from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Dropout, Sequential, Module
#from models.encoders.helpers import get_blocks, Flatten, bottleneck_IR, bottleneck_IR_SE, l2_norm
from helpers import get_blocks, Flatten, bottleneck_IR, bottleneck_IR_SE, l2_norm

"""
Modified Backbone implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch)
"""


class Backbone(Module):
	def __init__(self, input_size, num_layers, mode='ir', drop_ratio=0.4, affine=True):
		super(Backbone, self).__init__()
		assert input_size in [112, 224], "input_size should be 112 or 224"
		assert num_layers in [50, 100, 152], "num_layers should be 50, 100 or 152"
		assert mode in ['ir', 'ir_se'], "mode should be ir or ir_se"
		blocks = get_blocks(num_layers)
		if mode == 'ir':
			unit_module = bottleneck_IR
		elif mode == 'ir_se':
			unit_module = bottleneck_IR_SE
		self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False),
									  BatchNorm2d(64),
									  PReLU(64))
		if input_size == 112:
			self.output_layer = Sequential(BatchNorm2d(512),
			                               Dropout(drop_ratio),
			                               Flatten(),
			                               Linear(512 * 7 * 7, 512),
			                               BatchNorm1d(512, affine=affine))
		else:
			self.output_layer = Sequential(BatchNorm2d(512),
			                               Dropout(drop_ratio),
			                               Flatten(),
			                               Linear(512 * 14 * 14, 512),
			                               BatchNorm1d(512, affine=affine))

		modules = []
		for block in blocks:
			for bottleneck in block:
				modules.append(unit_module(bottleneck.in_channel,
										   bottleneck.depth,
										   bottleneck.stride))
		self.body = Sequential(*modules)

	def forward(self, x):
		x = self.input_layer(x)
		x = self.body(x)
		x = self.output_layer(x)
		return l2_norm(x)


def IR_50(input_size):
	"""Constructs a ir-50 model."""
	model = Backbone(input_size, num_layers=50, mode='ir', drop_ratio=0.4, affine=False)
	return model


def IR_101(input_size):
	"""Constructs a ir-101 model."""
	model = Backbone(input_size, num_layers=100, mode='ir', drop_ratio=0.4, affine=False)
	return model


def IR_152(input_size):
	"""Constructs a ir-152 model."""
	model = Backbone(input_size, num_layers=152, mode='ir', drop_ratio=0.4, affine=False)
	return model


def IR_SE_50(input_size):
	"""Constructs a ir_se-50 model."""
	model = Backbone(input_size, num_layers=50, mode='ir_se', drop_ratio=0.4, affine=False)
	return model


def IR_SE_101(input_size):
	"""Constructs a ir_se-101 model."""
	model = Backbone(input_size, num_layers=100, mode='ir_se', drop_ratio=0.4, affine=False)
	return model


def IR_SE_152(input_size):
	"""Constructs a ir_se-152 model."""
	model = Backbone(input_size, num_layers=152, mode='ir_se', drop_ratio=0.4, affine=False)
	return model