import torch
import torch.nn as nn
import torch.utils.checkpoint as checkpoint
from torch import Tensor
from torch.nn import functional as F
from torchvision.utils import make_grid

from timm.models.layers import DropPath, trunc_normal_
from einops.layers.torch import Rearrange
from einops import rearrange, repeat

import math
import numpy as np

import random
from typing import Union
import cv2
from loguru import logger


def img2windows(img, H_sp, W_sp):
    """
    Input: Image (B, C, H, W)
    Output: Window Partition (B', N, C)
    """
    B, C, H, W = img.shape
    img_reshape = img.view(B, C, H // H_sp, H_sp, W // W_sp, W_sp)
    img_perm = img_reshape.permute(0, 2, 4, 3, 5, 1).contiguous().reshape(-1, H_sp* W_sp, C)
    return img_perm


def windows2img(img_splits_hw, H_sp, W_sp, H, W):
    """
    Input: Window Partition (B', N, C)
    Output: Image (B, H, W, C)
    """
    B = int(img_splits_hw.shape[0] / (H * W / H_sp / W_sp))

    img = img_splits_hw.view(B, H // H_sp, W // W_sp, H_sp, W_sp, -1)
    img = img.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
    return img


class Gate(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.norm = nn.LayerNorm(dim)
        self.conv = nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1, groups=dim) # DW Conv

    def forward(self, x, H, W):
        # Split
        x1, x2 = x.chunk(2, dim = -1)
        B, N, C = x.shape
        x2 = self.conv(self.norm(x2).transpose(1, 2).contiguous().view(B, C//2, H, W)).flatten(2).transpose(-1, -2).contiguous()

        return x1 * x2


class MLP(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.sg = Gate(hidden_features//2)
        self.fc2 = nn.Linear(hidden_features//2, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x, H, W):
        """
        Input: x: (B, H*W, C), H, W
        Output: x: (B, H*W, C)
        """
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)

        x = self.sg(x, H, W)
        x = self.drop(x)

        x = self.fc2(x)
        x = self.drop(x)
        return x


class DynamicPosBias(nn.Module):
    # The implementation builds on Crossformer code https://github.com/cheerss/CrossFormer/blob/main/models/crossformer.py
    """ Dynamic Relative Position Bias.
    Args:
        dim (int): Number of input channels.
        num_heads (int): Number of attention heads.
        residual (bool):  If True, use residual strage to connect conv.
    """
    def __init__(self, dim, num_heads, residual):
        super().__init__()
        self.residual = residual
        self.num_heads = num_heads
        self.pos_dim = dim // 4
        self.pos_proj = nn.Linear(2, self.pos_dim)
        self.pos1 = nn.Sequential(
            nn.LayerNorm(self.pos_dim),
            nn.ReLU(inplace=True),
            nn.Linear(self.pos_dim, self.pos_dim),
        )
        self.pos2 = nn.Sequential(
            nn.LayerNorm(self.pos_dim),
            nn.ReLU(inplace=True),
            nn.Linear(self.pos_dim, self.pos_dim)
        )
        self.pos3 = nn.Sequential(
            nn.LayerNorm(self.pos_dim),
            nn.ReLU(inplace=True),
            nn.Linear(self.pos_dim, self.num_heads)
        )
    def forward(self, biases):
        if self.residual:
            pos = self.pos_proj(biases) # 2Gh-1 * 2Gw-1, heads
            pos = pos + self.pos1(pos)
            pos = pos + self.pos2(pos)
            pos = self.pos3(pos)
        else:
            pos = self.pos3(self.pos2(self.pos1(self.pos_proj(biases))))
        return pos


class WindowAttention(nn.Module):
    def __init__(self, dim, idx, split_size=[8,8], dim_out=None, num_heads=6, attn_drop=0., proj_drop=0., qk_scale=None, position_bias=True):
        super().__init__()
        self.dim = dim
        self.dim_out = dim_out or dim
        self.split_size = split_size
        self.num_heads = num_heads
        self.idx = idx
        self.position_bias = position_bias

