"""
Model Trainer Module
====================

Provides model training functionality with progress tracking,
checkpointing, and experiment logging.
"""

import os
# Set environment variables before transformers import
os.environ.setdefault('TF_CPP_MIN_LOG_LEVEL', '3')
os.environ.setdefault('TRANSFORMERS_NO_TF', '1')

import json
import time
import logging
from pathlib import Path
from datetime import datetime
from typing import Dict, List, Optional, Tuple, Callable, Any
from dataclasses import dataclass, field
import numpy as np

import torch
from torch.utils.data import Dataset, DataLoader
from transformers import (
    AutoTokenizer,
    AutoModelForSequenceClassification,
    TrainingArguments,
    Trainer,
    EarlyStoppingCallback,
    TrainerCallback
)
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, precision_recall_fscore_support

from .config import TrainingConfig

logger = logging.getLogger(__name__)


@dataclass
class TrainingMetrics:
    """Container for training metrics."""
    epoch: int = 0
    train_loss: float = 0.0
    eval_loss: float = 0.0
    accuracy: float = 0.0
    precision: float = 0.0
    recall: float = 0.0
    f1: float = 0.0
    learning_rate: float = 0.0
    timestamp: str = ""
    
    def to_dict(self) -> dict:
        return {
            "epoch": self.epoch,
            "train_loss": self.train_loss,
            "eval_loss": self.eval_loss,
            "accuracy": self.accuracy,
            "precision": self.precision,
            "recall": self.recall,
            "f1": self.f1,
            "learning_rate": self.learning_rate,
            "timestamp": self.timestamp
        }


@dataclass
class TrainingProgress:
    """Container for training progress information."""
    status: str = "idle"  # idle, training, completed, failed
    current_epoch: int = 0
    total_epochs: int = 0
    current_step: int = 0
    total_steps: int = 0
    progress_percent: float = 0.0
    eta_seconds: float = 0.0
    metrics_history: List[TrainingMetrics] = field(default_factory=list)
    error_message: str = ""
    model_path: Optional[str] = None
    final_metrics: Optional[TrainingMetrics] = None
    start_time: float = 0.0
    end_time: float = 0.0
    
    def update_progress(self):
        """Update progress percentage."""
        if self.total_steps > 0:
            self.progress_percent = (self.current_step / self.total_steps) * 100
    
    def get_elapsed_time(self) -> float:
        """Get elapsed training time in seconds."""
        if self.start_time == 0:
            return 0.0
        end = self.end_time if self.end_time > 0 else time.time()
        return end - self.start_time


class TextClassificationDataset(Dataset):
    """PyTorch Dataset for text classification."""
    
    def __init__(self, texts: List[str], labels: List[int], 
                 tokenizer, max_length: int = 256):
        self.texts = texts
        self.labels = labels
        self.tokenizer = tokenizer
        self.max_length = max_length
    
    def __len__(self):
        return len(self.texts)
    
    def __getitem__(self, idx):
        text = str(self.texts[idx])
        label = self.labels[idx]
        
        encoding = self.tokenizer(
            text,
            truncation=True,
            padding='max_length',
            max_length=self.max_length,
            return_tensors='pt'
        )
        
        return {
            'input_ids': encoding['input_ids'].flatten(),
            'attention_mask': encoding['attention_mask'].flatten(),
            'labels': torch.tensor(label, dtype=torch.long)
        }


class ProgressCallback(TrainerCallback):
    """Custom callback for tracking training progress."""
    
    def __init__(self, progress: TrainingProgress, 
                 update_callback: Optional[Callable] = None):
        self.progress = progress
        self.update_callback = update_callback
    
    def on_train_begin(self, args, state, control, **kwargs):
        self.progress.status = "training"
        self.progress.start_time = time.time()
        self.progress.total_steps = state.max_steps
    
    def on_step_end(self, args, state, control, **kwargs):
        self.progress.current_step = state.global_step
        self.progress.update_progress()
        
        # Calculate ETA
        if state.global_step > 0:
            elapsed = time.time() - self.progress.start_time
            steps_remaining = state.max_steps - state.global_step
            time_per_step = elapsed / state.global_step
            self.progress.eta_seconds = steps_remaining * time_per_step
        
        if self.update_callback:
            self.update_callback(self.progress)
    
    def on_epoch_end(self, args, state, control, **kwargs):
        self.progress.current_epoch = int(state.epoch)
    
    def on_log(self, args, state, control, logs=None, **kwargs):
        if logs:
            metrics = TrainingMetrics(
                epoch=int(state.epoch) if state.epoch else 0,
                train_loss=logs.get('loss', 0.0),
                eval_loss=logs.get('eval_loss', 0.0),
                learning_rate=logs.get('learning_rate', 0.0),
                timestamp=datetime.now().isoformat()
            )
            self.progress.metrics_history.append(metrics)
    
    def on_train_end(self, args, state, control, **kwargs):
        self.progress.status = "completed"
        self.progress.end_time = time.time()
        self.progress.progress_percent = 100.0


class ModelTrainer:
    """
    Main trainer class for text classification models.
    
