import re

import matplotlib.pyplot as plt

h1 = {
    "loss": [],
    "c_acc": [],
    "top1": [],
    "top3": [],
    "top5": [],
    "val_loss": [],
    "val_top1": [],
    "val_top3": [],
    "val_top5": [],
}
h2 = {
    "loss": [],
    "c_acc": [],
    "top1": [],
    "top3": [],
    "top5": [],
    "val_loss": [],
    "val_top1": [],
    "val_top3": [],
    "val_top5": [],
}
h3 = {
    "loss": [],
    "c_acc": [],
    "top1": [],
    "top3": [],
    "top5": [],
    "val_loss": [],
    "val_top1": [],
    "val_top3": [],
    "val_top5": [],
}

d1 = {
    "train": {
        "top1": [
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            0.3828125,
            0.515625,
            0.6015625,
            0.5625,
            0.6875,
            0.734375,
            0.7734375,
            0.6875,
            0.8046875,
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            0.84375,
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            0.7734375,
            0.90625,
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        "top3": [
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        ],
    },
    "val": {
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}
d2 = {
    "train": {
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d3 = {
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with open("/home/laurent/Documents/Cours/ENSEEIHT/S9 - IAM/Projet/src/recup_baseline.txt", "r") as file:
    for line in file:
        matches = re.finditer(r"(val_loss|loss): ([0-9.]+)", line)
        for match in matches:
            metric_name = match.group(1)
            metric_value = float(match.group(2))
            h1[metric_name].append(metric_value)

# with open("/home/laurent/Documents/Cours/ENSEEIHT/S9 - IAM/Projet/src/recup_semisup.txt", "r") as file:
#     for line in file:
#         matches = re.finditer(r"(val_top1|val_top3): ([0-9.]+)", line)
#         for match in matches:
#             metric_name = match.group(1)
#             metric_value = float(match.group(2))
#             h2[metric_name].append(metric_value)

# with open("/home/laurent/Documents/Cours/ENSEEIHT/S9 - IAM/Projet/src/recup_finetuning.txt", "r") as file:
#     for line in file:
#         matches = re.finditer(r"(val_top1|val_top3): ([0-9.]+)", line)
#         for match in matches:
#             metric_name = match.group(1)
#             metric_value = float(match.group(2))
#             h3[metric_name].append(metric_value)

# plt.figure(figsize=(15, 10))
# plt.plot(d1["val"]["top1"], label="baseline_val_top1")
# # plt.plot(h1["val_top3"], color="green", label="baseline_top3")
# plt.plot(d2["val"]["top1"], label="semisup_val_top1")
# # plt.plot(h2["val_top3"], color="green", label="semisup_top3")
# plt.plot(d3["val"]["top1"], label="pretrain_val_top1")
# # plt.plot(h3["val_top3"], color="green", label="finetuning_top3")
# plt.xlabel("Epochs")
# plt.ylabel("Accuracy")
# plt.legend()
# plt.grid()
# plt.savefig("comp_accuracy_sgan.png", dpi=300, transparent=True, bbox_inches="tight", pad_inches=0)

plt.figure(figsize=(15, 10))
plt.plot(h1["loss"], color="purple", label="loss")
plt.plot(h1["val_loss"], color="purple", label="val_loss", linestyle="--")
plt.ylabel("Loss")
plt.xlabel("Epochs")
plt.legend()
plt.grid()
plt.savefig("baseline_loss_simclr.png", dpi=300, transparent=True, bbox_inches="tight", pad_inches=0)