import contextlib
import decimal
import inspect
import math
import re
from unittest import mock

import numpy as np
import PIL.Image
import pytest

import torch
import torchvision.transforms.v2 as transforms
from common_utils import (
    assert_equal,
    assert_no_warnings,
    cache,
    cpu_and_cuda,
    freeze_rng_state,
    ignore_jit_no_profile_information_warning,
    make_bounding_box,
    make_detection_mask,
    make_image,
    make_image_pil,
    make_image_tensor,
    make_segmentation_mask,
    make_video,
    make_video_tensor,
    needs_cuda,
    set_rng_seed,
)

from torch import nn
from torch.testing import assert_close
from torch.utils._pytree import tree_map
from torch.utils.data import DataLoader, default_collate
from torchvision import datapoints

from torchvision.transforms._functional_tensor import _max_value as get_max_value
from torchvision.transforms.functional import pil_modes_mapping
from torchvision.transforms.v2 import functional as F
from torchvision.transforms.v2.functional._utils import _get_kernel, _register_kernel_internal


@pytest.fixture(autouse=True)
def fix_rng_seed():
    set_rng_seed(0)
    yield


def _to_tolerances(maybe_tolerance_dict):
    if not isinstance(maybe_tolerance_dict, dict):
        return dict(rtol=None, atol=None)

    tolerances = dict(rtol=0, atol=0)
    tolerances.update(maybe_tolerance_dict)
    return tolerances


def _check_kernel_cuda_vs_cpu(kernel, input, *args, rtol, atol, **kwargs):
    """Checks if the kernel produces closes results for inputs on GPU and CPU."""
    if input.device.type != "cuda":
        return

    input_cuda = input.as_subclass(torch.Tensor)
    input_cpu = input_cuda.to("cpu")

    with freeze_rng_state():
        actual = kernel(input_cuda, *args, **kwargs)
    with freeze_rng_state():
        expected = kernel(input_cpu, *args, **kwargs)

    assert_close(actual, expected, check_device=False, rtol=rtol, atol=atol)


@cache
def _script(obj):
    try:
        return torch.jit.script(obj)
    except Exception as error:
        name = getattr(obj, "__name__", obj.__class__.__name__)
        raise AssertionError(f"Trying to `torch.jit.script` '{name}' raised the error above.") from error


def _check_kernel_scripted_vs_eager(kernel, input, *args, rtol, atol, **kwargs):
    """Checks if the kernel is scriptable and if the scripted output is close to the eager one."""
    if input.device.type != "cpu":
        return

    kernel_scripted = _script(kernel)

    input = input.as_subclass(torch.Tensor)
    with ignore_jit_no_profile_information_warning():
        actual = kernel_scripted(input, *args, **kwargs)
    expected = kernel(input, *args, **kwargs)

    assert_close(actual, expected, rtol=rtol, atol=atol)


def _check_kernel_batched_vs_unbatched(kernel, input, *args, rtol, atol, **kwargs):
    """Checks if the kernel produces close results for batched and unbatched inputs."""
    unbatched_input = input.as_subclass(torch.Tensor)

    for batch_dims in [(2,), (2, 1)]:
        repeats = [*batch_dims, *[1] * input.ndim]

        actual = kernel(unbatched_input.repeat(repeats), *args, **kwargs)

        expected = kernel(unbatched_input, *args, **kwargs)
        # We can't directly call `.repeat()` on the output, since some kernel also return some additional metadata
        if isinstance(expected, torch.Tensor):
            expected = expected.repeat(repeats)
        else:
            tensor, *metadata = expected
            expected = (tensor.repeat(repeats), *metadata)

        assert_close(actual, expected, rtol=rtol, atol=atol)

    for degenerate_batch_dims in [(0,), (5, 0), (0, 5)]:
        degenerate_batched_input = torch.empty(
            degenerate_batch_dims + input.shape, dtype=input.dtype, device=input.device
        )

        output = kernel(degenerate_batched_input, *args, **kwargs)
        # Most kernels just return a tensor, but some also return some additional metadata
        if not isinstance(output, torch.Tensor):
            output, *_ = output

        assert output.shape[: -input.ndim] == degenerate_batch_dims


def check_kernel(
    kernel,
    input,
    *args,
    check_cuda_vs_cpu=True,
    check_scripted_vs_eager=True,
    check_batched_vs_unbatched=True,
    expect_same_dtype=True,
    **kwargs,
):
    initial_input_version = input._version

    output = kernel(input.as_subclass(torch.Tensor), *args, **kwargs)
    # Most kernels just return a tensor, but some also return some additional metadata
    if not isinstance(output, torch.Tensor):
        output, *_ = output

    # check that no inplace operation happened
    assert input._version == initial_input_version

    if expect_same_dtype:
        assert output.dtype == input.dtype
    assert output.device == input.device

    if check_cuda_vs_cpu:
        _check_kernel_cuda_vs_cpu(kernel, input, *args, **kwargs, **_to_tolerances(check_cuda_vs_cpu))

    if check_scripted_vs_eager:
        _check_kernel_scripted_vs_eager(kernel, input, *args, **kwargs, **_to_tolerances(check_scripted_vs_eager))

    if check_batched_vs_unbatched:
        _check_kernel_batched_vs_unbatched(kernel, input, *args, **kwargs, **_to_tolerances(check_batched_vs_unbatched))


def _check_functional_scripted_smoke(functional, input, *args, **kwargs):
    """Checks if the functional can be scripted and the scripted version can be called without error."""
    if not isinstance(input, datapoints.Image):
        return

    functional_scripted = _script(functional)
    with ignore_jit_no_profile_information_warning():
        functional_scripted(input.as_subclass(torch.Tensor), *args, **kwargs)


def check_functional(functional, input, *args, check_scripted_smoke=True, **kwargs):
    unknown_input = object()
    with pytest.raises(TypeError, match=re.escape(str(type(unknown_input)))):
        functional(unknown_input, *args, **kwargs)

    with mock.patch("torch._C._log_api_usage_once", wraps=torch._C._log_api_usage_once) as spy:
        output = functional(input, *args, **kwargs)

        spy.assert_any_call(f"{functional.__module__}.{functional.__name__}")

    assert isinstance(output, type(input))

    if isinstance(input, datapoints.BoundingBoxes):
        assert output.format == input.format

    if check_scripted_smoke:
        _check_functional_scripted_smoke(functional, input, *args, **kwargs)


def check_functional_kernel_signature_match(functional, *, kernel, input_type):
    """Checks if the signature of the functional matches the kernel signature."""
    functional_params = list(inspect.signature(functional).parameters.values())[1:]
    kernel_params = list(inspect.signature(kernel).parameters.values())[1:]

    if issubclass(input_type, datapoints.Datapoint):
        # We filter out metadata that is implicitly passed to the functional through the input datapoint, but has to be
        # explicitly passed to the kernel.
        explicit_metadata = {
            datapoints.BoundingBoxes: {"format", "canvas_size"},
        }
        kernel_params = [param for param in kernel_params if param.name not in explicit_metadata.get(input_type, set())]

    functional_params = iter(functional_params)
    for functional_param, kernel_param in zip(functional_params, kernel_params):
        try:
            # In general, the functional parameters are a superset of the kernel parameters. Thus, we filter out
            # functional parameters that have no kernel equivalent while keeping the order intact.
            while functional_param.name != kernel_param.name:
                functional_param = next(functional_params)
        except StopIteration:
            raise AssertionError(
                f"Parameter `{kernel_param.name}` of kernel `{kernel.__name__}` "
                f"has no corresponding parameter on the functional `{functional.__name__}`."
            ) from None

        if issubclass(input_type, PIL.Image.Image):
            # PIL kernels often have more correct annotations, since they are not limited by JIT. Thus, we don't check
            # them in the first place.
            functional_param._annotation = kernel_param._annotation = inspect.Parameter.empty

        assert functional_param == kernel_param


def _check_transform_v1_compatibility(transform, input):
    """If the transform defines the ``_v1_transform_cls`` attribute, checks if the transform has a public, static
    ``get_params`` method, is scriptable, and the scripted version can be called without error."""
    if transform._v1_transform_cls is None:
        return

    if type(input) is not torch.Tensor:
        return

    if hasattr(transform._v1_transform_cls, "get_params"):
        assert type(transform).get_params is transform._v1_transform_cls.get_params

    scripted_transform = _script(transform)
    with ignore_jit_no_profile_information_warning():
        scripted_transform(input)


def check_transform(transform_cls, input, *args, **kwargs):
    transform = transform_cls(*args, **kwargs)

    output = transform(input)
    assert isinstance(output, type(input))

    if isinstance(input, datapoints.BoundingBoxes):
        assert output.format == input.format

    _check_transform_v1_compatibility(transform, input)


def transform_cls_to_functional(transform_cls, **transform_specific_kwargs):
    def wrapper(input, *args, **kwargs):
        transform = transform_cls(*args, **transform_specific_kwargs, **kwargs)
        return transform(input)

    wrapper.__name__ = transform_cls.__name__

    return wrapper


def param_value_parametrization(**kwargs):
    """Helper function to turn

    @pytest.mark.parametrize(
        ("param", "value"),
        ("a", 1),
        ("a", 2),
        ("a", 3),
        ("b", -1.0)
        ("b", 1.0)
    )

    into

    @param_value_parametrization(a=[1, 2, 3], b=[-1.0, 1.0])
    """
    return pytest.mark.parametrize(
        ("param", "value"),
        [(param, value) for param, values in kwargs.items() for value in values],
    )


def adapt_fill(value, *, dtype):
    """Adapt fill values in the range [0.0, 1.0] to the value range of the dtype"""
    if value is None:
        return value

    max_value = get_max_value(dtype)

    if isinstance(value, (int, float)):
        return type(value)(value * max_value)
    elif isinstance(value, (list, tuple)):
        return type(value)(type(v)(v * max_value) for v in value)
    else:
        raise ValueError(f"fill should be an int or float, or a list or tuple of the former, but got '{value}'.")


