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from copy import copy
from typing import List, Any, Optional, Union, Tuple, Dict
import numpy as np

from modules import scripts, processing, shared
from modules.api import api
from .args import ControlNetUnit
from scripts import global_state
from scripts.logging import logger
from scripts.enums import (
    ResizeMode,
    BatchOption,  # noqa: F401
    ControlMode,  # noqa: F401
)
from scripts.supported_preprocessor import (
    Preprocessor,
    PreprocessorParameter,  # noqa: F401
)

import torch
import base64
import io
from modules.safe import unsafe_torch_load


def get_api_version() -> int:
    return 3


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 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("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))


def to_base64_nparray(encoding: str) -> np.ndarray:
    """
    Convert a base64 image into the image type the extension uses
    """

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


def get_all_units_in_processing(
    p: processing.StableDiffusionProcessing,
) -> List[ControlNetUnit]:
    """
    Fetch ControlNet processing units from a StableDiffusionProcessing.
    """

    return get_all_units(p.scripts, p.script_args)


def get_all_units(
    script_runner: scripts.ScriptRunner, script_args: List[Any]
) -> List[ControlNetUnit]:
    """
    Fetch ControlNet processing units from an existing script runner.
    Use this function to fetch units from the list of all scripts arguments.
    """

    cn_script = find_cn_script(script_runner)
    if cn_script:
        return get_all_units_from(script_args[cn_script.args_from : cn_script.args_to])

    return []


def get_all_units_from(script_args: List[Any]) -> List[ControlNetUnit]:
    """
    Fetch ControlNet processing units from ControlNet script arguments.
    Use `external_code.get_all_units` to fetch units from the list of all scripts arguments.
    """

    def is_stale_unit(script_arg: Any) -> bool:
        """Returns whether the script_arg is potentially an stale version of
        ControlNetUnit created before module reload."""
        return "ControlNetUnit" in type(script_arg).__name__ and not isinstance(
            script_arg, ControlNetUnit
        )

    def is_controlnet_unit(script_arg: Any) -> bool:
        """Returns whether the script_arg is ControlNetUnit or anything that
        can be treated like ControlNetUnit."""
        return isinstance(script_arg, (ControlNetUnit, dict)) or (
            hasattr(script_arg, "__dict__")
            and set(vars(ControlNetUnit()).keys()).issubset(
                set(vars(script_arg).keys())
            )
        )

    all_units = [
        to_processing_unit(script_arg)
        for script_arg in script_args
        if is_controlnet_unit(script_arg)
    ]
    if not all_units:
        logger.warning(
            "No ControlNetUnit detected in args. It is very likely that you are having an extension conflict."
            f"Here are args received by ControlNet: {script_args}."
        )
    if any(is_stale_unit(script_arg) for script_arg in script_args):
        logger.debug(
            "Stale version of ControlNetUnit detected. The ControlNetUnit received"
            "by ControlNet is created before the newest load of ControlNet extension."
            "They will still be used by ControlNet as long as they provide same fields"
            "defined in the newest version of ControlNetUnit."
        )

    return all_units


def get_single_unit_from(
    script_args: List[Any], index: int = 0
) -> Optional[ControlNetUnit]:
    """
    Fetch a single ControlNet processing unit from ControlNet script arguments.
    The list must not contain script positional arguments. It must only contain processing units.
    """

    i = 0
    while i < len(script_args) and index >= 0:
        if index == 0 and script_args[i] is not None:
            return to_processing_unit(script_args[i])
        i += 1

        index -= 1

    return None


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


def to_processing_unit(unit: Union[Dict, ControlNetUnit]) -> ControlNetUnit:
    """
    Convert different types to processing unit.
    """
    if isinstance(unit, dict):
        return ControlNetUnit.from_dict(unit)

    assert isinstance(unit, ControlNetUnit)
    return unit


def update_cn_script_in_processing(
    p: processing.StableDiffusionProcessing,
    cn_units: List[ControlNetUnit],
    **_kwargs,  # for backwards compatibility
):
    """
    Update the arguments of the ControlNet script in `p.script_args` in place, reading from `cn_units`.
    `cn_units` and its elements are not modified. You can call this function repeatedly, as many times as you want.
    Does not update `p.script_args` if any of the folling is true:
    - ControlNet is not present in `p.scripts`
    - `p.script_args` is not filled with script arguments for scripts that are processed before ControlNet
    """
    p.script_args = update_cn_script(p.scripts, p.script_args_value, cn_units)