        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5

        if idx == 0:
            H_sp, W_sp = self.split_size[0], self.split_size[1]
        elif idx == 1:
            W_sp, H_sp = self.split_size[0], self.split_size[1]
        else:
            print ("ERROR MODE", idx)
            exit(0)
        self.H_sp = H_sp
        self.W_sp = W_sp

        if self.position_bias:
            self.pos = DynamicPosBias(self.dim // 4, self.num_heads, residual=False)
            # generate mother-set
            position_bias_h = torch.arange(1 - self.H_sp, self.H_sp)
            position_bias_w = torch.arange(1 - self.W_sp, self.W_sp)
            biases = torch.stack(torch.meshgrid([position_bias_h, position_bias_w]))
            biases = biases.flatten(1).transpose(0, 1).contiguous().float()
            self.register_buffer('rpe_biases', biases)

            # get pair-wise relative position index for each token inside the window
            coords_h = torch.arange(self.H_sp)
            coords_w = torch.arange(self.W_sp)
            coords = torch.stack(torch.meshgrid([coords_h, coords_w]))
            coords_flatten = torch.flatten(coords, 1)
            relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]
            relative_coords = relative_coords.permute(1, 2, 0).contiguous()
            relative_coords[:, :, 0] += self.H_sp - 1
            relative_coords[:, :, 1] += self.W_sp - 1
            relative_coords[:, :, 0] *= 2 * self.W_sp - 1
            relative_position_index = relative_coords.sum(-1)
            self.register_buffer('relative_position_index', relative_position_index)

        self.attn_drop = nn.Dropout(attn_drop)

    def im2win(self, x, H, W):
        B, N, C = x.shape
        x = x.transpose(-2,-1).contiguous().view(B, C, H, W)
        x = img2windows(x, self.H_sp, self.W_sp)
        x = x.reshape(-1, self.H_sp* self.W_sp, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3).contiguous()
        return x

    def forward(self, qkv, H, W, mask=None):
        """
        Input: qkv: (B, 3*L, C), H, W, mask: (B, N, N), N is the window size
        Output: x (B, H, W, C)
        """
        q,k,v = qkv[0], qkv[1], qkv[2]

        B, L, C = q.shape
        assert L == H * W, "flatten img_tokens has wrong size"

        # partition the q,k,v, image to window
        q = self.im2win(q, H, W)
        k = self.im2win(k, H, W)
        v = self.im2win(v, H, W)

        q = q * self.scale
        attn = (q @ k.transpose(-2, -1))  # B head N C @ B head C N --> B head N N

        # calculate drpe
        if self.position_bias:
            pos = self.pos(self.rpe_biases)
            # select position bias
            relative_position_bias = pos[self.relative_position_index.view(-1)].view(
                self.H_sp * self.W_sp, self.H_sp * self.W_sp, -1)
            relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()
            attn = attn + relative_position_bias.unsqueeze(0)

        N = attn.shape[3]

        # use mask for shift window
        if mask is not None:
            nW = mask.shape[0]
            attn = attn.view(B, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
            attn = attn.view(-1, self.num_heads, N, N)

        attn = nn.functional.softmax(attn, dim=-1, dtype=attn.dtype)
        attn = self.attn_drop(attn)

        x = (attn @ v)
        x = x.transpose(1, 2).reshape(-1, self.H_sp* self.W_sp, C)  # B head N N @ B head N C

        # merge the window, window to image
        x = windows2img(x, self.H_sp, self.W_sp, H, W)  # B H' W' C

        return x


class L_SA(nn.Module):
    # The implementation builds on CAT code https://github.com/zhengchen1999/CAT/blob/main/basicsr/archs/cat_arch.py
    def __init__(self, dim, num_heads,
                 split_size=[2,4], shift_size=[1,2], qkv_bias=False, qk_scale=None,
                 drop=0., attn_drop=0., idx=0, reso=64, rs_id=0):
        super().__init__()
        self.dim = dim
        self.num_heads = num_heads
        self.split_size = split_size
        self.shift_size = shift_size
        self.idx = idx
        self.rs_id = rs_id
        self.patches_resolution = reso
        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)

        assert 0 <= self.shift_size[0] < self.split_size[0], "shift_size must in 0-split_size0"
        assert 0 <= self.shift_size[1] < self.split_size[1], "shift_size must in 0-split_size1"

        self.branch_num = 2

        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(drop)

        self.attns = nn.ModuleList([
                WindowAttention(
                    dim//2, idx = i,
                    split_size=split_size, num_heads=num_heads//2, dim_out=dim//2,
                    qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop, position_bias=True)
                for i in range(self.branch_num)])

        if (self.rs_id % 2 == 0 and self.idx > 0 and (self.idx - 2) % 4 == 0) or (self.rs_id % 2 != 0 and self.idx % 4 == 0):
            attn_mask = self.calculate_mask(self.patches_resolution, self.patches_resolution)