EXHAUSTIVE_TYPE_FILLS = [
    None,
    1,
    0.5,
    [1],
    [0.2],
    (0,),
    (0.7,),
    [1, 0, 1],
    [0.1, 0.2, 0.3],
    (0, 1, 0),
    (0.9, 0.234, 0.314),
]
CORRECTNESS_FILLS = [
    v for v in EXHAUSTIVE_TYPE_FILLS if v is None or isinstance(v, float) or (isinstance(v, list) and len(v) > 1)
]


# We cannot use `list(transforms.InterpolationMode)` here, since it includes some PIL-only ones as well
INTERPOLATION_MODES = [
    transforms.InterpolationMode.NEAREST,
    transforms.InterpolationMode.NEAREST_EXACT,
    transforms.InterpolationMode.BILINEAR,
    transforms.InterpolationMode.BICUBIC,
]


@contextlib.contextmanager
def assert_warns_antialias_default_value():
    with pytest.warns(UserWarning, match="The default value of the antialias parameter of all the resizing transforms"):
        yield


def reference_affine_bounding_boxes_helper(bounding_boxes, *, format, canvas_size, affine_matrix):
    def transform(bbox):
        # Go to float before converting to prevent precision loss in case of CXCYWH -> XYXY and W or H is 1
        in_dtype = bbox.dtype
        if not torch.is_floating_point(bbox):
            bbox = bbox.float()
        bbox_xyxy = F.convert_format_bounding_boxes(
            bbox.as_subclass(torch.Tensor),
            old_format=format,
            new_format=datapoints.BoundingBoxFormat.XYXY,
            inplace=True,
        )
        points = np.array(
            [
                [bbox_xyxy[0].item(), bbox_xyxy[1].item(), 1.0],
                [bbox_xyxy[2].item(), bbox_xyxy[1].item(), 1.0],
                [bbox_xyxy[0].item(), bbox_xyxy[3].item(), 1.0],
                [bbox_xyxy[2].item(), bbox_xyxy[3].item(), 1.0],
            ]
        )
        transformed_points = np.matmul(points, affine_matrix.T)
        out_bbox = torch.tensor(
            [
                np.min(transformed_points[:, 0]).item(),
                np.min(transformed_points[:, 1]).item(),
                np.max(transformed_points[:, 0]).item(),
                np.max(transformed_points[:, 1]).item(),
            ],
            dtype=bbox_xyxy.dtype,
        )
        out_bbox = F.convert_format_bounding_boxes(
            out_bbox, old_format=datapoints.BoundingBoxFormat.XYXY, new_format=format, inplace=True
        )
        # It is important to clamp before casting, especially for CXCYWH format, dtype=int64
        out_bbox = F.clamp_bounding_boxes(out_bbox, format=format, canvas_size=canvas_size)
        out_bbox = out_bbox.to(dtype=in_dtype)
        return out_bbox

    return torch.stack([transform(b) for b in bounding_boxes.reshape(-1, 4).unbind()]).reshape(bounding_boxes.shape)


class TestResize:
    INPUT_SIZE = (17, 11)
    OUTPUT_SIZES = [17, [17], (17,), [12, 13], (12, 13)]

    def _make_max_size_kwarg(self, *, use_max_size, size):
        if use_max_size:
            if not (isinstance(size, int) or len(size) == 1):
                # This would result in an `ValueError`
                return None

            max_size = (size if isinstance(size, int) else size[0]) + 1
        else:
            max_size = None

        return dict(max_size=max_size)

    def _compute_output_size(self, *, input_size, size, max_size):
        if not (isinstance(size, int) or len(size) == 1):
            return tuple(size)

        if not isinstance(size, int):
            size = size[0]

        old_height, old_width = input_size
        ratio = old_width / old_height
        if ratio > 1:
            new_height = size
            new_width = int(ratio * new_height)
        else:
            new_width = size
            new_height = int(new_width / ratio)

        if max_size is not None and max(new_height, new_width) > max_size:
            # Need to recompute the aspect ratio, since it might have changed due to rounding
            ratio = new_width / new_height
            if ratio > 1:
                new_width = max_size
                new_height = int(new_width / ratio)
            else:
                new_height = max_size
                new_width = int(new_height * ratio)

        return new_height, new_width

    @pytest.mark.parametrize("size", OUTPUT_SIZES)
    @pytest.mark.parametrize("interpolation", INTERPOLATION_MODES)
    @pytest.mark.parametrize("use_max_size", [True, False])
    @pytest.mark.parametrize("antialias", [True, False])
    @pytest.mark.parametrize("dtype", [torch.float32, torch.uint8])
    @pytest.mark.parametrize("device", cpu_and_cuda())
    def test_kernel_image_tensor(self, size, interpolation, use_max_size, antialias, dtype, device):
        if not (max_size_kwarg := self._make_max_size_kwarg(use_max_size=use_max_size, size=size)):
            return

        # In contrast to CPU, there is no native `InterpolationMode.BICUBIC` implementation for uint8 images on CUDA.
        # Internally, it uses the float path. Thus, we need to test with an enormous tolerance here to account for that.
        atol = 30 if transforms.InterpolationMode.BICUBIC and dtype is torch.uint8 else 1
        check_cuda_vs_cpu_tolerances = dict(rtol=0, atol=atol / 255 if dtype.is_floating_point else atol)

        check_kernel(
            F.resize_image_tensor,
            make_image(self.INPUT_SIZE, dtype=dtype, device=device),
            size=size,
            interpolation=interpolation,
            **max_size_kwarg,
            antialias=antialias,
            check_cuda_vs_cpu=check_cuda_vs_cpu_tolerances,
            check_scripted_vs_eager=not isinstance(size, int),
        )

    @pytest.mark.parametrize("format", list(datapoints.BoundingBoxFormat))
    @pytest.mark.parametrize("size", OUTPUT_SIZES)
    @pytest.mark.parametrize("use_max_size", [True, False])
    @pytest.mark.parametrize("dtype", [torch.float32, torch.int64])
    @pytest.mark.parametrize("device", cpu_and_cuda())
    def test_kernel_bounding_boxes(self, format, size, use_max_size, dtype, device):
        if not (max_size_kwarg := self._make_max_size_kwarg(use_max_size=use_max_size, size=size)):
            return

        bounding_boxes = make_bounding_box(
            format=format,
            canvas_size=self.INPUT_SIZE,
            dtype=dtype,
            device=device,
        )
        check_kernel(
            F.resize_bounding_boxes,
            bounding_boxes,
            canvas_size=bounding_boxes.canvas_size,
            size=size,
            **max_size_kwarg,
            check_scripted_vs_eager=not isinstance(size, int),
        )

    @pytest.mark.parametrize("make_mask", [make_segmentation_mask, make_detection_mask])
    def test_kernel_mask(self, make_mask):
        check_kernel(F.resize_mask, make_mask(self.INPUT_SIZE), size=self.OUTPUT_SIZES[-1])

    def test_kernel_video(self):
        check_kernel(F.resize_video, make_video(self.INPUT_SIZE), size=self.OUTPUT_SIZES[-1], antialias=True)

    @pytest.mark.parametrize("size", OUTPUT_SIZES)
    @pytest.mark.parametrize(
        "make_input",
        [make_image_tensor, make_image_pil, make_image, make_bounding_box, make_segmentation_mask, make_video],
    )
    def test_functional(self, size, make_input):
        check_functional(
            F.resize,
            make_input(self.INPUT_SIZE),
            size=size,
            antialias=True,
            check_scripted_smoke=not isinstance(size, int),
        )

    @pytest.mark.parametrize(
        ("kernel", "input_type"),
        [
            (F.resize_image_tensor, torch.Tensor),
            (F.resize_image_pil, PIL.Image.Image),
            (F.resize_image_tensor, datapoints.Image),
            (F.resize_bounding_boxes, datapoints.BoundingBoxes),
            (F.resize_mask, datapoints.Mask),
            (F.resize_video, datapoints.Video),
        ],
    )
    def test_functional_signature(self, kernel, input_type):
        check_functional_kernel_signature_match(F.resize, kernel=kernel, input_type=input_type)

    @pytest.mark.parametrize("size", OUTPUT_SIZES)
    @pytest.mark.parametrize("device", cpu_and_cuda())
    @pytest.mark.parametrize(
        "make_input",
        [
            make_image_tensor,
            make_image_pil,
            make_image,
            make_bounding_box,
            make_segmentation_mask,
            make_detection_mask,
            make_video,
        ],
    )
    def test_transform(self, size, device, make_input):
        check_transform(transforms.Resize, make_input(self.INPUT_SIZE, device=device), size=size, antialias=True)

    def _check_output_size(self, input, output, *, size, max_size):
        assert tuple(F.get_size(output)) == self._compute_output_size(
            input_size=F.get_size(input), size=size, max_size=max_size
        )

    @pytest.mark.parametrize("size", OUTPUT_SIZES)
    # `InterpolationMode.NEAREST` is modeled after the buggy `INTER_NEAREST` interpolation of CV2.
    # The PIL equivalent of `InterpolationMode.NEAREST` is `InterpolationMode.NEAREST_EXACT`
    @pytest.mark.parametrize("interpolation", set(INTERPOLATION_MODES) - {transforms.InterpolationMode.NEAREST})
    @pytest.mark.parametrize("use_max_size", [True, False])
    @pytest.mark.parametrize("fn", [F.resize, transform_cls_to_functional(transforms.Resize)])
    def test_image_correctness(self, size, interpolation, use_max_size, fn):
        if not (max_size_kwarg := self._make_max_size_kwarg(use_max_size=use_max_size, size=size)):
            return

        image = make_image(self.INPUT_SIZE, dtype=torch.uint8)

        actual = fn(image, size=size, interpolation=interpolation, **max_size_kwarg, antialias=True)
        expected = F.to_image_tensor(
            F.resize(F.to_image_pil(image), size=size, interpolation=interpolation, **max_size_kwarg)
        )

        self._check_output_size(image, actual, size=size, **max_size_kwarg)
        torch.testing.assert_close(actual, expected, atol=1, rtol=0)

    def _reference_resize_bounding_boxes(self, bounding_boxes, *, size, max_size=None):
        old_height, old_width = bounding_boxes.canvas_size
        new_height, new_width = self._compute_output_size(
            input_size=bounding_boxes.canvas_size, size=size, max_size=max_size
        )

        if (old_height, old_width) == (new_height, new_width):
            return bounding_boxes

        affine_matrix = np.array(
            [
                [new_width / old_width, 0, 0],
                [0, new_height / old_height, 0],
            ],
            dtype="float64" if bounding_boxes.dtype == torch.float64 else "float32",
        )

        expected_bboxes = reference_affine_bounding_boxes_helper(
            bounding_boxes,
            format=bounding_boxes.format,
            canvas_size=(new_height, new_width),
            affine_matrix=affine_matrix,
        )
        return datapoints.BoundingBoxes.wrap_like(bounding_boxes, expected_bboxes, canvas_size=(new_height, new_width))

    @pytest.mark.parametrize("format", list(datapoints.BoundingBoxFormat))
    @pytest.mark.parametrize("size", OUTPUT_SIZES)
    @pytest.mark.parametrize("use_max_size", [True, False])
    @pytest.mark.parametrize("fn", [F.resize, transform_cls_to_functional(transforms.Resize)])
    def test_bounding_boxes_correctness(self, format, size, use_max_size, fn):
        if not (max_size_kwarg := self._make_max_size_kwarg(use_max_size=use_max_size, size=size)):
            return

        bounding_boxes = make_bounding_box(format=format, canvas_size=self.INPUT_SIZE)

        actual = fn(bounding_boxes, size=size, **max_size_kwarg)
        expected = self._reference_resize_bounding_boxes(bounding_boxes, size=size, **max_size_kwarg)

        self._check_output_size(bounding_boxes, actual, size=size, **max_size_kwarg)
        torch.testing.assert_close(actual, expected)

    @pytest.mark.parametrize("interpolation", set(transforms.InterpolationMode) - set(INTERPOLATION_MODES))
    @pytest.mark.parametrize(
        "make_input",
        [make_image_tensor, make_image_pil, make_image, make_video],
    )
    def test_pil_interpolation_compat_smoke(self, interpolation, make_input):
        input = make_input(self.INPUT_SIZE)

        with (
            contextlib.nullcontext()
            if isinstance(input, PIL.Image.Image)
            # This error is triggered in PyTorch core
            else pytest.raises(NotImplementedError, match=f"got {interpolation.value.lower()}")
        ):
            F.resize(
                input,
                size=self.OUTPUT_SIZES[0],
                interpolation=interpolation,
            )

    def test_functional_pil_antialias_warning(self):
        with pytest.warns(UserWarning, match="Anti-alias option is always applied for PIL Image input"):
            F.resize(make_image_pil(self.INPUT_SIZE), size=self.OUTPUT_SIZES[0], antialias=False)

