tasks/task_order.py

"""
Task 3: Order - Generate temporal ordering questions

This task joins multiple audio sources and asks questions about their temporal order
(first, last, what comes after, what comes before).
"""

import csv
import random
import math
from pathlib import Path
from typing import Dict, List

import sys
sys.path.append(str(Path(__file__).parent.parent))

from utils import (
    AudioProcessor, ESC50Dataset, QuestionGenerator, LLMQuestionGenerator,
    setup_logger, set_random_seed, calculate_num_samples_for_task,
    generate_single_clip_duration, get_max_clip_num_to_be_joined,
    build_clip_sequence_with_silences, generate_sample_durations_for_task
)


class OrderTaskGenerator:
    """Generator for temporal ordering task dataset."""
    
    def __init__(self, config: Dict, logger):
        """
        Initialize order task generator.
        
        Args:
            config: Configuration dictionary
            logger: Logger instance
        """
        self.config = config
        self.logger = logger
        self.task_config = config['tasks']['order']
        
        # Initialize components
        self.dataset = ESC50Dataset(
            config['esc50']['metadata_path'],
            config['esc50']['audio_path'],
            config  # Pass config for class subset loading
        )
        self.audio_processor = AudioProcessor(
            crossfade_duration=config['audio']['crossfade_duration'],
            silence_duration=config['audio']['silence_duration'],
            with_silence=config['audio']['with_silence'],
            normalize=config['audio']['normalize'],
            normalize_target_dBFS=config['audio']['normalize_target_dBFS'],
            synthetic_silence_path=config['synthetic_silence']['path']
        )
        self.question_generator = QuestionGenerator(
            num_options=config['mcq']['num_options'],
            option_labels=config['mcq']['option_labels'],
            distractor_strategy=config['mcq']['distractor_strategy']
        )
        
        # Initialize LLM question generator
        self.llm_enabled = config.get('llm', {}).get('enabled', False)
        self.llm_generator = LLMQuestionGenerator(
            enabled=self.llm_enabled,
            template_questions=self.task_config
        )
        
        # Duration settings from config
        self.min_clip_duration = config['audio']['min_clip_duration']
        self.max_clip_duration = config['audio']['max_clip_duration']
        # Duration of individual source clips (ESC-50 default is 5s)
        self.source_clip_duration = config['audio'].get('source_clip_duration', 5.0)
        self.min_silence_ms = config['audio'].get('min_silence_duration', 100)
        self.max_extra_silence_per_gap_ms = config['audio'].get('max_extra_silence_per_gap', 500)
        self.crossfade_ms = config['audio'].get('crossfade_duration', 0)
        self.task_duration_hours = self.task_config['task_duration_size']
        
        # Order task specific settings
        self.allow_source_repetition = self.task_config.get('allow_source_repetition', False)
        self.min_clips_for_second = self.task_config.get('min_clips_for_second_questions', 4)
        
        # Set up output paths
        self.output_base = Path(config['output']['base_path']) / 'order'
        self.output_base.mkdir(parents=True, exist_ok=True)
        self.audio_output = self.output_base / 'audios'
        self.audio_output.mkdir(parents=True, exist_ok=True)
    
    def _get_valid_question_types(self, n_clips: int) -> List[str]:
        """
        Get question types valid for the given number of clips.
        
        "second" and "second_last" require at least min_clips_for_second clips.
        
        Args:
            n_clips: Number of clips in the sample
            
        Returns:
            List of valid question types
        """
        all_types = self.task_config['question_types']
        
        # Filter based on n_clips
        valid_types = []
        for qtype in all_types:
            if qtype in ['second', 'second_last']:
                if n_clips >= self.min_clips_for_second:
                    valid_types.append(qtype)
            elif qtype in ['after', 'before']:
                if n_clips >= 2:
                    valid_types.append(qtype)
            else:  # first, last
                valid_types.append(qtype)
        
        return valid_types if valid_types else ['first', 'last']
    
    def generate_sample(self, sample_id: int, target_question_type: str = None, target_duration_seconds: float = None) -> Dict:
        """
        Generate a single order task sample.
        
        Pipeline: pick dataset -> pick class -> pick audio clip -> get duration ->
        concatenate clips to reach target duration -> modulo to get num clips ->
        inserting silences randomly based on remainder.
        
