"""This file contains some base implementation for discrminators.

Copyright (2024) Bytedance Ltd. and/or its affiliates

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. 

TODO: Add reference to Mark Weber's tech report on the improved discriminator architecture.
"""
import functools
import math
from typing import Tuple


import torch
import torch.nn as nn
import torch.nn.functional as F

from .maskgit_vqgan import Conv2dSame


class BlurBlock(torch.nn.Module):
    def __init__(self,
                 kernel: Tuple[int] = (1, 3, 3, 1)
                 ):
        super().__init__()

        kernel = torch.tensor(kernel, dtype=torch.float32, requires_grad=False)
        kernel = kernel[None, :] * kernel[:, None]
        kernel /= kernel.sum()
        kernel = kernel.unsqueeze(0).unsqueeze(0)
        self.register_buffer("kernel", kernel)

    def calc_same_pad(self, i: int, k: int, s: int) -> int:
        return max((math.ceil(i / s) - 1) * s + (k - 1) + 1 - i, 0)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        ic, ih, iw = x.size()[-3:]
        pad_h = self.calc_same_pad(i=ih, k=4, s=2)
        pad_w = self.calc_same_pad(i=iw, k=4, s=2)
        if pad_h > 0 or pad_w > 0:
            x = F.pad(x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2])

        weight = self.kernel.expand(ic, -1, -1, -1)

        out = F.conv2d(input=x, weight=weight, stride=2, groups=x.shape[1])
        return out


class NLayerDiscriminator(torch.nn.Module):
    def __init__(
        self,
        num_channels: int = 3,
        hidden_channels: int = 128,
        num_stages: int = 3,
        blur_resample: bool = True,
        blur_kernel_size: int = 4
    ):
        """ Initializes the NLayerDiscriminator.

        Args:
            num_channels -> int: The number of input channels.
            hidden_channels -> int: The number of hidden channels.
            num_stages -> int: The number of stages.
            blur_resample -> bool: Whether to use blur resampling.
            blur_kernel_size -> int: The blur kernel size.
        """
        super().__init__()
        assert num_stages > 0, "Discriminator cannot have 0 stages"
        assert (not blur_resample) or (blur_kernel_size >= 3 and blur_kernel_size <= 5), "Blur kernel size must be in [3,5] when sampling]"

        in_channel_mult = (1,) + tuple(map(lambda t: 2**t, range(num_stages)))
        init_kernel_size = 5
        activation = functools.partial(torch.nn.LeakyReLU, negative_slope=0.1)

        self.block_in = torch.nn.Sequential(
            Conv2dSame(
                num_channels,
                hidden_channels,
                kernel_size=init_kernel_size
            ),
            activation(),
        )

        BLUR_KERNEL_MAP = {
            3: (1,2,1),
            4: (1,3,3,1),
            5: (1,4,6,4,1),
        }

        discriminator_blocks = []
        for i_level in range(num_stages):
            in_channels = hidden_channels * in_channel_mult[i_level]
            out_channels = hidden_channels * in_channel_mult[i_level + 1]
            block = torch.nn.Sequential(
                Conv2dSame(
                    in_channels,
                    out_channels,
                    kernel_size=3,
                ),
                torch.nn.AvgPool2d(kernel_size=2, stride=2) if not blur_resample else BlurBlock(BLUR_KERNEL_MAP[blur_kernel_size]),
                torch.nn.GroupNorm(32, out_channels),
                activation(),
            )
            discriminator_blocks.append(block)

        self.blocks = torch.nn.ModuleList(discriminator_blocks)

        self.pool = torch.nn.AdaptiveMaxPool2d((16, 16))

        self.to_logits = torch.nn.Sequential(
            Conv2dSame(out_channels, out_channels, 1),
            activation(),
            Conv2dSame(out_channels, 1, kernel_size=5)
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """ Forward pass.

        Args:
            x -> torch.Tensor: The input tensor.

        Returns:
            output -> torch.Tensor: The output tensor.
        """
        hidden_states = self.block_in(x)
        for block in self.blocks:
            hidden_states = block(hidden_states)

        hidden_states = self.pool(hidden_states)

        return self.to_logits(hidden_states)