import torch
from torch.utils.data import DataLoader
from sklearn.metrics import classification_report
import SetUp
from Data import *
from IdentitySubset import IdentitySubset
# models
from architectures.Model import Model, Architecture

# numbre of classes 
CLASS_SIZE = 30
# batch 
BATCH_SIZE = 16

# size of images per class trainset + testset
# 30 works best, more than that and we dont have enough data
SAMPLE_SIZE = 30

# this is then full sample - test sample
TRAINING_SMPLE = 28

# learning rate
LR_RATE = 0.0001
EPOCHS = 20

# depends on model architecture
# ResNet, DenseNet = 224
# Inception = 299
RESOLUTION = 299

# model architecture
arch = Architecture.INCEPTION

# DATA PREPARATION
# load data set and prepare
dataset = get_set(res = RESOLUTION)
# select identities for experiment
selected_identities = select_ids(
    dataset = dataset,
    sample_size = SAMPLE_SIZE,
    class_size = CLASS_SIZE
    )

print(f'> Selected {CLASS_SIZE} random identity classes from CelebA dataset.')
print(f'> A class has {TRAINING_SMPLE} train and {SAMPLE_SIZE-TRAINING_SMPLE} test samples')

# split class images to train/test indices
train_indices, test_indices = get_indices(
    dataset = dataset, 
    identities = selected_identities, 
    split_at = TRAINING_SMPLE
)

# helps map class id to index
id_map = {old_id: new_id for new_id, old_id in enumerate(selected_identities)}

# we remap identities because crossEntropyLoss requires in indices 0 -> (n-1)
# where n = class size.
train_data = IdentitySubset(
    dataset, 
    train_indices, 
    id_map)

train_loader = DataLoader(
    train_data, 
    batch_size = BATCH_SIZE, 
    shuffle = True)

print(f"> Total training images: {len(train_data)}")

print(f'> Constants : Classes = {CLASS_SIZE}, Batch = {BATCH_SIZE}, epochs = {EPOCHS}')

# MODEL PREPARATION
# cuda if exists (it does here)
device = SetUp.get_device()
# Create model using Factory
model = Model.create(
    arch = arch, 
    device = device, 
    size = CLASS_SIZE)

# FINETUNING
model.train(
    epochs = EPOCHS, 
    loader = train_loader, 
    rate = LR_RATE)

# save.
torch.save(
    model.get().state_dict(), 
    f'{arch.name}.pth')

# done tuning
print('Model saved!')

# EVALUATE
# Testing
test_data = IdentitySubset(
    dataset, 
    test_indices, 
    id_map)

test_loader = DataLoader(
    test_data, 
    batch_size=BATCH_SIZE, 
    shuffle=False)

print(f"Total test images for these {CLASS_SIZE} classes: {len(test_data)}")

# Evaluate
model.evaluate(
    loader = test_loader)