        Args:
            sample_id: Sample ID number
            target_question_type: Target question type for balanced distribution
            target_duration_seconds: Pre-generated target duration (from generate_sample_durations_for_task)
            
        Returns:
            Dictionary with sample metadata
        """
        # Use pre-generated duration or generate one (backward compatibility)
        if target_duration_seconds is not None:
            clip_duration_seconds = target_duration_seconds
        else:
            clip_duration_seconds = generate_single_clip_duration(
                self.min_clip_duration,
                self.max_clip_duration
            )

        # Calculate how many clips we need using the new helper
        max_clips, remainder_seconds = get_max_clip_num_to_be_joined(
            clip_duration_seconds,
            self.source_clip_duration,
            self.min_silence_ms
        )
        
        max_clips_per_sample = self.task_config.get('max_clips_per_sample', 10)
        
        # Silence reduction strategy: subsample from [max(2, max_clips-3), min(max_clips, max_clips_per_sample)]
        # This ensures we use close to max_clips that fit, reducing excessive silence
        
        # Calculate valid range for this sample's duration
        min_clips_for_sample = max(2, max_clips - 3)  # At least 2, preferably max_clips-3
        max_clips_for_sample = min(max_clips, max_clips_per_sample, len(self.dataset.CATEGORIES))
        
        # Validate range
        if max_clips_for_sample < 2:
            raise ValueError(
                f"Sample {sample_id}: Cannot generate order task - need at least 2 clips. "
                f"max_clips={max_clips}, max_clips_per_sample={max_clips_per_sample}, "
                f"duration={clip_duration_seconds:.1f}s. Increase min_clip_duration."
            )
        
        if min_clips_for_sample > max_clips_for_sample:
            raise ValueError(
                f"Sample {sample_id}: Invalid clip range - min_clips ({min_clips_for_sample}) > max_clips ({max_clips_for_sample}). "
                f"max_clips={max_clips}, max_clips_per_sample={max_clips_per_sample}, duration={clip_duration_seconds:.1f}s"
            )
        
        # Randomly select from valid range (NO balanced pool for order task)
        n_clips = random.randint(min_clips_for_sample, max_clips_for_sample)
        
        # Get valid question types for this n_clips
        valid_question_types = self._get_valid_question_types(n_clips)
        
        if not valid_question_types:
            raise ValueError(
                f"Sample {sample_id}: No valid question types for n_clips={n_clips}. "
                f"This should not happen - check _get_valid_question_types implementation."
            )
        
        # Pre-select question type to determine answer position
        if target_question_type is not None:
            if target_question_type not in valid_question_types:
                raise ValueError(
                    f"Sample {sample_id}: target_question_type='{target_question_type}' not valid for n_clips={n_clips}. "
                    f"Valid types: {valid_question_types}. Balanced distribution should only assign valid types."
                )
            question_type = target_question_type
        else:
            question_type = random.choice(valid_question_types)
        
        # Determine answer position based on question type
        if question_type == 'first':
            answer_position = 0
        elif question_type == 'last':
            answer_position = n_clips - 1
        elif question_type == 'second':
            answer_position = 1  # 0-indexed, so position 1 is second
        elif question_type == 'second_last':
            answer_position = n_clips - 2  # Second to last
        elif question_type == 'after':
            # Answer is after a reference, so position 1 to n-1
            answer_position = random.randint(1, n_clips - 1) if n_clips >= 2 else 0
        else:  # before
            # Answer is before a reference, so position 0 to n-2
            answer_position = random.randint(0, n_clips - 2) if n_clips >= 2 else 0
        
        # Select answer category from least-used categories
        answer_category = self.dataset.get_least_used_categories(1)[0]
        
        # Sample remaining categories, ensuring balanced distribution
        if n_clips <= len(self.dataset.CATEGORIES):
            other_categories = self.dataset.get_least_used_categories(
                n_clips - 1,
                exclude=[answer_category]
            )
        else:
            # Need more clips than unique categories - sample with some repetition
            other_categories = self.dataset.get_least_used_categories(
                min(n_clips - 1, len(self.dataset.CATEGORIES) - 1),
                exclude=[answer_category]
            )
            # Add random repetitions if needed
            while len(other_categories) < n_clips - 1:
                other_categories.append(random.choice(self.dataset.CATEGORIES))
        
        # Arrange categories with answer at correct position
        selected_categories = []
        other_idx = 0
        for i in range(n_clips):
            if i == answer_position:
                selected_categories.append(answer_category)
            else:
                selected_categories.append(other_categories[other_idx])
                other_idx += 1
        
