from datasets import Dataset, DatasetDict, load_dataset, load_from_disk
from transformers import ViTForImageClassification, ViTImageProcessor, Trainer, TrainingArguments
from PIL import Image
from torch.optim import AdamW
from torch.optim.lr_scheduler import StepLR
import torch
import numpy as np
from sklearn.metrics import f1_score, accuracy_score, recall_score, precision_score
import os


MODEL_NAME = "/Users/XXXXRT/vit_pretrain/vit-base-patch16-384"
SIZE = "base"
PATCH = 16
IMAGE_SIZE = 384
BATCH_SIZE = 8
OPTIMIZER = "AdamW"
SCHEDULER = "StepLR"

IMAGE_PATH = '/Users/XXXXRT/ISIC-2019'
TRAIN_CSV_PATH = '/Users/XXXXRT/ISIC-2019/train_labels.csv'
TEST_CSV_PATH = '/Users/XXXXRT/ISIC-2019/test_labels.csv'

processed_dataset_path = f"/Users/XXXXRT/ISIC-2019/dataset-{IMAGE_SIZE}"
processed_dataset_path = f"/Volumes/T9 APFS/ML Dataset/dataset-{IMAGE_SIZE}"

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device = torch.device("mps")

processor = ViTImageProcessor.from_pretrained(MODEL_NAME)

def preprocess_image_train(example):
    image = Image.open(os.path.join(IMAGE_PATH,'train', example["image"])).convert("RGB")
    example["pixel_values"] = processor(images=image, return_tensors="pt")["pixel_values"].squeeze(0).numpy()
    
    labels = example.copy()
    example["labels"] = np.array([labels["MEL"], labels["NV"], labels["BCC"], labels["AK"], labels["BKL"], labels["DF"], labels["VASC"], labels["SCC"], labels["UNK"]], dtype=np.float32)
    
    return example

def preprocess_image_test(example):
    image = Image.open(os.path.join(IMAGE_PATH,'test', example["image"])).convert("RGB")
    example["pixel_values"] = processor(images=image, return_tensors="pt")["pixel_values"].squeeze(0).numpy()
    
    labels = example.copy()
    example["labels"] = np.array([labels["MEL"], labels["NV"], labels["BCC"], labels["AK"], labels["BKL"], labels["DF"], labels["VASC"], labels["SCC"], labels["UNK"]], dtype=np.float32)
    
    return example

if os.path.exists(processed_dataset_path):
    dataset = load_from_disk(processed_dataset_path)
    print("LOADED")
else:
    train_dataset = load_dataset('csv', data_files=TRAIN_CSV_PATH)["train"]
    test_dataset = load_dataset('csv', data_files=TEST_CSV_PATH)["train"]

    train_dataset = train_dataset.map(preprocess_image_train, batched=False, num_proc=2)
    test_dataset = test_dataset.map(preprocess_image_test, batched=False, num_proc=2)

    dataset = DatasetDict({
        'train': train_dataset,
        'test': test_dataset
    })
    dataset.save_to_disk(processed_dataset_path,num_proc=2)
    print(f"SAVED TO {processed_dataset_path}")

train_dataset = dataset['train']
test_dataset = dataset['test']

num_labels = 9

model = ViTForImageClassification.from_pretrained(
    MODEL_NAME,
    num_labels=num_labels,
    problem_type="multi_label_classification"
).to(device)


training_args = TrainingArguments(
    output_dir=f"/Users/XXXXRT/ISIC-2019/logs/vit-{SIZE}-patch{PATCH}-{IMAGE_SIZE}-bs{BATCH_SIZE}-{OPTIMIZER}-{SCHEDULER}-lables-{num_labels}",
    evaluation_strategy="epoch",
    learning_rate=5e-5,
    per_device_train_batch_size=BATCH_SIZE,
    per_device_eval_batch_size=BATCH_SIZE,
    num_train_epochs=5,
    save_strategy="epoch",
    logging_dir=f"/Users/XXXXRT/ISIC-2019/logs/vit-{SIZE}-patch{PATCH}-{IMAGE_SIZE}-bs{BATCH_SIZE}-{OPTIMIZER}-{SCHEDULER}-lables-{num_labels}/logs",
    logging_steps=50,
    report_to="tensorboard"
)


def compute_metrics(pred):
    logits, labels = pred
    predictions = (logits >= 0.5).astype(int)
    f1 = f1_score(labels, predictions, average="macro")
    accuracy = accuracy_score(labels, predictions)
    recall = recall_score(labels, predictions, average="macro")
    precision = precision_score(labels, predictions, average="macro")
    return {
        "accuracy": accuracy,
        "f1": f1,
        "recall": recall,
        "precision": precision,
    }


learning_rate = 5e-5
weight_decay = 0.01
step_size = 100
gamma = 0.1
optimizer = AdamW(model.parameters(), lr=learning_rate, weight_decay=weight_decay)
scheduler = StepLR(optimizer, step_size=step_size, gamma=gamma)


trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=test_dataset,
    compute_metrics=compute_metrics,
    optimizers=(optimizer, scheduler)
)


trainer.train()