"""
Chapter-level structural analysis for Tibetan legal manuscripts.
Enhanced with Juxta/CollateX-inspired advanced alignment algorithms.
"""

import difflib
import re
import logging
from ..pipeline.advanced_alignment import enhanced_structural_analysis

logger = logging.getLogger(__name__)


def detect_structural_changes(text1: str, text2: str, 
                           min_change_length: int = 5,
                           context_window: int = 10) -> dict:
    """
    Detect structural changes between two Tibetan text chapters.
    
    Args:
        text1: First text chapter
        text2: Second text chapter
        min_change_length: Minimum length of change to report
        context_window: Number of characters to include as context
        
    Returns:
        Dictionary with detected changes: insertions, deletions, modifications
    """
    
    # Clean texts for comparison
    def clean_text(text):
        # Remove extra whitespace and normalize
        text = re.sub(r'\s+', ' ', text.strip())
        return text
    
    clean1 = clean_text(text1)
    clean2 = clean_text(text2)
    
    # Use difflib to detect changes
    differ = difflib.Differ()
    diff = list(differ.compare(clean1.split(), clean2.split()))
    
    changes = {
        'insertions': [],
        'deletions': [],
        'modifications': [],
        'unchanged': []
    }
    
    # Track current position in both texts
    pos1 = 0
    pos2 = 0
    
    for i, line in enumerate(diff):
        if line.startswith('  '):  # Unchanged
            word = line[2:]
            changes['unchanged'].append({
                'word': word,
                'position1': pos1,
                'position2': pos2,
                'length': len(word)
            })
            pos1 += len(word) + 1
            pos2 += len(word) + 1
            
        elif line.startswith('- '):  # Deletion
            word = line[2:]
            if len(word) >= min_change_length:
                changes['deletions'].append({
                    'word': word,
                    'position': pos1,
                    'length': len(word),
                    'context': get_context(clean1, pos1, context_window)
                })
            pos1 += len(word) + 1
            
        elif line.startswith('+ '):  # Insertion
            word = line[2:]
            if len(word) >= min_change_length:
                changes['insertions'].append({
                    'word': word,
                    'position': pos2,
                    'length': len(word),
                    'context': get_context(clean2, pos2, context_window)
                })
            pos2 += len(word) + 1
    
    # Detect modifications (adjacent deletions and insertions)
    modifications = detect_modifications(changes['deletions'], changes['insertions'])
    changes['modifications'] = modifications
    
    return changes


def get_context(text: str, position: int, window: int) -> str:
    """Get context around a position in text."""
    start = max(0, position - window)
    end = min(len(text), position + window)
    return text[start:end]


def detect_modifications(deletions: list[dict], insertions: list[dict]) -> list[dict]:
    """Detect modifications by pairing nearby deletions and insertions."""
    modifications = []
    
    for deletion in deletions[:]:  # Copy to avoid modification during iteration
        for insertion in insertions[:]:
            # If deletion and insertion are close (within 5 positions)
            if abs(deletion['position'] - insertion['position']) <= 5:
                modifications.append({
                    'original': deletion['word'],
                    'replacement': insertion['word'],
                    'position': deletion['position'],
                    'deletion_context': deletion['context'],
                    'insertion_context': insertion['context']
                })
                # Remove from original lists to avoid duplicates
                if deletion in deletions:
                    deletions.remove(deletion)
                if insertion in insertions:
                    insertions.remove(insertion)
                break
    
    return modifications


def generate_structural_alignment(text1: str, text2: str) -> dict[str, list]:
    """
    Generate enhanced structural alignment using advanced algorithms.
    
    Returns:
        Dictionary with Juxta/CollateX-inspired alignment information
    """
    
    try:
        # Use enhanced alignment from advanced_alignment module
        result = enhanced_structural_analysis(text1, text2)
        
        # Convert to legacy format for backward compatibility
        alignment = {
            'matches': [],
            'gaps': [],
            'mismatches': [],
            'segments1': [],
            'segments2': []
        }
        