        # Track usage of answer category
        self.dataset.category_usage_counts[answer_category] += 1
        
        # Sample one file from each category and load audio
        audio_segments = []
        filenames_list = []
        
        for category in selected_categories:
            filename, filepath = self.dataset.sample_file_from_category(category)
            audio = self.audio_processor.load_audio(filepath)
            audio_segments.append(audio)
            filenames_list.append(filename)
        
        # Build final audio with guaranteed silences between clips
        output_audio_path = self.audio_output / f"{sample_id}.wav"
        final_audio = build_clip_sequence_with_silences(
            audio_segments,
            clip_duration_seconds,
            min_silence_ms=self.min_silence_ms,
            max_extra_silence_per_gap_ms=self.max_extra_silence_per_gap_ms,
            crossfade_ms=self.crossfade_ms
        )
        
        # Save the audio
        final_audio.export(str(output_audio_path), format="wav")
        
        # Determine correct answer and generate questions based on question type
        # CRITICAL BUG FIX: Verify answer_category is actually at answer_position
        if selected_categories[answer_position] != answer_category:
            self.logger.error(f"Sample {sample_id}: Answer mismatch! Expected {answer_category} at position {answer_position}, got {selected_categories[answer_position]}")
            # Force correct by using actual category at answer_position
            correct_category = selected_categories[answer_position]
        else:
            correct_category = answer_category
        
        if question_type == 'first':
            mcq_question = self.task_config['mcq_questions']['first']
            open_text_question = self.task_config['open_text_questions']['first']
            
        elif question_type == 'last':
            mcq_question = self.task_config['mcq_questions']['last']
            open_text_question = self.task_config['open_text_questions']['last']
            
        elif question_type == 'second':
            mcq_question = self.task_config['mcq_questions']['second']
            open_text_question = self.task_config['open_text_questions']['second']
            
        elif question_type == 'second_last':
            mcq_question = self.task_config['mcq_questions']['second_last']
            open_text_question = self.task_config['open_text_questions']['second_last']
            
        elif question_type == 'after':
            # Reference is the sound before answer_position
            if answer_position > 0:
                reference_category = selected_categories[answer_position - 1]
                mcq_question = self.task_config['mcq_questions']['after'].format(sound1=reference_category)
                open_text_question = self.task_config['open_text_questions']['after'].format(sound1=reference_category)
            else:
                # Fallback shouldn't happen but handle gracefully
                mcq_question = self.task_config['mcq_questions']['first']
                open_text_question = self.task_config['open_text_questions']['first']
                
        else:  # before
            # Reference is the sound after answer_position
            if answer_position < n_clips - 1:
                reference_category = selected_categories[answer_position + 1]
                mcq_question = self.task_config['mcq_questions']['before'].format(sound2=reference_category)
                open_text_question = self.task_config['open_text_questions']['before'].format(sound2=reference_category)
            else:
                # Fallback to 'first' if only 1 clip
                correct_category = selected_categories[0]
                mcq_question = self.task_config['mcq_questions']['first']
                open_text_question = self.task_config['open_text_questions']['first']
                question_type = 'first'
        
        # Generate MCQ
        mcq_data = self.question_generator.generate_category_mcq(
            mcq_question,
            correct_category,
            selected_categories,
            self.dataset.CATEGORIES
        )
        
        # Generate open-text question
        open_text_data = self.question_generator.generate_category_open_text(
            open_text_question,
            correct_category
        )
        
        # Also generate a sequence question for open-text
        sequence_question = self.task_config['open_text_questions']['sequence']
        sequence_data = self.question_generator.generate_sequence_open_text(
            sequence_question,
            selected_categories
        )
        
        # Create metadata
        metadata = {
            'id': sample_id,
            'audio_path': str(output_audio_path.relative_to(self.output_base.parent)),
            'n_clips': n_clips,
            'question_type': question_type,
            'audio_sequence': selected_categories,
            'correct_answer_category': correct_category,
            'source_files': filenames_list,
            'mcq_question': mcq_data['question'],
            'mcq_options': mcq_data['options'],
            'mcq_correct_answer': mcq_data['correct_answer'],
            'open_text_question': open_text_data['question'],
            'open_text_answer': open_text_data['correct_answer'],
            'sequence_question': sequence_data['question'],
            'sequence_answer': sequence_data['correct_answer']
        }
        
        self.logger.info(f"Generated order sample {sample_id}: {question_type}, {n_clips} clips")
        
        return metadata
    
    def generate_dataset(self) -> tuple:
        """
        Generate the complete order task dataset.
        
