from typing import Any, Dict, List

from modules.datamodels.datamodelExtraction import ExtractedContent, ContentPart
from .utils import makeId
from .subRegistry import ExtractorRegistry, ChunkerRegistry
from .merging.text_merger import TextMerger
from .merging.table_merger import TableMerger
from .merging.default_merger import DefaultMerger


def runExtraction(extractorRegistry: ExtractorRegistry, chunkerRegistry: ChunkerRegistry, documentBytes: bytes, fileName: str, mimeType: str, options: Dict[str, Any]) -> ExtractedContent:
    extractor = extractorRegistry.resolve(mimeType, fileName)
    if extractor is None:
        # fallback: single binary part
        part = ContentPart(
            id=makeId(),
            parentId=None,
            label="file",
            typeGroup="binary",
            mimeType=mimeType or "application/octet-stream",
            data="",
            metadata={"warning": "No extractor registered"}
        )
        return ExtractedContent(id=makeId(), parts=[part])

    parts = extractor.extract(documentBytes, {"fileName": fileName, "mimeType": mimeType, "options": options})
    # Optional merge step
    mergeStrategy = options.get("mergeStrategy", {})
    if mergeStrategy:
        parts = _mergeParts(parts, mergeStrategy)
    return ExtractedContent(id=makeId(), parts=parts)


def poolAndLimit(parts: List[ContentPart], chunkerRegistry: ChunkerRegistry, options: Dict[str, Any]) -> List[ContentPart]:
    maxSize = int(options.get("maxSize", 0) or 0)
    chunkAllowed = bool(options.get("chunkAllowed", False))
    mergeStrategy = options.get("mergeStrategy", {})

    if maxSize <= 0:
        # Still apply merging if strategy provided
        if mergeStrategy:
            return _applyMerging(parts, mergeStrategy)
        return parts

    # First, try to fit within size limit
    current = 0
    kept: List[ContentPart] = []
    remaining: List[ContentPart] = []
    
    for p in parts:
        size = int(p.metadata.get("size", 0) or 0)
        if current + size <= maxSize:
            kept.append(p)
            current += size
        else:
            remaining.append(p)

    # If we have remaining parts and chunking is allowed, try chunking
    if remaining and chunkAllowed:
        for p in remaining:
            if p.typeGroup in ("text", "table", "structure"):
                chunks = chunkerRegistry.resolve(p.typeGroup).chunk(p, options)
                for ch in chunks:
                    chSize = int(ch.get("size", 0) or 0)
                    if current + chSize <= maxSize:
                        kept.append(ContentPart(
                            id=makeId(),
                            parentId=p.id,
                            label=f"chunk_{ch.get('order', 0)}",
                            typeGroup=p.typeGroup,
                            mimeType=p.mimeType,
                            data=ch.get("data", ""),
                            metadata={"size": chSize, "chunk": True}
                        ))
                        current += chSize
                    else:
                        break

    # Apply merging strategy if provided
    if mergeStrategy:
        kept = _applyMerging(kept, mergeStrategy)
        
        # Re-check size after merging
        totalSize = sum(int(p.metadata.get("size", 0) or 0) for p in kept)
        if totalSize > maxSize and mergeStrategy.get("maxSize"):
            # Apply size limit to merged parts
            kept = _applySizeLimit(kept, maxSize)

    return kept


def _applyMerging(parts: List[ContentPart], strategy: Dict[str, Any]) -> List[ContentPart]:
    """Apply merging strategy to parts."""
    textMerger = TextMerger()
    tableMerger = TableMerger()
    defaultMerger = DefaultMerger()
    
    # Group by typeGroup
    textParts = [p for p in parts if p.typeGroup == "text"]
    tableParts = [p for p in parts if p.typeGroup == "table"]
    structureParts = [p for p in parts if p.typeGroup == "structure"]
    otherParts = [p for p in parts if p.typeGroup not in ("text", "table", "structure")]
    
    merged: List[ContentPart] = []
    
    if textParts:
        merged.extend(textMerger.merge(textParts, strategy))
    if tableParts:
        merged.extend(tableMerger.merge(tableParts, strategy))
    if structureParts:
        # For now, treat structure like text
        merged.extend(textMerger.merge(structureParts, strategy))
    if otherParts:
        merged.extend(defaultMerger.merge(otherParts, strategy))
    
    return merged


def _applySizeLimit(parts: List[ContentPart], maxSize: int) -> List[ContentPart]:
    """Apply size limit by prioritizing parts and truncating if necessary."""
    # Sort by priority: text first, then others
    priority_order = {"text": 0, "table": 1, "structure": 2, "image": 3, "binary": 4, "metadata": 5, "container": 6}
    sorted_parts = sorted(parts, key=lambda p: priority_order.get(p.typeGroup, 99))
    
    kept: List[ContentPart] = []
    current_size = 0
    
    for part in sorted_parts:
        part_size = int(part.metadata.get("size", 0) or 0)
        if current_size + part_size <= maxSize:
            kept.append(part)
            current_size += part_size
        else:
            # Try to truncate text parts
            if part.typeGroup == "text" and part_size > 0:
                remaining_size = maxSize - current_size
                if remaining_size > 1000:  # Only truncate if we have meaningful space
                    truncated_data = part.data[:remaining_size * 4]  # Rough character estimate
                    truncated_part = ContentPart(
                        id=makeId(),
                        parentId=part.parentId,
                        label=f"{part.label}_truncated",
                        typeGroup=part.typeGroup,
                        mimeType=part.mimeType,
                        data=truncated_data,
                        metadata={**part.metadata, "size": len(truncated_data.encode('utf-8')), "truncated": True}
                    )
                    kept.append(truncated_part)
            break
    
    return kept


def applyAiIfRequested(extracted: ExtractedContent, options: Dict[str, Any]) -> ExtractedContent:
    """
    Apply AI processing if requested in options.
    This is a placeholder for actual AI integration.
    """
    prompt = options.get("prompt")
    operationType = options.get("operationType", "general")
    
    if not prompt:
        return extracted
    
    # Placeholder AI processing based on operationType
    if operationType == "analyse_content":
        # Add analysis metadata to parts
        for part in extracted.parts:
            if part.typeGroup in ("text", "table", "structure"):
                part.metadata["ai_processed"] = True
                part.metadata["operation_type"] = operationType
    elif operationType == "generate_plan":
        # Add plan generation metadata
        for part in extracted.parts:
            if part.typeGroup == "text":
                part.metadata["ai_processed"] = True
                part.metadata["operation_type"] = operationType
    elif operationType == "generate_content":
        # Add content generation metadata
        for part in extracted.parts:
            part.metadata["ai_processed"] = True
            part.metadata["operation_type"] = operationType
    
    return extracted