"""
NAIA-WEB API Service
NAI Image Generation API communication layer

Reference: NAIA2.0/core/api_service.py (260-460)
"""

import aiohttp
import asyncio
import zipfile
import io
import json
import random
import base64
from dataclasses import dataclass
from typing import Optional, Tuple, Dict, Any, List
from PIL import Image

from utils.constants import NAI_API_URL, MODEL_ID_MAP


def process_reference_image(file_path: str) -> str:
    """
    Process reference image for character reference API.
    Normalizes aspect ratio and encodes to base64.

    Reference: NAIA2.0/modules/character_reference_module.py _file_to_base64
    """
    try:
        original_image = Image.open(file_path)
        width, height = original_image.size
        aspect_ratio = width / height

        # Standard aspect ratios (ratio, canvas_width, canvas_height)
        ratios = {
            '2:3': (2/3, 1024, 1536),
            '3:2': (3/2, 1536, 1024),
            '1:1': (1/1, 1472, 1472)
        }

        # Find closest standard ratio
        closest_ratio = min(ratios.keys(), key=lambda k: abs(aspect_ratio - ratios[k][0]))
        target_ratio, canvas_width, canvas_height = ratios[closest_ratio]

        print(f"NAIA-WEB: Reference image {width}x{height} ({aspect_ratio:.2f}) → {closest_ratio} ({canvas_width}x{canvas_height})")

        # Create black canvas
        canvas = Image.new('RGB', (canvas_width, canvas_height), (0, 0, 0))

        # Resize to fit canvas (preserve aspect ratio)
        if width / canvas_width > height / canvas_height:
            new_width = canvas_width
            new_height = int(height * (canvas_width / width))
        else:
            new_height = canvas_height
            new_width = int(width * (canvas_height / height))

        resized_image = original_image.resize((new_width, new_height), Image.Resampling.LANCZOS)

        # Center on canvas
        x_offset = (canvas_width - new_width) // 2
        y_offset = (canvas_height - new_height) // 2

        # Handle RGBA transparency
        if resized_image.mode == 'RGBA':
            canvas = canvas.convert('RGBA')
            canvas.paste(resized_image, (x_offset, y_offset), resized_image)
            rgb_canvas = Image.new('RGB', (canvas_width, canvas_height), (0, 0, 0))
            rgb_canvas.paste(canvas, (0, 0), canvas)
            canvas = rgb_canvas
        else:
            canvas.paste(resized_image, (x_offset, y_offset))

        # Encode to base64
        buffer = io.BytesIO()
        canvas.save(buffer, format="PNG", optimize=False)
        return base64.b64encode(buffer.getvalue()).decode("utf-8")

    except Exception as e:
        print(f"NAIA-WEB: Failed to process reference image: {e}")
        # Fallback: use original file bytes
        with open(file_path, "rb") as f:
            return base64.b64encode(f.read()).decode("utf-8")


class NAIAPIError(Exception):
    """Custom exception for NAI API errors"""
    def __init__(self, status_code: int, message: str, debug_info: Optional[Dict] = None):
        self.status_code = status_code
        self.message = message
        self.debug_info = debug_info or {}
        super().__init__(f"NAI API Error ({status_code}): {message}")


@dataclass
class CharacterReferenceData:
    """Character reference data for NAID4.5"""
    image_base64: str  # Base64 encoded image
    style_aware: bool = True  # Include style from reference
    fidelity: float = 0.75  # How closely to follow the reference (0.0-1.0)


@dataclass
class GenerationParameters:
    """Parameters for image generation request"""
    prompt: str
    negative_prompt: str
    width: int
    height: int
    steps: int = 28
    scale: float = 5.0
    cfg_rescale: float = 0.4  # NAIA2.0 default
    sampler: str = "k_euler"
    seed: Optional[int] = None
    model: str = "NAID4.5F"
    noise_schedule: str = "native"
    variety_plus: bool = False  # VAR+ option (skip_cfg_above_sigma)
    # Character prompts: List of (prompt, negative) tuples
    character_prompts: List[Tuple[str, str]] = None
    # Character reference (NAID4.5 feature)
    character_reference: Optional[CharacterReferenceData] = None


class NAIAPIService:
    """
    Service for communicating with NAI image generation API.

