"""
ComfyUI Client for Qwen-Image-Edit-2511
========================================

Client to interact with ComfyUI API for running Qwen-Image-Edit-2511.

Model setup (download from HuggingFace):

  Lightning (default, 4-step):
    diffusion_models/  qwen_image_edit_2511_fp8_e4m3fn_scaled_lightning_comfyui_4steps_v1.0.safetensors
                         (lightx2v/Qwen-Image-Edit-2511-Lightning)

  Standard (20-step, optional):
    diffusion_models/  qwen_image_edit_2511_fp8mixed.safetensors
                         (Comfy-Org/Qwen-Image-Edit_ComfyUI)

  Shared:
    text_encoders/     qwen_2.5_vl_7b_fp8_scaled.safetensors         (Comfy-Org/Qwen-Image_ComfyUI)
    vae/               qwen_image_vae.safetensors                     (Comfy-Org/Qwen-Image_ComfyUI)

Required custom nodes:
  - Comfyui-QwenEditUtils (lrzjason) for TextEncodeQwenImageEditPlus
"""

import logging
import time
import uuid
import json
import io
import base64
from typing import Optional, List, Tuple
from PIL import Image
import websocket
import urllib.request
import urllib.parse

from .models import GenerationRequest, GenerationResult


logger = logging.getLogger(__name__)


class ComfyUIClient:
    """
    Client for ComfyUI API to run Qwen-Image-Edit-2511.

    Requires ComfyUI running with:
    - Qwen-Image-Edit-2511 model in models/diffusion_models/
    - Qwen 2.5 VL 7B text encoder in models/text_encoders/
    - Qwen Image VAE in models/vae/
    - Comfyui-QwenEditUtils custom node installed
    """

    # Default ComfyUI settings
    DEFAULT_HOST = "127.0.0.1"
    DEFAULT_PORT = 8188

    # Model file names (expected in ComfyUI models/ subfolders)
    # Lightning: baked model (LoRA pre-merged, ComfyUI-specific format)
    UNET_MODEL_LIGHTNING = "qwen_image_edit_2511_fp8_e4m3fn_scaled_lightning_comfyui_4steps_v1.0.safetensors"
    # Standard: base fp8mixed model (20-step, higher quality)
    UNET_MODEL_STANDARD = "qwen_image_edit_2511_fp8mixed.safetensors"
    TEXT_ENCODER = "qwen_2.5_vl_7b_fp8_scaled.safetensors"
    VAE_MODEL = "qwen_image_vae.safetensors"

    # Target output dimensions per aspect ratio.
    # Generation happens at 1024x1024, then crop+resize to these.
    ASPECT_RATIOS = {
        "1:1": (1024, 1024),
        "16:9": (1344, 768),
        "9:16": (768, 1344),
        "21:9": (1680, 720),
        "3:2": (1248, 832),
        "2:3": (832, 1248),
        "3:4": (896, 1152),
        "4:3": (1152, 896),
        "4:5": (1024, 1280),
        "5:4": (1280, 1024),
    }

    # Generate at 1024x1024 (proven safe for Qwen's VAE), then crop+resize
    NATIVE_RESOLUTION = (1024, 1024)

    # With Lightning LoRA: 4 steps, CFG 1.0  (fast, ~seconds per view)
    # Without LoRA:        20 steps, CFG 4.0
    DEFAULT_STEPS_LIGHTNING = 4
    DEFAULT_STEPS_STANDARD = 20
    DEFAULT_CFG_LIGHTNING = 1.0
    DEFAULT_CFG_STANDARD = 4.0

    def __init__(
        self,
        host: str = DEFAULT_HOST,
        port: int = DEFAULT_PORT,
        use_lightning: bool = True,
    ):
        """
        Initialize ComfyUI client.

        Args:
            host: ComfyUI server host
            port: ComfyUI server port
            use_lightning: Use Lightning LoRA for 4-step generation (much faster)
        """
        self.host = host
        self.port = port
        self.use_lightning = use_lightning
        self.client_id = str(uuid.uuid4())
        self.server_address = f"{host}:{port}"

        if use_lightning:
            self.num_inference_steps = self.DEFAULT_STEPS_LIGHTNING
            self.cfg_scale = self.DEFAULT_CFG_LIGHTNING
        else:
            self.num_inference_steps = self.DEFAULT_STEPS_STANDARD
            self.cfg_scale = self.DEFAULT_CFG_STANDARD

        logger.info(
            f"ComfyUIClient initialized for {self.server_address} "
            f"(lightning={use_lightning}, steps={self.num_inference_steps})"
        )

    def is_healthy(self) -> bool:
        """Check if ComfyUI server is running and accessible."""
        try:
            url = f"http://{self.server_address}/system_stats"
            with urllib.request.urlopen(url, timeout=5) as response:
                return response.status == 200
        except Exception:
            return False

    def _upload_image(self, image: Image.Image, name: str = "input.png") -> Optional[str]:
        """
        Upload an image to ComfyUI, pre-resized to fit within 1024x1024.

