import math
import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F

from .utils import freeze
from tqdm import tqdm

import time

def nonlinearity(x):
    return x*torch.sigmoid(x)


class SpatialNorm(nn.Module):
    def __init__(
        self, f_channels, zq_channels=None, norm_layer=nn.GroupNorm, freeze_norm_layer=False, add_conv=False, **norm_layer_params
    ):
        super().__init__()
        self.norm_layer = norm_layer(num_channels=f_channels, **norm_layer_params)
        if zq_channels is not None:
            if freeze_norm_layer:
                for p in self.norm_layer.parameters:
                    p.requires_grad = False
            self.add_conv = add_conv
            if self.add_conv:
                self.conv = nn.Conv2d(zq_channels, zq_channels, kernel_size=3, stride=1, padding=1)
            self.conv_y = nn.Conv2d(zq_channels, f_channels, kernel_size=1, stride=1, padding=0)
            self.conv_b = nn.Conv2d(zq_channels, f_channels, kernel_size=1, stride=1, padding=0)
    def forward(self, f, zq=None):
        norm_f = self.norm_layer(f)
        if zq is not None:
            f_size = f.shape[-2:]
            zq = torch.nn.functional.interpolate(zq, size=f_size, mode="nearest")
            if self.add_conv:
                zq = self.conv(zq)
            norm_f = norm_f * self.conv_y(zq) + self.conv_b(zq)
        return norm_f


def Normalize(in_channels, zq_ch=None, add_conv=None):
    return SpatialNorm(
            in_channels, zq_ch, norm_layer=nn.GroupNorm,
            freeze_norm_layer=False, add_conv=add_conv, num_groups=32, eps=1e-6, affine=True
        )


class Upsample(nn.Module):
    def __init__(self, in_channels, with_conv):
        super().__init__()
        self.with_conv = with_conv
        if self.with_conv:
            self.conv = torch.nn.Conv2d(in_channels,
                                        in_channels,
                                        kernel_size=3,
                                        stride=1,
                                        padding=1)

    def forward(self, x):
        x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
        if self.with_conv:
            x = self.conv(x)
        return x


class Downsample(nn.Module):
    def __init__(self, in_channels, with_conv):
        super().__init__()
        self.with_conv = with_conv
        if self.with_conv:
            self.conv = torch.nn.Conv2d(in_channels,
                                        in_channels,
                                        kernel_size=3,
                                        stride=2,
                                        padding=0)

    def forward(self, x):
        if self.with_conv:
            pad = (0,1,0,1)
            x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
            x = self.conv(x)
        else:
            x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
        return x


class ResnetBlock(nn.Module):
    def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
                 dropout, temb_channels=512, zq_ch=None, add_conv=False):
        super().__init__()
        self.in_channels = in_channels
        out_channels = in_channels if out_channels is None else out_channels
        self.out_channels = out_channels
        self.use_conv_shortcut = conv_shortcut

        self.norm1 = Normalize(in_channels, zq_ch, add_conv=add_conv)
        self.conv1 = torch.nn.Conv2d(in_channels,
                                     out_channels,
                                     kernel_size=3,
                                     stride=1,
                                     padding=1)
        if temb_channels > 0:
            self.temb_proj = torch.nn.Linear(temb_channels,
                                             out_channels)
        self.norm2 = Normalize(out_channels, zq_ch, add_conv=add_conv)
        self.dropout = torch.nn.Dropout(dropout)
        self.conv2 = torch.nn.Conv2d(out_channels,
                                     out_channels,
                                     kernel_size=3,
                                     stride=1,
                                     padding=1)
        if self.in_channels != self.out_channels:
            if self.use_conv_shortcut:
                self.conv_shortcut = torch.nn.Conv2d(in_channels,
                                                     out_channels,
                                                     kernel_size=3,
                                                     stride=1,
                                                     padding=1)
            else:
                self.nin_shortcut = torch.nn.Conv2d(in_channels,
                                                    out_channels,
                                                    kernel_size=1,
                                                    stride=1,
                                                    padding=0)

    def forward(self, x, temb, zq=None):
        h = x
        h = self.norm1(h, zq)
        h = nonlinearity(h)
        h = self.conv1(h)

        if temb is not None:
            h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]

        h = self.norm2(h, zq)
        h = nonlinearity(h)
        h = self.dropout(h)
        h = self.conv2(h)

        if self.in_channels != self.out_channels:
            if self.use_conv_shortcut:
                x = self.conv_shortcut(x)
            else:
                x = self.nin_shortcut(x)
        return x+h


