from typing import Tuple
import torch
import torch.nn.functional as F

from pyiqa.utils.color_util import to_y_channel
from pyiqa.matlab_utils import fspecial, imfilter
from .arch_util import excact_padding_2d

EPS = torch.finfo(torch.float32).eps


def preprocess_rgb(x, test_y_channel, data_range=1., color_space='yiq'):
    if test_y_channel and x.shape[1] == 3:
        x = to_y_channel(x, data_range, color_space)
    else:
        x = x * data_range
        x = x - x.detach() + x.round()
    return x


def extract_2d_patches(x, kernel, stride=1, dilation=1, padding='same'):
    """
    Ref: https://stackoverflow.com/a/65886666
    """
    b, c, h, w = x.shape
    if padding != 'none':
        x = excact_padding_2d(x, kernel, stride, dilation, mode=padding)

    # Extract patches
    patches = F.unfold(x, kernel, dilation, stride=stride)
    b, _, pnum = patches.shape
    patches = patches.transpose(1, 2).reshape(b, pnum, c, kernel, kernel)
    return patches


def torch_cov(tensor, rowvar=True, bias=False):
    r"""Estimate a covariance matrix (np.cov)
    Ref: https://gist.github.com/ModarTensai/5ab449acba9df1a26c12060240773110
    """
    tensor = tensor if rowvar else tensor.transpose(-1, -2)
    tensor = tensor - tensor.mean(dim=-1, keepdim=True)
    factor = 1 / (tensor.shape[-1] - int(not bool(bias)))
    return factor * tensor @ tensor.transpose(-1, -2)


def safe_sqrt(x: torch.Tensor) -> torch.Tensor:
    r"""Safe sqrt with EPS to ensure numeric stability.

    Args:
        x (torch.Tensor): should be non-negative
    """
    EPS = torch.finfo(x.dtype).eps
    return torch.sqrt(x + EPS)


def diff_round(x: torch.Tensor) -> torch.Tensor:
    r"""Differentiable round.
    """
    return x - x.detach() + x.round()


def normalize_img_with_guass(img: torch.Tensor,
                             kernel_size: int = 7,
                             sigma: float = 7. / 6,
                             C: int = 1,
                             padding: str = 'same'):

    kernel = fspecial(kernel_size, sigma, 1).to(img)
    mu = imfilter(img, kernel, padding=padding)
    std = imfilter(img**2, kernel, padding=padding)
    sigma = safe_sqrt((std - mu**2).abs())
    img_normalized = (img - mu) / (sigma + C)
    return img_normalized


# Gradient operator kernels
def scharr_filter() -> torch.Tensor:
    r"""Utility function that returns a normalized 3x3 Scharr kernel in X direction
    Returns:
        kernel: Tensor with shape (1, 3, 3)
    """
    return torch.tensor([[[-3., 0., 3.], [-10., 0., 10.], [-3., 0., 3.]]]) / 16


def gradient_map(x: torch.Tensor, kernels: torch.Tensor) -> torch.Tensor:
    r""" Compute gradient map for a given tensor and stack of kernels.
    Args:
        x: Tensor with shape (N, C, H, W).
        kernels: Stack of tensors for gradient computation with shape (k_N, k_H, k_W)
    Returns:
        Gradients of x per-channel with shape (N, C, H, W)
    """
    padding = kernels.size(-1) // 2
    grads = torch.nn.functional.conv2d(x, kernels.to(x), padding=padding)
    return safe_sqrt(torch.sum(grads**2, dim=-3, keepdim=True))


def similarity_map(map_x: torch.Tensor, map_y: torch.Tensor, constant: float, alpha: float = 0.0) -> torch.Tensor:
    r""" Compute similarity_map between two tensors using Dice-like equation.
    Args:
        map_x: Tensor with map to be compared
        map_y: Tensor with map to be compared
        constant: Used for numerical stability
        alpha: Masking coefficient. Substracts - `alpha` * map_x * map_y from denominator and nominator
    """
    return (2.0 * map_x * map_y - alpha * map_x * map_y + constant) / \
           (map_x ** 2 + map_y ** 2 - alpha * map_x * map_y + constant + EPS)


