#!/usr/bin/env python3

from __future__ import annotations

from collections import OrderedDict
from typing import Literal

import torch
import torch.nn as nn

####################
# Basic blocks
####################


def act(act_type: str, inplace=True, neg_slope=0.2, n_prelu=1):
    # helper selecting activation
    # neg_slope: for leakyrelu and init of prelu
    # n_prelu: for p_relu num_parameters
    act_type = act_type.lower()
    if act_type == "relu":
        layer = nn.ReLU(inplace)
    elif act_type == "leakyrelu":
        layer = nn.LeakyReLU(neg_slope, inplace)
    elif act_type == "prelu":
        layer = nn.PReLU(num_parameters=n_prelu, init=neg_slope)
    else:
        raise NotImplementedError(f"activation layer [{act_type:s}] is not found")
    return layer


def norm(norm_type: str, nc: int):
    # helper selecting normalization layer
    norm_type = norm_type.lower()
    if norm_type == "batch":
        layer = nn.BatchNorm2d(nc, affine=True)
    elif norm_type == "instance":
        layer = nn.InstanceNorm2d(nc, affine=False)
    else:
        raise NotImplementedError(f"normalization layer [{norm_type:s}] is not found")
    return layer


def pad(pad_type: str, padding):
    # helper selecting padding layer
    # if padding is 'zero', do by conv layers
    pad_type = pad_type.lower()
    if padding == 0:
        return None
    if pad_type == "reflect":
        layer = nn.ReflectionPad2d(padding)
    elif pad_type == "replicate":
        layer = nn.ReplicationPad2d(padding)
    else:
        raise NotImplementedError(f"padding layer [{pad_type:s}] is not implemented")
    return layer


def get_valid_padding(kernel_size, dilation):
    kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1)
    padding = (kernel_size - 1) // 2
    return padding


class ConcatBlock(nn.Module):
    # Concat the output of a submodule to its input
    def __init__(self, submodule):
        super().__init__()
        self.sub = submodule

    def forward(self, x):
        output = torch.cat((x, self.sub(x)), dim=1)
        return output

    def __repr__(self):
        tmpstr = "Identity .. \n|"
        modstr = self.sub.__repr__().replace("\n", "\n|")
        tmpstr = tmpstr + modstr
        return tmpstr


class ShortcutBlock(nn.Module):
    # Elementwise sum the output of a submodule to its input
    def __init__(self, submodule):
        super().__init__()
        self.sub = submodule

    def forward(self, x):
        output = x + self.sub(x)
        return output

    def __repr__(self):
        tmpstr = "Identity + \n|"
        modstr = self.sub.__repr__().replace("\n", "\n|")
        tmpstr = tmpstr + modstr
        return tmpstr


class ShortcutBlockSPSR(nn.Module):
    # Elementwise sum the output of a submodule to its input
    def __init__(self, submodule):
        super().__init__()
        self.sub = submodule

    def forward(self, x):
        return x, self.sub

    def __repr__(self):
        tmpstr = "Identity + \n|"
        modstr = self.sub.__repr__().replace("\n", "\n|")
        tmpstr = tmpstr + modstr
        return tmpstr


def sequential(*args):
    # Flatten Sequential. It unwraps nn.Sequential.
    if len(args) == 1:
        if isinstance(args[0], OrderedDict):
            raise NotImplementedError("sequential does not support OrderedDict input.")
        return args[0]  # No sequential is needed.
    modules = []
    for module in args:
        if isinstance(module, nn.Sequential):
            for submodule in module.children():
                modules.append(submodule)
        elif isinstance(module, nn.Module):
            modules.append(module)
    return nn.Sequential(*modules)


ConvMode = Literal["CNA", "NAC", "CNAC"]


# 2x2x2 Conv Block
def conv_block_2c2(
    in_nc,
    out_nc,
    act_type="relu",
):
    return sequential(
        nn.Conv2d(in_nc, out_nc, kernel_size=2, padding=1),
        nn.Conv2d(out_nc, out_nc, kernel_size=2, padding=0),
        act(act_type) if act_type else None,
    )


def conv_block(
    in_nc: int,
    out_nc: int,
    kernel_size,
    stride=1,
    dilation=1,
    groups=1,
    bias=True,
    pad_type="zero",
    norm_type: str | None = None,
    act_type: str | None = "relu",
    mode: ConvMode = "CNA",
):
    """
    Conv layer with padding, normalization, activation
    mode:
    - ``CNA`` --> Conv -> Norm -> Act
    - ``NAC`` --> Norm -> Act --> Conv (Identity Mappings in Deep Residual Networks, ECCV16)
    """

