import os

import numpy as np
import pytest
import torch
from common_utils import (
    get_tmp_dir,
    int_dtypes,
    float_dtypes,
    _create_data,
    _create_data_batch,
    _assert_equal_tensor_to_pil,
    _assert_approx_equal_tensor_to_pil,
    cpu_and_gpu,
    assert_equal,
)
from PIL import Image
from torchvision import transforms as T
from torchvision.transforms import InterpolationMode
from torchvision.transforms import functional as F
from torchvision.transforms.autoaugment import _apply_op

NEAREST, BILINEAR, BICUBIC = InterpolationMode.NEAREST, InterpolationMode.BILINEAR, InterpolationMode.BICUBIC


def _test_transform_vs_scripted(transform, s_transform, tensor, msg=None):
    torch.manual_seed(12)
    out1 = transform(tensor)
    torch.manual_seed(12)
    out2 = s_transform(tensor)
    assert_equal(out1, out2, msg=msg)


def _test_transform_vs_scripted_on_batch(transform, s_transform, batch_tensors, msg=None):
    torch.manual_seed(12)
    transformed_batch = transform(batch_tensors)

    for i in range(len(batch_tensors)):
        img_tensor = batch_tensors[i, ...]
        torch.manual_seed(12)
        transformed_img = transform(img_tensor)
        assert_equal(transformed_img, transformed_batch[i, ...], msg=msg)

    torch.manual_seed(12)
    s_transformed_batch = s_transform(batch_tensors)
    assert_equal(transformed_batch, s_transformed_batch, msg=msg)


def _test_functional_op(f, device, channels=3, fn_kwargs=None, test_exact_match=True, **match_kwargs):
    fn_kwargs = fn_kwargs or {}

    tensor, pil_img = _create_data(height=10, width=10, channels=channels, device=device)
    transformed_tensor = f(tensor, **fn_kwargs)
    transformed_pil_img = f(pil_img, **fn_kwargs)
    if test_exact_match:
        _assert_equal_tensor_to_pil(transformed_tensor, transformed_pil_img, **match_kwargs)
    else:
        _assert_approx_equal_tensor_to_pil(transformed_tensor, transformed_pil_img, **match_kwargs)


def _test_class_op(method, device, channels=3, meth_kwargs=None, test_exact_match=True, **match_kwargs):
    # TODO: change the name: it's not a method, it's a class.
    meth_kwargs = meth_kwargs or {}

    # test for class interface
    f = method(**meth_kwargs)
    scripted_fn = torch.jit.script(f)

    tensor, pil_img = _create_data(26, 34, channels, device=device)
    # set seed to reproduce the same transformation for tensor and PIL image
    torch.manual_seed(12)
    transformed_tensor = f(tensor)
    torch.manual_seed(12)
    transformed_pil_img = f(pil_img)
    if test_exact_match:
        _assert_equal_tensor_to_pil(transformed_tensor, transformed_pil_img, **match_kwargs)
    else:
        _assert_approx_equal_tensor_to_pil(transformed_tensor.float(), transformed_pil_img, **match_kwargs)

    torch.manual_seed(12)
    transformed_tensor_script = scripted_fn(tensor)
    assert_equal(transformed_tensor, transformed_tensor_script)

    batch_tensors = _create_data_batch(height=23, width=34, channels=channels, num_samples=4, device=device)
    _test_transform_vs_scripted_on_batch(f, scripted_fn, batch_tensors)

    with get_tmp_dir() as tmp_dir:
        scripted_fn.save(os.path.join(tmp_dir, f"t_{method.__name__}.pt"))


def _test_op(func, method, device, channels=3, fn_kwargs=None, meth_kwargs=None, test_exact_match=True, **match_kwargs):
    _test_functional_op(func, device, channels, fn_kwargs, test_exact_match=test_exact_match, **match_kwargs)
    _test_class_op(method, device, channels, meth_kwargs, test_exact_match=test_exact_match, **match_kwargs)


@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize(
    "func,method,fn_kwargs,match_kwargs",
    [
        (F.hflip, T.RandomHorizontalFlip, None, {}),
        (F.vflip, T.RandomVerticalFlip, None, {}),
        (F.invert, T.RandomInvert, None, {}),
        (F.posterize, T.RandomPosterize, {"bits": 4}, {}),
        (F.solarize, T.RandomSolarize, {"threshold": 192.0}, {}),
        (F.adjust_sharpness, T.RandomAdjustSharpness, {"sharpness_factor": 2.0}, {}),
        (
            F.autocontrast,
            T.RandomAutocontrast,
            None,
            {"test_exact_match": False, "agg_method": "max", "tol": (1 + 1e-5), "allowed_percentage_diff": 0.05},
        ),
        (F.equalize, T.RandomEqualize, None, {}),
    ],
)
@pytest.mark.parametrize("channels", [1, 3])
def test_random(func, method, device, channels, fn_kwargs, match_kwargs):
    _test_op(func, method, device, channels, fn_kwargs, fn_kwargs, **match_kwargs)


