from __future__ import annotations

import random
import warnings
from collections import OrderedDict, defaultdict
from collections.abc import Iterator, Sequence
from typing import Any, Union, cast

import cv2
import numpy as np

from albumentations import random_utils
from albumentations.core.types import NUM_MULTI_CHANNEL_DIMENSIONS

from .bbox_utils import BboxParams, BboxProcessor
from .hub_mixin import HubMixin
from .keypoints_utils import KeypointParams, KeypointsProcessor
from .serialization import (
    SERIALIZABLE_REGISTRY,
    Serializable,
    get_shortest_class_fullname,
    instantiate_nonserializable,
)
from .transforms_interface import BasicTransform
from .utils import DataProcessor, format_args, get_shape

__all__ = [
    "BaseCompose",
    "Compose",
    "SomeOf",
    "OneOf",
    "OneOrOther",
    "BboxParams",
    "KeypointParams",
    "ReplayCompose",
    "Sequential",
    "SelectiveChannelTransform",
    "RandomOrder",
]

NUM_ONEOF_TRANSFORMS = 2
REPR_INDENT_STEP = 2

TransformType = Union[BasicTransform, "BaseCompose"]
TransformsSeqType = list[TransformType]

AVAILABLE_KEYS = ("image", "mask", "masks", "bboxes", "keypoints")
MASK_KEYS = (
    "mask",
    "masks",
)
# Keys related to image data
IMAGE_KEYS = ("image", "images")
CHECKED_SINGLE = ("image", "mask")
CHECKED_MULTI = ("masks", "images")
CHECK_BBOX_PARAM = ("bboxes",)
CHECK_KEYPOINTS_PARAM = ("keypoints",)


def get_transforms_dict(transforms: TransformsSeqType) -> dict[int, BasicTransform]:
    result = {}
    for transform in transforms:
        if isinstance(transform, BaseCompose):
            result.update(get_transforms_dict(transform.transforms))
        else:
            result[id(transform)] = transform
    return result


class BaseCompose(Serializable):
    """Base class for composing multiple transforms together.

    This class serves as a foundation for creating compositions of transforms
    in the Albumentations library. It provides basic functionality for
    managing a sequence of transforms and applying them to data.

    Attributes:
        transforms (List[TransformType]): A list of transforms to be applied.
        p (float): Probability of applying the compose. Should be in the range [0, 1].
        replay_mode (bool): If True, the compose is in replay mode.
        applied_in_replay (bool): Indicates if the compose was applied during replay.
        _additional_targets (Dict[str, str]): Additional targets for transforms.
        _available_keys (Set[str]): Set of available keys for data.
        processors (Dict[str, Union[BboxProcessor, KeypointsProcessor]]): Processors for specific data types.

    Args:
        transforms (TransformsSeqType): A sequence of transforms to compose.
        p (float): Probability of applying the compose.

    Raises:
        ValueError: If an invalid additional target is specified.

    Note:
        - Subclasses should implement the __call__ method to define how
          the composition is applied to data.
        - The class supports serialization and deserialization of transforms.
        - It provides methods for adding targets, setting deterministic behavior,
          and checking data validity post-transform.
    """

    _transforms_dict: dict[int, BasicTransform] | None = None
    check_each_transform: tuple[DataProcessor, ...] | None = None
    main_compose: bool = True

    def __init__(self, transforms: TransformsSeqType, p: float, mask_interpolation: int | None = None):
        if isinstance(transforms, (BaseCompose, BasicTransform)):
            warnings.warn(
                "transforms is single transform, but a sequence is expected! Transform will be wrapped into list.",
                stacklevel=2,
            )
            transforms = [transforms]

        self.transforms = transforms
        self.p = p

        self.replay_mode = False
        self.applied_in_replay = False
        self._additional_targets: dict[str, str] = {}
        self._available_keys: set[str] = set()
        self.processors: dict[str, BboxProcessor | KeypointsProcessor] = {}
        self._set_keys()
        self.set_mask_interpolation(mask_interpolation)