            self.register_buffer("attn_mask_0", attn_mask[0])
            self.register_buffer("attn_mask_1", attn_mask[1])
        else:
            attn_mask = None

            self.register_buffer("attn_mask_0", None)
            self.register_buffer("attn_mask_1", None)

        self.get_v = nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1,groups=dim) # DW Conv

    def calculate_mask(self, H, W):
        # The implementation builds on Swin Transformer code https://github.com/microsoft/Swin-Transformer/blob/main/models/swin_transformer.py
        # calculate attention mask for Rwin
        img_mask_0 = torch.zeros((1, H, W, 1))  # 1 H W 1 idx=0
        img_mask_1 = torch.zeros((1, H, W, 1))  # 1 H W 1 idx=1
        h_slices_0 = (slice(0, -self.split_size[0]),
                    slice(-self.split_size[0], -self.shift_size[0]),
                    slice(-self.shift_size[0], None))
        w_slices_0 = (slice(0, -self.split_size[1]),
                    slice(-self.split_size[1], -self.shift_size[1]),
                    slice(-self.shift_size[1], None))

        h_slices_1 = (slice(0, -self.split_size[1]),
                    slice(-self.split_size[1], -self.shift_size[1]),
                    slice(-self.shift_size[1], None))
        w_slices_1 = (slice(0, -self.split_size[0]),
                    slice(-self.split_size[0], -self.shift_size[0]),
                    slice(-self.shift_size[0], None))
        cnt = 0
        for h in h_slices_0:
            for w in w_slices_0:
                img_mask_0[:, h, w, :] = cnt
                cnt += 1
        cnt = 0
        for h in h_slices_1:
            for w in w_slices_1:
                img_mask_1[:, h, w, :] = cnt
                cnt += 1

        # calculate mask for H-Shift
        img_mask_0 = img_mask_0.view(1, H // self.split_size[0], self.split_size[0], W // self.split_size[1], self.split_size[1], 1)
        img_mask_0 = img_mask_0.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, self.split_size[0], self.split_size[1], 1) # nW, sw[0], sw[1], 1
        mask_windows_0 = img_mask_0.view(-1, self.split_size[0] * self.split_size[1])
        attn_mask_0 = mask_windows_0.unsqueeze(1) - mask_windows_0.unsqueeze(2)
        attn_mask_0 = attn_mask_0.masked_fill(attn_mask_0 != 0, float(-100.0)).masked_fill(attn_mask_0 == 0, float(0.0))

        # calculate mask for V-Shift
        img_mask_1 = img_mask_1.view(1, H // self.split_size[1], self.split_size[1], W // self.split_size[0], self.split_size[0], 1)
        img_mask_1 = img_mask_1.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, self.split_size[1], self.split_size[0], 1) # nW, sw[1], sw[0], 1
        mask_windows_1 = img_mask_1.view(-1, self.split_size[1] * self.split_size[0])
        attn_mask_1 = mask_windows_1.unsqueeze(1) - mask_windows_1.unsqueeze(2)
        attn_mask_1 = attn_mask_1.masked_fill(attn_mask_1 != 0, float(-100.0)).masked_fill(attn_mask_1 == 0, float(0.0))

        return attn_mask_0, attn_mask_1

    def forward(self, x, H, W):
        """
        Input: x: (B, H*W, C), x_size: (H, W)
        Output: x: (B, H*W, C)
        """

        B, L, C = x.shape
        assert L == H * W, "flatten img_tokens has wrong size"

        qkv = self.qkv(x).reshape(B, -1, 3, C).permute(2, 0, 1, 3) # 3, B, HW, C
        # v without partition
        v = qkv[2].transpose(-2,-1).contiguous().view(B, C, H, W)


        max_split_size = max(self.split_size[0], self.split_size[1])
        pad_l = pad_t = 0
        pad_r = (max_split_size - W % max_split_size) % max_split_size
        pad_b = (max_split_size - H % max_split_size) % max_split_size

        qkv = qkv.reshape(3*B, H, W, C).permute(0, 3, 1, 2) # 3B C H W
        qkv = F.pad(qkv, (pad_l, pad_r, pad_t, pad_b)).reshape(3, B, C, -1).transpose(-2, -1) # l r t b
        _H = pad_b + H
        _W = pad_r + W
        _L = _H * _W