    @pytest.mark.parametrize("size", OUTPUT_SIZES)
    @pytest.mark.parametrize(
        "make_input",
        [
            make_image_tensor,
            make_image_pil,
            make_image,
            make_bounding_box,
            make_segmentation_mask,
            make_detection_mask,
            make_video,
        ],
    )
    def test_max_size_error(self, size, make_input):
        if isinstance(size, int) or len(size) == 1:
            max_size = (size if isinstance(size, int) else size[0]) - 1
            match = "must be strictly greater than the requested size"
        else:
            # value can be anything other than None
            max_size = -1
            match = "size should be an int or a sequence of length 1"

        with pytest.raises(ValueError, match=match):
            F.resize(make_input(self.INPUT_SIZE), size=size, max_size=max_size, antialias=True)

    @pytest.mark.parametrize("interpolation", INTERPOLATION_MODES)
    @pytest.mark.parametrize(
        "make_input",
        [make_image_tensor, make_image, make_video],
    )
    def test_antialias_warning(self, interpolation, make_input):
        with (
            assert_warns_antialias_default_value()
            if interpolation in {transforms.InterpolationMode.BILINEAR, transforms.InterpolationMode.BICUBIC}
            else assert_no_warnings()
        ):
            F.resize(
                make_input(self.INPUT_SIZE),
                size=self.OUTPUT_SIZES[0],
                interpolation=interpolation,
            )

    @pytest.mark.parametrize("interpolation", INTERPOLATION_MODES)
    @pytest.mark.parametrize(
        "make_input",
        [make_image_tensor, make_image_pil, make_image, make_video],
    )
    def test_interpolation_int(self, interpolation, make_input):
        input = make_input(self.INPUT_SIZE)

        # `InterpolationMode.NEAREST_EXACT` has no proper corresponding integer equivalent. Internally, we map it to
        # `0` to be the same as `InterpolationMode.NEAREST` for PIL. However, for the tensor backend there is a
        # difference and thus we don't test it here.
        if isinstance(input, torch.Tensor) and interpolation is transforms.InterpolationMode.NEAREST_EXACT:
            return

        expected = F.resize(input, size=self.OUTPUT_SIZES[0], interpolation=interpolation, antialias=True)
        actual = F.resize(
            input, size=self.OUTPUT_SIZES[0], interpolation=pil_modes_mapping[interpolation], antialias=True
        )

        assert_equal(actual, expected)

    def test_transform_unknown_size_error(self):
        with pytest.raises(ValueError, match="size can either be an integer or a list or tuple of one or two integers"):
            transforms.Resize(size=object())

    @pytest.mark.parametrize(
        "size", [min(INPUT_SIZE), [min(INPUT_SIZE)], (min(INPUT_SIZE),), list(INPUT_SIZE), tuple(INPUT_SIZE)]
    )
    @pytest.mark.parametrize(
        "make_input",
        [
            make_image_tensor,
            make_image_pil,
            make_image,
            make_bounding_box,
            make_segmentation_mask,
            make_detection_mask,
            make_video,
        ],
    )
    def test_noop(self, size, make_input):
        input = make_input(self.INPUT_SIZE)

        output = F.resize(input, size=F.get_size(input), antialias=True)

        # This identity check is not a requirement. It is here to avoid breaking the behavior by accident. If there
        # is a good reason to break this, feel free to downgrade to an equality check.
        if isinstance(input, datapoints.Datapoint):
            # We can't test identity directly, since that checks for the identity of the Python object. Since all
            # datapoints unwrap before a kernel and wrap again afterwards, the Python object changes. Thus, we check
            # that the underlying storage is the same
            assert output.data_ptr() == input.data_ptr()
        else:
            assert output is input

    @pytest.mark.parametrize(
        "make_input",
        [
            make_image_tensor,
            make_image_pil,
            make_image,
            make_bounding_box,
            make_segmentation_mask,
            make_detection_mask,
            make_video,
        ],
    )
    def test_no_regression_5405(self, make_input):
        # Checks that `max_size` is not ignored if `size == small_edge_size`
        # See https://github.com/pytorch/vision/issues/5405

        input = make_input(self.INPUT_SIZE)

        size = min(F.get_size(input))
        max_size = size + 1
        output = F.resize(input, size=size, max_size=max_size, antialias=True)

        assert max(F.get_size(output)) == max_size


class TestHorizontalFlip:
    @pytest.mark.parametrize("dtype", [torch.float32, torch.uint8])
    @pytest.mark.parametrize("device", cpu_and_cuda())
    def test_kernel_image_tensor(self, dtype, device):
        check_kernel(F.horizontal_flip_image_tensor, make_image(dtype=dtype, device=device))

    @pytest.mark.parametrize("format", list(datapoints.BoundingBoxFormat))
    @pytest.mark.parametrize("dtype", [torch.float32, torch.int64])
    @pytest.mark.parametrize("device", cpu_and_cuda())
    def test_kernel_bounding_boxes(self, format, dtype, device):
        bounding_boxes = make_bounding_box(format=format, dtype=dtype, device=device)
        check_kernel(
            F.horizontal_flip_bounding_boxes,
            bounding_boxes,
            format=format,
            canvas_size=bounding_boxes.canvas_size,
        )

    @pytest.mark.parametrize("make_mask", [make_segmentation_mask, make_detection_mask])
    def test_kernel_mask(self, make_mask):
        check_kernel(F.horizontal_flip_mask, make_mask())

    def test_kernel_video(self):
        check_kernel(F.horizontal_flip_video, make_video())

    @pytest.mark.parametrize(
        "make_input",
        [make_image_tensor, make_image_pil, make_image, make_bounding_box, make_segmentation_mask, make_video],
    )
    def test_functional(self, make_input):
        check_functional(F.horizontal_flip, make_input())

    @pytest.mark.parametrize(
        ("kernel", "input_type"),
        [
            (F.horizontal_flip_image_tensor, torch.Tensor),
            (F.horizontal_flip_image_pil, PIL.Image.Image),
            (F.horizontal_flip_image_tensor, datapoints.Image),
            (F.horizontal_flip_bounding_boxes, datapoints.BoundingBoxes),
            (F.horizontal_flip_mask, datapoints.Mask),
            (F.horizontal_flip_video, datapoints.Video),
        ],
    )
    def test_functional_signature(self, kernel, input_type):
        check_functional_kernel_signature_match(F.horizontal_flip, kernel=kernel, input_type=input_type)

    @pytest.mark.parametrize(
        "make_input",
        [make_image_tensor, make_image_pil, make_image, make_bounding_box, make_segmentation_mask, make_video],
    )
    @pytest.mark.parametrize("device", cpu_and_cuda())
    def test_transform(self, make_input, device):
        check_transform(transforms.RandomHorizontalFlip, make_input(device=device), p=1)

    @pytest.mark.parametrize(
        "fn", [F.horizontal_flip, transform_cls_to_functional(transforms.RandomHorizontalFlip, p=1)]
    )
    def test_image_correctness(self, fn):
        image = make_image(dtype=torch.uint8, device="cpu")

        actual = fn(image)
        expected = F.to_image_tensor(F.horizontal_flip(F.to_image_pil(image)))

        torch.testing.assert_close(actual, expected)

    def _reference_horizontal_flip_bounding_boxes(self, bounding_boxes):
        affine_matrix = np.array(
            [
                [-1, 0, bounding_boxes.canvas_size[1]],
                [0, 1, 0],
            ],
            dtype="float64" if bounding_boxes.dtype == torch.float64 else "float32",
        )

        expected_bboxes = reference_affine_bounding_boxes_helper(
            bounding_boxes,
            format=bounding_boxes.format,
            canvas_size=bounding_boxes.canvas_size,
            affine_matrix=affine_matrix,
        )

        return datapoints.BoundingBoxes.wrap_like(bounding_boxes, expected_bboxes)

    @pytest.mark.parametrize("format", list(datapoints.BoundingBoxFormat))
    @pytest.mark.parametrize(
        "fn", [F.horizontal_flip, transform_cls_to_functional(transforms.RandomHorizontalFlip, p=1)]
    )
    def test_bounding_boxes_correctness(self, format, fn):
        bounding_boxes = make_bounding_box(format=format)

        actual = fn(bounding_boxes)
        expected = self._reference_horizontal_flip_bounding_boxes(bounding_boxes)

        torch.testing.assert_close(actual, expected)

    @pytest.mark.parametrize(
        "make_input",
        [make_image_tensor, make_image_pil, make_image, make_bounding_box, make_segmentation_mask, make_video],
    )
    @pytest.mark.parametrize("device", cpu_and_cuda())
    def test_transform_noop(self, make_input, device):
        input = make_input(device=device)

        transform = transforms.RandomHorizontalFlip(p=0)

        output = transform(input)

        assert_equal(output, input)


class TestAffine:
    _EXHAUSTIVE_TYPE_AFFINE_KWARGS = dict(
        # float, int
        angle=[-10.9, 18],
        # two-list of float, two-list of int, two-tuple of float, two-tuple of int
        translate=[[6.3, -0.6], [1, -3], (16.6, -6.6), (-2, 4)],
        # float
        scale=[0.5],
        # float, int,
        # one-list of float, one-list of int, one-tuple of float, one-tuple of int
        # two-list of float, two-list of int, two-tuple of float, two-tuple of int
        shear=[35.6, 38, [-37.7], [-23], (5.3,), (-52,), [5.4, 21.8], [-47, 51], (-11.2, 36.7), (8, -53)],
        # None
        # two-list of float, two-list of int, two-tuple of float, two-tuple of int
        center=[None, [1.2, 4.9], [-3, 1], (2.5, -4.7), (3, 2)],
    )
    # The special case for shear makes sure we pick a value that is supported while JIT scripting
    _MINIMAL_AFFINE_KWARGS = {
        k: vs[0] if k != "shear" else next(v for v in vs if isinstance(v, list))
        for k, vs in _EXHAUSTIVE_TYPE_AFFINE_KWARGS.items()
    }
    _CORRECTNESS_AFFINE_KWARGS = {
        k: [v for v in vs if v is None or isinstance(v, float) or (isinstance(v, list) and len(v) > 1)]
        for k, vs in _EXHAUSTIVE_TYPE_AFFINE_KWARGS.items()
    }

    _EXHAUSTIVE_TYPE_TRANSFORM_AFFINE_RANGES = dict(
        degrees=[30, (-15, 20)],
        translate=[None, (0.5, 0.5)],
        scale=[None, (0.75, 1.25)],
        shear=[None, (12, 30, -17, 5), 10, (-5, 12)],
    )
    _CORRECTNESS_TRANSFORM_AFFINE_RANGES = {
        k: next(v for v in vs if v is not None) for k, vs in _EXHAUSTIVE_TYPE_TRANSFORM_AFFINE_RANGES.items()
    }

    def _check_kernel(self, kernel, input, *args, **kwargs):
        kwargs_ = self._MINIMAL_AFFINE_KWARGS.copy()
        kwargs_.update(kwargs)
        check_kernel(kernel, input, *args, **kwargs_)