    def set_mask_interpolation(self, mask_interpolation: int | None) -> None:
        self.mask_interpolation = mask_interpolation
        self._set_mask_interpolation_recursive(self.transforms)

    def _set_mask_interpolation_recursive(self, transforms: TransformsSeqType) -> None:
        for transform in transforms:
            if isinstance(transform, BasicTransform):
                if hasattr(transform, "mask_interpolation") and self.mask_interpolation is not None:
                    transform.mask_interpolation = self.mask_interpolation
            elif isinstance(transform, BaseCompose):
                transform.set_mask_interpolation(self.mask_interpolation)

    def __iter__(self) -> Iterator[TransformType]:
        return iter(self.transforms)

    def __len__(self) -> int:
        return len(self.transforms)

    def __call__(self, *args: Any, **data: Any) -> dict[str, Any]:
        raise NotImplementedError

    def __getitem__(self, item: int) -> TransformType:
        return self.transforms[item]

    def __repr__(self) -> str:
        return self.indented_repr()

    @property
    def additional_targets(self) -> dict[str, str]:
        return self._additional_targets

    @property
    def available_keys(self) -> set[str]:
        return self._available_keys

    def indented_repr(self, indent: int = REPR_INDENT_STEP) -> str:
        args = {k: v for k, v in self.to_dict_private().items() if not (k.startswith("__") or k == "transforms")}
        repr_string = self.__class__.__name__ + "(["
        for t in self.transforms:
            repr_string += "\n"
            t_repr = t.indented_repr(indent + REPR_INDENT_STEP) if hasattr(t, "indented_repr") else repr(t)
            repr_string += " " * indent + t_repr + ","
        repr_string += "\n" + " " * (indent - REPR_INDENT_STEP) + f"], {format_args(args)})"
        return repr_string

    @classmethod
    def get_class_fullname(cls) -> str:
        return get_shortest_class_fullname(cls)

    @classmethod
    def is_serializable(cls) -> bool:
        return True

    def to_dict_private(self) -> dict[str, Any]:
        return {
            "__class_fullname__": self.get_class_fullname(),
            "p": self.p,
            "transforms": [t.to_dict_private() for t in self.transforms],
        }

    def get_dict_with_id(self) -> dict[str, Any]:
        return {
            "__class_fullname__": self.get_class_fullname(),
            "id": id(self),
            "params": None,
            "transforms": [t.get_dict_with_id() for t in self.transforms],
        }

    def add_targets(self, additional_targets: dict[str, str] | None) -> None:
        if additional_targets:
            for k, v in additional_targets.items():
                if k in self._additional_targets and v != self._additional_targets[k]:
                    raise ValueError(
                        f"Trying to overwrite existed additional targets. "
                        f"Key={k} Exists={self._additional_targets[k]} New value: {v}",
                    )
            self._additional_targets.update(additional_targets)
            for t in self.transforms:
                t.add_targets(additional_targets)
            for proc in self.processors.values():
                proc.add_targets(additional_targets)
        self._set_keys()

    def _set_keys(self) -> None:
        """Set _available_keys"""
        self._available_keys.update(self._additional_targets.keys())
        for t in self.transforms:
            self._available_keys.update(t.available_keys)
            if hasattr(t, "targets_as_params"):
                self._available_keys.update(t.targets_as_params)
        if self.processors:
            self._available_keys.update(["labels"])
            for proc in self.processors.values():
                if proc.default_data_name not in self._available_keys:  # if no transform to process this data
                    warnings.warn(
                        f"Got processor for {proc.default_data_name}, but no transform to process it.",
                        stacklevel=2,
                    )
                self._available_keys.update(proc.data_fields)
                if proc.params.label_fields:
                    self._available_keys.update(proc.params.label_fields)

    def set_deterministic(self, flag: bool, save_key: str = "replay") -> None:
        for t in self.transforms:
            t.set_deterministic(flag, save_key)

    def check_data_post_transform(self, data: Any) -> dict[str, Any]:
        if self.check_each_transform:
            image_shape = get_shape(data["image"])

            for proc in self.check_each_transform:
                for data_name in data:
                    if data_name in proc.data_fields or (
                        data_name in self._additional_targets
                        and self._additional_targets[data_name] in proc.data_fields
                    ):
                        data[data_name] = proc.filter(data[data_name], image_shape)
        return data


class Compose(BaseCompose, HubMixin):
    """Compose multiple transforms together and apply them sequentially to input data.

