import random
from copy import deepcopy
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union, cast
from warnings import warn

import cv2
import numpy as np
from pydantic import BaseModel, ConfigDict, Field
from typing_extensions import Annotated

from albumentations.core.validation import ValidatedTransformMeta

from .serialization import Serializable, SerializableMeta, get_shortest_class_fullname
from .types import (
    BoxInternalType,
    BoxType,
    ColorType,
    KeypointInternalType,
    KeypointType,
    Targets,
)
from .utils import format_args

__all__ = ["BasicTransform", "DualTransform", "ImageOnlyTransform", "NoOp", "ReferenceBasedTransform"]

PAIR = 2


class Interpolation:
    def __init__(self, downscale: int = cv2.INTER_NEAREST, upscale: int = cv2.INTER_NEAREST):
        self.downscale = downscale
        self.upscale = upscale


class BaseTransformInitSchema(BaseModel):
    model_config = ConfigDict(arbitrary_types_allowed=True)
    always_apply: bool = Field(default=False, description="Always apply the transform")
    p: Annotated[float, Field(default=0.5, description="Probability of applying the transform", ge=0, le=1)]


class CombinedMeta(SerializableMeta, ValidatedTransformMeta):
    pass


class BasicTransform(Serializable, metaclass=CombinedMeta):
    # `_targets` defines the types of targets (e.g., image, mask) that the transform can be applied to.
    _targets: Union[Tuple[Targets, ...], Targets]
    call_backup = None
    interpolation: int
    fill_value: ColorType
    mask_fill_value: Optional[ColorType]
    # replay mode params
    deterministic: bool = False
    save_key = "replay"
    replay_mode = False
    applied_in_replay = False

    class InitSchema(BaseTransformInitSchema):
        pass

    def __init__(self, always_apply: bool = False, p: float = 0.5):
        self.p = p
        self.always_apply = always_apply
        self._additional_targets: Dict[str, str] = {}
        # replay mode params
        self.params: Dict[Any, Any] = {}

    def __call__(self, *args: Any, force_apply: bool = False, **kwargs: Any) -> Any:
        if args:
            msg = "You have to pass data to augmentations as named arguments, for example: aug(image=image)"
            raise KeyError(msg)
        if self.replay_mode:
            if self.applied_in_replay:
                return self.apply_with_params(self.params, **kwargs)

            return kwargs

        if force_apply or self.always_apply or (random.random() < self.p):
            params = self.get_params()

            if self.targets_as_params:
                if not all(key in kwargs for key in self.targets_as_params):
                    msg = f"{self.__class__.__name__} requires {self.targets_as_params}"
                    raise ValueError(msg)

                targets_as_params = {k: kwargs[k] for k in self.targets_as_params}
                params_dependent_on_targets = self.get_params_dependent_on_targets(targets_as_params)
                params.update(params_dependent_on_targets)
            if self.deterministic:
                kwargs[self.save_key][id(self)] = deepcopy(params)
            return self.apply_with_params(params, **kwargs)

        return kwargs

    def apply_with_params(self, params: Dict[str, Any], *args: Any, **kwargs: Any) -> Dict[str, Any]:
        """Apply transforms with parameters."""
        params = self.update_params(params, **kwargs)
        res = {}
        for key, arg in kwargs.items():
            if arg is not None:
                target_function = self._get_target_function(key)
                target_dependencies = {k: kwargs[k] for k in self.target_dependence.get(key, [])}
                res[key] = target_function(arg, **dict(params, **target_dependencies))
            else:
                res[key] = None
        return res

    def set_deterministic(self, flag: bool, save_key: str = "replay") -> "BasicTransform":
        """Set transform to be deterministic."""
        if save_key == "params":
            msg = "params save_key is reserved"
            raise KeyError(msg)

        self.deterministic = flag
        if self.deterministic and self.targets_as_params:
            warn(
                self.get_class_fullname() + " could work incorrectly in ReplayMode for other input data"
                " because its' params depend on targets.",
            )
        self.save_key = save_key
        return self

    def __repr__(self) -> str:
        state = self.get_base_init_args()
        state.update(self.get_transform_init_args())
        return f"{self.__class__.__name__}({format_args(state)})"

    def _get_target_function(self, key: str) -> Callable[..., Any]:
        """Returns function to process target"""
        transform_key = key
        if key in self._additional_targets:
            transform_key = self._additional_targets.get(key, key)

        return self.targets.get(transform_key, lambda x, **p: x)

    def apply(self, img: np.ndarray, *args: Any, **params: Any) -> np.ndarray:
        """Apply transform on image."""
        raise NotImplementedError

    def get_params(self) -> Dict[str, Any]:
        """Returns parameters independent of input"""
        return {}