class AttnBlock(nn.Module):
    def __init__(self, in_channels, zq_ch=None, add_conv=False):
        super().__init__()
        self.in_channels = in_channels

        self.norm = Normalize(in_channels, zq_ch, add_conv=add_conv)
        self.q = torch.nn.Conv2d(in_channels,
                                 in_channels,
                                 kernel_size=1,
                                 stride=1,
                                 padding=0)
        self.k = torch.nn.Conv2d(in_channels,
                                 in_channels,
                                 kernel_size=1,
                                 stride=1,
                                 padding=0)
        self.v = torch.nn.Conv2d(in_channels,
                                 in_channels,
                                 kernel_size=1,
                                 stride=1,
                                 padding=0)
        self.proj_out = torch.nn.Conv2d(in_channels,
                                        in_channels,
                                        kernel_size=1,
                                        stride=1,
                                        padding=0)


    def forward(self, x, zq=None):
        h_ = x
        h_ = self.norm(h_, zq)
        q = self.q(h_)
        k = self.k(h_)
        v = self.v(h_)

        # compute attention
        b,c,h,w = q.shape
        q = q.reshape(b,c,h*w)
        q = q.permute(0,2,1)   # b,hw,c
        k = k.reshape(b,c,h*w) # b,c,hw
        w_ = torch.bmm(q,k)     # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
        w_ = w_ * (int(c)**(-0.5))
        w_ = torch.nn.functional.softmax(w_, dim=2)

        # attend to values
        v = v.reshape(b,c,h*w)
        w_ = w_.permute(0,2,1)   # b,hw,hw (first hw of k, second of q)
        h_ = torch.bmm(v,w_)     # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
        h_ = h_.reshape(b,c,h,w)

        h_ = self.proj_out(h_)
        return x+h_
    
    
class Encoder(nn.Module):
    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
                 resolution, z_channels, double_z=True, **ignore_kwargs):
        super().__init__()
        self.ch = ch
        self.temb_ch = 0
        self.num_resolutions = len(ch_mult)
        self.num_res_blocks = num_res_blocks
        self.resolution = resolution
        self.in_channels = in_channels

        # downsampling
        self.conv_in = torch.nn.Conv2d(in_channels,
                                       self.ch,
                                       kernel_size=3,
                                       stride=1,
                                       padding=1)

        curr_res = resolution
        in_ch_mult = (1,)+tuple(ch_mult)
        self.down = nn.ModuleList()
        for i_level in range(self.num_resolutions):
            block = nn.ModuleList()
            attn = nn.ModuleList()
            block_in = ch*in_ch_mult[i_level]
            block_out = ch*ch_mult[i_level]
            for i_block in range(self.num_res_blocks):
                block.append(ResnetBlock(in_channels=block_in,
                                         out_channels=block_out,
                                         temb_channels=self.temb_ch,
                                         dropout=dropout))
                block_in = block_out
                if curr_res in attn_resolutions:
                    attn.append(AttnBlock(block_in))
            down = nn.Module()
            down.block = block
            down.attn = attn
            if i_level != self.num_resolutions-1:
                down.downsample = Downsample(block_in, resamp_with_conv)
                curr_res = curr_res // 2
            self.down.append(down)

        # middle
        self.mid = nn.Module()
        self.mid.block_1 = ResnetBlock(in_channels=block_in,
                                       out_channels=block_in,
                                       temb_channels=self.temb_ch,
                                       dropout=dropout)
        self.mid.attn_1 = AttnBlock(block_in)
        self.mid.block_2 = ResnetBlock(in_channels=block_in,
                                       out_channels=block_in,
                                       temb_channels=self.temb_ch,
                                       dropout=dropout)

        # end
        self.norm_out = Normalize(block_in)
        self.conv_out = torch.nn.Conv2d(block_in,
                                        2*z_channels if double_z else z_channels,
                                        kernel_size=3,
                                        stride=1,
                                        padding=1)


    def forward(self, x):
        temb = None

        # downsampling
        hs = [self.conv_in(x)]
        for i_level in range(self.num_resolutions):
            for i_block in range(self.num_res_blocks):
                h = self.down[i_level].block[i_block](hs[-1], temb)
                if len(self.down[i_level].attn) > 0:
                    h = self.down[i_level].attn[i_block](h)
                hs.append(h)
            if i_level != self.num_resolutions-1:
                hs.append(self.down[i_level].downsample(hs[-1]))

        # middle
        h = hs[-1]
        h = self.mid.block_1(h, temb)
        h = self.mid.attn_1(h)
        h = self.mid.block_2(h, temb)

