import numpy as np
import numpy.linalg as LA
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
from tqdm import tqdm, trange
from torch.utils.data import TensorDataset
from plotly.subplots import make_subplots
import plotly.graph_objects as go

def visualize3D(vis_net, x, y, dir1, dir2, dir3, len1 = 1, len2 = 1, len3 = 1, show_figure = True, save_figure = False, file_path = './temp.html'):
    
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    
    # Normalize the three directions
    print('Take three orthogonal directions')
    dir1 = dir1/torch.norm(dir1, p = float('2'))
    dir2 = dir2/torch.norm(dir2, p = float('2'))
    dir3 = dir3/torch.norm(dir3, p = float('2'))
    
    # Check if the three directions are orthogonal to each other
    inner_product1 = torch.abs(torch.dot(dir1.view(-1), dir2.view(-1)))
    inner_product2 = torch.abs(torch.dot(dir1.view(-1), dir3.view(-1)))
    inner_product3 = torch.abs(torch.dot(dir2.view(-1), dir3.view(-1)))
    
    check_inner_product1 = (inner_product1<0.01).item()
    check_inner_product2 = (inner_product2<0.01).item()
    check_inner_product3 = (inner_product3<0.01).item()

    assert check_inner_product1, "The three directions are not orthogonal"
    assert check_inner_product2, "The three directions are not orthogonal"
    assert check_inner_product3, "The three directions are not orthogonal"
    
    # Generate the visualization and data grid
    #lenx, leny, lenz = 51, 51, 51
    xx, yy, zz = np.mgrid[-len1:len1:50j, -len2:len2:50j, -len3:len3:50j]

    t = np.c_[xx.ravel(), yy.ravel(), zz.ravel()]
    vis_grid = torch.from_numpy(t).float().to(device)
    dirs_mat = torch.cat([dir1.reshape(1, -1), dir2.reshape(1, -1), dir3.reshape(1, -1)]).to(device)
    x_grid = torch.mm(vis_grid, dirs_mat).reshape(len(vis_grid), 3, 32, 32).to('cpu') + x
        
    grid_output = []
    grid_loader = torch.utils.data.DataLoader(TensorDataset(x_grid), batch_size=64, shuffle=False, num_workers=2)
    
    vis_net.eval()
    
    softmax1 = nn.Softmax()
    
    for grid_points in tqdm(grid_loader):
        
        grid_points = grid_points[0].to(device)
        grid_ys = vis_net(grid_points)   
        grid_ys = softmax1(grid_ys)
        grid_ys = grid_ys[:,y].detach().cpu().numpy()
        grid_output.append(grid_ys)
    
    y_pred0 = np.concatenate(grid_output)
    
    # and plot everything
    fig = go.Figure(data=go.Volume(
    x=xx.flatten(),
    y=yy.flatten(),
    z=zz.flatten(),
    value=y_pred0.flatten(),
    isomin=0,
    isomax=1,
    opacity=0.1, # needs to be small to see through all surfaces
    surface_count=17, # needs to be a large number for good volume rendering
    ))
    
    if show_figure:
        fig.show()
    
    if save_figure:
        plotly.offline.plot(fig, filename=file_path)
    
    return fig


def Assert_three_orthogonal(dirs):
    
    dir1, dir2, dir3 = dirs[0], dirs[1], dirs[2]
    # Check if the three directions are orthogonal to each other
    inner_product1 = torch.abs(torch.dot(dir1.view(-1), dir2.view(-1)))
    inner_product2 = torch.abs(torch.dot(dir1.view(-1), dir3.view(-1)))
    inner_product3 = torch.abs(torch.dot(dir2.view(-1), dir3.view(-1)))
    
    check_inner_product1 = (inner_product1<0.01).item()
    check_inner_product2 = (inner_product2<0.01).item()
    check_inner_product3 = (inner_product3<0.01).item()

    assert check_inner_product1, "The three directions are not orthogonal"
    assert check_inner_product2, "The three directions are not orthogonal"
    assert check_inner_product3, "The three directions are not orthogonal"
    

def Compute_grid_outputs(vis_net, x, y, dirs, lens=[[-1,1],[-1,1],[-1,1]], resolution = "high"):
    
