"""
Trainer: Main training loop for Vortex model.
Handles gradient accumulation, mixed precision, checkpointing.
"""

import os
import json
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, Dataset
from typing import Optional, Dict, List, Callable
from pathlib import Path
import logging

from ..training.losses import VortexLoss
from ..training.curriculum import CurriculumScheduler


class VortexDataset(Dataset):
    """Simple dataset wrapper."""

    def __init__(
        self,
        shard_files: List[str],
        tokenizer,
        max_seq_len: int = 16384,
    ):
        """
        Initialize dataset.

        Args:
            shard_files: List of parquet shard files
            tokenizer: Tokenizer for encoding text
            max_seq_len: Maximum sequence length
        """
        self.shard_files = shard_files
        self.tokenizer = tokenizer
        self.max_seq_len = max_seq_len

        # Load all shards into memory (for simplicity - would stream in practice)
        self.samples = []
        self._load_shards()

    def _load_shards(self):
        """Load all shards."""
        import pandas as pd

        for shard in self.shard_files:
            df = pd.read_parquet(shard)
            for _, row in df.iterrows():
                self.samples.append({
                    "text": row["text"],
                    "dataset": row.get("dataset", ""),
                    "domain": row.get("domain", ""),
                })

    def __len__(self) -> int:
        return len(self.samples)

    def __getitem__(self, idx) -> Dict:
        sample = self.samples[idx]
        text = sample["text"]

        # Tokenize
        encoding = self.tokenizer.encode(
            text,
            add_special_tokens=True,
            return_tensors="pt",
        )

        input_ids = encoding["input_ids"].squeeze(0)
        attention_mask = encoding["attention_mask"].squeeze(0)

        # Truncate if needed
        if len(input_ids) > self.max_seq_len:
            input_ids = input_ids[:self.max_seq_len]
            attention_mask = attention_mask[:self.max_seq_len]

        # Labels are same as input_ids (causal LM)
        labels = input_ids.clone()

        return {
            "input_ids": input_ids,
            "attention_mask": attention_mask,
            "labels": labels,
            "domain": sample["domain"],
        }


class VortexTrainer:
    """
    Main trainer for Vortex model.
    """

    def __init__(
        self,
        model: nn.Module,
        tokenizer,
        train_dataset: Dataset,
        config: Dict,
        eval_dataset: Optional[Dataset] = None,
        optimizer: Optional[torch.optim.Optimizer] = None,
        scheduler: Optional[torch.optim.lr_scheduler._LRScheduler] = None,
    ):
        """
        Initialize trainer.

        Args:
            model: VortexModel
            tokenizer: VortexScienceTokenizer
            train_dataset: Training dataset
            config: Training configuration
            eval_dataset: Optional evaluation dataset
            optimizer: Optional optimizer (created if None)
            scheduler: Optional LR scheduler
        """
        self.model = model
        self.tokenizer = tokenizer
        self.train_dataset = train_dataset
        self.eval_dataset = eval_dataset
        self.config = config

        self.device = torch.device(config["device"])
        self.use_amp = config.get("use_amp", True)
        self.amp_dtype = getattr(torch, config.get("amp_dtype", "bfloat16"))

        # Move model to device
        self.model.to(self.device)

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

        # Setup scheduler
        if scheduler is None:
            self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
                self.optimizer,
                T_max=config["max_steps"],
            )
        else:
            self.scheduler = scheduler

        # Setup AMP scaler
        self.scaler = torch.cuda.amp.GradScaler() if self.use_amp and self.device.type == "cuda" else None

        # Loss function
        self.loss_fn = VortexLoss(config)

        # Curriculum scheduler
        self.curriculum = CurriculumScheduler(config, config["max_steps"])

        # Logging
        self.log_dir = Path(config.get("log_dir", "logs"))
        self.log_dir.mkdir(parents=True, exist_ok=True)
        self.log_interval = config.get("log_interval", 100)

        # Checkpointing
        self.checkpoint_dir = Path(config.get("checkpoint_dir", "checkpoints"))
        self.checkpoint_dir.mkdir(parents=True, exist_ok=True)
        self.save_interval = config.get("save_interval", 5000)

