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, hidden_size=32, dropout=0.0):
        super(CNN3D, self).__init__()
        self.conv1 = nn.Conv3d(1, hidden_size, kernel_size=3, stride=1, padding=1)
        self.batchnorm = nn.BatchNorm3d(hidden_size)
        self.conv2 = nn.Conv3d(hidden_size, hidden_size*2, kernel_size=3, stride=1, padding=1)
        self.relu = nn.ReLU()
        self.maxpool = nn.MaxPool3d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(hidden_size*32, 256)  # Calculate input size based on output from conv3
        self.fc2 = nn.Linear(256, 6)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        x = self.conv1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = self.batchnorm(x)
        x = self.conv2(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = self.dropout(x)

        x = x.view(x.size(0), -1)  # Flatten features for fully connected layers
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        return x

def train(model, criterion, optimizer, loader, epochs=5):
    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




class Model():
    def __init__(self, batch_size=8,lr=0.001,epochs=10, dropout=0.0, hidden_size=32):
        self.batch_size = batch_size
        self.lr = lr
        self.epochs = epochs
        self.model = CNN3D(dropout=dropout, hidden_size=hidden_size)
        self.criterion = nn.CrossEntropyLoss()
        self.optimizer = torch.optim.Adam(self.model.parameters(), lr=self.lr)

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

    def predict(self, X):
        self.model.eval()
        with torch.no_grad():
            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)
            # TEMP
            threshold = 180
            tensor_videos[tensor_videos > threshold] = 255
            tensor_videos[tensor_videos < threshold] = 0
            # END TEMP

            # 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))
            result = self.model(X)
        return torch.max(result, dim=1)[1].numpy()
    def process_data(self, X, y, n_samples=600):
        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)
        # TEMP
        threshold = 180
        tensor_videos[tensor_videos > threshold] = 255
        tensor_videos[tensor_videos < threshold] = 0
        # END TEMP

        # 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
        # Get the indices of the samples to take
        indices_to_take = [np.random.choice(indices[i], n_samples, 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()


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]

print("init model")
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.