import torch
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import torch


def quantization_error(tensor, dequantized_tensor):
    return (dequantized_tensor - tensor).abs().square().mean()


def linear_q_with_scale_and_zero_point(
    r_tensor, scale, zero_point, dtype=torch.int8):
    """
    Performs simple linear quantization given
    the scale and zero-point.
    """

    # scale tensor and add the zero point
    scaled_and_shifted_tensor = r_tensor / scale + zero_point

    # round the tensor 
    rounded_tensor = torch.round(scaled_and_shifted_tensor)

    # we need to clamp to the min/max value of the specified dtype
    q_min, q_max = torch.iinfo(dtype).min, torch.iinfo(dtype).max
    q_tensor = rounded_tensor.clamp(q_min, q_max).to(dtype)
    return q_tensor


def linear_dequantization(quantized_tensor, scale, zero_point):
    """
    Linear de-quantization
    """
    dequantized_tensor = scale * (quantized_tensor.float() - zero_point)

    return dequantized_tensor

def plot_matrix(tensor, ax, title, vmin=0, vmax=1, cmap=None):
    """
    Plot a heatmap of tensors using seaborn
    """
    sns.heatmap(tensor.cpu().numpy(), ax=ax, vmin=vmin, vmax=vmax, cmap=cmap, annot=True, fmt=".2f", cbar=False)
    ax.set_title(title)
    ax.set_yticklabels([])
    ax.set_xticklabels([])


def plot_quantization_errors(original_tensor, quantized_tensor, dequantized_tensor, dtype = torch.int8, n_bits = 8):
    """
    A method that plots 4 matrices, the original tensor, the quantized tensor
    the de-quantized tensor and the error tensor.
    """
    # Get a figure of 4 plots
    fig, axes = plt.subplots(1, 4, figsize=(15, 4))

    # Plot the first matrix
    plot_matrix(original_tensor, axes[0], 'Original Tensor', cmap=ListedColormap(['white']))

    # Get the quantization range and plot the quantized tensor
    q_min, q_max = torch.iinfo(dtype).min, torch.iinfo(dtype).max
    plot_matrix(quantized_tensor, axes[1], f'{n_bits}-bit Linear Quantized Tensor', vmin=q_min, vmax=q_max, cmap='coolwarm')

    # Plot the de-quantized tensors
    plot_matrix(dequantized_tensor, axes[2], 'Dequantized Tensor', cmap='coolwarm')

    # Get the quantization errors
    q_error_tensor = abs(original_tensor - dequantized_tensor)
    plot_matrix(q_error_tensor, axes[3], 'Quantization Error Tensor', cmap=ListedColormap(['white']))

    fig.tight_layout()
    plt.show()

# From the previous lab 
def get_q_scale_and_zero_point(r_tensor, dtype=torch.int8):
    """
    Get quantization parameters (scale, zero point) 
    for a floating point tensor
    """
    q_min, q_max = torch.iinfo(dtype).min, torch.iinfo(dtype).max
    r_min, r_max = r_tensor.min().item(), r_tensor.max().item()

    scale = (r_max-r_min)/(q_max-q_min)

    zero_point = q_min-(r_min/scale)

    # clip the zero_point to fall in [quantized_min, quantized_max]
    if zero_point < q_min or zero_point > q_max:
        zero_point = q_min
    else:
        # round and cast to int
        zero_point = int(round(zero_point))
    return scale, zero_point

def linear_quantization(r_tensor, n_bits, dtype=torch.int8):
    """
    linear quantization
    """

    scale, zero_point = get_q_scale_and_zero_point(r_tensor)
    
    quantized_tensor = linear_q_with_scale_and_zero_point(
        r_tensor, scale=scale, zero_point=zero_point, dtype=dtype)

    return quantized_tensor, scale, zero_point