import inspect
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNet2DConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    deprecate,
    randn_tensor,
)


EXAMPLE_DOC_STRING = """
    Examples:
        ```
        from io import BytesIO

        import requests
        import torch
        from diffusers import DiffusionPipeline
        from PIL import Image
        from transformers import CLIPFeatureExtractor, CLIPModel

        feature_extractor = CLIPFeatureExtractor.from_pretrained(
            "laion/CLIP-ViT-B-32-laion2B-s34B-b79K"
        )
        clip_model = CLIPModel.from_pretrained(
            "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", torch_dtype=torch.float16
        )


        guided_pipeline = DiffusionPipeline.from_pretrained(
            "CompVis/stable-diffusion-v1-4",
            # custom_pipeline="clip_guided_stable_diffusion",
            custom_pipeline="/home/njindal/diffusers/examples/community/clip_guided_stable_diffusion.py",
            clip_model=clip_model,
            feature_extractor=feature_extractor,
            torch_dtype=torch.float16,
        )
        guided_pipeline.enable_attention_slicing()
        guided_pipeline = guided_pipeline.to("cuda")

        prompt = "fantasy book cover, full moon, fantasy forest landscape, golden vector elements, fantasy magic, dark light night, intricate, elegant, sharp focus, illustration, highly detailed, digital painting, concept art, matte, art by WLOP and Artgerm and Albert Bierstadt, masterpiece"

        url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"

        response = requests.get(url)
        init_image = Image.open(BytesIO(response.content)).convert("RGB")

        image = guided_pipeline(
            prompt=prompt,
            num_inference_steps=30,
            image=init_image,
            strength=0.75,
            guidance_scale=7.5,
            clip_guidance_scale=100,
            num_cutouts=4,
            use_cutouts=False,
        ).images[0]
        display(image)
        ```
"""


def preprocess(image, w, h):
    if isinstance(image, torch.Tensor):
        return image
    elif isinstance(image, PIL.Image.Image):
        image = [image]

    if isinstance(image[0], PIL.Image.Image):
        image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
        image = np.concatenate(image, axis=0)
        image = np.array(image).astype(np.float32) / 255.0
        image = image.transpose(0, 3, 1, 2)
        image = 2.0 * image - 1.0
        image = torch.from_numpy(image)
    elif isinstance(image[0], torch.Tensor):
        image = torch.cat(image, dim=0)
    return image


class MakeCutouts(nn.Module):
    def __init__(self, cut_size, cut_power=1.0):
        super().__init__()

        self.cut_size = cut_size
        self.cut_power = cut_power

    def forward(self, pixel_values, num_cutouts):
        sideY, sideX = pixel_values.shape[2:4]
        max_size = min(sideX, sideY)
        min_size = min(sideX, sideY, self.cut_size)
        cutouts = []
        for _ in range(num_cutouts):
            size = int(torch.rand([]) ** self.cut_power * (max_size - min_size) + min_size)
            offsetx = torch.randint(0, sideX - size + 1, ())
            offsety = torch.randint(0, sideY - size + 1, ())
            cutout = pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
            cutouts.append(F.adaptive_avg_pool2d(cutout, self.cut_size))
        return torch.cat(cutouts)


def spherical_dist_loss(x, y):
    x = F.normalize(x, dim=-1)
    y = F.normalize(y, dim=-1)
    return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)


def set_requires_grad(model, value):
    for param in model.parameters():
        param.requires_grad = value


class CLIPGuidedStableDiffusion(DiffusionPipeline):
    """CLIP guided stable diffusion based on the amazing repo by @crowsonkb and @Jack000
    - https://github.com/Jack000/glid-3-xl
    - https://github.dev/crowsonkb/k-diffusion
    """

    def __init__(
        self,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        clip_model: CLIPModel,
        tokenizer: CLIPTokenizer,
        unet: UNet2DConditionModel,
        scheduler: Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler],
        feature_extractor: CLIPFeatureExtractor,
    ):
        super().__init__()
        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            clip_model=clip_model,
            tokenizer=tokenizer,
            unet=unet,
            scheduler=scheduler,
            feature_extractor=feature_extractor,
        )

        self.normalize = transforms.Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std)
        self.cut_out_size = (
            feature_extractor.size
            if isinstance(feature_extractor.size, int)
            else feature_extractor.size["shortest_edge"]
        )
        self.make_cutouts = MakeCutouts(self.cut_out_size)

        set_requires_grad(self.text_encoder, False)
        set_requires_grad(self.clip_model, False)

