# coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# pylint: skip-file

from . import utils, layers, layerspp, normalization
import torch.nn as nn
import functools
import torch
import numpy as np
from models.utils import zero_module

from ipdb import set_trace as debug

ResnetBlockDDPM = layerspp.ResnetBlockDDPMpp
ResnetBlockBigGAN = layerspp.ResnetBlockBigGANpp
Combine = layerspp.Combine
conv3x3 = layerspp.conv3x3
conv1x1 = layerspp.conv1x1
get_act = layers.get_act
get_normalization = normalization.get_normalization
default_initializer = layers.default_init

def build_ncsnpp(config, compute_sigma, zero_out_last_layer):
    return NCSNpp(config, compute_sigma, zero_out_last_layer)

@utils.register_model(name='ncsnpp')
class NCSNpp(nn.Module):
    """NCSN++ model"""
    
    def __init__(self, config, compute_sigma, zero_out_last_layer):
        super().__init__()
        self.config = config
        self.compute_sigma = compute_sigma
        self.zero_out_last_layer = zero_out_last_layer
        self.act = act = get_act(config)
        # self.sigmas=self.sigmas.float()
        self.nf = nf = config.model.nf
        ch_mult = config.model.ch_mult
        self.num_res_blocks = num_res_blocks = config.model.num_res_blocks
        self.attn_resolutions = attn_resolutions = config.model.attn_resolutions
        dropout = config.model.dropout
        resamp_with_conv = config.model.resamp_with_conv
        self.num_resolutions = num_resolutions = len(ch_mult)
        self.all_resolutions = all_resolutions = [config.data.image_size // (2 ** i) for i in range(num_resolutions)]

        self.conditional = conditional = config.model.conditional  # noise-conditional
        fir = config.model.fir
        fir_kernel = config.model.fir_kernel
        self.skip_rescale = skip_rescale = config.model.skip_rescale
        self.resblock_type = resblock_type = config.model.resblock_type.lower()
        self.progressive = progressive = config.model.progressive.lower()
        self.progressive_input = progressive_input = config.model.progressive_input.lower()
        self.embedding_type = embedding_type = config.model.embedding_type.lower()
        init_scale = config.model.init_scale
        assert progressive in ['none', 'output_skip', 'residual']
        assert progressive_input in ['none', 'input_skip', 'residual']
        assert embedding_type in ['fourier', 'positional']
        combine_method = config.model.progressive_combine.lower()
        combiner = functools.partial(Combine, method=combine_method)

        modules = []
        # timestep/noise_level embedding; only for continuous training
        if embedding_type == 'fourier':
            # Gaussian Fourier features embeddings.
            assert config.training.continuous, "Fourier features are only used for continuous training."

            modules.append(layerspp.GaussianFourierProjection(
                embedding_size=nf, scale=config.model.fourier_scale
            ))
            embed_dim = 2 * nf

        elif embedding_type == 'positional':
            embed_dim = nf

        else:
            raise ValueError(f'embedding type {embedding_type} unknown.')

        if conditional:
            modules.append(nn.Linear(embed_dim, nf * 4))
            modules[-1].weight.data = default_initializer()(modules[-1].weight.shape)
            nn.init.zeros_(modules[-1].bias)
            modules.append(nn.Linear(nf * 4, nf * 4))
            modules[-1].weight.data = default_initializer()(modules[-1].weight.shape)
            nn.init.zeros_(modules[-1].bias)

        AttnBlock = functools.partial(layerspp.AttnBlockpp,
                                                                    init_scale=init_scale,
                                                                    skip_rescale=skip_rescale)

        Upsample = functools.partial(layerspp.Upsample,
                                                                 with_conv=resamp_with_conv, fir=fir, fir_kernel=fir_kernel)

        if progressive == 'output_skip':
            self.pyramid_upsample = layerspp.Upsample(fir=fir, fir_kernel=fir_kernel, with_conv=False)
        elif progressive == 'residual':
            pyramid_upsample = functools.partial(layerspp.Upsample,
                                                                                     fir=fir, fir_kernel=fir_kernel, with_conv=True)

        Downsample = functools.partial(layerspp.Downsample,
                                                                     with_conv=resamp_with_conv, fir=fir, fir_kernel=fir_kernel)

        if progressive_input == 'input_skip':
            self.pyramid_downsample = layerspp.Downsample(fir=fir, fir_kernel=fir_kernel, with_conv=False)
        elif progressive_input == 'residual':
            pyramid_downsample = functools.partial(layerspp.Downsample,
                                                                                         fir=fir, fir_kernel=fir_kernel, with_conv=True)

        if resblock_type == 'ddpm':
            ResnetBlock = functools.partial(ResnetBlockDDPM,
                                                                            act=act,
                                                                            dropout=dropout,
                                                                            init_scale=init_scale,
                                                                            skip_rescale=skip_rescale,
                                                                            temb_dim=nf * 4)

        elif resblock_type == 'biggan':
            ResnetBlock = functools.partial(ResnetBlockBigGAN,
                                                                            act=act,
                                                                            dropout=dropout,
                                                                            fir=fir,
                                                                            fir_kernel=fir_kernel,
                                                                            init_scale=init_scale,
                                                                            skip_rescale=skip_rescale,
                                                                            temb_dim=nf * 4)

        else:
            raise ValueError(f'resblock type {resblock_type} unrecognized.')