def update_cn_script(
    script_runner: scripts.ScriptRunner,
    script_args: Union[Tuple[Any], List[Any]],
    cn_units: List[ControlNetUnit],
) -> Union[Tuple[Any], List[Any]]:
    """
    Returns: The updated `script_args` with given `cn_units` used as ControlNet
    script args.
    Does not update `script_args` if any of the folling is true:
    - ControlNet is not present in `script_runner`
    - `script_args` is not filled with script arguments for scripts that are
    processed before ControlNet
    """
    script_args_type = type(script_args)
    assert script_args_type in (tuple, list), script_args_type
    updated_script_args = list(copy(script_args))

    cn_script = find_cn_script(script_runner)

    if cn_script is None or len(script_args) < cn_script.args_from:
        return script_args

    # fill in remaining parameters to satisfy max models, just in case script needs it.
    max_models = shared.opts.data.get("control_net_unit_count", 3)
    cn_units = cn_units + [ControlNetUnit(enabled=False)] * max(
        max_models - len(cn_units), 0
    )

    cn_script_args_diff = 0
    for script in script_runner.alwayson_scripts:
        if script is cn_script:
            cn_script_args_diff = len(cn_units) - (
                cn_script.args_to - cn_script.args_from
            )
            updated_script_args[script.args_from : script.args_to] = cn_units
            script.args_to = script.args_from + len(cn_units)
        else:
            script.args_from += cn_script_args_diff
            script.args_to += cn_script_args_diff

    return script_args_type(updated_script_args)


def update_cn_script_in_place(
    script_runner: scripts.ScriptRunner,
    script_args: List[Any],
    cn_units: List[ControlNetUnit],
    **_kwargs,  # for backwards compatibility
):
    """
    @Deprecated(Raises assertion error if script_args passed in is Tuple)
    Update the arguments of the ControlNet script in `script_args` in place, reading from `cn_units`.
    `cn_units` and its elements are not modified. You can call this function repeatedly, as many times as you want.
    Does not update `script_args` if any of the folling is true:
    - ControlNet is not present in `script_runner`
    - `script_args` is not filled with script arguments for scripts that are processed before ControlNet
    """
    assert isinstance(script_args, list), type(script_args)

    cn_script = find_cn_script(script_runner)
    if cn_script is None or len(script_args) < cn_script.args_from:
        return

    # fill in remaining parameters to satisfy max models, just in case script needs it.
    max_models = shared.opts.data.get("control_net_unit_count", 3)
    cn_units = cn_units + [ControlNetUnit(enabled=False)] * max(
        max_models - len(cn_units), 0
    )

    cn_script_args_diff = 0
    for script in script_runner.alwayson_scripts:
        if script is cn_script:
            cn_script_args_diff = len(cn_units) - (
                cn_script.args_to - cn_script.args_from
            )
            script_args[script.args_from : script.args_to] = cn_units
            script.args_to = script.args_from + len(cn_units)
        else:
            script.args_from += cn_script_args_diff
            script.args_to += cn_script_args_diff


def get_models(update: bool = False) -> List[str]:
    """
    Fetch the list of available models.
    Each value is a valid candidate of `ControlNetUnit.model`.
    Keyword arguments:
    update -- Whether to refresh the list from disk. (default False)
    """

    if update:
        global_state.update_cn_models()

    return list(global_state.cn_models_names.values())


def get_modules(alias_names: bool = False) -> List[str]:
    """
    Fetch the list of available preprocessors.
    Each value is a valid candidate of `ControlNetUnit.module`.
    Keyword arguments:
    alias_names -- Whether to get the ui alias names instead of internal keys
    """
    return [
        (p.label if alias_names else p.name)
        for p in Preprocessor.get_sorted_preprocessors()
    ]


def get_modules_detail(alias_names: bool = False) -> Dict[str, Any]:
    """
    get the detail of all preprocessors including
    sliders: the slider config in Auto1111 webUI
    Keyword arguments:
    alias_names -- Whether to get the module detail with alias names instead of internal keys
    """