def ifftshift(x: torch.Tensor) -> torch.Tensor:
    r""" Similar to np.fft.ifftshift but applies to PyTorch Tensors"""
    shift = [-(ax // 2) for ax in x.size()]
    return torch.roll(x, shift, tuple(range(len(shift))))


def get_meshgrid(size: Tuple[int, int]) -> torch.Tensor:
    r"""Return coordinate grid matrices centered at zero point.
    Args:
        size: Shape of meshgrid to create
    """
    if size[0] % 2:
        # Odd
        x = torch.arange(-(size[0] - 1) / 2, size[0] / 2) / (size[0] - 1)
    else:
        # Even
        x = torch.arange(-size[0] / 2, size[0] / 2) / size[0]

    if size[1] % 2:
        # Odd
        y = torch.arange(-(size[1] - 1) / 2, size[1] / 2) / (size[1] - 1)
    else:
        # Even
        y = torch.arange(-size[1] / 2, size[1] / 2) / size[1]
    return torch.meshgrid(x, y, indexing='ij')


def estimate_ggd_param(x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
    """Estimate general gaussian distribution.

    Args:
        x (Tensor): shape (b, 1, h, w)
    """
    gamma = torch.arange(0.2, 10 + 0.001, 0.001).to(x)
    r_table = (torch.lgamma(1. / gamma) + torch.lgamma(3. / gamma) - 2 * torch.lgamma(2. / gamma)).exp()
    r_table = r_table.repeat(x.size(0), 1)

    sigma_sq = x.pow(2).mean(dim=(-1, -2))
    sigma = sigma_sq.sqrt().squeeze(dim=-1)

    assert not torch.isclose(sigma, torch.zeros_like(sigma)).all(), \
        'Expected image with non zero variance of pixel values'

    E = x.abs().mean(dim=(-1, -2))
    rho = sigma_sq / E**2

    indexes = (rho - r_table).abs().argmin(dim=-1)
    solution = gamma[indexes]
    return solution, sigma


def estimate_aggd_param(block: torch.Tensor, return_sigma=False) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    """Estimate AGGD (Asymmetric Generalized Gaussian Distribution) parameters.
    Args:
        block (Tensor): Image block with shape (b, 1, h, w).
    Returns:
        Tensor: alpha, beta_l and beta_r for the AGGD distribution
        (Estimating the parames in Equation 7 in the paper).
    """
    gam = torch.arange(0.2, 10 + 0.001, 0.001).to(block)
    r_gam = (2 * torch.lgamma(2. / gam) - (torch.lgamma(1. / gam) + torch.lgamma(3. / gam))).exp()
    r_gam = r_gam.repeat(block.shape[0], 1)

    mask_left = block < 0
    mask_right = block > 0
    count_left = mask_left.sum(dim=(-1, -2), dtype=torch.float32)
    count_right = mask_right.sum(dim=(-1, -2), dtype=torch.float32)

    left_std = torch.sqrt((block * mask_left).pow(2).sum(dim=(-1, -2)) / (count_left))
    right_std = torch.sqrt((block * mask_right).pow(2).sum(dim=(-1, -2)) / (count_right))

    gammahat = left_std / right_std
    rhat = block.abs().mean(dim=(-1, -2)).pow(2) / block.pow(2).mean(dim=(-1, -2))
    rhatnorm = (rhat * (gammahat.pow(3) + 1) * (gammahat + 1)) / (gammahat.pow(2) + 1).pow(2)
    array_position = (r_gam - rhatnorm).abs().argmin(dim=-1)

    alpha = gam[array_position]
    beta_l = left_std.squeeze(-1) * (torch.lgamma(1 / alpha) - torch.lgamma(3 / alpha)).exp().sqrt()
    beta_r = right_std.squeeze(-1) * (torch.lgamma(1 / alpha) - torch.lgamma(3 / alpha)).exp().sqrt()

    if return_sigma:
        return alpha, left_std.squeeze(-1), right_std.squeeze(-1)
    else:
        return alpha, beta_l, beta_r
