import numpy as np
import torch
import os

from torch import nn

with open('data.npy', 'rb') as f:
    data = np.load(f, allow_pickle=True).item()
    X = data['data']
    y = data['label']

from torch import nn
from sklearn.model_selection import train_test_split

from torch import nn
import numpy as np
import torch
import os


class CNN3D(nn.Module):
    def __init__(self):
        super(CNN3D, self).__init__()
        self.conv1 = nn.Conv3d(1, 12, 2, 1, 2)
        self.mp = nn.AvgPool3d(2)
        self.relu = nn.LeakyReLU()
        self.fc1 = nn.Linear(3888, 6)
        self.fc2 = nn.Linear(128, 6)
        self.flatten = nn.Flatten()

    def forward(self, x):
        x = self.conv1(x)
        x = self.mp(x)
        x = self.relu(x)

        # print(x.shape)

        x = x.view(-1, 3888)
        x = self.fc1(x)
        # x = self.fc2(x)
        return x


def train(model, criterion, optimizer, loader, epochs=10):
    for epoch in range(epochs):
        for idx, (inputs, labels) in enumerate(loader):
            optimizer.zero_grad()
            outputs = model(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
        print(f'Epoch {epoch}, Loss: {loss.item()}')
    return model


def process_data(X, y):
    y = np.array(y)
    X = np.array([video[:6] for video in X])
    tensor_videos = torch.tensor(X, dtype=torch.float32)
    # Clip values to 0 and 255
    tensor_videos = np.clip(tensor_videos, 0, 255)
    # Replace NaNs in each frame, with the average of the frame. This was generated with GPT
    for i in range(tensor_videos.shape[0]):
        for j in range(tensor_videos.shape[1]):
            tensor_videos[i][j][torch.isnan(tensor_videos[i][j])] = torch.mean(
                tensor_videos[i][j][~torch.isnan(tensor_videos[i][j])])
    # Undersample the data for each of the 6 classes. Select max of 300 samples for each class
    # Very much generated with the assitance of chatGPT with some modifications
    # Get the indices of each class
    indices = [np.argwhere(y == i).squeeze(1) for i in range(6)]
    # Get the number of samples to take for each class
    num_samples_to_take = 300
    # Get the indices of the samples to take
    indices_to_take = [np.random.choice(indices[i], num_samples_to_take, replace=True) for i in range(6)]
    # Concatenate the indices
    indices_to_take = np.concatenate(indices_to_take)
    # Select the samples
    tensor_videos = tensor_videos[indices_to_take].unsqueeze(1)
    y = y[indices_to_take]
    return torch.Tensor(tensor_videos), torch.Tensor(y).long()


class Model():
    def __init__(self):
        self.model = CNN3D()
        self.criterion = nn.CrossEntropyLoss()
        self.optimizer = torch.optim.Adam(self.model.parameters(), lr=0.001)

    def fit(self, X, y):
        X, y = process_data(X, y)
        train_dataset = torch.utils.data.TensorDataset(X, y)
        train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)
        train(self.model, self.criterion, self.optimizer, train_loader)

    def predict(self, X):
        self.model.eval()

        X = np.array([video[:6] for video in X])
        tensor_videos = torch.tensor(X, dtype=torch.float32)
        # Clip values to 0 and 255
        tensor_videos = np.clip(tensor_videos, 0, 255)
        # Replace NaNs in each frame, with the average of the frame. This was generated with GPT
        for i in range(tensor_videos.shape[0]):
            for j in range(tensor_videos.shape[1]):
                tensor_videos[i][j][torch.isnan(tensor_videos[i][j])] = torch.mean(
                    tensor_videos[i][j][~torch.isnan(tensor_videos[i][j])])
        X = torch.Tensor(tensor_videos.unsqueeze(1))
        return np.argmax(self.model(X).detach().numpy(), axis=1)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)

not_nan_indices = np.argwhere(~np.isnan(np.array(y_test))).squeeze()
y_test = [y_test[i] for i in not_nan_indices]
X_test = [X_test[i] for i in not_nan_indices]

model = Model()
model.fit(X_train, y_train)

from sklearn.metrics import f1_score

y_pred = model.predict(X_test)
print("F1 Score (macro): {0:.2f}".format(f1_score(y_test, y_pred, average='macro'))) # You may encounter errors, you are expected to figure out what's the issue.