import logging

import torch
import torch.nn as nn
from .count_hooks import *

register_hooks = {
	nn.Conv1d: count_conv1d,
	nn.Conv2d: count_conv2d,
	nn.Conv3d: count_conv3d,
	nn.BatchNorm1d: count_bn2d,
	nn.BatchNorm2d: count_bn2d,
	nn.BatchNorm3d: count_bn2d,
	nn.ReLU: count_relu,
	nn.ReLU6: count_relu,
	nn.MaxPool1d: count_maxpool,
	nn.MaxPool2d: count_maxpool,
	nn.MaxPool3d: count_maxpool,
	nn.AvgPool1d: count_avgpool,
	nn.AvgPool2d: count_avgpool,
	nn.AvgPool3d: count_avgpool,
	nn.Linear: count_linear,
	nn.Dropout: None,
}


def profile(model, input_size, custom_ops={}):
	def add_hooks(m):
		if len(list(m.children())) > 0:
			return

		m.register_buffer('total_ops', torch.zeros(1))
		m.register_buffer('total_params', torch.zeros(1))

		for p in m.parameters():
			m.total_params += torch.Tensor([p.numel()])

		m_type = type(m)
		fn = None

		if m_type in custom_ops:
			fn = custom_ops[m_type]
		elif m_type in register_hooks:
			fn = register_hooks[m_type]
		else:
			logging.warning("Not implemented for ", m)

		if fn is not None:
			logging.info("Register FLOP counter for module %s" % str(m))
			m.register_forward_hook(fn)

	model.eval()
	model.apply(add_hooks)

	x = torch.zeros(input_size)
	model(x)

	total_ops = 0
	total_params = 0
	for m in model.modules():
		if len(list(m.children())) > 0: # skip for non-leaf module
			continue
		total_ops += m.total_ops
		total_params += m.total_params

	total_ops = total_ops.item()
	total_params = total_params.item()

	return total_ops, total_params