    assert mode in ("CNA", "NAC", "CNAC"), f"Wrong conv mode [{mode:s}]"
    padding = get_valid_padding(kernel_size, dilation)
    p = pad(pad_type, padding) if pad_type and pad_type != "zero" else None
    padding = padding if pad_type == "zero" else 0

    c = nn.Conv2d(
        in_nc,
        out_nc,
        kernel_size=kernel_size,
        stride=stride,
        padding=padding,
        dilation=dilation,
        bias=bias,
        groups=groups,
    )
    a = act(act_type) if act_type else None
    if mode in ("CNA", "CNAC"):
        n = norm(norm_type, out_nc) if norm_type else None
        return sequential(p, c, n, a)
    elif mode == "NAC":
        if norm_type is None and act_type is not None:
            a = act(act_type, inplace=False)
            # Important!
            # input----ReLU(inplace)----Conv--+----output
            #        |________________________|
            # inplace ReLU will modify the input, therefore wrong output
        n = norm(norm_type, in_nc) if norm_type else None
        return sequential(n, a, p, c)
    else:
        raise ValueError(f"Invalid conv mode {mode}")


####################
# Useful blocks
####################


class ResNetBlock(nn.Module):
    """
    ResNet Block, 3-3 style
    with extra residual scaling used in EDSR
    (Enhanced Deep Residual Networks for Single Image Super-Resolution, CVPRW 17)
    """

    def __init__(
        self,
        in_nc,
        mid_nc,
        out_nc,
        kernel_size=3,
        stride=1,
        dilation=1,
        groups=1,
        bias=True,
        pad_type="zero",
        norm_type=None,
        act_type="relu",
        mode: ConvMode = "CNA",
        res_scale=1,
    ):
        super().__init__()
        conv0 = conv_block(
            in_nc,
            mid_nc,
            kernel_size,
            stride,
            dilation,
            groups,
            bias,
            pad_type,
            norm_type,
            act_type,
            mode,
        )
        if mode == "CNA":
            act_type = None
        if mode == "CNAC":  # Residual path: |-CNAC-|
            act_type = None
            norm_type = None
        conv1 = conv_block(
            mid_nc,
            out_nc,
            kernel_size,
            stride,
            dilation,
            groups,
            bias,
            pad_type,
            norm_type,
            act_type,
            mode,
        )
        # if in_nc != out_nc:
        #     self.project = conv_block(in_nc, out_nc, 1, stride, dilation, 1, bias, pad_type, \
        #         None, None)
        #     print('Need a projecter in ResNetBlock.')
        # else:
        #     self.project = lambda x:x
        self.res = sequential(conv0, conv1)
        self.res_scale = res_scale

    def forward(self, x):
        res = self.res(x).mul(self.res_scale)
        return x + res


class RRDB(nn.Module):
    """
    Residual in Residual Dense Block
    (ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks)
    """

    def __init__(
        self,
        nf,
        kernel_size=3,
        gc=32,
        stride=1,
        bias: bool = True,
        pad_type="zero",
        norm_type=None,
        act_type="leakyrelu",
        mode: ConvMode = "CNA",
        _convtype="Conv2D",
        _spectral_norm=False,
        plus=False,
    ):
        super().__init__()
        self.RDB1 = ResidualDenseBlock_5C(
            nf,
            kernel_size,
            gc,
            stride,
            bias,
            pad_type,
            norm_type,
            act_type,
            mode,
            plus=plus,
        )
        self.RDB2 = ResidualDenseBlock_5C(
            nf,
            kernel_size,
            gc,
            stride,
            bias,
            pad_type,
            norm_type,
            act_type,
            mode,
            plus=plus,
        )
        self.RDB3 = ResidualDenseBlock_5C(
            nf,
            kernel_size,
            gc,
            stride,
            bias,
            pad_type,
            norm_type,
            act_type,
            mode,
            plus=plus,
        )

    def forward(self, x):
        out = self.RDB1(x)
        out = self.RDB2(out)
        out = self.RDB3(out)
        return out * 0.2 + x


class ResidualDenseBlock_5C(nn.Module):
    """
    Residual Dense Block
    Style: 5 convs
    The core module of paper: (Residual Dense Network for Image Super-Resolution, CVPR 18)
    Modified options that can be used:

    - "Partial Convolution based Padding" `arXiv:1811.11718`
    - "Spectral normalization" `arXiv:1802.05957`
    - "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C. {Rakotonirina} and A. {Rasoanaivo}

    Args:
        nf (int): Channel number of intermediate features (num_feat).
        gc (int): Channels for each growth (num_grow_ch: growth channel, i.e. intermediate channels).
        convtype (str): the type of convolution to use. Default: 'Conv2D'
        gaussian_noise (bool): enable the ESRGAN+ gaussian noise (no new trainable parameters)
        plus (bool): enable the additional residual paths from ESRGAN+ (adds trainable parameters)
    """

    def __init__(
        self,
        nf=64,
        kernel_size=3,
        gc=32,
        stride=1,
        bias: bool = True,
        pad_type="zero",
        norm_type=None,
        act_type="leakyrelu",
        mode: ConvMode = "CNA",
        plus=False,
    ):
        super().__init__()

        ## +
        self.conv1x1 = conv1x1(nf, gc) if plus else None
        ## +

        self.conv1 = conv_block(
            nf,
            gc,
            kernel_size,
            stride,
            bias=bias,
            pad_type=pad_type,
            norm_type=norm_type,
            act_type=act_type,
            mode=mode,
        )
        self.conv2 = conv_block(
            nf + gc,
            gc,
            kernel_size,
            stride,
            bias=bias,
            pad_type=pad_type,
            norm_type=norm_type,
            act_type=act_type,
            mode=mode,
        )
        self.conv3 = conv_block(
            nf + 2 * gc,
            gc,
            kernel_size,
            stride,
            bias=bias,
            pad_type=pad_type,
            norm_type=norm_type,
            act_type=act_type,
            mode=mode,
        )
        self.conv4 = conv_block(
            nf + 3 * gc,
            gc,
            kernel_size,
            stride,
            bias=bias,
            pad_type=pad_type,
            norm_type=norm_type,
            act_type=act_type,
            mode=mode,
        )
        if mode == "CNA":
            last_act = None
        else:
            last_act = act_type
        self.conv5 = conv_block(
            nf + 4 * gc,
            nf,
            3,
            stride,
            bias=bias,
            pad_type=pad_type,
            norm_type=norm_type,
            act_type=last_act,
            mode=mode,
        )

    def forward(self, x):
        x1 = self.conv1(x)
        x2 = self.conv2(torch.cat((x, x1), 1))
        if self.conv1x1:
            # pylint: disable=not-callable
            x2 = x2 + self.conv1x1(x)  # +
        x3 = self.conv3(torch.cat((x, x1, x2), 1))
        x4 = self.conv4(torch.cat((x, x1, x2, x3), 1))
        if self.conv1x1:
            x4 = x4 + x2  # +
        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
        return x5 * 0.2 + x


def conv1x1(in_planes, out_planes, stride=1):
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


####################
# Upsampler
####################


def pixelshuffle_block(
    in_nc: int,
    out_nc: int,
    upscale_factor=2,
    kernel_size=3,
    stride=1,
    bias=True,
    pad_type="zero",
    norm_type: str | None = None,
    act_type="relu",
):
    """
    Pixel shuffle layer
    (Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional
    Neural Network, CVPR17)
    """
    conv = conv_block(
        in_nc,
        out_nc * (upscale_factor**2),
        kernel_size,
        stride,
        bias=bias,
        pad_type=pad_type,
        norm_type=None,
        act_type=None,
    )
    pixel_shuffle = nn.PixelShuffle(upscale_factor)

    n = norm(norm_type, out_nc) if norm_type else None
    a = act(act_type) if act_type else None
    return sequential(conv, pixel_shuffle, n, a)


def upconv_block(
    in_nc: int,
    out_nc: int,
    upscale_factor=2,
    kernel_size=3,
    stride=1,
    bias=True,
    pad_type="zero",
    norm_type: str | None = None,
    act_type="relu",
    mode="nearest",
):
    # Up conv
    # described in https://distill.pub/2016/deconv-checkerboard/
    upsample = nn.Upsample(scale_factor=upscale_factor, mode=mode)
    conv = conv_block(
        in_nc,
        out_nc,
        kernel_size,
        stride,
        bias=bias,
        pad_type=pad_type,
        norm_type=norm_type,
        act_type=act_type,
    )
    return sequential(upsample, conv)