    @param_value_parametrization(
        angle=_EXHAUSTIVE_TYPE_AFFINE_KWARGS["angle"],
        translate=_EXHAUSTIVE_TYPE_AFFINE_KWARGS["translate"],
        shear=_EXHAUSTIVE_TYPE_AFFINE_KWARGS["shear"],
        center=_EXHAUSTIVE_TYPE_AFFINE_KWARGS["center"],
        interpolation=[transforms.InterpolationMode.NEAREST, transforms.InterpolationMode.BILINEAR],
        fill=EXHAUSTIVE_TYPE_FILLS,
    )
    @pytest.mark.parametrize("dtype", [torch.float32, torch.uint8])
    @pytest.mark.parametrize("device", cpu_and_cuda())
    def test_kernel_image_tensor(self, param, value, dtype, device):
        if param == "fill":
            value = adapt_fill(value, dtype=dtype)
        self._check_kernel(
            F.affine_image_tensor,
            make_image(dtype=dtype, device=device),
            **{param: value},
            check_scripted_vs_eager=not (param in {"shear", "fill"} and isinstance(value, (int, float))),
            check_cuda_vs_cpu=dict(atol=1, rtol=0)
            if dtype is torch.uint8 and param == "interpolation" and value is transforms.InterpolationMode.BILINEAR
            else True,
        )

    @param_value_parametrization(
        angle=_EXHAUSTIVE_TYPE_AFFINE_KWARGS["angle"],
        translate=_EXHAUSTIVE_TYPE_AFFINE_KWARGS["translate"],
        shear=_EXHAUSTIVE_TYPE_AFFINE_KWARGS["shear"],
        center=_EXHAUSTIVE_TYPE_AFFINE_KWARGS["center"],
    )
    @pytest.mark.parametrize("format", list(datapoints.BoundingBoxFormat))
    @pytest.mark.parametrize("dtype", [torch.float32, torch.int64])
    @pytest.mark.parametrize("device", cpu_and_cuda())
    def test_kernel_bounding_boxes(self, param, value, format, dtype, device):
        bounding_boxes = make_bounding_box(format=format, dtype=dtype, device=device)
        self._check_kernel(
            F.affine_bounding_boxes,
            bounding_boxes,
            format=format,
            canvas_size=bounding_boxes.canvas_size,
            **{param: value},
            check_scripted_vs_eager=not (param == "shear" and isinstance(value, (int, float))),
        )

    @pytest.mark.parametrize("make_mask", [make_segmentation_mask, make_detection_mask])
    def test_kernel_mask(self, make_mask):
        self._check_kernel(F.affine_mask, make_mask())

    def test_kernel_video(self):
        self._check_kernel(F.affine_video, make_video())

    @pytest.mark.parametrize(
        "make_input",
        [make_image_tensor, make_image_pil, make_image, make_bounding_box, make_segmentation_mask, make_video],
    )
    def test_functional(self, make_input):
        check_functional(F.affine, make_input(), **self._MINIMAL_AFFINE_KWARGS)

    @pytest.mark.parametrize(
        ("kernel", "input_type"),
        [
            (F.affine_image_tensor, torch.Tensor),
            (F.affine_image_pil, PIL.Image.Image),
            (F.affine_image_tensor, datapoints.Image),
            (F.affine_bounding_boxes, datapoints.BoundingBoxes),
            (F.affine_mask, datapoints.Mask),
            (F.affine_video, datapoints.Video),
        ],
    )
    def test_functional_signature(self, kernel, input_type):
        check_functional_kernel_signature_match(F.affine, kernel=kernel, input_type=input_type)

    @pytest.mark.parametrize(
        "make_input",
        [make_image_tensor, make_image_pil, make_image, make_bounding_box, make_segmentation_mask, make_video],
    )
    @pytest.mark.parametrize("device", cpu_and_cuda())
    def test_transform(self, make_input, device):
        input = make_input(device=device)

        check_transform(transforms.RandomAffine, input, **self._CORRECTNESS_TRANSFORM_AFFINE_RANGES)

    @pytest.mark.parametrize("angle", _CORRECTNESS_AFFINE_KWARGS["angle"])
    @pytest.mark.parametrize("translate", _CORRECTNESS_AFFINE_KWARGS["translate"])
    @pytest.mark.parametrize("scale", _CORRECTNESS_AFFINE_KWARGS["scale"])
    @pytest.mark.parametrize("shear", _CORRECTNESS_AFFINE_KWARGS["shear"])
    @pytest.mark.parametrize("center", _CORRECTNESS_AFFINE_KWARGS["center"])
    @pytest.mark.parametrize(
        "interpolation", [transforms.InterpolationMode.NEAREST, transforms.InterpolationMode.BILINEAR]
    )
    @pytest.mark.parametrize("fill", CORRECTNESS_FILLS)
    def test_functional_image_correctness(self, angle, translate, scale, shear, center, interpolation, fill):
        image = make_image(dtype=torch.uint8, device="cpu")

        fill = adapt_fill(fill, dtype=torch.uint8)

        actual = F.affine(
            image,
            angle=angle,
            translate=translate,
            scale=scale,
            shear=shear,
            center=center,
            interpolation=interpolation,
            fill=fill,
        )
        expected = F.to_image_tensor(
            F.affine(
                F.to_image_pil(image),
                angle=angle,
                translate=translate,
                scale=scale,
                shear=shear,
                center=center,
                interpolation=interpolation,
                fill=fill,
            )
        )

        mae = (actual.float() - expected.float()).abs().mean()
        assert mae < 2 if interpolation is transforms.InterpolationMode.NEAREST else 8

    @pytest.mark.parametrize("center", _CORRECTNESS_AFFINE_KWARGS["center"])
    @pytest.mark.parametrize(
        "interpolation", [transforms.InterpolationMode.NEAREST, transforms.InterpolationMode.BILINEAR]
    )
    @pytest.mark.parametrize("fill", CORRECTNESS_FILLS)
    @pytest.mark.parametrize("seed", list(range(5)))
    def test_transform_image_correctness(self, center, interpolation, fill, seed):
        image = make_image(dtype=torch.uint8, device="cpu")

        fill = adapt_fill(fill, dtype=torch.uint8)

        transform = transforms.RandomAffine(
            **self._CORRECTNESS_TRANSFORM_AFFINE_RANGES, center=center, interpolation=interpolation, fill=fill
        )

        torch.manual_seed(seed)
        actual = transform(image)

        torch.manual_seed(seed)
        expected = F.to_image_tensor(transform(F.to_image_pil(image)))

        mae = (actual.float() - expected.float()).abs().mean()
        assert mae < 2 if interpolation is transforms.InterpolationMode.NEAREST else 8

    def _compute_affine_matrix(self, *, angle, translate, scale, shear, center):
        rot = math.radians(angle)
        cx, cy = center
        tx, ty = translate
        sx, sy = [math.radians(s) for s in ([shear, 0.0] if isinstance(shear, (int, float)) else shear)]

        c_matrix = np.array([[1, 0, cx], [0, 1, cy], [0, 0, 1]])
        t_matrix = np.array([[1, 0, tx], [0, 1, ty], [0, 0, 1]])
        c_matrix_inv = np.linalg.inv(c_matrix)
        rs_matrix = np.array(
            [
                [scale * math.cos(rot), -scale * math.sin(rot), 0],
                [scale * math.sin(rot), scale * math.cos(rot), 0],
                [0, 0, 1],
            ]
        )
        shear_x_matrix = np.array([[1, -math.tan(sx), 0], [0, 1, 0], [0, 0, 1]])
        shear_y_matrix = np.array([[1, 0, 0], [-math.tan(sy), 1, 0], [0, 0, 1]])
        rss_matrix = np.matmul(rs_matrix, np.matmul(shear_y_matrix, shear_x_matrix))
        true_matrix = np.matmul(t_matrix, np.matmul(c_matrix, np.matmul(rss_matrix, c_matrix_inv)))
        return true_matrix

    def _reference_affine_bounding_boxes(self, bounding_boxes, *, angle, translate, scale, shear, center):
        if center is None:
            center = [s * 0.5 for s in bounding_boxes.canvas_size[::-1]]

        affine_matrix = self._compute_affine_matrix(
            angle=angle, translate=translate, scale=scale, shear=shear, center=center
        )
        affine_matrix = affine_matrix[:2, :]

        expected_bboxes = reference_affine_bounding_boxes_helper(
            bounding_boxes,
            format=bounding_boxes.format,
            canvas_size=bounding_boxes.canvas_size,
            affine_matrix=affine_matrix,
        )

        return expected_bboxes

    @pytest.mark.parametrize("format", list(datapoints.BoundingBoxFormat))
    @pytest.mark.parametrize("angle", _CORRECTNESS_AFFINE_KWARGS["angle"])
    @pytest.mark.parametrize("translate", _CORRECTNESS_AFFINE_KWARGS["translate"])
    @pytest.mark.parametrize("scale", _CORRECTNESS_AFFINE_KWARGS["scale"])
    @pytest.mark.parametrize("shear", _CORRECTNESS_AFFINE_KWARGS["shear"])
    @pytest.mark.parametrize("center", _CORRECTNESS_AFFINE_KWARGS["center"])
    def test_functional_bounding_boxes_correctness(self, format, angle, translate, scale, shear, center):
        bounding_boxes = make_bounding_box(format=format)

        actual = F.affine(
            bounding_boxes,
            angle=angle,
            translate=translate,
            scale=scale,
            shear=shear,
            center=center,
        )
        expected = self._reference_affine_bounding_boxes(
            bounding_boxes,
            angle=angle,
            translate=translate,
            scale=scale,
            shear=shear,
            center=center,
        )

        torch.testing.assert_close(actual, expected)

    @pytest.mark.parametrize("format", list(datapoints.BoundingBoxFormat))
    @pytest.mark.parametrize("center", _CORRECTNESS_AFFINE_KWARGS["center"])
    @pytest.mark.parametrize("seed", list(range(5)))
    def test_transform_bounding_boxes_correctness(self, format, center, seed):
        bounding_boxes = make_bounding_box(format=format)

        transform = transforms.RandomAffine(**self._CORRECTNESS_TRANSFORM_AFFINE_RANGES, center=center)

        torch.manual_seed(seed)
        params = transform._get_params([bounding_boxes])

        torch.manual_seed(seed)
        actual = transform(bounding_boxes)

        expected = self._reference_affine_bounding_boxes(bounding_boxes, **params, center=center)

        torch.testing.assert_close(actual, expected)

    @pytest.mark.parametrize("degrees", _EXHAUSTIVE_TYPE_TRANSFORM_AFFINE_RANGES["degrees"])
    @pytest.mark.parametrize("translate", _EXHAUSTIVE_TYPE_TRANSFORM_AFFINE_RANGES["translate"])
    @pytest.mark.parametrize("scale", _EXHAUSTIVE_TYPE_TRANSFORM_AFFINE_RANGES["scale"])
    @pytest.mark.parametrize("shear", _EXHAUSTIVE_TYPE_TRANSFORM_AFFINE_RANGES["shear"])
    @pytest.mark.parametrize("seed", list(range(10)))
    def test_transform_get_params_bounds(self, degrees, translate, scale, shear, seed):
        image = make_image()
        height, width = F.get_size(image)

        transform = transforms.RandomAffine(degrees=degrees, translate=translate, scale=scale, shear=shear)

        torch.manual_seed(seed)
        params = transform._get_params([image])

        if isinstance(degrees, (int, float)):
            assert -degrees <= params["angle"] <= degrees
        else:
            assert degrees[0] <= params["angle"] <= degrees[1]

        if translate is not None:
            width_max = int(round(translate[0] * width))
            height_max = int(round(translate[1] * height))
            assert -width_max <= params["translate"][0] <= width_max
            assert -height_max <= params["translate"][1] <= height_max
        else:
            assert params["translate"] == (0, 0)

        if scale is not None:
            assert scale[0] <= params["scale"] <= scale[1]
        else:
            assert params["scale"] == 1.0

        if shear is not None:
            if isinstance(shear, (int, float)):
                assert -shear <= params["shear"][0] <= shear
                assert params["shear"][1] == 0.0
            elif len(shear) == 2:
                assert shear[0] <= params["shear"][0] <= shear[1]
                assert params["shear"][1] == 0.0
            elif len(shear) == 4:
                assert shear[0] <= params["shear"][0] <= shear[1]
                assert shear[2] <= params["shear"][1] <= shear[3]
        else:
            assert params["shear"] == (0, 0)

