"""Advanced Trainer with Multi-Task Learning, Curriculum, and MoE Support"""

import logging
import os
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple, Union

import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.cuda.amp import GradScaler, autocast
from tqdm import tqdm
from transformers import PreTrainedModel

from ..configs import (
    DataConfig,
    TrainingConfig,
    ZenithConfig,
    get_7b_config,
    get_32b_config,
    get_70b_config,
)
from ..data import (
    OpenThoughtsProcessor,
    OpenThoughtsConfig,
    CurriculumSampler,
    QualityFilter,
)
from ..evaluation import BenchmarkSuite, BenchmarkConfig
from ..utils import CheckpointManager, MetricsLogger, setup_logging

logger = logging.getLogger(__name__)


@dataclass
class TrainerConfig:
    """Complete trainer configuration."""
    model_config: ZenithConfig
    data_config: DataConfig
    training_config: TrainingConfig

    # Paths
    output_dir: str = "./outputs"
    logging_dir: str = "./logs"
    checkpoint_dir: str = "./checkpoints"

    # Distributed training
    local_rank: int = -1
    world_size: int = 1
    distributed: bool = False

    # Mixed precision
    use_amp: bool = True
    amp_dtype: str = "bfloat16"

    # Gradient accumulation
    gradient_accumulation_steps: int = 4

    # Logging and evaluation
    log_interval: int = 10
    eval_interval: int = 500
    save_interval: int = 1000

    # Resume
    resume_from_checkpoint: Optional[str] = None

    def __post_init__(self):
        """Setup derived configs."""
        self.training_config.gradient_accumulation_steps = self.gradient_accumulation_steps


class MultiTaskLoss(nn.Module):
    """Multi-task loss for different objectives."""

    def __init__(self, task_weights: Dict[str, float]):
        super().__init__()
        self.task_weights = task_weights
        self.loss_fns = {
            "next_token": nn.CrossEntropyLoss(ignore_index=-100),
            "thoughts": nn.MSELoss(),
            "eq_classification": nn.CrossEntropyLoss(),
            "frustration_detection": nn.MSELoss(),
        }

    def forward(
        self,
        outputs: Dict[str, torch.Tensor],
        batch: Dict[str, torch.Tensor],
    ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
        """Compute weighted multi-task loss."""
        total_loss = 0.0
        losses = {}

        # Next token prediction (primary LM loss)
        if "next_token" in self.task_weights:
            lm_loss = self.loss_fns["next_token"](
                outputs["logits"].view(-1, outputs["logits"].size(-1)),
                batch["labels"].view(-1),
            )
            total_loss += self.task_weights["next_token"] * lm_loss
            losses["next_token"] = lm_loss

        # Thoughts prediction (auxiliary)
        if "thoughts" in self.task_weights and "thoughts_logits" in outputs:
            thoughts_loss = self.loss_fns["thoughts"](
                outputs["thoughts_logits"],
                batch.get("thoughts_labels", torch.zeros_like(outputs["thoughts_logits"])),
            )
            total_loss += self.task_weights["thoughts"] * thoughts_loss
            losses["thoughts"] = thoughts_loss

        # Emotion classification
        if "eq_classification" in self.task_weights and "emotion_logits" in outputs:
            emotion_loss = self.loss_fns["eq_classification"](
                outputs["emotion_logits"],
                batch.get("emotion_labels", torch.zeros_like(outputs["emotion_logits"][:, 0]).long()),
            )
            total_loss += self.task_weights["eq_classification"] * emotion_loss
            losses["eq_classification"] = emotion_loss

        # Frustration detection
        if "frustration_detection" in self.task_weights and "frustration_logits" in outputs:
            frustration_loss = self.loss_fns["frustration_detection"](
                outputs["frustration_logits"].squeeze(-1),
                batch.get("frustration_labels", torch.zeros_like(outputs["frustration_logits"].squeeze(-1))),
            )
            total_loss += self.task_weights["frustration_detection"] * frustration_loss
            losses["frustration_detection"] = frustration_loss

        losses["total"] = total_loss
        return total_loss, losses


class Trainer:
    """Advanced trainer with all Zenith features."""

    def __init__(
        self,
        model: nn.Module,
        config: TrainerConfig,
        train_loader: DataLoader,
        val_loader: Optional[DataLoader] = None,
        optimizer: Optional[torch.optim.Optimizer] = None,
        scheduler: Optional[Any] = None,
    ):
        self.model = model
        self.config = config
        self.train_loader = train_loader
        self.val_loader = val_loader

