import matplotlib.pyplot as plt
import numpy as np
from brokenaxes import brokenaxes
import random
random.seed(999)

import matplotlib.font_manager as fm

# Path to a downloaded Times New Roman font file (.ttf)
font_path = '/nfs/ywang29/GmNet/times.ttf'

# Add the font to Matplotlib's font manager
font_manager = fm.FontManager(fm.findSystemFonts())
font_manager.addfont(font_path)
prop = fm.FontProperties(fname=font_path)
plt.rcParams['font.family'] = prop.get_name()


# Randomly generating some fake data for the plot
my_color = (0.98,0.1,0.1)
models = [
    #{"name": "EfficientFormerV2-S0", "top1_accuracy": random.uniform(75, 76), "latency": 2.0, "param": random.uniform(100, 200), "color": np.random.rand(3,)},
    #{"name": "MobileNetV2", "top1_accuracy": random.uniform(71, 73), "latency": random.uniform(0.6, 0.9), "param": random.uniform(50, 100), "color": np.random.rand(3,)},
    #{"name": "EfficientFormer-L1", "top1_accuracy": random.uniform(78, 80), "latency": 12.1, "param": random.uniform(200, 300), "color": np.random.rand(3,)},
    #{"name": "ResNet50", "top1_accuracy": random.uniform(76, 78), "latency": random.uniform(2, 2.5), "param": random.uniform(150, 250), "color": np.random.rand(3,)},
    #{"name": "MobileViT-XS", "top1_accuracy": random.uniform(73, 74), "latency": random.uniform(5, 6), "param": random.uniform(80, 150), "color": np.random.rand(3,)},
    #
    # {"name": "", "top1_accuracy":72.5, "latency":0.67, "param":2.4, "color":my_color},
    # {"name": "", "top1_accuracy": 71.6, "latency": 0.63, "param": 2.2, "color": my_color, "shift": [0, 0]},
    #{"name": "", "top1_accuracy":73.5, "latency":0.716, "param":2.9, "color":my_color, "shift":[0,0]},
    {"name": "StarNet-S2", "top1_accuracy":74.8, "latency":1.9, "param":3.7, "color":np.random.rand(3,), "shift":[0,0.2]},
    {"name": "StarNet-S3", "top1_accuracy":77.3, "latency":2.4, "param":5.8, "color":np.random.rand(3,), "shift":[0,-0.2]},
    {"name": "StarNet-S4", "top1_accuracy":78.4, "latency":3.3, "param":7.5, "color":np.random.rand(3,), "shift":[-0.1,-0.3]},
    # others
    # {"name": "MobileOne-S0", "top1_accuracy":71.4, "latency":0.67, "param":2.1, "color":np.random.rand(3,),"shift":[0.13,-0.2]},
    #{"name": "MobileOne-S1", "top1_accuracy":75.9, "latency":0.854, "param":4.8, "color":np.random.rand(3,), "shift":[0.13,-0.2]},
    {"name": "MobileOne-S2", "top1_accuracy":77.2, "latency":1.8, "param":7.8, "color":np.random.rand(3,), "shift":[0.2,0.2]},
    {"name": "MobileOne-S4", "top1_accuracy":79.6, "latency":2.9, "param":7.8, "color":np.random.rand(3,), "shift":[0.15,0]},
   
    #{"name": "EfficientNet-B0", "top1_accuracy":77.1, "latency":, "param":5.3, "color":np.random.rand(3,), "shift":[0,0]},
    # {"name": "EdgeViT-xxs", "top1_accuracy":74.4, "latency":2.839, "param":4.1, "color":np.random.rand(3,), "shift":[0,0]},
    {"name": "EdgeViT-xs", "top1_accuracy":77.5, "latency":3.0, "param":4.1, "color":np.random.rand(3,), "shift":[0,0]},