    @pytest.mark.parametrize("param", ["degrees", "translate", "scale", "shear", "center"])
    @pytest.mark.parametrize("value", [0, [0], [0, 0, 0]])
    def test_transform_sequence_len_errors(self, param, value):
        if param in {"degrees", "shear"} and not isinstance(value, list):
            return

        kwargs = {param: value}
        if param != "degrees":
            kwargs["degrees"] = 0

        with pytest.raises(
            ValueError if isinstance(value, list) else TypeError, match=f"{param} should be a sequence of length 2"
        ):
            transforms.RandomAffine(**kwargs)

    def test_transform_negative_degrees_error(self):
        with pytest.raises(ValueError, match="If degrees is a single number, it must be positive"):
            transforms.RandomAffine(degrees=-1)

    @pytest.mark.parametrize("translate", [[-1, 0], [2, 0], [-1, 2]])
    def test_transform_translate_range_error(self, translate):
        with pytest.raises(ValueError, match="translation values should be between 0 and 1"):
            transforms.RandomAffine(degrees=0, translate=translate)

    @pytest.mark.parametrize("scale", [[-1, 0], [0, -1], [-1, -1]])
    def test_transform_scale_range_error(self, scale):
        with pytest.raises(ValueError, match="scale values should be positive"):
            transforms.RandomAffine(degrees=0, scale=scale)

    def test_transform_negative_shear_error(self):
        with pytest.raises(ValueError, match="If shear is a single number, it must be positive"):
            transforms.RandomAffine(degrees=0, shear=-1)

    def test_transform_unknown_fill_error(self):
        with pytest.raises(TypeError, match="Got inappropriate fill arg"):
            transforms.RandomAffine(degrees=0, fill="fill")


class TestVerticalFlip:
    @pytest.mark.parametrize("dtype", [torch.float32, torch.uint8])
    @pytest.mark.parametrize("device", cpu_and_cuda())
    def test_kernel_image_tensor(self, dtype, device):
        check_kernel(F.vertical_flip_image_tensor, make_image(dtype=dtype, device=device))

    @pytest.mark.parametrize("format", list(datapoints.BoundingBoxFormat))
    @pytest.mark.parametrize("dtype", [torch.float32, torch.int64])
    @pytest.mark.parametrize("device", cpu_and_cuda())
    def test_kernel_bounding_boxes(self, format, dtype, device):
        bounding_boxes = make_bounding_box(format=format, dtype=dtype, device=device)
        check_kernel(
            F.vertical_flip_bounding_boxes,
            bounding_boxes,
            format=format,
            canvas_size=bounding_boxes.canvas_size,
        )

    @pytest.mark.parametrize("make_mask", [make_segmentation_mask, make_detection_mask])
    def test_kernel_mask(self, make_mask):
        check_kernel(F.vertical_flip_mask, make_mask())

    def test_kernel_video(self):
        check_kernel(F.vertical_flip_video, make_video())

    @pytest.mark.parametrize(
        "make_input",
        [make_image_tensor, make_image_pil, make_image, make_bounding_box, make_segmentation_mask, make_video],
    )
    def test_functional(self, make_input):
        check_functional(F.vertical_flip, make_input())

    @pytest.mark.parametrize(
        ("kernel", "input_type"),
        [
            (F.vertical_flip_image_tensor, torch.Tensor),
            (F.vertical_flip_image_pil, PIL.Image.Image),
            (F.vertical_flip_image_tensor, datapoints.Image),
            (F.vertical_flip_bounding_boxes, datapoints.BoundingBoxes),
            (F.vertical_flip_mask, datapoints.Mask),
            (F.vertical_flip_video, datapoints.Video),
        ],
    )
    def test_functional_signature(self, kernel, input_type):
        check_functional_kernel_signature_match(F.vertical_flip, kernel=kernel, input_type=input_type)

    @pytest.mark.parametrize(
        "make_input",
        [make_image_tensor, make_image_pil, make_image, make_bounding_box, make_segmentation_mask, make_video],
    )
    @pytest.mark.parametrize("device", cpu_and_cuda())
    def test_transform(self, make_input, device):
        check_transform(transforms.RandomVerticalFlip, make_input(device=device), p=1)

    @pytest.mark.parametrize("fn", [F.vertical_flip, transform_cls_to_functional(transforms.RandomVerticalFlip, p=1)])
    def test_image_correctness(self, fn):
        image = make_image(dtype=torch.uint8, device="cpu")

        actual = fn(image)
        expected = F.to_image_tensor(F.vertical_flip(F.to_image_pil(image)))

        torch.testing.assert_close(actual, expected)

    def _reference_vertical_flip_bounding_boxes(self, bounding_boxes):
        affine_matrix = np.array(
            [
                [1, 0, 0],
                [0, -1, bounding_boxes.canvas_size[0]],
            ],
            dtype="float64" if bounding_boxes.dtype == torch.float64 else "float32",
        )

        expected_bboxes = reference_affine_bounding_boxes_helper(
            bounding_boxes,
            format=bounding_boxes.format,
            canvas_size=bounding_boxes.canvas_size,
            affine_matrix=affine_matrix,
        )

        return datapoints.BoundingBoxes.wrap_like(bounding_boxes, expected_bboxes)

    @pytest.mark.parametrize("format", list(datapoints.BoundingBoxFormat))
    @pytest.mark.parametrize("fn", [F.vertical_flip, transform_cls_to_functional(transforms.RandomVerticalFlip, p=1)])
    def test_bounding_boxes_correctness(self, format, fn):
        bounding_boxes = make_bounding_box(format=format)

        actual = fn(bounding_boxes)
        expected = self._reference_vertical_flip_bounding_boxes(bounding_boxes)

        torch.testing.assert_close(actual, expected)

    @pytest.mark.parametrize(
        "make_input",
        [make_image_tensor, make_image_pil, make_image, make_bounding_box, make_segmentation_mask, make_video],
    )
    @pytest.mark.parametrize("device", cpu_and_cuda())
    def test_transform_noop(self, make_input, device):
        input = make_input(device=device)

        transform = transforms.RandomVerticalFlip(p=0)

        output = transform(input)

        assert_equal(output, input)


class TestRotate:
    _EXHAUSTIVE_TYPE_AFFINE_KWARGS = dict(
        # float, int
        angle=[-10.9, 18],
        # None
        # two-list of float, two-list of int, two-tuple of float, two-tuple of int
        center=[None, [1.2, 4.9], [-3, 1], (2.5, -4.7), (3, 2)],
    )
    _MINIMAL_AFFINE_KWARGS = {k: vs[0] for k, vs in _EXHAUSTIVE_TYPE_AFFINE_KWARGS.items()}
    _CORRECTNESS_AFFINE_KWARGS = {
        k: [v for v in vs if v is None or isinstance(v, float) or isinstance(v, list)]
        for k, vs in _EXHAUSTIVE_TYPE_AFFINE_KWARGS.items()
    }

    _EXHAUSTIVE_TYPE_TRANSFORM_AFFINE_RANGES = dict(
        degrees=[30, (-15, 20)],
    )
    _CORRECTNESS_TRANSFORM_AFFINE_RANGES = {k: vs[0] for k, vs in _EXHAUSTIVE_TYPE_TRANSFORM_AFFINE_RANGES.items()}

    @param_value_parametrization(
        angle=_EXHAUSTIVE_TYPE_AFFINE_KWARGS["angle"],
        interpolation=[transforms.InterpolationMode.NEAREST, transforms.InterpolationMode.BILINEAR],
        expand=[False, True],
        center=_EXHAUSTIVE_TYPE_AFFINE_KWARGS["center"],
        fill=EXHAUSTIVE_TYPE_FILLS,
    )
    @pytest.mark.parametrize("dtype", [torch.float32, torch.uint8])
    @pytest.mark.parametrize("device", cpu_and_cuda())
    def test_kernel_image_tensor(self, param, value, dtype, device):
        kwargs = {param: value}
        if param != "angle":
            kwargs["angle"] = self._MINIMAL_AFFINE_KWARGS["angle"]
        check_kernel(
            F.rotate_image_tensor,
            make_image(dtype=dtype, device=device),
            **kwargs,
            check_scripted_vs_eager=not (param == "fill" and isinstance(value, (int, float))),
        )

    @param_value_parametrization(
        angle=_EXHAUSTIVE_TYPE_AFFINE_KWARGS["angle"],
        expand=[False, True],
        center=_EXHAUSTIVE_TYPE_AFFINE_KWARGS["center"],
    )
    @pytest.mark.parametrize("format", list(datapoints.BoundingBoxFormat))
    @pytest.mark.parametrize("dtype", [torch.float32, torch.uint8])
    @pytest.mark.parametrize("device", cpu_and_cuda())
    def test_kernel_bounding_boxes(self, param, value, format, dtype, device):
        kwargs = {param: value}
        if param != "angle":
            kwargs["angle"] = self._MINIMAL_AFFINE_KWARGS["angle"]

        bounding_boxes = make_bounding_box(format=format, dtype=dtype, device=device)

        check_kernel(
            F.rotate_bounding_boxes,
            bounding_boxes,
            format=format,
            canvas_size=bounding_boxes.canvas_size,
            **kwargs,
        )

    @pytest.mark.parametrize("make_mask", [make_segmentation_mask, make_detection_mask])
    def test_kernel_mask(self, make_mask):
        check_kernel(F.rotate_mask, make_mask(), **self._MINIMAL_AFFINE_KWARGS)

    def test_kernel_video(self):
        check_kernel(F.rotate_video, make_video(), **self._MINIMAL_AFFINE_KWARGS)

    @pytest.mark.parametrize(
        "make_input",
        [make_image_tensor, make_image_pil, make_image, make_bounding_box, make_segmentation_mask, make_video],
    )
    def test_functional(self, make_input):
        check_functional(F.rotate, make_input(), **self._MINIMAL_AFFINE_KWARGS)

    @pytest.mark.parametrize(
        ("kernel", "input_type"),
        [
            (F.rotate_image_tensor, torch.Tensor),
            (F.rotate_image_pil, PIL.Image.Image),
            (F.rotate_image_tensor, datapoints.Image),
            (F.rotate_bounding_boxes, datapoints.BoundingBoxes),
            (F.rotate_mask, datapoints.Mask),
            (F.rotate_video, datapoints.Video),
        ],
    )
    def test_functional_signature(self, kernel, input_type):
        check_functional_kernel_signature_match(F.rotate, kernel=kernel, input_type=input_type)

    @pytest.mark.parametrize(
        "make_input",
        [make_image_tensor, make_image_pil, make_image, make_bounding_box, make_segmentation_mask, make_video],
    )
    @pytest.mark.parametrize("device", cpu_and_cuda())
    def test_transform(self, make_input, device):
        check_transform(
            transforms.RandomRotation, make_input(device=device), **self._CORRECTNESS_TRANSFORM_AFFINE_RANGES
        )

