"""Ether is a  multi-modal loss function that minmizes the disparity between modalities while ensuring task specific accuracy"""

import torch
import torch.nn.functional as F
from torch import nn


class Ether(nn.Module):
    """

    **Algorithmic Pseudocode for MMOLF**:

    1. **Inputs**:
        - \( y_{pred} \) (Predicted values from the model)
        - \( y_{true} \) (True values or ground truth)
        - \( \alpha \) (Weighting factor for inter-modal loss)

    2. Calculate the intra-modal loss based on a standard loss function (for instance, the Mean Squared Error in the case of regression tasks).
        - \( \text{intra\_modal\_loss} = MSE(y_{pred}, y_{true}) \)

    3. Calculate the inter-modal discrepancy. This could be based on the variance or other discrepancy metrics between modalities.
        - **for** each modality **do**:
            - Calculate the mean and variance of the predictions for this modality
            - Compute the total variance from the mean of all modalities
        - \( \text{inter\_modal\_loss} = \text{Sum of discrepancies between each modality's predictions and the overall mean} \)

    4. Combine the intra-modal and inter-modal losses using the weight \( \alpha \).
        - \( \text{loss} = \text{intra\_modal\_loss} + \alpha \times \text{inter\_modal\_loss} \)

    5. **Return**: \( \text{loss} \)

    ---

    **PyTorch Implementation**:

    Let's implement this function in PyTorch:

    ```python
    import torch
    import torch.nn as nn
    import torch.nn.functional as F

    class MMOLF(nn.Module):
        def __init__(self, modalities, alpha=1.0):
            super(MMOLF, self).__init__()
            self.alpha = alpha
            self.modalities = modalities

        def forward(self, y_pred, y_true):
            # Intra-modal loss
            intra_modal_loss = F.mse_loss(y_pred, y_true)

            # Inter-modal loss
            modal_means = [torch.mean(y_pred[:, modality]) for modality in self.modalities]
            overall_mean = torch.mean(y_pred)
            inter_modal_loss = sum([torch.abs(mean - overall_mean) for mean in modal_means])

            return intra_modal_loss + self.alpha * inter_modal_loss

    class ModAct(nn.Module):
        def __init__(self, beta=1.0):
            super(ModAct, self).__init__()
            self.beta = beta

        def forward(self, x):
            gate = torch.sigmoid(self.beta * x)
            linear_part = x
            non_linear_part = torch.tanh(x)
            return gate * linear_part + (1 - gate) * non_linear_part
    ```

    In the code above, modalities is a list of slices or indices that specify where each modality's data lies within y_pred. For example, if y_pred is a tensor where the first half contains visual data predictions and the second half contains audio data predictions, modalities might look something like: [slice(0, y_pred.size(1)//2), slice(y_pred.size(1)//2, y_pred.size(1))].

    This MMOLF loss function considers the intra-modal discrepancies (similarities of predictions within the same modality) and the inter-modal discrepancies (variations of predictions between different modalities). The design is conceptual and may need further refinement and adjustments based on specific tasks and empirical results.


        #####################
    2. The Need for Specialized Algorithms

    Diverse Data Nature: Multi-modal data, by definition, originates from various sources. An algorithm that can dynamically adjust to this diversity can offer superior performance.

    Feature Interactions: Features from different modalities may interact in complex ways, requiring a more intricate processing mechanism.

    3. Multi-Modal Optimized Loss Function (MMOLF)

    The proposed MMOLF aims to minimize the disparity between modalities while ensuring task-specific accuracy. It incorporates intra-modal and inter-modal terms.

    Pseudocode:

    bash
    function MMOLF(y_pred, y_true, alpha):
        intra_modal_loss = standard_loss(y_pred, y_true) # e.g., MSE for regression tasks
        inter_modal_loss = calculate_inter_modal_discrepancy(y_pred) # Some metric that calculates discrepancy between modalities
        return intra_modal_loss + alpha * inter_modal_loss
    4. Why MMOLF?

    The intra-modal term ensures that the prediction aligns with the ground truth. Meanwhile, the inter-modal term minimizes the discrepancy between modalities, promoting uniformity and reducing conflicts that arise from data heterogeneity.

    5. Activation Function for Multi-Modal Tasks: ModAct

    Building on MMOLF, the ModAct activation function dynamically adjusts its transformation based on input data, ensuring efficient processing across modalities.

    Pseudocode:
    function ModAct(x, beta):
        gate = sigmoid(beta * x)
        linear_part = x
        non_linear_part = tanh(x)
        return gate * linear_part + (1 - gate) * non_linear_part
    6. PyTorch Implementation

    For brevity, here is a condensed version:

    import torch
    import torch.nn.functional as F

    class MMOLF(torch.nn.Module):
        def __init__(self, alpha=1.0):
            super(MMOLF, self).__init__()
            self.alpha = alpha

        def forward(self, y_pred, y_true):
            intra_modal_loss = F.mse_loss(y_pred, y_true) # This can change based on task
            inter_modal_loss = # Calculate inter-modal discrepancy here
            return intra_modal_loss + self.alpha * inter_modal_loss

    class ModAct(torch.nn.Module):
        def __init__(self, beta=1.0):
            super(ModAct, self).__init__()
            self.beta = beta

        def forward(self, x):
            gate = torch.sigmoid(self.beta * x)
            linear_part = x
            non_linear_part = torch.tanh(x)
            return gate * linear_part + (1 - gate) * non_linear_part
    7. Applications and Use Cases

    Data Fusion: MMOLF and ModAct can be particularly beneficial when fusing data from diverse sources.

