"""LoRA training service — train custom LoRA models from reference images.

Wraps Kohya's sd-scripts for LoRA training with sensible defaults for
character LoRAs on SD 1.5 / RealisticVision. Manages the full pipeline:
dataset preparation, config generation, training launch, and output handling.

Requirements (installed automatically on first use):
  - kohya sd-scripts (cloned from GitHub)
  - accelerate, lion-pytorch, prodigy-optimizer
"""

from __future__ import annotations

import asyncio
import json
import logging
import os
import shutil
import subprocess
import sys
import time
import uuid
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any

logger = logging.getLogger(__name__)

IS_HF_SPACES = os.environ.get("HF_SPACES") == "1" or os.environ.get("SPACE_ID") is not None

if IS_HF_SPACES:
    TRAINING_BASE_DIR = Path("/app/data/training")
    LORA_OUTPUT_DIR = Path("/app/data/loras")
else:
    TRAINING_BASE_DIR = Path("D:/AI automation/content_engine/training")
    LORA_OUTPUT_DIR = Path("D:/ComfyUI/Models/Lora")

SD_SCRIPTS_DIR = TRAINING_BASE_DIR / "sd-scripts"


def _default_base_model() -> str:
    """Get default base model path based on environment."""
    if IS_HF_SPACES:
        return "/app/models/realisticVisionV51_v51VAE.safetensors"
    return "D:/ComfyUI/Models/StableDiffusion/realisticVisionV51_v51VAE.safetensors"


@dataclass
class TrainingConfig:
    """Configuration for a LoRA training job."""

    name: str
    base_model: str = ""  # Set in __post_init__
    resolution: int = 512
    train_batch_size: int = 1
    num_epochs: int = 10
    learning_rate: float = 1e-4
    network_rank: int = 32  # LoRA rank (dim)
    network_alpha: int = 16
    optimizer: str = "AdamW8bit"  # AdamW8bit, Lion, Prodigy
    lr_scheduler: str = "cosine_with_restarts"
    max_train_steps: int | None = None  # If set, overrides epochs
    save_every_n_epochs: int = 2
    clip_skip: int = 1
    mixed_precision: str = "fp16"
    seed: int = 42
    caption_extension: str = ".txt"
    trigger_word: str = ""
    extra_args: dict[str, Any] = field(default_factory=dict)

    def __post_init__(self):
        if not self.base_model:
            self.base_model = _default_base_model()


@dataclass
class TrainingJob:
    """Tracks state of a running or completed training job."""

    id: str
    name: str
    config: TrainingConfig
    status: str = "pending"  # pending, preparing, training, completed, failed
    progress: float = 0.0
    current_epoch: int = 0
    total_epochs: int = 0
    current_step: int = 0
    total_steps: int = 0
    loss: float | None = None
    started_at: float | None = None
    completed_at: float | None = None
    output_path: str | None = None
    error: str | None = None
    log_lines: list[str] = field(default_factory=list)


class LoRATrainer:
    """Manages LoRA training jobs using Kohya sd-scripts."""

    def __init__(self):
        self._jobs: dict[str, TrainingJob] = {}
        self._processes: dict[str, asyncio.subprocess.Process] = {}
        TRAINING_BASE_DIR.mkdir(parents=True, exist_ok=True)

    @property
    def sd_scripts_installed(self) -> bool:
        return (SD_SCRIPTS_DIR / "train_network.py").exists()

    async def install_sd_scripts(self) -> str:
        """Clone and set up Kohya sd-scripts. Returns status message."""
        if self.sd_scripts_installed:
            return "sd-scripts already installed"

        SD_SCRIPTS_DIR.parent.mkdir(parents=True, exist_ok=True)

        logger.info("Cloning kohya sd-scripts...")
        proc = await asyncio.create_subprocess_exec(
            "git", "clone", "--depth", "1",
            "https://github.com/kohya-ss/sd-scripts.git",
            str(SD_SCRIPTS_DIR),
            stdout=asyncio.subprocess.PIPE,
            stderr=asyncio.subprocess.PIPE,
        )
        stdout, stderr = await proc.communicate()
        if proc.returncode != 0:
            raise RuntimeError(f"Failed to clone sd-scripts: {stderr.decode()}")