    Supports:
    - Multiple model architectures (BERT, RoBERTa, XLM-RoBERTa, etc.)
    - Progress tracking and callbacks
    - Checkpointing and model saving
    - Experiment logging
    """
    
    def __init__(self, config: TrainingConfig):
        """
        Initialize the trainer.
        
        Args:
            config: Training configuration
        """
        self.config = config
        self.model = None
        self.tokenizer = None
        self.trainer = None
        self.progress = TrainingProgress(total_epochs=config.num_epochs)
        self._setup_output_dir()
    
    def _setup_output_dir(self):
        """Create output directory for models and logs."""
        os.makedirs(self.config.output_dir, exist_ok=True)
        os.makedirs(os.path.join(self.config.output_dir, "logs"), exist_ok=True)
    
    def load_model(self, progress_callback: Optional[Callable] = None) -> bool:
        """
        Load model and tokenizer.
        
        Returns:
            True if successful, False otherwise
        """
        try:
            logger.info(f"Loading model: {self.config.model_name}")
            
            if progress_callback:
                progress_callback("Loading tokenizer...")
            
            self.tokenizer = AutoTokenizer.from_pretrained(
                self.config.model_name,
                use_fast=True
            )
            
            if progress_callback:
                progress_callback("Loading model...")
            
            self.model = AutoModelForSequenceClassification.from_pretrained(
                self.config.model_name,
                num_labels=self.config.num_labels,
                ignore_mismatched_sizes=True
            )
            
            logger.info("Model and tokenizer loaded successfully")
            return True
            
        except Exception as e:
            logger.error(f"Failed to load model: {str(e)}")
            self.progress.status = "failed"
            self.progress.error_message = str(e)
            return False
    
    def prepare_data(self, texts: List[str], labels: List[int]) -> Tuple[Dataset, Dataset, Dataset]:
        """
        Prepare datasets for training.
        
        Args:
            texts: List of text samples
            labels: List of corresponding labels
            
        Returns:
            Tuple of (train_dataset, val_dataset, test_dataset)
        """
        # Split data
        train_texts, temp_texts, train_labels, temp_labels = train_test_split(
            texts, labels,
            test_size=(1 - self.config.train_split),
            random_state=self.config.random_seed,
            stratify=labels if len(set(labels)) > 1 else None
        )
        
        # Split validation and test from remaining data
        val_ratio = self.config.validation_split / (1 - self.config.train_split)
        val_texts, test_texts, val_labels, test_labels = train_test_split(
            temp_texts, temp_labels,
            test_size=(1 - val_ratio),
            random_state=self.config.random_seed,
            stratify=temp_labels if len(set(temp_labels)) > 1 else None
        )
        
        # Create datasets
        train_dataset = TextClassificationDataset(
            train_texts, train_labels, self.tokenizer, self.config.max_length
        )
        val_dataset = TextClassificationDataset(
            val_texts, val_labels, self.tokenizer, self.config.max_length
        )
        test_dataset = TextClassificationDataset(
            test_texts, test_labels, self.tokenizer, self.config.max_length
        )
        
        logger.info(f"Data split: train={len(train_dataset)}, val={len(val_dataset)}, test={len(test_dataset)}")
        
        return train_dataset, val_dataset, test_dataset
    
    def compute_metrics(self, eval_pred) -> Dict[str, float]:
        """Compute metrics for evaluation."""
        predictions, labels = eval_pred
        predictions = np.argmax(predictions, axis=1)
        
        accuracy = accuracy_score(labels, predictions)
        precision, recall, f1, _ = precision_recall_fscore_support(
            labels, predictions, average='weighted', zero_division=0
        )
        
        return {
            'accuracy': accuracy,
            'precision': precision,
            'recall': recall,
            'f1': f1
        }
    
    def train(self, texts: List[str], labels: List[int],
              progress_callback: Optional[Callable] = None,
              status_callback: Optional[Callable] = None) -> TrainingProgress:
        """
        Train the model.
        