        # end
        h = self.norm_out(h)
        h = nonlinearity(h)
        h = self.conv_out(h)
        return h


class Decoder(nn.Module):
    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
                 resolution, z_channels, give_pre_end=False, zq_ch=None, add_conv=False, **ignorekwargs):
        super().__init__()
        self.ch = ch
        self.temb_ch = 0
        self.num_resolutions = len(ch_mult)
        self.num_res_blocks = num_res_blocks
        self.resolution = resolution
        self.in_channels = in_channels
        self.give_pre_end = give_pre_end

        # compute in_ch_mult, block_in and curr_res at lowest res
        in_ch_mult = (1,)+tuple(ch_mult)
        block_in = ch*ch_mult[self.num_resolutions-1]
        curr_res = resolution // 2**(self.num_resolutions-1)
        self.z_shape = (1,z_channels,curr_res,curr_res)

        # z to block_in
        self.conv_in = torch.nn.Conv2d(z_channels,
                                       block_in,
                                       kernel_size=3,
                                       stride=1,
                                       padding=1)

        # middle
        self.mid = nn.Module()
        self.mid.block_1 = ResnetBlock(in_channels=block_in,
                                       out_channels=block_in,
                                       temb_channels=self.temb_ch,
                                       dropout=dropout,
                                       zq_ch=zq_ch,
                                       add_conv=add_conv)
        self.mid.attn_1 = AttnBlock(block_in, zq_ch, add_conv=add_conv)
        self.mid.block_2 = ResnetBlock(in_channels=block_in,
                                       out_channels=block_in,
                                       temb_channels=self.temb_ch,
                                       dropout=dropout,
                                       zq_ch=zq_ch,
                                       add_conv=add_conv)

        # upsampling
        self.up = nn.ModuleList()
        for i_level in reversed(range(self.num_resolutions)):
            block = nn.ModuleList()
            attn = nn.ModuleList()
            block_out = ch*ch_mult[i_level]
            for i_block in range(self.num_res_blocks+1):
                block.append(ResnetBlock(in_channels=block_in,
                                         out_channels=block_out,
                                         temb_channels=self.temb_ch,
                                         dropout=dropout,
                                         zq_ch=zq_ch,
                                         add_conv=add_conv))
                block_in = block_out
                if curr_res in attn_resolutions:
                    attn.append(AttnBlock(block_in, zq_ch, add_conv=add_conv))
            up = nn.Module()
            up.block = block
            up.attn = attn
            if i_level != 0:
                up.upsample = Upsample(block_in, resamp_with_conv)
                curr_res = curr_res * 2
            self.up.insert(0, up) # prepend to get consistent order

        # end
        self.norm_out = Normalize(block_in, zq_ch, add_conv=add_conv)
        self.conv_out = torch.nn.Conv2d(block_in,
                                        out_ch,
                                        kernel_size=3,
                                        stride=1,
                                        padding=1)

    def forward(self, z, zq):
        #assert z.shape[1:] == self.z_shape[1:]
        self.last_z_shape = z.shape

        # timestep embedding
        temb = None

        # z to block_in
        h = self.conv_in(z)
        
        # middle
        h = self.mid.block_1(h, temb, zq)
        h = self.mid.attn_1(h, zq)
        h = self.mid.block_2(h, temb, zq)

        # upsampling
        for i_level in reversed(range(self.num_resolutions)):
            for i_block in range(self.num_res_blocks+1):
                h = self.up[i_level].block[i_block](h, temb, zq)
                if len(self.up[i_level].attn) > 0:
                    h = self.up[i_level].attn[i_block](h, zq)
            if i_level != 0:
                h = self.up[i_level].upsample(h)

        # end
        if self.give_pre_end:
            return h

        h = self.norm_out(h, zq)
        h = nonlinearity(h)
        h = self.conv_out(h)
        return h

    
class MoVQ(nn.Module):
    
    def __init__(self, generator_params):
        super().__init__()
        z_channels = generator_params["z_channels"]
        self.encoder = Encoder(**generator_params)
        self.quant_conv = torch.nn.Conv2d(z_channels, z_channels, 1)
        self.post_quant_conv = torch.nn.Conv2d(z_channels, z_channels, 1)
        self.decoder = Decoder(zq_ch=z_channels, **generator_params)
        
        self.tile_sample_min_size = generator_params["tile_sample_min_size"]
        self.scale_factor = 8
        self.tile_latent_min_size = int(self.tile_sample_min_size / self.scale_factor)
        self.tile_overlap_factor_enc = generator_params["tile_overlap_factor_enc"]
        self.tile_overlap_factor_dec = generator_params["tile_overlap_factor_dec"]
        self.use_tiling = generator_params["use_tiling"]
        