    _module_detail = {}
    _module_list = get_modules(False)
    _module_list_alias = get_modules(True)

    _output_list = _module_list if not alias_names else _module_list_alias
    for module_name in _output_list:
        preprocessor = Preprocessor.get_preprocessor(module_name)
        assert preprocessor is not None
        _module_detail[module_name] = dict(
            model_free=preprocessor.do_not_need_model,
            sliders=[
                s.api_json
                for s in (
                    preprocessor.slider_resolution,
                    preprocessor.slider_1,
                    preprocessor.slider_2,
                    preprocessor.slider_3,
                )
                if s.visible
            ],
        )

    return _module_detail


def find_cn_script(script_runner: scripts.ScriptRunner) -> Optional[scripts.Script]:
    """
    Find the ControlNet script in `script_runner`. Returns `None` if `script_runner` does not contain a ControlNet script.
    """

    if script_runner is None:
        return None

    for script in script_runner.alwayson_scripts:
        if is_cn_script(script):
            return script


def is_cn_script(script: scripts.Script) -> bool:
    """
    Determine whether `script` is a ControlNet script.
    """

    return script.title().lower() == "controlnet"


# TODO: Add model constraint
ControlNetUnit.cls_match_model = lambda model: True
ControlNetUnit.cls_match_module = (
    lambda module: Preprocessor.get_preprocessor(module) is not None
)
ControlNetUnit.cls_get_preprocessor = Preprocessor.get_preprocessor
ControlNetUnit.cls_decode_base64 = to_base64_nparray


def decode_base64(b: str) -> torch.Tensor:
    decoded_bytes = base64.b64decode(b)
    return unsafe_torch_load(io.BytesIO(decoded_bytes))


ControlNetUnit.cls_torch_load_base64 = decode_base64
ControlNetUnit.cls_logger = logger

logger.debug("ControlNetUnit initialized")

	from copy import copy
	from typing import List, Any, Optional, Union, Tuple, Dict
	import numpy as np

	from modules import scripts, processing, shared
	from modules.api import api
	from .args import ControlNetUnit
	from scripts import global_state
	from scripts.logging import logger
	from scripts.enums import (
	ResizeMode,
	BatchOption, # noqa: F401
	ControlMode, # noqa: F401
	)
	from scripts.supported_preprocessor import (
	Preprocessor,
	PreprocessorParameter, # noqa: F401
	)

	import torch
	import base64
	import io
	from modules.safe import unsafe_torch_load


	def get_api_version() -> int:
	return 3


	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 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("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))


	def to_base64_nparray(encoding: str) -> np.ndarray:
	"""
	Convert a base64 image into the image type the extension uses
	"""

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


	def get_all_units_in_processing(
	p: processing.StableDiffusionProcessing,
	) -> List[ControlNetUnit]:
	"""
	Fetch ControlNet processing units from a StableDiffusionProcessing.
	"""

	return get_all_units(p.scripts, p.script_args)


	def get_all_units(
	script_runner: scripts.ScriptRunner, script_args: List[Any]
	) -> List[ControlNetUnit]:
	"""
	Fetch ControlNet processing units from an existing script runner.
	Use this function to fetch units from the list of all scripts arguments.
	"""

	cn_script = find_cn_script(script_runner)
	if cn_script:
	return get_all_units_from(script_args[cn_script.args_from : cn_script.args_to])

	return []


	def get_all_units_from(script_args: List[Any]) -> List[ControlNetUnit]:
	"""
	Fetch ControlNet processing units from ControlNet script arguments.
	Use `external_code.get_all_units` to fetch units from the list of all scripts arguments.
	"""

	def is_stale_unit(script_arg: Any) -> bool:
	"""Returns whether the script_arg is potentially an stale version of
	ControlNetUnit created before module reload."""
	return "ControlNetUnit" in type(script_arg).__name__ and not isinstance(
	script_arg, ControlNetUnit
	)