        # Training state
        self.global_step = 0
        self.best_eval_loss = float('inf')

        # Data loader
        self.train_loader = DataLoader(
            train_dataset,
            batch_size=config["micro_batch_size"],
            shuffle=True,
            num_workers=config.get("num_workers", 4),
            pin_memory=config.get("pin_memory", True),
            prefetch_factor=config.get("prefetch_factor", 2),
        )

        if eval_dataset:
            self.eval_loader = DataLoader(
                eval_dataset,
                batch_size=config["micro_batch_size"],
                shuffle=False,
                num_workers=config.get("num_workers", 4),
            )

    def _create_optimizer(self) -> torch.optim.Optimizer:
        """Create AdamW optimizer."""
        return torch.optim.AdamW(
            self.model.parameters(),
            lr=self.config["learning_rate"],
            betas=(self.config["beta1"], self.config["beta2"]),
            weight_decay=self.config["weight_decay"],
        )

    def train_step(
        self,
        batch: Dict,
        current_step: int,
    ) -> Dict[str, torch.Tensor]:
        """
        Single training step.

        Args:
            batch: Batch dictionary
            current_step: Current step number

        Returns:
            Dictionary of losses
        """
        self.model.train()

        # Move batch to device
        input_ids = batch["input_ids"].to(self.device)
        attention_mask = batch["attention_mask"].to(self.device)
        labels = batch["labels"].to(self.device)

        # Domain info (placeholder - would extract from batch)
        domain_ids = None
        domain_tags = None

        # Forward pass with AMP
        with torch.cuda.amp.autocast(enabled=self.use_amp and self.device.type == "cuda"):
            outputs = self.model(
                input_ids=input_ids,
                attention_mask=attention_mask,
                domain_ids=domain_ids,
                domain_tags=domain_tags,
                return_dict=True,
            )
            logits = outputs["logits"]

            # Compute losses
            losses = self.loss_fn(
                logits=logits,
                labels=labels,
                # Pass modules and masks for auxiliary losses
            )

        # Backward pass
        if self.scaler:
            self.scaler.scale(losses["total_loss"]).backward()
        else:
            losses["total_loss"].backward()

        return losses

    def train_epoch(self):
        """Train for one epoch."""
        self.model.train()

        for batch_idx, batch in enumerate(self.train_loader):
            # Train step
            losses = self.train_step(batch, self.global_step)

            # Gradient accumulation
            if (self.global_step + 1) % self.config["gradient_accumulation_steps"] == 0:
                # Gradient clipping
                if self.config.get("clip_grad_norm", 0) > 0:
                    if self.scaler:
                        self.scaler.unscale_(self.optimizer)
                    torch.nn.utils.clip_grad_norm_(
                        self.model.parameters(),
                        self.config["clip_grad_norm"],
                    )

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

                self.optimizer.zero_grad()
                self.scheduler.step()

            # Logging
            if self.global_step % self.log_interval == 0:
                self._log_losses(losses, batch_idx)

            # Evaluation
            if self.eval_dataset and self.global_step % self.config.get("eval_interval", 1000) == 0:
                self.evaluate()

            # Checkpointing
            if self.global_step % self.save_interval == 0:
                self.save_checkpoint()

            self.global_step += 1

            if self.global_step >= self.config["max_steps"]:
                print("Reached max steps")
                return

    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 self.eval_loader:
                input_ids = batch["input_ids"].to(self.device)
                attention_mask = batch["attention_mask"].to(self.device)
                labels = batch["labels"].to(self.device)

                with torch.cuda.amp.autocast(enabled=self.use_amp and self.device.type == "cuda"):
                    outputs = self.model(input_ids=input_ids, attention_mask=attention_mask, return_dict=True)
                    logits = outputs["logits"]
                    loss = F.cross_entropy(
                        logits.view(-1, logits.size(-1)),
                        labels.view(-1),
                        ignore_index=-100,
                    )