        Args:
            image: PIL Image to upload
            name: Filename for the uploaded image

        Returns:
            Filename on server, or None if failed
        """
        try:
            # Pre-resize to keep total pixels around 1024x1024 (matching reference workflow)
            max_pixels = 1024 * 1024
            w, h = image.size
            if w * h > max_pixels:
                scale = (max_pixels / (w * h)) ** 0.5
                new_w = int(w * scale)
                new_h = int(h * scale)
                image = image.resize((new_w, new_h), Image.LANCZOS)
                logger.debug(f"Pre-resized input from {w}x{h} to {new_w}x{new_h}")

            # Convert image to bytes
            img_bytes = io.BytesIO()
            image.save(img_bytes, format='PNG')
            img_bytes.seek(0)

            # Create multipart form data
            boundary = uuid.uuid4().hex

            body = b''
            body += f'--{boundary}\r\n'.encode()
            body += f'Content-Disposition: form-data; name="image"; filename="{name}"\r\n'.encode()
            body += b'Content-Type: image/png\r\n\r\n'
            body += img_bytes.read()
            body += f'\r\n--{boundary}--\r\n'.encode()

            url = f"http://{self.server_address}/upload/image"
            req = urllib.request.Request(
                url,
                data=body,
                headers={
                    'Content-Type': f'multipart/form-data; boundary={boundary}'
                }
            )

            with urllib.request.urlopen(req) as response:
                result = json.loads(response.read())
                return result.get('name')

        except Exception as e:
            logger.error(f"Failed to upload image: {e}")
            return None

    def _queue_prompt(self, prompt: dict) -> str:
        """
        Queue a prompt for execution.

        Args:
            prompt: Workflow prompt dict

        Returns:
            Prompt ID
        """
        prompt_id = str(uuid.uuid4())
        p = {"prompt": prompt, "client_id": self.client_id, "prompt_id": prompt_id}
        data = json.dumps(p).encode('utf-8')

        url = f"http://{self.server_address}/prompt"
        req = urllib.request.Request(url, data=data)
        urllib.request.urlopen(req)

        return prompt_id

    def _get_history(self, prompt_id: str) -> dict:
        """Get execution history for a prompt."""
        url = f"http://{self.server_address}/history/{prompt_id}"
        with urllib.request.urlopen(url) as response:
            return json.loads(response.read())

    def _get_image(self, filename: str, subfolder: str, folder_type: str) -> bytes:
        """Get an image from ComfyUI."""
        data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
        url_values = urllib.parse.urlencode(data)
        url = f"http://{self.server_address}/view?{url_values}"
        with urllib.request.urlopen(url) as response:
            return response.read()

    def _wait_for_completion(self, prompt_id: str, timeout: float = 900.0) -> bool:
        """
        Wait for prompt execution to complete using websocket.

        Args:
            prompt_id: The prompt ID to wait for
            timeout: Maximum time to wait in seconds (default 15 min for image editing)

        Returns:
            True if completed successfully, False if timeout/error
        """
        ws = None
        try:
            ws_url = f"ws://{self.server_address}/ws?clientId={self.client_id}"
            ws = websocket.WebSocket()
            ws.settimeout(timeout)
            ws.connect(ws_url)

            start_time = time.time()
            while time.time() - start_time < timeout:
                try:
                    out = ws.recv()
                    if isinstance(out, str):
                        message = json.loads(out)
                        if message['type'] == 'executing':
                            data = message['data']
                            if data['node'] is None and data['prompt_id'] == prompt_id:
                                return True  # Execution complete
                        elif message['type'] == 'execution_error':
                            logger.error(f"Execution error: {message}")
                            return False
                except websocket.WebSocketTimeoutException:
                    continue

            logger.error("Timeout waiting for completion")
            return False

        except Exception as e:
            logger.error(f"WebSocket error: {e}")
            return False
        finally:
            if ws:
                try:
                    ws.close()
                except:
                    pass

    def _get_dimensions(self, aspect_ratio: str) -> Tuple[int, int]:
        """Get pixel dimensions for aspect ratio."""
        ratio = aspect_ratio.split()[0] if " " in aspect_ratio else aspect_ratio
        return self.ASPECT_RATIOS.get(ratio, (1024, 1024))

