"""
Training script for Darcy flow equation using neural operators.

This script trains a neural operator on the 2D Darcy flow equation,
which models fluid flow through porous media. The script supports
distributed training and multi-grid patching for high-resolution data.
"""

from pathlib import Path
import sys

import torch

from torch.utils.data import DataLoader, DistributedSampler
import wandb

from neuralop import H1Loss, LpLoss, Trainer, get_model
from neuralop.data.datasets import load_darcy_flow_small
from neuralop.data.transforms.data_processors import MGPatchingDataProcessor
from neuralop.training import setup, AdamW
from neuralop.mpu.comm import get_local_rank
from neuralop.utils import get_wandb_api_key, count_model_params


# Read the configuration
from zencfg import make_config_from_cli
import sys

sys.path.insert(0, "../")
from config.darcy_config import Default


config = make_config_from_cli(Default)
config = config.to_dict()

# Distributed training setup, if enabled
device, is_logger = setup(config)

# Set up WandB logging
wandb_args = None
if config.wandb.log and is_logger:
    wandb.login(key=get_wandb_api_key())
    if config.wandb.name:
        wandb_name = config.wandb.name
    else:
        wandb_name = "_".join(
            f"{var}"
            for var in [
                config.model.model_arch,
                config.model.n_layers,
                config.model.n_modes,
                config.model.hidden_channels,
            ]
        )
    wandb_args = dict(
        config=config,
        name=wandb_name,
        group=config.wandb.group,
        project=config.wandb.project,
        entity=config.wandb.entity,
    )
    if config.wandb.sweep:
        for key in wandb.config.keys():
            config.params[key] = wandb.config[key]
    wandb.init(**wandb_args)

# Make sure we only print information when needed
config.verbose = config.verbose and is_logger

# Print configuration details
if config.verbose and is_logger:
    print(f"##### CONFIG #####\n")
    print(config)
    sys.stdout.flush()

# Load the Darcy flow dataset
data_root = Path(config.data.folder).expanduser()
train_loader, test_loaders, data_processor = load_darcy_flow_small(
    data_root=data_root,
    n_train=config.data.n_train,
    batch_size=config.data.batch_size,
    test_resolutions=config.data.test_resolutions,
    n_tests=config.data.n_tests,
    test_batch_sizes=config.data.test_batch_sizes,
    encode_input=False,
    encode_output=False,
)
# Model initialization
model = get_model(config)

# convert dataprocessor to an MGPatchingDataProcessor if patching levels > 0
if config.patching.levels > 0:
    data_processor = MGPatchingDataProcessor(
        model=model,
        in_normalizer=data_processor.in_normalizer,
        out_normalizer=data_processor.out_normalizer,
        padding_fraction=config.patching.padding,
        stitching=config.patching.stitching,
        levels=config.patching.levels,
        use_distributed=config.distributed.use_distributed,
        device=device,
    )

# Distributed data parallel setup
# Reconfigure DataLoaders to use a DistributedSampler if in distributed mode
if config.distributed.use_distributed:
    train_db = train_loader.dataset
    train_sampler = DistributedSampler(train_db, rank=get_local_rank())
    train_loader = DataLoader(
        dataset=train_db, batch_size=config.data.batch_size, sampler=train_sampler
    )
    for (res, loader), batch_size in zip(
        test_loaders.items(), config.data.test_batch_sizes
    ):
        test_db = loader.dataset
        test_sampler = DistributedSampler(test_db, rank=get_local_rank())
        test_loaders[res] = DataLoader(
            dataset=test_db, batch_size=batch_size, shuffle=False, sampler=test_sampler
        )
# Create the optimizer
optimizer = AdamW(
    model.parameters(),
    lr=config.opt.learning_rate,
    weight_decay=config.opt.weight_decay,
)

if config.opt.scheduler == "ReduceLROnPlateau":
    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
        optimizer,
        factor=config.opt.gamma,
        patience=config.opt.scheduler_patience,
        mode="min",
    )
elif config.opt.scheduler == "CosineAnnealingLR":
    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
        optimizer, T_max=config.opt.scheduler_T_max
    )
elif config.opt.scheduler == "StepLR":
    scheduler = torch.optim.lr_scheduler.StepLR(
        optimizer, step_size=config.opt.step_size, gamma=config.opt.gamma
    )
else:
    raise ValueError(f"Got scheduler={config.opt.scheduler}")


# Create the losses functions
l2loss = LpLoss(d=2, p=2)
h1loss = H1Loss(d=2)
if config.opt.training_loss == "l2":
    train_loss = l2loss
elif config.opt.training_loss == "h1":
    train_loss = h1loss
else:
    raise ValueError(
        f"Got training_loss={config.opt.training_loss} "
        f'but expected one of ["l2", "h1"]'
    )
eval_losses = {"h1": h1loss, "l2": l2loss}

if config.verbose and is_logger:
    print("\n### MODEL ###\n", model)
    print("\n### OPTIMIZER ###\n", optimizer)
    print("\n### SCHEDULER ###\n", scheduler)
    print("\n### LOSSES ###")
    print(f"\n * Train: {train_loss}")
    print(f"\n * Test: {eval_losses}")
    print(f"\n### Beginning Training...\n")
    sys.stdout.flush()

trainer = Trainer(
    model=model,
    n_epochs=config.opt.n_epochs,
    device=device,
    data_processor=data_processor,
    mixed_precision=config.opt.mixed_precision,
    wandb_log=config.wandb.log,
    eval_interval=config.opt.eval_interval,
    log_output=config.wandb.log_output,
    use_distributed=config.distributed.use_distributed,
    verbose=config.verbose and is_logger,
)

# Log model parameter count
if is_logger:
    n_params = count_model_params(model)

    if config.verbose:
        print(f"\nn_params: {n_params}")
        sys.stdout.flush()

    if config.wandb.log:
        to_log = {"n_params": n_params}
        if config.n_params_baseline is not None:
            to_log["n_params_baseline"] = (config.n_params_baseline,)
            to_log["compression_ratio"] = (config.n_params_baseline / n_params,)
            to_log["space_savings"] = 1 - (n_params / config.n_params_baseline)
        wandb.log(to_log, commit=False)
        wandb.watch(model)

# Start training process
trainer.train(
    train_loader=train_loader,
    test_loaders=test_loaders,
    optimizer=optimizer,
    scheduler=scheduler,
    regularizer=False,
    training_loss=train_loss,
    eval_losses=eval_losses,
)

# Finalize WandB logging
if config.wandb.log and is_logger:
    wandb.finish()