        # Install requirements
        logger.info("Installing sd-scripts requirements...")
        proc = await asyncio.create_subprocess_exec(
            sys.executable, "-m", "pip", "install",
            "accelerate", "lion-pytorch", "prodigy-optimizer",
            "safetensors", "diffusers", "transformers",
            stdout=asyncio.subprocess.PIPE,
            stderr=asyncio.subprocess.PIPE,
        )
        await proc.communicate()

        logger.info("sd-scripts installation complete")
        return "sd-scripts installed successfully"

    def prepare_dataset(self, job_id: str, image_paths: list[str], trigger_word: str = "") -> Path:
        """Prepare a training dataset directory with proper structure.

        Creates: training/{job_id}/dataset/{num_repeats}_{trigger_word}/
        Each image gets a caption file with the trigger word.
        """
        dataset_dir = TRAINING_BASE_DIR / job_id / "dataset"
        # Convention: {repeats}_{concept_name}
        repeats = 10
        concept_dir = dataset_dir / f"{repeats}_{trigger_word or 'character'}"
        concept_dir.mkdir(parents=True, exist_ok=True)

        for img_path in image_paths:
            src = Path(img_path)
            if not src.exists():
                logger.warning("Image not found: %s", img_path)
                continue
            dst = concept_dir / src.name
            shutil.copy2(src, dst)

            # Create caption file
            caption_file = dst.with_suffix(".txt")
            caption_file.write_text(trigger_word or "")

        return dataset_dir

    async def start_training(self, config: TrainingConfig, image_paths: list[str]) -> str:
        """Start a LoRA training job. Returns the job ID."""
        job_id = str(uuid.uuid4())[:8]

        if not self.sd_scripts_installed:
            await self.install_sd_scripts()

        job = TrainingJob(
            id=job_id,
            name=config.name,
            config=config,
            status="preparing",
            total_epochs=config.num_epochs,
        )
        self._jobs[job_id] = job

        # Prepare dataset
        try:
            dataset_dir = self.prepare_dataset(job_id, image_paths, config.trigger_word)
        except Exception as e:
            job.status = "failed"
            job.error = f"Dataset preparation failed: {e}"
            return job_id

        # Create output directory
        output_dir = TRAINING_BASE_DIR / job_id / "output"
        output_dir.mkdir(parents=True, exist_ok=True)

        # Build training command
        cmd = self._build_training_command(config, dataset_dir, output_dir)
        job.log_lines.append(f"Command: {' '.join(cmd)}")

        # Launch training process
        job.status = "training"
        job.started_at = time.time()

        asyncio.create_task(self._run_training(job_id, cmd, output_dir, config))

        return job_id

    def _build_training_command(
        self, config: TrainingConfig, dataset_dir: Path, output_dir: Path
    ) -> list[str]:
        """Build the training command for Kohya sd-scripts."""
        cmd = [
            sys.executable,
            str(SD_SCRIPTS_DIR / "train_network.py"),
            f"--pretrained_model_name_or_path={config.base_model}",
            f"--train_data_dir={dataset_dir}",
            f"--output_dir={output_dir}",
            f"--output_name={config.name}",
            f"--resolution={config.resolution}",
            f"--train_batch_size={config.train_batch_size}",
            f"--max_train_epochs={config.num_epochs}",
            f"--learning_rate={config.learning_rate}",
            f"--network_module=networks.lora",
            f"--network_dim={config.network_rank}",
            f"--network_alpha={config.network_alpha}",
            f"--optimizer_type={config.optimizer}",
            f"--lr_scheduler={config.lr_scheduler}",
            f"--save_every_n_epochs={config.save_every_n_epochs}",
            f"--clip_skip={config.clip_skip}",
            f"--mixed_precision={config.mixed_precision}",
            f"--seed={config.seed}",
            f"--caption_extension={config.caption_extension}",
            "--cache_latents",
            "--enable_bucket",
            "--xformers",
            "--save_model_as=safetensors",
        ]

