import os
import math
import numpy as np
import pandas as pd
import seaborn as sn
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import matplotlib.pyplot as plt
import torch.nn as nn
import torch.nn.functional as F
from pl_bolts.datamodules import CIFAR10DataModule
from pl_bolts.transforms.dataset_normalizations import cifar10_normalization
from pytorch_lightning import LightningModule, Trainer, seed_everything
from pytorch_lightning.callbacks import LearningRateMonitor
from pytorch_lightning.callbacks.progress import TQDMProgressBar
from pytorch_lightning.loggers import CSVLogger
from torch.optim.lr_scheduler import OneCycleLR
from torch.optim.swa_utils import AveragedModel, update_bn
from torchmetrics.functional import accuracy
from pytorch_lightning.callbacks import ModelCheckpoint
from torchvision import datasets, transforms, utils
from PIL import Image
from pytorch_grad_cam import GradCAM
from pytorch_grad_cam.utils.image import show_cam_on_image



targets = None


# Yes - This is important predecessor3 for gradioMisClassGradCAM
def display_gradcam_output(data: list,
                           classes: list[str],
                           inv_normalize: transforms.Normalize,
                           model: 'DL Model',
                           target_layers: list['model_layer'],
                           targets=None,
                           number_of_samples: int = 10,
                           transparency: float = 0.60):
    """
    Function to visualize GradCam output on the data
    :param data: List[Tuple(image, label)]
    :param classes: Name of classes in the dataset
    :param inv_normalize: Mean and Standard deviation values of the dataset
    :param model: Model architecture
    :param target_layers: Layers on which GradCam should be executed
    :param targets: Classes to be focused on for GradCam
    :param number_of_samples: Number of images to print
    :param transparency: Weight of Normal image when mixed with activations
    """
    # Plot configuration
    fig = plt.figure(figsize=(10, 10))
    x_count = 5
    y_count = 1 if number_of_samples <= 5 else math.floor(number_of_samples / x_count)

    # Create an object for GradCam
    #cam = GradCAM(model=model, target_layers=target_layers, use_cuda=True)
    cam = GradCAM(model=model, target_layers=target_layers)

    # Iterate over number of specified images
    for i in range(number_of_samples):
        plt.subplot(y_count, x_count, i + 1)
        input_tensor = data[i][0]

        # Get the activations of the layer for the images
        grayscale_cam = cam(input_tensor=input_tensor, targets=targets)
        grayscale_cam = grayscale_cam[0, :]

        # Get back the original image
        img = input_tensor.squeeze(0).to('cpu')
        img = inv_normalize(img)
        rgb_img = np.transpose(img, (1, 2, 0))
        rgb_img = rgb_img.numpy()

        # Mix the activations on the original image
        visualization = show_cam_on_image(rgb_img, grayscale_cam, use_rgb=True, image_weight=transparency)

        # Display the images on the plot
        plt.imshow(visualization)
        # plt.title(r"Correct: " + classes[data[i][1].item()] + '\n' + 'Output: ' + classes[data[i][2].item()])
        plt.xticks([])
        plt.yticks([])
    plt.savefig('imshow_output_gradcam.png')
    return 'imshow_output_gradcam.png'