    This class allows you to chain multiple image augmentation transforms and apply them
    in a specified order. It also handles bounding box and keypoint transformations if
    the appropriate parameters are provided.

    Args:
        transforms (List[Union[BasicTransform, BaseCompose]]): A list of transforms to apply.
        bbox_params (Union[dict, BboxParams, None]): Parameters for bounding box transforms.
            Can be a dict of params or a BboxParams object. Default is None.
        keypoint_params (Union[dict, KeypointParams, None]): Parameters for keypoint transforms.
            Can be a dict of params or a KeypointParams object. Default is None.
        additional_targets (Dict[str, str], optional): A dictionary mapping additional target names
            to their types. For example, {'image2': 'image'}. Default is None.
        p (float): Probability of applying all transforms. Should be in range [0, 1]. Default is 1.0.
        is_check_shapes (bool): If True, checks consistency of shapes for image/mask/masks on each call.
            Disable only if you are sure about your data consistency. Default is True.
        strict (bool): If True, raises an error on unknown input keys. If False, ignores them. Default is True.
        return_params (bool): If True, returns parameters of applied transforms. Default is False.
        save_key (str): Key to save applied params if return_params is True. Default is 'applied_params'.
        mask_interpolation (int, optional): Interpolation method for mask transforms. When defined,
            it overrides the interpolation method specified in individual transforms. Default is None.

    Example:
        >>> import albumentations as A
        >>> transform = A.Compose([
        ...     A.RandomCrop(width=256, height=256),
        ...     A.HorizontalFlip(p=0.5),
        ...     A.RandomBrightnessContrast(p=0.2),
        ... ])
        >>> transformed = transform(image=image)

    Note:
        - The class checks the validity of input data and shapes if is_check_args and is_check_shapes are True.
        - When bbox_params or keypoint_params are provided, it sets up the corresponding processors.
        - The transform can handle additional targets specified in the additional_targets dictionary.
        - If return_params is True, it will return the parameters of applied transforms in the output.
    """

    def __init__(
        self,
        transforms: TransformsSeqType,
        bbox_params: dict[str, Any] | BboxParams | None = None,
        keypoint_params: dict[str, Any] | KeypointParams | None = None,
        additional_targets: dict[str, str] | None = None,
        p: float = 1.0,
        is_check_shapes: bool = True,
        strict: bool = True,
        return_params: bool = False,
        save_key: str = "applied_params",
        mask_interpolation: int | None = None,  # Add this parameter
    ):
        super().__init__(transforms=transforms, p=p, mask_interpolation=mask_interpolation)

        if bbox_params:
            if isinstance(bbox_params, dict):
                b_params = BboxParams(**bbox_params)
            elif isinstance(bbox_params, BboxParams):
                b_params = bbox_params
            else:
                msg = "unknown format of bbox_params, please use `dict` or `BboxParams`"
                raise ValueError(msg)
            self.processors["bboxes"] = BboxProcessor(b_params)

        if keypoint_params:
            if isinstance(keypoint_params, dict):
                k_params = KeypointParams(**keypoint_params)
            elif isinstance(keypoint_params, KeypointParams):
                k_params = keypoint_params
            else:
                msg = "unknown format of keypoint_params, please use `dict` or `KeypointParams`"
                raise ValueError(msg)
            self.processors["keypoints"] = KeypointsProcessor(k_params)

        for proc in self.processors.values():
            proc.ensure_transforms_valid(self.transforms)

        self.add_targets(additional_targets)
        if not self.transforms:  # if no transforms -> do nothing, all keys will be available
            self._available_keys.update(AVAILABLE_KEYS)