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

    # Generate the visualization and data grid
    
    if resolution == "high":
        xx, yy, zz = np.mgrid[lens[0][0]:lens[0][1]:50j, lens[1][0]:lens[1][1]:50j, lens[2][0]:lens[2][1]:50j]
    elif resolution == "medium":
        xx, yy, zz = np.mgrid[lens[0][0]:lens[0][1]:20j, lens[1][0]:lens[1][1]:20j, lens[2][0]:lens[2][1]:20j]
    elif resolution == "low":
        xx, yy, zz = np.mgrid[lens[0][0]:lens[0][1]:8j, lens[1][0]:lens[1][1]:8j, lens[2][0]:lens[2][1]:8j]
    else:
        raise NameError('The resolution has to be either high, medium, or low.')

    t = np.c_[xx.ravel(), yy.ravel(), zz.ravel()]
    vis_grid = torch.from_numpy(t).float().to(device)
    dirs_mat = torch.cat([dirs[0].reshape(1, -1), dirs[1].reshape(1, -1), dirs[2].reshape(1, -1)]).to(device)
    x_grid = torch.mm(vis_grid, dirs_mat).reshape(len(vis_grid), 3, 32, 32).to('cpu')
    x_grid = x_grid+ x
        
    grid_output = []
    grid_loader = torch.utils.data.DataLoader(TensorDataset(x_grid), batch_size=64, shuffle=False, num_workers=2)
    
    vis_net.eval()
    
    softmax1 = nn.Softmax()
    
    for grid_points in tqdm(grid_loader):
        
        grid_points = grid_points[0].to(device)
        grid_ys = vis_net(grid_points)   
        grid_ys = softmax1(grid_ys)
        grid_ys = grid_ys[:,y].detach().cpu().numpy()
        grid_output.append(grid_ys)
        
    y_pred0 = np.concatenate(grid_output)
    
    return xx.flatten(), yy.flatten(), zz.flatten(), y_pred0.flatten()

def find_specific_class(specific_class, labels):
    
    img_ind = -1
    
    for img_ind in range(labels.shape[0]):
        if labels[img_ind] == specific_class:
            break
    
    return img_ind

def run_many(PGD_attack, 
             data_loader, 
             model, 
             subplot_grid = [2,2], 
             num_adv_directions = 1, 
             lens = [[-1,1],[-1,1],[-1,1]], 
             resolution = "high",
             height = 1000,
             width = 1000,
             show_figure = False, 
             save_figure = False, 
             file_path = './temp.html',
             specific_class = -1,
             title = "",
             if_back_to_cpu = False):
    
    # Create a figure grid
    fig = make_subplots(
    rows=subplot_grid[0], cols=subplot_grid[1],
    specs = [[{'type': 'volume'} for _ in range(subplot_grid[1])] for ind2 in range(subplot_grid[0])])

    num_sub_figures_plotted = 0
        
    for i, (images, labels) in enumerate(data_loader):
        
        if if_back_to_cpu:
            images = images.cpu()
            labels = labels.cpu()
            
        num_figures_3D = subplot_grid[0]*subplot_grid[1]
        
        if num_sub_figures_plotted < num_figures_3D:
            
            print(f"Plotting figure {num_sub_figures_plotted+1}/{num_figures_3D}.")
            
            if specific_class == -1:
                
