import torch
import torch.nn as nn
from functools import reduce

class LambdaBase(nn.Sequential):
    def __init__(self, fn, *args):
        super(LambdaBase, self).__init__(*args)
        self.lambda_func = fn

    def forward_prepare(self, input):
        output = []
        for module in self._modules.values():
            output.append(module(input))
        return output if output else input

class Lambda(LambdaBase):
    def forward(self, input):
        return self.lambda_func(self.forward_prepare(input))

class LambdaMap(LambdaBase):
    def forward(self, input):
        return list(map(self.lambda_func,self.forward_prepare(input)))

class LambdaReduce(LambdaBase):
    def forward(self, input):
        return reduce(self.lambda_func,self.forward_prepare(input))


def load_estimation_model(inputfilename, outputfilename):
	with open(inputfilename, "r") as rf:
		contents = rf.read()
		contents = contents.split(",")

	data = torch.Tensor(1,350)
	name = ""
	for i in range(len(contents)):
		if i == 0:
			name = contents[i].strip()
		else:
			val = float(contents[i].strip())
			data[0][i-1] = val

	model = nn.Sequential( # Sequential,
        	nn.Sequential(Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x ),nn.Linear(350,1024)), # Linear,
       		nn.Sigmoid(),
        	nn.Sequential(Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x ),nn.Linear(1024,512)), # Linear,
        	nn.Sigmoid(),
        	nn.Sequential(Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x ),nn.Linear(512,256)), # Linear,
        	nn.Sigmoid(),
        	nn.Sequential(Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x ),nn.Linear(256,4)), # Linear,
	)

	model.load_state_dict(torch.load("em.pth"))
	my_prediction = model.forward(data)

	with open(outputfilename, "w") as wf:
		wf.write("NAME,F1,F2,F3,F4\n")
		wf.write(name + "," + str(1000 * float(my_prediction[0][0])) + "," + str(1000 * float(my_prediction[0][1])) + "," + str(1000 * float(my_prediction[0][2])) + "," + str(1000 * float(my_prediction[0][3])))