import numpy as np

def smartmean(x, dim=None):
    """
    Python implementation of the MATLAB smartmean function.
    Calculates the mean while ignoring NaN and Inf values.
    
    Parameters:
    x (ndarray): Input array
    dim (int, optional): Dimension along which to compute the mean
    
    Returns:
    ndarray: Mean values along the specified dimension, ignoring NaN/Inf
    """
    if isinstance(x, list):
        x = np.array(x)
    
    # If dimension is not specified, find the first non-singleton dimension
    if dim is None:
        # Find the first dimension with size > 1
        if len(x.shape) == 1:
            dim = 0
        else:
            non_singleton_dims = [i for i, size in enumerate(x.shape) if size > 1]
            dim = non_singleton_dims[0] if non_singleton_dims else 0
    
    # Create a mask of finite values
    mask = np.isfinite(x)
    
    # Replace non-finite values with zeros
    x_clean = np.where(mask, x, 0)
    
    # Sum the finite values and divide by the count of finite values
    with np.errstate(divide='ignore', invalid='ignore'):
        result = np.sum(x_clean, axis=dim) / np.sum(mask, axis=dim)
    
    # Set result to NaN where all values are non-finite
    if dim == 0:
        result[np.all(~mask, axis=dim)] = np.nan
    elif dim == 1:
        result[np.all(~mask, axis=dim)] = np.nan
    else:
        all_nan_indices = np.where(np.all(~mask, axis=dim))
        if all_nan_indices[0].size > 0:
            result[all_nan_indices] = np.nan
    
    return result