    Handles V4.5 model API calls with proper payload structure.
    """

    def __init__(self):
        self._session: Optional[aiohttp.ClientSession] = None
        # Debug info storage
        self._last_payload: Optional[Dict] = None
        self._last_response_status: Optional[int] = None
        self._last_response_text: Optional[str] = None

    async def _get_session(self) -> aiohttp.ClientSession:
        """Get or create aiohttp session"""
        if self._session is None or self._session.closed:
            self._session = aiohttp.ClientSession()
        return self._session

    async def generate_image(
        self,
        token: str,
        params: GenerationParameters
    ) -> Tuple[Image.Image, Dict[str, Any]]:
        """
        Call NAI API to generate an image.

        Args:
            token: NAI API authentication token
            params: Generation parameters

        Returns:
            Tuple of (PIL Image, metadata dict)

        Raises:
            NAIAPIError: If API call fails
        """
        session = await self._get_session()

        # Get model name from mapping
        model_name = MODEL_ID_MAP.get(params.model, "nai-diffusion-4-5-full")

        # Determine seed
        seed = params.seed if params.seed and params.seed > 0 else random.randint(0, 2**32 - 1)

        # Build V4 prompt structure
        v4_prompt = {
            "caption": {
                "base_caption": params.prompt,
                "char_captions": []
            },
            "use_coords": False,
            "use_order": True
        }

        v4_negative_prompt = {
            "caption": {
                "base_caption": params.negative_prompt,
                "char_captions": []
            },
            "legacy_uc": False
        }

        # Add character prompts if provided (NAID4.5 feature)
        if params.character_prompts:
            for char_prompt, char_negative in params.character_prompts:
                if char_prompt.strip():
                    # Default center position (no 5x5 grid feature)
                    centers = [{"x": 0.5, "y": 0.5}]
                    v4_prompt["caption"]["char_captions"].append({
                        "char_caption": char_prompt.strip(),
                        "centers": centers
                    })
                    v4_negative_prompt["caption"]["char_captions"].append({
                        "char_caption": char_negative.strip() if char_negative else "",
                        "centers": centers
                    })
            if v4_prompt["caption"]["char_captions"]:
                print(f"NAIA-WEB: Added {len(v4_prompt['caption']['char_captions'])} character prompt(s)")

        # Build API parameters (matching NAI V4 structure)
        api_parameters = {
            "width": params.width,
            "height": params.height,
            "n_samples": 1,
            "seed": seed,
            "extra_noise_seed": seed,
            "sampler": params.sampler,
            "steps": params.steps,
            "scale": params.scale,
            "cfg_rescale": params.cfg_rescale,
            "noise_schedule": params.noise_schedule,
            "negative_prompt": params.negative_prompt,
            # V4 specific parameters
            "params_version": 3,
            "add_original_image": True,
            "legacy": False,
            "legacy_uc": False,
            "autoSmea": True,
            "prefer_brownian": True,
            "ucPreset": 0,
            "use_coords": False,
            "v4_prompt": v4_prompt,
            "v4_negative_prompt": v4_negative_prompt,
        }

        # VAR+ (skip_cfg_above_sigma) handling
        # Reference: NAIA2.0/core/api_service.py:307-321
        if params.variety_plus:
            # NAID4.5: 58, NAID4.0/NAID3: 19
            if model_name in ['nai-diffusion-4-5-curated']:
                api_parameters["skip_cfg_above_sigma"] = 58
            elif model_name == 'nai-diffusion-4-5-full':
                api_parameters["skip_cfg_above_sigma"] = 58.93178654671047
            else:
                api_parameters["skip_cfg_above_sigma"] = 19
            print(f"NAIA-WEB: VAR+ enabled (skip_cfg_above_sigma={api_parameters['skip_cfg_above_sigma']})")
        else:
            api_parameters["skip_cfg_above_sigma"] = None

        # Add character reference if provided (NAID4.5 feature)
        if params.character_reference:
            ref = params.character_reference
            # Build description based on style_aware setting
            if ref.style_aware:
                description = {
                    "caption": {"base_caption": "character&style", "char_captions": []},
                    "legacy_uc": False
                }
            else:
                description = {
                    "caption": {"base_caption": "character", "char_captions": []},
                    "legacy_uc": False
                }

            api_parameters["director_reference_descriptions"] = [description]
            api_parameters["director_reference_images"] = [ref.image_base64]
            api_parameters["director_reference_information_extracted"] = [1]
            api_parameters["director_reference_secondary_strength_values"] = [ref.fidelity]
            api_parameters["director_reference_strength_values"] = [1]
            api_parameters["controlnet_strength"] = 1
            api_parameters["inpaintImg2ImgStrength"] = 1
            api_parameters["normalize_reference_strength_multiple"] = True