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

        avg_loss = total_loss / num_batches if num_batches > 0 else 0.0
        print(f"Evaluation at step {self.global_step}: loss = {avg_loss:.4f}")

        return {"eval_loss": avg_loss}

    def save_checkpoint(self, is_best: bool = False):
        """Save model checkpoint."""
        checkpoint = {
            "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(),
            "config": self.config,
            "best_eval_loss": self.best_eval_loss,
        }

        if self.scaler:
            checkpoint["scaler_state_dict"] = self.scaler.state_dict()

        # Save latest
        checkpoint_path = self.checkpoint_dir / f"checkpoint_{self.global_step:06d}.pt"
        torch.save(checkpoint, checkpoint_path)
        print(f"Saved checkpoint to {checkpoint_path}")

        # Save best
        if is_best:
            best_path = self.checkpoint_dir / "best_model.pt"
            torch.save(checkpoint, best_path)
            print(f"Saved best model to {best_path}")

        # Save latest link
        latest_path = self.checkpoint_dir / "latest.pt"
        torch.save(checkpoint, latest_path)

    def load_checkpoint(self, checkpoint_path: str):
        """Load checkpoint."""
        checkpoint = torch.load(checkpoint_path, map_location=self.device, weights_only=False)
        self.model.load_state_dict(checkpoint["model_state_dict"])
        self.optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
        self.scheduler.load_state_dict(checkpoint["scheduler_state_dict"])
        self.global_step = checkpoint["step"]
        self.best_eval_loss = checkpoint.get("best_eval_loss", float('inf'))

        if self.scaler and "scaler_state_dict" in checkpoint:
            self.scaler.load_state_dict(checkpoint["scaler_state_dict"])

        print(f"Loaded checkpoint from {checkpoint_path} at step {self.global_step}")

    def _log_losses(self, losses: Dict[str, torch.Tensor], batch_idx: int):
        """Log losses to console and file."""
        loss_str = " | ".join([f"{k}: {v.item():.4f}" for k, v in losses.items()])
        print(f"Step {self.global_step} | {loss_str}")

    def train(self):
        """Main training loop."""
        print("Starting training...")
        print(f"Total steps: {self.config['max_steps']}")
        print(f"Device: {self.device}")
        print(f"Batch size: {self.config['micro_batch_size']}")
        print(f"Gradient accumulation steps: {self.config['gradient_accumulation_steps']}")

        try:
            self.train_epoch()
        except KeyboardInterrupt:
            print("Training interrupted")
        finally:
            self.save_checkpoint()


def test_trainer():
    """Test trainer with small model."""
    from models.vortex_model import VortexModel
    from tokenizer.vortex_tokenizer import VortexScienceTokenizer
    from configs.vortex_7b_config import VORTEX_7B_CONFIG

    # Small config for testing
    config = VORTEX_7B_CONFIG.copy()
    config["d_model"] = 256
    config["num_layers"] = 2
    config["num_heads"] = 4
    config["vocab_size"] = 1000
    config["max_steps"] = 10
    config["device"] = "cpu"

    # Create model
    model = VortexModel(config)

    # Create dummy tokenizer
    class DummyTokenizer:
        def encode(self, text, add_special_tokens=True, return_tensors="pt"):
            return {"input_ids": torch.randint(0, 1000, (1, 10)), "attention_mask": torch.ones(1, 10)}

    tokenizer = DummyTokenizer()

    # Create dummy dataset
    class DummyDataset(torch.utils.data.Dataset):
        def __len__(self): return 10
        def __getitem__(self, idx):
            return {
                "input_ids": torch.randint(0, 1000, (32,)),
                "attention_mask": torch.ones(32),
                "labels": torch.randint(0, 1000, (32,)),
                "domain": "physics",
            }

    train_dataset = DummyDataset()
    eval_dataset = DummyDataset()

    # Create trainer
    trainer = VortexTrainer(
        model=model,
        tokenizer=tokenizer,
        train_dataset=train_dataset,
        config=config,
        eval_dataset=eval_dataset,
    )

    # Run a few steps
    trainer.train()

    print("Trainer test passed!")


if __name__ == "__main__":
    test_trainer()