import hashlib
import re
from typing import List, Dict, Optional
from pathlib import Path

class StructuredChunker:
    def __init__(self, chunk_size: int = 1000, overlap: int = 200):
        self.chunk_size = chunk_size
        self.overlap = overlap

    def chunk_text(self, text: str, metadata: Optional[Dict] = None) -> List[Dict]:
        if not text or len(text) < self.chunk_size:
            return [self._create_chunk(text, 0, metadata)]

        paragraphs = text.split('\n\n')
        chunks = []
        current_chunk = []
        current_size = 0
        chunk_idx = 0

        for para_idx, para in enumerate(paragraphs):
            para_size = len(para)

            if current_size + para_size > self.chunk_size and current_chunk:
                chunk_text = '\n\n'.join(current_chunk)
                chunks.append(self._create_chunk(chunk_text, chunk_idx, metadata, para_idx=para_idx))
                chunk_idx += 1

                overlap_text = self._get_overlap_text(current_chunk)
                current_chunk = [overlap_text] if overlap_text else []
                current_size = len(overlap_text) if overlap_text else 0

            current_chunk.append(para)
            current_size += para_size

        if current_chunk:
            chunk_text = '\n\n'.join(current_chunk)
            chunks.append(self._create_chunk(chunk_text, chunk_idx, metadata))

        return chunks

    def chunk_code(self, text: str, metadata: Optional[Dict] = None) -> List[Dict]:
        chunks = []
        functions = self._extract_functions(text)

        if functions:
            for idx, func in enumerate(functions):
                chunks.append(self._create_chunk(
                    func['code'],
                    idx,
                    {**(metadata or {}), 'type': 'function', 'name': func['name']},
                    section=func['name']
                ))
        else:
            return self.chunk_text(text, {**(metadata or {}), 'type': 'code'})

        return chunks

    def chunk_markdown(self, text: str, metadata: Optional[Dict] = None) -> List[Dict]:
        sections = self._extract_md_sections(text)

        if sections:
            chunks = []
            for idx, section in enumerate(sections):
                chunks.append(self._create_chunk(
                    section['content'],
                    idx,
                    {**(metadata or {}), 'type': 'section', 'heading': section['heading']},
                    section=section['heading']
                ))
            return chunks
        else:
            return self.chunk_text(text, {**(metadata or {}), 'type': 'markdown'})

    def _create_chunk(self, text: str, index: int, metadata: Optional[Dict], para_idx: Optional[int] = None, section: Optional[str] = None) -> Dict:
        chunk_id = hashlib.sha256(f'{text}{index}'.encode()).hexdigest()[:16]

        return {
            'chunk_id': chunk_id,
            'index': index,
            'text': text,
            'size': len(text),
            'offset_start': para_idx if para_idx is not None else index * (self.chunk_size - self.overlap),
            'offset_end': (para_idx if para_idx is not None else index * (self.chunk_size - self.overlap)) + len(text),
            'section': section,
            'metadata': metadata or {}
        }

    def _get_overlap_text(self, chunks: List[str]) -> str:
        combined = '\n\n'.join(chunks)
        if len(combined) <= self.overlap:
            return combined
        return combined[-self.overlap:]

    def _extract_functions(self, text: str) -> List[Dict]:
        patterns = [
            r'(def\s+(\w+)\s*\([^)]*\):.*?)(?=\ndef\s+\w+|class\s+\w+|\Z)',
            r'(function\s+(\w+)\s*\([^)]*\)\s*\{.*?\n\})',
            r'(public\s+(?:static\s+)?[\w<>]+\s+(\w+)\s*\([^)]*\)\s*\{.*?\n\})'
        ]

        functions = []
        for pattern in patterns:
            matches = re.finditer(pattern, text, re.DOTALL | re.MULTILINE)
            for match in matches:
                functions.append({
                    'name': match.group(2),
                    'code': match.group(1)
                })

        return functions

    def _extract_md_sections(self, text: str) -> List[Dict]:
        lines = text.split('\n')
        sections = []
        current_section = {'heading': 'Introduction', 'content': []}

        for line in lines:
            if line.startswith('#'):
                if current_section['content']:
                    sections.append({
                        'heading': current_section['heading'],
                        'content': '\n'.join(current_section['content'])
                    })
                heading = re.sub(r'^#+\s*', '', line)
                current_section = {'heading': heading, 'content': []}
            else:
                current_section['content'].append(line)

        if current_section['content']:
            sections.append({
                'heading': current_section['heading'],
                'content': '\n'.join(current_section['content'])
            })

        return sections if len(sections) > 1 else []