        # Downsampling block

        channels = config.data.num_channels
        if progressive_input != 'none':
            input_pyramid_ch = channels

        modules.append(conv3x3(channels, nf))
        hs_c = [nf]

        in_ch = nf
        for i_level in range(num_resolutions):
            # Residual blocks for this resolution
            for i_block in range(num_res_blocks):
                out_ch = nf * ch_mult[i_level]
                modules.append(ResnetBlock(in_ch=in_ch, out_ch=out_ch))
                in_ch = out_ch

                if all_resolutions[i_level] in attn_resolutions:
                    modules.append(AttnBlock(channels=in_ch))
                hs_c.append(in_ch)

            if i_level != num_resolutions - 1:
                if resblock_type == 'ddpm':
                    modules.append(Downsample(in_ch=in_ch))
                else:
                    modules.append(ResnetBlock(down=True, in_ch=in_ch))

                if progressive_input == 'input_skip':
                    modules.append(combiner(dim1=input_pyramid_ch, dim2=in_ch))
                    if combine_method == 'cat':
                        in_ch *= 2

                elif progressive_input == 'residual':
                    modules.append(pyramid_downsample(in_ch=input_pyramid_ch, out_ch=in_ch))
                    input_pyramid_ch = in_ch

                hs_c.append(in_ch)

        in_ch = hs_c[-1]
        modules.append(ResnetBlock(in_ch=in_ch))
        modules.append(AttnBlock(channels=in_ch))
        modules.append(ResnetBlock(in_ch=in_ch))

        pyramid_ch = 0
        # Upsampling block
        for i_level in reversed(range(num_resolutions)):
            for i_block in range(num_res_blocks + 1):
                out_ch = nf * ch_mult[i_level]
                modules.append(ResnetBlock(in_ch=in_ch + hs_c.pop(),
                                                                     out_ch=out_ch))
                in_ch = out_ch

            if all_resolutions[i_level] in attn_resolutions:
                modules.append(AttnBlock(channels=in_ch))

            if progressive != 'none':
                if i_level == num_resolutions - 1:
                    if progressive == 'output_skip':
                        modules.append(nn.GroupNorm(num_groups=min(in_ch // 4, 32),
                                                                                num_channels=in_ch, eps=1e-6))
                        modules.append(conv3x3(in_ch, channels, init_scale=init_scale))
                        pyramid_ch = channels
                    elif progressive == 'residual':
                        modules.append(nn.GroupNorm(num_groups=min(in_ch // 4, 32),
                                                                                num_channels=in_ch, eps=1e-6))
                        modules.append(conv3x3(in_ch, in_ch, bias=True))
                        pyramid_ch = in_ch
                    else:
                        raise ValueError(f'{progressive} is not a valid name.')
                else:
                    if progressive == 'output_skip':
                        modules.append(nn.GroupNorm(num_groups=min(in_ch // 4, 32),
                                                                                num_channels=in_ch, eps=1e-6))
                        modules.append(conv3x3(in_ch, channels, bias=True, init_scale=init_scale))
                        pyramid_ch = channels
                    elif progressive == 'residual':
                        modules.append(pyramid_upsample(in_ch=pyramid_ch, out_ch=in_ch))
                        pyramid_ch = in_ch
                    else:
                        raise ValueError(f'{progressive} is not a valid name')

            if i_level != 0:
                if resblock_type == 'ddpm':
                    modules.append(Upsample(in_ch=in_ch))
                else:
                    modules.append(ResnetBlock(in_ch=in_ch, up=True))

        assert not hs_c

        if progressive != 'output_skip':
            modules.append(nn.GroupNorm(num_groups=min(in_ch // 4, 32),
                                                                    num_channels=in_ch, eps=1e-6))
            modules.append(conv3x3(in_ch, channels, init_scale=init_scale))

        # zero out last layer
        if zero_out_last_layer:
            modules[-1] = zero_module(modules[-1])

        self.all_modules = nn.ModuleList(modules)

    def forward(self, x, time_cond):
        assert time_cond.shape[0] == x.shape[0]

        # timestep/noise_level embedding; only for continuous training
        modules = self.all_modules
        m_idx = 0
        # time_cond=torch.unsqueeze(time_cond,0)
        if self.embedding_type == 'fourier':
            # Gaussian Fourier features embeddings.
            time_cond=torch.ones(x.shape[0], device=time_cond.device) * time_cond
            used_sigmas = time_cond
            temb = modules[m_idx](torch.log(used_sigmas))
            m_idx += 1

        elif self.embedding_type == 'positional':
            analytic_sigma = self.compute_sigma(time_cond)

