embedding.unsqueeze(0)

            # parsing
            input = img2tensor(align_face, bgr2rgb=True).unsqueeze(0) / 255.0 # pylint: disable=redefined-builtin
            input = input.to(self.device)
            parsing_out = self.face_helper.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)
            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)
            # only keep the face features
            face_features_image = torch.where(bg, white_image, self.to_gray(input))
            self.debug_img_list.append(tensor2img(face_features_image, rgb2bgr=False))

            # transform img before sending to eva-clip-vit
            face_features_image = resize(face_features_image, self.clip_vision_model.image_size, InterpolationMode.BICUBIC)
            face_features_image = normalize(face_features_image, self.eva_transform_mean, self.eva_transform_std).to(self.dtype)
            id_cond_vit, id_vit_hidden = self.clip_vision_model(face_features_image, return_all_features=False, return_hidden=True, shuffle=False)
            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)

            id_cond_list.append(id_cond)
            id_vit_hidden_list.append(id_vit_hidden)

        self.id_adapter.to(self.device)
        id_uncond = torch.zeros_like(id_cond_list[0]).to(self.dtype)
        id_vit_hidden_uncond = []
        for layer_idx in range(0, len(id_vit_hidden_list[0])):
            id_vit_hidden_uncond.append(torch.zeros_like(id_vit_hidden_list[0][layer_idx]).to(self.dtype))

        id_cond = torch.stack(id_cond_list, dim=1).to(self.dtype)
        id_vit_hidden = id_vit_hidden_list[0]
        for i in range(1, len(image_list)):
            for j, x in enumerate(id_vit_hidden_list[i]):
                id_vit_hidden[j] = torch.cat([id_vit_hidden[j], x], dim=1).to(self.dtype)
        id_embedding = self.id_adapter(id_cond, id_vit_hidden)
        uncond_id_embedding = self.id_adapter(id_uncond, id_vit_hidden_uncond)

        if self.offload:
            self.face_helper.face_det.to('cpu')
            self.id_adapter.to('cpu')
            self.clip_vision_model.to('cpu')

        # return id_embedding
        debug(f'PulID embedding: cond={id_embedding.shape} uncond={uncond_id_embedding.shape}')
        return uncond_id_embedding, id_embedding

    def set_progress_bar_config(self, bar_format: str = None, ncols: int = 80, colour: str = None):
        import functools
        from tqdm.auto import trange as trange_orig
        import pulid_sampling
        pulid_sampling.trange = functools.partial(trange_orig, bar_format=bar_format, ncols=ncols, colour=colour)

    def sample(self, x, sigma, **extra_args):
        t = self.timestep(sigma)
        x_ddim_space = x / (sigma[:, None, None, None] ** 2 + self.sigma_data**2) ** 0.5
        cfg_scale = extra_args['cfg_scale']
        # debug(f'PulID sample start: step={self.step+1} x={x.shape} dtype={x.dtype} timestep={t.item()} sigma={sigma.shape} cfg={cfg_scale} args={extra_args.keys()}')
        eps_positive = self.pipe.unet(x_ddim_space, t, return_dict=False, **extra_args['positive'])[0]
        eps_negative = self.pipe.unet(x_ddim_space, t, return_dict=False, **extra_args['negative'])[0]
        noise_pred = eps_negative + cfg_scale * (eps_positive - eps_negative)
        latent = x - noise_pred * sigma[:, None, None, None]
        if self.callback_on_step_end is not None:
            self.step += 1
            self.callback_on_step_end(self.pipe, step=self.step, timestep=t, kwargs={ 'latents': latent })
        # debug(f'PulID sample end:   step={self.step} x={latent.shape} dtype={x.dtype} min={torch.amin(latent)} max={torch.amax(latent)}')
        return latent

    def init_latent(self, seed, size, image, mask_image, strength, width, height): # pylint: disable=unused-argument
        # standard txt2img will full noise
        noise = torch.randn((size[0], 4, size[1] // 8, size[2] // 8), device="cpu", generator=torch.manual_seed(seed))
        noise = noise.to(dtype=self.pipe.unet.dtype, device=self.device)
        if strength > 0 and image is not None:
            image = self.pipe.image_processor.preprocess(image)
            if mask_image is not None:  # Inpaint
                latents = self.pipe.prepare_latents(1,  # batch_size,
                                                    self.pipe.vae.config.latent_channels,  # num_channels_latents
                                                    height,
                                                    width,
                                                    noise.dtype,
                                                    noise.device,
                                                    None,  # generator
                                                    latents=None,
                                                    image=image,
                                                    timestep=1000,
                                                    is_strength_max=False,
                                                    add_noise=False,
                                                    return_noise=False,
                                                    return_image_latents=False,
                                                    )
                latents = latents[0]
                debug(f'PulID noise: op=inpaint latent={latents.shape} image={image} mask={mask_image} dtype={latents.dtype}')
            else:  # img2img
                latents = self.pipe.prepare_latents(image,
                                                    None,  # timestep (not needed)
                                                    1,  # batch_size
                                                    1,  # num_images_per_prompt
                                                    noise.dtype,
                                                    noise.device,
                                                    None,  # generator
                                                    False,  # add_noise
                                                    )
                debug(f'PulID noise: op=img2img latent={latents.shape} image={image} dtype={latents.dtype}')
        else:
            latents = torch.zeros_like(noise)
            debug(f'PulID noise: op=txt2img latent={latents.shape} dtype={latents.dtype}')
        return latents, noise

