# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.

import torch.nn.functional as F
import torch.nn as nn
import torch
from torch.nn.parameter import Parameter

from search.rm_search.ofa.utils import get_same_padding, sub_filter_start_end, make_divisible, SEModule, MyNetwork, MyConv2d

__all__ = ['DynamicSeparableConv2d', 'DynamicConv2d', 'DynamicGroupConv2d',
           'DynamicBatchNorm2d', 'DynamicGroupNorm', 'DynamicSE', 'DynamicLinear']


class DynamicSeparableConv2d(nn.Module):
	KERNEL_TRANSFORM_MODE = 1  # None or 1

	def __init__(self, max_in_channels, kernel_size_list, stride=1, dilation=1):
		super(DynamicSeparableConv2d, self).__init__()

		self.max_in_channels = max_in_channels
		self.kernel_size_list = kernel_size_list
		self.stride = stride
		self.dilation = dilation

		self.conv = nn.Conv2d(
			self.max_in_channels, self.max_in_channels, max(self.kernel_size_list), self.stride,
			groups=self.max_in_channels, bias=False,
		)

		self._ks_set = list(set(self.kernel_size_list))
		self._ks_set.sort()  # e.g., [3, 5, 7]
		if self.KERNEL_TRANSFORM_MODE is not None:
			# register scaling parameters
			# 7to5_matrix, 5to3_matrix
			scale_params = {}
			for i in range(len(self._ks_set) - 1):
				ks_small = self._ks_set[i]
				ks_larger = self._ks_set[i + 1]
				param_name = '%dto%d' % (ks_larger, ks_small)
				# noinspection PyArgumentList
				scale_params['%s_matrix' % param_name] = Parameter(torch.eye(ks_small ** 2))
			for name, param in scale_params.items():
				self.register_parameter(name, param)

		self.active_kernel_size = max(self.kernel_size_list)

	def get_active_filter(self, in_channel, kernel_size):
		out_channel = in_channel
		max_kernel_size = max(self.kernel_size_list)

		start, end = sub_filter_start_end(max_kernel_size, kernel_size)
		filters = self.conv.weight[:out_channel, :in_channel, start:end, start:end]
		if self.KERNEL_TRANSFORM_MODE is not None and kernel_size < max_kernel_size:
			start_filter = self.conv.weight[:out_channel, :in_channel, :, :]  # start with max kernel
			for i in range(len(self._ks_set) - 1, 0, -1):
				src_ks = self._ks_set[i]
				if src_ks <= kernel_size:
					break
				target_ks = self._ks_set[i - 1]
				start, end = sub_filter_start_end(src_ks, target_ks)
				_input_filter = start_filter[:, :, start:end, start:end]
				_input_filter = _input_filter.contiguous()
				_input_filter = _input_filter.view(_input_filter.size(0), _input_filter.size(1), -1)
				_input_filter = _input_filter.view(-1, _input_filter.size(2))
				_input_filter = F.linear(
					_input_filter, self.__getattr__('%dto%d_matrix' % (src_ks, target_ks)),
				)
				_input_filter = _input_filter.view(filters.size(0), filters.size(1), target_ks ** 2)
				_input_filter = _input_filter.view(filters.size(0), filters.size(1), target_ks, target_ks)
				start_filter = _input_filter
			filters = start_filter
		return filters

	def forward(self, x, kernel_size=None):
		if kernel_size is None:
			kernel_size = self.active_kernel_size
		in_channel = x.size(1)

		filters = self.get_active_filter(in_channel, kernel_size).contiguous()

		padding = get_same_padding(kernel_size)
		filters = self.conv.weight_standardization(filters) if isinstance(self.conv, MyConv2d) else filters
		y = F.conv2d(
			x, filters, None, self.stride, padding, self.dilation, in_channel
		)
		return y


class DynamicConv2d(nn.Module):

	def __init__(self, max_in_channels, max_out_channels, kernel_size=1, stride=1, dilation=1):
		super(DynamicConv2d, self).__init__()

		self.max_in_channels = max_in_channels
		self.max_out_channels = max_out_channels
		self.kernel_size = kernel_size
		self.stride = stride
		self.dilation = dilation