            # Character Reference 활성화 시 skip_cfg_above_sigma 제거
            # Reference: NAIA2.0/core/api_service.py:533-536
            if 'skip_cfg_above_sigma' in api_parameters:
                del api_parameters['skip_cfg_above_sigma']
                print("NAIA-WEB: skip_cfg_above_sigma removed (Character Reference enabled)")

            print(f"NAIA-WEB: Character reference enabled (style_aware={ref.style_aware}, fidelity={ref.fidelity})")

        # Build request payload
        payload = {
            "input": params.prompt,
            "model": model_name,
            "action": "generate",
            "parameters": api_parameters
        }

        # Headers - matching NAIA2.0 (no Accept header)
        headers = {
            "Authorization": f"Bearer {token}",
            "Content-Type": "application/json"
        }

        # Store for debugging
        self._last_payload = payload
        self._last_response_status = None
        self._last_response_text = None

        max_retries = 2
        last_error = None

        for attempt in range(max_retries):
            try:
                async with session.post(
                    NAI_API_URL,
                    json=payload,
                    headers=headers,
                    timeout=aiohttp.ClientTimeout(total=180)  # NAIA2.0 uses 180s
                ) as response:
                    self._last_response_status = response.status

                    if response.status == 200:
                        zip_data = await response.read()
                        image = self._extract_image_from_zip(zip_data)

                        metadata = {
                            "seed": seed,
                            "model": params.model,
                            "steps": params.steps,
                            "scale": params.scale,
                            "sampler": params.sampler,
                            "width": params.width,
                            "height": params.height,
                        }

                        return image, metadata
                    else:
                        error_text = await response.text()
                        self._last_response_text = error_text

                        debug_info = {
                            "model": model_name,
                            "status": response.status,
                            "response": error_text[:500],  # Truncate long responses
                            "token_length": len(token) if token else 0,
                            "token_prefix": token[:10] + "..." if token and len(token) > 10 else token
                        }
                        last_error = NAIAPIError(response.status, error_text, debug_info)

                        # Don't retry on client errors (4xx)
                        if 400 <= response.status < 500:
                            raise last_error

            except aiohttp.ClientError as e:
                self._last_response_text = str(e)
                last_error = NAIAPIError(0, f"Network error: {str(e)}")

            # Wait before retry
            if attempt < max_retries - 1:
                await asyncio.sleep(1)

        raise last_error or NAIAPIError(0, "Unknown error")

    def _extract_image_from_zip(self, zip_data: bytes) -> Image.Image:
        """Extract image from NAI response zip"""
        with zipfile.ZipFile(io.BytesIO(zip_data)) as zf:
            # Find PNG file in zip
            image_files = [f for f in zf.namelist() if f.endswith('.png')]
            if not image_files:
                raise NAIAPIError(0, "No image found in response")

            image_bytes = zf.read(image_files[0])
            return Image.open(io.BytesIO(image_bytes))

    async def close(self):
        """Close the aiohttp session"""
        if self._session and not self._session.closed:
            await self._session.close()

    def get_debug_info(self) -> Dict[str, Any]:
        """Return debug info from last request"""
        return {
            "last_status": self._last_response_status,
            "last_response": self._last_response_text,
            "last_payload_keys": list(self._last_payload.keys()) if self._last_payload else None,
            "last_model": self._last_payload.get("model") if self._last_payload else None,
        }


def format_api_error(error: NAIAPIError) -> str:
    """Format API error for user display with debug info"""
    base_msg = ""
    if error.status_code == 401:
        base_msg = "Authentication failed. Please check your API token."
    elif error.status_code == 402:
        base_msg = "Insufficient Anlas. Please check your account balance."
    elif error.status_code == 429:
        base_msg = "Rate limited. Please wait before trying again."
    elif error.status_code >= 500:
        base_msg = "NAI server error. Please try again later."
    elif error.status_code == 0:
        base_msg = f"Connection error: {error.message}"
    else:
        base_msg = f"API Error ({error.status_code}): {error.message}"

    # Add debug info if available
    if error.debug_info:
        debug_parts = []
        if "token_length" in error.debug_info:
            debug_parts.append(f"Token length: {error.debug_info['token_length']}")
        if "token_prefix" in error.debug_info:
            debug_parts.append(f"Token prefix: {error.debug_info['token_prefix']}")
        if "model" in error.debug_info:
            debug_parts.append(f"Model: {error.debug_info['model']}")
        if "response" in error.debug_info:
            debug_parts.append(f"Response: {error.debug_info['response']}")

        if debug_parts:
            base_msg += "\n\n[Debug Info]\n" + "\n".join(debug_parts)

    return base_msg