from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union, Tuple, Dict
import numpy as np
from modules import shared
from lib_controlnet.logging import logger
from lib_controlnet.enums import InputMode

from modules.api import api


def get_api_version() -> int:
    return 2


class ControlMode(Enum):
    """
    The improved guess mode.
    """

    BALANCED = "Balanced"
    PROMPT = "My prompt is more important"
    CONTROL = "ControlNet is more important"


class BatchOption(Enum):
    DEFAULT = "All ControlNet units for all images in a batch"
    SEPARATE = "Each ControlNet unit for each image in a batch"


class ResizeMode(Enum):
    """
    Resize modes for ControlNet input images.
    """

    RESIZE = "Just Resize"
    INNER_FIT = "Crop and Resize"
    OUTER_FIT = "Resize and Fill"

    def int_value(self):
        if self == ResizeMode.RESIZE:
            return 0
        elif self == ResizeMode.INNER_FIT:
            return 1
        elif self == ResizeMode.OUTER_FIT:
            return 2
        assert False, "NOTREACHED"


resize_mode_aliases = {
    'Inner Fit (Scale to Fit)': 'Crop and Resize',
    'Outer Fit (Shrink to Fit)': 'Resize and Fill',
    'Scale to Fit (Inner Fit)': 'Crop and Resize',
    'Envelope (Outer Fit)': 'Resize and Fill',
}


def resize_mode_from_value(value: Union[str, int, ResizeMode]) -> ResizeMode:
    if isinstance(value, str):
        return ResizeMode(resize_mode_aliases.get(value, value))
    elif isinstance(value, int):
        assert value >= 0
        if value == 3:  # 'Just Resize (Latent upscale)'
            return ResizeMode.RESIZE

        if value >= len(ResizeMode):
            logger.warning(f'Unrecognized ResizeMode int value {value}. Fall back to RESIZE.')
            return ResizeMode.RESIZE

        return [e for e in ResizeMode][value]
    else:
        return value


def control_mode_from_value(value: Union[str, int, ControlMode]) -> ControlMode:
    if isinstance(value, str):
        return ControlMode(value)
    elif isinstance(value, int):
        return [e for e in ControlMode][value]
    else:
        return value


def visualize_inpaint_mask(img):
    if img.ndim == 3 and img.shape[2] == 4:
        result = img.copy()
        mask = result[:, :, 3]
        mask = 255 - mask // 2
        result[:, :, 3] = mask
        return np.ascontiguousarray(result.copy())
    return img


def pixel_perfect_resolution(
        image: np.ndarray,
        target_H: int,
        target_W: int,
        resize_mode: ResizeMode,
) -> int:
    """
    Calculate the estimated resolution for resizing an image while preserving aspect ratio.

    The function first calculates scaling factors for height and width of the image based on the target
    height and width. Then, based on the chosen resize mode, it either takes the smaller or the larger
    scaling factor to estimate the new resolution.

    If the resize mode is OUTER_FIT, the function uses the smaller scaling factor, ensuring the whole image
    fits within the target dimensions, potentially leaving some empty space.

    If the resize mode is not OUTER_FIT, the function uses the larger scaling factor, ensuring the target
    dimensions are fully filled, potentially cropping the image.

    After calculating the estimated resolution, the function prints some debugging information.

    Args:
        image (np.ndarray): A 3D numpy array representing an image. The dimensions represent [height, width, channels].
        target_H (int): The target height for the image.
        target_W (int): The target width for the image.
        resize_mode (ResizeMode): The mode for resizing.

    Returns:
        int: The estimated resolution after resizing.
    """
    raw_H, raw_W, _ = image.shape

    k0 = float(target_H) / float(raw_H)
    k1 = float(target_W) / float(raw_W)

    if resize_mode == ResizeMode.OUTER_FIT:
        estimation = min(k0, k1) * float(min(raw_H, raw_W))
    else:
        estimation = max(k0, k1) * float(min(raw_H, raw_W))

    logger.debug(f"Pixel Perfect Computation:")
    logger.debug(f"resize_mode = {resize_mode}")
    logger.debug(f"raw_H = {raw_H}")
    logger.debug(f"raw_W = {raw_W}")
    logger.debug(f"target_H = {target_H}")
    logger.debug(f"target_W = {target_W}")
    logger.debug(f"estimation = {estimation}")

    return int(np.round(estimation))


InputImage = Union[np.ndarray, str]
InputImage = Union[Dict[str, InputImage], Tuple[InputImage, InputImage], InputImage]


@dataclass
class UiControlNetUnit:
    input_mode: InputMode = InputMode.SIMPLE
    use_preview_as_input: bool = False,
    batch_image_dir: str = '',
    batch_mask_dir: str = '',
    batch_input_gallery: list = [],
    batch_mask_gallery: list = [],
    generated_image: Optional[np.ndarray] = None,
    mask_image: Optional[np.ndarray] = None,
    enabled: bool = True
    module: str = "None"
    model: str = "None"
    weight: float = 1.0
    image: Optional[Union[InputImage, List[InputImage]]] = None
    resize_mode: Union[ResizeMode, int, str] = ResizeMode.INNER_FIT
    processor_res: int = -1
    threshold_a: float = -1
    threshold_b: float = -1
    guidance_start: float = 0.0
    guidance_end: float = 1.0
    pixel_perfect: bool = False
    control_mode: Union[ControlMode, int, str] = ControlMode.BALANCED

    @staticmethod
    def infotext_fields():
        """Fields that should be included in infotext.
        You should define a Gradio element with exact same name in ControlNetUiGroup
        as well, so that infotext can wire the value to correct field when pasting
        infotext.
        """
        return (
            "module",
            "model",
            "weight",
            "resize_mode",
            "processor_res",
            "threshold_a",
            "threshold_b",
            "guidance_start",
            "guidance_end",
            "pixel_perfect",
            "control_mode",
        )


# Backward Compatible
ControlNetUnit = UiControlNetUnit


def to_base64_nparray(encoding: str):
    """
    Convert a base64 image into the image type the extension uses
    """

    return np.array(api.decode_base64_to_image(encoding)).astype('uint8')


def get_max_models_num():
    """
    Fetch the maximum number of allowed ControlNet models.
    """

    max_models_num = shared.opts.data.get("control_net_unit_count", 3)
    return max_models_num