        self.is_check_args = True
        self.strict = strict

        self.is_check_shapes = is_check_shapes
        self.check_each_transform = tuple(  # processors that checks after each transform
            proc for proc in self.processors.values() if getattr(proc.params, "check_each_transform", False)
        )
        self._set_check_args_for_transforms(self.transforms)

        self.return_params = return_params

        if return_params:
            self.save_key = save_key
            self._available_keys.add(save_key)
            self._transforms_dict = get_transforms_dict(self.transforms)
            self.set_deterministic(True, save_key=save_key)

        self._set_processors_for_transforms(self.transforms)

    def _set_processors_for_transforms(self, transforms: TransformsSeqType) -> None:
        for transform in transforms:
            if isinstance(transform, BasicTransform):
                if hasattr(transform, "set_processors"):
                    transform.set_processors(self.processors)
            elif isinstance(transform, BaseCompose):
                self._set_processors_for_transforms(transform.transforms)

    def _set_check_args_for_transforms(self, transforms: TransformsSeqType) -> None:
        for transform in transforms:
            if isinstance(transform, BaseCompose):
                self._set_check_args_for_transforms(transform.transforms)
                transform.check_each_transform = self.check_each_transform
                transform.processors = self.processors
            if isinstance(transform, Compose):
                transform.disable_check_args_private()

    def disable_check_args_private(self) -> None:
        self.is_check_args = False
        self.strict = False
        self.main_compose = False

    def __call__(self, *args: Any, force_apply: bool = False, **data: Any) -> dict[str, Any]:
        if args:
            msg = "You have to pass data to augmentations as named arguments, for example: aug(image=image)"
            raise KeyError(msg)

        if not isinstance(force_apply, (bool, int)):
            msg = "force_apply must have bool or int type"
            raise TypeError(msg)

        if self.return_params and self.main_compose:
            data[self.save_key] = OrderedDict()

        need_to_run = force_apply or random.random() < self.p
        if not need_to_run:
            return data

        self.preprocess(data)

        for t in self.transforms:
            data = t(**data)
            data = self.check_data_post_transform(data)

        return self.postprocess(data)

    def run_with_params(self, *, params: dict[int, dict[str, Any]], **data: Any) -> dict[str, Any]:
        """Run transforms with given parameters. Available only for Compose with `return_params=True`."""
        if self._transforms_dict is None:
            raise RuntimeError("`run_with_params` is not available for Compose with `return_params=False`.")

        self.preprocess(data)

        for tr_id, param in params.items():
            tr = self._transforms_dict[tr_id]
            data = tr.apply_with_params(param, **data)
            data = self.check_data_post_transform(data)

        return self.postprocess(data)

    def preprocess(self, data: Any) -> None:
        if self.strict:
            for data_name in data:
                if data_name not in self._available_keys and data_name not in MASK_KEYS and data_name not in IMAGE_KEYS:
                    msg = f"Key {data_name} is not in available keys."
                    raise ValueError(msg)
        if self.is_check_args:
            self._check_args(**data)
        if self.main_compose:
            for p in self.processors.values():
                p.ensure_data_valid(data)
            for p in self.processors.values():
                p.preprocess(data)

    def postprocess(self, data: dict[str, Any]) -> dict[str, Any]:
        if self.main_compose:
            for p in self.processors.values():
                p.postprocess(data)
        return data

    def to_dict_private(self) -> dict[str, Any]:
        dictionary = super().to_dict_private()
        bbox_processor = self.processors.get("bboxes")
        keypoints_processor = self.processors.get("keypoints")
        dictionary.update(
            {
                "bbox_params": bbox_processor.params.to_dict_private() if bbox_processor else None,
                "keypoint_params": (keypoints_processor.params.to_dict_private() if keypoints_processor else None),
                "additional_targets": self.additional_targets,
                "is_check_shapes": self.is_check_shapes,
            },
        )
        return dictionary

    def get_dict_with_id(self) -> dict[str, Any]:
        dictionary = super().get_dict_with_id()
        bbox_processor = self.processors.get("bboxes")
        keypoints_processor = self.processors.get("keypoints")
        dictionary.update(
            {
                "bbox_params": bbox_processor.params.to_dict_private() if bbox_processor else None,
                "keypoint_params": (keypoints_processor.params.to_dict_private() if keypoints_processor else None),
                "additional_targets": self.additional_targets,
                "params": None,
                "is_check_shapes": self.is_check_shapes,
            },
        )
        return dictionary

