#!/usr/bin/python3
# -*- coding: utf-8 -*-
"""
DeepFilterNet 的原生实现不直接支持流式推理

社区开发者（如 Rikorose）提供了基于 Torch 的流式推理实现
https://github.com/grazder/DeepFilterNet/tree/1097015d53ced78fb234e7d7071a5dd4446e3952/torchDF

此文件试图实现一个支持流式推理的 dfnet

"""
import os
import math
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union

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

from toolbox.torchaudio.configuration_utils import CONFIG_FILE
from toolbox.torchaudio.models.dfnet.configuration_dfnet import DfNetConfig
from toolbox.torchaudio.modules.conv_stft import ConvSTFT, ConviSTFT
from toolbox.torchaudio.modules.local_snr_target import LocalSnrTarget
from toolbox.torchaudio.modules.freq_bands.erb_bands import ErbBands


MODEL_FILE = "model.pt"


norm_layer_dict = {
    "batch_norm_2d": torch.nn.BatchNorm2d
}


activation_layer_dict = {
    "relu": torch.nn.ReLU,
    "identity": torch.nn.Identity,
    "sigmoid": torch.nn.Sigmoid,
}


class CausalConv2d(nn.Module):
    def __init__(self,
                 in_channels: int,
                 out_channels: int,
                 kernel_size: Union[int, Iterable[int]],
                 fstride: int = 1,
                 dilation: int = 1,
                 pad_f_dim: bool = True,
                 bias: bool = True,
                 separable: bool = False,
                 norm_layer: str = "batch_norm_2d",
                 activation_layer: str = "relu",
                 ):
        super(CausalConv2d, self).__init__()
        kernel_size = (kernel_size, kernel_size) if isinstance(kernel_size, int) else tuple(kernel_size)

        if pad_f_dim:
            fpad = kernel_size[1] // 2 + dilation - 1
        else:
            fpad = 0

        # for last 2 dim, pad (left, right, top, bottom).
        self.lookback = kernel_size[0] - 1
        if self.lookback > 0:
            self.tpad = nn.ConstantPad2d(padding=(0, 0, self.lookback, 0), value=0.0)
        else:
            self.tpad = nn.Identity()

        groups = math.gcd(in_channels, out_channels) if separable else 1
        if groups == 1:
            separable = False
        if max(kernel_size) == 1:
            separable = False

        self.conv = nn.Conv2d(
            in_channels,
            out_channels,
            kernel_size=kernel_size,
            padding=(0, fpad),
            stride=(1, fstride),  # stride over time is always 1
            dilation=(1, dilation),  # dilation over time is always 1
            groups=groups,
            bias=bias,
        )

        if separable:
            self.convp = nn.Conv2d(
                out_channels,
                out_channels,
                kernel_size=1,
                bias=False,
            )
        else:
            self.convp = nn.Identity()

        if norm_layer is not None:
            norm_layer = norm_layer_dict[norm_layer]
            self.norm = norm_layer(out_channels)
        else:
            self.norm = nn.Identity()

        if activation_layer is not None:
            activation_layer = activation_layer_dict[activation_layer]
            self.activation = activation_layer()
        else:
            self.activation = nn.Identity()

        super().__init__()

    def forward(self, inputs: torch.Tensor, cache: Tuple[torch.Tensor, torch.Tensor] = None):
        """
        :param inputs: shape: [b, c, t, f]
        :param cache: shape: [b, c, lookback, f];
        :return:
        """
        x = inputs

        if cache is None:
            x = self.tpad(x)
        else:
            x = torch.concat(tensors=[cache, x], dim=2)
        new_cache = x[:, :, -self.lookback:, :]

        x = self.conv(x)

        x = self.convp(x)
        x = self.norm(x)
        x = self.activation(x)

        return x, new_cache


class CausalConvTranspose2d(nn.Module):
    def __init__(self,
                 in_channels: int,
                 out_channels: int,
                 kernel_size: Union[int, Iterable[int]],
                 fstride: int = 1,
                 dilation: int = 1,
                 pad_f_dim: bool = True,
                 bias: bool = True,
                 separable: bool = False,
                 norm_layer: str = "batch_norm_2d",
                 activation_layer: str = "relu",
                 ):
        super(CausalConvTranspose2d, self).__init__()

        kernel_size = (kernel_size, kernel_size) if isinstance(kernel_size, int) else kernel_size

        if pad_f_dim:
            fpad = kernel_size[1] // 2
        else:
            fpad = 0

        # for last 2 dim, pad (left, right, top, bottom).
        self.lookback = kernel_size[0] - 1

        groups = math.gcd(in_channels, out_channels) if separable else 1
        if groups == 1:
            separable = False

        self.convt = nn.ConvTranspose2d(
            in_channels,
            out_channels,
            kernel_size=kernel_size,
            padding=(0, fpad),
            output_padding=(0, 0),
            stride=(1, fstride),  # stride over time is always 1
            dilation=(1, dilation),  # dilation over time is always 1
            groups=groups,
            bias=bias,
        )

        if separable:
            self.convp = nn.Conv2d(
                out_channels,
                out_channels,
                kernel_size=1,
                bias=False,
            )
        else:
            self.convp = nn.Identity()

        if norm_layer is not None:
            norm_layer = norm_layer_dict[norm_layer]
            self.norm = norm_layer(out_channels)
        else:
            self.norm = nn.Identity()

        if activation_layer is not None:
            activation_layer = activation_layer_dict[activation_layer]
            self.activation = activation_layer()
        else:
            self.activation = nn.Identity()

    def forward(self, inputs: torch.Tensor, cache: Tuple[torch.Tensor, torch.Tensor] = None):
        """
        :param inputs: shape: [b, c, t, f]
        :param cache: shape: [b, c, lookback, f];
        :return:
        """
        x = inputs

        # x shape: [b, c, t, f]
        x = self.convt(x)
        # x shape: [b, c, t+lookback, f]

        if cache is not None:
            x = torch.concat(tensors=[
                x[:, :, :self.lookback, :] + cache,
                x[:, :, self.lookback:, :]
            ], dim=2)
        x = x[:, :, :-self.lookback, :]
        new_cache = x[:, :, -self.lookback:, :]

        x = self.convp(x)
        x = self.norm(x)
        x = self.activation(x)

        return x, new_cache


if __name__ == "__main__":
    pass