import torch
import torchvision
from torchvision import transforms
import torch.nn as nn
from torchvision.models import mobilenet_v2

# Load MobileNetV2 with pre-trained weights


def create_mobilenet_model(num_classes:int=4, 
                          seed:int=42):
    """Creates an EfficientNetB2 feature extractor model and transforms.
    Args:
        num_classes (int, optional): number of classes in the classifier head. 
            Defaults to 3.
        seed (int, optional): random seed value. Defaults to 42.
    Returns:
        model (torch.nn.Module): EffNetB2 feature extractor model. 
        transforms (torchvision.transforms): EffNetB2 image transforms.
    """
    # Create EffNetB2 pretrained weights, transforms and model
    
    transform = transforms.Compose([
        transforms.Resize((224, 224)), # 1. Reshape all images to 224x224 (though some models may require different sizes)
        transforms.ToTensor(), # 2. Turn image values to between 0 & 1
        transforms.Normalize(mean=[0.485, 0.456, 0.406], # 3. A mean of [0.485, 0.456, 0.406] (across each colour channel)
                         std=[0.229, 0.224, 0.225]) # 4. A standard deviation of [0.229, 0.224, 0.225] (across each colour channel),
                                                    ])
    model = mobilenet_v2(pretrained=True)

    # Freeze all layers in base model
    # Freeze all base layers by setting requires_grad attribute to False
    for param in model.parameters():
        param.requires_grad = False
    
    # Since we're creating a new layer with random weights (torch.nn.Linear),
    # let's set the seeds
    torch.manual_seed(42)
    
    # Update the classifier head to suit our problem
    model.classifier = nn.Sequential(
        nn.Dropout(p=0.2, inplace=True),
        nn.Linear(in_features=model.classifier[1].in_features,  # Accessing the last layer of the classifier
                  out_features=num_classes,
                  bias=True)
    )
    
    return model, transform