                # This means that we do not need to find a specific class
                img_ind = 0
                
            else:
                img_ind = find_specific_class(specific_class, labels)
                if img_ind == -1:
                    # This means that this batch does not contain any image of this particular class
                    print("No img of label {0}! Go to the next batch.".format(specific_class))
                    # So, go to the next batch
                    continue
            
            x = images[img_ind]
            y = labels[img_ind]
            
            dirs = [0, 0, 0]
            if num_adv_directions == 0:
                
                print("The number of adversarial directions is 0")
                
                dirs[0] = torch.rand(x.shape) - 0.5
                dirs[1] = torch.rand(x.shape) - 0.5
                dirs[2] = torch.rand(x.shape) - 0.5
            
            elif num_adv_directions == 1:
                
                print("The number of adversarial directions is 1")
                
                labels_change = torch.randint(1, 10, (labels.shape[0],))
                wrong_labels = torch.remainder(labels_change + labels, 10)
                adv_images = PGD_attack.__call__(images, wrong_labels)
                dirs[0] = adv_images[img_ind].cpu() - x    
            
                dirs[1] = torch.rand(x.shape) - 0.5
                dirs[2] = torch.rand(x.shape) - 0.5
                
            elif num_adv_directions == 3:
                
                print("The number of adversarial directions is 3")
                
                for dir_ind in range(3):
                    
                    labels_change = torch.ones(labels.shape[0]) * (dir_ind+1)
                    labels_change = labels_change.long()
                    wrong_labels = torch.remainder(labels_change + labels, 10)
                    adv_images = PGD_attack.__call__(images, wrong_labels)
                    dirs[dir_ind] = adv_images[img_ind].cpu() - x
                    
            else:
                raise NameError('The number of adversarial directions has to be either 0, 1, or 3.')
                
            # Normalize the first direction
            dirs[0] = dirs[0]/torch.norm(dirs[0], p=2)
            
            # Normalize the second direction
            dirs[1] = dirs[1]/torch.norm(dirs[1], p=2)
            dirs[1] = dirs[1] - torch.dot(dirs[1].view(-1), dirs[0].view(-1))*dirs[0]
            dirs[1] = dirs[1]/torch.norm(dirs[1], p=2)
                
            # Normalize the third direction

            dirs[2] = dirs[2]/torch.norm(dirs[2], p=2)
            proj1 = torch.dot(dirs[2].view(-1), dirs[0].view(-1))
            proj2 = torch.dot(dirs[2].view(-1), dirs[1].view(-1))
            dirs[2] = dirs[2] - proj1*dirs[0] - proj2*dirs[1]
            dirs[2] = dirs[2]/torch.norm(dirs[2], p=2)
            
            # Check if the three directions are orthogonal
            Assert_three_orthogonal(dirs)
            
            # Compute the grid outputs
            x, y, z, value = Compute_grid_outputs(model, x, y, dirs, lens = lens, resolution = resolution)
            
            # Figure out where to put the subfigure
            row_ind = int(num_sub_figures_plotted/subplot_grid[1])
            col_ind = num_sub_figures_plotted - row_ind*subplot_grid[1]
            
            row_ind += 1
            col_ind += 1
            
            # Add a subfigure
            fig.add_trace(
                go.Volume(
                    x=x,
                    y=y,
                    z=z,
                    value=value,
                    isomin=0,
                    isomax=1,
                    opacity=0.1, # needs to be small to see through all surfaces
                    surface_count=17, # needs to be a large number for good volume rendering
                ),
                row=row_ind, col=col_ind
            )
            
            num_sub_figures_plotted += 1 
            
        else:
            break
    
    if num_adv_directions == 0:
        title_text="All three directions are random."
    elif num_adv_directions == 1:
        title_text="X direction is adversarial."
    elif num_adv_directions == 3:
        title_text="All three directions are adversarial (with different classes)."
    else:
        raise NameError('The number of adversarial directions has to be either 0, 1, or 3.')
    
    title_text += " Exp name: "
    title_text += title
    
    fig.update_layout(height=height, width=width, title_text=title_text)
    
    if show_figure:
        fig.show()
    
    if save_figure:
        plotly.offline.plot(fig, filename=file_path)
            
    return fig


import numpy as np
import numpy.linalg as LA
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
from tqdm import tqdm, trange
from torch.utils.data import TensorDataset
from plotly.subplots import make_subplots
import plotly.graph_objects as go

def visualize3D(vis_net, x, y, dir1, dir2, dir3, len1 = 1, len2 = 1, len3 = 1, show_figure = True, save_figure = False, file_path = './temp.html'):
    