        if (self.rs_id % 2 == 0 and self.idx > 0 and (self.idx - 2) % 4 == 0) or (self.rs_id % 2 != 0 and self.idx % 4 == 0):
            qkv = qkv.view(3, B, _H, _W, C)
            # H-Shift
            qkv_0 = torch.roll(qkv[:,:,:,:,:C//2], shifts=(-self.shift_size[0], -self.shift_size[1]), dims=(2, 3))
            qkv_0 = qkv_0.view(3, B, _L, C//2)
            # V-Shift
            qkv_1 = torch.roll(qkv[:,:,:,:,C//2:], shifts=(-self.shift_size[1], -self.shift_size[0]), dims=(2, 3))
            qkv_1 = qkv_1.view(3, B, _L, C//2)

            if self.patches_resolution != _H or self.patches_resolution != _W:
                mask_tmp = self.calculate_mask(_H, _W)
                # H-Rwin
                x1_shift = self.attns[0](qkv_0, _H, _W, mask=mask_tmp[0].to(x.device))
                # V-Rwin
                x2_shift = self.attns[1](qkv_1, _H, _W, mask=mask_tmp[1].to(x.device))

            else:
                # H-Rwin
                x1_shift = self.attns[0](qkv_0, _H, _W, mask=self.attn_mask_0)
                # V-Rwin
                x2_shift = self.attns[1](qkv_1, _H, _W, mask=self.attn_mask_1)

            x1 = torch.roll(x1_shift, shifts=(self.shift_size[0], self.shift_size[1]), dims=(1, 2))
            x2 = torch.roll(x2_shift, shifts=(self.shift_size[1], self.shift_size[0]), dims=(1, 2))
            x1 = x1[:, :H, :W, :].reshape(B, L, C//2)
            x2 = x2[:, :H, :W, :].reshape(B, L, C//2)
            # Concat
            attened_x = torch.cat([x1,x2], dim=2)
        else:
            # V-Rwin
            x1 = self.attns[0](qkv[:,:,:,:C//2], _H, _W)[:, :H, :W, :].reshape(B, L, C//2)
            # H-Rwin
            x2 = self.attns[1](qkv[:,:,:,C//2:], _H, _W)[:, :H, :W, :].reshape(B, L, C//2)
            # Concat
            attened_x = torch.cat([x1,x2], dim=2)

        # mix
        lcm = self.get_v(v)
        lcm = lcm.permute(0, 2, 3, 1).contiguous().view(B, L, C)

        x = attened_x + lcm

        x = self.proj(x)
        x = self.proj_drop(x)

        return x


class RG_SA(nn.Module):
    """
    Recursive-Generalization Self-Attention (RG-SA).
    Args:
        dim (int): Number of input channels.
        num_heads (int): Number of attention heads.
        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
        c_ratio (float): channel adjustment factor.
    """
    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., c_ratio=0.5):
        super(RG_SA, self).__init__()
        assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
        self.num_heads = num_heads
        head_dim = dim // num_heads

        self.cr = int(dim * c_ratio) # scaled channel dimension

        # self.scale = qk_scale or head_dim ** -0.5
        self.scale = qk_scale or (head_dim * c_ratio) ** -0.5

        # RGM
        self.reduction1 = nn.Conv2d(dim, dim, kernel_size=4, stride=4, groups=dim)
        self.dwconv = nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1,groups=dim)
        self.conv = nn.Conv2d(dim, self.cr, kernel_size=1, stride=1)
        self.norm_act = nn.Sequential(
            nn.LayerNorm(self.cr),
            nn.GELU())
        # CA
        self.q = nn.Linear(dim, self.cr, bias=qkv_bias)
        self.k = nn.Linear(self.cr, self.cr, bias=qkv_bias)
        self.v = nn.Linear(self.cr, dim, bias=qkv_bias)

        # CPE
        self.cpe = nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1, groups=dim)

        self.proj = nn.Linear(dim, dim)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x, H, W):
        B, N, C = x.shape

        _scale = 1

        # reduction
        _x = x.permute(0, 2, 1).reshape(B, C, H, W).contiguous()

        if self.training:
            _time = max(int(math.log(H//4, 4)), int(math.log(W//4, 4)))
        else:
            _time = max(int(math.log(H//16, 4)), int(math.log(W//16, 4)))
            if _time < 2: _time = 2 # testing _time must equal or larger than training _time (2)

