from datasets import Dataset, DatasetDict, load_dataset, load_from_disk
from transformers import ViTForImageClassification, ViTImageProcessor, Trainer, TrainingArguments, ViTConfig
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 = "google/vit-base-patch16-224"
SIZE = "base"
PATCH = 16
IMAGE_SIZE = 224
BATCH_SIZE = 128
OPTIMIZER = "AdamW"
SCHEDULER = "StepLR"
NAME = 'no-pretrain'

IMAGE_PATH = '/root/autodl-tmp/ISIC-2019'
TRAIN_CSV_PATH = '/root/autodl-tmp/ISIC-2019/train_labels.csv'
TEST_CSV_PATH = '/root/autodl-tmp/ISIC-2019/test_labels.csv'

checkpoint_dir = "/root/autodl-tmp/ISIC-2019/logs/vit-base-patch16-224-bs128-AdamW-StepLR-lables-9"

if os.path.isdir(checkpoint_dir) and any(os.scandir(checkpoint_dir)):
    checkpoint = max([os.path.join(checkpoint_dir, d) for d in os.listdir(checkpoint_dir) if "checkpoint" in d], key=os.path.getctime)
else:
    checkpoint = None
    
checkpoint = "/root/autodl-tmp/ISIC-2019/logs/vit-base-patch16-224-bs128-AdamW-StepLR-lables-9-train-linear/checkpoint-895"
checkpoint = None
print(f"从检查点 {checkpoint} 恢复训练")

processed_dataset_path = f"/root/autodl-tmp/ISIC-2019/dataset-{IMAGE_SIZE}"
# processed_dataset_path = f"/root/autodl-tmp/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,keep_in_memory=True)
    print("LOADED")
else:
    train_dataset = load_dataset('csv', data_files=TRAIN_CSV_PATH, keep_in_memory=True)["train"]
    test_dataset = load_dataset('csv', data_files=TEST_CSV_PATH, keep_in_memory=True)["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",
#     ignore_mismatched_sizes=True
# ).to(device)

# for param in model.parameters():
#     param.requires_grad = False

# for param in model.classifier.parameters():
#     param.requires_grad = True

config = ViTConfig(image_size=IMAGE_SIZE, num_labels=num_labels, problem_type="multi_label_classification", patch_size = PATCH)
model = ViTForImageClassification(config)

training_args = TrainingArguments(
    output_dir=f"/root/autodl-tmp/ISIC-2019/logs/vit-{SIZE}-patch{PATCH}-{IMAGE_SIZE}-bs{BATCH_SIZE}-{OPTIMIZER}-{SCHEDULER}-lables-{num_labels}-{NAME}",
    evaluation_strategy="epoch",
    learning_rate=5e-5,
    per_device_train_batch_size=BATCH_SIZE,
    per_device_eval_batch_size=BATCH_SIZE,
    num_train_epochs=20,
    save_strategy="epoch",
    logging_dir=f"/root/autodl-tmp/ISIC-2019/logs/vit-{SIZE}-patch{PATCH}-{IMAGE_SIZE}-bs{BATCH_SIZE}-{OPTIMIZER}-{SCHEDULER}-lables-{num_labels}-{NAME}/logs",
    logging_steps=10,
    report_to="tensorboard",
    fp16=True,   
    dataloader_num_workers = 8
)


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 = 1000
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,None)
    # optimizers=(optimizer, scheduler)
)


# trainer.train()
trainer.train(resume_from_checkpoint=checkpoint)