# based on
# https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/stable_diffusion
import inspect
import time
from typing import List, Optional, Union
import PIL
import torch
import numpy as np

from tqdm.auto import tqdm
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer

# from ...models import AutoencoderKL, UNet2DConditionModel
# from ...pipeline_utils import DiffusionPipeline
# from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
# from .safety_checker import StableDiffusionSafetyChecker

from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.pipeline_utils import DiffusionPipeline
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.pipelines.stable_diffusion.safety_checker import (
    StableDiffusionSafetyChecker,
)


def preprocess(image):
    w, h = image.size
    w, h = map(lambda x: x - x % 64, (w, h))  # resize to integer multiple of 32
    image = image.resize((w, h), resample=PIL.Image.LANCZOS)
    image = np.array(image).astype(np.float32) / 255.0
    image = image[None].transpose(0, 3, 1, 2)
    image = torch.from_numpy(image)
    return 2.0 * image - 1.0


def preprocess_mask(mask):
    # mask = mask.convert("L")
    w, h = mask.size
    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
    mask = mask.resize((w // 8, h // 8), resample=PIL.Image.NEAREST)
    mask = np.array(mask).astype(np.float32) / 255.0
    mask = np.tile(mask, (4, 1, 1))
    mask = mask[None].transpose(0, 1, 2, 3)  # what does this step do?
    # mask = 1 - mask  # repaint white, keep black
    mask = torch.from_numpy(mask)
    return mask


def decode_image(
    latents,
    vae,
):
    latents = 1 / 0.18215 * latents
    image = vae.decode(latents.to(vae.dtype)).sample
    image = (image / 2 + 0.5).clamp(0, 1)
    image = image.cpu().permute(0, 2, 3, 1).numpy()
    return image


class StableDiffusionPipeline(DiffusionPipeline):
    def __init__(
        self,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        tokenizer: CLIPTokenizer,
        unet: UNet2DConditionModel,
        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
        feature_extractor: CLIPFeatureExtractor,
        safety_checker: StableDiffusionSafetyChecker,
    ):
        super().__init__()
        scheduler = scheduler.set_format("pt")
        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            unet=unet,
            scheduler=scheduler,
            feature_extractor=feature_extractor,
            safety_checker=safety_checker,
        )

    @torch.no_grad()
    def __call__(
        self,
        prompt: Union[str, List[str]],
        mode: str = "prompt",
        height: Optional[int] = 512,
        width: Optional[int] = 512,
        num_inference_steps: Optional[int] = 50,
        guidance_scale: Optional[float] = 7.5,
        eta: Optional[float] = 0.0,
        generator: Optional[torch.Generator] = None,
        strength: float = 0.8,
        init_image: Union[torch.FloatTensor, PIL.Image.Image] = None,
        return_intermediates: bool = False,
        seed: Optional[int] = 2147483647,
        attention_slice: Optional[Union[str, int]] = "auto",
        mask_image: Union[torch.FloatTensor, PIL.Image.Image] = None,
        **kwargs,
    ):
        if seed is not None:
            generator = torch.Generator(device=self.device).manual_seed(seed)

        if attention_slice:
            if attention_slice == "auto":
                self.unet.set_attention_slice(self.unet.config.attention_head_dim // 2)
            else:
                self.unet.set_attention_slice(attention_slice)
        start_time = time.time()
        if isinstance(prompt, str):
            batch_size = 1
        elif isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            raise ValueError(
                f"`prompt` has to be of type `str` or `list` but is {type(prompt)}"
            )

        # set timesteps
        accepts_offset = "offset" in set(
            inspect.signature(self.scheduler.set_timesteps).parameters.keys()
        )
        extra_set_kwargs = {}
        offset = 0
        if accepts_offset:
            offset = 1
            extra_set_kwargs["offset"] = 1

        self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)

        if mode == "prompt":
            if height % 8 != 0 or width % 8 != 0:
                raise ValueError(
                    f"`height` and `width` have to be divisible by 8 but are {height} and {width}."
                )
            # get the intial random noise
            latents = torch.randn(
                (batch_size, self.unet.in_channels, height // 8, width // 8),
                generator=generator,
                device=self.device,
            )
            t_start = 0
        elif mode == "image":
            if not init_image:
                raise ValueError(
                    "If `mode` is 'image' you have to provide an `init_image`."
                )
            if strength < 0 or strength > 1:
                raise ValueError(
                    f"The value of strength should in [0.0, 1.0] but is {strength}"
                )
            if not isinstance(init_image, torch.FloatTensor):
                init_image = preprocess(init_image)
            init_image = init_image.to(self.device)
            # encode the init image into latents and scale the latents
            init_latent_dist = self.vae.encode(init_image.to(self.device)).latent_dist
            init_latents = init_latent_dist.sample(generator=generator)
            init_latents = 0.18215 * init_latents

            # expand init_latents for batch_size
            init_latents = torch.cat([init_latents] * batch_size)
            init_latents_orig = init_latents

            # handle mask if provided
            if mode == "image" and mask_image is not None:
                if not isinstance(mask_image, torch.FloatTensor):
                    mask_image = preprocess_mask(mask_image)
                mask_image = mask_image.to(self.device)
                mask = torch.cat([mask_image] * batch_size)

                # check sizes
                if not mask.shape == init_latents.shape:
                    raise ValueError("The mask and init_image should be the same size!")