@pytest.mark.parametrize("seed", range(10))
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("channels", [1, 3])
class TestColorJitter:
    @pytest.fixture(autouse=True)
    def set_random_seed(self, seed):
        torch.random.manual_seed(seed)

    @pytest.mark.parametrize("brightness", [0.1, 0.5, 1.0, 1.34, (0.3, 0.7), [0.4, 0.5]])
    def test_color_jitter_brightness(self, brightness, device, channels):
        tol = 1.0 + 1e-10
        meth_kwargs = {"brightness": brightness}
        _test_class_op(
            T.ColorJitter,
            meth_kwargs=meth_kwargs,
            test_exact_match=False,
            device=device,
            tol=tol,
            agg_method="max",
            channels=channels,
        )

    @pytest.mark.parametrize("contrast", [0.2, 0.5, 1.0, 1.5, (0.3, 0.7), [0.4, 0.5]])
    def test_color_jitter_contrast(self, contrast, device, channels):
        tol = 1.0 + 1e-10
        meth_kwargs = {"contrast": contrast}
        _test_class_op(
            T.ColorJitter,
            meth_kwargs=meth_kwargs,
            test_exact_match=False,
            device=device,
            tol=tol,
            agg_method="max",
            channels=channels,
        )

    @pytest.mark.parametrize("saturation", [0.5, 0.75, 1.0, 1.25, (0.3, 0.7), [0.3, 0.4]])
    def test_color_jitter_saturation(self, saturation, device, channels):
        tol = 1.0 + 1e-10
        meth_kwargs = {"saturation": saturation}
        _test_class_op(
            T.ColorJitter,
            meth_kwargs=meth_kwargs,
            test_exact_match=False,
            device=device,
            tol=tol,
            agg_method="max",
            channels=channels,
        )

    @pytest.mark.parametrize("hue", [0.2, 0.5, (-0.2, 0.3), [-0.4, 0.5]])
    def test_color_jitter_hue(self, hue, device, channels):
        meth_kwargs = {"hue": hue}
        _test_class_op(
            T.ColorJitter,
            meth_kwargs=meth_kwargs,
            test_exact_match=False,
            device=device,
            tol=16.1,
            agg_method="max",
            channels=channels,
        )

    def test_color_jitter_all(self, device, channels):
        # All 4 parameters together
        meth_kwargs = {"brightness": 0.2, "contrast": 0.2, "saturation": 0.2, "hue": 0.2}
        _test_class_op(
            T.ColorJitter,
            meth_kwargs=meth_kwargs,
            test_exact_match=False,
            device=device,
            tol=12.1,
            agg_method="max",
            channels=channels,
        )


@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("m", ["constant", "edge", "reflect", "symmetric"])
@pytest.mark.parametrize("mul", [1, -1])
def test_pad(m, mul, device):
    fill = 127 if m == "constant" else 0

    # Test functional.pad (PIL and Tensor) with padding as single int
    _test_functional_op(F.pad, fn_kwargs={"padding": mul * 2, "fill": fill, "padding_mode": m}, device=device)
    # Test functional.pad and transforms.Pad with padding as [int, ]
    fn_kwargs = meth_kwargs = {
        "padding": [
            mul * 2,
        ],
        "fill": fill,
        "padding_mode": m,
    }
    _test_op(F.pad, T.Pad, device=device, fn_kwargs=fn_kwargs, meth_kwargs=meth_kwargs)
    # Test functional.pad and transforms.Pad with padding as list
    fn_kwargs = meth_kwargs = {"padding": [mul * 4, 4], "fill": fill, "padding_mode": m}
    _test_op(F.pad, T.Pad, device=device, fn_kwargs=fn_kwargs, meth_kwargs=meth_kwargs)
    # Test functional.pad and transforms.Pad with padding as tuple
    fn_kwargs = meth_kwargs = {"padding": (mul * 2, 2, 2, mul * 2), "fill": fill, "padding_mode": m}
    _test_op(F.pad, T.Pad, device=device, fn_kwargs=fn_kwargs, meth_kwargs=meth_kwargs)


@pytest.mark.parametrize("device", cpu_and_gpu())
def test_crop(device):
    fn_kwargs = {"top": 2, "left": 3, "height": 4, "width": 5}
    # Test transforms.RandomCrop with size and padding as tuple
    meth_kwargs = {
        "size": (4, 5),
        "padding": (4, 4),
        "pad_if_needed": True,
    }
    _test_op(F.crop, T.RandomCrop, device=device, fn_kwargs=fn_kwargs, meth_kwargs=meth_kwargs)

