import functools
import warnings
from collections import defaultdict
from typing import Any, Dict, Optional, Sequence, Tuple, Type, TypeVar, Union

import torch

from torchvision import datapoints
from torchvision.transforms.v2 import Transform

from torchvision.transforms.v2._utils import is_pure_tensor


T = TypeVar("T")


def _default_arg(value: T) -> T:
    return value


def _get_defaultdict(default: T) -> Dict[Any, T]:
    # This weird looking construct only exists, since `lambda`'s cannot be serialized by pickle.
    # If it were possible, we could replace this with `defaultdict(lambda: default)`
    return defaultdict(functools.partial(_default_arg, default))


class PermuteDimensions(Transform):
    _transformed_types = (is_pure_tensor, datapoints.Image, datapoints.Video)

    def __init__(self, dims: Union[Sequence[int], Dict[Type, Optional[Sequence[int]]]]) -> None:
        super().__init__()
        if not isinstance(dims, dict):
            dims = _get_defaultdict(dims)
        if torch.Tensor in dims and any(cls in dims for cls in [datapoints.Image, datapoints.Video]):
            warnings.warn(
                "Got `dims` values for `torch.Tensor` and either `datapoints.Image` or `datapoints.Video`. "
                "Note that a plain `torch.Tensor` will *not* be transformed by this (or any other transformation) "
                "in case a `datapoints.Image` or `datapoints.Video` is present in the input."
            )
        self.dims = dims

    def _transform(self, inpt: Any, params: Dict[str, Any]) -> torch.Tensor:
        dims = self.dims[type(inpt)]
        if dims is None:
            return inpt.as_subclass(torch.Tensor)
        return inpt.permute(*dims)


class TransposeDimensions(Transform):
    _transformed_types = (is_pure_tensor, datapoints.Image, datapoints.Video)

    def __init__(self, dims: Union[Tuple[int, int], Dict[Type, Optional[Tuple[int, int]]]]) -> None:
        super().__init__()
        if not isinstance(dims, dict):
            dims = _get_defaultdict(dims)
        if torch.Tensor in dims and any(cls in dims for cls in [datapoints.Image, datapoints.Video]):
            warnings.warn(
                "Got `dims` values for `torch.Tensor` and either `datapoints.Image` or `datapoints.Video`. "
                "Note that a plain `torch.Tensor` will *not* be transformed by this (or any other transformation) "
                "in case a `datapoints.Image` or `datapoints.Video` is present in the input."
            )
        self.dims = dims

    def _transform(self, inpt: Any, params: Dict[str, Any]) -> torch.Tensor:
        dims = self.dims[type(inpt)]
        if dims is None:
            return inpt.as_subclass(torch.Tensor)
        return inpt.transpose(*dims)

import functools
import warnings
from collections import defaultdict
from typing import Any, Dict, Optional, Sequence, Tuple, Type, TypeVar, Union

import torch

from torchvision import datapoints
from torchvision.transforms.v2 import Transform

from torchvision.transforms.v2.utils import is_pure_tensor


T = TypeVar("T")


def _default_arg(value: T) -> T:
    return value


def _get_defaultdict(default: T) -> Dict[Any, T]:
    # This weird looking construct only exists, since `lambda`'s cannot be serialized by pickle.
    # If it were possible, we could replace this with `defaultdict(lambda: default)`
    return defaultdict(functools.partial(_default_arg, default))


class PermuteDimensions(Transform):
    _transformed_types = (is_pure_tensor, datapoints.Image, datapoints.Video)

    def __init__(self, dims: Union[Sequence[int], Dict[Type, Optional[Sequence[int]]]]) -> None:
        super().__init__()
        if not isinstance(dims, dict):
            dims = _get_defaultdict(dims)
        if torch.Tensor in dims and any(cls in dims for cls in [datapoints.Image, datapoints.Video]):
            warnings.warn(
                "Got `dims` values for `torch.Tensor` and either `datapoints.Image` or `datapoints.Video`. "
                "Note that a plain `torch.Tensor` will *not* be transformed by this (or any other transformation) "
                "in case a `datapoints.Image` or `datapoints.Video` is present in the input."
            )
        self.dims = dims

    def _transform(self, inpt: Any, params: Dict[str, Any]) -> torch.Tensor:
        dims = self.dims[type(inpt)]
        if dims is None:
            return inpt.as_subclass(torch.Tensor)
        return inpt.permute(*dims)


class TransposeDimensions(Transform):
    _transformed_types = (is_pure_tensor, datapoints.Image, datapoints.Video)

    def __init__(self, dims: Union[Tuple[int, int], Dict[Type, Optional[Tuple[int, int]]]]) -> None:
        super().__init__()
        if not isinstance(dims, dict):
            dims = _get_defaultdict(dims)
        if torch.Tensor in dims and any(cls in dims for cls in [datapoints.Image, datapoints.Video]):
            warnings.warn(
                "Got `dims` values for `torch.Tensor` and either `datapoints.Image` or `datapoints.Video`. "
                "Note that a plain `torch.Tensor` will *not* be transformed by this (or any other transformation) "
                "in case a `datapoints.Image` or `datapoints.Video` is present in the input."
            )
        self.dims = dims

    def _transform(self, inpt: Any, params: Dict[str, Any]) -> torch.Tensor:
        dims = self.dims[type(inpt)]
        if dims is None:
            return inpt.as_subclass(torch.Tensor)
        return inpt.transpose(*dims)