        _scale = 4 ** _time

        # Recursion xT
        for _ in range(_time):
            _x = self.reduction1(_x)

        _x = self.conv(self.dwconv(_x)).reshape(B, self.cr, -1).permute(0, 2, 1).contiguous()  # shape=(B, N', C')
        _x = self.norm_act(_x)

        # q, k, v, where q_shape=(B, N, C'), k_shape=(B, N', C'), v_shape=(B, N', C)
        q = self.q(x).reshape(B, N, self.num_heads, int(self.cr / self.num_heads)).permute(0, 2, 1, 3)
        k = self.k(_x).reshape(B, -1, self.num_heads, int(self.cr / self.num_heads)).permute(0, 2, 1, 3)
        v = self.v(_x).reshape(B, -1, self.num_heads, int(C / self.num_heads)).permute(0, 2, 1, 3)
        
        # corss-attention
        attn = (q @ k.transpose(-2, -1)) * self.scale
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)

        # CPE
        # v_shape=(B, H, N', C//H)
        v = v + self.cpe(v.transpose(1, 2).reshape(B, -1, C).transpose(1, 2).contiguous().view(B, C, H // _scale, W // _scale)).view(B, C, -1).view(B, self.num_heads, int(C / self.num_heads), -1).transpose(-1, -2)

        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)

        return x


class Block(nn.Module):
    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0.,
                 attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, idx=0, 
                 rs_id=0, split_size=[2,4], shift_size=[1,2], reso=64, c_ratio=0.5, layerscale_value=1e-4):
        super().__init__()
        self.norm1 = norm_layer(dim)
        if idx % 2 == 0:
            self.attn = L_SA(
                dim, split_size=split_size, shift_size=shift_size, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop,
                drop=drop, idx=idx, reso=reso, rs_id=rs_id
            )
        else:
            self.attn = RG_SA(
                dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop,
                proj_drop=drop, c_ratio=c_ratio
            )
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()

        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = MLP(in_features=dim, hidden_features=mlp_hidden_dim, out_features=dim, act_layer=act_layer)
        self.norm2 = norm_layer(dim)

        # HAI
        self.gamma = nn.Parameter(layerscale_value * torch.ones((dim)), requires_grad=True)

    def forward(self, x, x_size):
        H , W = x_size

        res = x

        x = x + self.drop_path(self.attn(self.norm1(x), H, W))
        x = x + self.drop_path(self.mlp(self.norm2(x), H, W))

        # HAI
        x = x + (res * self.gamma)

        return x


class ResidualGroup(nn.Module):

    def __init__(   self,
                    dim,
                    reso,
                    num_heads,
                    mlp_ratio=4.,
                    qkv_bias=False,
                    qk_scale=None,
                    drop=0.,
                    attn_drop=0.,
                    drop_paths=None,
                    act_layer=nn.GELU,
                    norm_layer=nn.LayerNorm,
                    depth=2,
                    use_chk=False,
                    resi_connection='1conv',
                    rs_id=0,
                    split_size=[8,8],
                    c_ratio = 0.5):
        super().__init__()
        self.use_chk = use_chk
        self.reso = reso

        self.blocks = nn.ModuleList([
            Block(
                dim=dim,
                num_heads=num_heads,
                mlp_ratio=mlp_ratio,
                qkv_bias=qkv_bias,
                qk_scale=qk_scale,
                drop=drop,
                attn_drop=attn_drop,
                drop_path=drop_paths[i],
                act_layer=act_layer,
                norm_layer=norm_layer,
                idx = i,
                rs_id = rs_id,
                split_size = split_size,
                shift_size = [split_size[0]//2, split_size[1]//2],
                c_ratio = c_ratio,
                )for i in range(depth)])


        if resi_connection == '1conv':
            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
        elif resi_connection == '3conv':
            self.conv = nn.Sequential(
                nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
                nn.Conv2d(dim // 4, dim // 4, 1, 1, 0), nn.LeakyReLU(negative_slope=0.2, inplace=True),
                nn.Conv2d(dim // 4, dim, 3, 1, 1))

    def forward(self, x, x_size):
        """
        Input: x: (B, H*W, C), x_size: (H, W)
        Output: x: (B, H*W, C)
        """
        H, W = x_size
        res = x
        for blk in self.blocks:
            if self.use_chk:
                x = checkpoint.checkpoint(blk, x, x_size)
            else:
                x = blk(x, x_size)
        x = rearrange(x, "b (h w) c -> b c h w", h=H, w=W)
        x = self.conv(x)
        x = rearrange(x, "b c h w -> b (h w) c")
        x = res + x