    @staticmethod
    def _check_single_data(data_name: str, data: Any) -> tuple[int, int]:
        if not isinstance(data, np.ndarray):
            raise TypeError(f"{data_name} must be numpy array type")
        return data.shape[:2]

    @staticmethod
    def _check_masks_data(data_name: str, data: Any) -> tuple[int, int]:
        if isinstance(data, np.ndarray):
            if data.ndim not in [3, 4]:
                raise TypeError(f"{data_name} must be a 3D or 4D numpy array")
            return data.shape[1:3] if data.ndim == NUM_MULTI_CHANNEL_DIMENSIONS else data.shape[:2]
        if isinstance(data, Sequence):
            if not all(isinstance(m, np.ndarray) for m in data):
                raise TypeError(f"All elements in {data_name} must be numpy arrays")
            if any(m.ndim not in [2, 3] for m in data):
                raise TypeError(f"All masks in {data_name} must be 2D or 3D numpy arrays")
            return data[0].shape[:2]

        raise TypeError(f"{data_name} must be either a numpy array or a sequence of numpy arrays")

    @staticmethod
    def _check_multi_data(data_name: str, data: Any) -> tuple[int, int]:
        if not isinstance(data, Sequence) or not isinstance(data[0], np.ndarray):
            raise TypeError(f"{data_name} must be list of numpy arrays")
        return data[0].shape[:2]

    @staticmethod
    def _check_bbox_keypoint_params(internal_data_name: str, processors: dict[str, Any]) -> None:
        if internal_data_name in CHECK_BBOX_PARAM and processors.get("bboxes") is None:
            raise ValueError("bbox_params must be specified for bbox transformations")
        if internal_data_name in CHECK_KEYPOINTS_PARAM and processors.get("keypoints") is None:
            raise ValueError("keypoints_params must be specified for keypoint transformations")

    @staticmethod
    def _check_shapes(shapes: list[tuple[int, int]], is_check_shapes: bool) -> None:
        if is_check_shapes and shapes and shapes.count(shapes[0]) != len(shapes):
            raise ValueError(
                "Height and Width of image, mask or masks should be equal. You can disable shapes check "
                "by setting a parameter is_check_shapes=False of Compose class (do it only if you are sure "
                "about your data consistency).",
            )

    def _check_args(self, **kwargs: Any) -> None:
        shapes = []

        for data_name, data in kwargs.items():
            internal_data_name = self._additional_targets.get(data_name, data_name)

            if internal_data_name in CHECKED_SINGLE:
                shapes.append(self._check_single_data(data_name, data))

            if internal_data_name in CHECKED_MULTI and data is not None and len(data):
                if internal_data_name == "masks":
                    shapes.append(self._check_masks_data(data_name, data))
                else:
                    shapes.append(self._check_multi_data(data_name, data))

            self._check_bbox_keypoint_params(internal_data_name, self.processors)

        self._check_shapes(shapes, self.is_check_shapes)


class OneOf(BaseCompose):
    """Select one of transforms to apply. Selected transform will be called with `force_apply=True`.
    Transforms probabilities will be normalized to one 1, so in this case transforms probabilities works as weights.

    Args:
        transforms (list): list of transformations to compose.
        p (float): probability of applying selected transform. Default: 0.5.

    """

    def __init__(self, transforms: TransformsSeqType, p: float = 0.5):
        super().__init__(transforms, p)
        transforms_ps = [t.p for t in self.transforms]
        s = sum(transforms_ps)
        self.transforms_ps = [t / s for t in transforms_ps]

    def __call__(self, *args: Any, force_apply: bool = False, **data: Any) -> dict[str, Any]:
        if self.replay_mode:
            for t in self.transforms:
                data = t(**data)
            return data

        if self.transforms_ps and (force_apply or random.random() < self.p):
            idx: int = random_utils.choice(len(self.transforms), p=self.transforms_ps)
            t = self.transforms[idx]
            data = t(force_apply=True, **data)
        return data


class SomeOf(BaseCompose):
    """Apply a random subset of transforms from the given list.