		self.conv = nn.Conv2d(
			self.max_in_channels, self.max_out_channels, self.kernel_size, stride=self.stride, bias=False,
		)

		self.active_out_channel = self.max_out_channels

	def get_active_filter(self, out_channel, in_channel):
		return self.conv.weight[:out_channel, :in_channel, :, :]

	def forward(self, x, out_channel=None):
		if out_channel is None:
			out_channel = self.active_out_channel
		in_channel = x.size(1)
		filters = self.get_active_filter(out_channel, in_channel).contiguous()

		padding = get_same_padding(self.kernel_size)
		filters = self.conv.weight_standardization(filters) if isinstance(self.conv, MyConv2d) else filters
		y = F.conv2d(x, filters, None, self.stride, padding, self.dilation, 1)
		return y


class DynamicGroupConv2d(nn.Module):

	def __init__(self, in_channels, out_channels, kernel_size_list, groups_list, stride=1, dilation=1):
		super(DynamicGroupConv2d, self).__init__()

		self.in_channels = in_channels
		self.out_channels = out_channels
		self.kernel_size_list = kernel_size_list
		self.groups_list = groups_list
		self.stride = stride
		self.dilation = dilation

		self.conv = nn.Conv2d(
			self.in_channels, self.out_channels, max(self.kernel_size_list), self.stride,
			groups=min(self.groups_list), bias=False,
		)

		self.active_kernel_size = max(self.kernel_size_list)
		self.active_groups = min(self.groups_list)

	def get_active_filter(self, kernel_size, groups):
		start, end = sub_filter_start_end(max(self.kernel_size_list), kernel_size)
		filters = self.conv.weight[:, :, start:end, start:end]

		sub_filters = torch.chunk(filters, groups, dim=0)
		sub_in_channels = self.in_channels // groups
		sub_ratio = filters.size(1) // sub_in_channels

		filter_crops = []
		for i, sub_filter in enumerate(sub_filters):
			part_id = i % sub_ratio
			start = part_id * sub_in_channels
			filter_crops.append(sub_filter[:, start:start + sub_in_channels, :, :])
		filters = torch.cat(filter_crops, dim=0)
		return filters

	def forward(self, x, kernel_size=None, groups=None):
		if kernel_size is None:
			kernel_size = self.active_kernel_size
		if groups is None:
			groups = self.active_groups

		filters = self.get_active_filter(kernel_size, groups).contiguous()
		padding = get_same_padding(kernel_size)
		filters = self.conv.weight_standardization(filters) if isinstance(self.conv, MyConv2d) else filters
		y = F.conv2d(
			x, filters, None, self.stride, padding, self.dilation, groups,
		)
		return y


class DynamicBatchNorm2d(nn.Module):
	SET_RUNNING_STATISTICS = False

	def __init__(self, max_feature_dim):
		super(DynamicBatchNorm2d, self).__init__()

		self.max_feature_dim = max_feature_dim
		self.bn = nn.BatchNorm2d(self.max_feature_dim)

	@staticmethod
	def bn_forward(x, bn: nn.BatchNorm2d, feature_dim):
		if bn.num_features == feature_dim or DynamicBatchNorm2d.SET_RUNNING_STATISTICS:
			return bn(x)
		else:
			exponential_average_factor = 0.0

			if bn.training and bn.track_running_stats:
				if bn.num_batches_tracked is not None:
					bn.num_batches_tracked += 1
					if bn.momentum is None:  # use cumulative moving average
						exponential_average_factor = 1.0 / float(bn.num_batches_tracked)
					else:  # use exponential moving average
						exponential_average_factor = bn.momentum
			return F.batch_norm(
				x, bn.running_mean[:feature_dim], bn.running_var[:feature_dim], bn.weight[:feature_dim],
				bn.bias[:feature_dim], bn.training or not bn.track_running_stats,
				exponential_average_factor, bn.eps,
			)

	def forward(self, x):
		feature_dim = x.size(1)
		y = self.bn_forward(x, self.bn, feature_dim)
		return y


class DynamicGroupNorm(nn.GroupNorm):

	def __init__(self, num_groups, num_channels, eps=1e-5, affine=True, channel_per_group=None):
		super(DynamicGroupNorm, self).__init__(num_groups, num_channels, eps, affine)
		self.channel_per_group = channel_per_group

	def forward(self, x):
		n_channels = x.size(1)
		n_groups = n_channels // self.channel_per_group
		return F.group_norm(x, n_groups, self.weight[:n_channels], self.bias[:n_channels], self.eps)