        Uses generate_sample_durations_for_task() to pre-generate exact sample durations
        that sum to exactly the target task duration. This guarantees:
        - Exact coverage of target duration
        - No estimation errors from average-based calculation
        
        Returns:
            Tuple of (mcq_csv_path, open_text_csv_path, sequence_csv_path)
        """
        # Generate sample durations upfront (guarantees exact total duration)
        sample_durations = generate_sample_durations_for_task(
            self.task_duration_hours,
            self.min_clip_duration,
            self.max_clip_duration
        )
        num_samples = len(sample_durations)
        
        self.logger.info(f"Generating {num_samples} order task samples (target: {self.task_duration_hours}h, exact fill)...")
        
        # Calculate effective max clips each sample can use (accounting for silence reduction)
        # This matches the logic in generate_sample()
        max_clips_per_sample = self.task_config.get('max_clips_per_sample', 10)
        sample_effective_max_clips = []
        
        for duration in sample_durations:
            max_clips, _ = get_max_clip_num_to_be_joined(
                duration,
                self.source_clip_duration,
                self.min_silence_ms
            )
            # Apply the same constraints as generate_sample()
            effective_max = min(max_clips, max_clips_per_sample, len(self.dataset.CATEGORIES))
            sample_effective_max_clips.append(effective_max)
        
        # Create capacity-aware balanced question type distribution
        # Categorize question types by clip requirements
        question_types = self.task_config['question_types']
        
        # Separate into tiers based on clip requirements
        basic_types = ['first', 'last', 'after', 'before']  # Need >= 2 clips
        advanced_types = ['second', 'second_last']  # Need >= min_clips_for_second
        
        # Count how many samples can support each tier (use effective max, not raw max)
        samples_for_basic = sum(1 for emc in sample_effective_max_clips if emc >= 2)
        samples_for_advanced = sum(1 for emc in sample_effective_max_clips if emc >= self.min_clips_for_second)
        
        # Create list of (sample_idx, duration, effective_max_clips)
        sample_info = [(i, sample_durations[i], sample_effective_max_clips[i]) for i in range(num_samples)]
        
        # Sort by capacity (descending) - assign advanced types to high-capacity samples
        sample_info.sort(key=lambda x: x[2], reverse=True)
        
        # Calculate distribution: prefer advanced types for longer clips
        samples_per_type = num_samples // len(question_types)
        remainder = num_samples % len(question_types)
        
        # Build assignment pool - advanced types first (for high-capacity samples)
        assignment_pool = []
        for qtype in advanced_types:
            count = samples_per_type + (1 if remainder > 0 else 0)
            assignment_pool.extend([qtype] * count)
            remainder = max(0, remainder - 1)
        
        for qtype in basic_types:
            count = samples_per_type + (1 if remainder > 0 else 0)
            assignment_pool.extend([qtype] * count)
            remainder = max(0, remainder - 1)
        
        # Assign question types based on capacity
        balanced_assignments = [None] * num_samples
        
        for idx, (sample_idx, duration, capacity) in enumerate(sample_info):
            target_qtype = assignment_pool[idx]
            
            # Validate and adjust if needed
            valid_types = self._get_valid_question_types(capacity)
            
            if target_qtype not in valid_types:
                # Assign a valid alternative - prefer similar types
                if target_qtype in advanced_types and any(t in valid_types for t in basic_types):
                    # Downgrade to basic type
                    target_qtype = random.choice([t for t in basic_types if t in valid_types])
                else:
                    # Fallback to any valid type
                    target_qtype = random.choice(valid_types)
            
            balanced_assignments[sample_idx] = target_qtype
        
        # Log the actual distribution after capacity-aware assignment
        from collections import Counter
        type_dist = Counter(balanced_assignments)
        self.logger.info(f"Balanced question type distribution (after capacity-aware assignment): {dict(sorted(type_dist.items()))}")
        
        all_metadata = []
        
        for i, target_duration in enumerate(sample_durations):
            metadata = self.generate_sample(i, target_question_type=balanced_assignments[i], target_duration_seconds=target_duration)
            all_metadata.append(metadata)        # Save MCQ CSV
        mcq_csv_path = self.output_base / 'order_mcq.csv'
        self._save_mcq_csv(all_metadata, mcq_csv_path)
        