    def __call__(
        self,
        prompt: str='',
        negative_prompt: str='',
        width: int=1024,
        height: int=1024,
        guidance_scale: float=7.0,
        num_inference_steps: int=50,
        seed: int=-1,
        image: np.ndarray=None,
        mask_image: np.ndarray=None,
        strength: float=0.3,
        id_embedding=None,
        uncond_id_embedding=None,
        id_scale: float=1.0,
        output_type: str='pil',
        callback_on_step_end=None,
    ):
        debug(f'PulID call: width={width} height={height} cfg={guidance_scale} steps={num_inference_steps} seed={seed} strength={strength} id_scale={id_scale} output={output_type}')
        self.step = 0 # pylint: disable=attribute-defined-outside-init
        self.callback_on_step_end = callback_on_step_end # pylint: disable=attribute-defined-outside-init
        if isinstance(image, list) and len(image) > 0 and isinstance(image[0], Image.Image):
            if image[0].width != width or image[0].height != height: # override width/height if different
                width, height = image[0].width, image[0].height
        size = (1, height, width)
        # sigmas
        sigmas = self.get_sigmas_karras(num_inference_steps).to(self.device)
        if image is not None and strength > 0:
            _timesteps, num_inference_steps = self.pipe.get_timesteps(num_inference_steps, strength, self.device, None)  # denoising_start disabled
            sigmas = sigmas[-(num_inference_steps + 1):].to(self.device) # shorten sigmas in i2i
        debug(f'PulID sigmas: sigmas={sigmas.shape} dtype={sigmas.dtype}')

        # latents
        latent, noise = self.init_latent(seed, size, image, mask_image, strength, width, height)
        noisy_latent = latent + noise * sigmas[0].to(noise)
        debug(f'PulID noisy: latent={noisy_latent.shape} dtype={noisy_latent.dtype}')

        (
            prompt_embeds,
            negative_prompt_embeds,
            pooled_prompt_embeds,
            negative_pooled_prompt_embeds,
        ) = self.pipe.encode_prompt(
            prompt=prompt,
            negative_prompt=negative_prompt,
        )

        add_time_ids = list((size[1], size[2]) + (0, 0) + (size[1], size[2]))
        add_time_ids = torch.tensor([add_time_ids], dtype=self.pipe.unet.dtype, device=self.device)
        add_neg_time_ids = add_time_ids.clone()

        sampler_kwargs = dict(
            cfg_scale=guidance_scale,
            positive=dict(
                encoder_hidden_states=prompt_embeds,
                added_cond_kwargs={"text_embeds": pooled_prompt_embeds, "time_ids": add_time_ids},
                cross_attention_kwargs={'id_embedding': id_embedding, 'id_scale': id_scale},
            ),
            negative=dict(
                encoder_hidden_states=negative_prompt_embeds,
                added_cond_kwargs={"text_embeds": negative_pooled_prompt_embeds, "time_ids": add_neg_time_ids},
                cross_attention_kwargs={'id_embedding': uncond_id_embedding, 'id_scale': id_scale},
            ),
        )
        if mask_image is not None:
            latent_mask = torch.Tensor(np.asarray(mask_image.convert("L").resize((noisy_latent.shape[-1], noisy_latent.shape[-2])))).reshape((noisy_latent.shape[-2], noisy_latent.shape[-1]))
            latent_mask /= latent_mask.max()
            mask_args = dict(
                latent=latent,
                latent_mask=latent_mask,
                noise=noise,
                sigmas=sigmas,
            )
        else:
            mask_args = None

        # actual sampling loop
        latents = self.sampler(self.sample, noisy_latent, sigmas, extra_args=sampler_kwargs, disable=False, mask_args=mask_args)

        # process output
        latents = latents.to(dtype=self.pipe.vae.dtype, device=self.device)
        debug(f'PulID output: latent={latents.shape} dtype={latents.dtype}')
        if output_type == 'latent':
            images = self.pipe.image_processor.postprocess(latents, output_type='latent')
        elif output_type == 'np':
            images = self.pipe.image_processor.postprocess(latents, output_type='np')
        else:
            latents = latents / self.pipe.vae.config.scaling_factor
            images = self.pipe.vae.decode(latents).sample
            images = self.pipe.image_processor.postprocess(images, output_type='pil')
        debug(f'PulID output: type={type(images)} images={images.shape if hasattr(images, "shape") else images}')
        return StableDiffusionXLPipelineOutput(images)


class StableDiffusionXLPuLIDPipelineImage(StableDiffusionXLPuLIDPipeline):
    def __init__(self, pipe: StableDiffusionXLPipeline, device: torch.device, sampler=None, cache_dir=None): # pylint: disable=useless-parent-delegation
        super().__init__(pipe, device, sampler, cache_dir)
        # we dont do anything special here, just having different class so task-type can be detected/assigned


class StableDiffusionXLPuLIDPipelineInpaint(StableDiffusionXLPuLIDPipeline):
    def __init__(self, pipe: StableDiffusionXLPipeline, device: torch.device, sampler=None, cache_dir=None): # pylint: disable=useless-parent-delegation
        super().__init__(pipe, device, sampler, cache_dir)
        # we dont do anything special here, just having different class so task-type can be detected/assigned