    This class selects a specified number of transforms from the provided list
    and applies them to the input data. The selection can be done with or without
    replacement, allowing for the same transform to be potentially applied multiple times.

    Args:
        transforms (List[Union[BasicTransform, BaseCompose]]): A list of transforms to choose from.
        n (int): The number of transforms to apply. If greater than the number of
                 transforms and replace=False, it will be set to the number of transforms.
        replace (bool): Whether to sample transforms with replacement. Default is True.
        p (float): Probability of applying the selected transforms. Should be in the range [0, 1].
                   Default is 1.0.
        mask_interpolation (int, optional): Interpolation method for mask transforms.
                                            When defined, it overrides the interpolation method
                                            specified in individual transforms. Default is None.

    Note:
        - If `n` is greater than the number of transforms and `replace` is False,
          `n` will be set to the number of transforms with a warning.
        - The probabilities of individual transforms are used as weights for sampling.
        - When `replace` is True, the same transform can be selected multiple times.

    Example:
        >>> import albumentations as A
        >>> transform = A.SomeOf([
        ...     A.HorizontalFlip(p=1),
        ...     A.VerticalFlip(p=1),
        ...     A.RandomBrightnessContrast(p=1),
        ... ], n=2, replace=False, p=0.5)
        >>> # This will apply 2 out of the 3 transforms with 50% probability
    """

    def __init__(self, transforms: TransformsSeqType, n: int = 1, replace: bool = False, p: float = 1):
        super().__init__(transforms, p)
        self.n = n
        if not replace and n > len(self.transforms):
            self.n = len(self.transforms)
            warnings.warn(
                f"`n` is greater than number of transforms. `n` will be set to {self.n}.",
                UserWarning,
                stacklevel=2,
            )
        self.replace = replace
        transforms_ps = [t.p for t in self.transforms]
        s = sum(transforms_ps)
        self.transforms_ps = [t / s for t in transforms_ps]

    def __call__(self, *arg: Any, force_apply: bool = False, **data: Any) -> dict[str, Any]:
        if self.replay_mode:
            for t in self.transforms:
                data = t(**data)
                data = self.check_data_post_transform(data)
            return data

        if self.transforms_ps and (force_apply or random.random() < self.p):
            for i in self._get_idx():
                t = self.transforms[i]
                data = t(force_apply=True, **data)
                data = self.check_data_post_transform(data)
        return data

    def _get_idx(self) -> np.ndarray[np.int_]:
        idx = random_utils.choice(len(self.transforms), size=self.n, replace=self.replace, p=self.transforms_ps)
        idx.sort()
        return idx

    def to_dict_private(self) -> dict[str, Any]:
        dictionary = super().to_dict_private()
        dictionary.update({"n": self.n, "replace": self.replace})
        return dictionary


class RandomOrder(SomeOf):
    """Apply a random subset of transforms from the given list in a random order.

    The `RandomOrder` class allows you to select a specified number of transforms from a list and apply them
    to the input data in a random order. This is useful for creating more diverse augmentation pipelines
    where the order of transformations can vary, potentially leading to different results.

    Attributes:
        transforms (TransformsSeqType): A list of transformations to choose from.
        n (int): The number of transforms to apply. If `n` is greater than the number of available transforms
                 and `replace` is False, `n` will be set to the number of available transforms.
        replace (bool): Whether to sample transforms with replacement. If True, the same transform can be
                        selected multiple times. Default is False.
        p (float): Probability of applying the selected transforms. Should be in the range [0, 1]. Default is 1.0.