        # Save open-text CSV
        open_text_csv_path = self.output_base / 'order_open_text.csv'
        self._save_open_text_csv(all_metadata, open_text_csv_path)
        
        # Save sequence CSV
        sequence_csv_path = self.output_base / 'order_sequence.csv'
        self._save_sequence_csv(all_metadata, sequence_csv_path)
        
        # Save metadata CSV
        metadata_csv_path = self.output_base / 'order_metadata.csv'
        self._save_metadata_csv(all_metadata, metadata_csv_path)
        
        self.logger.info(f"Order task dataset generation complete!")
        self.logger.info(f"  - MCQ CSV: {mcq_csv_path}")
        self.logger.info(f"  - Open-text CSV: {open_text_csv_path}")
        self.logger.info(f"  - Sequence CSV: {sequence_csv_path}")
        self.logger.info(f"  - Metadata CSV: {metadata_csv_path}")
        self.logger.info(f"  - Audio files: {self.audio_output}")
        
        return mcq_csv_path, open_text_csv_path, sequence_csv_path
    
    def _save_mcq_csv(self, metadata_list: List[Dict], output_path: Path):
        """Save MCQ format CSV."""
        with open(output_path, 'w', newline='') as f:
            writer = csv.writer(f)
            # Header
            writer.writerow([
                'question', 'id', 'audio_path',
                'optionA', 'optionB', 'optionC', 'optionD',
                'correct', 'question_type', 'audio_sequence'
            ])
            
            # Data rows
            for meta in metadata_list:
                writer.writerow([
                    meta['mcq_question'],
                    meta['id'],
                    meta['audio_path'],
                    meta['mcq_options']['A'],
                    meta['mcq_options']['B'],
                    meta['mcq_options']['C'],
                    meta['mcq_options']['D'],
                    meta['mcq_correct_answer'],
                    meta['question_type'],
                    str(meta['audio_sequence'])
                ])
    
    def _save_open_text_csv(self, metadata_list: List[Dict], output_path: Path):
        """Save open-text format CSV."""
        with open(output_path, 'w', newline='') as f:
            writer = csv.writer(f)
            # Header
            writer.writerow([
                'question', 'id', 'audio_path', 'answer',
                'question_type', 'audio_sequence'
            ])
            
            # Data rows
            for meta in metadata_list:
                writer.writerow([
                    meta['open_text_question'],
                    meta['id'],
                    meta['audio_path'],
                    meta['open_text_answer'],
                    meta['question_type'],
                    str(meta['audio_sequence'])
                ])
    
    def _save_sequence_csv(self, metadata_list: List[Dict], output_path: Path):
        """Save sequence question CSV."""
        with open(output_path, 'w', newline='') as f:
            writer = csv.writer(f)
            # Header
            writer.writerow([
                'question', 'id', 'audio_path', 'answer', 'audio_sequence'
            ])
            
            # Data rows
            for meta in metadata_list:
                writer.writerow([
                    meta['sequence_question'],
                    meta['id'],
                    meta['audio_path'],
                    meta['sequence_answer'],
                    str(meta['audio_sequence'])
                ])
    
    def _save_metadata_csv(self, metadata_list: List[Dict], output_path: Path):
        """Save detailed metadata CSV."""
        with open(output_path, 'w', newline='') as f:
            writer = csv.writer(f)
            # Header
            writer.writerow([
                'id', 'audio_path', 'n_clips', 'question_type',
                'audio_sequence', 'correct_answer', 'source_files'
            ])
            
            # Data rows
            for meta in metadata_list:
                writer.writerow([
                    meta['id'],
                    meta['audio_path'],
                    meta['n_clips'],
                    meta['question_type'],
                    str(meta['audio_sequence']),
                    meta['correct_answer_category'],
                    str(meta['source_files'])
                ])


def main(config_path: str = None):
    """Main entry point for order task generation."""
    import yaml
    
    # Load configuration
    if config_path is None:
        config_path = Path(__file__).parent.parent / 'config.yaml'
    
    with open(config_path, 'r') as f:
        config = yaml.safe_load(f)
    
    # Set random seed
    set_random_seed(config['random_seed'])
    
    # Setup logger
    logger = setup_logger(
        'order_task',
        log_file=str(Path(config['output']['base_path']) / config['logging']['log_file']),
        level=config['logging']['level'],
        console_output=config['logging']['console_output']
    )
    
    # Generate dataset
    generator = OrderTaskGenerator(config, logger)
    generator.generate_dataset()


if __name__ == '__main__':
    main()