    # Test transforms.functional.crop including outside the image area
    fn_kwargs = {"top": -2, "left": 3, "height": 4, "width": 5}  # top
    _test_functional_op(F.crop, fn_kwargs=fn_kwargs, device=device)

    fn_kwargs = {"top": 1, "left": -3, "height": 4, "width": 5}  # left
    _test_functional_op(F.crop, fn_kwargs=fn_kwargs, device=device)

    fn_kwargs = {"top": 7, "left": 3, "height": 4, "width": 5}  # bottom
    _test_functional_op(F.crop, fn_kwargs=fn_kwargs, device=device)

    fn_kwargs = {"top": 3, "left": 8, "height": 4, "width": 5}  # right
    _test_functional_op(F.crop, fn_kwargs=fn_kwargs, device=device)

    fn_kwargs = {"top": -3, "left": -3, "height": 15, "width": 15}  # all
    _test_functional_op(F.crop, fn_kwargs=fn_kwargs, device=device)


@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize(
    "padding_config",
    [
        {"padding_mode": "constant", "fill": 0},
        {"padding_mode": "constant", "fill": 10},
        {"padding_mode": "constant", "fill": 20},
        {"padding_mode": "edge"},
        {"padding_mode": "reflect"},
    ],
)
@pytest.mark.parametrize(
    "size",
    [
        5,
        [
            5,
        ],
        [6, 6],
    ],
)
def test_crop_pad(size, padding_config, device):
    config = dict(padding_config)
    config["size"] = size
    _test_class_op(T.RandomCrop, device, meth_kwargs=config)


@pytest.mark.parametrize("device", cpu_and_gpu())
def test_center_crop(device, tmpdir):
    fn_kwargs = {"output_size": (4, 5)}
    meth_kwargs = {
        "size": (4, 5),
    }
    _test_op(F.center_crop, T.CenterCrop, device=device, fn_kwargs=fn_kwargs, meth_kwargs=meth_kwargs)
    fn_kwargs = {"output_size": (5,)}
    meth_kwargs = {"size": (5,)}
    _test_op(F.center_crop, T.CenterCrop, device=device, fn_kwargs=fn_kwargs, meth_kwargs=meth_kwargs)
    tensor = torch.randint(0, 256, (3, 10, 10), dtype=torch.uint8, device=device)
    # Test torchscript of transforms.CenterCrop with size as int
    f = T.CenterCrop(size=5)
    scripted_fn = torch.jit.script(f)
    scripted_fn(tensor)

    # Test torchscript of transforms.CenterCrop with size as [int, ]
    f = T.CenterCrop(
        size=[
            5,
        ]
    )
    scripted_fn = torch.jit.script(f)
    scripted_fn(tensor)

    # Test torchscript of transforms.CenterCrop with size as tuple
    f = T.CenterCrop(size=(6, 6))
    scripted_fn = torch.jit.script(f)
    scripted_fn(tensor)

    scripted_fn.save(os.path.join(tmpdir, "t_center_crop.pt"))


@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize(
    "fn, method, out_length",
    [
        # test_five_crop
        (F.five_crop, T.FiveCrop, 5),
        # test_ten_crop
        (F.ten_crop, T.TenCrop, 10),
    ],
)
@pytest.mark.parametrize(
    "size",
    [
        (5,),
        [
            5,
        ],
        (4, 5),
        [4, 5],
    ],
)
def test_x_crop(fn, method, out_length, size, device):
    meth_kwargs = fn_kwargs = {"size": size}
    scripted_fn = torch.jit.script(fn)

    tensor, pil_img = _create_data(height=20, width=20, device=device)
    transformed_t_list = fn(tensor, **fn_kwargs)
    transformed_p_list = fn(pil_img, **fn_kwargs)
    assert len(transformed_t_list) == len(transformed_p_list)
    assert len(transformed_t_list) == out_length
    for transformed_tensor, transformed_pil_img in zip(transformed_t_list, transformed_p_list):
        _assert_equal_tensor_to_pil(transformed_tensor, transformed_pil_img)

    transformed_t_list_script = scripted_fn(tensor.detach().clone(), **fn_kwargs)
    assert len(transformed_t_list) == len(transformed_t_list_script)
    assert len(transformed_t_list_script) == out_length
    for transformed_tensor, transformed_tensor_script in zip(transformed_t_list, transformed_t_list_script):
        assert_equal(transformed_tensor, transformed_tensor_script)

    # test for class interface
    fn = method(**meth_kwargs)
    scripted_fn = torch.jit.script(fn)
    output = scripted_fn(tensor)
    assert len(output) == len(transformed_t_list_script)

    # test on batch of tensors
    batch_tensors = _create_data_batch(height=23, width=34, channels=3, num_samples=4, device=device)
    torch.manual_seed(12)
    transformed_batch_list = fn(batch_tensors)

    for i in range(len(batch_tensors)):
        img_tensor = batch_tensors[i, ...]
        torch.manual_seed(12)
        transformed_img_list = fn(img_tensor)
        for transformed_img, transformed_batch in zip(transformed_img_list, transformed_batch_list):
            assert_equal(transformed_img, transformed_batch[i, ...])


