"""
Semantic Chunking Utilities

Strategies for splitting and merging document content
into semantically meaningful chunks.
"""

import re
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple

from ..chunks.models import (
    BoundingBox,
    ChunkType,
    DocumentChunk,
)


@dataclass
class ChunkingConfig:
    """Configuration for semantic chunking."""

    # Size limits
    min_chunk_chars: int = 50
    max_chunk_chars: int = 2000
    target_chunk_chars: int = 500

    # Overlap for context preservation
    overlap_chars: int = 100

    # Splitting behavior
    split_on_headings: bool = True
    split_on_paragraphs: bool = True
    preserve_sentences: bool = True

    # Merging behavior
    merge_small_chunks: bool = True
    merge_threshold_chars: int = 100


class SemanticChunker:
    """
    Semantic chunking engine.

    Splits text into meaningful chunks based on document structure,
    headings, paragraphs, and sentence boundaries.
    """

    # Patterns for text splitting
    HEADING_PATTERN = re.compile(r'^(?:#{1,6}\s+|[A-Z0-9][\.\)]\s+|\d+[\.\)]\s+)', re.MULTILINE)
    PARAGRAPH_PATTERN = re.compile(r'\n\s*\n')
    SENTENCE_PATTERN = re.compile(r'(?<=[.!?])\s+(?=[A-Z])')

    def __init__(self, config: Optional[ChunkingConfig] = None):
        self.config = config or ChunkingConfig()

    def chunk_text(
        self,
        text: str,
        metadata: Optional[Dict[str, Any]] = None,
    ) -> List[Dict[str, Any]]:
        """
        Split text into semantic chunks.

        Args:
            text: Input text to chunk
            metadata: Optional metadata to include with each chunk

        Returns:
            List of chunk dictionaries with text and metadata
        """
        if not text or not text.strip():
            return []

        metadata = metadata or {}
        chunks: List[Dict[str, Any]] = []

        # Split by headings first
        if self.config.split_on_headings:
            sections = self._split_by_headings(text)
        else:
            sections = [{"heading": None, "text": text}]

        for section in sections:
            section_chunks = self._chunk_section(
                section["text"],
                section.get("heading"),
            )
            for chunk_text in section_chunks:
                if len(chunk_text.strip()) >= self.config.min_chunk_chars:
                    chunks.append({
                        "text": chunk_text.strip(),
                        "heading": section.get("heading"),
                        **metadata,
                    })

        # Merge small chunks
        if self.config.merge_small_chunks:
            chunks = self._merge_small_chunks(chunks)

        return chunks

    def _split_by_headings(self, text: str) -> List[Dict[str, Any]]:
        """Split text by heading patterns."""
        sections = []
        current_heading = None
        current_text = []

        lines = text.split("\n")

        for line in lines:
            if self.HEADING_PATTERN.match(line):
                # Save previous section
                if current_text:
                    sections.append({
                        "heading": current_heading,
                        "text": "\n".join(current_text),
                    })
                current_heading = line.strip()
                current_text = []
            else:
                current_text.append(line)

        # Save last section
        if current_text:
            sections.append({
                "heading": current_heading,
                "text": "\n".join(current_text),
            })

        return sections if sections else [{"heading": None, "text": text}]

    def _chunk_section(
        self,
        text: str,
        heading: Optional[str],
    ) -> List[str]:
        """Chunk a single section."""
        if len(text) <= self.config.max_chunk_chars:
            return [text]

        # Split by paragraphs
        if self.config.split_on_paragraphs:
            paragraphs = self.PARAGRAPH_PATTERN.split(text)
        else:
            paragraphs = [text]

        chunks = []
        current_chunk = ""

        for para in paragraphs:
            para = para.strip()
            if not para:
                continue

            # Check if adding this paragraph exceeds limit
            if len(current_chunk) + len(para) + 1 <= self.config.target_chunk_chars:
                if current_chunk:
                    current_chunk += "\n\n" + para
                else:
                    current_chunk = para
            else:
                # Save current and start new
                if current_chunk:
                    chunks.append(current_chunk)

                # If paragraph is too long, split further
                if len(para) > self.config.max_chunk_chars:
                    sub_chunks = self._split_long_text(para)
                    chunks.extend(sub_chunks[:-1])
                    current_chunk = sub_chunks[-1] if sub_chunks else ""
                else:
                    current_chunk = para

        if current_chunk:
            chunks.append(current_chunk)

        return chunks

    def _split_long_text(self, text: str) -> List[str]:
        """Split long text by sentences."""
        if not self.config.preserve_sentences:
            # Simple character-based split
            return self._split_by_chars(text)

        sentences = self.SENTENCE_PATTERN.split(text)
        chunks = []
        current_chunk = ""

        for sentence in sentences:
            sentence = sentence.strip()
            if not sentence:
                continue

            if len(current_chunk) + len(sentence) + 1 <= self.config.target_chunk_chars:
                if current_chunk:
                    current_chunk += " " + sentence
                else:
                    current_chunk = sentence
            else:
                if current_chunk:
                    chunks.append(current_chunk)

                if len(sentence) > self.config.max_chunk_chars:
                    # Sentence too long - split by chars
                    sub_chunks = self._split_by_chars(sentence)
                    chunks.extend(sub_chunks[:-1])
                    current_chunk = sub_chunks[-1] if sub_chunks else ""
                else:
                    current_chunk = sentence

        if current_chunk:
            chunks.append(current_chunk)

        return chunks

    def _split_by_chars(self, text: str) -> List[str]:
        """Split text by character count with overlap."""
        chunks = []
        start = 0
        text_len = len(text)

        while start < text_len:
            end = min(start + self.config.target_chunk_chars, text_len)