        # Process alignment segments
        for segment in result.get('alignment_segments', []):
            if segment['type'] == 'match':
                alignment['matches'].append({
                    'segments1': [segment['content1']],
                    'segments2': [segment['content2']],
                    'type': 'match',
                    'confidence': segment['confidence']
                })
            elif segment['type'] == 'insertion':
                alignment['gaps'].append({
                    'segments': [segment['content2']],
                    'type': 'insertion',
                    'position': 'text2',
                    'confidence': segment['confidence']
                })
            elif segment['type'] == 'deletion':
                alignment['gaps'].append({
                    'segments': [segment['content1']],
                    'type': 'deletion',
                    'position': 'text1',
                    'confidence': segment['confidence']
                })
            elif segment['type'] in ['mismatch', 'modification']:
                alignment['mismatches'].append({
                    'original': [segment['content1']],
                    'replacement': [segment['content2']],
                    'type': 'modification',
                    'confidence': segment['confidence']
                })
        
        return alignment
        
    except Exception as e:
        logger.warning(f"Enhanced alignment failed, falling back to basic: {e}")
        
        # Fallback to basic alignment for robustness
        def split_into_segments(text):
            segments = re.split(r'[།༎༏༐༑༔]', text)
            return [seg.strip() for seg in segments if seg.strip()]
        
        segments1 = split_into_segments(text1)
        segments2 = split_into_segments(text2)
        
        matcher = difflib.SequenceMatcher(None, segments1, segments2)
        
        alignment = {
            'matches': [],
            'gaps': [],
            'mismatches': [],
            'segments1': segments1,
            'segments2': segments2
        }
        
        for tag, i1, i2, j1, j2 in matcher.get_opcodes():
            if tag == 'equal':
                alignment['matches'].append({
                    'segments1': segments1[i1:i2],
                    'segments2': segments2[j1:j2],
                    'type': 'match'
                })
            elif tag == 'delete':
                alignment['gaps'].append({
                    'segments': segments1[i1:i2],
                    'type': 'deletion',
                    'position': 'text1'
                })
            elif tag == 'insert':
                alignment['gaps'].append({
                    'segments': segments2[j1:j2],
                    'type': 'insertion',
                    'position': 'text2'
                })
            elif tag == 'replace':
                alignment['mismatches'].append({
                    'original': segments1[i1:i2],
                    'replacement': segments2[j1:j2],
                    'type': 'modification'
                })
        
        return alignment


def calculate_structural_similarity_score(text1: str, text2: str) -> dict[str, float]:
    """
    Calculate various structural similarity scores between two texts.
    
    Returns:
        Dictionary with multiple similarity metrics
    """
    
    changes = detect_structural_changes(text1, text2)
    alignment = generate_structural_alignment(text1, text2)
    
    # Calculate scores
    total_changes = len(changes['insertions']) + len(changes['deletions']) + len(changes['modifications'])
    
    # Structural similarity score (inverse of changes)
    text_length = max(len(text1.split()), len(text2.split()))
    structural_score = max(0, 1 - (total_changes / text_length)) if text_length > 0 else 0
    
    # Alignment-based score
    total_segments = len(alignment['segments1']) + len(alignment['segments2'])
    matches = len(alignment['matches'])
    alignment_score = matches / (total_segments / 2) if total_segments > 0 else 0
    
    return {
        'structural_similarity': structural_score,
        'alignment_score': alignment_score,
        'insertions': len(changes['insertions']),
        'deletions': len(changes['deletions']),
        'modifications': len(changes['modifications']),
        'total_changes': total_changes
    }


def generate_differential_report(text1: str, text2: str, 
                               file1_name: str = "Text 1", 
                               file2_name: str = "Text 2") -> dict[str, any]:
    """
    Generate a comprehensive differential report for two text chapters.
    
    Returns:
        Complete report with changes, alignment, and recommendations
    """
    
    changes = detect_structural_changes(text1, text2)
    alignment = generate_structural_alignment(text1, text2)
    scores = calculate_structural_similarity_score(text1, text2)
    
    report = {
        'file1': file1_name,
        'file2': file2_name,
        'changes': changes,
        'alignment': alignment,
        'scores': scores,
        'summary': {
            'significant_differences': len([c for c in changes['modifications'] if len(c['original']) > 10 or len(c['replacement']) > 10]),
            'minor_variants': len([c for c in changes['modifications'] if len(c['original']) <= 5 and len(c['replacement']) <= 5]),
            'structural_preservation': scores['alignment_score'] > 0.8,
            'recommendation': 'Manuscripts are structurally similar' if scores['alignment_score'] > 0.7 else 'Significant structural differences detected'
        }
    }
    
    return report