        return x


class Upsample(nn.Sequential):
    """Upsample module.
    Args:
        scale (int): Scale factor. Supported scales: 2^n and 3.
        num_feat (int): Channel number of intermediate features.
    """
    def __init__(self, scale, num_feat):
        m = []
        if (scale & (scale - 1)) == 0:  # scale = 2^n
            for _ in range(int(math.log(scale, 2))):
                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
                m.append(nn.PixelShuffle(2))
        elif scale == 3:
            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
            m.append(nn.PixelShuffle(3))
        else:
            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
        super(Upsample, self).__init__(*m)


class RGT(nn.Module):

    def __init__(self,
                img_size=64,
                in_chans=3,
                embed_dim=180,
                depth=[2,2,2,2],
                num_heads=[2,2,2,2],
                mlp_ratio=4.,
                qkv_bias=True,
                qk_scale=None,
                drop_rate=0.,
                attn_drop_rate=0.,
                drop_path_rate=0.1,
                act_layer=nn.GELU,
                norm_layer=nn.LayerNorm,
                use_chk=False,
                upscale=2,
                img_range=1.,
                resi_connection='1conv',
                split_size=[8,8],
                c_ratio=0.5,
                **kwargs):
        super().__init__()

        num_in_ch = in_chans
        num_out_ch = in_chans
        num_feat = 64
        self.img_range = img_range
        if in_chans == 3:
            rgb_mean = (0.4488, 0.4371, 0.4040)
            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
        else:
            self.mean = torch.zeros(1, 1, 1, 1)
        self.upscale = upscale

        # ------------------------- 1, Shallow Feature Extraction ------------------------- #
        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)

        # ------------------------- 2, Deep Feature Extraction ------------------------- #
        self.num_layers = len(depth)
        self.use_chk = use_chk
        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
        heads=num_heads

        self.before_RG = nn.Sequential(
            Rearrange('b c h w -> b (h w) c'),
            nn.LayerNorm(embed_dim)
        )

        curr_dim = embed_dim
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, np.sum(depth))]  # stochastic depth decay rule

        self.layers = nn.ModuleList()
        for i in range(self.num_layers):
            layer = ResidualGroup(
                dim=embed_dim,
                num_heads=heads[i],
                reso=img_size,
                mlp_ratio=mlp_ratio,
                qkv_bias=qkv_bias,
                qk_scale=qk_scale,
                drop=drop_rate,
                attn_drop=attn_drop_rate,
                drop_paths=dpr[sum(depth[:i]):sum(depth[:i + 1])],
                act_layer=act_layer,
                norm_layer=norm_layer,
                depth=depth[i],
                use_chk=use_chk,
                resi_connection=resi_connection,
                rs_id=i,
                split_size = split_size,
                c_ratio = c_ratio
                )
            self.layers.append(layer)

        self.norm = norm_layer(curr_dim)
        # build the last conv layer in deep feature extraction
        if resi_connection == '1conv':
            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
        elif resi_connection == '3conv':
            # to save parameters and memory
            self.conv_after_body = nn.Sequential(
                nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
                nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0), nn.LeakyReLU(negative_slope=0.2, inplace=True),
                nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))

        # ------------------------- 3, Reconstruction ------------------------- #
        self.conv_before_upsample = nn.Sequential(
                nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True))
        self.upsample = Upsample(upscale, num_feat)
        self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)

    def forward_features(self, x):
        _, _, H, W = x.shape
        x_size = [H, W]
        x = self.before_RG(x)
        for layer in self.layers:
            x = layer(x, x_size)
        x = self.norm(x)
        x = rearrange(x, "b (h w) c -> b c h w", h=H, w=W)

        return x

    def forward(self, x):
        """
        Input: x: (B, C, H, W)
        """
        self.mean = self.mean.type_as(x)
        x = (x - self.mean) * self.img_range

        x = self.conv_first(x)
        x = self.conv_after_body(self.forward_features(x)) + x
        x = self.conv_before_upsample(x)
        x = self.conv_last(self.upsample(x))

        x = x / self.img_range + self.mean
        return x


def img2tensor(imgs, bgr2rgb=True, float32=True):
    """Numpy array to tensor.

    Args:
        imgs (list[ndarray] | ndarray): Input images.
        bgr2rgb (bool): Whether to change bgr to rgb.
        float32 (bool): Whether to change to float32.