@pytest.mark.parametrize("method", ["FiveCrop", "TenCrop"])
def test_x_crop_save(method, tmpdir):
    fn = getattr(T, method)(
        size=[
            5,
        ]
    )
    scripted_fn = torch.jit.script(fn)
    scripted_fn.save(os.path.join(tmpdir, f"t_op_list_{method}.pt"))


class TestResize:
    @pytest.mark.parametrize("size", [32, 34, 35, 36, 38])
    def test_resize_int(self, size):
        # TODO: Minimal check for bug-fix, improve this later
        x = torch.rand(3, 32, 46)
        t = T.Resize(size=size)
        y = t(x)
        # If size is an int, smaller edge of the image will be matched to this number.
        # i.e, if height > width, then image will be rescaled to (size * height / width, size).
        assert isinstance(y, torch.Tensor)
        assert y.shape[1] == size
        assert y.shape[2] == int(size * 46 / 32)

    @pytest.mark.parametrize("device", cpu_and_gpu())
    @pytest.mark.parametrize("dt", [None, torch.float32, torch.float64])
    @pytest.mark.parametrize(
        "size",
        [
            [
                32,
            ],
            [32, 32],
            (32, 32),
            [34, 35],
        ],
    )
    @pytest.mark.parametrize("max_size", [None, 35, 1000])
    @pytest.mark.parametrize("interpolation", [BILINEAR, BICUBIC, NEAREST])
    def test_resize_scripted(self, dt, size, max_size, interpolation, device):
        tensor, _ = _create_data(height=34, width=36, device=device)
        batch_tensors = torch.randint(0, 256, size=(4, 3, 44, 56), dtype=torch.uint8, device=device)

        if dt is not None:
            # This is a trivial cast to float of uint8 data to test all cases
            tensor = tensor.to(dt)
        if max_size is not None and len(size) != 1:
            pytest.xfail("with max_size, size must be a sequence with 2 elements")

        transform = T.Resize(size=size, interpolation=interpolation, max_size=max_size)
        s_transform = torch.jit.script(transform)
        _test_transform_vs_scripted(transform, s_transform, tensor)
        _test_transform_vs_scripted_on_batch(transform, s_transform, batch_tensors)

    def test_resize_save(self, tmpdir):
        transform = T.Resize(
            size=[
                32,
            ]
        )
        s_transform = torch.jit.script(transform)
        s_transform.save(os.path.join(tmpdir, "t_resize.pt"))

    @pytest.mark.parametrize("device", cpu_and_gpu())
    @pytest.mark.parametrize("scale", [(0.7, 1.2), [0.7, 1.2]])
    @pytest.mark.parametrize("ratio", [(0.75, 1.333), [0.75, 1.333]])
    @pytest.mark.parametrize(
        "size",
        [
            (32,),
            [
                44,
            ],
            [
                32,
            ],
            [32, 32],
            (32, 32),
            [44, 55],
        ],
    )
    @pytest.mark.parametrize("interpolation", [NEAREST, BILINEAR, BICUBIC])
    def test_resized_crop(self, scale, ratio, size, interpolation, device):
        tensor = torch.randint(0, 256, size=(3, 44, 56), dtype=torch.uint8, device=device)
        batch_tensors = torch.randint(0, 256, size=(4, 3, 44, 56), dtype=torch.uint8, device=device)
        transform = T.RandomResizedCrop(size=size, scale=scale, ratio=ratio, interpolation=interpolation)
        s_transform = torch.jit.script(transform)
        _test_transform_vs_scripted(transform, s_transform, tensor)
        _test_transform_vs_scripted_on_batch(transform, s_transform, batch_tensors)

    def test_resized_crop_save(self, tmpdir):
        transform = T.RandomResizedCrop(
            size=[
                32,
            ]
        )
        s_transform = torch.jit.script(transform)
        s_transform.save(os.path.join(tmpdir, "t_resized_crop.pt"))


def _test_random_affine_helper(device, **kwargs):
    tensor = torch.randint(0, 256, size=(3, 44, 56), dtype=torch.uint8, device=device)
    batch_tensors = torch.randint(0, 256, size=(4, 3, 44, 56), dtype=torch.uint8, device=device)
    transform = T.RandomAffine(**kwargs)
    s_transform = torch.jit.script(transform)

