"""
train.py

Training script for Vision-Language-Action (VLA) Policies, built on top of pretrained VLMs, trained using mixtures of
the Open-X Embodiment dataset. Performs training in native PyTorch, using Fully-Sharded Data Parallel (FSDP) to run
distributed across GPUs (and nodes). By default, assumes that CUDA toolkit is >= 11.0 (to support BF16 mixed precision).

Notes & Prerequisites:
    - If you want to set a custom location for all HF / TIMM artifacts --> `export HF_HOME="<PATH>"` *before* running!
        => For example (add to end of .bashrc): `export HF_HOME="/mnt/fsx/skaramcheti/cache"`
    - If you want to suppress random Tensorflow logs --> `export TF_CPP_MIN_LOG_LEVEL=3`

Run with:
    - [Single Node One-GPU (Debug)] : torchrun --standalone --nnodes 1 --nproc-per-node 1 vla-scripts/train.py
    - [Single Node Multi-GPU (= $K)]: torchrun --standalone --nnodes 1 --nproc-per-node $K vla-scripts/train.py
"""

import json
import os
import re
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Tuple, Union

import draccus
import torch
import torch.distributed as dist
import yaml

from prismatic.conf import VLAConfig, VLARegistry
from prismatic.models import load, load_vla
from prismatic.overwatch import initialize_overwatch
from prismatic.training import VLAMetrics, get_train_strategy
from prismatic.util import set_global_seed
from prismatic.vla import get_vla_dataset_and_collator
from prismatic.vla.datasets.rlds.utils.data_utils import save_dataset_statistics

# Sane Defaults
os.environ["TOKENIZERS_PARALLELISM"] = "false"


# Initialize Overwatch =>> Wraps `logging.Logger`
overwatch = initialize_overwatch(__name__)


@dataclass
class TrainConfig:
    # fmt: off

    # VLAConfig (`prismatic/conf/vla.py`); override with --vla.type `VLARegistry.<VLA>.vla_id`
    vla: VLAConfig = field(
        default_factory=VLAConfig.get_choice_class(VLARegistry.DINOSIGLIP_224PX_MX_OXE_MAGIC_SOUP_PLUS.vla_id)
    )

    # Directory Paths
    data_root_dir: Path = Path(                                     # Path to Open-X dataset directory
        "datasets/open-x-embodiment"
    )
    run_root_dir: Path = Path("runs")                               # Path to directory to store logs & checkpoints

    # Resume Run Parameters
    pretrained_checkpoint: Optional[Path] = None                    # Absolute Path to Checkpoint
    is_resume: bool = True                                          # Whether we are continuing a prior training run
                                                                    #   (only applicable given pretrained checkpoint)
    resume_step: Optional[int] = None                               # Global Step to Resume (should match checkpoint)
    resume_epoch: Optional[int] = None                              # Epoch to Resume (should match checkpoint)

    # Run Arguments
    run_id: Optional[str] = None                                    # Run ID for logging, Weights & Biases
    run_id_note: Optional[str] = None                               # Extra note for logging, Weights & Biases
    save_interval: int = 2500                                       # Interval for saving checkpoints (in steps)
    image_aug: bool = False                                         # Whether to enable image augmentations
    seed: int = 7                                                   # Random seed (for reproducibility)

    # HF Hub Credentials (for any gated models)
    hf_token: Union[str, Path] = Path(".hf_token")                  # Environment variable or Path to HF Token

    # Tracking Parameters
    trackers: Tuple[str, ...] = ("jsonl", "wandb")                  # Trackers to initialize (if W&B, add config!)
    wandb_project: str = "openvla"                                  # Name of W&B project to log to (use default!)
    wandb_entity: str = "stanford-voltron"                          # Name of entity to log under

    def __post_init__(self) -> None:
        """Lift optimization parameters from `self.vla` for ease of use =>> validate on `expected_world_size`"""
        self.epochs = self.vla.epochs
        self.max_steps = self.vla.max_steps
        self.global_batch_size = self.vla.global_batch_size
        self.per_device_batch_size = self.vla.per_device_batch_size

        self.learning_rate = self.vla.learning_rate
        self.weight_decay = self.vla.weight_decay
        self.max_grad_norm = self.vla.max_grad_norm
        self.lr_scheduler_type = self.vla.lr_scheduler_type
        self.warmup_ratio = self.vla.warmup_ratio

        self.train_strategy = self.vla.train_strategy

        # [Validate] Assert on `expected_world_size`
        assert (
            self.vla.expected_world_size == overwatch.world_size()
        ), f"Expected World Size = {self.vla.expected_world_size} but Found {overwatch.world_size()} GPUs!"