        # Setup device
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.model.to(self.device)

        # Setup optimizer
        if optimizer is None:
            optimizer_config = config.training_config.optimizer
            self.optimizer = self._create_optimizer(optimizer_config)
        else:
            self.optimizer = optimizer

        # Setup scheduler
        self.scheduler = scheduler

        # Mixed precision
        self.scaler = GradScaler() if config.use_amp and torch.cuda.is_available() else None

        # Loss
        self.criterion = MultiTaskLoss(config.data_config.task_weights)

        # Logging
        self.metrics_logger = MetricsLogger(config.logging_dir)
        self.checkpoint_manager = CheckpointManager(
            config.checkpoint_dir,
            save_total_limit=config.training_config.save_total_limit,
        )

        # Curriculum sampler
        self.curriculum_sampler = None
        if isinstance(train_loader.sampler, CurriculumSampler):
            self.curriculum_sampler = train_loader.sampler

        # State
        self.global_step = 0
        self.epoch = 0

        logger.info(f"Trainer initialized on {self.device}")

    def _create_optimizer(self, optimizer_config) -> torch.optim.Optimizer:
        """Create optimizer from config."""
        if optimizer_config.use_8bit:
            import bitsandbytes as bnb
            optimizer = bnb.optim.AdamW8bit(
                self.model.parameters(),
                lr=optimizer_config.learning_rate,
                betas=(optimizer_config.beta1, optimizer_config.beta2),
                weight_decay=optimizer_config.weight_decay,
                eps=optimizer_config.epsilon,
            )
        else:
            optimizer = torch.optim.AdamW(
                self.model.parameters(),
                lr=optimizer_config.learning_rate,
                betas=(optimizer_config.beta1, optimizer_config.beta2),
                weight_decay=optimizer_config.weight_decay,
                eps=optimizer_config.epsilon,
            )
        return optimizer

    def train(self):
        """Main training loop."""
        logger.info("Starting training...")

        # Resume from checkpoint if specified
        if self.config.resume_from_checkpoint:
            self._load_checkpoint(self.config.resume_from_checkpoint)

        max_steps = self.config.training_config.max_steps
        num_epochs = self.config.training_config.num_train_epochs

        for epoch in range(self.epoch, num_epochs):
            self.epoch = epoch

            # Update curriculum sampler
            if self.curriculum_sampler:
                self.curriculum_sampler.set_epoch(epoch)

            # Train one epoch
            epoch_loss = self._train_epoch()

            # Evaluation
            if self.val_loader and (epoch + 1) % self.config.eval_interval == 0:
                eval_metrics = self.evaluate()
                self.metrics_logger.log(eval_metrics, self.global_step, prefix="eval")

            # Save checkpoint
            if (epoch + 1) % self.config.save_interval == 0:
                self._save_checkpoint()

            logger.info(f"Epoch {epoch} completed. Average loss: {epoch_loss:.4f}")

        # Final save
        self._save_checkpoint(final=True)

    def _train_epoch(self) -> float:
        """Train for one epoch."""
        self.model.train()

        total_loss = 0.0
        num_batches = 0

        progress_bar = tqdm(self.train_loader, desc=f"Epoch {self.epoch}")
        for batch_idx, batch in enumerate(progress_bar):
            # Move batch to device
            batch = {k: v.to(self.device) if isinstance(v, torch.Tensor) else v for k, v in batch.items()}

            # Forward pass with mixed precision
            with autocast(enabled=self.config.use_amp, dtype=getattr(torch, self.config.amp_dtype)):
                outputs = self.model(**batch)
                loss, task_losses = self.criterion(outputs, batch)

            # Normalize loss for gradient accumulation
            loss = loss / self.config.gradient_accumulation_steps

            # Backward pass
            if self.scaler:
                self.scaler.scale(loss).backward()
            else:
                loss.backward()

            # Gradient accumulation
            if (batch_idx + 1) % self.config.gradient_accumulation_steps == 0:
                # Gradient clipping
                if self.config.training_config.max_grad_norm > 0:
                    if self.scaler:
                        self.scaler.unscale_(self.optimizer)
                    torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.config.training_config.max_grad_norm)