    Transfer Learning Across Modalities: The specialized design allows for more effective knowledge transfer.

    8. Conclusion

    This report presented MMOLF and ModAct as next-gen algorithms tailored for multi-modal data processing. While theoretical in nature, they hold promise in tackling the unique challenges posed by multi-modal datasets.

    This outline and expanded sections serve as a foundation for a more comprehensive report. In a real-world scenario, the proposed algorithms would need rigorous empirical evaluations, iterative adjustments, and peer reviews.
    function MMOLF(y_pred, y_true, alpha):
        intra_modal_loss = standard_loss(y_pred, y_true) # e.g., MSE for regression tasks
        inter_modal_loss = calculate_inter_modal_discrepancy(y_pred) # Some metric that calculates discrepancy between modalities
        return intra_modal_loss + alpha * inter_modal_loss
    4. Why MMOLF?

    The intra-modal term ensures that the prediction aligns with the ground truth. Meanwhile, the inter-modal term minimizes the discrepancy between modalities, promoting uniformity and reducing conflicts that arise from data heterogeneity.
    7. Applications and Use Cases

    Data Fusion: MMOLF and ModAct can be particularly beneficial when fusing data from diverse sources.

    Transfer Learning Across Modalities: The specialized design allows for more effective knowledge transfer.



    # Usage

    x = torch.randn(10, 5)
    y = torch.randn(10, 5)
    model = Ether([0, 1, 2, 3, 4])
    out = model(x, y)
    print(out)


    """

    def __init__(self, modalities, alpha=1.0):
        """Ether init"""
        super(Ether, self).__init__()
        self.alpha = alpha
        self.modalities = modalities

    def forward(self, y_pred, y_true):
        """Ether forward"""
        # Intra-modal loss
        intra_modal_loss = F.mse_loss(y_pred, y_true)

        # Inter-modal loss
        modal_means = [
            torch.mean(y_pred[:, modality]) for modality in self.modalities
        ]
        overall_mean = torch.mean(y_pred)
        inter_modal_loss = sum(
            [torch.abs(mean - overall_mean) for mean in modal_means]
        )

        return intra_modal_loss + self.alpha * inter_modal_loss


# #####################

# import numpy as np
# import torch
# import torch.nn as nn
# import torch.optim as optim
# from torch.utils.data import DataLoader, Dataset
# import torchvision.transforms as transforms
# import torchvision.datasets as datasets
# import time

# class CIFAR10MultiModal(Dataset):
#     def __init__(self, cifar_dataset):
#         self.cifar_dataset = cifar_dataset

#     def __len__(self):
#         return len(self.cifar_dataset)

#     def __getitem__(self, index):
#         img, label = self.cifar_dataset[index]
#         # Random numerical feature (for illustration purposes)
#         numerical_feature = torch.tensor(np.random.randn(), dtype=torch.float32)
#         return (img, numerical_feature), label

# transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
# cifar_trainset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
# trainset = CIFAR10MultiModal(cifar_trainset)
# dataloader = DataLoader(trainset, batch_size=32, shuffle=True)

# class MultiModalNet(nn.Module):
#     def __init__(self):
#         super(MultiModalNet, self).__init__()

#         # Image processing branch
#         self.conv1 = nn.Conv2d(3, 16, 3, padding=1)
#         self.pool = nn.MaxPool2d(2, 2)
#         self.conv2 = nn.Conv2d(16, 32, 3, padding=1)

#         # Numerical feature processing branch
#         self.fc1_num = nn.Linear(1, 16)

#         # Classifier
#         self.fc1 = nn.Linear(8*8*32 + 16, 256)
#         self.fc2 = nn.Linear(256, 10)

#     def forward(self, x):
#         img, num = x

#         # Image branch
#         x1 = self.pool(F.relu(self.conv1(img)))
#         x1 = self.pool(F.relu(self.conv2(x1)))
#         x1 = x1.view(-1, 8*8*32)

#         # Numerical branch
#         x2 = F.relu(self.fc1_num(num.unsqueeze(1)))

#         # Merging the two branches
#         x = torch.cat((x1, x2), dim=1)
#         x = F.relu(self.fc1(x))
#         x = self.fc2(x)
#         return x

# def train_model(model, loss_fn, optimizer, dataloader, epochs=10):
#     model.train()
#     start_time = time.time()
#     for epoch in range(epochs):
#         epoch_loss = 0.0
#         for batch_data, batch_labels in dataloader:
#             optimizer.zero_grad()
#             outputs = model(batch_data)
#             loss = loss_fn(outputs, batch_labels)
#             loss.backward()
#             optimizer.step()
#             epoch_loss += loss.item()
#         print(f"Epoch {epoch+1}/{epochs}, Loss: {epoch_loss/len(dataloader)}")
#     end_time = time.time()
#     return end_time - start_time

# # Initialize model, loss, and optimizer
# model = MultiModalNet()
# modalities = [0, 1]
# loss_fn = Ether(alpha=0.5, modalities=modalities)
# optimizer = optim.Adam(model.parameters(), lr=0.001)

# # Train the model
# train_time = train_model(model, loss_fn, optimizer, dataloader)
# print(f"Training time: {train_time:.2f} seconds")