    # fmt: on


@draccus.wrap()
def train(cfg: TrainConfig) -> None:
    overwatch.info("OpenVLA Training :: Warming Up")

    # Note => Under `torchrun` initializing `overwatch` will automatically set up `torch.distributed`
    torch.cuda.set_device(device_id := overwatch.local_rank())
    torch.cuda.empty_cache()

    # Configure Unique Run Name & Save Directory
    vla_id = cfg.vla.vla_id
    cfg.run_id = (
        f"{vla_id}+n{cfg.vla.expected_world_size // 8}+b{cfg.per_device_batch_size}+x{cfg.seed}"
        if cfg.run_id is None
        else cfg.run_id
    )
    if cfg.run_id_note is not None:
        cfg.run_id += f"--{cfg.run_id_note}"
    if cfg.image_aug:
        cfg.run_id += "--image_aug"

    # Start =>> Build Directories and Set Randomness
    overwatch.info('"Do or do not; there is no try."', ctx_level=1)
    hf_token = cfg.hf_token.read_text().strip() if isinstance(cfg.hf_token, Path) else os.environ[cfg.hf_token]
    worker_init_fn = set_global_seed(cfg.seed, get_worker_init_fn=True)
    os.makedirs(run_dir := (cfg.run_root_dir / cfg.run_id), exist_ok=True)
    os.makedirs(cfg.run_root_dir / cfg.run_id / "checkpoints", exist_ok=True)

    # Save Configuration =>> additionally save a JSON version for later HF Integration
    if overwatch.is_rank_zero():
        draccus.dump(cfg, open(run_dir / "config.yaml", "w"))
        with open(run_dir / "config.yaml", "r") as f_yaml, open(run_dir / "config.json", "w") as f_json:
            yaml_cfg = yaml.safe_load(f_yaml)
            json.dump(yaml_cfg, f_json, indent=2)

    # Load VLA checkpoint (if resuming from training) or Base VLM otherwise (from `cfg.vla.base_vlm` ID or Path)
    #   =>> Note :: Verifies that all parameters are loaded in FP32 on load!
    overwatch.info(f"Loading Base VLM `{cfg.vla.base_vlm}` from ID/Path")
    if cfg.pretrained_checkpoint is not None:
        # [Validate] Pretrained Checkpoint `step` and `epoch` should match `resume_step` and `resume_epoch`
        #   =>> Note :: We make developers pass in `resume_*` arguments as an extra sanity check!
        if cfg.is_resume:
            assert int(re.search("step-(.+?)-", cfg.pretrained_checkpoint.name).group(1)) == cfg.resume_step
            assert int(re.search("epoch-(.+?)-", cfg.pretrained_checkpoint.name).group(1)) == cfg.resume_epoch

        vlm = load_vla(cfg.pretrained_checkpoint, hf_token=hf_token, load_for_training=True)

    else:
        vlm = load(cfg.vla.base_vlm, hf_token=hf_token, load_for_training=True)

    # [Validate] Model should be in Full Precision!
    for param in vlm.parameters():
        assert param.dtype == torch.float32, f"Loaded VLM parameter not in full precision: {param}"

    # Determine training "stage" based on frozen vs unfrozen parameters --> supports different fine-tuning schemes!
    if not cfg.vla.freeze_vision_backbone and not cfg.vla.freeze_llm_backbone:
        stage = "vla-full-train"  # Full fine-tuning
    elif cfg.vla.freeze_vision_backbone and not cfg.vla.freeze_llm_backbone:
        stage = "vla-train"  # Frozen vision encoder
    elif not cfg.vla.freeze_vision_backbone and cfg.vla.freeze_llm_backbone:
        assert cfg.vla.unfreeze_last_llm_layer, "You should unfreeze at least the last layer of your LLM!"
        stage = "vla-sandwich-train"  # Fine-tuning vision encoder, projector, and LLM last layer
    elif cfg.vla.freeze_vision_backbone and cfg.vla.freeze_llm_backbone:
        assert cfg.vla.unfreeze_last_llm_layer, "Need to unfreeze at least last LLM layer to train!"
        stage = "vla-last-layer-train"  # Fine-tuning LLM last layer only
    else:
        raise ValueError(
            "Weight freezing configuration not supported. VLA config has the following parameters: "
            f"freeze_vision_backbone: {cfg.vla.freeze_vision_backbone}"
            f"freeze_llm_backbone: {cfg.vla.freeze_llm_backbone}"
            f"unfreeze_last_llm_layer: {cfg.vla.unfreeze_last_llm_layer}"
        )