            # TODO(ghliu) map time_cond from [0,1] to [0,999] so that pretrained model works as usual
            idx = (1.0 - time_cond) * 1000 #### Hardcoded to transform [0,1] to [1000,0]

            time_cond = torch.round(idx)
            time_cond = torch.clamp(time_cond, min=0)
            # Sinusoidal positional embeddings.
            timesteps = time_cond
            used_sigmas=analytic_sigma
            # used_sigmas = self.sigmas[time_cond.long()]
            temb = layers.get_timestep_embedding(timesteps, self.nf) # batch_t x nf

        else:
            raise ValueError(f'embedding type {self.embedding_type} unknown.')

        if self.conditional:
            temb = modules[m_idx](temb)
            m_idx += 1
            temb = modules[m_idx](self.act(temb))
            m_idx += 1
        else:
            temb = None

        if not self.config.data.centered:
            # If input data is in [0, 1]
            x = 2 * x - 1.

        # Downsampling block
        input_pyramid = None
        if self.progressive_input != 'none':
            input_pyramid = x

        hs = [modules[m_idx](x)]

        m_idx += 1
        for i_level in range(self.num_resolutions):
            # Residual blocks for this resolution
            for i_block in range(self.num_res_blocks):
                h = modules[m_idx](hs[-1], temb)
                m_idx += 1
                if h.shape[-1] in self.attn_resolutions:
                    h = modules[m_idx](h)
                    m_idx += 1

                hs.append(h)

            if i_level != self.num_resolutions - 1:
                if self.resblock_type == 'ddpm':
                    h = modules[m_idx](hs[-1])
                    m_idx += 1
                else:
                    h = modules[m_idx](hs[-1], temb)
                    m_idx += 1

                if self.progressive_input == 'input_skip':
                    input_pyramid = self.pyramid_downsample(input_pyramid)
                    h = modules[m_idx](input_pyramid, h)
                    m_idx += 1

                elif self.progressive_input == 'residual':
                    input_pyramid = modules[m_idx](input_pyramid)
                    m_idx += 1
                    if self.skip_rescale:
                        input_pyramid = (input_pyramid + h) / np.sqrt(2.)
                    else:
                        input_pyramid = input_pyramid + h
                    h = input_pyramid

                hs.append(h)

        h = hs[-1]
        h = modules[m_idx](h, temb)
        m_idx += 1
        h = modules[m_idx](h)
        m_idx += 1
        h = modules[m_idx](h, temb)
        m_idx += 1

        pyramid = None

        # Upsampling block
        for i_level in reversed(range(self.num_resolutions)):
            for i_block in range(self.num_res_blocks + 1):
                h = modules[m_idx](torch.cat([h, hs.pop()], dim=1), temb)
                m_idx += 1

            if h.shape[-1] in self.attn_resolutions:
                h = modules[m_idx](h)
                m_idx += 1

            if self.progressive != 'none':
                if i_level == self.num_resolutions - 1:
                    if self.progressive == 'output_skip':
                        pyramid = self.act(modules[m_idx](h))
                        m_idx += 1
                        pyramid = modules[m_idx](pyramid)
                        m_idx += 1
                    elif self.progressive == 'residual':
                        pyramid = self.act(modules[m_idx](h))
                        m_idx += 1
                        pyramid = modules[m_idx](pyramid)
                        m_idx += 1
                    else:
                        raise ValueError(f'{self.progressive} is not a valid name.')
                else:
                    if self.progressive == 'output_skip':
                        pyramid = self.pyramid_upsample(pyramid)
                        pyramid_h = self.act(modules[m_idx](h))
                        m_idx += 1
                        pyramid_h = modules[m_idx](pyramid_h)
                        m_idx += 1
                        pyramid = pyramid + pyramid_h
                    elif self.progressive == 'residual':
                        pyramid = modules[m_idx](pyramid)
                        m_idx += 1
                        if self.skip_rescale:
                            pyramid = (pyramid + h) / np.sqrt(2.)
                        else:
                            pyramid = pyramid + h
                        h = pyramid
                    else:
                        raise ValueError(f'{self.progressive} is not a valid name')

            if i_level != 0:
                if self.resblock_type == 'ddpm':
                    h = modules[m_idx](h)
                    m_idx += 1
                else:
                    h = modules[m_idx](h, temb)
                    m_idx += 1

        assert not hs

        if self.progressive == 'output_skip':
            h = pyramid
        else:
            h = self.act(modules[m_idx](h))
            m_idx += 1
            h = modules[m_idx](h)
            m_idx += 1

        assert m_idx == len(modules)
        if self.config.model.scale_by_sigma:
            used_sigmas = used_sigmas.reshape(x.shape[0], *([1,]*(x.dim()-1)))
            h = h / used_sigmas
        return h