    Returns:
        list[tensor] | tensor: Tensor images. If returned results only have
            one element, just return tensor.
    """

    def _totensor(img, bgr2rgb, float32):
        if img.shape[2] == 3 and bgr2rgb:
            if img.dtype == 'float64':
                img = img.astype('float32')
            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        img = torch.from_numpy(img.transpose(2, 0, 1))
        if float32:
            img = img.float()
        return img

    if isinstance(imgs, list):
        return [_totensor(img, bgr2rgb, float32) for img in imgs]
    else:
        return _totensor(imgs, bgr2rgb, float32)


def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1)):
    """Convert torch Tensors into image numpy arrays.

    After clamping to [min, max], values will be normalized to [0, 1].

    Args:
        tensor (Tensor or list[Tensor]): Accept shapes:
            1) 4D mini-batch Tensor of shape (B x 3/1 x H x W);
            2) 3D Tensor of shape (3/1 x H x W);
            3) 2D Tensor of shape (H x W).
            Tensor channel should be in RGB order.
        rgb2bgr (bool): Whether to change rgb to bgr.
        out_type (numpy type): output types. If ``np.uint8``, transform outputs
            to uint8 type with range [0, 255]; otherwise, float type with
            range [0, 1]. Default: ``np.uint8``.
        min_max (tuple[int]): min and max values for clamp.

    Returns:
        (Tensor or list): 3D ndarray of shape (H x W x C) OR 2D ndarray of
        shape (H x W). The channel order is BGR.
    """
    if not (torch.is_tensor(tensor) or (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))):
        raise TypeError(f'tensor or list of tensors expected, got {type(tensor)}')

    if torch.is_tensor(tensor):
        tensor = [tensor]
    result = []
    for _tensor in tensor:
        _tensor = _tensor.squeeze(0).float().detach().cpu().clamp_(*min_max)
        _tensor = (_tensor - min_max[0]) / (min_max[1] - min_max[0])

        n_dim = _tensor.dim()
        if n_dim == 4:
            img_np = make_grid(_tensor, nrow=int(math.sqrt(_tensor.size(0))), normalize=False).numpy()
            img_np = img_np.transpose(1, 2, 0)
            if rgb2bgr:
                img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
        elif n_dim == 3:
            img_np = _tensor.numpy()
            img_np = img_np.transpose(1, 2, 0)
            if img_np.shape[2] == 1:  # gray image
                img_np = np.squeeze(img_np, axis=2)
            else:
                if rgb2bgr:
                    img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
        elif n_dim == 2:
            img_np = _tensor.numpy()
        else:
            raise TypeError(f'Only support 4D, 3D or 2D tensor. But received with dimension: {n_dim}')
        if out_type == np.uint8:
            # Unlike MATLAB, numpy.unit8() WILL NOT round by default.
            img_np = (img_np * 255.0).round()
        img_np = img_np.astype(out_type)
        result.append(img_np)
    if len(result) == 1:
        result = result[0]
    return result


def data_pre_preprocess(image):
    img = cv2.imread(image)
    img = img.astype(np.float32) / 255.
    img_lq = img2tensor(img, bgr2rgb=True, float32=True)
    return img_lq



class RGTModel:
    def __init__(self, name:str, upscale:int, device:str, model_path:str):
        self.name = None
        self.upscale = None
        self. v = None
        self.device = None

        self.load_model(name, upscale, device, model_path)

    def load_model(self, name, upscale, device, model_path):
        if self.name == name and self.upscale == upscale and self.device == upscale:
            logger.debug(f"Nothing changed, continued use loaded model")
            return 
        

        # init model by config
        if name == "RGT":
            num_heads = depth = [6,6,6,6,6,6,6,6]
        elif name == "RGT_S":
            num_heads = depth = [6,6,6,6,6,6]
        else:
            raise ValueError(f"model name wrong, should be RGT or RGT-S, but your input is: {name}")
        

        self.device = device
        self.upscale = upscale

        self.net_g = RGT(
            img_size=64,
            in_chans=3,
            embed_dim=180,
            depth=depth,
            num_heads=num_heads,
            mlp_ratio=2,
            qkv_bias=True,
            qk_scale=None,
            drop_rate=0.,
            attn_drop_rate=0.,
            drop_path_rate=0.1,
            use_chk=False,
            upscale=upscale,
            img_range=1.,
            split_size=[8,32]
        )