    _test_transform_vs_scripted(transform, s_transform, tensor)
    _test_transform_vs_scripted_on_batch(transform, s_transform, batch_tensors)


@pytest.mark.parametrize("device", cpu_and_gpu())
def test_random_affine(device, tmpdir):
    transform = T.RandomAffine(degrees=45.0)
    s_transform = torch.jit.script(transform)
    s_transform.save(os.path.join(tmpdir, "t_random_affine.pt"))


@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("interpolation", [NEAREST, BILINEAR])
@pytest.mark.parametrize("shear", [15, 10.0, (5.0, 10.0), [-15, 15], [-10.0, 10.0, -11.0, 11.0]])
def test_random_affine_shear(device, interpolation, shear):
    _test_random_affine_helper(device, degrees=0.0, interpolation=interpolation, shear=shear)


@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("interpolation", [NEAREST, BILINEAR])
@pytest.mark.parametrize("scale", [(0.7, 1.2), [0.7, 1.2]])
def test_random_affine_scale(device, interpolation, scale):
    _test_random_affine_helper(device, degrees=0.0, interpolation=interpolation, scale=scale)


@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("interpolation", [NEAREST, BILINEAR])
@pytest.mark.parametrize("translate", [(0.1, 0.2), [0.2, 0.1]])
def test_random_affine_translate(device, interpolation, translate):
    _test_random_affine_helper(device, degrees=0.0, interpolation=interpolation, translate=translate)


@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("interpolation", [NEAREST, BILINEAR])
@pytest.mark.parametrize("degrees", [45, 35.0, (-45, 45), [-90.0, 90.0]])
def test_random_affine_degrees(device, interpolation, degrees):
    _test_random_affine_helper(device, degrees=degrees, interpolation=interpolation)


@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("interpolation", [NEAREST, BILINEAR])
@pytest.mark.parametrize(
    "fill",
    [
        85,
        (10, -10, 10),
        0.7,
        [0.0, 0.0, 0.0],
        [
            1,
        ],
        1,
    ],
)
def test_random_affine_fill(device, interpolation, fill):
    _test_random_affine_helper(device, degrees=0.0, interpolation=interpolation, fill=fill)


@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("center", [(0, 0), [10, 10], None, (56, 44)])
@pytest.mark.parametrize("expand", [True, False])
@pytest.mark.parametrize("degrees", [45, 35.0, (-45, 45), [-90.0, 90.0]])
@pytest.mark.parametrize("interpolation", [NEAREST, BILINEAR])
@pytest.mark.parametrize(
    "fill",
    [
        85,
        (10, -10, 10),
        0.7,
        [0.0, 0.0, 0.0],
        [
            1,
        ],
        1,
    ],
)
def test_random_rotate(device, center, expand, degrees, interpolation, fill):
    tensor = torch.randint(0, 256, size=(3, 44, 56), dtype=torch.uint8, device=device)
    batch_tensors = torch.randint(0, 256, size=(4, 3, 44, 56), dtype=torch.uint8, device=device)

    transform = T.RandomRotation(degrees=degrees, interpolation=interpolation, expand=expand, center=center, fill=fill)
    s_transform = torch.jit.script(transform)

    _test_transform_vs_scripted(transform, s_transform, tensor)
    _test_transform_vs_scripted_on_batch(transform, s_transform, batch_tensors)


def test_random_rotate_save(tmpdir):
    transform = T.RandomRotation(degrees=45.0)
    s_transform = torch.jit.script(transform)
    s_transform.save(os.path.join(tmpdir, "t_random_rotate.pt"))


@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("distortion_scale", np.linspace(0.1, 1.0, num=20))
@pytest.mark.parametrize("interpolation", [NEAREST, BILINEAR])
@pytest.mark.parametrize(
    "fill",
    [
        85,
        (10, -10, 10),
        0.7,
        [0.0, 0.0, 0.0],
        [
            1,
        ],
        1,
    ],
)
def test_random_perspective(device, distortion_scale, interpolation, fill):
    tensor = torch.randint(0, 256, size=(3, 44, 56), dtype=torch.uint8, device=device)
    batch_tensors = torch.randint(0, 256, size=(4, 3, 44, 56), dtype=torch.uint8, device=device)

    transform = T.RandomPerspective(distortion_scale=distortion_scale, interpolation=interpolation, fill=fill)
    s_transform = torch.jit.script(transform)

