import numpy as np
import torch
from torch.autograd import Function
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from torchvision import datasets, transforms
from qiskit import QuantumRegister, QuantumCircuit, ClassicalRegister, execute
from qiskit.circuit import Parameter
from qiskit import Aer
from tqdm import tqdm
from matplotlib import pyplot as plt
%matplotlib inline

def to_numbers(tensor_list):
    num_list = []
    for tensor in tensor_list:
        num_list += [tensor.item()]
    return num_list

class QiskitCircuit():
    
    # Specify initial parameters and the quantum circuit
    
    def __init__(self,shots):
        self.theta = Parameter('Theta')
        self.shots = shots
        
        def create_circuit():
            qr = QuantumRegister(1,'q')
            cr = ClassicalRegister(1,'c')
            ckt = QuantumCircuit(qr,cr)
            ckt.h(qr[0])
            ckt.barrier()
            ckt.ry(self.theta,qr[0])
            ckt.barrier()
            ckt.measure(qr,cr)
            return ckt
        
        self.circuit = create_circuit()
        
    def N_qubit_expectation_Z(self,counts, shots, nr_qubits):
        expects = np.zeros(nr_qubits)
        for key in counts.keys():
            perc = counts[key]/shots
            check = np.array([(float(key[i])-1/2)*2*perc for i in range(nr_qubits)])
            expects += check   
        return expects    
    
    def bind(self, parameters):
        [self.theta] = to_numbers(parameters)
        self.circuit.data[2][0]._params = to_numbers(parameters)
    
    def run(self, i):
        self.bind(i)
        backend = Aer.get_backend('qasm_simulator')
        job_sim = execute(self.circuit,backend,shots=self.shots)
        result_sim = job_sim.result()
        counts = result_sim.get_counts(self.circuit)
        return self.N_qubit_expectation_Z(counts,self.shots,1)

class TorchCircuit(Function):    

    @staticmethod
    def forward(ctx, i):
        if not hasattr(ctx, 'QiskitCirc'):
            ctx.QiskitCirc = QiskitCircuit(shots=100)
            
        exp_value = ctx.QiskitCirc.run(i[0])
        
        result = torch.tensor([exp_value]) # store the result as a torch tensor
        
        ctx.save_for_backward(result, i)
        
        return result
    
    @staticmethod
    def backward(ctx, grad_output):
        s = np.pi/2
        
        forward_tensor, i = ctx.saved_tensors  
        
        # Obtain paramaters 
        input_numbers = to_numbers(i[0])
        
        gradient = []
        
        for k in range(len(input_numbers)):
            input_plus_s = input_numbers
            input_plus_s[k] = input_numbers[k] + s  # Shift up by s
            
            exp_value_plus = ctx.QiskitCirc.run(torch.tensor(input_plus_s))[0]
            result_plus_s = torch.tensor([exp_value_plus])
            
            input_minus_s = input_numbers
            input_minus_s[k] = input_numbers[k] - s # Shift down by s
            
            exp_value_minus = ctx.QiskitCirc.run(torch.tensor(input_minus_s))[0]
            result_minus_s = torch.tensor([exp_value_minus])

            gradient_result = (result_plus_s - result_minus_s)

            gradient.append(gradient_result)
            
        result = torch.tensor([gradient])
        
        return result.float() * grad_output.float()

transform = torchvision.transforms.Compose([torchvision.transforms.ToTensor()]) # transform images to tensors/vectors
mnist_trainset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)

labels = mnist_trainset.targets # get the labels for the data
labels = labels.numpy()

idx1 = np.where(labels == 0) # filter on zeros
idx2 = np.where(labels == 1) # filter on ones

# Specify number of datapoints per class (i.e. there will be n pictures of 1 and n pictures of 0 in the training set)
n=100

# concatenate the data indices
idx = np.concatenate((idx1[0][0:n],idx2[0][0:n])) 

# create the filtered dataset for our training set
mnist_trainset.targets = labels[idx] 
mnist_trainset.data = mnist_trainset.data[idx]

train_loader = torch.utils.data.DataLoader(mnist_trainset, batch_size=1, shuffle=True)

qc = TorchCircuit.apply 

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.h1 = nn.Linear(320, 50)
        self.h2 = nn.Linear(50, 1)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.h1(x))
        x = F.dropout(x, training=self.training)
        x = self.h2(x)
        x = qc(x)
        x = (x+1)/2  # Normalise the inputs to 1 or 0
        x = torch.cat((x, 1-x), -1)
        return x

network = Net()
optimizer = optim.Adam(network.parameters(), lr=0.001)

epochs = 30
loss_list = []
for epoch in range(epochs):
    total_loss = []
    target_list = []
    for batch_idx, (data, target) in enumerate(train_loader):
        target_list.append(target.item())
        optimizer.zero_grad()
        output = network(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        total_loss.append(loss.item())
    loss_list.append(sum(total_loss)/len(total_loss))
    print(loss_list[-1])

# Normalise the loss between 0 and 1
for i in range(len(loss_list)):
    loss_list[i] += 1

# Plot the loss per epoch
plt.plot(loss_list)