    @torch.no_grad()
    def encode(self, x):
        if self.use_tiling and (
            x.shape[-1] > self.tile_sample_min_size
            or x.shape[-2] > self.tile_sample_min_size
        ):
            print('tiled_encode')
            return self.tiled_encode(x)
        h = self.encoder(x)
        h = self.quant_conv(h)
        return h

    @torch.no_grad()
    def decode(self, quant):
        if self.use_tiling and (
            quant.shape[-1] > self.tile_latent_min_size
            or quant.shape[-2] > self.tile_latent_min_size
        ):
            print('tiled_decode')
            return self.tiled_decode(quant)
        decoder_input = self.post_quant_conv(quant)
        decoded = self.decoder(decoder_input, quant)
        return decoded
    
    def blend_v(
        self, a: torch.Tensor, b: torch.Tensor, blend_extent: int
    ) -> torch.Tensor:
        blend_extent = min(a.shape[2], b.shape[2], blend_extent)
        for y in range(blend_extent):
            b[ :, :, y, :] = a[ :, :, -blend_extent + y, :] * (
                1 - y / blend_extent
            ) + b[ :, :, y, :] * (y / blend_extent)
        return b

    def blend_h(
        self, a: torch.Tensor, b: torch.Tensor, blend_extent: int
    ) -> torch.Tensor:
        blend_extent = min(a.shape[3], b.shape[3], blend_extent)
        for x in range(blend_extent):
            b[ :, :, :, x] = a[ :, :, :, -blend_extent + x] * (
                1 - x / blend_extent
            ) + b[ :, :, :, x] * (x / blend_extent)
        return b
    
    def tiled_encode(self, x):
        overlap_size = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor_enc))
        blend_extent = int(self.tile_latent_min_size * self.tile_overlap_factor_enc)
        row_limit = self.tile_latent_min_size - blend_extent

        # Split the image into tiles and encode them separately.
        rows = []
        for i in tqdm(range(0, x.shape[2], overlap_size)):
            row = []
            for j in range(0, x.shape[3], overlap_size):
                tile = x[
                    :,
                    :,
                    i : i + self.tile_sample_min_size,
                    j : j + self.tile_sample_min_size,
                ]
                tile = self.encode(tile)
                row.append(tile)
            rows.append(row)
        result_rows = []
        for i, row in enumerate(rows):
            result_row = []
            for j, tile in enumerate(row):
                # blend the above tile and the left tile
                # to the current tile and add the current tile to the result row
                if i > 0:
                    tile = self.blend_v(rows[i - 1][j], tile, blend_extent)
                if j > 0:
                    tile = self.blend_h(row[j - 1], tile, blend_extent)
                result_row.append(tile[ :, :, :row_limit, :row_limit])
            result_rows.append(torch.cat(result_row, dim=3))

        h = torch.cat(result_rows, dim=2)
        return h
    
    def tiled_decode(self, z):
        overlap_size = int(self.tile_latent_min_size * (1 - self.tile_overlap_factor_dec))
        blend_extent = int(self.tile_sample_min_size * self.tile_overlap_factor_dec)
        row_limit = self.tile_sample_min_size - blend_extent

        # Split z into overlapping tiles and decode them separately.
        # The tiles have an overlap to avoid seams between tiles.
        rows = []
        for i in tqdm(range(0, z.shape[2], overlap_size)):
            row = []
            for j in range(0, z.shape[3], overlap_size):
                tile = z[
                    :,
                    :,
                    i : i + self.tile_latent_min_size,
                    j : j + self.tile_latent_min_size,
                ]
                decoded = self.decode(tile)
                row.append(decoded)
            rows.append(row)
        result_rows = []
        for i, row in enumerate(rows):
            result_row = []
            for j, tile in enumerate(row):
                # blend the above tile and the left tile
                # to the current tile and add the current tile to the result row
                if i > 0:
                    tile = self.blend_v(rows[i - 1][j], tile, blend_extent)
                if j > 0:
                    tile = self.blend_h(row[j - 1], tile, blend_extent)
                result_row.append(tile[ :, :, :row_limit, :row_limit])
            result_rows.append(torch.cat(result_row, dim=3))

        dec = torch.cat(result_rows, dim=2)
        return dec

    
def get_vae(conf):
    movq = MoVQ(conf.params)
    if conf.checkpoint is not None:
        movq_state_dict = torch.load(conf.checkpoint)
        movq.load_state_dict(movq_state_dict)
    movq = freeze(movq)
    return movq