""" Conditional Convolution

Hacked together by Ross Wightman
"""

import math
from functools import partial
import numpy as np
import torch
from torch import nn as nn
from torch.nn import functional as F

from .conv2d_same import get_padding_value, conv2d_same
from .conv_helpers import tup_pair


def get_condconv_initializer(initializer, num_experts, expert_shape):
    def condconv_initializer(weight):
        """CondConv initializer function."""
        num_params = np.prod(expert_shape)
        if (len(weight.shape) != 2 or weight.shape[0] != num_experts or
                weight.shape[1] != num_params):
            raise (ValueError(
                'CondConv variables must have shape [num_experts, num_params]'))
        for i in range(num_experts):
            initializer(weight[i].view(expert_shape))
    return condconv_initializer


class CondConv2d(nn.Module):
    """ Conditional Convolution
    Inspired by: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/condconv/condconv_layers.py

    Grouped convolution hackery for parallel execution of the per-sample kernel filters inspired by this discussion:
    https://github.com/pytorch/pytorch/issues/17983
    """
    __constants__ = ['bias', 'in_channels', 'out_channels', 'dynamic_padding']

    def __init__(self, in_channels, out_channels, kernel_size=3,
                 stride=1, padding='', dilation=1, groups=1, bias=False, num_experts=4):
        super(CondConv2d, self).__init__()

        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = tup_pair(kernel_size)
        self.stride = tup_pair(stride)
        padding_val, is_padding_dynamic = get_padding_value(
            padding, kernel_size, stride=stride, dilation=dilation)
        self.dynamic_padding = is_padding_dynamic  # if in forward to work with torchscript
        self.padding = tup_pair(padding_val)
        self.dilation = tup_pair(dilation)
        self.groups = groups
        self.num_experts = num_experts

        self.weight_shape = (self.out_channels, self.in_channels // self.groups) + self.kernel_size
        weight_num_param = 1
        for wd in self.weight_shape:
            weight_num_param *= wd
        self.weight = torch.nn.Parameter(torch.Tensor(self.num_experts, weight_num_param))

        if bias:
            self.bias_shape = (self.out_channels,)
            self.bias = torch.nn.Parameter(torch.Tensor(self.num_experts, self.out_channels))
        else:
            self.register_parameter('bias', None)

        self.reset_parameters()

    def reset_parameters(self):
        init_weight = get_condconv_initializer(
            partial(nn.init.kaiming_uniform_, a=math.sqrt(5)), self.num_experts, self.weight_shape)
        init_weight(self.weight)
        if self.bias is not None:
            fan_in = np.prod(self.weight_shape[1:])
            bound = 1 / math.sqrt(fan_in)
            init_bias = get_condconv_initializer(
                partial(nn.init.uniform_, a=-bound, b=bound), self.num_experts, self.bias_shape)
            init_bias(self.bias)

    def forward(self, x, routing_weights):
        B, C, H, W = x.shape
        weight = torch.matmul(routing_weights, self.weight)
        new_weight_shape = (B * self.out_channels, self.in_channels // self.groups) + self.kernel_size
        weight = weight.view(new_weight_shape)
        bias = None
        if self.bias is not None:
            bias = torch.matmul(routing_weights, self.bias)
            bias = bias.view(B * self.out_channels)
        # move batch elements with channels so each batch element can be efficiently convolved with separate kernel
        x = x.view(1, B * C, H, W)
        if self.dynamic_padding:
            out = conv2d_same(
                x, weight, bias, stride=self.stride, padding=self.padding,
                dilation=self.dilation, groups=self.groups * B)
        else:
            out = F.conv2d(
                x, weight, bias, stride=self.stride, padding=self.padding,
                dilation=self.dilation, groups=self.groups * B)
        out = out.permute([1, 0, 2, 3]).view(B, self.out_channels, out.shape[-2], out.shape[-1])

        # Literal port (from TF definition)
        # x = torch.split(x, 1, 0)
        # weight = torch.split(weight, 1, 0)
        # if self.bias is not None:
        #     bias = torch.matmul(routing_weights, self.bias)
        #     bias = torch.split(bias, 1, 0)
        # else:
        #     bias = [None] * B
        # out = []
        # for xi, wi, bi in zip(x, weight, bias):
        #     wi = wi.view(*self.weight_shape)
        #     if bi is not None:
        #         bi = bi.view(*self.bias_shape)
        #     out.append(self.conv_fn(
        #         xi, wi, bi, stride=self.stride, padding=self.padding,
        #         dilation=self.dilation, groups=self.groups))
        # out = torch.cat(out, 0)
        return out