    _test_transform_vs_scripted(transform, s_transform, tensor)
    _test_transform_vs_scripted_on_batch(transform, s_transform, batch_tensors)


def test_random_perspective_save(tmpdir):
    transform = T.RandomPerspective()
    s_transform = torch.jit.script(transform)
    s_transform.save(os.path.join(tmpdir, "t_perspective.pt"))


@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize(
    "Klass, meth_kwargs",
    [(T.Grayscale, {"num_output_channels": 1}), (T.Grayscale, {"num_output_channels": 3}), (T.RandomGrayscale, {})],
)
def test_to_grayscale(device, Klass, meth_kwargs):
    tol = 1.0 + 1e-10
    _test_class_op(Klass, meth_kwargs=meth_kwargs, test_exact_match=False, device=device, tol=tol, agg_method="max")


@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("in_dtype", int_dtypes() + float_dtypes())
@pytest.mark.parametrize("out_dtype", int_dtypes() + float_dtypes())
def test_convert_image_dtype(device, in_dtype, out_dtype):
    tensor, _ = _create_data(26, 34, device=device)
    batch_tensors = torch.rand(4, 3, 44, 56, device=device)

    in_tensor = tensor.to(in_dtype)
    in_batch_tensors = batch_tensors.to(in_dtype)

    fn = T.ConvertImageDtype(dtype=out_dtype)
    scripted_fn = torch.jit.script(fn)

    if (in_dtype == torch.float32 and out_dtype in (torch.int32, torch.int64)) or (
        in_dtype == torch.float64 and out_dtype == torch.int64
    ):
        with pytest.raises(RuntimeError, match=r"cannot be performed safely"):
            _test_transform_vs_scripted(fn, scripted_fn, in_tensor)
        with pytest.raises(RuntimeError, match=r"cannot be performed safely"):
            _test_transform_vs_scripted_on_batch(fn, scripted_fn, in_batch_tensors)
        return

    _test_transform_vs_scripted(fn, scripted_fn, in_tensor)
    _test_transform_vs_scripted_on_batch(fn, scripted_fn, in_batch_tensors)


def test_convert_image_dtype_save(tmpdir):
    fn = T.ConvertImageDtype(dtype=torch.uint8)
    scripted_fn = torch.jit.script(fn)
    scripted_fn.save(os.path.join(tmpdir, "t_convert_dtype.pt"))


@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("policy", [policy for policy in T.AutoAugmentPolicy])
@pytest.mark.parametrize(
    "fill",
    [
        None,
        85,
        (10, -10, 10),
        0.7,
        [0.0, 0.0, 0.0],
        [
            1,
        ],
        1,
    ],
)
def test_autoaugment(device, policy, fill):
    tensor = torch.randint(0, 256, size=(3, 44, 56), dtype=torch.uint8, device=device)
    batch_tensors = torch.randint(0, 256, size=(4, 3, 44, 56), dtype=torch.uint8, device=device)

    transform = T.AutoAugment(policy=policy, fill=fill)
    s_transform = torch.jit.script(transform)
    for _ in range(25):
        _test_transform_vs_scripted(transform, s_transform, tensor)
        _test_transform_vs_scripted_on_batch(transform, s_transform, batch_tensors)


@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("num_ops", [1, 2, 3])
@pytest.mark.parametrize("magnitude", [7, 9, 11])
@pytest.mark.parametrize(
    "fill",
    [
        None,
        85,
        (10, -10, 10),
        0.7,
        [0.0, 0.0, 0.0],
        [
            1,
        ],
        1,
    ],
)
def test_randaugment(device, num_ops, magnitude, fill):
    tensor = torch.randint(0, 256, size=(3, 44, 56), dtype=torch.uint8, device=device)
    batch_tensors = torch.randint(0, 256, size=(4, 3, 44, 56), dtype=torch.uint8, device=device)

    transform = T.RandAugment(num_ops=num_ops, magnitude=magnitude, fill=fill)
    s_transform = torch.jit.script(transform)
    for _ in range(25):
        _test_transform_vs_scripted(transform, s_transform, tensor)
        _test_transform_vs_scripted_on_batch(transform, s_transform, batch_tensors)


@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize(
    "fill",
    [
        None,
        85,
        (10, -10, 10),
        0.7,
        [0.0, 0.0, 0.0],
        [
            1,
        ],
        1,
    ],
)
def test_trivialaugmentwide(device, fill):
    tensor = torch.randint(0, 256, size=(3, 44, 56), dtype=torch.uint8, device=device)
    batch_tensors = torch.randint(0, 256, size=(4, 3, 44, 56), dtype=torch.uint8, device=device)

    transform = T.TrivialAugmentWide(fill=fill)
    s_transform = torch.jit.script(transform)
    for _ in range(25):
        _test_transform_vs_scripted(transform, s_transform, tensor)
        _test_transform_vs_scripted_on_batch(transform, s_transform, batch_tensors)


