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#
# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""DeepSpeed integration via accelerate's built-in capabilities.

Instead of manually calling deepspeed.initialize() and syncing config,
this module leverages accelerate's Accelerator + DeepSpeedPlugin to handle
initialization, backward, gradient accumulation, and model saving.
"""

from typing import Any, Optional

import torch
from accelerate import Accelerator
from accelerate.utils import DeepSpeedPlugin

from ....utils.logging import get_logger
from ....utils.types import HFModel, Processor


logger = get_logger(__name__)


class DeepSpeedEngine:
    """DeepSpeed integration using accelerate's built-in capabilities.

    This replaces the manual DeepSpeedConfigHelper / DeepSpeedEngine approach
    with accelerate's Accelerator + DeepSpeedPlugin, which handles:
    - Config syncing (auto values, batch size, lr, etc.)
    - deepspeed.initialize() call
    - Optimizer / LR scheduler wrapping
    - Backward + gradient accumulation boundary
    - ZeRO-3 parameter gathering for saving
    """

    def __init__(self, dist_config: dict[str, Any], num_micro_batch: int = 1, micro_batch_size: int = 1):
        config_file = dist_config.get("config_file")
        if not config_file:
            raise ValueError("DeepSpeed config_file is required in dist_config")

        ds_plugin = DeepSpeedPlugin(hf_ds_config=config_file)

        self.accelerator = Accelerator(
            deepspeed_plugin=ds_plugin,
            gradient_accumulation_steps=num_micro_batch,
        )

        # Resolve "auto" for train_micro_batch_size_per_gpu so that
        # accelerate.prepare() does not require a DataLoader to infer it.
        ds_config = self.accelerator.state.deepspeed_plugin.deepspeed_config
        if ds_config.get("train_micro_batch_size_per_gpu") in (None, "auto"):
            ds_config["train_micro_batch_size_per_gpu"] = micro_batch_size

        logger.info_rank0(f"DeepSpeedEngine initialized with config: {config_file}")

    def shard_model(self, model: HFModel) -> "DeepSpeedEngine":
        """No-op shard — actual model wrapping happens in prepare().

        Returns self so the caller gets the engine instance via the hub interface.
        """
        return self

    def prepare(
        self,
        model: HFModel,
        optimizer: torch.optim.Optimizer,
        lr_scheduler: Optional[Any] = None,
    ) -> tuple[HFModel, torch.optim.Optimizer, Any]:
        """Prepare model, optimizer, and lr_scheduler using accelerate.

        Internally calls deepspeed.initialize() and wraps the returned objects.
        """
        if lr_scheduler is not None:
            model, optimizer, lr_scheduler = self.accelerator.prepare(model, optimizer, lr_scheduler)
        else:
            model, optimizer = self.accelerator.prepare(model, optimizer)

        model._accelerator = self.accelerator  # type: ignore[assignment]

        logger.info_rank0("Model, optimizer, and lr_scheduler prepared via accelerate")
        return model, optimizer, lr_scheduler

    def backward(self, loss: torch.Tensor) -> None:
        """Backward pass using accelerate.

        Delegates to DeepSpeedEngineWrapper.backward() which respects
        sync_gradients to control gradient accumulation boundaries.
        When sync_gradients=True: engine.backward(loss) + engine.step()
        When sync_gradients=False: engine.backward(loss) only
        """
        self.accelerator.backward(loss)

    def get_grad_norm(self) -> float:
        """Get the global gradient norm from the DeepSpeed engine."""
        engine_wrapper = getattr(self.accelerator, "deepspeed_engine_wrapped", None)
        if engine_wrapper is not None:
            return engine_wrapper.engine.get_global_grad_norm() or 0.0
        return 0.0


def save_model(model: HFModel, output_dir: str, processor: Processor) -> None:
    """Save model using accelerate's built-in ZeRO-aware utilities.

    Expects model._accelerator to be set during prepare().
    Handles ZeRO-3 parameter gathering automatically via
    accelerator.get_state_dict().
    """
    accelerator: Accelerator = model._accelerator  # type: ignore[union-attr]

    unwrapped_model = accelerator.unwrap_model(model)
    state_dict = accelerator.get_state_dict(model)

    if accelerator.is_main_process:
        unwrapped_model.save_pretrained(output_dir, state_dict=state_dict, max_shard_size="4GB")
        processor.save_pretrained(output_dir, max_shard_size="4GB")

    accelerator.wait_for_everyone()
    logger.info_rank0(f"Model saved to {output_dir}")