    @staticmethod
    def _crop_and_resize(image: Image.Image, target_w: int, target_h: int) -> Image.Image:
        """Crop to target aspect ratio, then resize. Centers the crop."""
        src_w, src_h = image.size
        target_ratio = target_w / target_h
        src_ratio = src_w / src_h

        if abs(target_ratio - src_ratio) < 0.01:
            return image.resize((target_w, target_h), Image.LANCZOS)

        if target_ratio < src_ratio:
            crop_w = int(src_h * target_ratio)
            offset = (src_w - crop_w) // 2
            image = image.crop((offset, 0, offset + crop_w, src_h))
        else:
            crop_h = int(src_w / target_ratio)
            offset = (src_h - crop_h) // 2
            image = image.crop((0, offset, src_w, offset + crop_h))

        return image.resize((target_w, target_h), Image.LANCZOS)

    def _build_workflow(
        self,
        prompt: str,
        width: int,
        height: int,
        input_images: List[str] = None,
        negative_prompt: str = ""
    ) -> dict:
        """
        Build the ComfyUI workflow for Qwen-Image-Edit-2511.

        Workflow graph:
          UNETLoader → KSampler
          CLIPLoader → TextEncodeQwenImageEditPlus (pos/neg)
          VAELoader → TextEncode + VAEDecode
          LoadImage(s) → TextEncodeQwenImageEditPlus
          EmptyQwenImageLayeredLatentImage → KSampler
          KSampler → VAEDecode → PreviewImage

        Lightning mode uses a baked model (LoRA pre-merged), no separate
        LoRA or ModelSamplingAuraFlow nodes needed.
        """
        workflow = {}
        node_id = 1

        # --- Model loading ---

        # Select model based on lightning mode
        unet_name = (self.UNET_MODEL_LIGHTNING if self.use_lightning
                     else self.UNET_MODEL_STANDARD)

        # UNETLoader - weight_dtype "default" lets ComfyUI auto-detect fp8
        unet_id = str(node_id)
        workflow[unet_id] = {
            "class_type": "UNETLoader",
            "inputs": {
                "unet_name": unet_name,
                "weight_dtype": "default"
            }
        }
        node_id += 1

        # CLIPLoader
        clip_id = str(node_id)
        workflow[clip_id] = {
            "class_type": "CLIPLoader",
            "inputs": {
                "clip_name": self.TEXT_ENCODER,
                "type": "qwen_image"
            }
        }
        node_id += 1

        # VAELoader
        vae_id = str(node_id)
        workflow[vae_id] = {
            "class_type": "VAELoader",
            "inputs": {
                "vae_name": self.VAE_MODEL
            }
        }
        node_id += 1

        model_out_id = unet_id

        # --- Input images ---

        image_loader_ids = []
        if input_images:
            for img_name in input_images[:3]:  # Max 3 reference images
                img_loader_id = str(node_id)
                workflow[img_loader_id] = {
                    "class_type": "LoadImage",
                    "inputs": {
                        "image": img_name
                    }
                }
                image_loader_ids.append(img_loader_id)
                node_id += 1

        # --- Text encoding ---

        # Positive: prompt + vision references + VAE
        pos_encode_id = str(node_id)
        pos_inputs = {
            "clip": [clip_id, 0],
            "prompt": prompt,
            "vae": [vae_id, 0]
        }
        for i, loader_id in enumerate(image_loader_ids):
            pos_inputs[f"image{i+1}"] = [loader_id, 0]

        workflow[pos_encode_id] = {
            "class_type": "TextEncodeQwenImageEditPlus",
            "inputs": pos_inputs
        }
        node_id += 1

        # Negative: text only, no images
        neg_encode_id = str(node_id)
        workflow[neg_encode_id] = {
            "class_type": "TextEncodeQwenImageEditPlus",
            "inputs": {
                "clip": [clip_id, 0],
                "prompt": negative_prompt or " ",
                "vae": [vae_id, 0]
            }
        }
        node_id += 1

        # --- Latent + sampling ---

        latent_id = str(node_id)
        workflow[latent_id] = {
            "class_type": "EmptySD3LatentImage",
            "inputs": {
                "width": width,
                "height": height,
                "batch_size": 1
            }
        }
        node_id += 1

        sampler_id = str(node_id)
        workflow[sampler_id] = {
            "class_type": "KSampler",
            "inputs": {
                "model": [model_out_id, 0],
                "positive": [pos_encode_id, 0],
                "negative": [neg_encode_id, 0],
                "latent_image": [latent_id, 0],
                "seed": int(time.time()) % 2**32,
                "steps": self.num_inference_steps,
                "cfg": self.cfg_scale,
                "sampler_name": "euler",
                "scheduler": "simple",
                "denoise": 1.0
            }
        }
        node_id += 1

        # --- Decode + output ---

        decode_id = str(node_id)
        workflow[decode_id] = {
            "class_type": "VAEDecode",
            "inputs": {
                "samples": [sampler_id, 0],
                "vae": [vae_id, 0]
            }
        }
        node_id += 1

        preview_id = str(node_id)
        workflow[preview_id] = {
            "class_type": "PreviewImage",
            "inputs": {
                "images": [decode_id, 0]
            }
        }

        return workflow

    def generate(
        self,
        request: GenerationRequest,
        num_inference_steps: Optional[int] = None,
        cfg_scale: Optional[float] = None
    ) -> GenerationResult:
        """
        Generate/edit image using Qwen-Image-Edit-2511 via ComfyUI.