                # Optimizer step
                if self.scaler:
                    self.scaler.step(self.optimizer)
                    self.scaler.update()
                else:
                    self.optimizer.step()

                self.optimizer.zero_grad()
                self.global_step += 1

                # Scheduler step
                if self.scheduler:
                    self.scheduler.step()

                # Logging
                if self.global_step % self.config.log_interval == 0:
                    self._log_metrics(loss, task_losses, progress_bar)

            total_loss += loss.item() * self.config.gradient_accumulation_steps
            num_batches += 1

        return total_loss / num_batches if num_batches > 0 else 0.0

    def _log_metrics(self, loss: torch.Tensor, task_losses: Dict[str, torch.Tensor], progress_bar: tqdm):
        """Log metrics to console and logger."""
        metrics = {"loss": loss.item()}
        metrics.update({f"{k}_loss": v.item() for k, v in task_losses.items()})

        if self.scheduler:
            metrics["lr"] = self.scheduler.get_last_lr()[0]

        self.metrics_logger.log(metrics, self.global_step, prefix="train")

        # Update progress bar
        progress_bar.set_postfix(metrics)

    def evaluate(self) -> Dict[str, float]:
        """Run evaluation."""
        self.model.eval()
        total_loss = 0.0
        num_batches = 0

        with torch.no_grad():
            for batch in tqdm(self.val_loader, desc="Evaluation"):
                batch = {k: v.to(self.device) if isinstance(v, torch.Tensor) else v for k, v in batch.items()}

                with autocast(enabled=self.config.use_amp, dtype=getattr(torch, self.config.amp_dtype)):
                    outputs = self.model(**batch)
                    loss, _ = self.criterion(outputs, batch)

                total_loss += loss.item()
                num_batches += 1

        avg_loss = total_loss / num_batches if num_batches > 0 else 0.0
        perplexity = torch.exp(torch.tensor(avg_loss)).item()

        self.model.train()
        return {"loss": avg_loss, "perplexity": perplexity}

    def _save_checkpoint(self, final: bool = False):
        """Save checkpoint."""
        checkpoint = {
            "epoch": self.epoch,
            "global_step": self.global_step,
            "model_state_dict": self.model.state_dict(),
            "optimizer_state_dict": self.optimizer.state_dict(),
            "scheduler_state_dict": self.scheduler.state_dict() if self.scheduler else None,
            "scaler_state_dict": self.scaler.state_dict() if self.scaler else None,
            "config": self.config,
        }

        if final:
            path = self.checkpoint_manager.save_checkpoint(checkpoint, f"final")
        else:
            path = self.checkpoint_manager.save_checkpoint(checkpoint, f"step-{self.global_step}")

        logger.info(f"Checkpoint saved to {path}")

    def _load_checkpoint(self, path: str):
        """Load checkpoint."""
        logger.info(f"Loading checkpoint from {path}")
        checkpoint = torch.load(path, map_location=self.device)

        self.model.load_state_dict(checkpoint["model_state_dict"])
        self.optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
        if self.scheduler and checkpoint["scheduler_state_dict"]:
            self.scheduler.load_state_dict(checkpoint["scheduler_state_dict"])
        if self.scaler and checkpoint["scaler_state_dict"]:
            self.scaler.load_state_dict(checkpoint["scaler_state_dict"])

        self.epoch = checkpoint["epoch"]
        self.global_step = checkpoint["global_step"]

        logger.info(f"Resumed from epoch {self.epoch}, step {self.global_step}")


def train_zenith_model(
    model: nn.Module,
    tokenizer: Any,
    config: TrainerConfig,
    train_dataset: Any,
    val_dataset: Optional[Any] = None,
) -> Trainer:
    """Main training function."""

    # Create data processor
    data_processor = OpenThoughtsProcessor(config.data_config)

    # Create dataloaders
    train_loader = data_processor.create_dataloader(
        train_dataset,
        batch_size=config.training_config.train_batch_size,
        shuffle=True,
        num_workers=config.training_config.dataloader_num_workers,
        curriculum_epoch=0,
    )

    if val_dataset:
        val_loader = data_processor.create_dataloader(
            val_dataset,
            batch_size=config.training_config.eval_batch_size,
            shuffle=False,
            num_workers=config.training_config.dataloader_num_workers,
        )
    else:
        val_loader = None

    # Create trainer
    trainer = Trainer(
        model=model,
        config=config,
        train_loader=train_loader,
        val_loader=val_loader,
    )

    # Train
    trainer.train()

    return trainer