            # get the original timestep using init_timestep
            offset = self.scheduler.config.get("steps_offset", 0)
            init_timestep = int(num_inference_steps * strength) + offset
            init_timestep = min(init_timestep, num_inference_steps)
            if isinstance(self.scheduler, LMSDiscreteScheduler):
                timesteps = torch.tensor(
                    [num_inference_steps - init_timestep] * batch_size, dtype=torch.long,
                    device=self.device)
            else:
                timesteps = self.scheduler.timesteps[-init_timestep]
                timesteps = torch.tensor(
                    [timesteps] * batch_size,
                    dtype=torch.long,
                    device=self.device)

            # add noise to latents using the timesteps
            noise = torch.randn(
                init_latents.shape, generator=generator, device=self.device
            )
            init_latents = self.scheduler.add_noise(init_latents, noise, timesteps).to(self.device)
            latents = init_latents
            t_start = max(num_inference_steps - init_timestep + offset, 0)

        # get prompt text embeddings
        text_input = self.tokenizer(
            prompt,
            padding="max_length",
            max_length=self.tokenizer.model_max_length,
            truncation=True,
            return_tensors="pt",
        )
        text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]

        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        do_classifier_free_guidance = guidance_scale > 1.0
        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance:
            max_length = text_input.input_ids.shape[-1]
            uncond_input = self.tokenizer(
                [""] * batch_size,
                padding="max_length",
                max_length=max_length,
                return_tensors="pt",
            )
            uncond_embeddings = self.text_encoder(
                uncond_input.input_ids.to(self.device)
            )[0]

            # For classifier free guidance, we need to do two forward passes.
            # Here we concatenate the unconditional and text embeddings into a single batch
            # to avoid doing two forward passes
            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])

        # if we use LMSDiscreteScheduler, let's make sure latents are mulitplied by sigmas
        # if isinstance(self.scheduler, LMSDiscreteScheduler):
        #     latents = latents * self.scheduler.sigmas[0]

        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]
        accepts_eta = "eta" in set(
            inspect.signature(self.scheduler.step).parameters.keys()
        )
        extra_step_kwargs = {}
        if accepts_eta:
            extra_step_kwargs["eta"] = eta

        intermediate_images = []
        for i, t in tqdm(enumerate(self.scheduler.timesteps[t_start:])):
            # expand the latents if we are doing classifier free guidance
            latent_model_input = (
                torch.cat([latents] * 2) if do_classifier_free_guidance else latents
            )
            if isinstance(self.scheduler, LMSDiscreteScheduler):
                sigma = self.scheduler.sigmas[i]
                latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)

            # predict the noise residual
            noise_pred = self.unet(
                latent_model_input, t, encoder_hidden_states=text_embeddings
            )["sample"]

            # perform guidance
            if do_classifier_free_guidance:
                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                noise_pred = noise_pred_uncond + guidance_scale * (
                    noise_pred_text - noise_pred_uncond
                )

            # compute the previous noisy sample x_t -> x_t-1
            if isinstance(self.scheduler, LMSDiscreteScheduler):
                latents = self.scheduler.step(
                    noise_pred, i, latents, **extra_step_kwargs
                )["prev_sample"]
            else:
                latents = self.scheduler.step(
                    noise_pred, t, latents, **extra_step_kwargs
                )["prev_sample"]

            if mode == "image" and mask_image is not None:
                init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, t)
                latents = (init_latents_proper * mask) + (latents * (1 - mask))

            if return_intermediates:
                decoded_image = decode_image(latents, self.vae)
                intermediate_images.append(self.numpy_to_pil(decoded_image))

        # scale and decode the image latents with vae
        has_nsfw_concept = None
        if return_intermediates:
            image = intermediate_images[-1]
        else:
            image = decode_image(latents, self.vae)
            safety_cheker_input = self.feature_extractor(
                self.numpy_to_pil(image), return_tensors="pt"
            ).to(self.device)
            image, has_nsfw_concept = self.safety_checker(
                images=np.asarray(image), clip_input=safety_cheker_input.pixel_values
            )
            image = self.numpy_to_pil(image)

        return {
            "images": image,
            "nsfw_content_detected": has_nsfw_concept,
            "intermediates": intermediate_images,
            "time": time.time() - start_time,
        }