@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize(
    "fill",
    [
        None,
        85,
        (10, -10, 10),
        0.7,
        [0.0, 0.0, 0.0],
        [
            1,
        ],
        1,
    ],
)
def test_augmix(device, fill):
    tensor = torch.randint(0, 256, size=(3, 44, 56), dtype=torch.uint8, device=device)
    batch_tensors = torch.randint(0, 256, size=(4, 3, 44, 56), dtype=torch.uint8, device=device)

    class DeterministicAugMix(T.AugMix):
        def _sample_dirichlet(self, params: torch.Tensor) -> torch.Tensor:
            # patch the method to ensure that the order of rand calls doesn't affect the outcome
            return params.softmax(dim=-1)

    transform = DeterministicAugMix(fill=fill)
    s_transform = torch.jit.script(transform)
    for _ in range(25):
        _test_transform_vs_scripted(transform, s_transform, tensor)
        _test_transform_vs_scripted_on_batch(transform, s_transform, batch_tensors)


@pytest.mark.parametrize("augmentation", [T.AutoAugment, T.RandAugment, T.TrivialAugmentWide, T.AugMix])
def test_autoaugment_save(augmentation, tmpdir):
    transform = augmentation()
    s_transform = torch.jit.script(transform)
    s_transform.save(os.path.join(tmpdir, "t_autoaugment.pt"))


@pytest.mark.parametrize("interpolation", [F.InterpolationMode.NEAREST, F.InterpolationMode.BILINEAR])
@pytest.mark.parametrize("mode", ["X", "Y"])
def test_autoaugment__op_apply_shear(interpolation, mode):
    # We check that torchvision's implementation of shear is equivalent
    # to official CIFAR10 autoaugment implementation:
    # https://github.com/tensorflow/models/blob/885fda091c46c59d6c7bb5c7e760935eacc229da/research/autoaugment/augmentation_transforms.py#L273-L290
    image_size = 32

    def shear(pil_img, level, mode, resample):
        if mode == "X":
            matrix = (1, level, 0, 0, 1, 0)
        elif mode == "Y":
            matrix = (1, 0, 0, level, 1, 0)
        return pil_img.transform((image_size, image_size), Image.AFFINE, matrix, resample=resample)

    t_img, pil_img = _create_data(image_size, image_size)

    resample_pil = {
        F.InterpolationMode.NEAREST: Image.NEAREST,
        F.InterpolationMode.BILINEAR: Image.BILINEAR,
    }[interpolation]

    level = 0.3
    expected_out = shear(pil_img, level, mode=mode, resample=resample_pil)

    # Check pil output vs expected pil
    out = _apply_op(pil_img, op_name=f"Shear{mode}", magnitude=level, interpolation=interpolation, fill=0)
    assert out == expected_out

    if interpolation == F.InterpolationMode.BILINEAR:
        # We skip bilinear mode for tensors as
        # affine transformation results are not exactly the same
        # between tensors and pil images
        # MAE as around 1.40
        # Max Abs error can be 163 or 170
        return

    # Check tensor output vs expected pil
    out = _apply_op(t_img, op_name=f"Shear{mode}", magnitude=level, interpolation=interpolation, fill=0)
    _assert_approx_equal_tensor_to_pil(out, expected_out)


@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize(
    "config",
    [{"value": 0.2}, {"value": "random"}, {"value": (0.2, 0.2, 0.2)}, {"value": "random", "ratio": (0.1, 0.2)}],
)
def test_random_erasing(device, config):
    tensor, _ = _create_data(24, 32, channels=3, device=device)
    batch_tensors = torch.rand(4, 3, 44, 56, device=device)

    fn = T.RandomErasing(**config)
    scripted_fn = torch.jit.script(fn)
    _test_transform_vs_scripted(fn, scripted_fn, tensor)
    _test_transform_vs_scripted_on_batch(fn, scripted_fn, batch_tensors)


def test_random_erasing_save(tmpdir):
    fn = T.RandomErasing(value=0.2)
    scripted_fn = torch.jit.script(fn)
    scripted_fn.save(os.path.join(tmpdir, "t_random_erasing.pt"))


def test_random_erasing_with_invalid_data():
    img = torch.rand(3, 60, 60)
    # Test Set 0: invalid value
    random_erasing = T.RandomErasing(value=(0.1, 0.2, 0.3, 0.4), p=1.0)
    with pytest.raises(ValueError, match="If value is a sequence, it should have either a single value or 3"):
        random_erasing(img)