    {"name": "Mobilev2-x1.4", "top1_accuracy":74.7, "latency":1.7, "param":6.9, "color":np.random.rand(3,), "shift":[0,-0.2]},
    #{"name": "MobileNetv2-x1.0", "top1_accuracy":72, "latency":1.7, "param":3.4, "color":np.random.rand(3,), "shift":[0,0]},
    #{"name": "Mobile v2-x2.0", "top1_accuracy":74.7, "latency":2.0, "param":7.4, "color":np.random.rand(3,), "shift":[0.2,0]},
    #{"name": "MobileNetv2-x1.4", "param":6.9, "top1_accuracy":74, "latency":1.7, "color":np.random.rand(3,), "shift":[0,0]},
    #{"name": "MobileNetv2-x1.0", "param":3.4, "top1_accuracy":72, "latency":1.7, "color":np.random.rand(3,), "shift":[0,0]},
    #{"name": "MobileNetv2-x2.0", "param":7.4, "top1_accuracy":74, "latency":2.0, "color":np.random.rand(3,), "shift":[0,0]},
    {"name": "RepViT-M0.9", "param":6.9, "top1_accuracy":77.4, "latency":3.2, "color":np.random.rand(3,), "shift":[0,-0.2]},
    # {"name": "RepViT-M1.0", "param":3.4, "top1_accuracy":78.6, "latency":3.6, "color":np.random.rand(3,), "shift":[-0.1,0]},
    # {"name": "RepViT-M1.1", "param":7.4, "top1_accuracy":79.4, "latency":5.1, "color":np.random.rand(3,), "shift":[-0.3,0]},
    # {"name": "RepViT-M1.5", "param":7.4, "top1_accuracy":81.2, "latency":6.4, "color":np.random.rand(3,), "shift":[-0.3,0]},

    #{"name": "GhostNet0.5", "param":6.9, "top1_accuracy":77.4, "latency":3.2, "color":np.random.rand(3,), "shift":[0,0]},
    {"name": "GhostNet1.0", "param":3.4, "top1_accuracy":73.9, "latency":2.9, "color":np.random.rand(3,), "shift":[0,0]},
    {"name": "GhostNet1.3", "param":7.4, "top1_accuracy":75.7, "latency":3.0, "color":np.random.rand(3,), "shift":[-0,0]},

    # {"name": "FastViT-S12", "param":3.4, "top1_accuracy":79.8, "latency":5.3, "color":np.random.rand(3,), "shift":[-0.3,-0.3]},
   # {"name": "RepViT-M1.1", "param":7.4, "top1_accuracy":81.2, "latency":5.1, "color":np.random.rand(3,), "shift":[0,0]},
    # {"name": "LSNet-B", "param":3.4, "top1_accuracy":80.3, "latency":6.2, "color":np.random.rand(3,), "shift":[-0.3,-0.3]},
    {"name": "LSNet-T", "param":3.4, "top1_accuracy":74.9, "latency":2.9, "color":np.random.rand(3,), "shift":[-0,-0]},

    {"name": "UniRepLKNet-F", "param":3.4, "top1_accuracy":78.6, "latency":3.1, "color":np.random.rand(3,), "shift":[-0,-0]},

    {"name": "EfficientFormerV2-S0", "param":6.9, "top1_accuracy":73.7, "latency":2.0, "color":np.random.rand(3,), "shift":[0,0.3]},
    #{"name": "EfficientFormerV2-S1", "param":3.4, "top1_accuracy":74.7, "latency":2.8, "color":np.random.rand(3,), "shift":[0,0.3]},
    # {"name": "EfficientFormerV2-S2", "param":7.4, "top1_accuracy":80.4, "latency":5.4, "color":np.random.rand(3,), "shift":[-0.35,0.15]},

    {"name": "EfficientMod-xxs", "param":6.9, "top1_accuracy":73.7, "latency":2.3, "color":np.random.rand(3,), "shift":[0,-0]},
    {"name": "EfficientMod-xs", "param":3.4, "top1_accuracy":74.7, "latency":2.9, "color":np.random.rand(3,), "shift":[-0,-0.1]},
    # {"name": "EfficientMod-s", "param":7.4, "top1_accuracy":80.4, "latency":4.5, "color":np.random.rand(3,), "shift":[0,-0.3]},
    # {"name": "FasterNet-T0", "top1_accuracy":71.9, "latency":0.708, "param":3.9, "color":np.random.rand(3,), "shift":[0,0]},
    #{"name": "Fasternet-T0", "param":6.9, "top1_accuracy":71.9, "latency":2.3, "color":np.random.rand(3,), "shift":[0,0]},
    {"name": "Fasternet-T1", "param":3.4, "top1_accuracy":76.2, "latency":2.6, "color":np.random.rand(3,), "shift":[0,0]},
    # {"name": "Fasternet-T2", "param":7.4, "top1_accuracy":78.9, "latency":4.5, "color":np.random.rand(3,), "shift":[0,0]},

