import torch
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt

# colors for visualization
COLORS = [[0.000, 0.447, 0.741], [0.850, 0.325, 0.098], [0.929, 0.694, 0.125],
          [0.494, 0.184, 0.556], [0.466, 0.674, 0.188], [0.301, 0.745, 0.933]]

def plot_results(model, pil_img, results):
    plt.figure(figsize=(16,10))
    plt.imshow(pil_img)
    ax = plt.gca()
    scores, labels, boxes = results["scores"], results["labels"], results["boxes"]
    colors = COLORS * 100
    for score, label, (xmin, ymin, xmax, ymax),c  in zip(scores.tolist(), labels.tolist(), boxes.tolist(), colors):
        ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin,
                                   fill=False, color=c, linewidth=3))
        text = f'{model.config.id2label[label]}: {score:0.2f}'
        ax.text(xmin, ymin, text, fontsize=15,
                bbox=dict(facecolor='yellow', alpha=0.5))
    plt.axis('off')
    plt.show()

############# From the previous lesson(s) of "Building your own Quantizer"
def w8_a16_forward(weight, input, scales, bias=None):
    
    casted_weights = weight.to(input.dtype)
    output = F.linear(input, casted_weights) * scales
    
    if bias is not None:
        output = output + bias
      
    return output

class W8A16LinearLayer(nn.Module):
    def __init__(self, in_features, out_features, 
                 bias=True, dtype=torch.float32):
        super().__init__()
        
        
        self.register_buffer(
            "int8_weights",
            torch.randint(
                -128, 127, (out_features, in_features), dtype=torch.int8
            )
        )
        
        self.register_buffer("scales", 
                             torch.randn((out_features), dtype=dtype))
        
        if bias:
            self.register_buffer("bias", 
                                 torch.randn((1, out_features), 
                                             dtype=dtype))
        
        else:
            self.bias = None

    def quantize(self, weights):
        w_fp32 = weights.clone().to(torch.float32)

        scales = w_fp32.abs().max(dim=-1).values / 127
        scales = scales.to(weights.dtype)

        int8_weights = torch.round(weights
                        /scales.unsqueeze(1)).to(torch.int8)

        self.int8_weights = int8_weights
        self.scales = scales
    
    def forward(self, input):
        return w8_a16_forward(self.int8_weights, 
                              input, self.scales, self.bias)


def replace_linear_with_target_and_quantize(module, 
                               target_class, module_name_to_exclude):
    for name, child in module.named_children():
        if isinstance(child, nn.Linear) and not \
        any([x == name for x in module_name_to_exclude]):
            old_bias = child.bias
            old_weight = child.weight

            new_module = target_class(child.in_features, 
                                      child.out_features, 
                                      old_bias is not None, 
                                      child.weight.dtype)
            setattr(module, name, new_module)

            getattr(module, name).quantize(old_weight)
            
            if old_bias is not None:
              getattr(module, name).bias = old_bias
        else:
            # Recursively call the function for nested modules
            replace_linear_with_target_and_quantize(child, 
                     target_class, module_name_to_exclude)
###################################