import math
from sys import path

path.append("./taming-transformers")
from taming.models import cond_transformer, vqgan

import torch
from torch import nn
from torch.nn import functional

from omegaconf import OmegaConf
import kornia.augmentation as K


def sinc(x):
    return torch.where(x != 0, torch.sin(math.pi * x) / (math.pi * x), x.new_ones([]))


def lanczos(x, a):
    cond = torch.logical_and(-a < x, x < a)
    out = torch.where(cond, sinc(x) * sinc(x / a), x.new_zeros([]))
    return out / out.sum()


def ramp(ratio, width):
    n = math.ceil(width / ratio + 1)
    out = torch.empty([n])
    cur = 0
    for i in range(out.shape[0]):
        out[i] = cur
        cur += ratio
    return torch.cat([-out[1:].flip([0]), out])[1:-1]


def resample(input, size, align_corners=True):
    n, c, h, w = input.shape
    dh, dw = size

    input = input.view([n * c, 1, h, w])

    if dh < h:
        kernel_h = lanczos(ramp(dh / h, 2), 2).to(input.device, input.dtype)
        pad_h = (kernel_h.shape[0] - 1) // 2
        input = functional.pad(input, (0, 0, pad_h, pad_h), "reflect")
        input = functional.conv2d(input, kernel_h[None, None, :, None])

    if dw < w:
        kernel_w = lanczos(ramp(dw / w, 2), 2).to(input.device, input.dtype)
        pad_w = (kernel_w.shape[0] - 1) // 2
        input = functional.pad(input, (pad_w, pad_w, 0, 0), "reflect")
        input = functional.conv2d(input, kernel_w[None, None, None, :])

    input = input.view([n, c, h, w])
    return functional.interpolate(
        input, size, mode="bicubic", align_corners=align_corners
    )


class ReplaceGrad(torch.autograd.Function):
    @staticmethod
    def forward(ctx, x_forward, x_backward):
        ctx.shape = x_backward.shape
        return x_forward

    @staticmethod
    def backward(ctx, grad_in):
        return None, grad_in.sum_to_size(ctx.shape)


replace_grad = ReplaceGrad.apply


class ClampWithGrad(torch.autograd.Function):
    @staticmethod
    def forward(ctx, input, min, max):
        ctx.min = min
        ctx.max = max
        ctx.save_for_backward(input)
        return input.clamp(min, max)

    @staticmethod
    def backward(ctx, grad_in):
        (input,) = ctx.saved_tensors
        return (
            grad_in * (grad_in * (input - input.clamp(ctx.min, ctx.max)) >= 0),
            None,
            None,
        )


clamp_with_grad = ClampWithGrad.apply


def vector_quantize(x, codebook):
    d = (
        x.pow(2).sum(dim=-1, keepdim=True)
        + codebook.pow(2).sum(dim=1)
        - 2 * x @ codebook.T
    )
    indices = d.argmin(-1)
    x_q = functional.one_hot(indices, codebook.shape[0]).to(d.dtype) @ codebook
    return replace_grad(x_q, x)


class Prompt(nn.Module):
    def __init__(self, embed, weight=1.0, stop=float("-inf")):
        super().__init__()
        self.register_buffer("embed", embed)
        self.register_buffer("weight", torch.as_tensor(weight))
        self.register_buffer("stop", torch.as_tensor(stop))

    def forward(self, input):
        input_normed = functional.normalize(input.unsqueeze(1), dim=2)
        embed_normed = functional.normalize(self.embed.unsqueeze(0), dim=2)
        dists = input_normed.sub(embed_normed).norm(dim=2).div(2).arcsin().pow(2).mul(2)
        dists = dists * self.weight.sign()
        return (
            self.weight.abs()
            * replace_grad(dists, torch.maximum(dists, self.stop)).mean()
        )


def parse_prompt(prompt):
    vals = prompt.rsplit(":", 2)
    vals = vals + ["", "1", "-inf"][len(vals) :]
    return vals[0], float(vals[1]), float(vals[2])


class MakeCutouts(nn.Module):
    def __init__(self, cut_size, cutn, cut_pow=1.0):
        super().__init__()
        self.cut_size = cut_size
        self.cutn = cutn
        self.cut_pow = cut_pow
        self.augs = nn.Sequential(
            K.RandomHorizontalFlip(p=0.5),
            # K.RandomSolarize(0.01, 0.01, p=0.7),
            K.RandomSharpness(0.3, p=0.4),
            K.RandomAffine(degrees=30, translate=0.1, p=0.8, padding_mode="border"),
            K.RandomPerspective(0.2, p=0.4),
            K.ColorJitter(hue=0.01, saturation=0.01, p=0.7),
        )
        self.noise_fac = 0.1

    def forward(self, input):
        sideY, sideX = input.shape[2:4]
        max_size = min(sideX, sideY)
        min_size = min(sideX, sideY, self.cut_size)
        cutouts = []
        for _ in range(self.cutn):
            size = int(
                torch.rand([]) ** self.cut_pow * (max_size - min_size) + min_size
            )
            offsetx = torch.randint(0, sideX - size + 1, ())
            offsety = torch.randint(0, sideY - size + 1, ())
            cutout = input[:, :, offsety : offsety + size, offsetx : offsetx + size]
            cutouts.append(resample(cutout, (self.cut_size, self.cut_size)))
        batch = self.augs(torch.cat(cutouts, dim=0))
        if self.noise_fac:
            facs = batch.new_empty([self.cutn, 1, 1, 1]).uniform_(0, self.noise_fac)
            batch = batch + facs * torch.randn_like(batch)
        return batch


def load_vqgan_model(config_path, checkpoint_path):
    config = OmegaConf.load(config_path)
    if config.model.target == "taming.models.vqgan.VQModel":
        model = vqgan.VQModel(**config.model.params)
        model.eval().requires_grad_(False)
        model.init_from_ckpt(checkpoint_path)
    elif config.model.target == "taming.models.cond_transformer.Net2NetTransformer":
        parent_model = cond_transformer.Net2NetTransformer(**config.model.params)
        parent_model.eval().requires_grad_(False)
        parent_model.init_from_ckpt(checkpoint_path)
        model = parent_model.first_stage_model
    else:
        raise ValueError(f"unknown model type: {config.model.target}")
    del model.loss
    return model