"""
Taken from https://github.com/rwightman/pytorch-image-models

MIT License
"""
import torch
import torch.nn as nn
import torch.nn.functional as F


def drop_block_2d(
    x,
    drop_prob: float = 0.1,
    block_size: int = 7,
    gamma_scale: float = 1.0,
    with_noise: bool = False,
    inplace: bool = False,
    batchwise: bool = False,
):
    """DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
    DropBlock with an experimental gaussian noise option. This layer has been tested on a few training
    runs with success, but needs further validation and possibly optimization for lower runtime impact.
    """
    B, C, H, W = x.shape
    total_size = W * H
    clipped_block_size = min(block_size, min(W, H))
    # seed_drop_rate, the gamma parameter
    gamma = (
        gamma_scale
        * drop_prob
        * total_size
        / clipped_block_size ** 2
        / ((W - block_size + 1) * (H - block_size + 1))
    )

    # Forces the block to be inside the feature map.
    w_i, h_i = torch.meshgrid(
        torch.arange(W).to(x.device), torch.arange(H).to(x.device)
    )
    valid_block = (
        (w_i >= clipped_block_size // 2) & (w_i < W - (clipped_block_size - 1) // 2)
    ) & ((h_i >= clipped_block_size // 2) & (h_i < H - (clipped_block_size - 1) // 2))
    valid_block = torch.reshape(valid_block, (1, 1, H, W)).to(dtype=x.dtype)

    if batchwise:
        # one mask for whole batch, quite a bit faster
        uniform_noise = torch.rand((1, C, H, W), dtype=x.dtype, device=x.device)
    else:
        uniform_noise = torch.rand_like(x)
    block_mask = ((2 - gamma - valid_block + uniform_noise) >= 1).to(dtype=x.dtype)
    block_mask = -F.max_pool2d(
        -block_mask,
        kernel_size=clipped_block_size,  # block_size,
        stride=1,
        padding=clipped_block_size // 2,
    )

    if with_noise:
        normal_noise = (
            torch.randn((1, C, H, W), dtype=x.dtype, device=x.device)
            if batchwise
            else torch.randn_like(x)
        )
        if inplace:
            x.mul_(block_mask).add_(normal_noise * (1 - block_mask))
        else:
            x = x * block_mask + normal_noise * (1 - block_mask)
    else:
        normalize_scale = (
            block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7)
        ).to(x.dtype)
        if inplace:
            x.mul_(block_mask * normalize_scale)
        else:
            x = x * block_mask * normalize_scale
    return x


def drop_block_fast_2d(
    x: torch.Tensor,
    drop_prob: float = 0.1,
    block_size: int = 7,
    gamma_scale: float = 1.0,
    with_noise: bool = False,
    inplace: bool = False,
    batchwise: bool = False,
):
    """DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
    DropBlock with an experimental gaussian noise option. Simplied from above without concern for valid
    block mask at edges.
    """
    B, C, H, W = x.shape
    total_size = W * H
    clipped_block_size = min(block_size, min(W, H))
    gamma = (
        gamma_scale
        * drop_prob
        * total_size
        / clipped_block_size ** 2
        / ((W - block_size + 1) * (H - block_size + 1))
    )

    if batchwise:
        # one mask for whole batch, quite a bit faster
        block_mask = torch.rand((1, C, H, W), dtype=x.dtype, device=x.device) < gamma
    else:
        # mask per batch element
        block_mask = torch.rand_like(x) < gamma
    block_mask = F.max_pool2d(
        block_mask.to(x.dtype),
        kernel_size=clipped_block_size,
        stride=1,
        padding=clipped_block_size // 2,
    )

    if with_noise:
        normal_noise = (
            torch.randn((1, C, H, W), dtype=x.dtype, device=x.device)
            if batchwise
            else torch.randn_like(x)
        )
        if inplace:
            x.mul_(1.0 - block_mask).add_(normal_noise * block_mask)
        else:
            x = x * (1.0 - block_mask) + normal_noise * block_mask
    else:
        block_mask = 1 - block_mask
        normalize_scale = (
            block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7)
        ).to(dtype=x.dtype)
        if inplace:
            x.mul_(block_mask * normalize_scale)
        else:
            x = x * block_mask * normalize_scale
    return x


class DropBlock2d(nn.Module):
    """DropBlock. See https://arxiv.org/pdf/1810.12890.pdf"""

    def __init__(
        self,
        drop_prob=0.1,
        block_size=7,
        gamma_scale=1.0,
        with_noise=False,
        inplace=False,
        batchwise=False,
        fast=True,
    ):
        super(DropBlock2d, self).__init__()
        self.drop_prob = drop_prob
        self.gamma_scale = gamma_scale
        self.block_size = block_size
        self.with_noise = with_noise
        self.inplace = inplace
        self.batchwise = batchwise
        self.fast = fast  # FIXME finish comparisons of fast vs not

    def forward(self, x):
        if not self.training or not self.drop_prob:
            return x
        if self.fast:
            return drop_block_fast_2d(
                x,
                self.drop_prob,
                self.block_size,
                self.gamma_scale,
                self.with_noise,
                self.inplace,
                self.batchwise,
            )
        else:
            return drop_block_2d(
                x,
                self.drop_prob,
                self.block_size,
                self.gamma_scale,
                self.with_noise,
                self.inplace,
                self.batchwise,
            )