            # Try to break at word boundary
            if end < text_len:
                # Look for last space before limit
                space_idx = text.rfind(" ", start, end)
                if space_idx > start:
                    end = space_idx

            chunks.append(text[start:end].strip())

            # Apply overlap
            start = end - self.config.overlap_chars
            if start < 0 or start >= text_len:
                break

        return chunks

    def _merge_small_chunks(
        self,
        chunks: List[Dict[str, Any]],
    ) -> List[Dict[str, Any]]:
        """Merge chunks smaller than threshold."""
        if not chunks:
            return chunks

        merged = []
        current = None

        for chunk in chunks:
            text = chunk["text"]

            if current is None:
                current = chunk.copy()
                continue

            # Check if should merge
            current_len = len(current["text"])
            new_len = len(text)

            if (current_len < self.config.merge_threshold_chars and
                current_len + new_len <= self.config.max_chunk_chars and
                current.get("heading") == chunk.get("heading")):
                # Merge
                current["text"] = current["text"] + "\n\n" + text
            else:
                merged.append(current)
                current = chunk.copy()

        if current:
            merged.append(current)

        return merged


class DocumentChunkBuilder:
    """
    Builder for creating DocumentChunk objects.

    Provides a fluent interface for chunk construction with
    automatic ID generation and validation.
    """

    def __init__(
        self,
        doc_id: str,
        page: int,
    ):
        self.doc_id = doc_id
        self.page = page
        self._chunks: List[DocumentChunk] = []
        self._sequence_index = 0

    def add_chunk(
        self,
        text: str,
        chunk_type: ChunkType,
        bbox: BoundingBox,
        confidence: float = 1.0,
        metadata: Optional[Dict[str, Any]] = None,
    ) -> "DocumentChunkBuilder":
        """Add a chunk."""
        chunk_id = DocumentChunk.generate_chunk_id(
            doc_id=self.doc_id,
            page=self.page,
            bbox=bbox,
            chunk_type_str=chunk_type.value,
        )

        chunk = DocumentChunk(
            chunk_id=chunk_id,
            doc_id=self.doc_id,
            chunk_type=chunk_type,
            text=text,
            page=self.page,
            bbox=bbox,
            confidence=confidence,
            sequence_index=self._sequence_index,
            metadata=metadata or {},
        )

        self._chunks.append(chunk)
        self._sequence_index += 1
        return self

    def add_text(
        self,
        text: str,
        bbox: BoundingBox,
        confidence: float = 1.0,
    ) -> "DocumentChunkBuilder":
        """Add a text chunk."""
        return self.add_chunk(text, ChunkType.TEXT, bbox, confidence)

    def add_title(
        self,
        text: str,
        bbox: BoundingBox,
        confidence: float = 1.0,
    ) -> "DocumentChunkBuilder":
        """Add a title chunk."""
        return self.add_chunk(text, ChunkType.TITLE, bbox, confidence)

    def add_heading(
        self,
        text: str,
        bbox: BoundingBox,
        confidence: float = 1.0,
    ) -> "DocumentChunkBuilder":
        """Add a heading chunk."""
        return self.add_chunk(text, ChunkType.HEADING, bbox, confidence)

    def add_paragraph(
        self,
        text: str,
        bbox: BoundingBox,
        confidence: float = 1.0,
    ) -> "DocumentChunkBuilder":
        """Add a paragraph chunk."""
        return self.add_chunk(text, ChunkType.PARAGRAPH, bbox, confidence)

    def build(self) -> List[DocumentChunk]:
        """Build and return the list of chunks."""
        return self._chunks.copy()

    def reset(self) -> "DocumentChunkBuilder":
        """Reset the builder."""
        self._chunks = []
        self._sequence_index = 0
        return self


def estimate_tokens(text: str) -> int:
    """
    Estimate token count for text.

    Uses simple heuristic: ~4 characters per token.
    """
    return len(text) // 4


def split_for_embedding(
    text: str,
    max_tokens: int = 512,
    overlap_tokens: int = 50,
) -> List[str]:
    """
    Split text for embedding model input.

    Args:
        text: Text to split
        max_tokens: Maximum tokens per chunk
        overlap_tokens: Overlap between chunks

    Returns:
        List of text chunks
    """
    max_chars = max_tokens * 4
    overlap_chars = overlap_tokens * 4

    config = ChunkingConfig(
        max_chunk_chars=max_chars,
        target_chunk_chars=max_chars - 100,
        overlap_chars=overlap_chars,
    )

    chunker = SemanticChunker(config)
    chunks = chunker.chunk_text(text)

    return [c["text"] for c in chunks]