    @pytest.mark.parametrize("angle", _CORRECTNESS_AFFINE_KWARGS["angle"])
    @pytest.mark.parametrize("center", _CORRECTNESS_AFFINE_KWARGS["center"])
    @pytest.mark.parametrize(
        "interpolation", [transforms.InterpolationMode.NEAREST, transforms.InterpolationMode.BILINEAR]
    )
    @pytest.mark.parametrize("expand", [False, True])
    @pytest.mark.parametrize("fill", CORRECTNESS_FILLS)
    def test_functional_image_correctness(self, angle, center, interpolation, expand, fill):
        image = make_image(dtype=torch.uint8, device="cpu")

        fill = adapt_fill(fill, dtype=torch.uint8)

        actual = F.rotate(image, angle=angle, center=center, interpolation=interpolation, expand=expand, fill=fill)
        expected = F.to_image_tensor(
            F.rotate(
                F.to_image_pil(image), angle=angle, center=center, interpolation=interpolation, expand=expand, fill=fill
            )
        )

        mae = (actual.float() - expected.float()).abs().mean()
        assert mae < 1 if interpolation is transforms.InterpolationMode.NEAREST else 6

    @pytest.mark.parametrize("center", _CORRECTNESS_AFFINE_KWARGS["center"])
    @pytest.mark.parametrize(
        "interpolation", [transforms.InterpolationMode.NEAREST, transforms.InterpolationMode.BILINEAR]
    )
    @pytest.mark.parametrize("expand", [False, True])
    @pytest.mark.parametrize("fill", CORRECTNESS_FILLS)
    @pytest.mark.parametrize("seed", list(range(5)))
    def test_transform_image_correctness(self, center, interpolation, expand, fill, seed):
        image = make_image(dtype=torch.uint8, device="cpu")

        fill = adapt_fill(fill, dtype=torch.uint8)

        transform = transforms.RandomRotation(
            **self._CORRECTNESS_TRANSFORM_AFFINE_RANGES,
            center=center,
            interpolation=interpolation,
            expand=expand,
            fill=fill,
        )

        torch.manual_seed(seed)
        actual = transform(image)

        torch.manual_seed(seed)
        expected = F.to_image_tensor(transform(F.to_image_pil(image)))

        mae = (actual.float() - expected.float()).abs().mean()
        assert mae < 1 if interpolation is transforms.InterpolationMode.NEAREST else 6

    def _reference_rotate_bounding_boxes(self, bounding_boxes, *, angle, expand, center):
        # FIXME
        if expand:
            raise ValueError("This reference currently does not support expand=True")

        if center is None:
            center = [s * 0.5 for s in bounding_boxes.canvas_size[::-1]]

        a = np.cos(angle * np.pi / 180.0)
        b = np.sin(angle * np.pi / 180.0)
        cx = center[0]
        cy = center[1]
        affine_matrix = np.array(
            [
                [a, b, cx - cx * a - b * cy],
                [-b, a, cy + cx * b - a * cy],
            ],
            dtype="float64" if bounding_boxes.dtype == torch.float64 else "float32",
        )

        expected_bboxes = reference_affine_bounding_boxes_helper(
            bounding_boxes,
            format=bounding_boxes.format,
            canvas_size=bounding_boxes.canvas_size,
            affine_matrix=affine_matrix,
        )

        return expected_bboxes

    @pytest.mark.parametrize("format", list(datapoints.BoundingBoxFormat))
    @pytest.mark.parametrize("angle", _CORRECTNESS_AFFINE_KWARGS["angle"])
    # TODO: add support for expand=True in the reference
    @pytest.mark.parametrize("expand", [False])
    @pytest.mark.parametrize("center", _CORRECTNESS_AFFINE_KWARGS["center"])
    def test_functional_bounding_boxes_correctness(self, format, angle, expand, center):
        bounding_boxes = make_bounding_box(format=format)

        actual = F.rotate(bounding_boxes, angle=angle, expand=expand, center=center)
        expected = self._reference_rotate_bounding_boxes(bounding_boxes, angle=angle, expand=expand, center=center)

        torch.testing.assert_close(actual, expected)

    @pytest.mark.parametrize("format", list(datapoints.BoundingBoxFormat))
    # TODO: add support for expand=True in the reference
    @pytest.mark.parametrize("expand", [False])
    @pytest.mark.parametrize("center", _CORRECTNESS_AFFINE_KWARGS["center"])
    @pytest.mark.parametrize("seed", list(range(5)))
    def test_transform_bounding_boxes_correctness(self, format, expand, center, seed):
        bounding_boxes = make_bounding_box(format=format)

        transform = transforms.RandomRotation(**self._CORRECTNESS_TRANSFORM_AFFINE_RANGES, expand=expand, center=center)

        torch.manual_seed(seed)
        params = transform._get_params([bounding_boxes])

        torch.manual_seed(seed)
        actual = transform(bounding_boxes)

        expected = self._reference_rotate_bounding_boxes(bounding_boxes, **params, expand=expand, center=center)

        torch.testing.assert_close(actual, expected)

    @pytest.mark.parametrize("degrees", _EXHAUSTIVE_TYPE_TRANSFORM_AFFINE_RANGES["degrees"])
    @pytest.mark.parametrize("seed", list(range(10)))
    def test_transform_get_params_bounds(self, degrees, seed):
        transform = transforms.RandomRotation(degrees=degrees)

        torch.manual_seed(seed)
        params = transform._get_params([])

        if isinstance(degrees, (int, float)):
            assert -degrees <= params["angle"] <= degrees
        else:
            assert degrees[0] <= params["angle"] <= degrees[1]

    @pytest.mark.parametrize("param", ["degrees", "center"])
    @pytest.mark.parametrize("value", [0, [0], [0, 0, 0]])
    def test_transform_sequence_len_errors(self, param, value):
        if param == "degrees" and not isinstance(value, list):
            return

        kwargs = {param: value}
        if param != "degrees":
            kwargs["degrees"] = 0

        with pytest.raises(
            ValueError if isinstance(value, list) else TypeError, match=f"{param} should be a sequence of length 2"
        ):
            transforms.RandomRotation(**kwargs)

    def test_transform_negative_degrees_error(self):
        with pytest.raises(ValueError, match="If degrees is a single number, it must be positive"):
            transforms.RandomAffine(degrees=-1)

    def test_transform_unknown_fill_error(self):
        with pytest.raises(TypeError, match="Got inappropriate fill arg"):
            transforms.RandomAffine(degrees=0, fill="fill")


class TestCompose:
    class BuiltinTransform(transforms.Transform):
        def _transform(self, inpt, params):
            return inpt

    class PackedInputTransform(nn.Module):
        def forward(self, sample):
            assert len(sample) == 2
            return sample

    class UnpackedInputTransform(nn.Module):
        def forward(self, image, label):
            return image, label

    @pytest.mark.parametrize(
        "transform_clss",
        [
            [BuiltinTransform],
            [PackedInputTransform],
            [UnpackedInputTransform],
            [BuiltinTransform, BuiltinTransform],
            [PackedInputTransform, PackedInputTransform],
            [UnpackedInputTransform, UnpackedInputTransform],
            [BuiltinTransform, PackedInputTransform, BuiltinTransform],
            [BuiltinTransform, UnpackedInputTransform, BuiltinTransform],
            [PackedInputTransform, BuiltinTransform, PackedInputTransform],
            [UnpackedInputTransform, BuiltinTransform, UnpackedInputTransform],
        ],
    )
    @pytest.mark.parametrize("unpack", [True, False])
    def test_packed_unpacked(self, transform_clss, unpack):
        needs_packed_inputs = any(issubclass(cls, self.PackedInputTransform) for cls in transform_clss)
        needs_unpacked_inputs = any(issubclass(cls, self.UnpackedInputTransform) for cls in transform_clss)
        assert not (needs_packed_inputs and needs_unpacked_inputs)

        transform = transforms.Compose([cls() for cls in transform_clss])

        image = make_image()
        label = 3
        packed_input = (image, label)

        def call_transform():
            if unpack:
                return transform(*packed_input)
            else:
                return transform(packed_input)

        if needs_unpacked_inputs and not unpack:
            with pytest.raises(TypeError, match="missing 1 required positional argument"):
                call_transform()
        elif needs_packed_inputs and unpack:
            with pytest.raises(TypeError, match="takes 2 positional arguments but 3 were given"):
                call_transform()
        else:
            output = call_transform()

            assert isinstance(output, tuple) and len(output) == 2
            assert output[0] is image
            assert output[1] is label


class TestToDtype:
    @pytest.mark.parametrize(
        ("kernel", "make_input"),
        [
            (F.to_dtype_image_tensor, make_image_tensor),
            (F.to_dtype_image_tensor, make_image),
            (F.to_dtype_video, make_video),
        ],
    )
    @pytest.mark.parametrize("input_dtype", [torch.float32, torch.float64, torch.uint8])
    @pytest.mark.parametrize("output_dtype", [torch.float32, torch.float64, torch.uint8])
    @pytest.mark.parametrize("device", cpu_and_cuda())
    @pytest.mark.parametrize("scale", (True, False))
    def test_kernel(self, kernel, make_input, input_dtype, output_dtype, device, scale):
        check_kernel(
            kernel,
            make_input(dtype=input_dtype, device=device),
            expect_same_dtype=input_dtype is output_dtype,
            dtype=output_dtype,
            scale=scale,
        )

    @pytest.mark.parametrize("make_input", [make_image_tensor, make_image, make_video])
    @pytest.mark.parametrize("input_dtype", [torch.float32, torch.float64, torch.uint8])
    @pytest.mark.parametrize("output_dtype", [torch.float32, torch.float64, torch.uint8])
    @pytest.mark.parametrize("device", cpu_and_cuda())
    @pytest.mark.parametrize("scale", (True, False))
    def test_functional(self, make_input, input_dtype, output_dtype, device, scale):
        check_functional(
            F.to_dtype,
            make_input(dtype=input_dtype, device=device),
            dtype=output_dtype,
            scale=scale,
        )

    @pytest.mark.parametrize(
        "make_input",
        [make_image_tensor, make_image, make_bounding_box, make_segmentation_mask, make_video],
    )
    @pytest.mark.parametrize("input_dtype", [torch.float32, torch.float64, torch.uint8])
    @pytest.mark.parametrize("output_dtype", [torch.float32, torch.float64, torch.uint8])
    @pytest.mark.parametrize("device", cpu_and_cuda())
    @pytest.mark.parametrize("scale", (True, False))
    @pytest.mark.parametrize("as_dict", (True, False))
    def test_transform(self, make_input, input_dtype, output_dtype, device, scale, as_dict):
        input = make_input(dtype=input_dtype, device=device)
        if as_dict:
            output_dtype = {type(input): output_dtype}
        check_transform(transforms.ToDtype, input, dtype=output_dtype, scale=scale)

    def reference_convert_dtype_image_tensor(self, image, dtype=torch.float, scale=False):
        input_dtype = image.dtype
        output_dtype = dtype

        if not scale:
            return image.to(dtype)

        if output_dtype == input_dtype:
            return image

        def fn(value):
            if input_dtype.is_floating_point:
                if output_dtype.is_floating_point:
                    return value
                else:
                    return round(decimal.Decimal(value) * torch.iinfo(output_dtype).max)
            else:
                input_max_value = torch.iinfo(input_dtype).max

                if output_dtype.is_floating_point:
                    return float(decimal.Decimal(value) / input_max_value)
                else:
                    output_max_value = torch.iinfo(output_dtype).max

                    if input_max_value > output_max_value:
                        factor = (input_max_value + 1) // (output_max_value + 1)
                        return value / factor
                    else:
                        factor = (output_max_value + 1) // (input_max_value + 1)
                        return value * factor

        return torch.tensor(tree_map(fn, image.tolist()), dtype=dtype, device=image.device)