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    
    # Normalize the three directions
    print('Take three orthogonal directions')
    dir1 = dir1/torch.norm(dir1, p = float('2'))
    dir2 = dir2/torch.norm(dir2, p = float('2'))
    dir3 = dir3/torch.norm(dir3, p = float('2'))
    
    # Check if the three directions are orthogonal to each other
    inner_product1 = torch.abs(torch.dot(dir1.view(-1), dir2.view(-1)))
    inner_product2 = torch.abs(torch.dot(dir1.view(-1), dir3.view(-1)))
    inner_product3 = torch.abs(torch.dot(dir2.view(-1), dir3.view(-1)))
    
    check_inner_product1 = (inner_product1<0.01).item()
    check_inner_product2 = (inner_product2<0.01).item()
    check_inner_product3 = (inner_product3<0.01).item()

    assert check_inner_product1, "The three directions are not orthogonal"
    assert check_inner_product2, "The three directions are not orthogonal"
    assert check_inner_product3, "The three directions are not orthogonal"
    
    # Generate the visualization and data grid
    #lenx, leny, lenz = 51, 51, 51
    xx, yy, zz = np.mgrid[-len1:len1:50j, -len2:len2:50j, -len3:len3:50j]

    t = np.c_[xx.ravel(), yy.ravel(), zz.ravel()]
    vis_grid = torch.from_numpy(t).float().to(device)
    dirs_mat = torch.cat([dir1.reshape(1, -1), dir2.reshape(1, -1), dir3.reshape(1, -1)]).to(device)
    x_grid = torch.mm(vis_grid, dirs_mat).reshape(len(vis_grid), 3, 32, 32).to('cpu') + x
        
    grid_output = []
    grid_loader = torch.utils.data.DataLoader(TensorDataset(x_grid), batch_size=64, shuffle=False, num_workers=2)
    
    vis_net.eval()
    
    softmax1 = nn.Softmax()
    
    for grid_points in tqdm(grid_loader):
        
        grid_points = grid_points[0].to(device)
        grid_ys = vis_net(grid_points)   
        grid_ys = softmax1(grid_ys)
        grid_ys = grid_ys[:,y].detach().cpu().numpy()
        grid_output.append(grid_ys)
    
    y_pred0 = np.concatenate(grid_output)
    
    # and plot everything
    fig = go.Figure(data=go.Volume(
    x=xx.flatten(),
    y=yy.flatten(),
    z=zz.flatten(),
    value=y_pred0.flatten(),
    isomin=0,
    isomax=1,
    opacity=0.1, # needs to be small to see through all surfaces
    surface_count=17, # needs to be a large number for good volume rendering
    ))
    
    if show_figure:
        fig.show()
    
    if save_figure:
        plotly.offline.plot(fig, filename=file_path)
    
    return fig


def Assert_three_orthogonal(dirs):
    
    dir1, dir2, dir3 = dirs[0], dirs[1], dirs[2]
    # Check if the three directions are orthogonal to each other
    inner_product1 = torch.abs(torch.dot(dir1.view(-1), dir2.view(-1)))
    inner_product2 = torch.abs(torch.dot(dir1.view(-1), dir3.view(-1)))
    inner_product3 = torch.abs(torch.dot(dir2.view(-1), dir3.view(-1)))
    
    check_inner_product1 = (inner_product1<0.01).item()
    check_inner_product2 = (inner_product2<0.01).item()
    check_inner_product3 = (inner_product3<0.01).item()

    assert check_inner_product1, "The three directions are not orthogonal"
    assert check_inner_product2, "The three directions are not orthogonal"
    assert check_inner_product3, "The three directions are not orthogonal"
    

def Compute_grid_outputs(vis_net, x, y, dirs, lens=[[-1,1],[-1,1],[-1,1]], resolution = "high"):
    