        logger.debug(f"checkpoints path:{model_path}")
        checkpoints = torch.load(model_path)
        self.net_g.load_state_dict(checkpoints["params"])
        self.net_g.to(torch.device(device))
        self.net_g.eval()
        


    def __call__(self, image: Union[str, torch.Tensor], use_chop: bool = True):
        if isinstance(image, str):
            image = data_pre_preprocess(image)
        logger.debug(f"image: {image.shape}")

        image = image.to(self.device)
        
        if use_chop:
            _, C, h, w = image.size()
            split_token_h = h // 200 + 1  # number of horizontal cut sections
            split_token_w = w // 200 + 1  # number of vertical cut sections

            patch_size_tmp_h = split_token_h
            patch_size_tmp_w = split_token_w
            
            # padding
            mod_pad_h, mod_pad_w = 0, 0
            if h % patch_size_tmp_h != 0:
                mod_pad_h = patch_size_tmp_h - h % patch_size_tmp_h
            if w % patch_size_tmp_w != 0:
                mod_pad_w = patch_size_tmp_w - w % patch_size_tmp_w
                
            img = image
            img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, :h+mod_pad_h, :]
            img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, :w+mod_pad_w]

            _, _, H, W = img.size()
            split_h = H // split_token_h  # height of each partition
            split_w = W // split_token_w  # width of each partition

            # overlapping
            shave_h = 16
            shave_w = 16
            scale = self.upscale
            ral = H // split_h
            row = W // split_w
            slices = []  # list of partition borders
            for i in range(ral):
                for j in range(row):
                    if i == 0 and i == ral - 1:
                        top = slice(i * split_h, (i + 1) * split_h)
                    elif i == 0:
                        top = slice(i*split_h, (i+1)*split_h+shave_h)
                    elif i == ral - 1:
                        top = slice(i*split_h-shave_h, (i+1)*split_h)
                    else:
                        top = slice(i*split_h-shave_h, (i+1)*split_h+shave_h)
                    if j == 0 and j == row - 1:
                        left = slice(j*split_w, (j+1)*split_w)
                    elif j == 0:
                        left = slice(j*split_w, (j+1)*split_w+shave_w)
                    elif j == row - 1:
                        left = slice(j*split_w-shave_w, (j+1)*split_w)
                    else:
                        left = slice(j*split_w-shave_w, (j+1)*split_w+shave_w)
                    temp = (top, left)
                    slices.append(temp)
            img_chops = []  # list of partitions
            for temp in slices:
                top, left = temp
                img_chops.append(img[..., top, left])

            with torch.no_grad():
                outputs = []
                for chop in img_chops:
                    out = self.net_g(chop)  # image processing of each partition
                    outputs.append(out)
                _img = torch.zeros(1, C, H * scale, W * scale)
                # merge
                for i in range(ral):
                    for j in range(row):
                        top = slice(i * split_h * scale, (i + 1) * split_h * scale)
                        left = slice(j * split_w * scale, (j + 1) * split_w * scale)
                        if i == 0:
                            _top = slice(0, split_h * scale)
                        else:
                            _top = slice(shave_h * scale, (shave_h + split_h) * scale)
                        if j == 0:
                            _left = slice(0, split_w * scale)
                        else:
                            _left = slice(shave_w * scale, (shave_w + split_w) * scale)
                        _img[..., top, left] = outputs[i * row + j][..., _top, _left]
                output = _img
            self.net_g.train()
            _, _, h, w = output.size()
            output = output[:, :, 0:h - mod_pad_h * scale, 0:w - mod_pad_w * scale]

        else:
            with torch.no_grad():
                output = self.net_g(image)


        return output
            


            




if __name__ == "__main__":
    model_path = "/root/code/ComfyUI/models/RGT/RGT/RGT_x2.pth"
    rgt_model = RGTModel(name="RGT", upscale=2, device="cuda:0", model_path=model_path)
    res = rgt_model(image="/root/code/RGT/datasets/single/test_4.png", use_chop=True)
    # index = 0
    # for name in ["RGT", "RGT_S"]:
    #     for upsacle in [2, 3, 4]:
    #         rgt_model = RGTModel(name=name, upscale=upsacle, device="cuda:0")
    #         res = rgt_model(image="/root/code/RGT/datasets/single/test_4.png", use_chop=True)
    #         cv2.imwrite(f"/root/code/RGT/datasets/single/res_{index}.png", res)
    #         index += 1