import gc
import os
import random
import numpy as np
import json
import uuid
from PIL import Image, PngImagePlugin
from datetime import datetime
from dataclasses import dataclass
from typing import Callable, Dict, Optional, Tuple


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 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 free_memory() -> None:
    torch.cuda.empty_cache()
    gc.collect()


def preprocess_prompt(
    style_dict,
    style_name: str,
    positive: str,
    negative: str = "",
    add_style: bool = True,
) -> Tuple[str, str]:
    p, n = style_dict.get(style_name, style_dict["(None)"])

    if add_style and positive.strip():
        formatted_positive = p.format(prompt=positive)
    else:
        formatted_positive = positive

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

    return formatted_positive, combined_negative



def get_random_line_from_file(file_path: str) -> str:
    with open(file_path, "r") as file:
        lines = file.readlines()
        if not lines:
            return ""
        return random.choice(lines).strip()

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

from PIL import Image
import requests
from io import BytesIO
def save_image_replicate(image_url, metadata, output_dir, is_colab):
    if 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)
    response = requests.get(image_url)
    image = Image.open(BytesIO(response.content))
    filepath = os.path.join(output_dir, filename)
    metadata_str = json.dumps(metadata)
    info = PngImagePlugin.PngInfo()
    info.add_text("metadata", metadata_str)
    image.save(filepath, "PNG", pnginfo=info)
    return filepath

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("metadata", metadata_str)
    image.save(filepath, "PNG", pnginfo=info)
    return filepath