        Generates at native 1024x1024, then crop+resize to requested
        aspect ratio for clean VAE output.
        """
        if not self.is_healthy():
            return GenerationResult.error_result(
                "ComfyUI server is not accessible. Make sure ComfyUI is running on "
                f"{self.server_address}"
            )

        try:
            start_time = time.time()

            # Target dimensions for post-processing
            target_w, target_h = self._get_dimensions(request.aspect_ratio)
            # Generate at native resolution (VAE-safe)
            native_w, native_h = self.NATIVE_RESOLUTION

            # Upload input images (max 3)
            uploaded_images = []
            if request.has_input_images:
                for i, img in enumerate(request.input_images):
                    if img is not None:
                        name = f"input_{i}_{uuid.uuid4().hex[:8]}.png"
                        uploaded_name = self._upload_image(img, name)
                        if uploaded_name:
                            uploaded_images.append(uploaded_name)
                        else:
                            logger.warning(f"Failed to upload image {i}")

            steps = num_inference_steps or self.num_inference_steps
            cfg = cfg_scale or self.cfg_scale

            # Temporarily set for workflow build
            old_steps, old_cfg = self.num_inference_steps, self.cfg_scale
            self.num_inference_steps, self.cfg_scale = steps, cfg

            workflow = self._build_workflow(
                prompt=request.prompt,
                width=native_w,
                height=native_h,
                input_images=uploaded_images or None,
                negative_prompt=request.negative_prompt or ""
            )

            self.num_inference_steps, self.cfg_scale = old_steps, old_cfg

            logger.info(f"Generating with ComfyUI/Qwen: {request.prompt[:80]}...")
            logger.info(
                f"Native: {native_w}x{native_h}, target: {target_w}x{target_h}, "
                f"steps: {steps}, cfg: {cfg}, images: {len(uploaded_images)}, "
                f"lightning: {self.use_lightning}"
            )

            # Queue and wait
            prompt_id = self._queue_prompt(workflow)
            logger.info(f"Queued prompt: {prompt_id}")

            if not self._wait_for_completion(prompt_id):
                return GenerationResult.error_result("Generation failed or timed out")

            # Retrieve output
            history = self._get_history(prompt_id)
            if prompt_id not in history:
                return GenerationResult.error_result("No history found for prompt")

            outputs = history[prompt_id].get('outputs', {})
            for nid, node_output in outputs.items():
                if 'images' in node_output:
                    for img_info in node_output['images']:
                        img_data = self._get_image(
                            img_info['filename'],
                            img_info.get('subfolder', ''),
                            img_info.get('type', 'temp')
                        )
                        image = Image.open(io.BytesIO(img_data))
                        generation_time = time.time() - start_time
                        logger.info(f"Generated in {generation_time:.2f}s: {image.size}")

                        # Crop+resize to target aspect ratio
                        if (target_w, target_h) != (native_w, native_h):
                            image = self._crop_and_resize(image, target_w, target_h)
                            logger.info(f"Post-processed to: {image.size}")

                        return GenerationResult.success_result(
                            image=image,
                            message=f"Generated with ComfyUI/Qwen in {generation_time:.2f}s",
                            generation_time=generation_time
                        )

            return GenerationResult.error_result("No output images found")

        except Exception as e:
            logger.error(f"ComfyUI generation failed: {e}", exc_info=True)
            return GenerationResult.error_result(f"ComfyUI error: {str(e)}")

    def unload_model(self):
        """
        Request ComfyUI to free memory.
        Note: ComfyUI manages models automatically, but we can request cleanup.
        """
        try:
            url = f"http://{self.server_address}/free"
            data = json.dumps({"unload_models": True}).encode('utf-8')
            req = urllib.request.Request(url, data=data, method='POST')
            urllib.request.urlopen(req)
            logger.info("Requested ComfyUI to free memory")
        except Exception as e:
            logger.warning(f"Failed to request memory cleanup: {e}")

    @classmethod
    def get_dimensions(cls, aspect_ratio: str) -> Tuple[int, int]:
        """Get pixel dimensions for aspect ratio."""
        ratio = aspect_ratio.split()[0] if " " in aspect_ratio else aspect_ratio
        return cls.ASPECT_RATIOS.get(ratio, (1024, 1024))