import queue
import time
import uuid
from pathlib import Path
from typing import Optional
from urllib.parse import urlparse

import httpx
import torch.multiprocessing as mp
from loguru import logger

from ..infer import init_runner
from ..utils.set_config import set_config
from .distributed_utils import create_distributed_worker
from .schema import TaskRequest, TaskResponse
from .utils import ServiceStatus

mp.set_start_method("spawn", force=True)


class FileService:
    def __init__(self, cache_dir: Path):
        self.cache_dir = cache_dir
        self.input_image_dir = cache_dir / "inputs" / "imgs"
        self.input_audio_dir = cache_dir / "inputs" / "audios"
        self.output_video_dir = cache_dir / "outputs"

        # Create directories
        for directory in [
            self.input_image_dir,
            self.output_video_dir,
            self.input_audio_dir,
        ]:
            directory.mkdir(parents=True, exist_ok=True)

    async def download_image(self, image_url: str) -> Path:
        try:
            async with httpx.AsyncClient(verify=False) as client:
                response = await client.get(image_url)

            if response.status_code != 200:
                raise ValueError(f"Failed to download image from {image_url}")

            image_name = Path(urlparse(image_url).path).name
            if not image_name:
                raise ValueError(f"Invalid image URL: {image_url}")

            image_path = self.input_image_dir / image_name
            image_path.parent.mkdir(parents=True, exist_ok=True)

            with open(image_path, "wb") as f:
                f.write(response.content)

            return image_path
        except Exception as e:
            logger.error(f"Failed to download image: {e}")
            raise

    def save_uploaded_file(self, file_content: bytes, filename: str) -> Path:
        file_extension = Path(filename).suffix
        unique_filename = f"{uuid.uuid4()}{file_extension}"
        file_path = self.input_image_dir / unique_filename

        with open(file_path, "wb") as f:
            f.write(file_content)

        return file_path

    def get_output_path(self, save_video_path: str) -> Path:
        video_path = Path(save_video_path)
        if not video_path.is_absolute():
            return self.output_video_dir / save_video_path
        return video_path


def _distributed_inference_worker(rank, world_size, master_addr, master_port, args, task_queue, result_queue):
    task_data = None
    worker = None

    try:
        logger.info(f"Process {rank}/{world_size - 1} initializing distributed inference service...")

        # Create and initialize distributed worker process
        worker = create_distributed_worker(rank, world_size, master_addr, master_port)
        if not worker.init():
            raise RuntimeError(f"Rank {rank} distributed environment initialization failed")

        # Initialize configuration and model
        config = set_config(args)
        logger.info(f"Rank {rank} config: {config}")

        runner = init_runner(config)
        logger.info(f"Process {rank}/{world_size - 1} distributed inference service initialization completed")

        while True:
            # Only rank=0 reads tasks from queue
            if rank == 0:
                try:
                    task_data = task_queue.get(timeout=1.0)
                    if task_data is None:  # Stop signal
                        logger.info(f"Process {rank} received stop signal, exiting inference service")
                        # Broadcast stop signal to other processes
                        worker.dist_manager.broadcast_task_data(None)
                        break
                    # Broadcast task data to other processes
                    worker.dist_manager.broadcast_task_data(task_data)
                except queue.Empty:
                    # Queue is empty, continue waiting
                    continue
            else:
                # Non-rank=0 processes receive task data from rank=0
                task_data = worker.dist_manager.broadcast_task_data()
                if task_data is None:  # Stop signal
                    logger.info(f"Process {rank} received stop signal, exiting inference service")
                    break

            # All processes handle the task
            if task_data is not None:
                logger.info(f"Process {rank} received inference task: {task_data['task_id']}")

                try:
                    # Set inputs and run inference
                    runner.set_inputs(task_data)  # type: ignore
                    runner.run_pipeline()

                    # Synchronize and report results
                    worker.sync_and_report(
                        task_data["task_id"],
                        "success",
                        result_queue,
                        save_video_path=task_data["save_video_path"],
                        message="Inference completed",
                    )
                except Exception as e:
                    logger.error(f"Process {rank} error occurred while processing task: {str(e)}")

                    # Synchronize and report error
                    worker.sync_and_report(
                        task_data.get("task_id", "unknown"),
                        "failed",
                        result_queue,
                        error=str(e),
                        message=f"Inference failed: {str(e)}",
                    )

    except KeyboardInterrupt:
        logger.info(f"Process {rank} received KeyboardInterrupt, gracefully exiting")
    except Exception as e:
        logger.error(f"Distributed inference service process {rank} startup failed: {str(e)}")
        if rank == 0:
            error_result = {
                "task_id": "startup",
                "status": "startup_failed",
                "error": str(e),
                "message": f"Inference service startup failed: {str(e)}",
            }
            result_queue.put(error_result)
    finally:
        try:
            if worker:
                worker.cleanup()
        except:  # noqa: E722
            pass


class DistributedInferenceService:
    def __init__(self):
        self.task_queue = None
        self.result_queue = None
        self.processes = []
        self.is_running = False

    def start_distributed_inference(self, args) -> bool:
        if hasattr(args, "lora_path") and args.lora_path:
            args.lora_configs = [{"path": args.lora_path, "strength": getattr(args, "lora_strength", 1.0)}]
            delattr(args, "lora_path")
            if hasattr(args, "lora_strength"):
                delattr(args, "lora_strength")

        self.args = args
        if self.is_running:
            logger.warning("Distributed inference service is already running")
            return True

        nproc_per_node = args.nproc_per_node
        if nproc_per_node <= 0:
            logger.error("nproc_per_node must be greater than 0")
            return False

        try:
            import random

            master_addr = "127.0.0.1"
            master_port = str(random.randint(20000, 29999))
            logger.info(f"Distributed inference service Master Addr: {master_addr}, Master Port: {master_port}")