    {"name": "", "top1_accuracy":75.5, "latency":1.6, "param":7.6, "color":'r', "shift":[0.15,0]},
     {"name": "", "top1_accuracy":78.3, "latency":1.8, "param":7.6, "color":'r', "shift":[0.15,0]},
    {"name": "", "top1_accuracy":79.3, "latency":2.1, "param":7.8, "color":'r', "shift":[0.15,0]},
    {"name": "GmNet", "top1_accuracy":81.5, "latency":2.9, "param":13.0, "color":'r', "shift":[0.1,0]},

    # Add more models as needed
]

#GmNet
plot_data_x = [ 1.6, 1.8, 2.1, 2.9]
plot_data_y = [75.5,78.3, 79.3, 81.5]

#StarNet
#plot_data_x1 = [ 2.0,2.3, 3.3]
#plot_data_y1 = [74.8,77.3,78.4]
plot_data_x1 = [ 2.0, 3.3]
plot_data_y1 = [74.8,78.4]
#RepViT
plot_data_x2 = [ 3.2, 3.6, 5.1]
plot_data_y2 = [77.4, 78.6, 81.2]

#EfficientFormerV2
plot_data_x3 = [ 2.0, 2.8, 5.4]
plot_data_y3 = [73.7, 74.7, 80.4]


#EfficientMod
plot_data_x4 = [ 2.3, 2.9, 4.5]
plot_data_y4 = [76, 78.3, 81.0]

#Fasternet
plot_data_x5 = [ 2.3, 2.6, 4.5]
plot_data_y5 = [71.9, 76.2, 78.9]

#Fasternet
plot_data_x5 = [ 2.3, 2.6, 4.5]
plot_data_y5 = [71.9, 76.2, 78.9]

# 71.9,
# 76.2,
# 78.9,
#MobileOne
plot_data_x5 = [ 1.8, 2.9]
plot_data_y5 = [77.4, 79.4]

#GmNet
#plot_data_x = [ 1.6, 2.2, 2.9]
#plot_data_y = [74.6, 79.3, 81.2]

# Set up the figure and axis
fig, ax = plt.subplots(figsize=(6, 6))
ax.grid(True, linestyle='dashed', color=(0.95,0.95,0.95))
# fig = plt.figure(figsize=(4, 3))
# ax = brokenaxes(xlims=((0, 2), ()),
#                 ylims=((-4.3, -4), (-0.5, 0.2), (0.65, 1.5))) # !! These lims need to be manually adjusted for the best view

# Scatter plot, each model is a point whose size is proportional to the model 'size' and color is unique
ax.plot(plot_data_x, plot_data_y, linewidth=3, color=my_color, linestyle="-.", alpha=0.4)
#ax.plot(plot_data_x1, plot_data_y1, linewidth=3, color=my_color, linestyle="-.", alpha=0.4)
#ax.plot(plot_data_x2, plot_data_y2, linewidth=3, color=my_color, linestyle="-.", alpha=0.4)
#ax.plot(plot_data_x3, plot_data_y3, linewidth=3, color=my_color, linestyle="-.", alpha=0.4)
#ax.plot(plot_data_x4, plot_data_y4, linewidth=3, color=my_color, linestyle="-.", alpha=0.4)
#ax.plot(plot_data_x5, plot_data_y5, linewidth=3, color=my_color, linestyle="-.", alpha=0.4)


for spine in ax.spines.values():
        spine.set_edgecolor((0.5, 0.5, 0.5))
for model in models:
    ax.scatter(model['latency'], model['top1_accuracy'], s =40, alpha=0.8, color=model['color'], marker='o')
    ax.text(model['latency']+model['shift'][0], model['top1_accuracy']+model['shift'][1], model['name'], fontsize=11, ha='center')
# s=int(model['param']*100),
# Setting the axis labels
ax.set_xlabel('Latency (ms)', fontsize=12)
ax.set_ylabel('Top-1 Accuracy (%)', fontsize=12)
#ax.annotate('(Bubble size indicates model parameter number)', (1.45, 72.5),fontsize=11, horizontalalignment='center', verticalalignment='center', color=(0.4,0.4,0.4))

# Title of the plot
# ax.set_title('Model Performance Comparison')

# Show the plot
plt.show()
out = 'acc_latency3.pdf'
fig.savefig(out, bbox_inches='tight')
out = 'acc_latency3.png'
fig.savefig(out, bbox_inches='tight')