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

    # Generate the visualization and data grid
    
    if resolution == "high":
        xx, yy, zz = np.mgrid[lens[0][0]:lens[0][1]:50j, lens[1][0]:lens[1][1]:50j, lens[2][0]:lens[2][1]:50j]
    elif resolution == "medium":
        xx, yy, zz = np.mgrid[lens[0][0]:lens[0][1]:20j, lens[1][0]:lens[1][1]:20j, lens[2][0]:lens[2][1]:20j]
    elif resolution == "low":
        xx, yy, zz = np.mgrid[lens[0][0]:lens[0][1]:8j, lens[1][0]:lens[1][1]:8j, lens[2][0]:lens[2][1]:8j]
    else:
        raise NameError('The resolution has to be either high, medium, or low.')

    t = np.c_[xx.ravel(), yy.ravel(), zz.ravel()]
    vis_grid = torch.from_numpy(t).float().to(device)
    dirs_mat = torch.cat([dirs[0].reshape(1, -1), dirs[1].reshape(1, -1), dirs[2].reshape(1, -1)]).to(device)
    x_grid = torch.mm(vis_grid, dirs_mat).reshape(len(vis_grid), 3, 32, 32).to('cpu')
    x_grid = x_grid+ x
        
    grid_output = []
    grid_loader = torch.utils.data.DataLoader(TensorDataset(x_grid), batch_size=64, shuffle=False, num_workers=2)
    
    vis_net.eval()
    
    softmax1 = nn.Softmax()
    
    for grid_points in tqdm(grid_loader):
        
        grid_points = grid_points[0].to(device)
        grid_ys = vis_net(grid_points)   
        grid_ys = softmax1(grid_ys)
        grid_ys = grid_ys[:,y].detach().cpu().numpy()
        grid_output.append(grid_ys)
        
    y_pred0 = np.concatenate(grid_output)
    
    return xx.flatten(), yy.flatten(), zz.flatten(), y_pred0.flatten()

def find_specific_class(specific_class, labels):
    
    img_ind = -1
    
    for img_ind in range(labels.shape[0]):
        if labels[img_ind] == specific_class:
            break
    
    return img_ind

def run_many(PGD_attack, 
             data_loader, 
             model, 
             subplot_grid = [2,2], 
             num_adv_directions = 1, 
             lens = [[-1,1],[-1,1],[-1,1]], 
             resolution = "high",
             height = 1000,
             width = 1000,
             show_figure = False, 
             save_figure = False, 
             file_path = './temp.html',
             specific_class = -1,
             title = "",
             if_back_to_cpu = False):
    
    # Create a figure grid
    fig = make_subplots(
    rows=subplot_grid[0], cols=subplot_grid[1],
    specs = [[{'type': 'volume'} for _ in range(subplot_grid[1])] for ind2 in range(subplot_grid[0])])

    num_sub_figures_plotted = 0
        
    for i, (images, labels) in enumerate(data_loader):
        
        if if_back_to_cpu:
            images = images.cpu()
            labels = labels.cpu()
            
        num_figures_3D = subplot_grid[0]*subplot_grid[1]
        
        if num_sub_figures_plotted < num_figures_3D:
            
            print(f"Plotting figure {num_sub_figures_plotted+1}/{num_figures_3D}.")
            
            if specific_class == -1:
                
                # This means that we do not need to find a specific class
                img_ind = 0
                
            else:
                img_ind = find_specific_class(specific_class, labels)
                if img_ind == -1:
                    # This means that this batch does not contain any image of this particular class
                    print("No img of label {0}! Go to the next batch.".format(specific_class))
                    # So, go to the next batch
                    continue
            
            x = images[img_ind]
            y = labels[img_ind]
            
            dirs = [0, 0, 0]
            if num_adv_directions == 0:
                
                print("The number of adversarial directions is 0")
                
                dirs[0] = torch.rand(x.shape) - 0.5
                dirs[1] = torch.rand(x.shape) - 0.5
                dirs[2] = torch.rand(x.shape) - 0.5
            
            elif num_adv_directions == 1:
                
                print("The number of adversarial directions is 1")
                