	def is_controlnet_unit(script_arg: Any) -> bool:
	"""Returns whether the script_arg is ControlNetUnit or anything that
	can be treated like ControlNetUnit."""
	return isinstance(script_arg, (ControlNetUnit, dict)) or (
	hasattr(script_arg, "__dict__")
	and set(vars(ControlNetUnit()).keys()).issubset(
	set(vars(script_arg).keys())
	)
	)

	all_units = [
	to_processing_unit(script_arg)
	for script_arg in script_args
	if is_controlnet_unit(script_arg)
	]
	if not all_units:
	logger.warning(
	"No ControlNetUnit detected in args. It is very likely that you are having an extension conflict."
	f"Here are args received by ControlNet: {script_args}."
	)
	if any(is_stale_unit(script_arg) for script_arg in script_args):
	logger.debug(
	"Stale version of ControlNetUnit detected. The ControlNetUnit received"
	"by ControlNet is created before the newest load of ControlNet extension."
	"They will still be used by ControlNet as long as they provide same fields"
	"defined in the newest version of ControlNetUnit."
	)

	return all_units


	def get_single_unit_from(
	script_args: List[Any], index: int = 0
	) -> Optional[ControlNetUnit]:
	"""
	Fetch a single ControlNet processing unit from ControlNet script arguments.
	The list must not contain script positional arguments. It must only contain processing units.
	"""

	i = 0
	while i < len(script_args) and index >= 0:
	if index == 0 and script_args[i] is not None:
	return to_processing_unit(script_args[i])
	i += 1

	index -= 1

	return None


	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


	def to_processing_unit(unit: Union[Dict, ControlNetUnit]) -> ControlNetUnit:
	"""
	Convert different types to processing unit.
	"""
	if isinstance(unit, dict):
	return ControlNetUnit.from_dict(unit)

	assert isinstance(unit, ControlNetUnit)
	return unit


	def update_cn_script_in_processing(
	p: processing.StableDiffusionProcessing,
	cn_units: List[ControlNetUnit],
	**_kwargs, # for backwards compatibility
	):
	"""
	Update the arguments of the ControlNet script in `p.script_args` in place, reading from `cn_units`.
	`cn_units` and its elements are not modified. You can call this function repeatedly, as many times as you want.

	Does not update `p.script_args` if any of the folling is true:
	- ControlNet is not present in `p.scripts`
	- `p.script_args` is not filled with script arguments for scripts that are processed before ControlNet
	"""
	p.script_args = update_cn_script(p.scripts, p.script_args_value, cn_units)


	def update_cn_script(
	script_runner: scripts.ScriptRunner,
	script_args: Union[Tuple[Any], List[Any]],
	cn_units: List[ControlNetUnit],
	) -> Union[Tuple[Any], List[Any]]:
	"""
	Returns: The updated `script_args` with given `cn_units` used as ControlNet
	script args.

	Does not update `script_args` if any of the folling is true:
	- ControlNet is not present in `script_runner`
	- `script_args` is not filled with script arguments for scripts that are
	processed before ControlNet
	"""
	script_args_type = type(script_args)
	assert script_args_type in (tuple, list), script_args_type
	updated_script_args = list(copy(script_args))

	cn_script = find_cn_script(script_runner)

	if cn_script is None or len(script_args) < cn_script.args_from:
	return script_args

	# fill in remaining parameters to satisfy max models, just in case script needs it.
	max_models = shared.opts.data.get("control_net_unit_count", 3)
	cn_units = cn_units + [ControlNetUnit(enabled=False)] * max(
	max_models - len(cn_units), 0
	)

	cn_script_args_diff = 0
	for script in script_runner.alwayson_scripts:
	if script is cn_script:
	cn_script_args_diff = len(cn_units) - (
	cn_script.args_to - cn_script.args_from
	)
	updated_script_args[script.args_from : script.args_to] = cn_units
	script.args_to = script.args_from + len(cn_units)
	else:
	script.args_from += cn_script_args_diff
	script.args_to += cn_script_args_diff

	return script_args_type(updated_script_args)


	def update_cn_script_in_place(
	script_runner: scripts.ScriptRunner,
	script_args: List[Any],
	cn_units: List[ControlNetUnit],
	**_kwargs, # for backwards compatibility
	):
	"""
	@Deprecated(Raises assertion error if script_args passed in is Tuple)

	Update the arguments of the ControlNet script in `script_args` in place, reading from `cn_units`.
	`cn_units` and its elements are not modified. You can call this function repeatedly, as many times as you want.