    Example:
        >>> import albumentations as A
        >>> transform = A.RandomOrder([
        ...     A.HorizontalFlip(p=1),
        ...     A.VerticalFlip(p=1),
        ...     A.RandomBrightnessContrast(p=1),
        ... ], n=2, replace=False, p=0.5)
        >>> # This will apply 2 out of the 3 transforms in a random order with 50% probability

    Note:
        - The probabilities of individual transforms are used as weights for sampling.
        - When `replace` is True, the same transform can be selected multiple times.
        - The random order of transforms will not be replayed in `ReplayCompose`.
    """

    def __init__(self, transforms: TransformsSeqType, n: int = 1, replace: bool = False, p: float = 1):
        super().__init__(transforms=transforms, n=n, replace=replace, p=p)

    def _get_idx(self) -> np.ndarray[np.int_]:
        return random_utils.choice(len(self.transforms), size=self.n, replace=self.replace, p=self.transforms_ps)


class OneOrOther(BaseCompose):
    """Select one or another transform to apply. Selected transform will be called with `force_apply=True`."""

    def __init__(
        self,
        first: TransformType | None = None,
        second: TransformType | None = None,
        transforms: TransformsSeqType | None = None,
        p: float = 0.5,
    ):
        if transforms is None:
            if first is None or second is None:
                msg = "You must set both first and second or set transforms argument."
                raise ValueError(msg)
            transforms = [first, second]
        super().__init__(transforms, p)
        if len(self.transforms) != NUM_ONEOF_TRANSFORMS:
            warnings.warn("Length of transforms is not equal to 2.", stacklevel=2)

    def __call__(self, *args: Any, force_apply: bool = False, **data: Any) -> dict[str, Any]:
        if self.replay_mode:
            for t in self.transforms:
                data = t(**data)
            return data

        if random.random() < self.p:
            return self.transforms[0](force_apply=True, **data)

        return self.transforms[-1](force_apply=True, **data)


class SelectiveChannelTransform(BaseCompose):
    """A transformation class to apply specified transforms to selected channels of an image.

    This class extends BaseCompose to allow selective application of transformations to
    specified image channels. It extracts the selected channels, applies the transformations,
    and then reinserts the transformed channels back into their original positions in the image.

    Parameters:
        transforms (TransformsSeqType):
            A sequence of transformations (from Albumentations) to be applied to the specified channels.
        channels (Sequence[int]):
            A sequence of integers specifying the indices of the channels to which the transforms should be applied.
        p (float):
            Probability that the transform will be applied; the default is 1.0 (always apply).

    Methods:
        __call__(*args, **kwargs):
            Applies the transforms to the image according to the specified channels.
            The input data should include 'image' key with the image array.

    Returns:
        dict[str, Any]: The transformed data dictionary, which includes the transformed 'image' key.
    """

    def __init__(
        self,
        transforms: TransformsSeqType,
        channels: Sequence[int] = (0, 1, 2),
        p: float = 1.0,
    ) -> None:
        super().__init__(transforms, p)
        self.channels = channels

    def __call__(self, *args: Any, force_apply: bool = False, **data: Any) -> dict[str, Any]:
        if force_apply or random.random() < self.p:
            image = data["image"]

            selected_channels = image[:, :, self.channels]
            sub_image = np.ascontiguousarray(selected_channels)

            for t in self.transforms:
                sub_image = t(image=sub_image)["image"]

            transformed_channels = cv2.split(sub_image)
            output_img = image.copy()

            for idx, channel in zip(self.channels, transformed_channels):
                output_img[:, :, idx] = channel

            data["image"] = np.ascontiguousarray(output_img)

        return data


class ReplayCompose(Compose):
    def __init__(
        self,
        transforms: TransformsSeqType,
        bbox_params: dict[str, Any] | BboxParams | None = None,
        keypoint_params: dict[str, Any] | KeypointParams | None = None,
        additional_targets: dict[str, str] | None = None,
        p: float = 1.0,
        is_check_shapes: bool = True,
        save_key: str = "replay",
    ):
        super().__init__(transforms, bbox_params, keypoint_params, additional_targets, p, is_check_shapes)
        self.set_deterministic(True, save_key=save_key)
        self.save_key = save_key
        self._available_keys.add(save_key)

    def __call__(self, *args: Any, force_apply: bool = False, **kwargs: Any) -> dict[str, Any]:
        kwargs[self.save_key] = defaultdict(dict)
        result = super().__call__(force_apply=force_apply, **kwargs)
        serialized = self.get_dict_with_id()
        self.fill_with_params(serialized, result[self.save_key])
        self.fill_applied(serialized)
        result[self.save_key] = serialized
        return result