        if config.max_train_steps:
            cmd.append(f"--max_train_steps={config.max_train_steps}")

        return cmd

    async def _run_training(
        self, job_id: str, cmd: list[str], output_dir: Path, config: TrainingConfig
    ):
        """Run the training process and monitor progress."""
        job = self._jobs[job_id]
        try:
            proc = await asyncio.create_subprocess_exec(
                *cmd,
                stdout=asyncio.subprocess.PIPE,
                stderr=asyncio.subprocess.STDOUT,
                cwd=str(SD_SCRIPTS_DIR),
            )
            self._processes[job_id] = proc

            # Read output lines and parse progress
            async for line_bytes in proc.stdout:
                line = line_bytes.decode("utf-8", errors="replace").strip()
                if not line:
                    continue
                job.log_lines.append(line)
                # Keep last 200 lines
                if len(job.log_lines) > 200:
                    job.log_lines = job.log_lines[-200:]

                # Parse progress from Kohya output
                if "epoch" in line.lower() and "/" in line:
                    try:
                        # Look for patterns like "epoch 3/10"
                        parts = line.lower().split("epoch")
                        if len(parts) > 1:
                            ep_part = parts[1].strip().split()[0]
                            if "/" in ep_part:
                                current, total = ep_part.split("/")
                                job.current_epoch = int(current)
                                job.total_epochs = int(total)
                                job.progress = job.current_epoch / max(job.total_epochs, 1)
                    except (ValueError, IndexError):
                        pass

                if "loss=" in line or "loss:" in line:
                    try:
                        loss_str = line.split("loss")[1].strip("=: ").split()[0].strip(",")
                        job.loss = float(loss_str)
                    except (ValueError, IndexError):
                        pass

                if "steps:" in line.lower() or "step " in line.lower():
                    try:
                        import re
                        step_match = re.search(r"(\d+)/(\d+)", line)
                        if step_match:
                            job.current_step = int(step_match.group(1))
                            job.total_steps = int(step_match.group(2))
                            if job.total_steps > 0:
                                job.progress = job.current_step / job.total_steps
                    except (ValueError, IndexError):
                        pass

            await proc.wait()

            if proc.returncode == 0:
                job.status = "completed"
                job.progress = 1.0
                job.completed_at = time.time()

                # Find the output LoRA file and copy to ComfyUI
                lora_file = output_dir / f"{config.name}.safetensors"
                if lora_file.exists():
                    dest = LORA_OUTPUT_DIR / lora_file.name
                    LORA_OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
                    shutil.copy2(lora_file, dest)
                    job.output_path = str(dest)
                    logger.info("Training complete! LoRA saved to %s", dest)
                else:
                    # Check for epoch-saved versions
                    for f in sorted(output_dir.glob("*.safetensors")):
                        dest = LORA_OUTPUT_DIR / f.name
                        shutil.copy2(f, dest)
                        job.output_path = str(dest)
                    logger.info("Training complete! Output in %s", output_dir)
            else:
                job.status = "failed"
                job.error = f"Training process exited with code {proc.returncode}"
                logger.error("Training failed: %s", job.error)

        except Exception as e:
            job.status = "failed"
            job.error = str(e)
            logger.error("Training error: %s", e, exc_info=True)
        finally:
            self._processes.pop(job_id, None)

    def get_job(self, job_id: str) -> TrainingJob | None:
        return self._jobs.get(job_id)

    def list_jobs(self) -> list[TrainingJob]:
        return list(self._jobs.values())

    async def cancel_job(self, job_id: str) -> bool:
        """Cancel a running training job."""
        proc = self._processes.get(job_id)
        if proc:
            proc.terminate()
            self._processes.pop(job_id, None)
            job = self._jobs.get(job_id)
            if job:
                job.status = "failed"
                job.error = "Cancelled by user"
            return True
        return False