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


class CustomLinear(nn.Linear):
    def __init__(self, *args, init_eye_val=0.0, is_diagonal=False, **kwargs):
        super().__init__(*args, **kwargs)
        self.init_eye_val = init_eye_val

class CustomLinearInitialized(nn.Linear):
    def __init__(self, in_features: int, out_features: int, bias: bool = True,
                 device=None, dtype=None, init_fun=None) -> None:
        super().__init__(in_features, out_features, bias, device, dtype)
        self.init_fun = init_fun

class CustomDiagonalLinear(nn.Module):
    def __init__(self, d_model, bias=True, init_eye_val=0.0):
        super().__init__()
        self.init_eye_val = init_eye_val
        self.weight = nn.Parameter(torch.full((d_model,), init_eye_val))
        self.bias = nn.Parameter(torch.zeros(d_model)) if bias else None

    def forward(self, input):
        out = input * self.weight
        if self.bias is not None:
            out += self.bias
        return out

class Gate(nn.Module):
    def __init__(self, items, init_val=0.0):
        super().__init__()
        self.init_val = init_val
        self.gate = nn.Parameter(torch.full((items,), init_val))

    def forward(self, input, dim):
        if input.ndim != 4:
            raise ValueError('input must be a 4D tensor')
        shape = [1] * 4
        shape[dim] = -1
        return input * self.gate.view(*shape)


class AttentivePoolingClassifier(nn.Module):
    def __init__(self, d_model, num_classes, hidden_dim=128):
        """
        Attentive Pooling Classifier

        Args:
            d_model: Input feature dimension (D)
            num_classes: Number of output classes (V)
            hidden_dim: Hidden dimension for attention mechanism
        """
        super(AttentivePoolingClassifier, self).__init__()

        # Attention mechanism for pooling [B,T,D] -> [B,D]
        self.attention_projection = nn.Linear(d_model, hidden_dim)
        self.attention_weights = nn.Linear(hidden_dim, 1)

        # Classifier [B,D] -> [B,V]
        self.classifier = nn.Sequential(
            nn.Linear(d_model, hidden_dim),
            nn.ReLU(),
            nn.Dropout(0.1),
            nn.Linear(hidden_dim, num_classes)
        )

    def forward(self, x, apply_stop_gradient=True):
        """
        Forward pass

        Args:
            x: Input tensor of shape [B, T, D]
            apply_stop_gradient: Whether to apply stop gradient

        Returns:
            logits: Output logits [B, V]
            attention_weights: Attention weights [B, T]
            pooled_features: Pooled features [B, D]
        """
        # Apply stop gradient if specified
        if apply_stop_gradient:
            x = x.detach()

        # Compute attention weights
        # x: [B, T, D] -> [B, T, hidden_dim]
        att_proj = torch.tanh(self.attention_projection(x))

        # att_proj: [B, T, hidden_dim] -> [B, T, 1] -> [B, T]
        attention_scores = self.attention_weights(att_proj).squeeze(-1)
        attention_weights = F.softmax(attention_scores, dim=-1)

        # Apply attentive pooling: [B, T, D] * [B, T, 1] -> [B, D]
        pooled_features = torch.sum(x * attention_weights.unsqueeze(-1), dim=1)

        # Classification
        logits = self.classifier(pooled_features)

        return logits