import gc
import os
import random
import numpy as np
import json
import torch
import uuid
from PIL import PngImagePlugin
from datetime import datetime
from dataclasses import dataclass
from typing import Callable, Dict, Optional, Tuple, Any
from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    AutoencoderKL,
)
from lpw_stable_diffusion_xl import SDXLLongPromptWeightingPipeline
import logging

MAX_SEED = np.iinfo(np.int32).max


@dataclass
class StyleConfig:
    prompt: str
    negative_prompt: str


def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    return seed


def seed_everything(seed: int) -> torch.Generator:
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    np.random.seed(seed)
    generator = torch.Generator()
    generator.manual_seed(seed)
    return generator


def parse_aspect_ratio(aspect_ratio: str) -> Optional[Tuple[int, int]]:
    if aspect_ratio == "Custom":
        return None
    width, height = aspect_ratio.split(" x ")
    return int(width), int(height)


def aspect_ratio_handler(
    aspect_ratio: str, custom_width: int, custom_height: int
) -> Tuple[int, int]:
    if aspect_ratio == "Custom":
        return custom_width, custom_height
    else:
        width, height = parse_aspect_ratio(aspect_ratio)
        return width, height


def get_scheduler(scheduler_config: Dict, name: str) -> Optional[Callable]:
    scheduler_factory_map = {
        "DPM++ 2M Karras": lambda: DPMSolverMultistepScheduler.from_config(
            scheduler_config, use_karras_sigmas=True
        ),
        "DPM++ SDE Karras": lambda: DPMSolverSinglestepScheduler.from_config(
            scheduler_config, use_karras_sigmas=True
        ),
        "DPM++ 2M SDE Karras": lambda: DPMSolverMultistepScheduler.from_config(
            scheduler_config, use_karras_sigmas=True, algorithm_type="sde-dpmsolver++"
        ),
        "Euler": lambda: EulerDiscreteScheduler.from_config(scheduler_config),
        "Euler a": lambda: EulerAncestralDiscreteScheduler.from_config(
            scheduler_config
        ),
        "DDIM": lambda: DDIMScheduler.from_config(scheduler_config),
    }
    return scheduler_factory_map.get(name, lambda: None)()


def free_memory() -> None:
    """Free up GPU and system memory."""
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
        torch.cuda.ipc_collect()
    gc.collect()


def preprocess_prompt(
    positive: str,
    negative: str = "",
    add_style: bool = True,
) -> Tuple[str, str]:
    formatted_positive = positive

    combined_negative = ""
    if negative.strip():
        if combined_negative:
            combined_negative += ", " + negative
        else:
            combined_negative = negative

    return formatted_positive, combined_negative


def common_upscale(
    samples: torch.Tensor,
    width: int,
    height: int,
    upscale_method: str,
) -> torch.Tensor:
    return torch.nn.functional.interpolate(
        samples, size=(height, width), mode=upscale_method
    )


def upscale(
    samples: torch.Tensor, upscale_method: str, scale_by: float
) -> torch.Tensor:
    width = round(samples.shape[3] * scale_by)
    height = round(samples.shape[2] * scale_by)
    return common_upscale(samples, width, height, upscale_method)


def preprocess_image_dimensions(width, height):
    if width % 8 != 0:
        width = width - (width % 8)
    if height % 8 != 0:
        height = height - (height % 8)
    return width, height


def save_image(image, metadata, output_dir, is_colab):
    if is_colab:
        current_time = datetime.now().strftime("%Y%m%d_%H%M%S")
        filename = f"image_{current_time}.png"   
    else:
        filename = str(uuid.uuid4()) + ".png"
    os.makedirs(output_dir, exist_ok=True)
    filepath = os.path.join(output_dir, filename)
    metadata_str = json.dumps(metadata)
    info = PngImagePlugin.PngInfo()
    info.add_text("parameters", metadata_str)
    image.save(filepath, "PNG", pnginfo=info)
    return filepath
    
    
def is_google_colab():
    try:
        import google.colab
        return True
    except:
        return False


def load_pipeline(model_name: str, device: torch.device, hf_token: Optional[str] = None, vae: Optional[AutoencoderKL] = None) -> Any:
    """Load the Stable Diffusion pipeline."""
    try:
        pipeline = (
            SDXLLongPromptWeightingPipeline.from_single_file
            if model_name.endswith(".safetensors")
            else SDXLLongPromptWeightingPipeline.from_pretrained
        )

        pipe = pipeline(
            model_name,
            vae=vae,
            torch_dtype=torch.float16,
            use_safetensors=True,
            add_watermarker=False
        )
        pipe.to(device)
        return pipe
    except Exception as e:
        logging.error(f"Failed to load pipeline: {str(e)}", exc_info=True)
        raise