    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
        if slice_size == "auto":
            # half the attention head size is usually a good trade-off between
            # speed and memory
            slice_size = self.unet.config.attention_head_dim // 2
        self.unet.set_attention_slice(slice_size)

    def disable_attention_slicing(self):
        self.enable_attention_slicing(None)

    def freeze_vae(self):
        set_requires_grad(self.vae, False)

    def unfreeze_vae(self):
        set_requires_grad(self.vae, True)

    def freeze_unet(self):
        set_requires_grad(self.unet, False)

    def unfreeze_unet(self):
        set_requires_grad(self.unet, True)

    def get_timesteps(self, num_inference_steps, strength, device):
        # get the original timestep using init_timestep
        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)

        t_start = max(num_inference_steps - init_timestep, 0)
        timesteps = self.scheduler.timesteps[t_start:]

        return timesteps, num_inference_steps - t_start

    def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
        if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
            raise ValueError(
                f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
            )

        image = image.to(device=device, dtype=dtype)

        batch_size = batch_size * num_images_per_prompt
        if isinstance(generator, list) and len(generator) != batch_size:
            raise ValueError(
                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
            )

        if isinstance(generator, list):
            init_latents = [
                self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
            ]
            init_latents = torch.cat(init_latents, dim=0)
        else:
            init_latents = self.vae.encode(image).latent_dist.sample(generator)

        init_latents = self.vae.config.scaling_factor * init_latents

        if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
            # expand init_latents for batch_size
            deprecation_message = (
                f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
                " images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
                " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
                " your script to pass as many initial images as text prompts to suppress this warning."
            )
            deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
            additional_image_per_prompt = batch_size // init_latents.shape[0]
            init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)
        elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
            raise ValueError(
                f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
            )
        else:
            init_latents = torch.cat([init_latents], dim=0)

        shape = init_latents.shape
        noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)

        # get latents
        init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
        latents = init_latents

        return latents

    @torch.enable_grad()
    def cond_fn(
        self,
        latents,
        timestep,
        index,
        text_embeddings,
        noise_pred_original,
        text_embeddings_clip,
        clip_guidance_scale,
        num_cutouts,
        use_cutouts=True,
    ):
        latents = latents.detach().requires_grad_()

        latent_model_input = self.scheduler.scale_model_input(latents, timestep)

        # predict the noise residual
        noise_pred = self.unet(latent_model_input, timestep, encoder_hidden_states=text_embeddings).sample

        if isinstance(self.scheduler, (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)):
            alpha_prod_t = self.scheduler.alphas_cumprod[timestep]
            beta_prod_t = 1 - alpha_prod_t
            # compute predicted original sample from predicted noise also called
            # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
            pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)

            fac = torch.sqrt(beta_prod_t)
            sample = pred_original_sample * (fac) + latents * (1 - fac)
        elif isinstance(self.scheduler, LMSDiscreteScheduler):
            sigma = self.scheduler.sigmas[index]
            sample = latents - sigma * noise_pred
        else:
            raise ValueError(f"scheduler type {type(self.scheduler)} not supported")

        sample = 1 / self.vae.config.scaling_factor * sample
        image = self.vae.decode(sample).sample
        image = (image / 2 + 0.5).clamp(0, 1)

        if use_cutouts:
            image = self.make_cutouts(image, num_cutouts)
        else:
            image = transforms.Resize(self.cut_out_size)(image)
        image = self.normalize(image).to(latents.dtype)

        image_embeddings_clip = self.clip_model.get_image_features(image)
        image_embeddings_clip = image_embeddings_clip / image_embeddings_clip.norm(p=2, dim=-1, keepdim=True)

        if use_cutouts:
            dists = spherical_dist_loss(image_embeddings_clip, text_embeddings_clip)
            dists = dists.view([num_cutouts, sample.shape[0], -1])
            loss = dists.sum(2).mean(0).sum() * clip_guidance_scale
        else:
            loss = spherical_dist_loss(image_embeddings_clip, text_embeddings_clip).mean() * clip_guidance_scale

        grads = -torch.autograd.grad(loss, latents)[0]

        if isinstance(self.scheduler, LMSDiscreteScheduler):
            latents = latents.detach() + grads * (sigma**2)
            noise_pred = noise_pred_original
        else:
            noise_pred = noise_pred_original - torch.sqrt(beta_prod_t) * grads
        return noise_pred, latents