        Args:
            texts: Training texts
            labels: Training labels
            progress_callback: Optional callback for progress updates
            status_callback: Optional callback for status messages
            
        Returns:
            TrainingProgress object with training results
        """
        try:
            self.progress = TrainingProgress(total_epochs=self.config.num_epochs)
            
            # Load model if not already loaded
            if self.model is None:
                if status_callback:
                    status_callback("Loading model...")
                if not self.load_model(status_callback):
                    return self.progress
            
            # Prepare data
            if status_callback:
                status_callback("Preparing datasets...")
            
            train_dataset, val_dataset, test_dataset = self.prepare_data(texts, labels)
            
            # Create unique output directory for this run
            run_timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
            run_output_dir = os.path.join(
                self.config.output_dir, 
                f"run_{run_timestamp}"
            )
            os.makedirs(run_output_dir, exist_ok=True)
            
            # Save config
            config_path = os.path.join(run_output_dir, "training_config.json")
            with open(config_path, 'w', encoding='utf-8') as f:
                json.dump(self.config.to_dict(), f, indent=2, ensure_ascii=False)
            
            # Setup training arguments
            training_args = TrainingArguments(
                output_dir=run_output_dir,
                num_train_epochs=self.config.num_epochs,
                per_device_train_batch_size=self.config.batch_size,
                per_device_eval_batch_size=self.config.batch_size,
                warmup_ratio=self.config.warmup_ratio,
                weight_decay=self.config.weight_decay,
                learning_rate=self.config.learning_rate,
                logging_dir=os.path.join(run_output_dir, "logs"),
                logging_steps=self.config.logging_steps,
                eval_strategy=self.config.eval_strategy,
                save_strategy=self.config.eval_strategy,
                load_best_model_at_end=self.config.save_best_model,
                metric_for_best_model="f1",
                greater_is_better=True,
                save_total_limit=2,
                fp16=self.config.use_fp16 and torch.cuda.is_available(),
                gradient_accumulation_steps=self.config.gradient_accumulation_steps,
                report_to="none",  # Disable default reporting
                seed=self.config.random_seed,
                dataloader_pin_memory=False,  # For CPU compatibility
            )
            
            # Create trainer with custom callback
            progress_tracker = ProgressCallback(self.progress, progress_callback)
            
            self.trainer = Trainer(
                model=self.model,
                args=training_args,
                train_dataset=train_dataset,
                eval_dataset=val_dataset,
                compute_metrics=self.compute_metrics,
                callbacks=[progress_tracker]
            )
            
            # Start training
            if status_callback:
                status_callback("Training started...")
            
            logger.info("Starting model training...")
            self.trainer.train()
            logger.info("Training loop completed successfully")
            
            # Evaluate on test set
            if status_callback:
                status_callback("Evaluating on test set...")
            
            logger.info("Starting test set evaluation...")
            test_results = self.trainer.evaluate(test_dataset)
            logger.info(f"Test evaluation completed: {test_results}")
            
            # Add final metrics
            final_metrics = TrainingMetrics(
                epoch=self.config.num_epochs,
                eval_loss=test_results.get('eval_loss', 0),
                accuracy=test_results.get('eval_accuracy', 0),
                precision=test_results.get('eval_precision', 0),
                recall=test_results.get('eval_recall', 0),
                f1=test_results.get('eval_f1', 0),
                timestamp=datetime.now().isoformat()
            )
            self.progress.metrics_history.append(final_metrics)
            
            # Save model
            if status_callback:
                status_callback("Saving model...")
            
            model_save_path = os.path.join(run_output_dir, "final_model")
            logger.info(f"Saving model to {model_save_path}...")
            os.makedirs(model_save_path, exist_ok=True)
            self.trainer.save_model(model_save_path)
            self.tokenizer.save_pretrained(model_save_path)
            logger.info(f"Model saved successfully to {model_save_path}")
            
            # Save training metrics
            metrics_path = os.path.join(run_output_dir, "metrics.json")
            with open(metrics_path, 'w', encoding='utf-8') as f:
                json.dump({
                    "final_metrics": final_metrics.to_dict(),
                    "history": [m.to_dict() for m in self.progress.metrics_history],
                    "test_results": test_results
                }, f, indent=2, ensure_ascii=False)
            
            self.progress.status = "completed"
            self.progress.model_path = model_save_path
            self.progress.final_metrics = final_metrics
            
            logger.info(f"Training completed! Model saved to {model_save_path}")
            
            return self.progress
            
        except Exception as e:
            logger.error(f"Training failed: {str(e)}")
            self.progress.status = "failed"
            self.progress.error_message = str(e)
            self.progress.end_time = time.time()
            return self.progress
    
    def get_model_path(self) -> Optional[str]:
        """Get path to the trained model."""
        if hasattr(self.progress, 'model_path'):
            return self.progress.model_path
        return None
    
    def cleanup(self):
        """Cleanup resources."""
        if self.model is not None:
            del self.model
            self.model = None
        if self.tokenizer is not None:
            del self.tokenizer
            self.tokenizer = None
        if torch.cuda.is_available():
            torch.cuda.empty_cache()


def create_trainer(config: TrainingConfig) -> ModelTrainer:
    """Factory function to create a ModelTrainer instance."""
    return ModelTrainer(config)