    # [Explicit] Call to `freeze_backbones` here for clarity =>> will log exactly what is/is not frozen
    overwatch.info(f"Invoking `VLM.freeze_backbones()` for `{vla_id}` => Stage: `{stage}`")
    vlm.freeze_backbones(stage)

    # Print number of total/trainable model parameters
    num_params = sum(p.numel() for p in vlm.parameters())
    num_trainable_params = sum(p.numel() for p in vlm.parameters() if p.requires_grad)
    overwatch.info(
        f"# Parameters (in millions): {num_params / 10**6:.3f} Total, {num_trainable_params / 10**6:.3f} Trainable"
    )

    # Get VLA Dataset & Collator
    overwatch.info(f"Creating VLA Open-X Dataset with Mixture `{cfg.vla.data_mix}`")
    vla_dataset, action_tokenizer, collator = get_vla_dataset_and_collator(
        cfg.data_root_dir,
        cfg.vla.data_mix,
        image_transform=vlm.vision_backbone.get_image_transform(),
        tokenizer=vlm.llm_backbone.get_tokenizer(),
        prompt_builder_fn=vlm.llm_backbone.prompt_builder_fn,
        default_image_resolution=vlm.vision_backbone.default_image_resolution,
        shuffle_buffer_size=cfg.vla.shuffle_buffer_size,
        image_aug=cfg.image_aug,
    )

    # Save dataset statistics for de-normalization at inference time
    if overwatch.is_rank_zero():
        save_dataset_statistics(vla_dataset.dataset_statistics, run_dir)

    # Create Train Strategy
    overwatch.info(f"Initializing Train Strategy `{cfg.train_strategy}`")
    train_strategy = get_train_strategy(

        # 基础配置
        train_strategy=cfg.train_strategy,
        vlm=vlm,
        device_id=device_id,
        stage=stage,

        # 训练周期参数
        epochs=cfg.epochs,
        max_steps=cfg.max_steps,

        # 批处理配置
        global_batch_size=cfg.global_batch_size,
        per_device_batch_size=cfg.per_device_batch_size,

        # 优化器参数
        learning_rate=cfg.learning_rate,
        weight_decay=cfg.weight_decay,
        max_grad_norm=cfg.max_grad_norm,

        # 学习率调度
        lr_scheduler_type=cfg.lr_scheduler_type,
        warmup_ratio=cfg.warmup_ratio,

        # 性能优化选项
        enable_gradient_checkpointing=cfg.vla.enable_gradient_checkpointing,
        enable_mixed_precision_training=cfg.vla.enable_mixed_precision_training,
        reduce_in_full_precision=cfg.vla.reduce_in_full_precision,

        # 工作进程初始化
        worker_init_fn=worker_init_fn,
    )
    train_strategy.run_setup(run_dir=run_dir, n_train_examples=len(vla_dataset))

    # Create Metrics =>> Handles on the fly tracking, logging to specified trackers (e.g., JSONL, Weights & Biases)
    overwatch.info(f"Creating Metrics with Active Trackers => `{cfg.trackers}`")
    metrics = VLAMetrics(
        cfg.trackers, # 追踪器列表(JSONL, W&B)
        cfg.run_id, # 运行ID
        run_dir, # 运行目录
        draccus.encode(cfg), # 编码后的配置
        wandb_project=cfg.wandb_project,
        wandb_entity=cfg.wandb_entity,
        resume_step=cfg.resume_step,
        resume_epoch=cfg.resume_epoch,
    )

    # Run VLA Training
    overwatch.info("Starting VLA Training Loop")
    train_strategy.run_vla_training(
        vla_dataset,
        collator,
        action_tokenizer,
        metrics,
        save_interval=cfg.save_interval,
    )

    # Finalize
    overwatch.info("Done with Training =>> Finalizing Metrics")
    metrics.finalize()

    # And... we're done!
    overwatch.info("... and that's all, folks!")
    dist.barrier()
    dist.destroy_process_group()


if __name__ == "__main__":
    train()