    @torch.no_grad()
    def __call__(
        self,
        prompt: Union[str, List[str]],
        height: Optional[int] = 512,
        width: Optional[int] = 512,
        image: Union[torch.FloatTensor, PIL.Image.Image] = None,
        strength: float = 0.8,
        num_inference_steps: Optional[int] = 50,
        guidance_scale: Optional[float] = 7.5,
        num_images_per_prompt: Optional[int] = 1,
        eta: float = 0.0,
        clip_guidance_scale: Optional[float] = 100,
        clip_prompt: Optional[Union[str, List[str]]] = None,
        num_cutouts: Optional[int] = 4,
        use_cutouts: Optional[bool] = True,
        generator: Optional[torch.Generator] = None,
        latents: Optional[torch.FloatTensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
    ):
        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)}")

        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 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]
        # duplicate text embeddings for each generation per prompt
        text_embeddings = text_embeddings.repeat_interleave(num_images_per_prompt, dim=0)

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

        self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
        # Some schedulers like PNDM have timesteps as arrays
        # It's more optimized to move all timesteps to correct device beforehand
        self.scheduler.timesteps.to(self.device)

        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, self.device)
        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)

        # Preprocess image
        image = preprocess(image, width, height)
        latents = self.prepare_latents(
            image, latent_timestep, batch_size, num_images_per_prompt, text_embeddings.dtype, self.device, generator
        )

        if clip_guidance_scale > 0:
            if clip_prompt is not None:
                clip_text_input = self.tokenizer(
                    clip_prompt,
                    padding="max_length",
                    max_length=self.tokenizer.model_max_length,
                    truncation=True,
                    return_tensors="pt",
                ).input_ids.to(self.device)
            else:
                clip_text_input = text_input.input_ids.to(self.device)
            text_embeddings_clip = self.clip_model.get_text_features(clip_text_input)
            text_embeddings_clip = text_embeddings_clip / text_embeddings_clip.norm(p=2, dim=-1, keepdim=True)
            # duplicate text embeddings clip for each generation per prompt
            text_embeddings_clip = text_embeddings_clip.repeat_interleave(num_images_per_prompt, dim=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([""], padding="max_length", max_length=max_length, return_tensors="pt")
            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
            # duplicate unconditional embeddings for each generation per prompt
            uncond_embeddings = uncond_embeddings.repeat_interleave(num_images_per_prompt, dim=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])

        # get the initial random noise unless the user supplied it

        # Unlike in other pipelines, latents need to be generated in the target device
        # for 1-to-1 results reproducibility with the CompVis implementation.
        # However this currently doesn't work in `mps`.
        latents_shape = (batch_size * num_images_per_prompt, self.unet.in_channels, height // 8, width // 8)
        latents_dtype = text_embeddings.dtype
        if latents is None:
            if self.device.type == "mps":
                # randn does not work reproducibly on mps
                latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
                    self.device
                )
            else:
                latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
        else:
            if latents.shape != latents_shape:
                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
            latents = latents.to(self.device)

        # scale the initial noise by the standard deviation required by the scheduler
        latents = latents * self.scheduler.init_noise_sigma

        # 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

        # check if the scheduler accepts generator
        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
        if accepts_generator:
            extra_step_kwargs["generator"] = generator

        with self.progress_bar(total=num_inference_steps):
            for i, t in enumerate(timesteps):
                # expand the latents if we are doing classifier free guidance
                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

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

                # perform classifier free 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)

                # perform clip guidance
                if clip_guidance_scale > 0:
                    text_embeddings_for_guidance = (
                        text_embeddings.chunk(2)[1] if do_classifier_free_guidance else text_embeddings
                    )
                    noise_pred, latents = self.cond_fn(
                        latents,
                        t,
                        i,
                        text_embeddings_for_guidance,
                        noise_pred,
                        text_embeddings_clip,
                        clip_guidance_scale,
                        num_cutouts,
                        use_cutouts,
                    )

                # compute the previous noisy sample x_t -> x_t-1
                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample

        # scale and decode the image latents with vae
        latents = 1 / self.vae.config.scaling_factor * latents
        image = self.vae.decode(latents).sample

        image = (image / 2 + 0.5).clamp(0, 1)
        image = image.cpu().permute(0, 2, 3, 1).numpy()

        if output_type == "pil":
            image = self.numpy_to_pil(image)

        if not return_dict:
            return (image, None)

        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=None)