    @property
    def targets(self) -> Dict[str, Callable[..., Any]]:
        # mapping for targets and methods for which they depend
        # for example:
        # >>  {"image": self.apply}
        # >>  {"masks": self.apply_to_masks}
        raise NotImplementedError

    def update_params(self, params: Dict[str, Any], **kwargs: Any) -> Dict[str, Any]:
        """Update parameters with transform specific params"""
        if hasattr(self, "interpolation"):
            params["interpolation"] = self.interpolation
        if hasattr(self, "fill_value"):
            params["fill_value"] = self.fill_value
        if hasattr(self, "mask_fill_value"):
            params["mask_fill_value"] = self.mask_fill_value
        params.update({"cols": kwargs["image"].shape[1], "rows": kwargs["image"].shape[0]})
        return params

    @property
    def target_dependence(self) -> Dict[str, Any]:
        return {}

    def add_targets(self, additional_targets: Dict[str, str]) -> None:
        """Add targets to transform them the same way as one of existing targets
        ex: {'target_image': 'image'}
        ex: {'obj1_mask': 'mask', 'obj2_mask': 'mask'}
        by the way you must have at least one object with key 'image'

        Args:
            additional_targets (dict): keys - new target name, values - old target name. ex: {'image2': 'image'}

        """
        self._additional_targets = {**self._additional_targets, **additional_targets}

    @property
    def targets_as_params(self) -> List[str]:
        """Targets used to get params"""
        return []

    def get_params_dependent_on_targets(self, params: Dict[str, Any]) -> Dict[str, Any]:
        """Returns parameters dependent on targets.
        Dependent target is defined in `self.targets_as_params`
        """
        raise NotImplementedError(
            "Method get_params_dependent_on_targets is not implemented in class " + self.__class__.__name__,
        )

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

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

    def get_transform_init_args_names(self) -> Tuple[str, ...]:
        """Returns names of arguments that are used in __init__ method of the transform"""
        msg = f"Class {self.get_class_fullname()} is not serializable because the `get_transform_init_args_names` "
        "method is not implemented"
        raise NotImplementedError(msg)

    def get_base_init_args(self) -> Dict[str, Any]:
        """Returns base init args - always_apply and p"""
        return {"always_apply": self.always_apply, "p": self.p}

    def get_transform_init_args(self) -> Dict[str, Any]:
        return {k: getattr(self, k) for k in self.get_transform_init_args_names()}

    def to_dict_private(self) -> Dict[str, Any]:
        state = {"__class_fullname__": self.get_class_fullname()}
        state.update(self.get_base_init_args())
        state.update(self.get_transform_init_args())

        return state

    def get_dict_with_id(self) -> Dict[str, Any]:
        d = self.to_dict_private()
        d["id"] = id(self)
        return d


class DualTransform(BasicTransform):
    """A base class for transformations that should be applied both to an image and its corresponding properties
    such as masks, bounding boxes, and keypoints. This class ensures that when a transform is applied to an image,
    all associated entities are transformed accordingly to maintain consistency between the image and its annotations.

    Properties:
        targets (Dict[str, Callable[..., Any]]): Defines the types of targets (e.g., image, mask, bboxes, keypoints)
            that the transform should be applied to and maps them to the corresponding methods.

    Methods:
        apply_to_bbox(bbox: BoxInternalType, *args: Any, **params: Any) -> BoxInternalType:
            Applies the transform to a single bounding box. Should be implemented in the subclass.

        apply_to_keypoint(keypoint: KeypointInternalType, *args: Any, **params: Any) -> KeypointInternalType:
            Applies the transform to a single keypoint. Should be implemented in the subclass.

        apply_to_bboxes(bboxes: Sequence[BoxType], *args: Any, **params: Any) -> Sequence[BoxType]:
            Applies the transform to a list of bounding boxes. Delegates to `apply_to_bbox` for each bounding box.

        apply_to_keypoints(keypoints: Sequence[KeypointType], *args: Any, **params: Any) -> Sequence[KeypointType]:
            Applies the transform to a list of keypoints. Delegates to `apply_to_keypoint` for each keypoint.

        apply_to_mask(mask: np.ndarray, *args: Any, **params: Any) -> np.ndarray:
            Applies the transform specifically to a single mask.

        apply_to_masks(masks: Sequence[np.ndarray], **params: Any) -> List[np.ndarray]:
            Applies the transform to a list of masks. Delegates to `apply_to_mask` for each mask.