    @pytest.mark.parametrize("input_dtype", [torch.float32, torch.float64, torch.uint8])
    @pytest.mark.parametrize("output_dtype", [torch.float32, torch.float64, torch.uint8])
    @pytest.mark.parametrize("device", cpu_and_cuda())
    @pytest.mark.parametrize("scale", (True, False))
    def test_image_correctness(self, input_dtype, output_dtype, device, scale):
        if input_dtype.is_floating_point and output_dtype == torch.int64:
            pytest.xfail("float to int64 conversion is not supported")

        input = make_image(dtype=input_dtype, device=device)

        out = F.to_dtype(input, dtype=output_dtype, scale=scale)
        expected = self.reference_convert_dtype_image_tensor(input, dtype=output_dtype, scale=scale)

        if input_dtype.is_floating_point and not output_dtype.is_floating_point and scale:
            torch.testing.assert_close(out, expected, atol=1, rtol=0)
        else:
            torch.testing.assert_close(out, expected)

    def was_scaled(self, inpt):
        # this assumes the target dtype is float
        return inpt.max() <= 1

    def make_inpt_with_bbox_and_mask(self, make_input):
        H, W = 10, 10
        inpt_dtype = torch.uint8
        bbox_dtype = torch.float32
        mask_dtype = torch.bool
        sample = {
            "inpt": make_input(size=(H, W), dtype=inpt_dtype),
            "bbox": make_bounding_box(canvas_size=(H, W), dtype=bbox_dtype),
            "mask": make_detection_mask(size=(H, W), dtype=mask_dtype),
        }

        return sample, inpt_dtype, bbox_dtype, mask_dtype

    @pytest.mark.parametrize("make_input", (make_image_tensor, make_image, make_video))
    @pytest.mark.parametrize("scale", (True, False))
    def test_dtype_not_a_dict(self, make_input, scale):
        # assert only inpt gets transformed when dtype isn't a dict

        sample, inpt_dtype, bbox_dtype, mask_dtype = self.make_inpt_with_bbox_and_mask(make_input)
        out = transforms.ToDtype(dtype=torch.float32, scale=scale)(sample)

        assert out["inpt"].dtype != inpt_dtype
        assert out["inpt"].dtype == torch.float32
        if scale:
            assert self.was_scaled(out["inpt"])
        else:
            assert not self.was_scaled(out["inpt"])
        assert out["bbox"].dtype == bbox_dtype
        assert out["mask"].dtype == mask_dtype

    @pytest.mark.parametrize("make_input", (make_image_tensor, make_image, make_video))
    def test_others_catch_all_and_none(self, make_input):
        # make sure "others" works as a catch-all and that None means no conversion

        sample, inpt_dtype, bbox_dtype, mask_dtype = self.make_inpt_with_bbox_and_mask(make_input)
        out = transforms.ToDtype(dtype={datapoints.Mask: torch.int64, "others": None})(sample)
        assert out["inpt"].dtype == inpt_dtype
        assert out["bbox"].dtype == bbox_dtype
        assert out["mask"].dtype != mask_dtype
        assert out["mask"].dtype == torch.int64

    @pytest.mark.parametrize("make_input", (make_image_tensor, make_image, make_video))
    def test_typical_use_case(self, make_input):
        # Typical use-case: want to convert dtype and scale for inpt and just dtype for masks.
        # This just makes sure we now have a decent API for this

        sample, inpt_dtype, bbox_dtype, mask_dtype = self.make_inpt_with_bbox_and_mask(make_input)
        out = transforms.ToDtype(
            dtype={type(sample["inpt"]): torch.float32, datapoints.Mask: torch.int64, "others": None}, scale=True
        )(sample)
        assert out["inpt"].dtype != inpt_dtype
        assert out["inpt"].dtype == torch.float32
        assert self.was_scaled(out["inpt"])
        assert out["bbox"].dtype == bbox_dtype
        assert out["mask"].dtype != mask_dtype
        assert out["mask"].dtype == torch.int64

    @pytest.mark.parametrize("make_input", (make_image_tensor, make_image, make_video))
    def test_errors_warnings(self, make_input):
        sample, inpt_dtype, bbox_dtype, mask_dtype = self.make_inpt_with_bbox_and_mask(make_input)

        with pytest.raises(ValueError, match="No dtype was specified for"):
            out = transforms.ToDtype(dtype={datapoints.Mask: torch.float32})(sample)
        with pytest.warns(UserWarning, match=re.escape("plain `torch.Tensor` will *not* be transformed")):
            transforms.ToDtype(dtype={torch.Tensor: torch.float32, datapoints.Image: torch.float32})
        with pytest.warns(UserWarning, match="no scaling will be done"):
            out = transforms.ToDtype(dtype={"others": None}, scale=True)(sample)
        assert out["inpt"].dtype == inpt_dtype
        assert out["bbox"].dtype == bbox_dtype
        assert out["mask"].dtype == mask_dtype


class TestAdjustBrightness:
    _CORRECTNESS_BRIGHTNESS_FACTORS = [0.5, 0.0, 1.0, 5.0]
    _DEFAULT_BRIGHTNESS_FACTOR = _CORRECTNESS_BRIGHTNESS_FACTORS[0]

    @pytest.mark.parametrize(
        ("kernel", "make_input"),
        [
            (F.adjust_brightness_image_tensor, make_image),
            (F.adjust_brightness_video, make_video),
        ],
    )
    @pytest.mark.parametrize("dtype", [torch.float32, torch.uint8])
    @pytest.mark.parametrize("device", cpu_and_cuda())
    def test_kernel(self, kernel, make_input, dtype, device):
        check_kernel(kernel, make_input(dtype=dtype, device=device), brightness_factor=self._DEFAULT_BRIGHTNESS_FACTOR)

    @pytest.mark.parametrize("make_input", [make_image_tensor, make_image_pil, make_image, make_video])
    def test_functional(self, make_input):
        check_functional(F.adjust_brightness, make_input(), brightness_factor=self._DEFAULT_BRIGHTNESS_FACTOR)

    @pytest.mark.parametrize(
        ("kernel", "input_type"),
        [
            (F.adjust_brightness_image_tensor, torch.Tensor),
            (F.adjust_brightness_image_pil, PIL.Image.Image),
            (F.adjust_brightness_image_tensor, datapoints.Image),
            (F.adjust_brightness_video, datapoints.Video),
        ],
    )
    def test_functional_signature(self, kernel, input_type):
        check_functional_kernel_signature_match(F.adjust_brightness, kernel=kernel, input_type=input_type)

    @pytest.mark.parametrize("brightness_factor", _CORRECTNESS_BRIGHTNESS_FACTORS)
    def test_image_correctness(self, brightness_factor):
        image = make_image(dtype=torch.uint8, device="cpu")

        actual = F.adjust_brightness(image, brightness_factor=brightness_factor)
        expected = F.to_image_tensor(F.adjust_brightness(F.to_image_pil(image), brightness_factor=brightness_factor))

        torch.testing.assert_close(actual, expected)


class TestCutMixMixUp:
    class DummyDataset:
        def __init__(self, size, num_classes):
            self.size = size
            self.num_classes = num_classes
            assert size < num_classes

        def __getitem__(self, idx):
            img = torch.rand(3, 100, 100)
            label = idx  # This ensures all labels in a batch are unique and makes testing easier
            return img, label

        def __len__(self):
            return self.size

    @pytest.mark.parametrize("T", [transforms.CutMix, transforms.MixUp])
    def test_supported_input_structure(self, T):

        batch_size = 32
        num_classes = 100

        dataset = self.DummyDataset(size=batch_size, num_classes=num_classes)

        cutmix_mixup = T(num_classes=num_classes)

        dl = DataLoader(dataset, batch_size=batch_size)

        # Input sanity checks
        img, target = next(iter(dl))
        input_img_size = img.shape[-3:]
        assert isinstance(img, torch.Tensor) and isinstance(target, torch.Tensor)
        assert target.shape == (batch_size,)

        def check_output(img, target):
            assert img.shape == (batch_size, *input_img_size)
            assert target.shape == (batch_size, num_classes)
            torch.testing.assert_close(target.sum(axis=-1), torch.ones(batch_size))
            num_non_zero_labels = (target != 0).sum(axis=-1)
            assert (num_non_zero_labels == 2).all()

        # After Dataloader, as unpacked input
        img, target = next(iter(dl))
        assert target.shape == (batch_size,)
        img, target = cutmix_mixup(img, target)
        check_output(img, target)

        # After Dataloader, as packed input
        packed_from_dl = next(iter(dl))
        assert isinstance(packed_from_dl, list)
        img, target = cutmix_mixup(packed_from_dl)
        check_output(img, target)

        # As collation function. We expect default_collate to be used by users.
        def collate_fn_1(batch):
            return cutmix_mixup(default_collate(batch))

        def collate_fn_2(batch):
            return cutmix_mixup(*default_collate(batch))

        for collate_fn in (collate_fn_1, collate_fn_2):
            dl = DataLoader(dataset, batch_size=batch_size, collate_fn=collate_fn)
            img, target = next(iter(dl))
            check_output(img, target)

    @needs_cuda
    @pytest.mark.parametrize("T", [transforms.CutMix, transforms.MixUp])
    def test_cpu_vs_gpu(self, T):
        num_classes = 10
        batch_size = 3
        H, W = 12, 12

        imgs = torch.rand(batch_size, 3, H, W)
        labels = torch.randint(0, num_classes, (batch_size,))
        cutmix_mixup = T(alpha=0.5, num_classes=num_classes)

        _check_kernel_cuda_vs_cpu(cutmix_mixup, imgs, labels, rtol=None, atol=None)

    @pytest.mark.parametrize("T", [transforms.CutMix, transforms.MixUp])
    def test_error(self, T):

        num_classes = 10
        batch_size = 9

        imgs = torch.rand(batch_size, 3, 12, 12)
        cutmix_mixup = T(alpha=0.5, num_classes=num_classes)

        for input_with_bad_type in (
            F.to_pil_image(imgs[0]),
            datapoints.Mask(torch.rand(12, 12)),
            datapoints.BoundingBoxes(torch.rand(2, 4), format="XYXY", canvas_size=12),
        ):
            with pytest.raises(ValueError, match="does not support PIL images, "):
                cutmix_mixup(input_with_bad_type)

        with pytest.raises(ValueError, match="Could not infer where the labels are"):
            cutmix_mixup({"img": imgs, "Nothing_else": 3})

        with pytest.raises(ValueError, match="labels tensor should be of shape"):
            # Note: the error message isn't ideal, but that's because the label heuristic found the img as the label
            # It's OK, it's an edge-case. The important thing is that this fails loudly instead of passing silently
            cutmix_mixup(imgs)

        with pytest.raises(ValueError, match="When using the default labels_getter"):
            cutmix_mixup(imgs, "not_a_tensor")

        with pytest.raises(ValueError, match="labels tensor should be of shape"):
            cutmix_mixup(imgs, torch.randint(0, 2, size=(2, 3)))

        with pytest.raises(ValueError, match="Expected a batched input with 4 dims"):
            cutmix_mixup(imgs[None, None], torch.randint(0, num_classes, size=(batch_size,)))

        with pytest.raises(ValueError, match="does not match the batch size of the labels"):
            cutmix_mixup(imgs, torch.randint(0, num_classes, size=(batch_size + 1,)))

        with pytest.raises(ValueError, match="labels tensor should be of shape"):
            # The purpose of this check is more about documenting the current
            # behaviour of what happens on a Compose(), rather than actually
            # asserting the expected behaviour. We may support Compose() in the
            # future, e.g. for 2 consecutive CutMix?
            labels = torch.randint(0, num_classes, size=(batch_size,))
            transforms.Compose([cutmix_mixup, cutmix_mixup])(imgs, labels)


