import torch
import torchvision
from torch import nn


def create_effnetb4_model(num_classes: int = 101, seed: int = 42):
    """
    Creates an EfficientNet-B4 feature extractor model and its preprocessing transforms.

    Args:
        num_classes (int, optional): Number of output classes. Defaults to 101 (Food-101 dataset).
        seed (int, optional): Random seed for reproducibility. Defaults to 42.

    Returns:
        model (torch.nn.Module): EfficientNet-B4 feature extractor model.
        transforms (torchvision.transforms.Compose): Corresponding image transforms.
    """

    # Use pretrained EfficientNet-B4 weights
    weights = torchvision.models.EfficientNet_B4_Weights.DEFAULT
    transforms = weights.transforms()

    # Initialize model
    model = torchvision.models.efficientnet_b4(weights=weights)

    # Freeze feature extractor layers
    for param in model.parameters():
        param.requires_grad = False

    # Set seed for reproducibility
    torch.manual_seed(seed)

    # Replace classifier head
    model.classifier = nn.Sequential(
        nn.Dropout(p=0.3, inplace=True),
        nn.Linear(in_features=1792, out_features=num_classes)
    )

    return model, transforms