import torch
import torchvision.transforms as transforms
from torchvision.datasets import CIFAR10
from torch.utils.data import DataLoader
from transformers import ViTImageProcessor, ViTForImageClassification, ViTFeatureExtractor
import matplotlib.pyplot as plt
import torch.nn.functional as F
from sklearn.metrics import accuracy_score, classification_report
from torch.optim import AdamW
from torch.nn import CrossEntropyLoss
#import os
import numpy as np

#os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'expandable_segments:True'

# Загрузка предобученной модели ViT-16
# model = ViTForImageClassification.from_pretrained('WinKawaks/vit-small-patch16-224') #-in21k')
processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224')
model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224')

# Изменение количества классов на 10 (для CIFAR-10)
model.classifier = torch.nn.Linear(model.classifier.in_features, 10)

# Перенос модели на GPU, если доступно
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# model.to(device)
model.half().to(device)  # Load in half precision

# Преобразования для данных
transform = transforms.Compose([
    transforms.Resize((224, 224)),  # ViT-16 ожидает изображения размером 224x224
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# Загрузка данных
train_dataset = CIFAR10(root='./data', train=True, download=True, transform=transform)
test_dataset = CIFAR10(root='./data', train=False, download=True, transform=transform)

train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)
# train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
# test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)

"""
# Дообучение модели
# Выполняется 2 дня
# Оптимизатор и функция потерь
optimizer = AdamW(model.parameters(), lr=1e-4)
criterion = CrossEntropyLoss()

model.train()
num_epochs = 10

for epoch in range(num_epochs):
    for images, labels in train_loader:
        images = images.to(device)
        labels = labels.to(device)

        optimizer.zero_grad()
        outputs = model(images).logits
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

    print(f'Epoch {epoch+1}/{num_epochs}, Loss: {loss.item()}')

# for epoch in range(num_epochs):
#     epoch_loss = 0.0
#     for batch_idx, (images, labels) in enumerate(train_loader):
#         images = images.to(device)
#         labels = labels.to(device)
#
#         optimizer.zero_grad()
#         outputs = model(images).logits
#         loss = criterion(outputs, labels)
#         loss.backward()
#         optimizer.step()
#
#         epoch_loss += loss.item()
#
#         # Печать прогресса каждые 10 батчей
#         if (batch_idx + 1) % 10 == 0:
#             print(f'Epoch {epoch+1}/{num_epochs}, Batch {batch_idx+1}/{len(train_loader)}, Loss: {loss.item()}')
#
#     # Печать средней потери за эпоху
#     avg_epoch_loss = epoch_loss / len(train_loader)
#     print(f'Epoch {epoch+1}/{num_epochs}, Average Loss: {avg_epoch_loss}')
"""

# Функция для оценки модели
def evaluate_model(model, data_loader, device):
    model.eval()
    all_preds = []
    all_labels = []

    with torch.no_grad():
        for images, labels in data_loader:
            images = images.to(device)
            labels = labels.to(device)

            outputs = model(images).logits
            preds = torch.argmax(outputs, dim=1)

            all_preds.extend(preds.cpu().numpy())
            all_labels.extend(labels.cpu().numpy())

    accuracy = accuracy_score(all_labels, all_preds)
    report = classification_report(all_labels, all_preds, target_names=train_dataset.classes)

    return accuracy, report

# Оценка модели на тестовых данных
accuracy, report = evaluate_model(model, test_loader, device)

print(f'Test Accuracy: {accuracy:.4f}')
print('Classification Report:')
print(report)

# Загрузка 25 тестовых изображений
test_images = []
true_labels = []
predicted_labels = []

model.eval()
with torch.no_grad():
    for images, labels in test_loader:
        images = images.to(device)
        labels = labels.to(device)

        outputs = model(images).logits
        preds = torch.argmax(outputs, dim=1)

        test_images.extend(images.cpu())
        true_labels.extend(labels.cpu())
        predicted_labels.extend(preds.cpu())

        if len(test_images) >= 25:
            break

fig, axes = plt.subplots(5, 5, figsize=(15, 15))
axes = axes.ravel()

for i in range(5 * 5):
    axes[i].imshow(np.transpose(test_images[:25][i].cpu().numpy(), (1, 2, 0)))
    axes[i].set_title(f'True: {train_dataset.classes[true_labels[i]]}\nPred: {train_dataset.classes[predicted_labels[i]]}')
    axes[i].axis('off')

plt.subplots_adjust(hspace=0.4)
plt.show()