    Note:
        This class is intended to be subclassed and should not be used directly. Subclasses are expected to
        implement the specific logic for each type of target (e.g., image, mask, bboxes, keypoints) in the
        corresponding `apply_to_*` methods.

    """

    @property
    def targets(self) -> Dict[str, Callable[..., Any]]:
        return {
            "image": self.apply,
            "mask": self.apply_to_mask,
            "masks": self.apply_to_masks,
            "bboxes": self.apply_to_bboxes,
            "keypoints": self.apply_to_keypoints,
        }

    def apply_to_bbox(self, bbox: BoxInternalType, *args: Any, **params: Any) -> BoxInternalType:
        msg = f"Method apply_to_bbox is not implemented in class {self.__class__.__name__}"
        raise NotImplementedError(msg)

    def apply_to_keypoint(self, keypoint: KeypointInternalType, *args: Any, **params: Any) -> KeypointInternalType:
        msg = f"Method apply_to_keypoint is not implemented in class {self.__class__.__name__}"
        raise NotImplementedError(msg)

    def apply_to_global_label(self, label: np.ndarray, *args: Any, **params: Any) -> np.ndarray:
        msg = f"Method apply_to_global_label is not implemented in class {self.__class__.__name__}"
        raise NotImplementedError(msg)

    def apply_to_bboxes(self, bboxes: Sequence[BoxType], *args: Any, **params: Any) -> Sequence[BoxType]:
        return [
            self.apply_to_bbox(cast(BoxInternalType, tuple(cast(BoxInternalType, bbox[:4]))), **params)
            + tuple(bbox[4:])
            for bbox in bboxes
        ]

    def apply_to_keypoints(
        self,
        keypoints: Sequence[KeypointType],
        *args: Any,
        **params: Any,
    ) -> Sequence[KeypointType]:
        return [
            self.apply_to_keypoint(cast(KeypointInternalType, tuple(keypoint[:4])), **params) + tuple(keypoint[4:])
            for keypoint in keypoints
        ]

    def apply_to_mask(self, mask: np.ndarray, *args: Any, **params: Any) -> np.ndarray:
        return self.apply(mask, **{k: cv2.INTER_NEAREST if k == "interpolation" else v for k, v in params.items()})

    def apply_to_masks(self, masks: Sequence[np.ndarray], **params: Any) -> List[np.ndarray]:
        return [self.apply_to_mask(mask, **params) for mask in masks]

    def apply_to_global_labels(self, labels: Sequence[np.ndarray], **params: Any) -> List[np.ndarray]:
        return [self.apply_to_global_label(label, **params) for label in labels]


class ImageOnlyTransform(BasicTransform):
    """Transform applied to image only."""

    _targets = Targets.IMAGE

    @property
    def targets(self) -> Dict[str, Callable[..., Any]]:
        return {"image": self.apply}


class NoOp(DualTransform):
    """Identity transform (does nothing).

    Targets:
        image, mask, bboxes, keypoints, global_label
    """

    _targets = (Targets.IMAGE, Targets.MASK, Targets.BBOXES, Targets.KEYPOINTS, Targets.GLOBAL_LABEL)

    def apply_to_keypoint(self, keypoint: KeypointInternalType, **params: Any) -> KeypointInternalType:
        return keypoint

    def apply_to_bbox(self, bbox: BoxInternalType, **params: Any) -> BoxInternalType:
        return bbox

    def apply(self, img: np.ndarray, **params: Any) -> np.ndarray:
        return img

    def apply_to_mask(self, mask: np.ndarray, **params: Any) -> np.ndarray:
        return mask

    def apply_to_global_label(self, label: np.ndarray, **params: Any) -> np.ndarray:
        return label

    def get_transform_init_args_names(self) -> Tuple[str, ...]:
        return ()


class ReferenceBasedTransform(DualTransform):
    @property
    def targets(self) -> Dict[str, Callable[..., Any]]:
        return {
            "global_label": self.apply_to_global_label,
            "image": self.apply,
            "mask": self.apply_to_mask,
            "bboxes": self.apply_to_bboxes,
            "keypoints": self.apply_to_keypoints,
        }