@pytest.mark.parametrize("device", cpu_and_gpu())
def test_normalize(device, tmpdir):
    fn = T.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
    tensor, _ = _create_data(26, 34, device=device)

    with pytest.raises(TypeError, match="Input tensor should be a float tensor"):
        fn(tensor)

    batch_tensors = torch.rand(4, 3, 44, 56, device=device)
    tensor = tensor.to(dtype=torch.float32) / 255.0
    # test for class interface
    scripted_fn = torch.jit.script(fn)

    _test_transform_vs_scripted(fn, scripted_fn, tensor)
    _test_transform_vs_scripted_on_batch(fn, scripted_fn, batch_tensors)

    scripted_fn.save(os.path.join(tmpdir, "t_norm.pt"))


@pytest.mark.parametrize("device", cpu_and_gpu())
def test_linear_transformation(device, tmpdir):
    c, h, w = 3, 24, 32

    tensor, _ = _create_data(h, w, channels=c, device=device)

    matrix = torch.rand(c * h * w, c * h * w, device=device)
    mean_vector = torch.rand(c * h * w, device=device)

    fn = T.LinearTransformation(matrix, mean_vector)
    scripted_fn = torch.jit.script(fn)

    _test_transform_vs_scripted(fn, scripted_fn, tensor)

    batch_tensors = torch.rand(4, c, h, w, device=device)
    # We skip some tests from _test_transform_vs_scripted_on_batch as
    # results for scripted and non-scripted transformations are not exactly the same
    torch.manual_seed(12)
    transformed_batch = fn(batch_tensors)
    torch.manual_seed(12)
    s_transformed_batch = scripted_fn(batch_tensors)
    assert_equal(transformed_batch, s_transformed_batch)

    scripted_fn.save(os.path.join(tmpdir, "t_norm.pt"))


@pytest.mark.parametrize("device", cpu_and_gpu())
def test_compose(device):
    tensor, _ = _create_data(26, 34, device=device)
    tensor = tensor.to(dtype=torch.float32) / 255.0
    transforms = T.Compose(
        [
            T.CenterCrop(10),
            T.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
        ]
    )
    s_transforms = torch.nn.Sequential(*transforms.transforms)

    scripted_fn = torch.jit.script(s_transforms)
    torch.manual_seed(12)
    transformed_tensor = transforms(tensor)
    torch.manual_seed(12)
    transformed_tensor_script = scripted_fn(tensor)
    assert_equal(transformed_tensor, transformed_tensor_script, msg=f"{transforms}")

    t = T.Compose(
        [
            lambda x: x,
        ]
    )
    with pytest.raises(RuntimeError, match="cannot call a value of type 'Tensor'"):
        torch.jit.script(t)


@pytest.mark.parametrize("device", cpu_and_gpu())
def test_random_apply(device):
    tensor, _ = _create_data(26, 34, device=device)
    tensor = tensor.to(dtype=torch.float32) / 255.0

    transforms = T.RandomApply(
        [
            T.RandomHorizontalFlip(),
            T.ColorJitter(),
        ],
        p=0.4,
    )
    s_transforms = T.RandomApply(
        torch.nn.ModuleList(
            [
                T.RandomHorizontalFlip(),
                T.ColorJitter(),
            ]
        ),
        p=0.4,
    )

    scripted_fn = torch.jit.script(s_transforms)
    torch.manual_seed(12)
    transformed_tensor = transforms(tensor)
    torch.manual_seed(12)
    transformed_tensor_script = scripted_fn(tensor)
    assert_equal(transformed_tensor, transformed_tensor_script, msg=f"{transforms}")

    if device == "cpu":
        # Can't check this twice, otherwise
        # "Can't redefine method: forward on class: __torch__.torchvision.transforms.transforms.RandomApply"
        transforms = T.RandomApply(
            [
                T.ColorJitter(),
            ],
            p=0.3,
        )
        with pytest.raises(RuntimeError, match="Module 'RandomApply' has no attribute 'transforms'"):
            torch.jit.script(transforms)


@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize(
    "meth_kwargs",
    [
        {"kernel_size": 3, "sigma": 0.75},
        {"kernel_size": 23, "sigma": [0.1, 2.0]},
        {"kernel_size": 23, "sigma": (0.1, 2.0)},
        {"kernel_size": [3, 3], "sigma": (1.0, 1.0)},
        {"kernel_size": (3, 3), "sigma": (0.1, 2.0)},
        {"kernel_size": [23], "sigma": 0.75},
    ],
)
@pytest.mark.parametrize("channels", [1, 3])
def test_gaussian_blur(device, channels, meth_kwargs):
    tol = 1.0 + 1e-10
    torch.manual_seed(12)
    _test_class_op(
        T.GaussianBlur,
        meth_kwargs=meth_kwargs,
        channels=channels,
        test_exact_match=False,
        device=device,
        agg_method="max",
        tol=tol,
    )