@pytest.mark.parametrize("key", ("labels", "LABELS", "LaBeL", "SOME_WEIRD_KEY_THAT_HAS_LABeL_IN_IT"))
@pytest.mark.parametrize("sample_type", (tuple, list, dict))
def test_labels_getter_default_heuristic(key, sample_type):
    labels = torch.arange(10)
    sample = {key: labels, "another_key": "whatever"}
    if sample_type is not dict:
        sample = sample_type((None, sample, "whatever_again"))
    assert transforms._utils._find_labels_default_heuristic(sample) is labels

    if key.lower() != "labels":
        # If "labels" is in the dict (case-insensitive),
        # it takes precedence over other keys which would otherwise be a match
        d = {key: "something_else", "labels": labels}
        assert transforms._utils._find_labels_default_heuristic(d) is labels


class TestShapeGetters:
    @pytest.mark.parametrize(
        ("kernel", "make_input"),
        [
            (F.get_dimensions_image_tensor, make_image_tensor),
            (F.get_dimensions_image_pil, make_image_pil),
            (F.get_dimensions_image_tensor, make_image),
            (F.get_dimensions_video, make_video),
        ],
    )
    def test_get_dimensions(self, kernel, make_input):
        size = (10, 10)
        color_space, num_channels = "RGB", 3

        input = make_input(size, color_space=color_space)

        assert kernel(input) == F.get_dimensions(input) == [num_channels, *size]

    @pytest.mark.parametrize(
        ("kernel", "make_input"),
        [
            (F.get_num_channels_image_tensor, make_image_tensor),
            (F.get_num_channels_image_pil, make_image_pil),
            (F.get_num_channels_image_tensor, make_image),
            (F.get_num_channels_video, make_video),
        ],
    )
    def test_get_num_channels(self, kernel, make_input):
        color_space, num_channels = "RGB", 3

        input = make_input(color_space=color_space)

        assert kernel(input) == F.get_num_channels(input) == num_channels

    @pytest.mark.parametrize(
        ("kernel", "make_input"),
        [
            (F.get_size_image_tensor, make_image_tensor),
            (F.get_size_image_pil, make_image_pil),
            (F.get_size_image_tensor, make_image),
            (F.get_size_bounding_boxes, make_bounding_box),
            (F.get_size_mask, make_detection_mask),
            (F.get_size_mask, make_segmentation_mask),
            (F.get_size_video, make_video),
        ],
    )
    def test_get_size(self, kernel, make_input):
        size = (10, 10)

        input = make_input(size)

        assert kernel(input) == F.get_size(input) == list(size)

    @pytest.mark.parametrize(
        ("kernel", "make_input"),
        [
            (F.get_num_frames_video, make_video_tensor),
            (F.get_num_frames_video, make_video),
        ],
    )
    def test_get_num_frames(self, kernel, make_input):
        num_frames = 4

        input = make_input(num_frames=num_frames)

        assert kernel(input) == F.get_num_frames(input) == num_frames

    @pytest.mark.parametrize(
        ("functional", "make_input"),
        [
            (F.get_dimensions, make_bounding_box),
            (F.get_dimensions, make_detection_mask),
            (F.get_dimensions, make_segmentation_mask),
            (F.get_num_channels, make_bounding_box),
            (F.get_num_channels, make_detection_mask),
            (F.get_num_channels, make_segmentation_mask),
            (F.get_num_frames, make_image_pil),
            (F.get_num_frames, make_image),
            (F.get_num_frames, make_bounding_box),
            (F.get_num_frames, make_detection_mask),
            (F.get_num_frames, make_segmentation_mask),
        ],
    )
    def test_unsupported_types(self, functional, make_input):
        input = make_input()

        with pytest.raises(TypeError, match=re.escape(str(type(input)))):
            functional(input)


class TestRegisterKernel:
    @pytest.mark.parametrize("functional", (F.resize, "resize"))
    def test_register_kernel(self, functional):
        class CustomDatapoint(datapoints.Datapoint):
            pass

        kernel_was_called = False

        @F.register_kernel(functional, CustomDatapoint)
        def new_resize(dp, *args, **kwargs):
            nonlocal kernel_was_called
            kernel_was_called = True
            return dp

        t = transforms.Resize(size=(224, 224), antialias=True)

        my_dp = CustomDatapoint(torch.rand(3, 10, 10))
        out = t(my_dp)
        assert out is my_dp
        assert kernel_was_called

        # Sanity check to make sure we didn't override the kernel of other types
        t(torch.rand(3, 10, 10)).shape == (3, 224, 224)
        t(datapoints.Image(torch.rand(3, 10, 10))).shape == (3, 224, 224)

    def test_errors(self):
        with pytest.raises(ValueError, match="Could not find functional with name"):
            F.register_kernel("bad_name", datapoints.Image)

        with pytest.raises(ValueError, match="Kernels can only be registered on functionals"):
            F.register_kernel(datapoints.Image, F.resize)

        with pytest.raises(ValueError, match="Kernels can only be registered for subclasses"):
            F.register_kernel(F.resize, object)

        with pytest.raises(ValueError, match="cannot be registered for the builtin datapoint classes"):
            F.register_kernel(F.resize, datapoints.Image)(F.resize_image_tensor)

        class CustomDatapoint(datapoints.Datapoint):
            pass

        def resize_custom_datapoint():
            pass

        F.register_kernel(F.resize, CustomDatapoint)(resize_custom_datapoint)

        with pytest.raises(ValueError, match="already has a kernel registered for type"):
            F.register_kernel(F.resize, CustomDatapoint)(resize_custom_datapoint)


class TestGetKernel:
    # We are using F.resize as functional and the kernels below as proxy. Any other functional / kernels combination
    # would also be fine
    KERNELS = {
        torch.Tensor: F.resize_image_tensor,
        PIL.Image.Image: F.resize_image_pil,
        datapoints.Image: F.resize_image_tensor,
        datapoints.BoundingBoxes: F.resize_bounding_boxes,
        datapoints.Mask: F.resize_mask,
        datapoints.Video: F.resize_video,
    }

    def test_unsupported_types(self):
        class MyTensor(torch.Tensor):
            pass

        class MyPILImage(PIL.Image.Image):
            pass

        for input_type in [str, int, object, MyTensor, MyPILImage]:
            with pytest.raises(TypeError, match="supports inputs of type"):
                _get_kernel(F.resize, input_type)

    def test_exact_match(self):
        # We cannot use F.resize together with self.KERNELS mapping here directly here, since this is only the
        # ideal wrapping. Practically, we have an intermediate wrapper layer. Thus, we create a new resize functional
        # here, register the kernels without wrapper, and check the exact matching afterwards.
        def resize_with_pure_kernels():
            pass

        for input_type, kernel in self.KERNELS.items():
            _register_kernel_internal(resize_with_pure_kernels, input_type, datapoint_wrapper=False)(kernel)

            assert _get_kernel(resize_with_pure_kernels, input_type) is kernel

    def test_builtin_datapoint_subclass(self):
        # We cannot use F.resize together with self.KERNELS mapping here directly here, since this is only the
        # ideal wrapping. Practically, we have an intermediate wrapper layer. Thus, we create a new resize functional
        # here, register the kernels without wrapper, and check if subclasses of our builtin datapoints get dispatched
        # to the kernel of the corresponding superclass
        def resize_with_pure_kernels():
            pass

        class MyImage(datapoints.Image):
            pass

        class MyBoundingBoxes(datapoints.BoundingBoxes):
            pass

        class MyMask(datapoints.Mask):
            pass

        class MyVideo(datapoints.Video):
            pass

        for custom_datapoint_subclass in [
            MyImage,
            MyBoundingBoxes,
            MyMask,
            MyVideo,
        ]:
            builtin_datapoint_class = custom_datapoint_subclass.__mro__[1]
            builtin_datapoint_kernel = self.KERNELS[builtin_datapoint_class]
            _register_kernel_internal(resize_with_pure_kernels, builtin_datapoint_class, datapoint_wrapper=False)(
                builtin_datapoint_kernel
            )

            assert _get_kernel(resize_with_pure_kernels, custom_datapoint_subclass) is builtin_datapoint_kernel

    def test_datapoint_subclass(self):
        class MyDatapoint(datapoints.Datapoint):
            pass

        with pytest.raises(TypeError, match="supports inputs of type"):
            _get_kernel(F.resize, MyDatapoint)

        def resize_my_datapoint():
            pass

        _register_kernel_internal(F.resize, MyDatapoint, datapoint_wrapper=False)(resize_my_datapoint)

        assert _get_kernel(F.resize, MyDatapoint) is resize_my_datapoint


class TestPermuteChannels:
    _DEFAULT_PERMUTATION = [2, 0, 1]

    @pytest.mark.parametrize(
        ("kernel", "make_input"),
        [
            (F.permute_channels_image_tensor, make_image_tensor),
            # FIXME
            # check_kernel does not support PIL kernel, but it should
            (F.permute_channels_image_tensor, make_image),
            (F.permute_channels_video, make_video),
        ],
    )
    @pytest.mark.parametrize("dtype", [torch.float32, torch.uint8])
    @pytest.mark.parametrize("device", cpu_and_cuda())
    def test_kernel(self, kernel, make_input, dtype, device):
        check_kernel(kernel, make_input(dtype=dtype, device=device), permutation=self._DEFAULT_PERMUTATION)

    @pytest.mark.parametrize("make_input", [make_image_tensor, make_image_pil, make_image, make_video])
    def test_functional(self, make_input):
        check_functional(F.permute_channels, make_input(), permutation=self._DEFAULT_PERMUTATION)

    @pytest.mark.parametrize(
        ("kernel", "input_type"),
        [
            (F.permute_channels_image_tensor, torch.Tensor),
            (F.permute_channels_image_pil, PIL.Image.Image),
            (F.permute_channels_image_tensor, datapoints.Image),
            (F.permute_channels_video, datapoints.Video),
        ],
    )
    def test_functional_signature(self, kernel, input_type):
        check_functional_kernel_signature_match(F.permute_channels, kernel=kernel, input_type=input_type)

    def reference_image_correctness(self, image, permutation):
        channel_images = image.split(1, dim=-3)
        permuted_channel_images = [channel_images[channel_idx] for channel_idx in permutation]
        return datapoints.Image(torch.concat(permuted_channel_images, dim=-3))

    @pytest.mark.parametrize("permutation", [[2, 0, 1], [1, 2, 0], [2, 0, 1], [0, 1, 2]])
    @pytest.mark.parametrize("batch_dims", [(), (2,), (2, 1)])
    def test_image_correctness(self, permutation, batch_dims):
        image = make_image(batch_dims=batch_dims)

        actual = F.permute_channels(image, permutation=permutation)
        expected = self.reference_image_correctness(image, permutation=permutation)

        torch.testing.assert_close(actual, expected)