	@property
	def bn(self):
		return self


class DynamicSE(SEModule):

	def __init__(self, max_channel):
		super(DynamicSE, self).__init__(max_channel)

	def get_active_reduce_weight(self, num_mid, in_channel, groups=None):
		if groups is None or groups == 1:
			return self.fc.reduce.weight[:num_mid, :in_channel, :, :]
		else:
			assert in_channel % groups == 0
			sub_in_channels = in_channel // groups
			sub_filters = torch.chunk(self.fc.reduce.weight[:num_mid, :, :, :], groups, dim=1)
			return torch.cat([
				sub_filter[:, :sub_in_channels, :, :] for sub_filter in sub_filters
			], dim=1)

	def get_active_reduce_bias(self, num_mid):
		return self.fc.reduce.bias[:num_mid] if self.fc.reduce.bias is not None else None

	def get_active_expand_weight(self, num_mid, in_channel, groups=None):
		if groups is None or groups == 1:
			return self.fc.expand.weight[:in_channel, :num_mid, :, :]
		else:
			assert in_channel % groups == 0
			sub_in_channels = in_channel // groups
			sub_filters = torch.chunk(self.fc.expand.weight[:, :num_mid, :, :], groups, dim=0)
			return torch.cat([
				sub_filter[:sub_in_channels, :, :, :] for sub_filter in sub_filters
			], dim=0)

	def get_active_expand_bias(self, in_channel, groups=None):
		if groups is None or groups == 1:
			return self.fc.expand.bias[:in_channel] if self.fc.expand.bias is not None else None
		else:
			assert in_channel % groups == 0
			sub_in_channels = in_channel // groups
			sub_bias_list = torch.chunk(self.fc.expand.bias, groups, dim=0)
			return torch.cat([
				sub_bias[:sub_in_channels] for sub_bias in sub_bias_list
			], dim=0)

	def forward(self, x, groups=None):
		in_channel = x.size(1)
		num_mid = make_divisible(in_channel // self.reduction, divisor=MyNetwork.CHANNEL_DIVISIBLE)

		y = x.mean(3, keepdim=True).mean(2, keepdim=True)
		# reduce
		reduce_filter = self.get_active_reduce_weight(num_mid, in_channel, groups=groups).contiguous()
		reduce_bias = self.get_active_reduce_bias(num_mid)
		y = F.conv2d(y, reduce_filter, reduce_bias, 1, 0, 1, 1)
		# relu
		y = self.fc.relu(y)
		# expand
		expand_filter = self.get_active_expand_weight(num_mid, in_channel, groups=groups).contiguous()
		expand_bias = self.get_active_expand_bias(in_channel, groups=groups)
		y = F.conv2d(y, expand_filter, expand_bias, 1, 0, 1, 1)
		# hard sigmoid
		y = self.fc.h_sigmoid(y)

		return x * y


class DynamicLinear(nn.Module):

	def __init__(self, max_in_features, max_out_features, bias=True):
		super(DynamicLinear, self).__init__()

		self.max_in_features = max_in_features
		self.max_out_features = max_out_features
		self.bias = bias

		self.linear = nn.Linear(self.max_in_features, self.max_out_features, self.bias)

		self.active_out_features = self.max_out_features

	def get_active_weight(self, out_features, in_features):
		return self.linear.weight[:out_features, :in_features]

	def get_active_bias(self, out_features):
		return self.linear.bias[:out_features] if self.bias else None

	def forward(self, x, out_features=None):
		if out_features is None:
			out_features = self.active_out_features

		in_features = x.size(1)
		weight = self.get_active_weight(out_features, in_features).contiguous()
		bias = self.get_active_bias(out_features)
		y = F.linear(x, weight, bias)
		return y