                labels_change = torch.randint(1, 10, (labels.shape[0],))
                wrong_labels = torch.remainder(labels_change + labels, 10)
                adv_images = PGD_attack.__call__(images, wrong_labels)
                dirs[0] = adv_images[img_ind].cpu() - x    
            
                dirs[1] = torch.rand(x.shape) - 0.5
                dirs[2] = torch.rand(x.shape) - 0.5
                
            elif num_adv_directions == 3:
                
                print("The number of adversarial directions is 3")
                
                for dir_ind in range(3):
                    
                    labels_change = torch.ones(labels.shape[0]) * (dir_ind+1)
                    labels_change = labels_change.long()
                    wrong_labels = torch.remainder(labels_change + labels, 10)
                    adv_images = PGD_attack.__call__(images, wrong_labels)
                    dirs[dir_ind] = adv_images[img_ind].cpu() - x
                    
            else:
                raise NameError('The number of adversarial directions has to be either 0, 1, or 3.')
                
            # Normalize the first direction
            dirs[0] = dirs[0]/torch.norm(dirs[0], p=2)
            
            # Normalize the second direction
            dirs[1] = dirs[1]/torch.norm(dirs[1], p=2)
            dirs[1] = dirs[1] - torch.dot(dirs[1].view(-1), dirs[0].view(-1))*dirs[0]
            dirs[1] = dirs[1]/torch.norm(dirs[1], p=2)
                
            # Normalize the third direction

            dirs[2] = dirs[2]/torch.norm(dirs[2], p=2)
            proj1 = torch.dot(dirs[2].view(-1), dirs[0].view(-1))
            proj2 = torch.dot(dirs[2].view(-1), dirs[1].view(-1))
            dirs[2] = dirs[2] - proj1*dirs[0] - proj2*dirs[1]
            dirs[2] = dirs[2]/torch.norm(dirs[2], p=2)
            
            # Check if the three directions are orthogonal
            Assert_three_orthogonal(dirs)
            
            # Compute the grid outputs
            x, y, z, value = Compute_grid_outputs(model, x, y, dirs, lens = lens, resolution = resolution)
            
            # Figure out where to put the subfigure
            row_ind = int(num_sub_figures_plotted/subplot_grid[1])
            col_ind = num_sub_figures_plotted - row_ind*subplot_grid[1]
            
            row_ind += 1
            col_ind += 1
            
            # Add a subfigure
            fig.add_trace(
                go.Volume(
                    x=x,
                    y=y,
                    z=z,
                    value=value,
                    isomin=0,
                    isomax=1,
                    opacity=0.1, # needs to be small to see through all surfaces
                    surface_count=17, # needs to be a large number for good volume rendering
                ),
                row=row_ind, col=col_ind
            )
            
            num_sub_figures_plotted += 1 
            
        else:
            break
    
    if num_adv_directions == 0:
        title_text="All three directions are random."
    elif num_adv_directions == 1:
        title_text="X direction is adversarial."
    elif num_adv_directions == 3:
        title_text="All three directions are adversarial (with different classes)."
    else:
        raise NameError('The number of adversarial directions has to be either 0, 1, or 3.')
    
    title_text += " Exp name: "
    title_text += title
    
    fig.update_layout(height=height, width=width, title_text=title_text)
    
    if show_figure:
        fig.show()
    
    if save_figure:
        plotly.offline.plot(fig, filename=file_path)
            
    return fig


# Normalize the three directions # Check if the three directions are orthogonal to each other # Generate the visualization and data grid #lenx, leny, lenz = 51, 51, 51 # and plot everything # needs to be small to see through all surfaces # needs to be a large number for good volume rendering # Check if the three directions are orthogonal to each other # Generate the visualization and data grid # Create a figure grid # This means that we do not need to find a specific class # This means that this batch does not contain any image of this particular class # So, go to the next batch # Normalize the first direction # Normalize the second direction # Normalize the third direction # Check if the three directions are orthogonal # Compute the grid outputs # Figure out where to put the subfigure # Add a subfigure # needs to be small to see through all surfaces # needs to be a large number for good volume rendering