    @staticmethod
    def replay(saved_augmentations: dict[str, Any], **kwargs: Any) -> dict[str, Any]:
        augs = ReplayCompose._restore_for_replay(saved_augmentations)
        return augs(force_apply=True, **kwargs)

    @staticmethod
    def _restore_for_replay(
        transform_dict: dict[str, Any],
        lambda_transforms: dict[str, Any] | None = None,
    ) -> TransformType:
        """Args:
        lambda_transforms (dict): A dictionary that contains lambda transforms, that
        is instances of the Lambda class.
            This dictionary is required when you are restoring a pipeline that contains lambda transforms. Keys
            in that dictionary should be named same as `name` arguments in respective lambda transforms from
            a serialized pipeline.

        """
        applied = transform_dict["applied"]
        params = transform_dict["params"]
        lmbd = instantiate_nonserializable(transform_dict, lambda_transforms)
        if lmbd:
            transform = lmbd
        else:
            name = transform_dict["__class_fullname__"]
            args = {k: v for k, v in transform_dict.items() if k not in ["__class_fullname__", "applied", "params"]}
            cls = SERIALIZABLE_REGISTRY[name]
            if "transforms" in args:
                args["transforms"] = [
                    ReplayCompose._restore_for_replay(t, lambda_transforms=lambda_transforms)
                    for t in args["transforms"]
                ]
            transform = cls(**args)

        transform = cast(BasicTransform, transform)
        if isinstance(transform, BasicTransform):
            transform.params = params
        transform.replay_mode = True
        transform.applied_in_replay = applied
        return transform

    def fill_with_params(self, serialized: dict[str, Any], all_params: Any) -> None:
        params = all_params.get(serialized.get("id"))
        serialized["params"] = params
        del serialized["id"]
        for transform in serialized.get("transforms", []):
            self.fill_with_params(transform, all_params)

    def fill_applied(self, serialized: dict[str, Any]) -> bool:
        if "transforms" in serialized:
            applied = [self.fill_applied(t) for t in serialized["transforms"]]
            serialized["applied"] = any(applied)
        else:
            serialized["applied"] = serialized.get("params") is not None
        return serialized["applied"]

    def to_dict_private(self) -> dict[str, Any]:
        dictionary = super().to_dict_private()
        dictionary.update({"save_key": self.save_key})
        return dictionary


class Sequential(BaseCompose):
    """Sequentially applies all transforms to targets.

    Note:
        This transform is not intended to be a replacement for `Compose`. Instead, it should be used inside `Compose`
        the same way `OneOf` or `OneOrOther` are used. For instance, you can combine `OneOf` with `Sequential` to
        create an augmentation pipeline that contains multiple sequences of augmentations and applies one randomly
        chose sequence to input data (see the `Example` section for an example definition of such pipeline).

    Example:
        >>> import albumentations as A
        >>> transform = A.Compose([
        >>>    A.OneOf([
        >>>        A.Sequential([
        >>>            A.HorizontalFlip(p=0.5),
        >>>            A.ShiftScaleRotate(p=0.5),
        >>>        ]),
        >>>        A.Sequential([
        >>>            A.VerticalFlip(p=0.5),
        >>>            A.RandomBrightnessContrast(p=0.5),
        >>>        ]),
        >>>    ], p=1)
        >>> ])

    """

    def __init__(self, transforms: TransformsSeqType, p: float = 0.5):
        super().__init__(transforms, p)

    def __call__(self, *args: Any, force_apply: bool = False, **data: Any) -> dict[str, Any]:
        if self.replay_mode or force_apply or random.random() < self.p:
            for t in self.transforms:
                data = t(**data)
                data = self.check_data_post_transform(data)
        return data