	Does not update `script_args` if any of the folling is true:
	- ControlNet is not present in `script_runner`
	- `script_args` is not filled with script arguments for scripts that are processed before ControlNet
	"""
	assert isinstance(script_args, list), type(script_args)

	cn_script = find_cn_script(script_runner)
	if cn_script is None or len(script_args) < cn_script.args_from:
	return

	# fill in remaining parameters to satisfy max models, just in case script needs it.
	max_models = shared.opts.data.get("control_net_unit_count", 3)
	cn_units = cn_units + [ControlNetUnit(enabled=False)] * max(
	max_models - len(cn_units), 0
	)

	cn_script_args_diff = 0
	for script in script_runner.alwayson_scripts:
	if script is cn_script:
	cn_script_args_diff = len(cn_units) - (
	cn_script.args_to - cn_script.args_from
	)
	script_args[script.args_from : script.args_to] = cn_units
	script.args_to = script.args_from + len(cn_units)
	else:
	script.args_from += cn_script_args_diff
	script.args_to += cn_script_args_diff


	def get_models(update: bool = False) -> List[str]:
	"""
	Fetch the list of available models.
	Each value is a valid candidate of `ControlNetUnit.model`.

	Keyword arguments:
	update -- Whether to refresh the list from disk. (default False)
	"""

	if update:
	global_state.update_cn_models()

	return list(global_state.cn_models_names.values())


	def get_modules(alias_names: bool = False) -> List[str]:
	"""
	Fetch the list of available preprocessors.
	Each value is a valid candidate of `ControlNetUnit.module`.

	Keyword arguments:
	alias_names -- Whether to get the ui alias names instead of internal keys
	"""
	return [
	(p.label if alias_names else p.name)
	for p in Preprocessor.get_sorted_preprocessors()
	]


	def get_modules_detail(alias_names: bool = False) -> Dict[str, Any]:
	"""
	get the detail of all preprocessors including
	sliders: the slider config in Auto1111 webUI

	Keyword arguments:
	alias_names -- Whether to get the module detail with alias names instead of internal keys
	"""

	_module_detail = {}
	_module_list = get_modules(False)
	_module_list_alias = get_modules(True)

	_output_list = _module_list if not alias_names else _module_list_alias
	for module_name in _output_list:
	preprocessor = Preprocessor.get_preprocessor(module_name)
	assert preprocessor is not None
	_module_detail[module_name] = dict(
	model_free=preprocessor.do_not_need_model,
	sliders=[
	s.api_json
	for s in (
	preprocessor.slider_resolution,
	preprocessor.slider_1,
	preprocessor.slider_2,
	preprocessor.slider_3,
	)
	if s.visible
	],
	)

	return _module_detail


	def find_cn_script(script_runner: scripts.ScriptRunner) -> Optional[scripts.Script]:
	"""
	Find the ControlNet script in `script_runner`. Returns `None` if `script_runner` does not contain a ControlNet script.
	"""

	if script_runner is None:
	return None

	for script in script_runner.alwayson_scripts:
	if is_cn_script(script):
	return script


	def is_cn_script(script: scripts.Script) -> bool:
	"""
	Determine whether `script` is a ControlNet script.
	"""

	return script.title().lower() == "controlnet"


	# TODO: Add model constraint
	ControlNetUnit.cls_match_model = lambda model: True
	ControlNetUnit.cls_match_module = (
	lambda module: Preprocessor.get_preprocessor(module) is not None
	)
	ControlNetUnit.cls_get_preprocessor = Preprocessor.get_preprocessor
	ControlNetUnit.cls_decode_base64 = to_base64_nparray


	def decode_base64(b: str) -> torch.Tensor:
	decoded_bytes = base64.b64decode(b)
	return unsafe_torch_load(io.BytesIO(decoded_bytes))


	ControlNetUnit.cls_torch_load_base64 = decode_base64
	ControlNetUnit.cls_logger = logger

	logger.debug("ControlNetUnit initialized")