            # Create shared queues
            self.task_queue = mp.Queue()
            self.result_queue = mp.Queue()

            # Start processes
            for rank in range(nproc_per_node):
                p = mp.Process(
                    target=_distributed_inference_worker,
                    args=(
                        rank,
                        nproc_per_node,
                        master_addr,
                        master_port,
                        args,
                        self.task_queue,
                        self.result_queue,
                    ),
                    daemon=True,
                )
                p.start()
                self.processes.append(p)

            self.is_running = True
            logger.info(f"Distributed inference service started successfully with {nproc_per_node} processes")
            return True

        except Exception as e:
            logger.exception(f"Error occurred while starting distributed inference service: {str(e)}")
            self.stop_distributed_inference()
            return False

    def stop_distributed_inference(self):
        if not self.is_running:
            return

        try:
            logger.info(f"Stopping {len(self.processes)} distributed inference service processes...")

            # Send stop signal
            if self.task_queue:
                for _ in self.processes:
                    self.task_queue.put(None)

            # Wait for processes to end
            for p in self.processes:
                try:
                    p.join(timeout=10)
                    if p.is_alive():
                        logger.warning(f"Process {p.pid} did not end within the specified time, forcing termination...")
                        p.terminate()
                        p.join(timeout=5)
                except:  # noqa: E722
                    pass

            logger.info("All distributed inference service processes have stopped")

        except Exception as e:
            logger.error(f"Error occurred while stopping distributed inference service: {str(e)}")
        finally:
            # Clean up resources
            self._clean_queues()
            self.processes = []
            self.task_queue = None
            self.result_queue = None
            self.is_running = False

    def _clean_queues(self):
        for queue_obj in [self.task_queue, self.result_queue]:
            if queue_obj:
                try:
                    while not queue_obj.empty():
                        queue_obj.get_nowait()
                except:  # noqa: E722
                    pass

    def submit_task(self, task_data: dict) -> bool:
        if not self.is_running or not self.task_queue:
            logger.error("Distributed inference service is not started")
            return False

        try:
            self.task_queue.put(task_data)
            return True
        except Exception as e:
            logger.error(f"Failed to submit task: {str(e)}")
            return False

    def wait_for_result(self, task_id: str, timeout: int = 300) -> Optional[dict]:
        if not self.is_running or not self.result_queue:
            return None

        start_time = time.time()

        while time.time() - start_time < timeout:
            try:
                result = self.result_queue.get(timeout=1.0)

                if result.get("task_id") == task_id:
                    return result
                else:
                    # Not the result for current task, put back in queue
                    self.result_queue.put(result)
                    time.sleep(0.1)

            except queue.Empty:
                continue

        return None

    def server_metadata(self):
        assert hasattr(self, "args"), "Distributed inference service has not been started. Call start_distributed_inference() first."
        return {"nproc_per_node": self.args.nproc_per_node, "model_cls": self.args.model_cls, "model_path": self.args.model_path}


class VideoGenerationService:
    def __init__(self, file_service: FileService, inference_service: DistributedInferenceService):
        self.file_service = file_service
        self.inference_service = inference_service

    async def generate_video(self, message: TaskRequest) -> TaskResponse:
        try:
            task_data = {field: getattr(message, field) for field in message.model_fields_set if field != "task_id"}
            task_data["task_id"] = message.task_id

            if "image_path" in message.model_fields_set and message.image_path.startswith("http"):
                image_path = await self.file_service.download_image(message.image_path)
                task_data["image_path"] = str(image_path)

            save_video_path = self.file_service.get_output_path(message.save_video_path)
            task_data["save_video_path"] = str(save_video_path)

            if not self.inference_service.submit_task(task_data):
                raise RuntimeError("Distributed inference service is not started")

            result = self.inference_service.wait_for_result(message.task_id)

            if result is None:
                raise RuntimeError("Task processing timeout")

            if result.get("status") == "success":
                ServiceStatus.complete_task(message)
                return TaskResponse(
                    task_id=message.task_id,
                    task_status="completed",
                    save_video_path=str(save_video_path),
                )
            else:
                error_msg = result.get("error", "Inference failed")
                ServiceStatus.record_failed_task(message, error=error_msg)
                raise RuntimeError(error_msg)

        except Exception as e:
            logger.error(f"Task {message.task_id} processing failed: {str(e)}")
            ServiceStatus.record_failed_task(message, error=str(e))
            raise
